stuff...
+ data = "\n" + data
+ if self.mark_code:
+ self.out("\n[code]")
+ self.p_p = 0
+
+ bq = ">" * self.blockquote
+ if not (force and data and data[0] == ">") and self.blockquote:
+ bq += " "
+
+ if self.pre:
+ if not self.list:
+ bq += " "
+ # else: list content is already partially indented
+ bq += " " * len(self.list)
+ data = data.replace("\n", "\n" + bq)
+
+ if self.startpre:
+ self.startpre = False
+ if self.list:
+ # use existing initial indentation
+ data = data.lstrip("\n")
+
+ if self.start:
+ self.space = False
+ self.p_p = 0
+ self.start = False
+
+ if force == "end":
+ # It's the end.
+ self.p_p = 0
+ self.out("\n")
+ self.space = False
+
+ if self.p_p:
+ self.out((self.br_toggle + "\n" + bq) * self.p_p)
+ self.space = False
+ self.br_toggle = ""
+
+ if self.space:
+ if not self.lastWasNL:
+ self.out(" ")
+ self.space = False
+
+ if self.a and (
+ (self.p_p == 2 and self.links_each_paragraph) or force == "end"
+ ):
+ if force == "end":
+ self.out("\n")
+
+ newa = []
+ for link in self.a:
+ if self.outcount > link.outcount:
+ self.out(
+ " ["
+ + str(link.count)
+ + "]: "
+ + urlparse.urljoin(self.baseurl, link.attrs["href"])
+ )
+ if "title" in link.attrs and link.attrs["title"] is not None:
+ self.out(" (" + link.attrs["title"] + ")")
+ self.out("\n")
+ else:
+ newa.append(link)
+
+ # Don't need an extra line when nothing was done.
+ if self.a != newa:
+ self.out("\n")
+
+ self.a = newa
+
+ if self.abbr_list and force == "end":
+ for abbr, definition in self.abbr_list.items():
+ self.out(" *[" + abbr + "]: " + definition + "\n")
+
+ self.p_p = 0
+ self.out(data)
+ self.outcount += 1
+
+ def handle_data(self, data: str, entity_char: bool = False) -> None:
+ if not data:
+ # Data may be empty for some HTML entities. For example,
+ # LEFT-TO-RIGHT MARK.
+ return
+
+ if self.stressed:
+ data = data.strip()
+ self.stressed = False
+ self.preceding_stressed = True
+ elif self.preceding_stressed:
+ if (
+ re.match(r"[^][(){}\s.!?]", data[0])
+ and not hn(self.current_tag)
+ and self.current_tag not in ["a", "code", "pre"]
+ ):
+ # should match a letter or common punctuation
+ data = " " + data
+ self.preceding_stressed = False
+
+ if self.style:
+ self.style_def.update(dumb_css_parser(data))
+
+ if self.maybe_automatic_link is not None:
+ href = self.maybe_automatic_link
+ if (
+ href == data
+ and self.absolute_url_matcher.match(href)
+ and self.use_automatic_links
+ ):
+ self.o("<" + data + ">")
+ self.empty_link = False
+ return
+ else:
+ self.o("[")
+ self.maybe_automatic_link = None
+ self.empty_link = False
+
+ if not self.code and not self.pre and not entity_char:
+ data = escape_md_section(data, snob=self.escape_snob, escape_dot=self.escape_dot, escape_plus=self.escape_plus, escape_dash=self.escape_dash)
+ self.preceding_data = data
+ self.o(data, puredata=True)
+
+ def charref(self, name: str) -> str:
+ if name[0] in ["x", "X"]:
+ c = int(name[1:], 16)
+ else:
+ c = int(name)
+
+ if not self.unicode_snob and c in unifiable_n:
+ return unifiable_n[c]
+ else:
+ try:
+ return chr(c)
+ except ValueError: # invalid unicode
+ return ""
+
+ def entityref(self, c: str) -> str:
+ if not self.unicode_snob and c in config.UNIFIABLE:
+ return config.UNIFIABLE[c]
+ try:
+ ch = html.entities.html5[c + ";"]
+ except KeyError:
+ return "&" + c + ";"
+ return config.UNIFIABLE[c] if c == "nbsp" else ch
+
+ def google_nest_count(self, style: Dict[str, str]) -> int:
+ """
+ Calculate the nesting count of google doc lists
+
+ :type style: dict
+
+ :rtype: int
+ """
+ nest_count = 0
+ if "margin-left" in style:
+ nest_count = int(style["margin-left"][:-2]) // self.google_list_indent
+
+ return nest_count
+
+ def optwrap(self, text: str) -> str:
+ """
+ Wrap all paragraphs in the provided text.
+
+ :type text: str
+
+ :rtype: str
+ """
+ if not self.body_width:
+ return text
+
+ result = ""
+ newlines = 0
+ # I cannot think of a better solution for now.
+ # To avoid the non-wrap behaviour for entire paras
+ # because of the presence of a link in it
+ if not self.wrap_links:
+ self.inline_links = False
+ for para in text.split("\n"):
+ if len(para) > 0:
+ if not skipwrap(
+ para, self.wrap_links, self.wrap_list_items, self.wrap_tables
+ ):
+ indent = ""
+ if para.startswith(" " + self.ul_item_mark):
+ # list item continuation: add a double indent to the
+ # new lines
+ indent = " "
+ elif para.startswith("> "):
+ # blockquote continuation: add the greater than symbol
+ # to the new lines
+ indent = "> "
+ wrapped = wrap(
+ para,
+ self.body_width,
+ break_long_words=False,
+ subsequent_indent=indent,
+ )
+ result += "\n".join(wrapped)
+ if para.endswith(" "):
+ result += " \n"
+ newlines = 1
+ elif indent:
+ result += "\n"
+ newlines = 1
+ else:
+ result += "\n\n"
+ newlines = 2
+ else:
+ # Warning for the tempted!!!
+ # Be aware that obvious replacement of this with
+ # line.isspace()
+ # DOES NOT work! Explanations are welcome.
+ if not config.RE_SPACE.match(para):
+ result += para + "\n"
+ newlines = 1
+ else:
+ if newlines < 2:
+ result += "\n"
+ newlines += 1
+ return result
+
+
+def html2text(html: str, baseurl: str = "", bodywidth: Optional[int] = None) -> str:
+ if bodywidth is None:
+ bodywidth = config.BODY_WIDTH
+ h = HTML2Text(baseurl=baseurl, bodywidth=bodywidth)
+
+ return h.handle(html)
diff --git a/crawl4ai/html2text/__main__.py b/crawl4ai/html2text/__main__.py
new file mode 100644
index 00000000..4e28416e
--- /dev/null
+++ b/crawl4ai/html2text/__main__.py
@@ -0,0 +1,3 @@
+from .cli import main
+
+main()
diff --git a/crawl4ai/html2text/_typing.py b/crawl4ai/html2text/_typing.py
new file mode 100644
index 00000000..eed83251
--- /dev/null
+++ b/crawl4ai/html2text/_typing.py
@@ -0,0 +1,2 @@
+class OutCallback:
+ def __call__(self, s: str) -> None: ...
diff --git a/crawl4ai/html2text/cli.py b/crawl4ai/html2text/cli.py
new file mode 100644
index 00000000..01532274
--- /dev/null
+++ b/crawl4ai/html2text/cli.py
@@ -0,0 +1,330 @@
+import argparse
+import sys
+
+from . import HTML2Text, __version__, config
+
+
+def main() -> None:
+ baseurl = ""
+
+ class bcolors:
+ HEADER = "\033[95m"
+ OKBLUE = "\033[94m"
+ OKGREEN = "\033[92m"
+ WARNING = "\033[93m"
+ FAIL = "\033[91m"
+ ENDC = "\033[0m"
+ BOLD = "\033[1m"
+ UNDERLINE = "\033[4m"
+
+ p = argparse.ArgumentParser()
+ p.add_argument(
+ "--default-image-alt",
+ dest="default_image_alt",
+ default=config.DEFAULT_IMAGE_ALT,
+ help="The default alt string for images with missing ones",
+ )
+ p.add_argument(
+ "--pad-tables",
+ dest="pad_tables",
+ action="store_true",
+ default=config.PAD_TABLES,
+ help="pad the cells to equal column width in tables",
+ )
+ p.add_argument(
+ "--no-wrap-links",
+ dest="wrap_links",
+ action="store_false",
+ default=config.WRAP_LINKS,
+ help="don't wrap links during conversion",
+ )
+ p.add_argument(
+ "--wrap-list-items",
+ dest="wrap_list_items",
+ action="store_true",
+ default=config.WRAP_LIST_ITEMS,
+ help="wrap list items during conversion",
+ )
+ p.add_argument(
+ "--wrap-tables",
+ dest="wrap_tables",
+ action="store_true",
+ default=config.WRAP_TABLES,
+ help="wrap tables",
+ )
+ p.add_argument(
+ "--ignore-emphasis",
+ dest="ignore_emphasis",
+ action="store_true",
+ default=config.IGNORE_EMPHASIS,
+ help="don't include any formatting for emphasis",
+ )
+ p.add_argument(
+ "--reference-links",
+ dest="inline_links",
+ action="store_false",
+ default=config.INLINE_LINKS,
+ help="use reference style links instead of inline links",
+ )
+ p.add_argument(
+ "--ignore-links",
+ dest="ignore_links",
+ action="store_true",
+ default=config.IGNORE_ANCHORS,
+ help="don't include any formatting for links",
+ )
+ p.add_argument(
+ "--ignore-mailto-links",
+ action="store_true",
+ dest="ignore_mailto_links",
+ default=config.IGNORE_MAILTO_LINKS,
+ help="don't include mailto: links",
+ )
+ p.add_argument(
+ "--protect-links",
+ dest="protect_links",
+ action="store_true",
+ default=config.PROTECT_LINKS,
+ help="protect links from line breaks surrounding them with angle brackets",
+ )
+ p.add_argument(
+ "--ignore-images",
+ dest="ignore_images",
+ action="store_true",
+ default=config.IGNORE_IMAGES,
+ help="don't include any formatting for images",
+ )
+ p.add_argument(
+ "--images-as-html",
+ dest="images_as_html",
+ action="store_true",
+ default=config.IMAGES_AS_HTML,
+ help=(
+ "Always write image tags as raw html; preserves `height`, `width` and "
+ "`alt` if possible."
+ ),
+ )
+ p.add_argument(
+ "--images-to-alt",
+ dest="images_to_alt",
+ action="store_true",
+ default=config.IMAGES_TO_ALT,
+ help="Discard image data, only keep alt text",
+ )
+ p.add_argument(
+ "--images-with-size",
+ dest="images_with_size",
+ action="store_true",
+ default=config.IMAGES_WITH_SIZE,
+ help=(
+ "Write image tags with height and width attrs as raw html to retain "
+ "dimensions"
+ ),
+ )
+ p.add_argument(
+ "-g",
+ "--google-doc",
+ action="store_true",
+ dest="google_doc",
+ default=False,
+ help="convert an html-exported Google Document",
+ )
+ p.add_argument(
+ "-d",
+ "--dash-unordered-list",
+ action="store_true",
+ dest="ul_style_dash",
+ default=False,
+ help="use a dash rather than a star for unordered list items",
+ )
+ p.add_argument(
+ "-e",
+ "--asterisk-emphasis",
+ action="store_true",
+ dest="em_style_asterisk",
+ default=False,
+ help="use an asterisk rather than an underscore for emphasized text",
+ )
+ p.add_argument(
+ "-b",
+ "--body-width",
+ dest="body_width",
+ type=int,
+ default=config.BODY_WIDTH,
+ help="number of characters per output line, 0 for no wrap",
+ )
+ p.add_argument(
+ "-i",
+ "--google-list-indent",
+ dest="list_indent",
+ type=int,
+ default=config.GOOGLE_LIST_INDENT,
+ help="number of pixels Google indents nested lists",
+ )
+ p.add_argument(
+ "-s",
+ "--hide-strikethrough",
+ action="store_true",
+ dest="hide_strikethrough",
+ default=False,
+ help="hide strike-through text. only relevant when -g is " "specified as well",
+ )
+ p.add_argument(
+ "--escape-all",
+ action="store_true",
+ dest="escape_snob",
+ default=False,
+ help=(
+ "Escape all special characters. Output is less readable, but avoids "
+ "corner case formatting issues."
+ ),
+ )
+ p.add_argument(
+ "--bypass-tables",
+ action="store_true",
+ dest="bypass_tables",
+ default=config.BYPASS_TABLES,
+ help="Format tables in HTML rather than Markdown syntax.",
+ )
+ p.add_argument(
+ "--ignore-tables",
+ action="store_true",
+ dest="ignore_tables",
+ default=config.IGNORE_TABLES,
+ help="Ignore table-related tags (table, th, td, tr) " "while keeping rows.",
+ )
+ p.add_argument(
+ "--single-line-break",
+ action="store_true",
+ dest="single_line_break",
+ default=config.SINGLE_LINE_BREAK,
+ help=(
+ "Use a single line break after a block element rather than two line "
+ "breaks. NOTE: Requires --body-width=0"
+ ),
+ )
+ p.add_argument(
+ "--unicode-snob",
+ action="store_true",
+ dest="unicode_snob",
+ default=config.UNICODE_SNOB,
+ help="Use unicode throughout document",
+ )
+ p.add_argument(
+ "--no-automatic-links",
+ action="store_false",
+ dest="use_automatic_links",
+ default=config.USE_AUTOMATIC_LINKS,
+ help="Do not use automatic links wherever applicable",
+ )
+ p.add_argument(
+ "--no-skip-internal-links",
+ action="store_false",
+ dest="skip_internal_links",
+ default=config.SKIP_INTERNAL_LINKS,
+ help="Do not skip internal links",
+ )
+ p.add_argument(
+ "--links-after-para",
+ action="store_true",
+ dest="links_each_paragraph",
+ default=config.LINKS_EACH_PARAGRAPH,
+ help="Put links after each paragraph instead of document",
+ )
+ p.add_argument(
+ "--mark-code",
+ action="store_true",
+ dest="mark_code",
+ default=config.MARK_CODE,
+ help="Mark program code blocks with [code]...[/code]",
+ )
+ p.add_argument(
+ "--decode-errors",
+ dest="decode_errors",
+ default=config.DECODE_ERRORS,
+ help=(
+ "What to do in case of decode errors.'ignore', 'strict' and 'replace' are "
+ "acceptable values"
+ ),
+ )
+ p.add_argument(
+ "--open-quote",
+ dest="open_quote",
+ default=config.OPEN_QUOTE,
+ help="The character used to open quotes",
+ )
+ p.add_argument(
+ "--close-quote",
+ dest="close_quote",
+ default=config.CLOSE_QUOTE,
+ help="The character used to close quotes",
+ )
+ p.add_argument(
+ "--version", action="version", version=".".join(map(str, __version__))
+ )
+ p.add_argument("filename", nargs="?")
+ p.add_argument("encoding", nargs="?", default="utf-8")
+ p.add_argument(
+ "--include-sup-sub",
+ dest="include_sup_sub",
+ action="store_true",
+ default=config.INCLUDE_SUP_SUB,
+ help="Include the sup and sub tags",
+ )
+ args = p.parse_args()
+
+ if args.filename and args.filename != "-":
+ with open(args.filename, "rb") as fp:
+ data = fp.read()
+ else:
+ data = sys.stdin.buffer.read()
+
+ try:
+ html = data.decode(args.encoding, args.decode_errors)
+ except UnicodeDecodeError as err:
+ warning = bcolors.WARNING + "Warning:" + bcolors.ENDC
+ warning += " Use the " + bcolors.OKGREEN
+ warning += "--decode-errors=ignore" + bcolors.ENDC + " flag."
+ print(warning)
+ raise err
+
+ h = HTML2Text(baseurl=baseurl)
+ # handle options
+ if args.ul_style_dash:
+ h.ul_item_mark = "-"
+ if args.em_style_asterisk:
+ h.emphasis_mark = "*"
+ h.strong_mark = "__"
+
+ h.body_width = args.body_width
+ h.google_list_indent = args.list_indent
+ h.ignore_emphasis = args.ignore_emphasis
+ h.ignore_links = args.ignore_links
+ h.ignore_mailto_links = args.ignore_mailto_links
+ h.protect_links = args.protect_links
+ h.ignore_images = args.ignore_images
+ h.images_as_html = args.images_as_html
+ h.images_to_alt = args.images_to_alt
+ h.images_with_size = args.images_with_size
+ h.google_doc = args.google_doc
+ h.hide_strikethrough = args.hide_strikethrough
+ h.escape_snob = args.escape_snob
+ h.bypass_tables = args.bypass_tables
+ h.ignore_tables = args.ignore_tables
+ h.single_line_break = args.single_line_break
+ h.inline_links = args.inline_links
+ h.unicode_snob = args.unicode_snob
+ h.use_automatic_links = args.use_automatic_links
+ h.skip_internal_links = args.skip_internal_links
+ h.links_each_paragraph = args.links_each_paragraph
+ h.mark_code = args.mark_code
+ h.wrap_links = args.wrap_links
+ h.wrap_list_items = args.wrap_list_items
+ h.wrap_tables = args.wrap_tables
+ h.pad_tables = args.pad_tables
+ h.default_image_alt = args.default_image_alt
+ h.open_quote = args.open_quote
+ h.close_quote = args.close_quote
+ h.include_sup_sub = args.include_sup_sub
+
+ sys.stdout.write(h.handle(html))
diff --git a/crawl4ai/html2text/config.py b/crawl4ai/html2text/config.py
new file mode 100644
index 00000000..d14ed64f
--- /dev/null
+++ b/crawl4ai/html2text/config.py
@@ -0,0 +1,172 @@
+import re
+
+# Use Unicode characters instead of their ascii pseudo-replacements
+UNICODE_SNOB = False
+
+# Marker to use for marking tables for padding post processing
+TABLE_MARKER_FOR_PAD = "special_marker_for_table_padding"
+# Escape all special characters. Output is less readable, but avoids
+# corner case formatting issues.
+ESCAPE_SNOB = False
+ESCAPE_BACKSLASH = False
+ESCAPE_DOT = False
+ESCAPE_PLUS = False
+ESCAPE_DASH = False
+
+# Put the links after each paragraph instead of at the end.
+LINKS_EACH_PARAGRAPH = False
+
+# Wrap long lines at position. 0 for no wrapping.
+BODY_WIDTH = 78
+
+# Don't show internal links (href="#local-anchor") -- corresponding link
+# targets won't be visible in the plain text file anyway.
+SKIP_INTERNAL_LINKS = True
+
+# Use inline, rather than reference, formatting for images and links
+INLINE_LINKS = True
+
+# Protect links from line breaks surrounding them with angle brackets (in
+# addition to their square brackets)
+PROTECT_LINKS = False
+# WRAP_LINKS = True
+WRAP_LINKS = True
+
+# Wrap list items.
+WRAP_LIST_ITEMS = False
+
+# Wrap tables
+WRAP_TABLES = False
+
+# Number of pixels Google indents nested lists
+GOOGLE_LIST_INDENT = 36
+
+# Values Google and others may use to indicate bold text
+BOLD_TEXT_STYLE_VALUES = ("bold", "700", "800", "900")
+
+IGNORE_ANCHORS = False
+IGNORE_MAILTO_LINKS = False
+IGNORE_IMAGES = False
+IMAGES_AS_HTML = False
+IMAGES_TO_ALT = False
+IMAGES_WITH_SIZE = False
+IGNORE_EMPHASIS = False
+MARK_CODE = False
+DECODE_ERRORS = "strict"
+DEFAULT_IMAGE_ALT = ""
+PAD_TABLES = False
+
+# Convert links with same href and text to format
+# if they are absolute links
+USE_AUTOMATIC_LINKS = True
+
+# For checking space-only lines on line 771
+RE_SPACE = re.compile(r"\s\+")
+
+RE_ORDERED_LIST_MATCHER = re.compile(r"\d+\.\s")
+RE_UNORDERED_LIST_MATCHER = re.compile(r"[-\*\+]\s")
+RE_MD_CHARS_MATCHER = re.compile(r"([\\\[\]\(\)])")
+RE_MD_CHARS_MATCHER_ALL = re.compile(r"([`\*_{}\[\]\(\)#!])")
+
+# to find links in the text
+RE_LINK = re.compile(r"(\[.*?\] ?\(.*?\))|(\[.*?\]:.*?)")
+
+# to find table separators
+RE_TABLE = re.compile(r" \| ")
+
+RE_MD_DOT_MATCHER = re.compile(
+ r"""
+ ^ # start of line
+ (\s*\d+) # optional whitespace and a number
+ (\.) # dot
+ (?=\s) # lookahead assert whitespace
+ """,
+ re.MULTILINE | re.VERBOSE,
+)
+RE_MD_PLUS_MATCHER = re.compile(
+ r"""
+ ^
+ (\s*)
+ (\+)
+ (?=\s)
+ """,
+ flags=re.MULTILINE | re.VERBOSE,
+)
+RE_MD_DASH_MATCHER = re.compile(
+ r"""
+ ^
+ (\s*)
+ (-)
+ (?=\s|\-) # followed by whitespace (bullet list, or spaced out hr)
+ # or another dash (header or hr)
+ """,
+ flags=re.MULTILINE | re.VERBOSE,
+)
+RE_SLASH_CHARS = r"\`*_{}[]()#+-.!"
+RE_MD_BACKSLASH_MATCHER = re.compile(
+ r"""
+ (\\) # match one slash
+ (?=[%s]) # followed by a char that requires escaping
+ """
+ % re.escape(RE_SLASH_CHARS),
+ flags=re.VERBOSE,
+)
+
+UNIFIABLE = {
+ "rsquo": "'",
+ "lsquo": "'",
+ "rdquo": '"',
+ "ldquo": '"',
+ "copy": "(C)",
+ "mdash": "--",
+ "nbsp": " ",
+ "rarr": "->",
+ "larr": "<-",
+ "middot": "*",
+ "ndash": "-",
+ "oelig": "oe",
+ "aelig": "ae",
+ "agrave": "a",
+ "aacute": "a",
+ "acirc": "a",
+ "atilde": "a",
+ "auml": "a",
+ "aring": "a",
+ "egrave": "e",
+ "eacute": "e",
+ "ecirc": "e",
+ "euml": "e",
+ "igrave": "i",
+ "iacute": "i",
+ "icirc": "i",
+ "iuml": "i",
+ "ograve": "o",
+ "oacute": "o",
+ "ocirc": "o",
+ "otilde": "o",
+ "ouml": "o",
+ "ugrave": "u",
+ "uacute": "u",
+ "ucirc": "u",
+ "uuml": "u",
+ "lrm": "",
+ "rlm": "",
+}
+
+# Format tables in HTML rather than Markdown syntax
+BYPASS_TABLES = False
+# Ignore table-related tags (table, th, td, tr) while keeping rows
+IGNORE_TABLES = False
+
+
+# Use a single line break after a block element rather than two line breaks.
+# NOTE: Requires body width setting to be 0.
+SINGLE_LINE_BREAK = False
+
+
+# Use double quotation marks when converting the tag.
+OPEN_QUOTE = '"'
+CLOSE_QUOTE = '"'
+
+# Include the and tags
+INCLUDE_SUP_SUB = False
diff --git a/crawl4ai/html2text/elements.py b/crawl4ai/html2text/elements.py
new file mode 100644
index 00000000..2533ec08
--- /dev/null
+++ b/crawl4ai/html2text/elements.py
@@ -0,0 +1,18 @@
+from typing import Dict, Optional
+
+
+class AnchorElement:
+ __slots__ = ["attrs", "count", "outcount"]
+
+ def __init__(self, attrs: Dict[str, Optional[str]], count: int, outcount: int):
+ self.attrs = attrs
+ self.count = count
+ self.outcount = outcount
+
+
+class ListElement:
+ __slots__ = ["name", "num"]
+
+ def __init__(self, name: str, num: int):
+ self.name = name
+ self.num = num
diff --git a/crawl4ai/html2text/utils.py b/crawl4ai/html2text/utils.py
new file mode 100644
index 00000000..1909d2cf
--- /dev/null
+++ b/crawl4ai/html2text/utils.py
@@ -0,0 +1,303 @@
+import html.entities
+from typing import Dict, List, Optional
+
+from . import config
+
+unifiable_n = {
+ html.entities.name2codepoint[k]: v
+ for k, v in config.UNIFIABLE.items()
+ if k != "nbsp"
+}
+
+
+def hn(tag: str) -> int:
+ if tag[0] == "h" and len(tag) == 2:
+ n = tag[1]
+ if "0" < n <= "9":
+ return int(n)
+ return 0
+
+
+def dumb_property_dict(style: str) -> Dict[str, str]:
+ """
+ :returns: A hash of css attributes
+ """
+ return {
+ x.strip().lower(): y.strip().lower()
+ for x, y in [z.split(":", 1) for z in style.split(";") if ":" in z]
+ }
+
+
+def dumb_css_parser(data: str) -> Dict[str, Dict[str, str]]:
+ """
+ :type data: str
+
+ :returns: A hash of css selectors, each of which contains a hash of
+ css attributes.
+ :rtype: dict
+ """
+ # remove @import sentences
+ data += ";"
+ importIndex = data.find("@import")
+ while importIndex != -1:
+ data = data[0:importIndex] + data[data.find(";", importIndex) + 1 :]
+ importIndex = data.find("@import")
+
+ # parse the css. reverted from dictionary comprehension in order to
+ # support older pythons
+ pairs = [x.split("{") for x in data.split("}") if "{" in x.strip()]
+ try:
+ elements = {a.strip(): dumb_property_dict(b) for a, b in pairs}
+ except ValueError:
+ elements = {} # not that important
+
+ return elements
+
+
+def element_style(
+ attrs: Dict[str, Optional[str]],
+ style_def: Dict[str, Dict[str, str]],
+ parent_style: Dict[str, str],
+) -> Dict[str, str]:
+ """
+ :type attrs: dict
+ :type style_def: dict
+ :type style_def: dict
+
+ :returns: A hash of the 'final' style attributes of the element
+ :rtype: dict
+ """
+ style = parent_style.copy()
+ if "class" in attrs:
+ assert attrs["class"] is not None
+ for css_class in attrs["class"].split():
+ css_style = style_def.get("." + css_class, {})
+ style.update(css_style)
+ if "style" in attrs:
+ assert attrs["style"] is not None
+ immediate_style = dumb_property_dict(attrs["style"])
+ style.update(immediate_style)
+
+ return style
+
+
+def google_list_style(style: Dict[str, str]) -> str:
+ """
+ Finds out whether this is an ordered or unordered list
+
+ :type style: dict
+
+ :rtype: str
+ """
+ if "list-style-type" in style:
+ list_style = style["list-style-type"]
+ if list_style in ["disc", "circle", "square", "none"]:
+ return "ul"
+
+ return "ol"
+
+
+def google_has_height(style: Dict[str, str]) -> bool:
+ """
+ Check if the style of the element has the 'height' attribute
+ explicitly defined
+
+ :type style: dict
+
+ :rtype: bool
+ """
+ return "height" in style
+
+
+def google_text_emphasis(style: Dict[str, str]) -> List[str]:
+ """
+ :type style: dict
+
+ :returns: A list of all emphasis modifiers of the element
+ :rtype: list
+ """
+ emphasis = []
+ if "text-decoration" in style:
+ emphasis.append(style["text-decoration"])
+ if "font-style" in style:
+ emphasis.append(style["font-style"])
+ if "font-weight" in style:
+ emphasis.append(style["font-weight"])
+
+ return emphasis
+
+
+def google_fixed_width_font(style: Dict[str, str]) -> bool:
+ """
+ Check if the css of the current element defines a fixed width font
+
+ :type style: dict
+
+ :rtype: bool
+ """
+ font_family = ""
+ if "font-family" in style:
+ font_family = style["font-family"]
+ return "courier new" == font_family or "consolas" == font_family
+
+
+def list_numbering_start(attrs: Dict[str, Optional[str]]) -> int:
+ """
+ Extract numbering from list element attributes
+
+ :type attrs: dict
+
+ :rtype: int or None
+ """
+ if "start" in attrs:
+ assert attrs["start"] is not None
+ try:
+ return int(attrs["start"]) - 1
+ except ValueError:
+ pass
+
+ return 0
+
+
+def skipwrap(
+ para: str, wrap_links: bool, wrap_list_items: bool, wrap_tables: bool
+) -> bool:
+ # If it appears to contain a link
+ # don't wrap
+ if not wrap_links and config.RE_LINK.search(para):
+ return True
+ # If the text begins with four spaces or one tab, it's a code block;
+ # don't wrap
+ if para[0:4] == " " or para[0] == "\t":
+ return True
+
+ # If the text begins with only two "--", possibly preceded by
+ # whitespace, that's an emdash; so wrap.
+ stripped = para.lstrip()
+ if stripped[0:2] == "--" and len(stripped) > 2 and stripped[2] != "-":
+ return False
+
+ # I'm not sure what this is for; I thought it was to detect lists,
+ # but there's a
-inside- case in one of the tests that
+ # also depends upon it.
+ if stripped[0:1] in ("-", "*") and not stripped[0:2] == "**":
+ return not wrap_list_items
+
+ # If text contains a pipe character it is likely a table
+ if not wrap_tables and config.RE_TABLE.search(para):
+ return True
+
+ # If the text begins with a single -, *, or +, followed by a space,
+ # or an integer, followed by a ., followed by a space (in either
+ # case optionally proceeded by whitespace), it's a list; don't wrap.
+ return bool(
+ config.RE_ORDERED_LIST_MATCHER.match(stripped)
+ or config.RE_UNORDERED_LIST_MATCHER.match(stripped)
+ )
+
+
+def escape_md(text: str) -> str:
+ """
+ Escapes markdown-sensitive characters within other markdown
+ constructs.
+ """
+ return config.RE_MD_CHARS_MATCHER.sub(r"\\\1", text)
+
+
+def escape_md_section(
+ text: str,
+ escape_backslash: bool = True,
+ snob: bool = False,
+ escape_dot: bool = True,
+ escape_plus: bool = True,
+ escape_dash: bool = True
+) -> str:
+ """
+ Escapes markdown-sensitive characters across whole document sections.
+ Each escaping operation can be controlled individually.
+ """
+ if escape_backslash:
+ text = config.RE_MD_BACKSLASH_MATCHER.sub(r"\\\1", text)
+
+ if snob:
+ text = config.RE_MD_CHARS_MATCHER_ALL.sub(r"\\\1", text)
+
+ if escape_dot:
+ text = config.RE_MD_DOT_MATCHER.sub(r"\1\\\2", text)
+
+ if escape_plus:
+ text = config.RE_MD_PLUS_MATCHER.sub(r"\1\\\2", text)
+
+ if escape_dash:
+ text = config.RE_MD_DASH_MATCHER.sub(r"\1\\\2", text)
+
+ return text
+
+def reformat_table(lines: List[str], right_margin: int) -> List[str]:
+ """
+ Given the lines of a table
+ padds the cells and returns the new lines
+ """
+ # find the maximum width of the columns
+ max_width = [len(x.rstrip()) + right_margin for x in lines[0].split("|")]
+ max_cols = len(max_width)
+ for line in lines:
+ cols = [x.rstrip() for x in line.split("|")]
+ num_cols = len(cols)
+
+ # don't drop any data if colspan attributes result in unequal lengths
+ if num_cols < max_cols:
+ cols += [""] * (max_cols - num_cols)
+ elif max_cols < num_cols:
+ max_width += [len(x) + right_margin for x in cols[-(num_cols - max_cols) :]]
+ max_cols = num_cols
+
+ max_width = [
+ max(len(x) + right_margin, old_len) for x, old_len in zip(cols, max_width)
+ ]
+
+ # reformat
+ new_lines = []
+ for line in lines:
+ cols = [x.rstrip() for x in line.split("|")]
+ if set(line.strip()) == set("-|"):
+ filler = "-"
+ new_cols = [
+ x.rstrip() + (filler * (M - len(x.rstrip())))
+ for x, M in zip(cols, max_width)
+ ]
+ new_lines.append("|-" + "|".join(new_cols) + "|")
+ else:
+ filler = " "
+ new_cols = [
+ x.rstrip() + (filler * (M - len(x.rstrip())))
+ for x, M in zip(cols, max_width)
+ ]
+ new_lines.append("| " + "|".join(new_cols) + "|")
+ return new_lines
+
+
+def pad_tables_in_text(text: str, right_margin: int = 1) -> str:
+ """
+ Provide padding for tables in the text
+ """
+ lines = text.split("\n")
+ table_buffer = [] # type: List[str]
+ table_started = False
+ new_lines = []
+ for line in lines:
+ # Toggle table started
+ if config.TABLE_MARKER_FOR_PAD in line:
+ table_started = not table_started
+ if not table_started:
+ table = reformat_table(table_buffer, right_margin)
+ new_lines.extend(table)
+ table_buffer = []
+ new_lines.append("")
+ continue
+ # Process lines
+ if table_started:
+ table_buffer.append(line)
+ else:
+ new_lines.append(line)
+ return "\n".join(new_lines)
diff --git a/crawl4ai/model_loader.py b/crawl4ai/model_loader.py
index e33e53f4..d1872d7e 100644
--- a/crawl4ai/model_loader.py
+++ b/crawl4ai/model_loader.py
@@ -56,7 +56,7 @@ def set_model_device(model):
@lru_cache()
def get_home_folder():
- home_folder = os.path.join(Path.home(), ".crawl4ai")
+ home_folder = os.path.join(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai")
os.makedirs(home_folder, exist_ok=True)
os.makedirs(f"{home_folder}/cache", exist_ok=True)
os.makedirs(f"{home_folder}/models", exist_ok=True)
@@ -72,10 +72,18 @@ def load_bert_base_uncased():
return tokenizer, model
@lru_cache()
-def load_bge_small_en_v1_5():
+def load_HF_embedding_model(model_name="BAAI/bge-small-en-v1.5") -> tuple:
+ """Load the Hugging Face model for embedding.
+
+ Args:
+ model_name (str, optional): The model name to load. Defaults to "BAAI/bge-small-en-v1.5".
+
+ Returns:
+ tuple: The tokenizer and model.
+ """
from transformers import BertTokenizer, BertModel, AutoTokenizer, AutoModel
- tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5', resume_download=None)
- model = AutoModel.from_pretrained('BAAI/bge-small-en-v1.5', resume_download=None)
+ tokenizer = AutoTokenizer.from_pretrained(model_name, resume_download=None)
+ model = AutoModel.from_pretrained(model_name, resume_download=None)
model.eval()
model, device = set_model_device(model)
return tokenizer, model
diff --git a/crawl4ai/models.py b/crawl4ai/models.py
index 151ccb4f..4ac06797 100644
--- a/crawl4ai/models.py
+++ b/crawl4ai/models.py
@@ -14,6 +14,8 @@ class CrawlResult(BaseModel):
links: Dict[str, List[Dict]] = {}
screenshot: Optional[str] = None
markdown: Optional[str] = None
+ fit_markdown: Optional[str] = None
+ fit_html: Optional[str] = None
extracted_content: Optional[str] = None
metadata: Optional[dict] = None
error_message: Optional[str] = None
diff --git a/crawl4ai/models/onnx/config.json b/crawl4ai/models/onnx/config.json
deleted file mode 100644
index 6171b914..00000000
--- a/crawl4ai/models/onnx/config.json
+++ /dev/null
@@ -1,25 +0,0 @@
-{
- "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
- "architectures": [
- "BertModel"
- ],
- "attention_probs_dropout_prob": 0.1,
- "classifier_dropout": null,
- "gradient_checkpointing": false,
- "hidden_act": "gelu",
- "hidden_dropout_prob": 0.1,
- "hidden_size": 384,
- "initializer_range": 0.02,
- "intermediate_size": 1536,
- "layer_norm_eps": 1e-12,
- "max_position_embeddings": 512,
- "model_type": "bert",
- "num_attention_heads": 12,
- "num_hidden_layers": 6,
- "pad_token_id": 0,
- "position_embedding_type": "absolute",
- "transformers_version": "4.27.4",
- "type_vocab_size": 2,
- "use_cache": true,
- "vocab_size": 30522
-}
diff --git a/crawl4ai/models/onnx/model.onnx b/crawl4ai/models/onnx/model.onnx
deleted file mode 100644
index 9117c9bb..00000000
Binary files a/crawl4ai/models/onnx/model.onnx and /dev/null differ
diff --git a/crawl4ai/models/onnx/special_tokens_map.json b/crawl4ai/models/onnx/special_tokens_map.json
deleted file mode 100644
index a8b3208c..00000000
--- a/crawl4ai/models/onnx/special_tokens_map.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
- "cls_token": "[CLS]",
- "mask_token": "[MASK]",
- "pad_token": "[PAD]",
- "sep_token": "[SEP]",
- "unk_token": "[UNK]"
-}
diff --git a/crawl4ai/models/onnx/tokenizer.json b/crawl4ai/models/onnx/tokenizer.json
deleted file mode 100644
index c17ed520..00000000
--- a/crawl4ai/models/onnx/tokenizer.json
+++ /dev/null
@@ -1,30686 +0,0 @@
-{
- "version": "1.0",
- "truncation": {
- "direction": "Right",
- "max_length": 128,
- "strategy": "LongestFirst",
- "stride": 0
- },
- "padding": {
- "strategy": {
- "Fixed": 128
- },
- "direction": "Right",
- "pad_to_multiple_of": null,
- "pad_id": 0,
- "pad_type_id": 0,
- "pad_token": "[PAD]"
- },
- "added_tokens": [
- {
- "id": 0,
- "content": "[PAD]",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": false,
- "special": true
- },
- {
- "id": 100,
- "content": "[UNK]",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": false,
- "special": true
- },
- {
- "id": 101,
- "content": "[CLS]",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": false,
- "special": true
- },
- {
- "id": 102,
- "content": "[SEP]",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": false,
- "special": true
- },
- {
- "id": 103,
- "content": "[MASK]",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": false,
- "special": true
- }
- ],
- "normalizer": {
- "type": "BertNormalizer",
- "clean_text": true,
- "handle_chinese_chars": true,
- "strip_accents": null,
- "lowercase": true
- },
- "pre_tokenizer": {
- "type": "BertPreTokenizer"
- },
- "post_processor": {
- "type": "TemplateProcessing",
- "single": [
- {
- "SpecialToken": {
- "id": "[CLS]",
- "type_id": 0
- }
- },
- {
- "Sequence": {
- "id": "A",
- "type_id": 0
- }
- },
- {
- "SpecialToken": {
- "id": "[SEP]",
- "type_id": 0
- }
- }
- ],
- "pair": [
- {
- "SpecialToken": {
- "id": "[CLS]",
- "type_id": 0
- }
- },
- {
- "Sequence": {
- "id": "A",
- "type_id": 0
- }
- },
- {
- "SpecialToken": {
- "id": "[SEP]",
- "type_id": 0
- }
- },
- {
- "Sequence": {
- "id": "B",
- "type_id": 1
- }
- },
- {
- "SpecialToken": {
- "id": "[SEP]",
- "type_id": 1
- }
- }
- ],
- "special_tokens": {
- "[CLS]": {
- "id": "[CLS]",
- "ids": [
- 101
- ],
- "tokens": [
- "[CLS]"
- ]
- },
- "[SEP]": {
- "id": "[SEP]",
- "ids": [
- 102
- ],
- "tokens": [
- "[SEP]"
- ]
- }
- }
- },
- "decoder": {
- "type": "WordPiece",
- "prefix": "##",
- "cleanup": true
- },
- "model": {
- "type": "WordPiece",
- "unk_token": "[UNK]",
- "continuing_subword_prefix": "##",
- "max_input_chars_per_word": 100,
- "vocab": {
- "[PAD]": 0,
- "[unused0]": 1,
- "[unused1]": 2,
- "[unused2]": 3,
- "[unused3]": 4,
- "[unused4]": 5,
- "[unused5]": 6,
- "[unused6]": 7,
- "[unused7]": 8,
- "[unused8]": 9,
- "[unused9]": 10,
- "[unused10]": 11,
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- }
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-}
\ No newline at end of file
diff --git a/crawl4ai/models/onnx/tokenizer_config.json b/crawl4ai/models/onnx/tokenizer_config.json
deleted file mode 100644
index 0d9932d7..00000000
--- a/crawl4ai/models/onnx/tokenizer_config.json
+++ /dev/null
@@ -1,15 +0,0 @@
-{
- "cls_token": "[CLS]",
- "do_basic_tokenize": true,
- "do_lower_case": true,
- "mask_token": "[MASK]",
- "model_max_length": 512,
- "never_split": null,
- "pad_token": "[PAD]",
- "sep_token": "[SEP]",
- "special_tokens_map_file": "/Users/hammad/.cache/huggingface/hub/models--sentence-transformers--all-MiniLM-L6-v2/snapshots/7dbbc90392e2f80f3d3c277d6e90027e55de9125/special_tokens_map.json",
- "strip_accents": null,
- "tokenize_chinese_chars": true,
- "tokenizer_class": "BertTokenizer",
- "unk_token": "[UNK]"
-}
diff --git a/crawl4ai/models/onnx/vocab.txt b/crawl4ai/models/onnx/vocab.txt
deleted file mode 100644
index fb140275..00000000
--- a/crawl4ai/models/onnx/vocab.txt
+++ /dev/null
@@ -1,30522 +0,0 @@
-[PAD]
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-[unused1]
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-[unused84]
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-[unused94]
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-[unused98]
-[UNK]
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-[SEP]
-[MASK]
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-35
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-##„
-##†
-##‡
-##•
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-##‰
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-##€
-##₱
-##₹
-##ℓ
-##№
-##ℝ
-##™
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-##←
-##↑
-##→
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-##≤
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-##⊗
-##⋅
-##─
-##│
-##■
-##▪
-##●
-##★
-##☆
-##☉
-##♠
-##♣
-##♥
-##♦
-##♯
-##⟨
-##⟩
-##ⱼ
-##⺩
-##⺼
-##⽥
-##、
-##。
-##〈
-##〉
-##《
-##》
-##「
-##」
-##『
-##』
-##〜
-##あ
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-##ワ
-##ン
-##・
-##ー
-##一
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-##上
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-##不
-##世
-##中
-##主
-##久
-##之
-##也
-##事
-##二
-##五
-##井
-##京
-##人
-##亻
-##仁
-##介
-##代
-##仮
-##伊
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-##佐
-##侍
-##保
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-##元
-##光
-##八
-##公
-##内
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-##北
-##区
-##十
-##千
-##南
-##博
-##原
-##口
-##古
-##史
-##司
-##合
-##吉
-##同
-##名
-##和
-##囗
-##四
-##国
-##國
-##土
-##地
-##坂
-##城
-##堂
-##場
-##士
-##夏
-##外
-##大
-##天
-##太
-##夫
-##奈
-##女
-##子
-##学
-##宀
-##宇
-##安
-##宗
-##定
-##宣
-##宮
-##家
-##宿
-##寺
-##將
-##小
-##尚
-##山
-##岡
-##島
-##崎
-##川
-##州
-##巿
-##帝
-##平
-##年
-##幸
-##广
-##弘
-##張
-##彳
-##後
-##御
-##德
-##心
-##忄
-##志
-##忠
-##愛
-##成
-##我
-##戦
-##戸
-##手
-##扌
-##政
-##文
-##新
-##方
-##日
-##明
-##星
-##春
-##昭
-##智
-##曲
-##書
-##月
-##有
-##朝
-##木
-##本
-##李
-##村
-##東
-##松
-##林
-##森
-##楊
-##樹
-##橋
-##歌
-##止
-##正
-##武
-##比
-##氏
-##民
-##水
-##氵
-##氷
-##永
-##江
-##沢
-##河
-##治
-##法
-##海
-##清
-##漢
-##瀬
-##火
-##版
-##犬
-##王
-##生
-##田
-##男
-##疒
-##発
-##白
-##的
-##皇
-##目
-##相
-##省
-##真
-##石
-##示
-##社
-##神
-##福
-##禾
-##秀
-##秋
-##空
-##立
-##章
-##竹
-##糹
-##美
-##義
-##耳
-##良
-##艹
-##花
-##英
-##華
-##葉
-##藤
-##行
-##街
-##西
-##見
-##訁
-##語
-##谷
-##貝
-##貴
-##車
-##軍
-##辶
-##道
-##郎
-##郡
-##部
-##都
-##里
-##野
-##金
-##鈴
-##镇
-##長
-##門
-##間
-##阝
-##阿
-##陳
-##陽
-##雄
-##青
-##面
-##風
-##食
-##香
-##馬
-##高
-##龍
-##龸
-##fi
-##fl
-##！
-##（
-##）
-##，
-##－
-##．
-##／
-##：
-##？
-##～
diff --git a/crawl4ai/train.py b/crawl4ai/train.py
deleted file mode 100644
index f7e7c1a9..00000000
--- a/crawl4ai/train.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import spacy
-from spacy.training import Example
-import random
-import nltk
-from nltk.corpus import reuters
-import torch
-
-def save_spacy_model_as_torch(nlp, model_dir="models/reuters"):
- # Extract the TextCategorizer component
- textcat = nlp.get_pipe("textcat_multilabel")
-
- # Convert the weights to a PyTorch state dictionary
- state_dict = {name: torch.tensor(param.data) for name, param in textcat.model.named_parameters()}
-
- # Save the state dictionary
- torch.save(state_dict, f"{model_dir}/model_weights.pth")
-
- # Extract and save the vocabulary
- vocab = extract_vocab(nlp)
- with open(f"{model_dir}/vocab.txt", "w") as vocab_file:
- for word, idx in vocab.items():
- vocab_file.write(f"{word}\t{idx}\n")
-
- print(f"Model weights and vocabulary saved to: {model_dir}")
-
-def extract_vocab(nlp):
- # Extract vocabulary from the SpaCy model
- vocab = {word: i for i, word in enumerate(nlp.vocab.strings)}
- return vocab
-
-nlp = spacy.load("models/reuters")
-save_spacy_model_as_torch(nlp, model_dir="models")
-
-def train_and_save_reuters_model(model_dir="models/reuters"):
- # Ensure the Reuters corpus is downloaded
- nltk.download('reuters')
- nltk.download('punkt')
- if not reuters.fileids():
- print("Reuters corpus not found.")
- return
-
- # Load a blank English spaCy model
- nlp = spacy.blank("en")
-
- # Create a TextCategorizer with the ensemble model for multi-label classification
- textcat = nlp.add_pipe("textcat_multilabel")
-
- # Add labels to text classifier
- for label in reuters.categories():
- textcat.add_label(label)
-
- # Prepare training data
- train_examples = []
- for fileid in reuters.fileids():
- categories = reuters.categories(fileid)
- text = reuters.raw(fileid)
- cats = {label: label in categories for label in reuters.categories()}
- # Prepare spacy Example objects
- doc = nlp.make_doc(text)
- example = Example.from_dict(doc, {'cats': cats})
- train_examples.append(example)
-
- # Initialize the text categorizer with the example objects
- nlp.initialize(lambda: train_examples)
-
- # Train the model
- random.seed(1)
- spacy.util.fix_random_seed(1)
- for i in range(5): # Adjust iterations for better accuracy
- random.shuffle(train_examples)
- losses = {}
- # Create batches of data
- batches = spacy.util.minibatch(train_examples, size=8)
- for batch in batches:
- nlp.update(batch, drop=0.2, losses=losses)
- print(f"Losses at iteration {i}: {losses}")
-
- # Save the trained model
- nlp.to_disk(model_dir)
- print(f"Model saved to: {model_dir}")
-
-def train_model(model_dir, additional_epochs=0):
- # Load the model if it exists, otherwise start with a blank model
- try:
- nlp = spacy.load(model_dir)
- print("Model loaded from disk.")
- except IOError:
- print("No existing model found. Starting with a new model.")
- nlp = spacy.blank("en")
- textcat = nlp.add_pipe("textcat_multilabel")
- for label in reuters.categories():
- textcat.add_label(label)
-
- # Prepare training data
- train_examples = []
- for fileid in reuters.fileids():
- categories = reuters.categories(fileid)
- text = reuters.raw(fileid)
- cats = {label: label in categories for label in reuters.categories()}
- doc = nlp.make_doc(text)
- example = Example.from_dict(doc, {'cats': cats})
- train_examples.append(example)
-
- # Initialize the model if it was newly created
- if 'textcat_multilabel' not in nlp.pipe_names:
- nlp.initialize(lambda: train_examples)
- else:
- print("Continuing training with existing model.")
-
- # Train the model
- random.seed(1)
- spacy.util.fix_random_seed(1)
- num_epochs = 5 + additional_epochs
- for i in range(num_epochs):
- random.shuffle(train_examples)
- losses = {}
- batches = spacy.util.minibatch(train_examples, size=8)
- for batch in batches:
- nlp.update(batch, drop=0.2, losses=losses)
- print(f"Losses at iteration {i}: {losses}")
-
- # Save the trained model
- nlp.to_disk(model_dir)
- print(f"Model saved to: {model_dir}")
-
-def load_model_and_predict(model_dir, text, tok_k = 3):
- # Load the trained model from the specified directory
- nlp = spacy.load(model_dir)
-
- # Process the text with the loaded model
- doc = nlp(text)
-
- # gee top 3 categories
- top_categories = sorted(doc.cats.items(), key=lambda x: x[1], reverse=True)[:tok_k]
- print(f"Top {tok_k} categories:")
-
- return top_categories
-
-if __name__ == "__main__":
- train_and_save_reuters_model()
- train_model("models/reuters", additional_epochs=5)
- model_directory = "reuters_model_10"
- print(reuters.categories())
- example_text = "Apple Inc. is reportedly buying a startup for $1 billion"
- r =load_model_and_predict(model_directory, example_text)
- print(r)
\ No newline at end of file
diff --git a/crawl4ai/utils.py b/crawl4ai/utils.py
index efb5d79b..1f15dea1 100644
--- a/crawl4ai/utils.py
+++ b/crawl4ai/utils.py
@@ -1,13 +1,12 @@
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from bs4 import BeautifulSoup, Comment, element, Tag, NavigableString
-import html2text
import json
import html
import re
import os
import platform
-from html2text import HTML2Text
+from .html2text import HTML2Text
from .prompts import PROMPT_EXTRACT_BLOCKS
from .config import *
from pathlib import Path
@@ -61,7 +60,7 @@ def get_system_memory():
raise OSError("Unsupported operating system")
def get_home_folder():
- home_folder = os.path.join(Path.home(), ".crawl4ai")
+ home_folder = os.path.join(os.getenv("CRAWL4_AI_BASE_DIRECTORY", os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home())), ".crawl4ai")
os.makedirs(home_folder, exist_ok=True)
os.makedirs(f"{home_folder}/cache", exist_ok=True)
os.makedirs(f"{home_folder}/models", exist_ok=True)
@@ -179,12 +178,25 @@ def escape_json_string(s):
return s
-class CustomHTML2Text(HTML2Text):
+class CustomHTML2Text_v0(HTML2Text):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
- self.ignore_links = True
self.inside_pre = False
self.inside_code = False
+
+ self.skip_internal_links = False
+ self.single_line_break = False
+ self.mark_code = False
+ self.include_sup_sub = False
+ self.body_width = 0
+ self.ignore_mailto_links = True
+ self.ignore_links = False
+ self.escape_backslash = False
+ self.escape_dot = False
+ self.escape_plus = False
+ self.escape_dash = False
+ self.escape_snob = False
+
def handle_tag(self, tag, attrs, start):
if tag == 'pre':
@@ -194,6 +206,10 @@ class CustomHTML2Text(HTML2Text):
else:
self.o('\n```')
self.inside_pre = False
+ elif tag in ["h1", "h2", "h3", "h4", "h5", "h6"]:
+ pass
+
+
# elif tag == 'code' and not self.inside_pre:
# if start:
# if not self.inside_pre:
@@ -690,10 +706,13 @@ def get_content_of_website_optimized(url: str, html: str, word_count_threshold:
body = flatten_nested_elements(body)
base64_pattern = re.compile(r'data:image/[^;]+;base64,([^"]+)')
for img in imgs:
- src = img.get('src', '')
- if base64_pattern.match(src):
- # Replace base64 data with empty string
- img['src'] = base64_pattern.sub('', src)
+ try:
+ src = img.get('src', '')
+ if base64_pattern.match(src):
+ img['src'] = base64_pattern.sub('', src)
+ except:
+ pass
+
cleaned_html = str(body).replace('\n\n', '\n').replace(' ', ' ')
cleaned_html = sanitize_html(cleaned_html)
@@ -964,4 +983,66 @@ def format_html(html_string):
soup = BeautifulSoup(html_string, 'html.parser')
return soup.prettify()
+def normalize_url(href, base_url):
+ """Normalize URLs to ensure consistent format"""
+ from urllib.parse import urljoin, urlparse
+ # Parse base URL to get components
+ parsed_base = urlparse(base_url)
+ if not parsed_base.scheme or not parsed_base.netloc:
+ raise ValueError(f"Invalid base URL format: {base_url}")
+
+ # Use urljoin to handle all cases
+ normalized = urljoin(base_url, href.strip())
+ return normalized
+
+def normalize_url_tmp(href, base_url):
+ """Normalize URLs to ensure consistent format"""
+ # Extract protocol and domain from base URL
+ try:
+ base_parts = base_url.split('/')
+ protocol = base_parts[0]
+ domain = base_parts[2]
+ except IndexError:
+ raise ValueError(f"Invalid base URL format: {base_url}")
+
+ # Handle special protocols
+ special_protocols = {'mailto:', 'tel:', 'ftp:', 'file:', 'data:', 'javascript:'}
+ if any(href.lower().startswith(proto) for proto in special_protocols):
+ return href.strip()
+
+ # Handle anchor links
+ if href.startswith('#'):
+ return f"{base_url}{href}"
+
+ # Handle protocol-relative URLs
+ if href.startswith('//'):
+ return f"{protocol}{href}"
+
+ # Handle root-relative URLs
+ if href.startswith('/'):
+ return f"{protocol}//{domain}{href}"
+
+ # Handle relative URLs
+ if not href.startswith(('http://', 'https://')):
+ # Remove leading './' if present
+ href = href.lstrip('./')
+ return f"{protocol}//{domain}/{href}"
+
+ return href.strip()
+
+def is_external_url(url, base_domain):
+ """Determine if a URL is external"""
+ special_protocols = {'mailto:', 'tel:', 'ftp:', 'file:', 'data:', 'javascript:'}
+ if any(url.lower().startswith(proto) for proto in special_protocols):
+ return True
+
+ try:
+ # Handle URLs with protocol
+ if url.startswith(('http://', 'https://')):
+ url_domain = url.split('/')[2]
+ return base_domain.lower() not in url_domain.lower()
+ except IndexError:
+ return False
+
+ return False
diff --git a/crawl4ai/web_crawler.back.py b/crawl4ai/web_crawler.back.py
deleted file mode 100644
index af78f126..00000000
--- a/crawl4ai/web_crawler.back.py
+++ /dev/null
@@ -1,357 +0,0 @@
-import os, time
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-from pathlib import Path
-
-from .models import UrlModel, CrawlResult
-from .database import init_db, get_cached_url, cache_url, DB_PATH, flush_db
-from .utils import *
-from .chunking_strategy import *
-from .extraction_strategy import *
-from .crawler_strategy import *
-from typing import List
-from concurrent.futures import ThreadPoolExecutor
-from .config import *
-
-
-class WebCrawler:
- def __init__(
- self,
- # db_path: str = None,
- crawler_strategy: CrawlerStrategy = None,
- always_by_pass_cache: bool = False,
- verbose: bool = False,
- ):
- # self.db_path = db_path
- self.crawler_strategy = crawler_strategy or LocalSeleniumCrawlerStrategy(verbose=verbose)
- self.always_by_pass_cache = always_by_pass_cache
-
- # Create the .crawl4ai folder in the user's home directory if it doesn't exist
- self.crawl4ai_folder = os.path.join(Path.home(), ".crawl4ai")
- os.makedirs(self.crawl4ai_folder, exist_ok=True)
- os.makedirs(f"{self.crawl4ai_folder}/cache", exist_ok=True)
-
- # If db_path is not provided, use the default path
- # if not db_path:
- # self.db_path = f"{self.crawl4ai_folder}/crawl4ai.db"
-
- # flush_db()
- init_db()
-
- self.ready = False
-
- def warmup(self):
- print("[LOG] 🌤️ Warming up the WebCrawler")
- result = self.run(
- url='https://crawl4ai.uccode.io/',
- word_count_threshold=5,
- extraction_strategy= NoExtractionStrategy(),
- bypass_cache=False,
- verbose = False
- )
- self.ready = True
- print("[LOG] 🌞 WebCrawler is ready to crawl")
-
- def fetch_page(
- self,
- url_model: UrlModel,
- provider: str = DEFAULT_PROVIDER,
- api_token: str = None,
- extract_blocks_flag: bool = True,
- word_count_threshold=MIN_WORD_THRESHOLD,
- css_selector: str = None,
- screenshot: bool = False,
- use_cached_html: bool = False,
- extraction_strategy: ExtractionStrategy = None,
- chunking_strategy: ChunkingStrategy = RegexChunking(),
- **kwargs,
- ) -> CrawlResult:
- return self.run(
- url_model.url,
- word_count_threshold,
- extraction_strategy or NoExtractionStrategy(),
- chunking_strategy,
- bypass_cache=url_model.forced,
- css_selector=css_selector,
- screenshot=screenshot,
- **kwargs,
- )
- pass
-
- def run_old(
- self,
- url: str,
- word_count_threshold=MIN_WORD_THRESHOLD,
- extraction_strategy: ExtractionStrategy = None,
- chunking_strategy: ChunkingStrategy = RegexChunking(),
- bypass_cache: bool = False,
- css_selector: str = None,
- screenshot: bool = False,
- user_agent: str = None,
- verbose=True,
- **kwargs,
- ) -> CrawlResult:
- if user_agent:
- self.crawler_strategy.update_user_agent(user_agent)
- extraction_strategy = extraction_strategy or NoExtractionStrategy()
- extraction_strategy.verbose = verbose
- # Check if extraction strategy is an instance of ExtractionStrategy if not raise an error
- if not isinstance(extraction_strategy, ExtractionStrategy):
- raise ValueError("Unsupported extraction strategy")
- if not isinstance(chunking_strategy, ChunkingStrategy):
- raise ValueError("Unsupported chunking strategy")
-
- # make sure word_count_threshold is not lesser than MIN_WORD_THRESHOLD
- if word_count_threshold < MIN_WORD_THRESHOLD:
- word_count_threshold = MIN_WORD_THRESHOLD
-
- # Check cache first
- if not bypass_cache and not self.always_by_pass_cache:
- cached = get_cached_url(url)
- if cached:
- return CrawlResult(
- **{
- "url": cached[0],
- "html": cached[1],
- "cleaned_html": cached[2],
- "markdown": cached[3],
- "extracted_content": cached[4],
- "success": cached[5],
- "media": json.loads(cached[6] or "{}"),
- "links": json.loads(cached[7] or "{}"),
- "metadata": json.loads(cached[8] or "{}"), # "metadata": "{}
- "screenshot": cached[9],
- "error_message": "",
- }
- )
-
- # Initialize WebDriver for crawling
- t = time.time()
- if kwargs.get("js", None):
- self.crawler_strategy.js_code = kwargs.get("js")
- html = self.crawler_strategy.crawl(url)
- base64_image = None
- if screenshot:
- base64_image = self.crawler_strategy.take_screenshot()
- success = True
- error_message = ""
- # Extract content from HTML
- try:
- result = get_content_of_website(url, html, word_count_threshold, css_selector=css_selector)
- metadata = extract_metadata(html)
- if result is None:
- raise ValueError(f"Failed to extract content from the website: {url}")
- except InvalidCSSSelectorError as e:
- raise ValueError(str(e))
-
- cleaned_html = result.get("cleaned_html", "")
- markdown = result.get("markdown", "")
- media = result.get("media", [])
- links = result.get("links", [])
-
- # Print a profession LOG style message, show time taken and say crawling is done
- if verbose:
- print(
- f"[LOG] 🚀 Crawling done for {url}, success: {success}, time taken: {time.time() - t} seconds"
- )
-
- extracted_content = []
- if verbose:
- print(f"[LOG] 🔥 Extracting semantic blocks for {url}, Strategy: {extraction_strategy.name}")
- t = time.time()
- # Split markdown into sections
- sections = chunking_strategy.chunk(markdown)
- # sections = merge_chunks_based_on_token_threshold(sections, CHUNK_TOKEN_THRESHOLD)
-
- extracted_content = extraction_strategy.run(
- url, sections,
- )
- extracted_content = json.dumps(extracted_content)
-
- if verbose:
- print(
- f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t} seconds."
- )
-
- # Cache the result
- cleaned_html = beautify_html(cleaned_html)
- cache_url(
- url,
- html,
- cleaned_html,
- markdown,
- extracted_content,
- success,
- json.dumps(media),
- json.dumps(links),
- json.dumps(metadata),
- screenshot=base64_image,
- )
-
- return CrawlResult(
- url=url,
- html=html,
- cleaned_html=cleaned_html,
- markdown=markdown,
- media=media,
- links=links,
- metadata=metadata,
- screenshot=base64_image,
- extracted_content=extracted_content,
- success=success,
- error_message=error_message,
- )
-
- def fetch_pages(
- self,
- url_models: List[UrlModel],
- provider: str = DEFAULT_PROVIDER,
- api_token: str = None,
- extract_blocks_flag: bool = True,
- word_count_threshold=MIN_WORD_THRESHOLD,
- use_cached_html: bool = False,
- css_selector: str = None,
- screenshot: bool = False,
- extraction_strategy: ExtractionStrategy = None,
- chunking_strategy: ChunkingStrategy = RegexChunking(),
- **kwargs,
- ) -> List[CrawlResult]:
- extraction_strategy = extraction_strategy or NoExtractionStrategy()
- def fetch_page_wrapper(url_model, *args, **kwargs):
- return self.fetch_page(url_model, *args, **kwargs)
-
- with ThreadPoolExecutor() as executor:
- results = list(
- executor.map(
- fetch_page_wrapper,
- url_models,
- [provider] * len(url_models),
- [api_token] * len(url_models),
- [extract_blocks_flag] * len(url_models),
- [word_count_threshold] * len(url_models),
- [css_selector] * len(url_models),
- [screenshot] * len(url_models),
- [use_cached_html] * len(url_models),
- [extraction_strategy] * len(url_models),
- [chunking_strategy] * len(url_models),
- *[kwargs] * len(url_models),
- )
- )
-
- return results
-
- def run(
- self,
- url: str,
- word_count_threshold=MIN_WORD_THRESHOLD,
- extraction_strategy: ExtractionStrategy = None,
- chunking_strategy: ChunkingStrategy = RegexChunking(),
- bypass_cache: bool = False,
- css_selector: str = None,
- screenshot: bool = False,
- user_agent: str = None,
- verbose=True,
- **kwargs,
- ) -> CrawlResult:
- extraction_strategy = extraction_strategy or NoExtractionStrategy()
- extraction_strategy.verbose = verbose
- if not isinstance(extraction_strategy, ExtractionStrategy):
- raise ValueError("Unsupported extraction strategy")
- if not isinstance(chunking_strategy, ChunkingStrategy):
- raise ValueError("Unsupported chunking strategy")
-
- if word_count_threshold < MIN_WORD_THRESHOLD:
- word_count_threshold = MIN_WORD_THRESHOLD
-
- # Check cache first
- cached = None
- extracted_content = None
- if not bypass_cache and not self.always_by_pass_cache:
- cached = get_cached_url(url)
-
- if cached:
- html = cached[1]
- extracted_content = cached[2]
- if screenshot:
- screenshot = cached[9]
-
- else:
- if user_agent:
- self.crawler_strategy.update_user_agent(user_agent)
- html = self.crawler_strategy.crawl(url)
- if screenshot:
- screenshot = self.crawler_strategy.take_screenshot()
-
- return self.process_html(url, html, extracted_content, word_count_threshold, extraction_strategy, chunking_strategy, css_selector, screenshot, verbose, bool(cached), **kwargs)
-
- def process_html(
- self,
- url: str,
- html: str,
- extracted_content: str,
- word_count_threshold: int,
- extraction_strategy: ExtractionStrategy,
- chunking_strategy: ChunkingStrategy,
- css_selector: str,
- screenshot: bool,
- verbose: bool,
- is_cached: bool,
- **kwargs,
- ) -> CrawlResult:
- t = time.time()
- # Extract content from HTML
- try:
- result = get_content_of_website(url, html, word_count_threshold, css_selector=css_selector)
- metadata = extract_metadata(html)
- if result is None:
- raise ValueError(f"Failed to extract content from the website: {url}")
- except InvalidCSSSelectorError as e:
- raise ValueError(str(e))
-
- cleaned_html = result.get("cleaned_html", "")
- markdown = result.get("markdown", "")
- media = result.get("media", [])
- links = result.get("links", [])
-
- if verbose:
- print(f"[LOG] 🚀 Crawling done for {url}, success: True, time taken: {time.time() - t} seconds")
-
- if extracted_content is None:
- if verbose:
- print(f"[LOG] 🔥 Extracting semantic blocks for {url}, Strategy: {extraction_strategy.name}")
-
- sections = chunking_strategy.chunk(markdown)
- extracted_content = extraction_strategy.run(url, sections)
- extracted_content = json.dumps(extracted_content)
-
- if verbose:
- print(f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t} seconds.")
-
- screenshot = None if not screenshot else screenshot
-
- if not is_cached:
- cache_url(
- url,
- html,
- cleaned_html,
- markdown,
- extracted_content,
- True,
- json.dumps(media),
- json.dumps(links),
- json.dumps(metadata),
- screenshot=screenshot,
- )
-
- return CrawlResult(
- url=url,
- html=html,
- cleaned_html=cleaned_html,
- markdown=markdown,
- media=media,
- links=links,
- metadata=metadata,
- screenshot=screenshot,
- extracted_content=extracted_content,
- success=True,
- error_message="",
- )
\ No newline at end of file
diff --git a/crawl4ai/web_crawler.py b/crawl4ai/web_crawler.py
index 20e9b04e..d44de183 100644
--- a/crawl4ai/web_crawler.py
+++ b/crawl4ai/web_crawler.py
@@ -20,7 +20,7 @@ class WebCrawler:
def __init__(self, crawler_strategy: CrawlerStrategy = None, always_by_pass_cache: bool = False, verbose: bool = False):
self.crawler_strategy = crawler_strategy or LocalSeleniumCrawlerStrategy(verbose=verbose)
self.always_by_pass_cache = always_by_pass_cache
- self.crawl4ai_folder = os.path.join(Path.home(), ".crawl4ai")
+ self.crawl4ai_folder = os.path.join(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai")
os.makedirs(self.crawl4ai_folder, exist_ok=True)
os.makedirs(f"{self.crawl4ai_folder}/cache", exist_ok=True)
init_db()
diff --git a/docs/.DS_Store b/docs/.DS_Store
deleted file mode 100644
index 2502f79f..00000000
Binary files a/docs/.DS_Store and /dev/null differ
diff --git a/docs/assets/pitch-dark.png b/docs/assets/pitch-dark.png
new file mode 100644
index 00000000..9b9b37b5
Binary files /dev/null and b/docs/assets/pitch-dark.png differ
diff --git a/docs/assets/pitch-dark.svg b/docs/assets/pitch-dark.svg
new file mode 100644
index 00000000..0913b2da
--- /dev/null
+++ b/docs/assets/pitch-dark.svg
@@ -0,0 +1,64 @@
+
\ No newline at end of file
diff --git a/docs/chunking_strategies.json b/docs/chunking_strategies.json
deleted file mode 100644
index b0d2a6bc..00000000
--- a/docs/chunking_strategies.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
- "RegexChunking": "### RegexChunking\n\n`RegexChunking` is a text chunking strategy that splits a given text into smaller parts using regular expressions.\nThis is useful for preparing large texts for processing by language models, ensuring they are divided into manageable segments.\n\n#### Constructor Parameters:\n- `patterns` (list, optional): A list of regular expression patterns used to split the text. Default is to split by double newlines (`['\\n\\n']`).\n\n#### Example usage:\n```python\nchunker = RegexChunking(patterns=[r'\\n\\n', r'\\. '])\nchunks = chunker.chunk(\"This is a sample text. It will be split into chunks.\")\n```",
-
- "NlpSentenceChunking": "### NlpSentenceChunking\n\n`NlpSentenceChunking` uses a natural language processing model to chunk a given text into sentences. This approach leverages SpaCy to accurately split text based on sentence boundaries.\n\n#### Constructor Parameters:\n- None.\n\n#### Example usage:\n```python\nchunker = NlpSentenceChunking()\nchunks = chunker.chunk(\"This is a sample text. It will be split into sentences.\")\n```",
-
- "TopicSegmentationChunking": "### TopicSegmentationChunking\n\n`TopicSegmentationChunking` uses the TextTiling algorithm to segment a given text into topic-based chunks. This method identifies thematic boundaries in the text.\n\n#### Constructor Parameters:\n- `num_keywords` (int, optional): The number of keywords to extract for each topic segment. Default is `3`.\n\n#### Example usage:\n```python\nchunker = TopicSegmentationChunking(num_keywords=3)\nchunks = chunker.chunk(\"This is a sample text. It will be split into topic-based segments.\")\n```",
-
- "FixedLengthWordChunking": "### FixedLengthWordChunking\n\n`FixedLengthWordChunking` splits a given text into chunks of fixed length, based on the number of words.\n\n#### Constructor Parameters:\n- `chunk_size` (int, optional): The number of words in each chunk. Default is `100`.\n\n#### Example usage:\n```python\nchunker = FixedLengthWordChunking(chunk_size=100)\nchunks = chunker.chunk(\"This is a sample text. It will be split into fixed-length word chunks.\")\n```",
-
- "SlidingWindowChunking": "### SlidingWindowChunking\n\n`SlidingWindowChunking` uses a sliding window approach to chunk a given text. Each chunk has a fixed length, and the window slides by a specified step size.\n\n#### Constructor Parameters:\n- `window_size` (int, optional): The number of words in each chunk. Default is `100`.\n- `step` (int, optional): The number of words to slide the window. Default is `50`.\n\n#### Example usage:\n```python\nchunker = SlidingWindowChunking(window_size=100, step=50)\nchunks = chunker.chunk(\"This is a sample text. It will be split using a sliding window approach.\")\n```"
- }
-
\ No newline at end of file
diff --git a/docs/examples/docker_example.py b/docs/examples/docker_example.py
new file mode 100644
index 00000000..c22acd55
--- /dev/null
+++ b/docs/examples/docker_example.py
@@ -0,0 +1,300 @@
+import requests
+import json
+import time
+import sys
+import base64
+import os
+from typing import Dict, Any
+
+class Crawl4AiTester:
+ def __init__(self, base_url: str = "http://localhost:11235"):
+ self.base_url = base_url
+
+ def submit_and_wait(self, request_data: Dict[str, Any], timeout: int = 300) -> Dict[str, Any]:
+ # Submit crawl job
+ response = requests.post(f"{self.base_url}/crawl", json=request_data)
+ task_id = response.json()["task_id"]
+ print(f"Task ID: {task_id}")
+
+ # Poll for result
+ start_time = time.time()
+ while True:
+ if time.time() - start_time > timeout:
+ raise TimeoutError(f"Task {task_id} did not complete within {timeout} seconds")
+
+ result = requests.get(f"{self.base_url}/task/{task_id}")
+ status = result.json()
+
+ if status["status"] == "failed":
+ print("Task failed:", status.get("error"))
+ raise Exception(f"Task failed: {status.get('error')}")
+
+ if status["status"] == "completed":
+ return status
+
+ time.sleep(2)
+
+def test_docker_deployment(version="basic"):
+ tester = Crawl4AiTester()
+ print(f"Testing Crawl4AI Docker {version} version")
+
+ # Health check with timeout and retry
+ max_retries = 5
+ for i in range(max_retries):
+ try:
+ health = requests.get(f"{tester.base_url}/health", timeout=10)
+ print("Health check:", health.json())
+ break
+ except requests.exceptions.RequestException as e:
+ if i == max_retries - 1:
+ print(f"Failed to connect after {max_retries} attempts")
+ sys.exit(1)
+ print(f"Waiting for service to start (attempt {i+1}/{max_retries})...")
+ time.sleep(5)
+
+ # Test cases based on version
+ test_basic_crawl(tester)
+
+ # if version in ["full", "transformer"]:
+ # test_cosine_extraction(tester)
+
+ # test_js_execution(tester)
+ # test_css_selector(tester)
+ # test_structured_extraction(tester)
+ # test_llm_extraction(tester)
+ # test_llm_with_ollama(tester)
+ # test_screenshot(tester)
+
+
+def test_basic_crawl(tester: Crawl4AiTester):
+ print("\n=== Testing Basic Crawl ===")
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 10
+ }
+
+ result = tester.submit_and_wait(request)
+ print(f"Basic crawl result length: {len(result['result']['markdown'])}")
+ assert result["result"]["success"]
+ assert len(result["result"]["markdown"]) > 0
+
+def test_js_execution(tester: Crawl4AiTester):
+ print("\n=== Testing JS Execution ===")
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 8,
+ "js_code": [
+ "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
+ ],
+ "wait_for": "article.tease-card:nth-child(10)",
+ "crawler_params": {
+ "headless": True
+ }
+ }
+
+ result = tester.submit_and_wait(request)
+ print(f"JS execution result length: {len(result['result']['markdown'])}")
+ assert result["result"]["success"]
+
+def test_css_selector(tester: Crawl4AiTester):
+ print("\n=== Testing CSS Selector ===")
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 7,
+ "css_selector": ".wide-tease-item__description",
+ "crawler_params": {
+ "headless": True
+ },
+ "extra": {"word_count_threshold": 10}
+
+ }
+
+ result = tester.submit_and_wait(request)
+ print(f"CSS selector result length: {len(result['result']['markdown'])}")
+ assert result["result"]["success"]
+
+def test_structured_extraction(tester: Crawl4AiTester):
+ print("\n=== Testing Structured Extraction ===")
+ schema = {
+ "name": "Coinbase Crypto Prices",
+ "baseSelector": ".cds-tableRow-t45thuk",
+ "fields": [
+ {
+ "name": "crypto",
+ "selector": "td:nth-child(1) h2",
+ "type": "text",
+ },
+ {
+ "name": "symbol",
+ "selector": "td:nth-child(1) p",
+ "type": "text",
+ },
+ {
+ "name": "price",
+ "selector": "td:nth-child(2)",
+ "type": "text",
+ }
+ ],
+ }
+
+ request = {
+ "urls": "https://www.coinbase.com/explore",
+ "priority": 9,
+ "extraction_config": {
+ "type": "json_css",
+ "params": {
+ "schema": schema
+ }
+ }
+ }
+
+ result = tester.submit_and_wait(request)
+ extracted = json.loads(result["result"]["extracted_content"])
+ print(f"Extracted {len(extracted)} items")
+ print("Sample item:", json.dumps(extracted[0], indent=2))
+ assert result["result"]["success"]
+ assert len(extracted) > 0
+
+def test_llm_extraction(tester: Crawl4AiTester):
+ print("\n=== Testing LLM Extraction ===")
+ schema = {
+ "type": "object",
+ "properties": {
+ "model_name": {
+ "type": "string",
+ "description": "Name of the OpenAI model."
+ },
+ "input_fee": {
+ "type": "string",
+ "description": "Fee for input token for the OpenAI model."
+ },
+ "output_fee": {
+ "type": "string",
+ "description": "Fee for output token for the OpenAI model."
+ }
+ },
+ "required": ["model_name", "input_fee", "output_fee"]
+ }
+
+ request = {
+ "urls": "https://openai.com/api/pricing",
+ "priority": 8,
+ "extraction_config": {
+ "type": "llm",
+ "params": {
+ "provider": "openai/gpt-4o-mini",
+ "api_token": os.getenv("OPENAI_API_KEY"),
+ "schema": schema,
+ "extraction_type": "schema",
+ "instruction": """From the crawled content, extract all mentioned model names along with their fees for input and output tokens."""
+ }
+ },
+ "crawler_params": {"word_count_threshold": 1}
+ }
+
+ try:
+ result = tester.submit_and_wait(request)
+ extracted = json.loads(result["result"]["extracted_content"])
+ print(f"Extracted {len(extracted)} model pricing entries")
+ print("Sample entry:", json.dumps(extracted[0], indent=2))
+ assert result["result"]["success"]
+ except Exception as e:
+ print(f"LLM extraction test failed (might be due to missing API key): {str(e)}")
+
+def test_llm_with_ollama(tester: Crawl4AiTester):
+ print("\n=== Testing LLM with Ollama ===")
+ schema = {
+ "type": "object",
+ "properties": {
+ "article_title": {
+ "type": "string",
+ "description": "The main title of the news article"
+ },
+ "summary": {
+ "type": "string",
+ "description": "A brief summary of the article content"
+ },
+ "main_topics": {
+ "type": "array",
+ "items": {"type": "string"},
+ "description": "Main topics or themes discussed in the article"
+ }
+ }
+ }
+
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 8,
+ "extraction_config": {
+ "type": "llm",
+ "params": {
+ "provider": "ollama/llama2",
+ "schema": schema,
+ "extraction_type": "schema",
+ "instruction": "Extract the main article information including title, summary, and main topics."
+ }
+ },
+ "extra": {"word_count_threshold": 1},
+ "crawler_params": {"verbose": True}
+ }
+
+ try:
+ result = tester.submit_and_wait(request)
+ extracted = json.loads(result["result"]["extracted_content"])
+ print("Extracted content:", json.dumps(extracted, indent=2))
+ assert result["result"]["success"]
+ except Exception as e:
+ print(f"Ollama extraction test failed: {str(e)}")
+
+def test_cosine_extraction(tester: Crawl4AiTester):
+ print("\n=== Testing Cosine Extraction ===")
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 8,
+ "extraction_config": {
+ "type": "cosine",
+ "params": {
+ "semantic_filter": "business finance economy",
+ "word_count_threshold": 10,
+ "max_dist": 0.2,
+ "top_k": 3
+ }
+ }
+ }
+
+ try:
+ result = tester.submit_and_wait(request)
+ extracted = json.loads(result["result"]["extracted_content"])
+ print(f"Extracted {len(extracted)} text clusters")
+ print("First cluster tags:", extracted[0]["tags"])
+ assert result["result"]["success"]
+ except Exception as e:
+ print(f"Cosine extraction test failed: {str(e)}")
+
+def test_screenshot(tester: Crawl4AiTester):
+ print("\n=== Testing Screenshot ===")
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 5,
+ "screenshot": True,
+ "crawler_params": {
+ "headless": True
+ }
+ }
+
+ result = tester.submit_and_wait(request)
+ print("Screenshot captured:", bool(result["result"]["screenshot"]))
+
+ if result["result"]["screenshot"]:
+ # Save screenshot
+ screenshot_data = base64.b64decode(result["result"]["screenshot"])
+ with open("test_screenshot.jpg", "wb") as f:
+ f.write(screenshot_data)
+ print("Screenshot saved as test_screenshot.jpg")
+
+ assert result["result"]["success"]
+
+if __name__ == "__main__":
+ version = sys.argv[1] if len(sys.argv) > 1 else "basic"
+ # version = "full"
+ test_docker_deployment(version)
\ No newline at end of file
diff --git a/docs/examples/quickstart.ipynb b/docs/examples/quickstart.ipynb
index 09ad623b..4751dec8 100644
--- a/docs/examples/quickstart.ipynb
+++ b/docs/examples/quickstart.ipynb
@@ -1,736 +1,664 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6yLvrXn7yZQI"
- },
- "source": [
- "# Crawl4AI: Advanced Web Crawling and Data Extraction\n",
- "\n",
- "Welcome to this interactive notebook showcasing Crawl4AI, an advanced asynchronous web crawling and data extraction library.\n",
- "\n",
- "- GitHub Repository: [https://github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)\n",
- "- Twitter: [@unclecode](https://twitter.com/unclecode)\n",
- "- Website: [https://crawl4ai.com](https://crawl4ai.com)\n",
- "\n",
- "Let's explore the powerful features of Crawl4AI!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "KIn_9nxFyZQK"
- },
- "source": [
- "## Installation\n",
- "\n",
- "First, let's install Crawl4AI from GitHub:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "mSnaxLf3zMog"
- },
- "outputs": [],
- "source": [
- "!sudo apt-get update && sudo apt-get install -y libwoff1 libopus0 libwebp6 libwebpdemux2 libenchant1c2a libgudev-1.0-0 libsecret-1-0 libhyphen0 libgdk-pixbuf2.0-0 libegl1 libnotify4 libxslt1.1 libevent-2.1-7 libgles2 libvpx6 libxcomposite1 libatk1.0-0 libatk-bridge2.0-0 libepoxy0 libgtk-3-0 libharfbuzz-icu0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "xlXqaRtayZQK"
- },
- "outputs": [],
- "source": [
- "# !pip install \"crawl4ai @ git+https://github.com/unclecode/crawl4ai.git\"\n",
- "!pip install \"crawl4ai @ git+https://github.com/unclecode/crawl4ai.git@staging\"\n",
- "!pip install nest-asyncio\n",
- "!playwright install"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "qKCE7TI7yZQL"
- },
- "source": [
- "Now, let's import the necessary libraries:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "id": "I67tr7aAyZQL"
- },
- "outputs": [],
- "source": [
- "import asyncio\n",
- "import nest_asyncio\n",
- "from crawl4ai import AsyncWebCrawler\n",
- "from crawl4ai.extraction_strategy import JsonCssExtractionStrategy, LLMExtractionStrategy\n",
- "import json\n",
- "import time\n",
- "from pydantic import BaseModel, Field\n",
- "\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "h7yR_Rt_yZQM"
- },
- "source": [
- "## Basic Usage\n",
- "\n",
- "Let's start with a simple crawl example:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "yBh6hf4WyZQM",
- "outputId": "0f83af5c-abba-4175-ed95-70b7512e6bcc"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
- "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
- "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.05 seconds\n",
- "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.05 seconds.\n",
- "18102\n"
- ]
- }
- ],
- "source": [
- "async def simple_crawl():\n",
- " async with AsyncWebCrawler(verbose=True) as crawler:\n",
- " result = await crawler.arun(url=\"https://www.nbcnews.com/business\")\n",
- " print(len(result.markdown))\n",
- "await simple_crawl()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9rtkgHI28uI4"
- },
- "source": [
- "💡 By default, **Crawl4AI** caches the result of every URL, so the next time you call it, you’ll get an instant result. But if you want to bypass the cache, just set `bypass_cache=True`."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "MzZ0zlJ9yZQM"
- },
- "source": [
- "## Advanced Features\n",
- "\n",
- "### Executing JavaScript and Using CSS Selectors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "gHStF86xyZQM",
- "outputId": "34d0fb6d-4dec-4677-f76e-85a1f082829b"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
- "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
- "[LOG] 🕸️ Crawling https://www.nbcnews.com/business using AsyncPlaywrightCrawlerStrategy...\n",
- "[LOG] ✅ Crawled https://www.nbcnews.com/business successfully!\n",
- "[LOG] 🚀 Crawling done for https://www.nbcnews.com/business, success: True, time taken: 6.06 seconds\n",
- "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.10 seconds\n",
- "[LOG] 🔥 Extracting semantic blocks for https://www.nbcnews.com/business, Strategy: AsyncWebCrawler\n",
- "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.11 seconds.\n",
- "41135\n"
- ]
- }
- ],
- "source": [
- "async def js_and_css():\n",
- " async with AsyncWebCrawler(verbose=True) as crawler:\n",
- " js_code = [\"const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();\"]\n",
- " result = await crawler.arun(\n",
- " url=\"https://www.nbcnews.com/business\",\n",
- " js_code=js_code,\n",
- " # css_selector=\"YOUR_CSS_SELECTOR_HERE\",\n",
- " bypass_cache=True\n",
- " )\n",
- " print(len(result.markdown))\n",
- "\n",
- "await js_and_css()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "cqE_W4coyZQM"
- },
- "source": [
- "### Using a Proxy\n",
- "\n",
- "Note: You'll need to replace the proxy URL with a working proxy for this example to run successfully."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "QjAyiAGqyZQM"
- },
- "outputs": [],
- "source": [
- "async def use_proxy():\n",
- " async with AsyncWebCrawler(verbose=True, proxy=\"http://your-proxy-url:port\") as crawler:\n",
- " result = await crawler.arun(\n",
- " url=\"https://www.nbcnews.com/business\",\n",
- " bypass_cache=True\n",
- " )\n",
- " print(result.markdown[:500]) # Print first 500 characters\n",
- "\n",
- "# Uncomment the following line to run the proxy example\n",
- "# await use_proxy()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "XTZ88lbayZQN"
- },
- "source": [
- "### Extracting Structured Data with OpenAI\n",
- "\n",
- "Note: You'll need to set your OpenAI API key as an environment variable for this example to work."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "fIOlDayYyZQN",
- "outputId": "cb8359cc-dee0-4762-9698-5dfdcee055b8"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
- "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
- "[LOG] 🕸️ Crawling https://openai.com/api/pricing/ using AsyncPlaywrightCrawlerStrategy...\n",
- "[LOG] ✅ Crawled https://openai.com/api/pricing/ successfully!\n",
- "[LOG] 🚀 Crawling done for https://openai.com/api/pricing/, success: True, time taken: 3.77 seconds\n",
- "[LOG] 🚀 Content extracted for https://openai.com/api/pricing/, success: True, time taken: 0.21 seconds\n",
- "[LOG] 🔥 Extracting semantic blocks for https://openai.com/api/pricing/, Strategy: AsyncWebCrawler\n",
- "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 0\n",
- "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 1\n",
- "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 2\n",
- "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 3\n",
- "[LOG] Extracted 4 blocks from URL: https://openai.com/api/pricing/ block index: 3\n",
- "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 4\n",
- "[LOG] Extracted 5 blocks from URL: https://openai.com/api/pricing/ block index: 0\n",
- "[LOG] Extracted 1 blocks from URL: https://openai.com/api/pricing/ block index: 4\n",
- "[LOG] Extracted 8 blocks from URL: https://openai.com/api/pricing/ block index: 1\n",
- "[LOG] Extracted 12 blocks from URL: https://openai.com/api/pricing/ block index: 2\n",
- "[LOG] 🚀 Extraction done for https://openai.com/api/pricing/, time taken: 8.55 seconds.\n",
- "5029\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "from google.colab import userdata\n",
- "os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')\n",
- "\n",
- "class OpenAIModelFee(BaseModel):\n",
- " model_name: str = Field(..., description=\"Name of the OpenAI model.\")\n",
- " input_fee: str = Field(..., description=\"Fee for input token for the OpenAI model.\")\n",
- " output_fee: str = Field(..., description=\"Fee for output token for the OpenAI model.\")\n",
- "\n",
- "async def extract_openai_fees():\n",
- " async with AsyncWebCrawler(verbose=True) as crawler:\n",
- " result = await crawler.arun(\n",
- " url='https://openai.com/api/pricing/',\n",
- " word_count_threshold=1,\n",
- " extraction_strategy=LLMExtractionStrategy(\n",
- " provider=\"openai/gpt-4o\", api_token=os.getenv('OPENAI_API_KEY'),\n",
- " schema=OpenAIModelFee.schema(),\n",
- " extraction_type=\"schema\",\n",
- " instruction=\"\"\"From the crawled content, extract all mentioned model names along with their fees for input and output tokens.\n",
- " Do not miss any models in the entire content. One extracted model JSON format should look like this:\n",
- " {\"model_name\": \"GPT-4\", \"input_fee\": \"US$10.00 / 1M tokens\", \"output_fee\": \"US$30.00 / 1M tokens\"}.\"\"\"\n",
- " ),\n",
- " bypass_cache=True,\n",
- " )\n",
- " print(len(result.extracted_content))\n",
- "\n",
- "# Uncomment the following line to run the OpenAI extraction example\n",
- "await extract_openai_fees()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "BypA5YxEyZQN"
- },
- "source": [
- "### Advanced Multi-Page Crawling with JavaScript Execution"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "tfkcVQ0b7mw-"
- },
- "source": [
- "## Advanced Multi-Page Crawling with JavaScript Execution\n",
- "\n",
- "This example demonstrates Crawl4AI's ability to handle complex crawling scenarios, specifically extracting commits from multiple pages of a GitHub repository. The challenge here is that clicking the \"Next\" button doesn't load a new page, but instead uses asynchronous JavaScript to update the content. This is a common hurdle in modern web crawling.\n",
- "\n",
- "To overcome this, we use Crawl4AI's custom JavaScript execution to simulate clicking the \"Next\" button, and implement a custom hook to detect when new data has loaded. Our strategy involves comparing the first commit's text before and after \"clicking\" Next, waiting until it changes to confirm new data has rendered. This showcases Crawl4AI's flexibility in handling dynamic content and its ability to implement custom logic for even the most challenging crawling tasks."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "qUBKGpn3yZQN",
- "outputId": "3e555b6a-ed33-42f4-cce9-499a923fbe17"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
- "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
- "[LOG] 🕸️ Crawling https://github.com/microsoft/TypeScript/commits/main using AsyncPlaywrightCrawlerStrategy...\n",
- "[LOG] ✅ Crawled https://github.com/microsoft/TypeScript/commits/main successfully!\n",
- "[LOG] 🚀 Crawling done for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 5.16 seconds\n",
- "[LOG] 🚀 Content extracted for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.28 seconds\n",
- "[LOG] 🔥 Extracting semantic blocks for https://github.com/microsoft/TypeScript/commits/main, Strategy: AsyncWebCrawler\n",
- "[LOG] 🚀 Extraction done for https://github.com/microsoft/TypeScript/commits/main, time taken: 0.28 seconds.\n",
- "Page 1: Found 35 commits\n",
- "[LOG] 🕸️ Crawling https://github.com/microsoft/TypeScript/commits/main using AsyncPlaywrightCrawlerStrategy...\n",
- "[LOG] ✅ Crawled https://github.com/microsoft/TypeScript/commits/main successfully!\n",
- "[LOG] 🚀 Crawling done for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.78 seconds\n",
- "[LOG] 🚀 Content extracted for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.90 seconds\n",
- "[LOG] 🔥 Extracting semantic blocks for https://github.com/microsoft/TypeScript/commits/main, Strategy: AsyncWebCrawler\n",
- "[LOG] 🚀 Extraction done for https://github.com/microsoft/TypeScript/commits/main, time taken: 0.90 seconds.\n",
- "Page 2: Found 35 commits\n",
- "[LOG] 🕸️ Crawling https://github.com/microsoft/TypeScript/commits/main using AsyncPlaywrightCrawlerStrategy...\n",
- "[LOG] ✅ Crawled https://github.com/microsoft/TypeScript/commits/main successfully!\n",
- "[LOG] 🚀 Crawling done for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 2.00 seconds\n",
- "[LOG] 🚀 Content extracted for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.74 seconds\n",
- "[LOG] 🔥 Extracting semantic blocks for https://github.com/microsoft/TypeScript/commits/main, Strategy: AsyncWebCrawler\n",
- "[LOG] 🚀 Extraction done for https://github.com/microsoft/TypeScript/commits/main, time taken: 0.75 seconds.\n",
- "Page 3: Found 35 commits\n",
- "Successfully crawled 105 commits across 3 pages\n"
- ]
- }
- ],
- "source": [
- "import re\n",
- "from bs4 import BeautifulSoup\n",
- "\n",
- "async def crawl_typescript_commits():\n",
- " first_commit = \"\"\n",
- " async def on_execution_started(page):\n",
- " nonlocal first_commit\n",
- " try:\n",
- " while True:\n",
- " await page.wait_for_selector('li.Box-sc-g0xbh4-0 h4')\n",
- " commit = await page.query_selector('li.Box-sc-g0xbh4-0 h4')\n",
- " commit = await commit.evaluate('(element) => element.textContent')\n",
- " commit = re.sub(r'\\s+', '', commit)\n",
- " if commit and commit != first_commit:\n",
- " first_commit = commit\n",
- " break\n",
- " await asyncio.sleep(0.5)\n",
- " except Exception as e:\n",
- " print(f\"Warning: New content didn't appear after JavaScript execution: {e}\")\n",
- "\n",
- " async with AsyncWebCrawler(verbose=True) as crawler:\n",
- " crawler.crawler_strategy.set_hook('on_execution_started', on_execution_started)\n",
- "\n",
- " url = \"https://github.com/microsoft/TypeScript/commits/main\"\n",
- " session_id = \"typescript_commits_session\"\n",
- " all_commits = []\n",
- "\n",
- " js_next_page = \"\"\"\n",
- " const button = document.querySelector('a[data-testid=\"pagination-next-button\"]');\n",
- " if (button) button.click();\n",
- " \"\"\"\n",
- "\n",
- " for page in range(3): # Crawl 3 pages\n",
- " result = await crawler.arun(\n",
- " url=url,\n",
- " session_id=session_id,\n",
- " css_selector=\"li.Box-sc-g0xbh4-0\",\n",
- " js=js_next_page if page > 0 else None,\n",
- " bypass_cache=True,\n",
- " js_only=page > 0\n",
- " )\n",
- "\n",
- " assert result.success, f\"Failed to crawl page {page + 1}\"\n",
- "\n",
- " soup = BeautifulSoup(result.cleaned_html, 'html.parser')\n",
- " commits = soup.select(\"li\")\n",
- " all_commits.extend(commits)\n",
- "\n",
- " print(f\"Page {page + 1}: Found {len(commits)} commits\")\n",
- "\n",
- " await crawler.crawler_strategy.kill_session(session_id)\n",
- " print(f\"Successfully crawled {len(all_commits)} commits across 3 pages\")\n",
- "\n",
- "await crawl_typescript_commits()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "EJRnYsp6yZQN"
- },
- "source": [
- "### Using JsonCssExtractionStrategy for Fast Structured Output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "1ZMqIzB_8SYp"
- },
- "source": [
- "The JsonCssExtractionStrategy is a powerful feature of Crawl4AI that allows for precise, structured data extraction from web pages. Here's how it works:\n",
- "\n",
- "1. You define a schema that describes the pattern of data you're interested in extracting.\n",
- "2. The schema includes a base selector that identifies repeating elements on the page.\n",
- "3. Within the schema, you define fields, each with its own selector and type.\n",
- "4. These field selectors are applied within the context of each base selector element.\n",
- "5. The strategy supports nested structures, lists within lists, and various data types.\n",
- "6. You can even include computed fields for more complex data manipulation.\n",
- "\n",
- "This approach allows for highly flexible and precise data extraction, transforming semi-structured web content into clean, structured JSON data. It's particularly useful for extracting consistent data patterns from pages like product listings, news articles, or search results.\n",
- "\n",
- "For more details and advanced usage, check out the full documentation on the Crawl4AI website."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "trCMR2T9yZQN",
- "outputId": "718d36f4-cccf-40f4-8d8c-c3ba73524d16"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
- "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
- "[LOG] 🕸️ Crawling https://www.nbcnews.com/business using AsyncPlaywrightCrawlerStrategy...\n",
- "[LOG] ✅ Crawled https://www.nbcnews.com/business successfully!\n",
- "[LOG] 🚀 Crawling done for https://www.nbcnews.com/business, success: True, time taken: 7.00 seconds\n",
- "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.32 seconds\n",
- "[LOG] 🔥 Extracting semantic blocks for https://www.nbcnews.com/business, Strategy: AsyncWebCrawler\n",
- "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.48 seconds.\n",
- "Successfully extracted 11 news teasers\n",
- "{\n",
- " \"category\": \"Business News\",\n",
- " \"headline\": \"NBC ripped up its Olympics playbook for 2024 \\u2014 so far, the new strategy paid off\",\n",
- " \"summary\": \"The Olympics have long been key to NBCUniversal. Paris marked the 18th Olympic Games broadcast by NBC in the U.S.\",\n",
- " \"time\": \"13h ago\",\n",
- " \"image\": {\n",
- " \"src\": \"https://media-cldnry.s-nbcnews.com/image/upload/t_focal-200x100,f_auto,q_auto:best/rockcms/2024-09/240903-nbc-olympics-ch-1344-c7a486.jpg\",\n",
- " \"alt\": \"Mike Tirico.\"\n",
- " },\n",
- " \"link\": \"https://www.nbcnews.com/business\"\n",
- "}\n"
- ]
- }
- ],
- "source": [
- "async def extract_news_teasers():\n",
- " schema = {\n",
- " \"name\": \"News Teaser Extractor\",\n",
- " \"baseSelector\": \".wide-tease-item__wrapper\",\n",
- " \"fields\": [\n",
- " {\n",
- " \"name\": \"category\",\n",
- " \"selector\": \".unibrow span[data-testid='unibrow-text']\",\n",
- " \"type\": \"text\",\n",
- " },\n",
- " {\n",
- " \"name\": \"headline\",\n",
- " \"selector\": \".wide-tease-item__headline\",\n",
- " \"type\": \"text\",\n",
- " },\n",
- " {\n",
- " \"name\": \"summary\",\n",
- " \"selector\": \".wide-tease-item__description\",\n",
- " \"type\": \"text\",\n",
- " },\n",
- " {\n",
- " \"name\": \"time\",\n",
- " \"selector\": \"[data-testid='wide-tease-date']\",\n",
- " \"type\": \"text\",\n",
- " },\n",
- " {\n",
- " \"name\": \"image\",\n",
- " \"type\": \"nested\",\n",
- " \"selector\": \"picture.teasePicture img\",\n",
- " \"fields\": [\n",
- " {\"name\": \"src\", \"type\": \"attribute\", \"attribute\": \"src\"},\n",
- " {\"name\": \"alt\", \"type\": \"attribute\", \"attribute\": \"alt\"},\n",
- " ],\n",
- " },\n",
- " {\n",
- " \"name\": \"link\",\n",
- " \"selector\": \"a[href]\",\n",
- " \"type\": \"attribute\",\n",
- " \"attribute\": \"href\",\n",
- " },\n",
- " ],\n",
- " }\n",
- "\n",
- " extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)\n",
- "\n",
- " async with AsyncWebCrawler(verbose=True) as crawler:\n",
- " result = await crawler.arun(\n",
- " url=\"https://www.nbcnews.com/business\",\n",
- " extraction_strategy=extraction_strategy,\n",
- " bypass_cache=True,\n",
- " )\n",
- "\n",
- " assert result.success, \"Failed to crawl the page\"\n",
- "\n",
- " news_teasers = json.loads(result.extracted_content)\n",
- " print(f\"Successfully extracted {len(news_teasers)} news teasers\")\n",
- " print(json.dumps(news_teasers[0], indent=2))\n",
- "\n",
- "await extract_news_teasers()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "FnyVhJaByZQN"
- },
- "source": [
- "## Speed Comparison\n",
- "\n",
- "Let's compare the speed of Crawl4AI with Firecrawl, a paid service. Note that we can't run Firecrawl in this Colab environment, so we'll simulate its performance based on previously recorded data."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "agDD186f3wig"
- },
- "source": [
- "💡 **Note on Speed Comparison:**\n",
- "\n",
- "The speed test conducted here is running on Google Colab, where the internet speed and performance can vary and may not reflect optimal conditions. When we call Firecrawl's API, we're seeing its best performance, while Crawl4AI's performance is limited by Colab's network speed.\n",
- "\n",
- "For a more accurate comparison, it's recommended to run these tests on your own servers or computers with a stable and fast internet connection. Despite these limitations, Crawl4AI still demonstrates faster performance in this environment.\n",
- "\n",
- "If you run these tests locally, you may observe an even more significant speed advantage for Crawl4AI compared to other services."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "F7KwHv8G1LbY"
- },
- "outputs": [],
- "source": [
- "!pip install firecrawl"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "91813zILyZQN",
- "outputId": "663223db-ab89-4976-b233-05ceca62b19b"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Firecrawl (simulated):\n",
- "Time taken: 4.38 seconds\n",
- "Content length: 41967 characters\n",
- "Images found: 49\n",
- "\n",
- "Crawl4AI (simple crawl):\n",
- "Time taken: 4.22 seconds\n",
- "Content length: 18221 characters\n",
- "Images found: 49\n",
- "\n",
- "Crawl4AI (with JavaScript execution):\n",
- "Time taken: 9.13 seconds\n",
- "Content length: 34243 characters\n",
- "Images found: 89\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "from google.colab import userdata\n",
- "os.environ['FIRECRAWL_API_KEY'] = userdata.get('FIRECRAWL_API_KEY')\n",
- "import time\n",
- "from firecrawl import FirecrawlApp\n",
- "\n",
- "async def speed_comparison():\n",
- " # Simulated Firecrawl performance\n",
- " app = FirecrawlApp(api_key=os.environ['FIRECRAWL_API_KEY'])\n",
- " start = time.time()\n",
- " scrape_status = app.scrape_url(\n",
- " 'https://www.nbcnews.com/business',\n",
- " params={'formats': ['markdown', 'html']}\n",
- " )\n",
- " end = time.time()\n",
- " print(\"Firecrawl (simulated):\")\n",
- " print(f\"Time taken: {end - start:.2f} seconds\")\n",
- " print(f\"Content length: {len(scrape_status['markdown'])} characters\")\n",
- " print(f\"Images found: {scrape_status['markdown'].count('cldnry.s-nbcnews.com')}\")\n",
- " print()\n",
- "\n",
- " async with AsyncWebCrawler() as crawler:\n",
- " # Crawl4AI simple crawl\n",
- " start = time.time()\n",
- " result = await crawler.arun(\n",
- " url=\"https://www.nbcnews.com/business\",\n",
- " word_count_threshold=0,\n",
- " bypass_cache=True,\n",
- " verbose=False\n",
- " )\n",
- " end = time.time()\n",
- " print(\"Crawl4AI (simple crawl):\")\n",
- " print(f\"Time taken: {end - start:.2f} seconds\")\n",
- " print(f\"Content length: {len(result.markdown)} characters\")\n",
- " print(f\"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}\")\n",
- " print()\n",
- "\n",
- " # Crawl4AI with JavaScript execution\n",
- " start = time.time()\n",
- " result = await crawler.arun(\n",
- " url=\"https://www.nbcnews.com/business\",\n",
- " js_code=[\"const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();\"],\n",
- " word_count_threshold=0,\n",
- " bypass_cache=True,\n",
- " verbose=False\n",
- " )\n",
- " end = time.time()\n",
- " print(\"Crawl4AI (with JavaScript execution):\")\n",
- " print(f\"Time taken: {end - start:.2f} seconds\")\n",
- " print(f\"Content length: {len(result.markdown)} characters\")\n",
- " print(f\"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}\")\n",
- "\n",
- "await speed_comparison()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "OBFFYVJIyZQN"
- },
- "source": [
- "If you run on a local machine with a proper internet speed:\n",
- "- Simple crawl: Crawl4AI is typically over 3-4 times faster than Firecrawl.\n",
- "- With JavaScript execution: Even when executing JavaScript to load more content (potentially doubling the number of images found), Crawl4AI is still faster than Firecrawl's simple crawl.\n",
- "\n",
- "Please note that actual performance may vary depending on network conditions and the specific content being crawled."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "A6_1RK1_yZQO"
- },
- "source": [
- "## Conclusion\n",
- "\n",
- "In this notebook, we've explored the powerful features of Crawl4AI, including:\n",
- "\n",
- "1. Basic crawling\n",
- "2. JavaScript execution and CSS selector usage\n",
- "3. Proxy support\n",
- "4. Structured data extraction with OpenAI\n",
- "5. Advanced multi-page crawling with JavaScript execution\n",
- "6. Fast structured output using JsonCssExtractionStrategy\n",
- "7. Speed comparison with other services\n",
- "\n",
- "Crawl4AI offers a fast, flexible, and powerful solution for web crawling and data extraction tasks. Its asynchronous architecture and advanced features make it suitable for a wide range of applications, from simple web scraping to complex, multi-page data extraction scenarios.\n",
- "\n",
- "For more information and advanced usage, please visit the [Crawl4AI documentation](https://crawl4ai.com/mkdocs/).\n",
- "\n",
- "Happy crawling!"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.13"
- }
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0cba38e5",
+ "metadata": {},
+ "source": [
+ "# Crawl4AI 🕷️🤖\n",
+ "![\"unclecode%2Fcrawl4ai](\"https://trendshift.io/api/badge/repositories/11716\")
\n",
+ "\n",
+ "[](https://github.com/unclecode/crawl4ai/stargazers)\n",
+ "\n",
+ "[](https://github.com/unclecode/crawl4ai/network/members)\n",
+ "[](https://github.com/unclecode/crawl4ai/issues)\n",
+ "[](https://github.com/unclecode/crawl4ai/pulls)\n",
+ "[](https://github.com/unclecode/crawl4ai/blob/main/LICENSE)\n",
+ "\n",
+ "Crawl4AI simplifies asynchronous web crawling and data extraction, making it accessible for large language models (LLMs) and AI applications. 🆓🌐\n",
+ "\n",
+ "- GitHub Repository: [https://github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)\n",
+ "- Twitter: [@unclecode](https://twitter.com/unclecode)\n",
+ "- Website: [https://crawl4ai.com](https://crawl4ai.com)\n",
+ "\n",
+ "## 🌟 Meet the Crawl4AI Assistant: Your Copilot for Crawling\n",
+ "Use the [Crawl4AI GPT Assistant](https://tinyurl.com/crawl4ai-gpt) as your AI-powered copilot! With this assistant, you can:\n",
+ "- 🧑💻 Generate code for complex crawling and extraction tasks\n",
+ "- 💡 Get tailored support and examples\n",
+ "- 📘 Learn Crawl4AI faster with step-by-step guidance"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 0
+ {
+ "cell_type": "markdown",
+ "id": "41de6458",
+ "metadata": {},
+ "source": [
+ "### **Quickstart with Crawl4AI**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1380e951",
+ "metadata": {},
+ "source": [
+ "#### 1. **Installation**\n",
+ "Install Crawl4AI and necessary dependencies:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "05fecfad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %%capture\n",
+ "!pip install crawl4ai\n",
+ "!pip install nest_asyncio\n",
+ "!playwright install "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "2c2a74c8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import asyncio\n",
+ "import nest_asyncio\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3c558d7",
+ "metadata": {},
+ "source": [
+ "#### 2. **Basic Setup and Simple Crawl**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "003376f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LOG] 🚀 Crawling done for https://www.nbcnews.com/business, success: True, time taken: 1.49 seconds\n",
+ "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.10 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://www.nbcnews.com/business, Strategy: AsyncWebCrawler\n",
+ "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.10 seconds.\n",
+ "IE 11 is not supported. For an optimal experience visit our site on another browser.\n",
+ "\n",
+ "[Morning Rundown: Trump and Harris' vastly different closing pitches, why Kim Jong Un is helping Russia, and an ancient city is discovered by accident](https://www.nbcnews.com/news/harris-speech-ellipse-ancient-mayan-city-morning-rundown-rcna177973)[](https://www.nbcnews.com/news/harris-speech-ellipse-ancient-mayan-city-morning-rundown-rcna177973)\n",
+ "\n",
+ "Skip to Content\n",
+ "\n",
+ "[NBC News Logo](https://www.nbcnews.com)\n",
+ "\n",
+ "Spon\n"
+ ]
+ }
+ ],
+ "source": [
+ "import asyncio\n",
+ "from crawl4ai import AsyncWebCrawler\n",
+ "\n",
+ "async def simple_crawl():\n",
+ " async with AsyncWebCrawler() as crawler:\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://www.nbcnews.com/business\",\n",
+ " bypass_cache=True # By default this is False, meaning the cache will be used\n",
+ " )\n",
+ " print(result.markdown[:500]) # Print the first 500 characters\n",
+ " \n",
+ "asyncio.run(simple_crawl())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da9b4d50",
+ "metadata": {},
+ "source": [
+ "#### 3. **Dynamic Content Handling**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "5bb8c1e4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
+ "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+ "[LOG] 🕸️ Crawling https://www.nbcnews.com/business using AsyncPlaywrightCrawlerStrategy...\n",
+ "[LOG] ✅ Crawled https://www.nbcnews.com/business successfully!\n",
+ "[LOG] 🚀 Crawling done for https://www.nbcnews.com/business, success: True, time taken: 4.52 seconds\n",
+ "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.15 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://www.nbcnews.com/business, Strategy: AsyncWebCrawler\n",
+ "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.15 seconds.\n",
+ "IE 11 is not supported. For an optimal experience visit our site on another browser.\n",
+ "\n",
+ "[Morning Rundown: Trump and Harris' vastly different closing pitches, why Kim Jong Un is helping Russia, and an ancient city is discovered by accident](https://www.nbcnews.com/news/harris-speech-ellipse-ancient-mayan-city-morning-rundown-rcna177973)[](https://www.nbcnews.com/news/harris-speech-ellipse-ancient-mayan-city-morning-rundown-rcna177973)\n",
+ "\n",
+ "Skip to Content\n",
+ "\n",
+ "[NBC News Logo](https://www.nbcnews.com)\n",
+ "\n",
+ "Spon\n"
+ ]
+ }
+ ],
+ "source": [
+ "async def crawl_dynamic_content():\n",
+ " # You can use wait_for to wait for a condition to be met before returning the result\n",
+ " # wait_for = \"\"\"() => {\n",
+ " # return Array.from(document.querySelectorAll('article.tease-card')).length > 10;\n",
+ " # }\"\"\"\n",
+ "\n",
+ " # wait_for can be also just a css selector\n",
+ " # wait_for = \"article.tease-card:nth-child(10)\"\n",
+ "\n",
+ " async with AsyncWebCrawler(verbose=True) as crawler:\n",
+ " js_code = [\n",
+ " \"const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();\"\n",
+ " ]\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://www.nbcnews.com/business\",\n",
+ " js_code=js_code,\n",
+ " # wait_for=wait_for,\n",
+ " bypass_cache=True,\n",
+ " )\n",
+ " print(result.markdown[:500]) # Print first 500 characters\n",
+ "\n",
+ "asyncio.run(crawl_dynamic_content())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "86febd8d",
+ "metadata": {},
+ "source": [
+ "#### 4. **Content Cleaning and Fit Markdown**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e8ab01f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "async def clean_content():\n",
+ " async with AsyncWebCrawler() as crawler:\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://janineintheworld.com/places-to-visit-in-central-mexico\",\n",
+ " excluded_tags=['nav', 'footer', 'aside'],\n",
+ " remove_overlay_elements=True,\n",
+ " word_count_threshold=10,\n",
+ " bypass_cache=True\n",
+ " )\n",
+ " full_markdown_length = len(result.markdown)\n",
+ " fit_markdown_length = len(result.fit_markdown)\n",
+ " print(f\"Full Markdown Length: {full_markdown_length}\")\n",
+ " print(f\"Fit Markdown Length: {fit_markdown_length}\")\n",
+ " print(result.fit_markdown[:1000])\n",
+ " \n",
+ "\n",
+ "asyncio.run(clean_content())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "55715146",
+ "metadata": {},
+ "source": [
+ "#### 5. **Link Analysis and Smart Filtering**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "2ae47c69",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LOG] 🚀 Crawling done for https://www.nbcnews.com/business, success: True, time taken: 0.93 seconds\n",
+ "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.11 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://www.nbcnews.com/business, Strategy: AsyncWebCrawler\n",
+ "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.11 seconds.\n",
+ "Found 107 internal links\n",
+ "Found 58 external links\n",
+ "Href: https://www.nbcnews.com/news/harris-speech-ellipse-ancient-mayan-city-morning-rundown-rcna177973\n",
+ "Text: Morning Rundown: Trump and Harris' vastly different closing pitches, why Kim Jong Un is helping Russia, and an ancient city is discovered by accident\n",
+ "\n",
+ "Href: https://www.nbcnews.com\n",
+ "Text: NBC News Logo\n",
+ "\n",
+ "Href: https://www.nbcnews.com/politics/2024-election/live-blog/kamala-harris-donald-trump-rally-election-live-updates-rcna177529\n",
+ "Text: 2024 Election\n",
+ "\n",
+ "Href: https://www.nbcnews.com/politics\n",
+ "Text: Politics\n",
+ "\n",
+ "Href: https://www.nbcnews.com/us-news\n",
+ "Text: U.S. News\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "async def link_analysis():\n",
+ " async with AsyncWebCrawler() as crawler:\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://www.nbcnews.com/business\",\n",
+ " bypass_cache=True,\n",
+ " exclude_external_links=True,\n",
+ " exclude_social_media_links=True,\n",
+ " # exclude_domains=[\"facebook.com\", \"twitter.com\"]\n",
+ " )\n",
+ " print(f\"Found {len(result.links['internal'])} internal links\")\n",
+ " print(f\"Found {len(result.links['external'])} external links\")\n",
+ "\n",
+ " for link in result.links['internal'][:5]:\n",
+ " print(f\"Href: {link['href']}\\nText: {link['text']}\\n\")\n",
+ " \n",
+ "\n",
+ "asyncio.run(link_analysis())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "80cceef3",
+ "metadata": {},
+ "source": [
+ "#### 6. **Media Handling**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "1fed7f99",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LOG] 🚀 Crawling done for https://www.nbcnews.com/business, success: True, time taken: 1.42 seconds\n",
+ "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.11 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://www.nbcnews.com/business, Strategy: AsyncWebCrawler\n",
+ "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.12 seconds.\n",
+ "Image URL: https://media-cldnry.s-nbcnews.com/image/upload/t_focal-762x508,f_auto,q_auto:best/rockcms/2024-10/241023-NM-Chilccare-jg-27b982.jpg, Alt: , Score: 4\n",
+ "Image URL: https://media-cldnry.s-nbcnews.com/image/upload/t_focal-80x80,f_auto,q_auto:best/rockcms/2024-10/241030-china-ev-electric-mb-0746-cae05c.jpg, Alt: Volkswagen Workshop in Hefei, Score: 5\n",
+ "Image URL: https://media-cldnry.s-nbcnews.com/image/upload/t_focal-80x80,f_auto,q_auto:best/rockcms/2024-10/241029-nyc-subway-sandwich-2021-ac-922p-a92374.jpg, Alt: A sub is prepared at a Subway restaurant in Manhattan, New York City, Score: 5\n",
+ "Image URL: https://media-cldnry.s-nbcnews.com/image/upload/t_focal-80x80,f_auto,q_auto:best/rockcms/2024-10/241029-suv-gravity-ch-1618-752415.jpg, Alt: The Lucid Gravity car., Score: 5\n",
+ "Image URL: https://media-cldnry.s-nbcnews.com/image/upload/t_focal-80x80,f_auto,q_auto:best/rockcms/2024-10/241029-dearborn-michigan-f-150-ford-ranger-trucks-assembly-line-ac-426p-614f0b.jpg, Alt: Ford Introduces new F-150 And Ranger Trucks At Their Dearborn Plant, Score: 5\n"
+ ]
+ }
+ ],
+ "source": [
+ "async def media_handling():\n",
+ " async with AsyncWebCrawler() as crawler:\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://www.nbcnews.com/business\", \n",
+ " bypass_cache=True,\n",
+ " exclude_external_images=False,\n",
+ " screenshot=True\n",
+ " )\n",
+ " for img in result.media['images'][:5]:\n",
+ " print(f\"Image URL: {img['src']}, Alt: {img['alt']}, Score: {img['score']}\")\n",
+ " \n",
+ "asyncio.run(media_handling())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9290499a",
+ "metadata": {},
+ "source": [
+ "#### 7. **Using Hooks for Custom Workflow**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d069c2b",
+ "metadata": {},
+ "source": [
+ "Hooks in Crawl4AI allow you to run custom logic at specific stages of the crawling process. This can be invaluable for scenarios like setting custom headers, logging activities, or processing content before it is returned. Below is an example of a basic workflow using a hook, followed by a complete list of available hooks and explanations on their usage."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "bc4d2fc8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Hook] Preparing to navigate...\n",
+ "[LOG] 🚀 Crawling done for https://crawl4ai.com, success: True, time taken: 3.49 seconds\n",
+ "[LOG] 🚀 Content extracted for https://crawl4ai.com, success: True, time taken: 0.03 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://crawl4ai.com, Strategy: AsyncWebCrawler\n",
+ "[LOG] 🚀 Extraction done for https://crawl4ai.com, time taken: 0.03 seconds.\n",
+ "[Crawl4AI Documentation](https://docs.crawl4ai.com/)\n",
+ "\n",
+ " * [ Home ](.)\n",
+ " * [ Installation ](basic/installation/)\n",
+ " * [ Quick Start ](basic/quickstart/)\n",
+ " * [ Search ](#)\n",
+ "\n",
+ "\n",
+ "\n",
+ " * Home\n",
+ " * [Installation](basic/installation/)\n",
+ " * [Quick Start](basic/quickstart/)\n",
+ " * Basic\n",
+ " * [Simple Crawling](basic/simple-crawling/)\n",
+ " * [Output Formats](basic/output-formats/)\n",
+ " * [Browser Configuration](basic/browser-config/)\n",
+ " * [Page Interaction](basic/page-interaction/)\n",
+ " * [Content Selection](basic/con\n"
+ ]
+ }
+ ],
+ "source": [
+ "async def custom_hook_workflow():\n",
+ " async with AsyncWebCrawler() as crawler:\n",
+ " # Set a 'before_goto' hook to run custom code just before navigation\n",
+ " crawler.crawler_strategy.set_hook(\"before_goto\", lambda page: print(\"[Hook] Preparing to navigate...\"))\n",
+ " \n",
+ " # Perform the crawl operation\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://crawl4ai.com\",\n",
+ " bypass_cache=True\n",
+ " )\n",
+ " print(result.markdown[:500]) # Display the first 500 characters\n",
+ "\n",
+ "asyncio.run(custom_hook_workflow())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3ff45e21",
+ "metadata": {},
+ "source": [
+ "List of available hooks and examples for each stage of the crawling process:\n",
+ "\n",
+ "- **on_browser_created**\n",
+ " ```python\n",
+ " async def on_browser_created_hook(browser):\n",
+ " print(\"[Hook] Browser created\")\n",
+ " ```\n",
+ "\n",
+ "- **before_goto**\n",
+ " ```python\n",
+ " async def before_goto_hook(page):\n",
+ " await page.set_extra_http_headers({\"X-Test-Header\": \"test\"})\n",
+ " ```\n",
+ "\n",
+ "- **after_goto**\n",
+ " ```python\n",
+ " async def after_goto_hook(page):\n",
+ " print(f\"[Hook] Navigated to {page.url}\")\n",
+ " ```\n",
+ "\n",
+ "- **on_execution_started**\n",
+ " ```python\n",
+ " async def on_execution_started_hook(page):\n",
+ " print(\"[Hook] JavaScript execution started\")\n",
+ " ```\n",
+ "\n",
+ "- **before_return_html**\n",
+ " ```python\n",
+ " async def before_return_html_hook(page, html):\n",
+ " print(f\"[Hook] HTML length: {len(html)}\")\n",
+ " ```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d56ebb1",
+ "metadata": {},
+ "source": [
+ "#### 8. **Session-Based Crawling**\n",
+ "\n",
+ "When to Use Session-Based Crawling: \n",
+ "Session-based crawling is especially beneficial when navigating through multi-page content where each page load needs to maintain the same session context. For instance, in cases where a “Next Page” button must be clicked to load subsequent data, the new data often replaces the previous content. Here, session-based crawling keeps the browser state intact across each interaction, allowing for sequential actions within the same session.\n",
+ "\n",
+ "Example: Multi-Page Navigation Using JavaScript\n",
+ "In this example, we’ll navigate through multiple pages by clicking a \"Next Page\" button. After each page load, we extract the new content and repeat the process."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e7bfebae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "async def multi_page_session_crawl():\n",
+ " async with AsyncWebCrawler() as crawler:\n",
+ " session_id = \"page_navigation_session\"\n",
+ " url = \"https://example.com/paged-content\"\n",
+ "\n",
+ " for page_number in range(1, 4):\n",
+ " result = await crawler.arun(\n",
+ " url=url,\n",
+ " session_id=session_id,\n",
+ " js_code=\"document.querySelector('.next-page-button').click();\" if page_number > 1 else None,\n",
+ " css_selector=\".content-section\",\n",
+ " bypass_cache=True\n",
+ " )\n",
+ " print(f\"Page {page_number} Content:\")\n",
+ " print(result.markdown[:500]) # Print first 500 characters\n",
+ "\n",
+ "# asyncio.run(multi_page_session_crawl())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ad32a778",
+ "metadata": {},
+ "source": [
+ "#### 9. **Using Extraction Strategies**\n",
+ "\n",
+ "**LLM Extraction**\n",
+ "\n",
+ "This example demonstrates how to use language model-based extraction to retrieve structured data from a pricing page on OpenAI’s site."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "3011a7c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "--- Extracting Structured Data with openai/gpt-4o-mini ---\n",
+ "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
+ "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+ "[LOG] 🕸️ Crawling https://openai.com/api/pricing/ using AsyncPlaywrightCrawlerStrategy...\n",
+ "[LOG] ✅ Crawled https://openai.com/api/pricing/ successfully!\n",
+ "[LOG] 🚀 Crawling done for https://openai.com/api/pricing/, success: True, time taken: 1.29 seconds\n",
+ "[LOG] 🚀 Content extracted for https://openai.com/api/pricing/, success: True, time taken: 0.13 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://openai.com/api/pricing/, Strategy: AsyncWebCrawler\n",
+ "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 0\n",
+ "[LOG] Extracted 26 blocks from URL: https://openai.com/api/pricing/ block index: 0\n",
+ "[LOG] 🚀 Extraction done for https://openai.com/api/pricing/, time taken: 15.12 seconds.\n",
+ "[{'model_name': 'gpt-4o', 'input_fee': '$2.50 / 1M input tokens', 'output_fee': '$10.00 / 1M output tokens', 'error': False}, {'model_name': 'gpt-4o-2024-08-06', 'input_fee': '$2.50 / 1M input tokens', 'output_fee': '$10.00 / 1M output tokens', 'error': False}, {'model_name': 'gpt-4o-audio-preview', 'input_fee': '$2.50 / 1M input tokens', 'output_fee': '$10.00 / 1M output tokens', 'error': False}, {'model_name': 'gpt-4o-audio-preview-2024-10-01', 'input_fee': '$2.50 / 1M input tokens', 'output_fee': '$10.00 / 1M output tokens', 'error': False}, {'model_name': 'gpt-4o-2024-05-13', 'input_fee': '$5.00 / 1M input tokens', 'output_fee': '$15.00 / 1M output tokens', 'error': False}]\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/unclecode/devs/crawl4ai/venv/lib/python3.10/site-packages/pydantic/main.py:347: UserWarning: Pydantic serializer warnings:\n",
+ " Expected `PromptTokensDetails` but got `dict` - serialized value may not be as expected\n",
+ " return self.__pydantic_serializer__.to_python(\n"
+ ]
+ }
+ ],
+ "source": [
+ "from crawl4ai.extraction_strategy import LLMExtractionStrategy\n",
+ "from pydantic import BaseModel, Field\n",
+ "import os, json\n",
+ "\n",
+ "class OpenAIModelFee(BaseModel):\n",
+ " model_name: str = Field(..., description=\"Name of the OpenAI model.\")\n",
+ " input_fee: str = Field(..., description=\"Fee for input token for the OpenAI model.\")\n",
+ " output_fee: str = Field(\n",
+ " ..., description=\"Fee for output token for the OpenAI model.\"\n",
+ " )\n",
+ "\n",
+ "async def extract_structured_data_using_llm(provider: str, api_token: str = None, extra_headers: dict = None):\n",
+ " print(f\"\\n--- Extracting Structured Data with {provider} ---\")\n",
+ " \n",
+ " # Skip if API token is missing (for providers that require it)\n",
+ " if api_token is None and provider != \"ollama\":\n",
+ " print(f\"API token is required for {provider}. Skipping this example.\")\n",
+ " return\n",
+ "\n",
+ " extra_args = {\"extra_headers\": extra_headers} if extra_headers else {}\n",
+ "\n",
+ " async with AsyncWebCrawler(verbose=True) as crawler:\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://openai.com/api/pricing/\",\n",
+ " word_count_threshold=1,\n",
+ " extraction_strategy=LLMExtractionStrategy(\n",
+ " provider=provider,\n",
+ " api_token=api_token,\n",
+ " schema=OpenAIModelFee.schema(),\n",
+ " extraction_type=\"schema\",\n",
+ " instruction=\"\"\"Extract all model names along with fees for input and output tokens.\"\n",
+ " \"{model_name: 'GPT-4', input_fee: 'US$10.00 / 1M tokens', output_fee: 'US$30.00 / 1M tokens'}.\"\"\",\n",
+ " **extra_args\n",
+ " ),\n",
+ " bypass_cache=True,\n",
+ " )\n",
+ " print(json.loads(result.extracted_content)[:5])\n",
+ "\n",
+ "# Usage:\n",
+ "await extract_structured_data_using_llm(\"openai/gpt-4o-mini\", os.getenv(\"OPENAI_API_KEY\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6532db9d",
+ "metadata": {},
+ "source": [
+ "**Cosine Similarity Strategy**\n",
+ "\n",
+ "This strategy uses semantic clustering to extract relevant content based on contextual similarity, which is helpful when extracting related sections from a single topic."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "ec079108",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LOG] Loading Extraction Model for mps device.\n",
+ "[LOG] Loading Multilabel Classifier for mps device.\n",
+ "[LOG] Model loaded sentence-transformers/all-MiniLM-L6-v2, models/reuters, took 5.193778038024902 seconds\n",
+ "[LOG] 🚀 Crawling done for https://www.nbcnews.com/business/consumer/how-mcdonalds-e-coli-crisis-inflation-politics-reflect-american-story-rcna177156, success: True, time taken: 1.37 seconds\n",
+ "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business/consumer/how-mcdonalds-e-coli-crisis-inflation-politics-reflect-american-story-rcna177156, success: True, time taken: 0.07 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://www.nbcnews.com/business/consumer/how-mcdonalds-e-coli-crisis-inflation-politics-reflect-american-story-rcna177156, Strategy: AsyncWebCrawler\n",
+ "[LOG] 🚀 Assign tags using mps\n",
+ "[LOG] 🚀 Categorization done in 0.55 seconds\n",
+ "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business/consumer/how-mcdonalds-e-coli-crisis-inflation-politics-reflect-american-story-rcna177156, time taken: 6.63 seconds.\n",
+ "[{'index': 1, 'tags': ['news_&_social_concern'], 'content': \"McDonald's 2024 combo: Inflation, a health crisis and a side of politics # McDonald's 2024 combo: Inflation, a health crisis and a side of politics\"}, {'index': 2, 'tags': ['business_&_entrepreneurs', 'news_&_social_concern'], 'content': 'Like many major brands, McDonald’s raked in big profits as the economy reopened from the pandemic. In October 2022, [executives were boasting](https://www.cnbc.com/2022/10/27/mcdonalds-mcd-earnings-q3-2022.html) that they’d been raising prices without crimping traffic, even as competitors began to warn that some customers were closing their wallets after inflation peaked above 9% that summer. Still, the U.S. had repeatedly dodged a much-forecast recession, and [Americans kept spending on nonessentials](https://www.nbcnews.com/business/economy/year-peak-inflation-travel-leisure-mostly-cost-less-rcna92760) like travel and dining out — despite regularly relaying to pollsters their dismal views of an otherwise solid economy. Even so, 64% of consumers said they noticed price increases at quick-service restaurants in September, more than at any other type of venue, according to a survey by Datassential, a food and beverage market researcher. Politicians are still drawing attention to fast-food costs, too, as the election season barrels toward a tumultuous finish. A group of Democratic senators this month [denounced McDonald’s for menu prices](https://www.nbcnews.com/news/us-news/democratic-senators-slam-mcdonalds-menu-price-hikes-rcna176380) that they said outstripped inflation, accusing the company of looking to profit “at the expense of people’s ability to put food on the table.” The financial results come toward the end of a humbling year for the nearly $213 billion restaurant chain, whose shares remained steady on the heels of its latest earnings. Kempczinski [sought to reassure investors](https://www.cnbc.com/2024/10/29/mcdonalds-e-coli-outbreak-ceo-comments.html) that [the E. coli outbreak](https://www.nbcnews.com/health/health-news/illnesses-linked-mcdonalds-e-coli-outbreak-rise-75-cdc-says-rcna177260), linked to Quarter Pounder burgers, was under control after the health crisis temporarily dented the company’s stock and caused U.S. foot traffic to drop nearly 10% in the days afterward, according to estimates by Gordon Haskett financial researchers. The fast-food giant [reported Tuesday](https://www.cnbc.com/2024/10/29/mcdonalds-mcd-earnings-q3-2024.html) that it had reversed its recent U.S. sales drop, posting a 0.3% uptick in the third quarter. Foot traffic was still down slightly, but the company said its summer of discounts was paying off. But by early this year, [photos of eye-watering menu prices](https://x.com/sam_learner/status/1681367351143301129) at some McDonald’s locations — including an $18 Big Mac combo at a Connecticut rest stop from July 2023 — went viral, bringing diners’ long-simmering frustrations to a boiling point that the company couldn’t ignore. On an earnings call in April, Kempczinski acknowledged that foot traffic had fallen. “We will stay laser-focused on providing an unparalleled experience with simple, everyday value and affordability that our consumers can count on as they continue to be mindful about their spending,” CEO Chris Kempczinski [said in a statement](https://www.prnewswire.com/news-releases/mcdonalds-reports-third-quarter-2024-results-302289216.html?Fds-Load-Behavior=force-external) alongside the earnings report.'}, {'index': 3, 'tags': ['food_&_dining', 'news_&_social_concern'], 'content': 'McDonald’s has had some success leaning into discounts this year. Eric Thayer / Bloomberg via Getty Images file'}, {'index': 4, 'tags': ['business_&_entrepreneurs', 'food_&_dining', 'news_&_social_concern'], 'content': 'McDonald’s has faced a customer revolt over pricey Big Macs, an unsolicited cameo in election-season crossfire, and now an E. coli outbreak — just as the company had been luring customers back with more affordable burgers. Despite a difficult quarter, McDonald’s looks resilient in the face of various pressures, analysts say — something the company shares with U.S. consumers overall. “Consumers continue to be even more discriminating with every dollar that they spend,” he said at the time. Going forward, McDonald’s would be “laser-focused” on affordability. “McDonald’s has also done a good job of embedding the brand in popular culture to enhance its relevance and meaning around fun and family. But it also needed to modify the product line to meet the expectations of a consumer who is on a tight budget,” he said. “The thing that McDonald’s had struggled with, and why I think we’re seeing kind of an inflection point, is a value proposition,” Senatore said. “McDonald’s menu price increases had run ahead of a lot of its restaurant peers. … Consumers are savvy enough to know that.” For many consumers, the fast-food giant’s menus serve as an informal gauge of the economy overall, said Sara Senatore, a Bank of America analyst covering restaurants. “The spotlight is always on McDonald’s because it’s so big” and something of a “bellwether,” she said. McDonald’s didn’t respond to requests for comment.'}, {'index': 5, 'tags': ['business_&_entrepreneurs', 'food_&_dining'], 'content': 'Mickey D’s’ $5 meal deal, which it launched in late June to jumpstart slumping sales, has given the company an appealing price point to advertise nationwide, Senatore said, speculating that it could open the door to a new permanent value offering. But before that promotion rolled out, the company’s reputation as a low-cost option had taken a bruising hit.'}]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from crawl4ai.extraction_strategy import CosineStrategy\n",
+ "\n",
+ "async def cosine_similarity_extraction():\n",
+ " async with AsyncWebCrawler() as crawler:\n",
+ " strategy = CosineStrategy(\n",
+ " word_count_threshold=10,\n",
+ " max_dist=0.2, # Maximum distance between two words\n",
+ " linkage_method=\"ward\", # Linkage method for hierarchical clustering (ward, complete, average, single)\n",
+ " top_k=3, # Number of top keywords to extract\n",
+ " sim_threshold=0.3, # Similarity threshold for clustering\n",
+ " semantic_filter=\"McDonald's economic impact, American consumer trends\", # Keywords to filter the content semantically using embeddings\n",
+ " verbose=True\n",
+ " )\n",
+ " \n",
+ " result = await crawler.arun(\n",
+ " url=\"https://www.nbcnews.com/business/consumer/how-mcdonalds-e-coli-crisis-inflation-politics-reflect-american-story-rcna177156\",\n",
+ " extraction_strategy=strategy\n",
+ " )\n",
+ " print(json.loads(result.extracted_content)[:5])\n",
+ "\n",
+ "asyncio.run(cosine_similarity_extraction())\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ff423629",
+ "metadata": {},
+ "source": [
+ "#### 10. **Conclusion and Next Steps**\n",
+ "\n",
+ "You’ve explored core features of Crawl4AI, including dynamic content handling, link analysis, and advanced extraction strategies. Visit our documentation for further details on using Crawl4AI’s extensive features.\n",
+ "\n",
+ "- GitHub Repository: [https://github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)\n",
+ "- Twitter: [@unclecode](https://twitter.com/unclecode)\n",
+ "- Website: [https://crawl4ai.com](https://crawl4ai.com)\n",
+ "\n",
+ "Happy Crawling with Crawl4AI! 🕷️🤖\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d34c1d35",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/quickstart_async.py b/docs/examples/quickstart_async.py
index f6c16a4e..9c57f57d 100644
--- a/docs/examples/quickstart_async.py
+++ b/docs/examples/quickstart_async.py
@@ -10,7 +10,7 @@ import time
import json
import os
import re
-from typing import Dict
+from typing import Dict, List
from bs4 import BeautifulSoup
from pydantic import BaseModel, Field
from crawl4ai import AsyncWebCrawler
@@ -379,6 +379,19 @@ async def crawl_custom_browser_type():
print(result.markdown[:500])
print("Time taken: ", time.time() - start)
+async def crawl_with_user_simultion():
+ async with AsyncWebCrawler(verbose=True, headless=True) as crawler:
+ url = "YOUR-URL-HERE"
+ result = await crawler.arun(
+ url=url,
+ bypass_cache=True,
+ magic = True, # Automatically detects and removes overlays, popups, and other elements that block content
+ # simulate_user = True,# Causes a series of random mouse movements and clicks to simulate user interaction
+ # override_navigator = True # Overrides the navigator object to make it look like a real user
+ )
+
+ print(result.markdown)
+
async def speed_comparison():
# print("\n--- Speed Comparison ---")
# print("Firecrawl (simulated):")
@@ -444,6 +457,57 @@ async def speed_comparison():
print("If you run these tests in an environment with better network conditions,")
print("you may observe an even more significant speed advantage for Crawl4AI.")
+async def generate_knowledge_graph():
+ class Entity(BaseModel):
+ name: str
+ description: str
+
+ class Relationship(BaseModel):
+ entity1: Entity
+ entity2: Entity
+ description: str
+ relation_type: str
+
+ class KnowledgeGraph(BaseModel):
+ entities: List[Entity]
+ relationships: List[Relationship]
+
+ extraction_strategy = LLMExtractionStrategy(
+ provider='openai/gpt-4o-mini', # Or any other provider, including Ollama and open source models
+ api_token=os.getenv('OPENAI_API_KEY'), # In case of Ollama just pass "no-token"
+ schema=KnowledgeGraph.model_json_schema(),
+ extraction_type="schema",
+ instruction="""Extract entities and relationships from the given text."""
+ )
+ async with AsyncWebCrawler() as crawler:
+ url = "https://paulgraham.com/love.html"
+ result = await crawler.arun(
+ url=url,
+ bypass_cache=True,
+ extraction_strategy=extraction_strategy,
+ # magic=True
+ )
+ # print(result.extracted_content)
+ with open(os.path.join(__location__, "kb.json"), "w") as f:
+ f.write(result.extracted_content)
+
+async def fit_markdown_remove_overlay():
+ async with AsyncWebCrawler(headless = False) as crawler:
+ url = "https://janineintheworld.com/places-to-visit-in-central-mexico"
+ result = await crawler.arun(
+ url=url,
+ bypass_cache=True,
+ word_count_threshold = 10,
+ remove_overlay_elements=True,
+ screenshot = True
+ )
+ # Save markdown to file
+ with open(os.path.join(__location__, "mexico_places.md"), "w") as f:
+ f.write(result.fit_markdown)
+
+ print("Done")
+
+
async def main():
await simple_crawl()
await simple_example_with_running_js_code()
@@ -455,7 +519,7 @@ async def main():
# LLM extraction examples
await extract_structured_data_using_llm()
await extract_structured_data_using_llm("huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct", os.getenv("HUGGINGFACE_API_KEY"))
- await extract_structured_data_using_llm("openai/gpt-4", os.getenv("OPENAI_API_KEY"))
+ await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
await extract_structured_data_using_llm("ollama/llama3.2")
# You always can pass custom headers to the extraction strategy
diff --git a/docs/examples/quickstart_v0.ipynb b/docs/examples/quickstart_v0.ipynb
new file mode 100644
index 00000000..71f23acb
--- /dev/null
+++ b/docs/examples/quickstart_v0.ipynb
@@ -0,0 +1,735 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6yLvrXn7yZQI"
+ },
+ "source": [
+ "# Crawl4AI: Advanced Web Crawling and Data Extraction\n",
+ "\n",
+ "Welcome to this interactive notebook showcasing Crawl4AI, an advanced asynchronous web crawling and data extraction library.\n",
+ "\n",
+ "- GitHub Repository: [https://github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)\n",
+ "- Twitter: [@unclecode](https://twitter.com/unclecode)\n",
+ "- Website: [https://crawl4ai.com](https://crawl4ai.com)\n",
+ "\n",
+ "Let's explore the powerful features of Crawl4AI!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KIn_9nxFyZQK"
+ },
+ "source": [
+ "## Installation\n",
+ "\n",
+ "First, let's install Crawl4AI from GitHub:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "mSnaxLf3zMog"
+ },
+ "outputs": [],
+ "source": [
+ "!sudo apt-get update && sudo apt-get install -y libwoff1 libopus0 libwebp6 libwebpdemux2 libenchant1c2a libgudev-1.0-0 libsecret-1-0 libhyphen0 libgdk-pixbuf2.0-0 libegl1 libnotify4 libxslt1.1 libevent-2.1-7 libgles2 libvpx6 libxcomposite1 libatk1.0-0 libatk-bridge2.0-0 libepoxy0 libgtk-3-0 libharfbuzz-icu0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xlXqaRtayZQK"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install crawl4ai\n",
+ "!pip install nest-asyncio\n",
+ "!playwright install"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qKCE7TI7yZQL"
+ },
+ "source": [
+ "Now, let's import the necessary libraries:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "I67tr7aAyZQL"
+ },
+ "outputs": [],
+ "source": [
+ "import asyncio\n",
+ "import nest_asyncio\n",
+ "from crawl4ai import AsyncWebCrawler\n",
+ "from crawl4ai.extraction_strategy import JsonCssExtractionStrategy, LLMExtractionStrategy\n",
+ "import json\n",
+ "import time\n",
+ "from pydantic import BaseModel, Field\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "h7yR_Rt_yZQM"
+ },
+ "source": [
+ "## Basic Usage\n",
+ "\n",
+ "Let's start with a simple crawl example:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "yBh6hf4WyZQM",
+ "outputId": "0f83af5c-abba-4175-ed95-70b7512e6bcc"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
+ "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+ "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.05 seconds\n",
+ "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.05 seconds.\n",
+ "18102\n"
+ ]
+ }
+ ],
+ "source": [
+ "async def simple_crawl():\n",
+ " async with AsyncWebCrawler(verbose=True) as crawler:\n",
+ " result = await crawler.arun(url=\"https://www.nbcnews.com/business\")\n",
+ " print(len(result.markdown))\n",
+ "await simple_crawl()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9rtkgHI28uI4"
+ },
+ "source": [
+ "💡 By default, **Crawl4AI** caches the result of every URL, so the next time you call it, you’ll get an instant result. But if you want to bypass the cache, just set `bypass_cache=True`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MzZ0zlJ9yZQM"
+ },
+ "source": [
+ "## Advanced Features\n",
+ "\n",
+ "### Executing JavaScript and Using CSS Selectors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gHStF86xyZQM",
+ "outputId": "34d0fb6d-4dec-4677-f76e-85a1f082829b"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
+ "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+ "[LOG] 🕸️ Crawling https://www.nbcnews.com/business using AsyncPlaywrightCrawlerStrategy...\n",
+ "[LOG] ✅ Crawled https://www.nbcnews.com/business successfully!\n",
+ "[LOG] 🚀 Crawling done for https://www.nbcnews.com/business, success: True, time taken: 6.06 seconds\n",
+ "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.10 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://www.nbcnews.com/business, Strategy: AsyncWebCrawler\n",
+ "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.11 seconds.\n",
+ "41135\n"
+ ]
+ }
+ ],
+ "source": [
+ "async def js_and_css():\n",
+ " async with AsyncWebCrawler(verbose=True) as crawler:\n",
+ " js_code = [\"const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();\"]\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://www.nbcnews.com/business\",\n",
+ " js_code=js_code,\n",
+ " # css_selector=\"YOUR_CSS_SELECTOR_HERE\",\n",
+ " bypass_cache=True\n",
+ " )\n",
+ " print(len(result.markdown))\n",
+ "\n",
+ "await js_and_css()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cqE_W4coyZQM"
+ },
+ "source": [
+ "### Using a Proxy\n",
+ "\n",
+ "Note: You'll need to replace the proxy URL with a working proxy for this example to run successfully."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "QjAyiAGqyZQM"
+ },
+ "outputs": [],
+ "source": [
+ "async def use_proxy():\n",
+ " async with AsyncWebCrawler(verbose=True, proxy=\"http://your-proxy-url:port\") as crawler:\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://www.nbcnews.com/business\",\n",
+ " bypass_cache=True\n",
+ " )\n",
+ " print(result.markdown[:500]) # Print first 500 characters\n",
+ "\n",
+ "# Uncomment the following line to run the proxy example\n",
+ "# await use_proxy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XTZ88lbayZQN"
+ },
+ "source": [
+ "### Extracting Structured Data with OpenAI\n",
+ "\n",
+ "Note: You'll need to set your OpenAI API key as an environment variable for this example to work."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fIOlDayYyZQN",
+ "outputId": "cb8359cc-dee0-4762-9698-5dfdcee055b8"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
+ "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+ "[LOG] 🕸️ Crawling https://openai.com/api/pricing/ using AsyncPlaywrightCrawlerStrategy...\n",
+ "[LOG] ✅ Crawled https://openai.com/api/pricing/ successfully!\n",
+ "[LOG] 🚀 Crawling done for https://openai.com/api/pricing/, success: True, time taken: 3.77 seconds\n",
+ "[LOG] 🚀 Content extracted for https://openai.com/api/pricing/, success: True, time taken: 0.21 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://openai.com/api/pricing/, Strategy: AsyncWebCrawler\n",
+ "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 0\n",
+ "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 1\n",
+ "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 2\n",
+ "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 3\n",
+ "[LOG] Extracted 4 blocks from URL: https://openai.com/api/pricing/ block index: 3\n",
+ "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 4\n",
+ "[LOG] Extracted 5 blocks from URL: https://openai.com/api/pricing/ block index: 0\n",
+ "[LOG] Extracted 1 blocks from URL: https://openai.com/api/pricing/ block index: 4\n",
+ "[LOG] Extracted 8 blocks from URL: https://openai.com/api/pricing/ block index: 1\n",
+ "[LOG] Extracted 12 blocks from URL: https://openai.com/api/pricing/ block index: 2\n",
+ "[LOG] 🚀 Extraction done for https://openai.com/api/pricing/, time taken: 8.55 seconds.\n",
+ "5029\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "from google.colab import userdata\n",
+ "os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')\n",
+ "\n",
+ "class OpenAIModelFee(BaseModel):\n",
+ " model_name: str = Field(..., description=\"Name of the OpenAI model.\")\n",
+ " input_fee: str = Field(..., description=\"Fee for input token for the OpenAI model.\")\n",
+ " output_fee: str = Field(..., description=\"Fee for output token for the OpenAI model.\")\n",
+ "\n",
+ "async def extract_openai_fees():\n",
+ " async with AsyncWebCrawler(verbose=True) as crawler:\n",
+ " result = await crawler.arun(\n",
+ " url='https://openai.com/api/pricing/',\n",
+ " word_count_threshold=1,\n",
+ " extraction_strategy=LLMExtractionStrategy(\n",
+ " provider=\"openai/gpt-4o\", api_token=os.getenv('OPENAI_API_KEY'),\n",
+ " schema=OpenAIModelFee.schema(),\n",
+ " extraction_type=\"schema\",\n",
+ " instruction=\"\"\"From the crawled content, extract all mentioned model names along with their fees for input and output tokens.\n",
+ " Do not miss any models in the entire content. One extracted model JSON format should look like this:\n",
+ " {\"model_name\": \"GPT-4\", \"input_fee\": \"US$10.00 / 1M tokens\", \"output_fee\": \"US$30.00 / 1M tokens\"}.\"\"\"\n",
+ " ),\n",
+ " bypass_cache=True,\n",
+ " )\n",
+ " print(len(result.extracted_content))\n",
+ "\n",
+ "# Uncomment the following line to run the OpenAI extraction example\n",
+ "await extract_openai_fees()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BypA5YxEyZQN"
+ },
+ "source": [
+ "### Advanced Multi-Page Crawling with JavaScript Execution"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tfkcVQ0b7mw-"
+ },
+ "source": [
+ "## Advanced Multi-Page Crawling with JavaScript Execution\n",
+ "\n",
+ "This example demonstrates Crawl4AI's ability to handle complex crawling scenarios, specifically extracting commits from multiple pages of a GitHub repository. The challenge here is that clicking the \"Next\" button doesn't load a new page, but instead uses asynchronous JavaScript to update the content. This is a common hurdle in modern web crawling.\n",
+ "\n",
+ "To overcome this, we use Crawl4AI's custom JavaScript execution to simulate clicking the \"Next\" button, and implement a custom hook to detect when new data has loaded. Our strategy involves comparing the first commit's text before and after \"clicking\" Next, waiting until it changes to confirm new data has rendered. This showcases Crawl4AI's flexibility in handling dynamic content and its ability to implement custom logic for even the most challenging crawling tasks."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qUBKGpn3yZQN",
+ "outputId": "3e555b6a-ed33-42f4-cce9-499a923fbe17"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
+ "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+ "[LOG] 🕸️ Crawling https://github.com/microsoft/TypeScript/commits/main using AsyncPlaywrightCrawlerStrategy...\n",
+ "[LOG] ✅ Crawled https://github.com/microsoft/TypeScript/commits/main successfully!\n",
+ "[LOG] 🚀 Crawling done for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 5.16 seconds\n",
+ "[LOG] 🚀 Content extracted for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.28 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://github.com/microsoft/TypeScript/commits/main, Strategy: AsyncWebCrawler\n",
+ "[LOG] 🚀 Extraction done for https://github.com/microsoft/TypeScript/commits/main, time taken: 0.28 seconds.\n",
+ "Page 1: Found 35 commits\n",
+ "[LOG] 🕸️ Crawling https://github.com/microsoft/TypeScript/commits/main using AsyncPlaywrightCrawlerStrategy...\n",
+ "[LOG] ✅ Crawled https://github.com/microsoft/TypeScript/commits/main successfully!\n",
+ "[LOG] 🚀 Crawling done for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.78 seconds\n",
+ "[LOG] 🚀 Content extracted for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.90 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://github.com/microsoft/TypeScript/commits/main, Strategy: AsyncWebCrawler\n",
+ "[LOG] 🚀 Extraction done for https://github.com/microsoft/TypeScript/commits/main, time taken: 0.90 seconds.\n",
+ "Page 2: Found 35 commits\n",
+ "[LOG] 🕸️ Crawling https://github.com/microsoft/TypeScript/commits/main using AsyncPlaywrightCrawlerStrategy...\n",
+ "[LOG] ✅ Crawled https://github.com/microsoft/TypeScript/commits/main successfully!\n",
+ "[LOG] 🚀 Crawling done for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 2.00 seconds\n",
+ "[LOG] 🚀 Content extracted for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.74 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://github.com/microsoft/TypeScript/commits/main, Strategy: AsyncWebCrawler\n",
+ "[LOG] 🚀 Extraction done for https://github.com/microsoft/TypeScript/commits/main, time taken: 0.75 seconds.\n",
+ "Page 3: Found 35 commits\n",
+ "Successfully crawled 105 commits across 3 pages\n"
+ ]
+ }
+ ],
+ "source": [
+ "import re\n",
+ "from bs4 import BeautifulSoup\n",
+ "\n",
+ "async def crawl_typescript_commits():\n",
+ " first_commit = \"\"\n",
+ " async def on_execution_started(page):\n",
+ " nonlocal first_commit\n",
+ " try:\n",
+ " while True:\n",
+ " await page.wait_for_selector('li.Box-sc-g0xbh4-0 h4')\n",
+ " commit = await page.query_selector('li.Box-sc-g0xbh4-0 h4')\n",
+ " commit = await commit.evaluate('(element) => element.textContent')\n",
+ " commit = re.sub(r'\\s+', '', commit)\n",
+ " if commit and commit != first_commit:\n",
+ " first_commit = commit\n",
+ " break\n",
+ " await asyncio.sleep(0.5)\n",
+ " except Exception as e:\n",
+ " print(f\"Warning: New content didn't appear after JavaScript execution: {e}\")\n",
+ "\n",
+ " async with AsyncWebCrawler(verbose=True) as crawler:\n",
+ " crawler.crawler_strategy.set_hook('on_execution_started', on_execution_started)\n",
+ "\n",
+ " url = \"https://github.com/microsoft/TypeScript/commits/main\"\n",
+ " session_id = \"typescript_commits_session\"\n",
+ " all_commits = []\n",
+ "\n",
+ " js_next_page = \"\"\"\n",
+ " const button = document.querySelector('a[data-testid=\"pagination-next-button\"]');\n",
+ " if (button) button.click();\n",
+ " \"\"\"\n",
+ "\n",
+ " for page in range(3): # Crawl 3 pages\n",
+ " result = await crawler.arun(\n",
+ " url=url,\n",
+ " session_id=session_id,\n",
+ " css_selector=\"li.Box-sc-g0xbh4-0\",\n",
+ " js=js_next_page if page > 0 else None,\n",
+ " bypass_cache=True,\n",
+ " js_only=page > 0\n",
+ " )\n",
+ "\n",
+ " assert result.success, f\"Failed to crawl page {page + 1}\"\n",
+ "\n",
+ " soup = BeautifulSoup(result.cleaned_html, 'html.parser')\n",
+ " commits = soup.select(\"li\")\n",
+ " all_commits.extend(commits)\n",
+ "\n",
+ " print(f\"Page {page + 1}: Found {len(commits)} commits\")\n",
+ "\n",
+ " await crawler.crawler_strategy.kill_session(session_id)\n",
+ " print(f\"Successfully crawled {len(all_commits)} commits across 3 pages\")\n",
+ "\n",
+ "await crawl_typescript_commits()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EJRnYsp6yZQN"
+ },
+ "source": [
+ "### Using JsonCssExtractionStrategy for Fast Structured Output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1ZMqIzB_8SYp"
+ },
+ "source": [
+ "The JsonCssExtractionStrategy is a powerful feature of Crawl4AI that allows for precise, structured data extraction from web pages. Here's how it works:\n",
+ "\n",
+ "1. You define a schema that describes the pattern of data you're interested in extracting.\n",
+ "2. The schema includes a base selector that identifies repeating elements on the page.\n",
+ "3. Within the schema, you define fields, each with its own selector and type.\n",
+ "4. These field selectors are applied within the context of each base selector element.\n",
+ "5. The strategy supports nested structures, lists within lists, and various data types.\n",
+ "6. You can even include computed fields for more complex data manipulation.\n",
+ "\n",
+ "This approach allows for highly flexible and precise data extraction, transforming semi-structured web content into clean, structured JSON data. It's particularly useful for extracting consistent data patterns from pages like product listings, news articles, or search results.\n",
+ "\n",
+ "For more details and advanced usage, check out the full documentation on the Crawl4AI website."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "trCMR2T9yZQN",
+ "outputId": "718d36f4-cccf-40f4-8d8c-c3ba73524d16"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LOG] 🌤️ Warming up the AsyncWebCrawler\n",
+ "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+ "[LOG] 🕸️ Crawling https://www.nbcnews.com/business using AsyncPlaywrightCrawlerStrategy...\n",
+ "[LOG] ✅ Crawled https://www.nbcnews.com/business successfully!\n",
+ "[LOG] 🚀 Crawling done for https://www.nbcnews.com/business, success: True, time taken: 7.00 seconds\n",
+ "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.32 seconds\n",
+ "[LOG] 🔥 Extracting semantic blocks for https://www.nbcnews.com/business, Strategy: AsyncWebCrawler\n",
+ "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.48 seconds.\n",
+ "Successfully extracted 11 news teasers\n",
+ "{\n",
+ " \"category\": \"Business News\",\n",
+ " \"headline\": \"NBC ripped up its Olympics playbook for 2024 \\u2014 so far, the new strategy paid off\",\n",
+ " \"summary\": \"The Olympics have long been key to NBCUniversal. Paris marked the 18th Olympic Games broadcast by NBC in the U.S.\",\n",
+ " \"time\": \"13h ago\",\n",
+ " \"image\": {\n",
+ " \"src\": \"https://media-cldnry.s-nbcnews.com/image/upload/t_focal-200x100,f_auto,q_auto:best/rockcms/2024-09/240903-nbc-olympics-ch-1344-c7a486.jpg\",\n",
+ " \"alt\": \"Mike Tirico.\"\n",
+ " },\n",
+ " \"link\": \"https://www.nbcnews.com/business\"\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "async def extract_news_teasers():\n",
+ " schema = {\n",
+ " \"name\": \"News Teaser Extractor\",\n",
+ " \"baseSelector\": \".wide-tease-item__wrapper\",\n",
+ " \"fields\": [\n",
+ " {\n",
+ " \"name\": \"category\",\n",
+ " \"selector\": \".unibrow span[data-testid='unibrow-text']\",\n",
+ " \"type\": \"text\",\n",
+ " },\n",
+ " {\n",
+ " \"name\": \"headline\",\n",
+ " \"selector\": \".wide-tease-item__headline\",\n",
+ " \"type\": \"text\",\n",
+ " },\n",
+ " {\n",
+ " \"name\": \"summary\",\n",
+ " \"selector\": \".wide-tease-item__description\",\n",
+ " \"type\": \"text\",\n",
+ " },\n",
+ " {\n",
+ " \"name\": \"time\",\n",
+ " \"selector\": \"[data-testid='wide-tease-date']\",\n",
+ " \"type\": \"text\",\n",
+ " },\n",
+ " {\n",
+ " \"name\": \"image\",\n",
+ " \"type\": \"nested\",\n",
+ " \"selector\": \"picture.teasePicture img\",\n",
+ " \"fields\": [\n",
+ " {\"name\": \"src\", \"type\": \"attribute\", \"attribute\": \"src\"},\n",
+ " {\"name\": \"alt\", \"type\": \"attribute\", \"attribute\": \"alt\"},\n",
+ " ],\n",
+ " },\n",
+ " {\n",
+ " \"name\": \"link\",\n",
+ " \"selector\": \"a[href]\",\n",
+ " \"type\": \"attribute\",\n",
+ " \"attribute\": \"href\",\n",
+ " },\n",
+ " ],\n",
+ " }\n",
+ "\n",
+ " extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)\n",
+ "\n",
+ " async with AsyncWebCrawler(verbose=True) as crawler:\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://www.nbcnews.com/business\",\n",
+ " extraction_strategy=extraction_strategy,\n",
+ " bypass_cache=True,\n",
+ " )\n",
+ "\n",
+ " assert result.success, \"Failed to crawl the page\"\n",
+ "\n",
+ " news_teasers = json.loads(result.extracted_content)\n",
+ " print(f\"Successfully extracted {len(news_teasers)} news teasers\")\n",
+ " print(json.dumps(news_teasers[0], indent=2))\n",
+ "\n",
+ "await extract_news_teasers()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FnyVhJaByZQN"
+ },
+ "source": [
+ "## Speed Comparison\n",
+ "\n",
+ "Let's compare the speed of Crawl4AI with Firecrawl, a paid service. Note that we can't run Firecrawl in this Colab environment, so we'll simulate its performance based on previously recorded data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "agDD186f3wig"
+ },
+ "source": [
+ "💡 **Note on Speed Comparison:**\n",
+ "\n",
+ "The speed test conducted here is running on Google Colab, where the internet speed and performance can vary and may not reflect optimal conditions. When we call Firecrawl's API, we're seeing its best performance, while Crawl4AI's performance is limited by Colab's network speed.\n",
+ "\n",
+ "For a more accurate comparison, it's recommended to run these tests on your own servers or computers with a stable and fast internet connection. Despite these limitations, Crawl4AI still demonstrates faster performance in this environment.\n",
+ "\n",
+ "If you run these tests locally, you may observe an even more significant speed advantage for Crawl4AI compared to other services."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "F7KwHv8G1LbY"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install firecrawl"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "91813zILyZQN",
+ "outputId": "663223db-ab89-4976-b233-05ceca62b19b"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Firecrawl (simulated):\n",
+ "Time taken: 4.38 seconds\n",
+ "Content length: 41967 characters\n",
+ "Images found: 49\n",
+ "\n",
+ "Crawl4AI (simple crawl):\n",
+ "Time taken: 4.22 seconds\n",
+ "Content length: 18221 characters\n",
+ "Images found: 49\n",
+ "\n",
+ "Crawl4AI (with JavaScript execution):\n",
+ "Time taken: 9.13 seconds\n",
+ "Content length: 34243 characters\n",
+ "Images found: 89\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "from google.colab import userdata\n",
+ "os.environ['FIRECRAWL_API_KEY'] = userdata.get('FIRECRAWL_API_KEY')\n",
+ "import time\n",
+ "from firecrawl import FirecrawlApp\n",
+ "\n",
+ "async def speed_comparison():\n",
+ " # Simulated Firecrawl performance\n",
+ " app = FirecrawlApp(api_key=os.environ['FIRECRAWL_API_KEY'])\n",
+ " start = time.time()\n",
+ " scrape_status = app.scrape_url(\n",
+ " 'https://www.nbcnews.com/business',\n",
+ " params={'formats': ['markdown', 'html']}\n",
+ " )\n",
+ " end = time.time()\n",
+ " print(\"Firecrawl (simulated):\")\n",
+ " print(f\"Time taken: {end - start:.2f} seconds\")\n",
+ " print(f\"Content length: {len(scrape_status['markdown'])} characters\")\n",
+ " print(f\"Images found: {scrape_status['markdown'].count('cldnry.s-nbcnews.com')}\")\n",
+ " print()\n",
+ "\n",
+ " async with AsyncWebCrawler() as crawler:\n",
+ " # Crawl4AI simple crawl\n",
+ " start = time.time()\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://www.nbcnews.com/business\",\n",
+ " word_count_threshold=0,\n",
+ " bypass_cache=True,\n",
+ " verbose=False\n",
+ " )\n",
+ " end = time.time()\n",
+ " print(\"Crawl4AI (simple crawl):\")\n",
+ " print(f\"Time taken: {end - start:.2f} seconds\")\n",
+ " print(f\"Content length: {len(result.markdown)} characters\")\n",
+ " print(f\"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}\")\n",
+ " print()\n",
+ "\n",
+ " # Crawl4AI with JavaScript execution\n",
+ " start = time.time()\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://www.nbcnews.com/business\",\n",
+ " js_code=[\"const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();\"],\n",
+ " word_count_threshold=0,\n",
+ " bypass_cache=True,\n",
+ " verbose=False\n",
+ " )\n",
+ " end = time.time()\n",
+ " print(\"Crawl4AI (with JavaScript execution):\")\n",
+ " print(f\"Time taken: {end - start:.2f} seconds\")\n",
+ " print(f\"Content length: {len(result.markdown)} characters\")\n",
+ " print(f\"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}\")\n",
+ "\n",
+ "await speed_comparison()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OBFFYVJIyZQN"
+ },
+ "source": [
+ "If you run on a local machine with a proper internet speed:\n",
+ "- Simple crawl: Crawl4AI is typically over 3-4 times faster than Firecrawl.\n",
+ "- With JavaScript execution: Even when executing JavaScript to load more content (potentially doubling the number of images found), Crawl4AI is still faster than Firecrawl's simple crawl.\n",
+ "\n",
+ "Please note that actual performance may vary depending on network conditions and the specific content being crawled."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A6_1RK1_yZQO"
+ },
+ "source": [
+ "## Conclusion\n",
+ "\n",
+ "In this notebook, we've explored the powerful features of Crawl4AI, including:\n",
+ "\n",
+ "1. Basic crawling\n",
+ "2. JavaScript execution and CSS selector usage\n",
+ "3. Proxy support\n",
+ "4. Structured data extraction with OpenAI\n",
+ "5. Advanced multi-page crawling with JavaScript execution\n",
+ "6. Fast structured output using JsonCssExtractionStrategy\n",
+ "7. Speed comparison with other services\n",
+ "\n",
+ "Crawl4AI offers a fast, flexible, and powerful solution for web crawling and data extraction tasks. Its asynchronous architecture and advanced features make it suitable for a wide range of applications, from simple web scraping to complex, multi-page data extraction scenarios.\n",
+ "\n",
+ "For more information and advanced usage, please visit the [Crawl4AI documentation](https://crawl4ai.com/mkdocs/).\n",
+ "\n",
+ "Happy crawling!"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/docs/extraction_strategies.json b/docs/extraction_strategies.json
deleted file mode 100644
index 570e1e32..00000000
--- a/docs/extraction_strategies.json
+++ /dev/null
@@ -1,10 +0,0 @@
-{
- "NoExtractionStrategy": "### NoExtractionStrategy\n\n`NoExtractionStrategy` is a basic extraction strategy that returns the entire HTML content without any modification. It is useful for cases where no specific extraction is required. Only clean html, and amrkdown.\n\n#### Constructor Parameters:\nNone.\n\n#### Example usage:\n```python\nextractor = NoExtractionStrategy()\nextracted_content = extractor.extract(url, html)\n```",
-
- "LLMExtractionStrategy": "### LLMExtractionStrategy\n\n`LLMExtractionStrategy` uses a Language Model (LLM) to extract meaningful blocks or chunks from the given HTML content. This strategy leverages an external provider for language model completions.\n\n#### Constructor Parameters:\n- `provider` (str, optional): The provider to use for the language model completions. Default is `DEFAULT_PROVIDER` (e.g., openai/gpt-4).\n- `api_token` (str, optional): The API token for the provider. If not provided, it will try to load from the environment variable `OPENAI_API_KEY`.\n- `instruction` (str, optional): An instruction to guide the LLM on how to perform the extraction. This allows users to specify the type of data they are interested in or set the tone of the response. Default is `None`.\n\n#### Example usage:\n```python\nextractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token', instruction='Extract only news about AI.')\nextracted_content = extractor.extract(url, html)\n```\n\nBy providing clear instructions, users can tailor the extraction process to their specific needs, enhancing the relevance and utility of the extracted content.",
-
- "CosineStrategy": "### CosineStrategy\n\n`CosineStrategy` uses hierarchical clustering based on cosine similarity to extract clusters of text from the given HTML content. This strategy is suitable for identifying related content sections.\n\n#### Constructor Parameters:\n- `semantic_filter` (str, optional): A string containing keywords for filtering relevant documents before clustering. If provided, documents are filtered based on their cosine similarity to the keyword filter embedding. Default is `None`.\n- `word_count_threshold` (int, optional): Minimum number of words per cluster. Default is `20`.\n- `max_dist` (float, optional): The maximum cophenetic distance on the dendrogram to form clusters. Default is `0.2`.\n- `linkage_method` (str, optional): The linkage method for hierarchical clustering. Default is `'ward'`.\n- `top_k` (int, optional): Number of top categories to extract. Default is `3`.\n- `model_name` (str, optional): The model name for embedding generation. Default is `'BAAI/bge-small-en-v1.5'`.\n\n#### Example usage:\n```python\nextractor = CosineStrategy(semantic_filter='artificial intelligence', word_count_threshold=10, max_dist=0.2, linkage_method='ward', top_k=3, model_name='BAAI/bge-small-en-v1.5')\nextracted_content = extractor.extract(url, html)\n```\n\n#### Cosine Similarity Filtering\n\nWhen a `semantic_filter` is provided, the `CosineStrategy` applies an embedding-based filtering process to select relevant documents before performing hierarchical clustering.",
-
- "TopicExtractionStrategy": "### TopicExtractionStrategy\n\n`TopicExtractionStrategy` uses the TextTiling algorithm to segment the HTML content into topics and extracts keywords for each segment. This strategy is useful for identifying and summarizing thematic content.\n\n#### Constructor Parameters:\n- `num_keywords` (int, optional): Number of keywords to represent each topic segment. Default is `3`.\n\n#### Example usage:\n```python\nextractor = TopicExtractionStrategy(num_keywords=3)\nextracted_content = extractor.extract(url, html)\n```"
- }
-
\ No newline at end of file
diff --git a/docs/md _sync/api/core_classes_and_functions.md b/docs/md _sync/api/core_classes_and_functions.md
deleted file mode 100644
index 198fd427..00000000
--- a/docs/md _sync/api/core_classes_and_functions.md
+++ /dev/null
@@ -1,141 +0,0 @@
-# Core Classes and Functions
-
-## Overview
-
-In this section, we will delve into the core classes and functions that make up the Crawl4AI library. This includes the `WebCrawler` class, various `CrawlerStrategy` classes, `ChunkingStrategy` classes, and `ExtractionStrategy` classes. Understanding these core components will help you leverage the full power of Crawl4AI for your web crawling and data extraction needs.
-
-## WebCrawler Class
-
-The `WebCrawler` class is the main class you'll interact with. It provides the interface for crawling web pages and extracting data.
-
-### Initialization
-
-```python
-from crawl4ai import WebCrawler
-
-# Create an instance of WebCrawler
-crawler = WebCrawler()
-```
-
-### Methods
-
-- **`warmup()`**: Prepares the crawler for use, such as loading necessary models.
-- **`run(url: str, **kwargs)`**: Runs the crawler on the specified URL with optional parameters for customization.
-
-```python
-crawler.warmup()
-result = crawler.run(url="https://www.nbcnews.com/business")
-print(result)
-```
-
-## CrawlerStrategy Classes
-
-The `CrawlerStrategy` classes define how the web crawling is executed. The base class is `CrawlerStrategy`, which is extended by specific implementations like `LocalSeleniumCrawlerStrategy`.
-
-### CrawlerStrategy Base Class
-
-An abstract base class that defines the interface for different crawler strategies.
-
-```python
-from abc import ABC, abstractmethod
-
-class CrawlerStrategy(ABC):
- @abstractmethod
- def crawl(self, url: str, **kwargs) -> str:
- pass
-
- @abstractmethod
- def take_screenshot(self, save_path: str):
- pass
-
- @abstractmethod
- def update_user_agent(self, user_agent: str):
- pass
-
- @abstractmethod
- def set_hook(self, hook_type: str, hook: Callable):
- pass
-```
-
-### LocalSeleniumCrawlerStrategy Class
-
-A concrete implementation of `CrawlerStrategy` that uses Selenium to crawl web pages.
-
-#### Initialization
-
-```python
-from crawl4ai.crawler_strategy import LocalSeleniumCrawlerStrategy
-
-strategy = LocalSeleniumCrawlerStrategy(js_code=["console.log('Hello, world!');"])
-```
-
-#### Methods
-
-- **`crawl(url: str, **kwargs)`**: Crawls the specified URL.
-- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
-- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
-- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
-
-```python
-result = strategy.crawl("https://www.example.com")
-strategy.take_screenshot("screenshot.png")
-strategy.update_user_agent("Mozilla/5.0")
-strategy.set_hook("before_get_url", lambda: print("About to get URL"))
-```
-
-## ChunkingStrategy Classes
-
-The `ChunkingStrategy` classes define how the text from a web page is divided into chunks. Here are a few examples:
-
-### RegexChunking Class
-
-Splits text using regular expressions.
-
-```python
-from crawl4ai.chunking_strategy import RegexChunking
-
-chunker = RegexChunking(patterns=[r'\n\n'])
-chunks = chunker.chunk("This is a sample text. It will be split into chunks.")
-```
-
-### NlpSentenceChunking Class
-
-Uses NLP to split text into sentences.
-
-```python
-from crawl4ai.chunking_strategy import NlpSentenceChunking
-
-chunker = NlpSentenceChunking()
-chunks = chunker.chunk("This is a sample text. It will be split into sentences.")
-```
-
-## ExtractionStrategy Classes
-
-The `ExtractionStrategy` classes define how meaningful content is extracted from the chunks. Here are a few examples:
-
-### CosineStrategy Class
-
-Clusters text chunks based on cosine similarity.
-
-```python
-from crawl4ai.extraction_strategy import CosineStrategy
-
-extractor = CosineStrategy(semantic_filter="finance", word_count_threshold=10)
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-### LLMExtractionStrategy Class
-
-Uses a Language Model to extract meaningful blocks from HTML.
-
-```python
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-
-extractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token', instruction='Extract only news about AI.')
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-## Conclusion
-
-By understanding these core classes and functions, you can customize and extend Crawl4AI to suit your specific web crawling and data extraction needs. Happy crawling! 🕷️🤖
-
diff --git a/docs/md _sync/api/detailed_api_documentation.md b/docs/md _sync/api/detailed_api_documentation.md
deleted file mode 100644
index aaf0e34f..00000000
--- a/docs/md _sync/api/detailed_api_documentation.md
+++ /dev/null
@@ -1,338 +0,0 @@
-# Detailed API Documentation
-
-## Overview
-
-This section provides comprehensive documentation for the Crawl4AI API, covering all classes, methods, and their parameters. This guide will help you understand how to utilize the API to its full potential, enabling efficient web crawling and data extraction.
-
-## WebCrawler Class
-
-The `WebCrawler` class is the primary interface for crawling web pages and extracting data.
-
-### Initialization
-
-```python
-from crawl4ai import WebCrawler
-
-crawler = WebCrawler()
-```
-
-### Methods
-
-#### `warmup()`
-
-Prepares the crawler for use, such as loading necessary models.
-
-```python
-crawler.warmup()
-```
-
-#### `run(url: str, **kwargs) -> CrawlResult`
-
-Crawls the specified URL and returns the result.
-
-- **Parameters:**
- - `url` (str): The URL to crawl.
- - `**kwargs`: Additional parameters for customization.
-
-- **Returns:**
- - `CrawlResult`: An object containing the crawl result.
-
-- **Example:**
-
-```python
-result = crawler.run(url="https://www.nbcnews.com/business")
-print(result)
-```
-
-### CrawlResult Class
-
-Represents the result of a crawl operation.
-
-- **Attributes:**
- - `url` (str): The URL of the crawled page.
- - `html` (str): The raw HTML of the page.
- - `success` (bool): Whether the crawl was successful.
- - `cleaned_html` (Optional[str]): The cleaned HTML.
- - `media` (Dict[str, List[Dict]]): Media tags in the page (images, audio, video).
- - `links` (Dict[str, List[Dict]]): Links in the page (external, internal).
- - `screenshot` (Optional[str]): Base64 encoded screenshot.
- - `markdown` (Optional[str]): Extracted content in Markdown format.
- - `extracted_content` (Optional[str]): Extracted meaningful content.
- - `metadata` (Optional[dict]): Metadata from the page.
- - `error_message` (Optional[str]): Error message if any.
-
-## CrawlerStrategy Classes
-
-The `CrawlerStrategy` classes define how the web crawling is executed.
-
-### CrawlerStrategy Base Class
-
-An abstract base class for different crawler strategies.
-
-#### Methods
-
-- **`crawl(url: str, **kwargs) -> str`**: Crawls the specified URL.
-- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
-- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
-- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
-
-### LocalSeleniumCrawlerStrategy Class
-
-Uses Selenium to crawl web pages.
-
-#### Initialization
-
-```python
-from crawl4ai.crawler_strategy import LocalSeleniumCrawlerStrategy
-
-strategy = LocalSeleniumCrawlerStrategy(js_code=["console.log('Hello, world!');"])
-```
-
-#### Methods
-
-- **`crawl(url: str, **kwargs)`**: Crawls the specified URL.
-- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
-- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
-- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
-
-#### Example
-
-```python
-result = strategy.crawl("https://www.example.com")
-strategy.take_screenshot("screenshot.png")
-strategy.update_user_agent("Mozilla/5.0")
-strategy.set_hook("before_get_url", lambda: print("About to get URL"))
-```
-
-## ChunkingStrategy Classes
-
-The `ChunkingStrategy` classes define how the text from a web page is divided into chunks.
-
-### RegexChunking Class
-
-Splits text using regular expressions.
-
-#### Initialization
-
-```python
-from crawl4ai.chunking_strategy import RegexChunking
-
-chunker = RegexChunking(patterns=[r'\n\n'])
-```
-
-#### Methods
-
-- **`chunk(text: str) -> List[str]`**: Splits the text into chunks.
-
-#### Example
-
-```python
-chunks = chunker.chunk("This is a sample text. It will be split into chunks.")
-```
-
-### NlpSentenceChunking Class
-
-Uses NLP to split text into sentences.
-
-#### Initialization
-
-```python
-from crawl4ai.chunking_strategy import NlpSentenceChunking
-
-chunker = NlpSentenceChunking()
-```
-
-#### Methods
-
-- **`chunk(text: str) -> List[str]`**: Splits the text into sentences.
-
-#### Example
-
-```python
-chunks = chunker.chunk("This is a sample text. It will be split into sentences.")
-```
-
-### TopicSegmentationChunking Class
-
-Uses the TextTiling algorithm to segment text into topics.
-
-#### Initialization
-
-```python
-from crawl4ai.chunking_strategy import TopicSegmentationChunking
-
-chunker = TopicSegmentationChunking(num_keywords=3)
-```
-
-#### Methods
-
-- **`chunk(text: str) -> List[str]`**: Splits the text into topic-based segments.
-
-#### Example
-
-```python
-chunks = chunker.chunk("This is a sample text. It will be split into topic-based segments.")
-```
-
-### FixedLengthWordChunking Class
-
-Splits text into chunks of fixed length based on the number of words.
-
-#### Initialization
-
-```python
-from crawl4ai.chunking_strategy import FixedLengthWordChunking
-
-chunker = FixedLengthWordChunking(chunk_size=100)
-```
-
-#### Methods
-
-- **`chunk(text: str) -> List[str]`**: Splits the text into fixed-length word chunks.
-
-#### Example
-
-```python
-chunks = chunker.chunk("This is a sample text. It will be split into fixed-length word chunks.")
-```
-
-### SlidingWindowChunking Class
-
-Uses a sliding window approach to chunk text.
-
-#### Initialization
-
-```python
-from crawl4ai.chunking_strategy import SlidingWindowChunking
-
-chunker = SlidingWindowChunking(window_size=100, step=50)
-```
-
-#### Methods
-
-- **`chunk(text: str) -> List[str]`**: Splits the text using a sliding window approach.
-
-#### Example
-
-```python
-chunks = chunker.chunk("This is a sample text. It will be split using a sliding window approach.")
-```
-
-## ExtractionStrategy Classes
-
-The `ExtractionStrategy` classes define how meaningful content is extracted from the chunks.
-
-### NoExtractionStrategy Class
-
-Returns the entire HTML content without any modification.
-
-#### Initialization
-
-```python
-from crawl4ai.extraction_strategy import NoExtractionStrategy
-
-extractor = NoExtractionStrategy()
-```
-
-#### Methods
-
-- **`extract(url: str, html: str) -> str`**: Returns the HTML content.
-
-#### Example
-
-```python
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-### LLMExtractionStrategy Class
-
-Uses a Language Model to extract meaningful blocks from HTML.
-
-#### Initialization
-
-```python
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-
-extractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token', instruction='Extract only news about AI.')
-```
-
-#### Methods
-
-- **`extract(url: str, html: str) -> str`**: Extracts meaningful content using the LLM.
-
-#### Example
-
-```python
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-### CosineStrategy Class
-
-Clusters text chunks based on cosine similarity.
-
-#### Initialization
-
-```python
-from crawl4ai.extraction_strategy import CosineStrategy
-
-extractor = CosineStrategy(semantic_filter="finance", word_count_threshold=10)
-```
-
-#### Methods
-
-- **`extract(url: str, html: str) -> str`**: Extracts clusters of text based on cosine similarity.
-
-#### Example
-
-```python
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-### TopicExtractionStrategy Class
-
-Uses the TextTiling algorithm to segment HTML content into topics and extract keywords.
-
-#### Initialization
-
-```python
-from crawl4ai.extraction_strategy import TopicExtractionStrategy
-
-extractor = TopicExtractionStrategy(num_keywords=3)
-```
-
-#### Methods
-
-- **`extract(url: str, html: str) -> str`**: Extracts topic-based segments and keywords.
-
-#### Example
-
-```python
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-## Parameters
-
-Here are the common parameters used across various classes and methods:
-
-- **`url`** (str): The URL to crawl.
-- **`html`** (str): The HTML content of the page.
-- **`user_agent`** (str): The user agent for the HTTP requests.
-- **`patterns`** (list): A list of regular expression patterns for chunking.
-- **`num_keywords`** (int): Number of keywords for topic extraction.
-- **`chunk_size`** (int): Number of words in each chunk.
-- **`window_size`** (int): Number of words in the sliding window.
-- **`step`** (int): Step size for the sliding window.
-- **`semantic_filter`** (str): Keywords for filtering relevant documents.
-- **`word_count_threshold`** (int): Minimum number of words per cluster.
-- **`max_dist`** (float): Maximum cophenetic distance for clustering.
-- **`linkage_method`** (str): Linkage method for hierarchical clustering.
-- **`top_k`** (int): Number of top categories to extract.
-- **`provider`** (
-
-str): Provider for language model completions.
-- **`api_token`** (str): API token for the provider.
-- **`instruction`** (str): Instruction to guide the LLM extraction.
-
-## Conclusion
-
-This detailed API documentation provides a thorough understanding of the classes, methods, and parameters in the Crawl4AI library. With this knowledge, you can effectively use the API to perform advanced web crawling and data extraction tasks.
\ No newline at end of file
diff --git a/docs/md _sync/changelog.md b/docs/md _sync/changelog.md
deleted file mode 100644
index 03e490f6..00000000
--- a/docs/md _sync/changelog.md
+++ /dev/null
@@ -1,102 +0,0 @@
-# Changelog
-
-## [v0.2.77] - 2024-08-04
-
-Significant improvements in text processing and performance:
-
-- 🚀 **Dependency reduction**: Removed dependency on spaCy model for text chunk labeling in cosine extraction strategy.
-- 🤖 **Transformer upgrade**: Implemented text sequence classification using a transformer model for labeling text chunks.
-- ⚡ **Performance enhancement**: Improved model loading speed due to removal of spaCy dependency.
-- 🔧 **Future-proofing**: Laid groundwork for potential complete removal of spaCy dependency in future versions.
-
-These changes address issue #68 and provide a foundation for faster, more efficient text processing in Crawl4AI.
-
-## [v0.2.76] - 2024-08-02
-
-Major improvements in functionality, performance, and cross-platform compatibility! 🚀
-
-- 🐳 **Docker enhancements**: Significantly improved Dockerfile for easy installation on Linux, Mac, and Windows.
-- 🌐 **Official Docker Hub image**: Launched our first official image on Docker Hub for streamlined deployment.
-- 🔧 **Selenium upgrade**: Removed dependency on ChromeDriver, now using Selenium's built-in capabilities for better compatibility.
-- 🖼️ **Image description**: Implemented ability to generate textual descriptions for extracted images from web pages.
-- ⚡ **Performance boost**: Various improvements to enhance overall speed and performance.
-
-A big shoutout to our amazing community contributors:
-- [@aravindkarnam](https://github.com/aravindkarnam) for developing the textual description extraction feature.
-- [@FractalMind](https://github.com/FractalMind) for creating the first official Docker Hub image and fixing Dockerfile errors.
-- [@ketonkss4](https://github.com/ketonkss4) for identifying Selenium's new capabilities, helping us reduce dependencies.
-
-Your contributions are driving Crawl4AI forward! 🙌
-
-## [v0.2.75] - 2024-07-19
-
-Minor improvements for a more maintainable codebase:
-
-- 🔄 Fixed typos in `chunking_strategy.py` and `crawler_strategy.py` to improve code readability
-- 🔄 Removed `.test_pads/` directory from `.gitignore` to keep our repository clean and organized
-
-These changes may seem small, but they contribute to a more stable and sustainable codebase. By fixing typos and updating our `.gitignore` settings, we're ensuring that our code is easier to maintain and scale in the long run.
-
-
-## v0.2.74 - 2024-07-08
-A slew of exciting updates to improve the crawler's stability and robustness! 🎉
-
-- 💻 **UTF encoding fix**: Resolved the Windows \"charmap\" error by adding UTF encoding.
-- 🛡️ **Error handling**: Implemented MaxRetryError exception handling in LocalSeleniumCrawlerStrategy.
-- 🧹 **Input sanitization**: Improved input sanitization and handled encoding issues in LLMExtractionStrategy.
-- 🚮 **Database cleanup**: Removed existing database file and initialized a new one.
-
-## [v0.2.73] - 2024-07-03
-
-💡 In this release, we've bumped the version to v0.2.73 and refreshed our documentation to ensure you have the best experience with our project.
-
-* Supporting website need "with-head" mode to crawl the website with head.
-* Fixing the installation issues for setup.py and dockerfile.
-* Resolve multiple issues.
-
-## [v0.2.72] - 2024-06-30
-
-This release brings exciting updates and improvements to our project! 🎉
-
-* 📚 **Documentation Updates**: Our documentation has been revamped to reflect the latest changes and additions.
-* 🚀 **New Modes in setup.py**: We've added support for three new modes in setup.py: default, torch, and transformers. This enhances the project's flexibility and usability.
-* 🐳 **Docker File Updates**: The Docker file has been updated to ensure seamless compatibility with the new modes and improvements.
-* 🕷️ **Temporary Solution for Headless Crawling**: We've implemented a temporary solution to overcome issues with crawling websites in headless mode.
-
-These changes aim to improve the overall user experience, provide more flexibility, and enhance the project's performance. We're thrilled to share these updates with you and look forward to continuing to evolve and improve our project!
-
-## [0.2.71] - 2024-06-26
-
-**Improved Error Handling and Performance** 🚧
-
-* 🚫 Refactored `crawler_strategy.py` to handle exceptions and provide better error messages, making it more robust and reliable.
-* 💻 Optimized the `get_content_of_website_optimized` function in `utils.py` for improved performance, reducing potential bottlenecks.
-* 💻 Updated `utils.py` with the latest changes, ensuring consistency and accuracy.
-* 🚫 Migrated to `ChromeDriverManager` to resolve Chrome driver download issues, providing a smoother user experience.
-
-These changes focus on refining the existing codebase, resulting in a more stable, efficient, and user-friendly experience. With these improvements, you can expect fewer errors and better performance in the crawler strategy and utility functions.
-
-## [0.2.71] - 2024-06-25
-### Fixed
-- Speed up twice the extraction function.
-
-## [0.2.6] - 2024-06-22
-### Fixed
-- Fix issue #19: Update Dockerfile to ensure compatibility across multiple platforms.
-
-## [0.2.5] - 2024-06-18
-### Added
-- Added five important hooks to the crawler:
- - on_driver_created: Called when the driver is ready for initializations.
- - before_get_url: Called right before Selenium fetches the URL.
- - after_get_url: Called after Selenium fetches the URL.
- - before_return_html: Called when the data is parsed and ready.
- - on_user_agent_updated: Called when the user changes the user_agent, causing the driver to reinitialize.
-- Added an example in `quickstart.py` in the example folder under the docs.
-- Enhancement issue #24: Replaced inline HTML tags (e.g., DEL, INS, SUB, ABBR) with textual format for better context handling in LLM.
-- Maintaining the semantic context of inline tags (e.g., abbreviation, DEL, INS) for improved LLM-friendliness.
-- Updated Dockerfile to ensure compatibility across multiple platforms (Hopefully!).
-
-## [0.2.4] - 2024-06-17
-### Fixed
-- Fix issue #22: Use MD5 hash for caching HTML files to handle long URLs
diff --git a/docs/md _sync/contact.md b/docs/md _sync/contact.md
deleted file mode 100644
index 85faa217..00000000
--- a/docs/md _sync/contact.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# Contact
-If you have any questions, suggestions, or feedback, please feel free to reach out to us:
-
-- GitHub: [unclecode](https://github.com/unclecode)
-- Twitter: [@unclecode](https://twitter.com/unclecode)
-- Website: [crawl4ai.com](https://crawl4ai.com)
-
-
-## Contributing 🤝
-
-We welcome contributions from the open-source community to help improve Crawl4AI and make it even more valuable for AI enthusiasts and developers. To contribute, please follow these steps:
-
-1. Fork the repository.
-2. Create a new branch for your feature or bug fix.
-3. Make your changes and commit them with descriptive messages.
-4. Push your changes to your forked repository.
-5. Submit a pull request to the main repository.
-
-For more information on contributing, please see our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTING.md).
-
-## License 📄
-
-Crawl4AI is released under the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE).
-
-Let's work together to make the web more accessible and useful for AI applications! 💪🌐🤖
diff --git a/docs/md _sync/demo.md b/docs/md _sync/demo.md
deleted file mode 100644
index 4ca6c754..00000000
--- a/docs/md _sync/demo.md
+++ /dev/null
@@ -1,231 +0,0 @@
-# Interactive Demo for Crowler
-
-
-
-
-
-
- Response
-
-
- - Markdown
- - Cleaned HTML
- - Media
- - Extracted Content
- - Screenshot
- - Python Code
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/md _sync/examples/hooks_auth.md b/docs/md _sync/examples/hooks_auth.md
deleted file mode 100644
index 2b4c2701..00000000
--- a/docs/md _sync/examples/hooks_auth.md
+++ /dev/null
@@ -1,100 +0,0 @@
-# Hooks & Auth
-
-Crawl4AI allows you to customize the behavior of the web crawler using hooks. Hooks are functions that are called at specific points in the crawling process, allowing you to modify the crawler's behavior or perform additional actions. This example demonstrates how to use various hooks to customize the crawling process.
-
-## Example: Using Crawler Hooks
-
-Let's see how we can customize the crawler using hooks! In this example, we'll:
-
-1. Maximize the browser window and log in to a website when the driver is created.
-2. Add a custom header before fetching the URL.
-3. Log the current URL after fetching it.
-4. Log the length of the HTML before returning it.
-
-### Hook Definitions
-
-```python
-from crawl4ai.web_crawler import WebCrawler
-from crawl4ai.crawler_strategy import *
-
-def on_driver_created(driver):
- print("[HOOK] on_driver_created")
- # Example customization: maximize the window
- driver.maximize_window()
-
- # Example customization: logging in to a hypothetical website
- driver.get('https://example.com/login')
-
- from selenium.webdriver.support.ui import WebDriverWait
- from selenium.webdriver.common.by import By
- from selenium.webdriver.support import expected_conditions as EC
-
- WebDriverWait(driver, 10).until(
- EC.presence_of_element_located((By.NAME, 'username'))
- )
- driver.find_element(By.NAME, 'username').send_keys('testuser')
- driver.find_element(By.NAME, 'password').send_keys('password123')
- driver.find_element(By.NAME, 'login').click()
- WebDriverWait(driver, 10).until(
- EC.presence_of_element_located((By.ID, 'welcome'))
- )
- # Add a custom cookie
- driver.add_cookie({'name': 'test_cookie', 'value': 'cookie_value'})
- return driver
-
-
-def before_get_url(driver):
- print("[HOOK] before_get_url")
- # Example customization: add a custom header
- # Enable Network domain for sending headers
- driver.execute_cdp_cmd('Network.enable', {})
- # Add a custom header
- driver.execute_cdp_cmd('Network.setExtraHTTPHeaders', {'headers': {'X-Test-Header': 'test'}})
- return driver
-
-def after_get_url(driver):
- print("[HOOK] after_get_url")
- # Example customization: log the URL
- print(driver.current_url)
- return driver
-
-def before_return_html(driver, html):
- print("[HOOK] before_return_html")
- # Example customization: log the HTML
- print(len(html))
- return driver
-```
-
-### Using the Hooks with the WebCrawler
-
-```python
-print("\n🔗 [bold cyan]Using Crawler Hooks: Let's see how we can customize the crawler using hooks![/bold cyan]", True)
-crawler_strategy = LocalSeleniumCrawlerStrategy(verbose=True)
-crawler_strategy.set_hook('on_driver_created', on_driver_created)
-crawler_strategy.set_hook('before_get_url', before_get_url)
-crawler_strategy.set_hook('after_get_url', after_get_url)
-crawler_strategy.set_hook('before_return_html', before_return_html)
-crawler = WebCrawler(verbose=True, crawler_strategy=crawler_strategy)
-crawler.warmup()
-
-result = crawler.run(url="https://example.com")
-
-print("[LOG] 📦 [bold yellow]Crawler Hooks result:[/bold yellow]")
-print(result)
-```
-
-### Explanation
-
-- `on_driver_created`: This hook is called when the Selenium driver is created. In this example, it maximizes the window, logs in to a website, and adds a custom cookie.
-- `before_get_url`: This hook is called right before Selenium fetches the URL. In this example, it adds a custom HTTP header.
-- `after_get_url`: This hook is called after Selenium fetches the URL. In this example, it logs the current URL.
-- `before_return_html`: This hook is called before returning the HTML content. In this example, it logs the length of the HTML content.
-
-### Additional Ideas
-
-- **Add custom headers to requests**: You can add custom headers to the requests using the `before_get_url` hook.
-- **Perform safety checks**: Use the hooks to perform safety checks before the crawling process starts.
-- **Modify the HTML content**: Use the `before_return_html` hook to modify the HTML content before it is returned.
-- **Log additional information**: Use the hooks to log additional information for debugging or monitoring purposes.
-
-By using these hooks, you can customize the behavior of the crawler to suit your specific needs.
diff --git a/docs/md _sync/examples/index.md b/docs/md _sync/examples/index.md
deleted file mode 100644
index 14380e3d..00000000
--- a/docs/md _sync/examples/index.md
+++ /dev/null
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-# Examples
-
-Welcome to the examples section of Crawl4AI documentation! In this section, you will find practical examples demonstrating how to use Crawl4AI for various web crawling and data extraction tasks. Each example is designed to showcase different features and capabilities of the library.
-
-## Examples Index
-
-### [LLM Extraction](llm_extraction.md)
-
-This example demonstrates how to use Crawl4AI to extract information using Large Language Models (LLMs). You will learn how to configure the `LLMExtractionStrategy` to get structured data from web pages.
-
-### [JS Execution & CSS Filtering](js_execution_css_filtering.md)
-
-Learn how to execute custom JavaScript code and filter data using CSS selectors. This example shows how to perform complex web interactions and extract specific content from web pages.
-
-### [Hooks & Auth](hooks_auth.md)
-
-This example covers the use of custom hooks for authentication and other pre-crawling tasks. You will see how to set up hooks to modify headers, authenticate sessions, and perform other preparatory actions before crawling.
-
-### [Summarization](summarization.md)
-
-Discover how to use Crawl4AI to summarize web page content. This example demonstrates the summarization capabilities of the library, helping you extract concise information from lengthy web pages.
-
-### [Research Assistant](research_assistant.md)
-
-In this example, Crawl4AI is used as a research assistant to gather and organize information from multiple sources. You will learn how to use various extraction and chunking strategies to compile a comprehensive report.
-
----
-
-Each example includes detailed explanations and code snippets to help you understand and implement the features in your projects. Click on the links to explore each example and start making the most of Crawl4AI!
diff --git a/docs/md _sync/examples/js_execution_css_filtering.md b/docs/md _sync/examples/js_execution_css_filtering.md
deleted file mode 100644
index 0592f094..00000000
--- a/docs/md _sync/examples/js_execution_css_filtering.md
+++ /dev/null
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-# JS Execution & CSS Filtering
-
-In this example, we'll demonstrate how to use Crawl4AI to execute JavaScript, filter data with CSS selectors, and use a cosine similarity strategy to extract relevant content. This approach is particularly useful when you need to interact with dynamic content on web pages, such as clicking "Load More" buttons.
-
-## Example: Extracting Structured Data
-
-```python
-# Import necessary modules
-from crawl4ai import WebCrawler
-from crawl4ai.chunking_strategy import *
-from crawl4ai.extraction_strategy import *
-from crawl4ai.crawler_strategy import *
-
-# Define the JavaScript code to click the "Load More" button
-js_code = ["""
-const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
-loadMoreButton && loadMoreButton.click();
-"""]
-
-crawler = WebCrawler(verbose=True)
-crawler.warmup()
-# Run the crawler with keyword filtering and CSS selector
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- js=js_code,
- css_selector="p",
- extraction_strategy=CosineStrategy(
- semantic_filter="technology",
- ),
-)
-
-# Display the extracted result
-print(result)
-```
-
-### Explanation
-
-1. **JavaScript Execution**: The `js_code` variable contains JavaScript code that simulates clicking a "Load More" button. This is useful for loading additional content dynamically.
-2. **CSS Selector**: The `css_selector="p"` parameter ensures that only paragraph (``) tags are extracted from the web page.
-3. **Extraction Strategy**: The `CosineStrategy` is used with a semantic filter for "technology" to extract relevant content based on cosine similarity.
-
-## Try It Yourself
-
-This example demonstrates the power and flexibility of Crawl4AI in handling complex web interactions and extracting meaningful data. You can customize the JavaScript code, CSS selectors, and extraction strategies to suit your specific requirements.
diff --git a/docs/md _sync/examples/llm_extraction.md b/docs/md _sync/examples/llm_extraction.md
deleted file mode 100644
index b7805726..00000000
--- a/docs/md _sync/examples/llm_extraction.md
+++ /dev/null
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-# LLM Extraction
-
-Crawl4AI allows you to use Language Models (LLMs) to extract structured data or relevant content from web pages. Below are two examples demonstrating how to use LLMExtractionStrategy for different purposes.
-
-## Example 1: Extract Structured Data
-
-In this example, we use the `LLMExtractionStrategy` to extract structured data (model names and their fees) from the OpenAI pricing page.
-
-```python
-import os
-import time
-from crawl4ai.web_crawler import WebCrawler
-from crawl4ai.chunking_strategy import *
-from crawl4ai.extraction_strategy import *
-from crawl4ai.crawler_strategy import *
-
-url = r'https://openai.com/api/pricing/'
-
-crawler = WebCrawler()
-crawler.warmup()
-
-from pydantic import BaseModel, Field
-
-class OpenAIModelFee(BaseModel):
- model_name: str = Field(..., description="Name of the OpenAI model.")
- input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
- output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
-
-result = crawler.run(
- url=url,
- word_count_threshold=1,
- extraction_strategy= LLMExtractionStrategy(
- provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'),
- schema=OpenAIModelFee.model_json_schema(),
- extraction_type="schema",
- instruction="From the crawled content, extract all mentioned model names along with their "\
- "fees for input and output tokens. Make sure not to miss anything in the entire content. "\
- 'One extracted model JSON format should look like this: '\
- '{ "model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens" }'
- ),
- bypass_cache=True,
-)
-
-model_fees = json.loads(result.extracted_content)
-
-print(len(model_fees))
-
-with open(".data/data.json", "w", encoding="utf-8") as f:
- f.write(result.extracted_content)
-```
-
-## Example 2: Extract Relevant Content
-
-In this example, we instruct the LLM to extract only content related to technology from the NBC News business page.
-
-```python
-crawler = WebCrawler()
-crawler.warmup()
-
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- extraction_strategy=LLMExtractionStrategy(
- provider="openai/gpt-4o",
- api_token=os.getenv('OPENAI_API_KEY'),
- instruction="Extract only content related to technology"
- ),
- bypass_cache=True,
- )
-
-model_fees = json.loads(result.extracted_content)
-
-print(len(model_fees))
-
-with open(".data/data.json", "w", encoding="utf-8") as f:
- f.write(result.extracted_content)
-```
-
-## Customizing LLM Provider
-
-Under the hood, Crawl4AI uses the `litellm` library, which allows you to use any LLM provider you want. Just pass the correct model name and API token.
-
-```python
-extraction_strategy=LLMExtractionStrategy(
- provider="your_llm_provider/model_name",
- api_token="your_api_token",
- instruction="Your extraction instruction"
-)
-```
-
-This flexibility allows you to integrate with various LLM providers and tailor the extraction process to your specific needs.
diff --git a/docs/md _sync/examples/research_assistant.md b/docs/md _sync/examples/research_assistant.md
deleted file mode 100644
index 9284fbef..00000000
--- a/docs/md _sync/examples/research_assistant.md
+++ /dev/null
@@ -1,248 +0,0 @@
-## Research Assistant Example
-
-This example demonstrates how to build a research assistant using `Chainlit` and `Crawl4AI`. The assistant will be capable of crawling web pages for information and answering questions based on the crawled content. Additionally, it integrates speech-to-text functionality for audio inputs.
-
-### Step-by-Step Guide
-
-1. **Install Required Packages**
-
- Ensure you have the necessary packages installed. You need `chainlit`, `groq`, `requests`, and `openai`.
-
- ```bash
- pip install chainlit groq requests openai
- ```
-
-2. **Import Libraries**
-
- Import all the necessary modules and initialize the OpenAI client.
-
- ```python
- import os
- import time
- from openai import AsyncOpenAI
- import chainlit as cl
- import re
- import requests
- from io import BytesIO
- from chainlit.element import ElementBased
- from groq import Groq
-
- from concurrent.futures import ThreadPoolExecutor
-
- client = AsyncOpenAI(base_url="https://api.groq.com/openai/v1", api_key=os.getenv("GROQ_API_KEY"))
-
- # Instrument the OpenAI client
- cl.instrument_openai()
- ```
-
-3. **Set Configuration**
-
- Define the model settings for the assistant.
-
- ```python
- settings = {
- "model": "llama3-8b-8192",
- "temperature": 0.5,
- "max_tokens": 500,
- "top_p": 1,
- "frequency_penalty": 0,
- "presence_penalty": 0,
- }
- ```
-
-4. **Define Utility Functions**
-
- - **Extract URLs from Text**: Use regex to find URLs in messages.
-
- ```python
- def extract_urls(text):
- url_pattern = re.compile(r'(https?://\S+)')
- return url_pattern.findall(text)
- ```
-
- - **Crawl URL**: Send a request to `Crawl4AI` to fetch the content of a URL.
-
- ```python
- def crawl_url(url):
- data = {
- "urls": [url],
- "include_raw_html": True,
- "word_count_threshold": 10,
- "extraction_strategy": "NoExtractionStrategy",
- "chunking_strategy": "RegexChunking"
- }
- response = requests.post("https://crawl4ai.com/crawl", json=data)
- response_data = response.json()
- response_data = response_data['results'][0]
- return response_data['markdown']
- ```
-
-5. **Initialize Chat Start Event**
-
- Set up the initial chat message and user session.
-
- ```python
- @cl.on_chat_start
- async def on_chat_start():
- cl.user_session.set("session", {
- "history": [],
- "context": {}
- })
- await cl.Message(
- content="Welcome to the chat! How can I assist you today?"
- ).send()
- ```
-
-6. **Handle Incoming Messages**
-
- Process user messages, extract URLs, and crawl them concurrently. Update the chat history and system message.
-
- ```python
- @cl.on_message
- async def on_message(message: cl.Message):
- user_session = cl.user_session.get("session")
-
- # Extract URLs from the user's message
- urls = extract_urls(message.content)
-
- futures = []
- with ThreadPoolExecutor() as executor:
- for url in urls:
- futures.append(executor.submit(crawl_url, url))
-
- results = [future.result() for future in futures]
-
- for url, result in zip(urls, results):
- ref_number = f"REF_{len(user_session['context']) + 1}"
- user_session["context"][ref_number] = {
- "url": url,
- "content": result
- }
-
- user_session["history"].append({
- "role": "user",
- "content": message.content
- })
-
- # Create a system message that includes the context
- context_messages = [
- f'\n{data["content"]}\n '
- for ref, data in user_session["context"].items()
- ]
- if context_messages:
- system_message = {
- "role": "system",
- "content": (
- "You are a helpful bot. Use the following context for answering questions. "
- "Refer to the sources using the REF number in square brackets, e.g., [1], only if the source is given in the appendices below.\n\n"
- "If the question requires any information from the provided appendices or context, refer to the sources. "
- "If not, there is no need to add a references section. "
- "At the end of your response, provide a reference section listing the URLs and their REF numbers only if sources from the appendices were used.\n\n"
- "\n\n".join(context_messages)
- )
- }
- else:
- system_message = {
- "role": "system",
- "content": "You are a helpful assistant."
- }
-
- msg = cl.Message(content="")
- await msg.send()
-
- # Get response from the LLM
- stream = await client.chat.completions.create(
- messages=[
- system_message,
- *user_session["history"]
- ],
- stream=True,
- **settings
- )
-
- assistant_response = ""
- async for part in stream:
- if token := part.choices[0].delta.content:
- assistant_response += token
- await msg.stream_token(token)
-
- # Add assistant message to the history
- user_session["history"].append({
- "role": "assistant",
- "content": assistant_response
- })
- await msg.update()
-
- # Append the reference section to the assistant's response
- reference_section = "\n\nReferences:\n"
- for ref, data in user_session["context"].items():
- reference_section += f"[{ref.split('_')[1]}]: {data['url']}\n"
-
- msg.content += reference_section
- await msg.update()
- ```
-
-7. **Handle Audio Input**
-
- Capture and transcribe audio input. Store the audio buffer and transcribe it when the audio ends.
-
- ```python
- @cl.on_audio_chunk
- async def on_audio_chunk(chunk: cl.AudioChunk):
- if chunk.isStart:
- buffer = BytesIO()
- buffer.name = f"input_audio.{chunk.mimeType.split('/')[1]}"
- cl.user_session.set("audio_buffer", buffer)
- cl.user_session.set("audio_mime_type", chunk.mimeType)
-
- cl.user_session.get("audio_buffer").write(chunk.data)
-
- @cl.step(type="tool")
- async def speech_to_text(audio_file):
- cli = Groq()
- response = await client.audio.transcriptions.create(
- model="whisper-large-v3", file=audio_file
- )
- return response.text
-
- @cl.on_audio_end
- async def on_audio_end(elements: list[ElementBased]):
- audio_buffer: BytesIO = cl.user_session.get("audio_buffer")
- audio_buffer.seek(0)
- audio_file = audio_buffer.read()
- audio_mime_type: str = cl.user_session.get("audio_mime_type")
-
- start_time = time.time()
- transcription = await speech_to_text((audio_buffer.name, audio_file, audio_mime_type))
- end_time = time.time()
- print(f"Transcription took {end_time - start_time} seconds")
-
- user_msg = cl.Message(
- author="You",
- type="user_message",
- content=transcription
- )
- await user_msg.send()
- await on_message(user_msg)
- ```
-
-8. **Run the Chat Application**
-
- Start the Chainlit application.
-
- ```python
- if __name__ == "__main__":
- from chainlit.cli import run_chainlit
- run_chainlit(__file__)
- ```
-
-### Explanation
-
-- **Libraries and Configuration**: Import necessary libraries and configure the OpenAI client.
-- **Utility Functions**: Define functions to extract URLs and crawl them.
-- **Chat Start Event**: Initialize chat session and welcome message.
-- **Message Handling**: Extract URLs, crawl them concurrently, and update chat history and context.
-- **Audio Handling**: Capture, buffer, and transcribe audio input, then process the transcription as text.
-- **Running the Application**: Start the Chainlit server to interact with the assistant.
-
-This example showcases how to create an interactive research assistant that can fetch, process, and summarize web content, along with handling audio inputs for a seamless user experience.
diff --git a/docs/md _sync/examples/summarization.md b/docs/md _sync/examples/summarization.md
deleted file mode 100644
index b817f691..00000000
--- a/docs/md _sync/examples/summarization.md
+++ /dev/null
@@ -1,108 +0,0 @@
-## Summarization Example
-
-This example demonstrates how to use `Crawl4AI` to extract a summary from a web page. The goal is to obtain the title, a detailed summary, a brief summary, and a list of keywords from the given page.
-
-### Step-by-Step Guide
-
-1. **Import Necessary Modules**
-
- First, import the necessary modules and classes.
-
- ```python
- import os
- import time
- import json
- from crawl4ai.web_crawler import WebCrawler
- from crawl4ai.chunking_strategy import *
- from crawl4ai.extraction_strategy import *
- from crawl4ai.crawler_strategy import *
- from pydantic import BaseModel, Field
- ```
-
-2. **Define the URL to be Crawled**
-
- Set the URL of the web page you want to summarize.
-
- ```python
- url = r'https://marketplace.visualstudio.com/items?itemName=Unclecode.groqopilot'
- ```
-
-3. **Initialize the WebCrawler**
-
- Create an instance of the `WebCrawler` and call the `warmup` method.
-
- ```python
- crawler = WebCrawler()
- crawler.warmup()
- ```
-
-4. **Define the Data Model**
-
- Use Pydantic to define the structure of the extracted data.
-
- ```python
- class PageSummary(BaseModel):
- title: str = Field(..., description="Title of the page.")
- summary: str = Field(..., description="Summary of the page.")
- brief_summary: str = Field(..., description="Brief summary of the page.")
- keywords: list = Field(..., description="Keywords assigned to the page.")
- ```
-
-5. **Run the Crawler**
-
- Set up and run the crawler with the `LLMExtractionStrategy`. Provide the necessary parameters, including the schema for the extracted data and the instruction for the LLM.
-
- ```python
- result = crawler.run(
- url=url,
- word_count_threshold=1,
- extraction_strategy=LLMExtractionStrategy(
- provider="openai/gpt-4o",
- api_token=os.getenv('OPENAI_API_KEY'),
- schema=PageSummary.model_json_schema(),
- extraction_type="schema",
- apply_chunking=False,
- instruction=(
- "From the crawled content, extract the following details: "
- "1. Title of the page "
- "2. Summary of the page, which is a detailed summary "
- "3. Brief summary of the page, which is a paragraph text "
- "4. Keywords assigned to the page, which is a list of keywords. "
- 'The extracted JSON format should look like this: '
- '{ "title": "Page Title", "summary": "Detailed summary of the page.", '
- '"brief_summary": "Brief summary in a paragraph.", "keywords": ["keyword1", "keyword2", "keyword3"] }'
- )
- ),
- bypass_cache=True,
- )
- ```
-
-6. **Process the Extracted Data**
-
- Load the extracted content into a JSON object and print it.
-
- ```python
- page_summary = json.loads(result.extracted_content)
- print(page_summary)
- ```
-
-7. **Save the Extracted Data**
-
- Save the extracted data to a file for further use.
-
- ```python
- with open(".data/page_summary.json", "w", encoding="utf-8") as f:
- f.write(result.extracted_content)
- ```
-
-### Explanation
-
-- **Importing Modules**: Import the necessary modules, including `WebCrawler` and `LLMExtractionStrategy` from `Crawl4AI`.
-- **URL Definition**: Set the URL of the web page you want to crawl and summarize.
-- **WebCrawler Initialization**: Create an instance of `WebCrawler` and call the `warmup` method to prepare the crawler.
-- **Data Model Definition**: Define the structure of the data you want to extract using Pydantic's `BaseModel`.
-- **Crawler Execution**: Run the crawler with the `LLMExtractionStrategy`, providing the schema and detailed instructions for the extraction process.
-- **Data Processing**: Load the extracted content into a JSON object and print it to verify the results.
-- **Data Saving**: Save the extracted data to a file for further use.
-
-This example demonstrates how to harness the power of `Crawl4AI` to perform advanced web crawling and data extraction tasks with minimal code.
diff --git a/docs/md _sync/full_details/advanced_features.md b/docs/md _sync/full_details/advanced_features.md
deleted file mode 100644
index 944f6e0d..00000000
--- a/docs/md _sync/full_details/advanced_features.md
+++ /dev/null
@@ -1,138 +0,0 @@
-# Advanced Features
-
-Crawl4AI offers a range of advanced features that allow you to fine-tune your web crawling and data extraction process. This section will cover some of these advanced features, including taking screenshots, extracting media and links, customizing the user agent, using custom hooks, and leveraging CSS selectors.
-
-## Taking Screenshots 📸
-
-One of the cool features of Crawl4AI is the ability to take screenshots of the web pages you're crawling. This can be particularly useful for visual verification or for capturing the state of dynamic content.
-
-Here's how you can take a screenshot:
-
-```python
-from crawl4ai import WebCrawler
-import base64
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-crawler.warmup()
-
-# Run the crawler with the screenshot parameter
-result = crawler.run(url="https://www.nbcnews.com/business", screenshot=True)
-
-# Save the screenshot to a file
-with open("screenshot.png", "wb") as f:
- f.write(base64.b64decode(result.screenshot))
-
-print("Screenshot saved to 'screenshot.png'!")
-```
-
-In this example, we create a `WebCrawler` instance, warm it up, and then run it with the `screenshot` parameter set to `True`. The screenshot is saved as a base64 encoded string in the result, which we then decode and save as a PNG file.
-
-## Extracting Media and Links 🎨🔗
-
-Crawl4AI can extract all media tags (images, audio, and video) and links (both internal and external) from a web page. This feature is useful for collecting multimedia content or analyzing link structures.
-
-Here's an example:
-
-```python
-from crawl4ai import WebCrawler
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-crawler.warmup()
-
-# Run the crawler
-result = crawler.run(url="https://www.nbcnews.com/business")
-
-print("Extracted media:", result.media)
-print("Extracted links:", result.links)
-```
-
-In this example, the `result` object contains dictionaries for media and links, which you can access and use as needed.
-
-## Customizing the User Agent 🕵️♂️
-
-Crawl4AI allows you to set a custom user agent for your HTTP requests. This can help you avoid detection by web servers or simulate different browsing environments.
-
-Here's how to set a custom user agent:
-
-```python
-from crawl4ai import WebCrawler
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-crawler.warmup()
-
-# Run the crawler with a custom user agent
-result = crawler.run(url="https://www.nbcnews.com/business", user_agent="Mozilla/5.0 (compatible; MyCrawler/1.0)")
-
-print("Crawl result:", result)
-```
-
-In this example, we specify a custom user agent string when running the crawler.
-
-## Using Custom Hooks 🪝
-
-Hooks are a powerful feature in Crawl4AI that allow you to customize the crawling process at various stages. You can define hooks for actions such as driver initialization, before and after URL fetching, and before returning the HTML.
-
-Here's an example of using hooks:
-
-```python
-from crawl4ai import WebCrawler
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support.ui import WebDriverWait
-from selenium.webdriver.support import expected_conditions as EC
-
-# Define the hooks
-def on_driver_created(driver):
- driver.maximize_window()
- driver.get('https://example.com/login')
- WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.NAME, 'username'))).send_keys('testuser')
- driver.find_element(By.NAME, 'password').send_keys('password123')
- driver.find_element(By.NAME, 'login').click()
- return driver
-
-def before_get_url(driver):
- driver.execute_cdp_cmd('Network.setExtraHTTPHeaders', {'headers': {'X-Test-Header': 'test'}})
- return driver
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-crawler.warmup()
-
-# Set the hooks
-crawler.set_hook('on_driver_created', on_driver_created)
-crawler.set_hook('before_get_url', before_get_url)
-
-# Run the crawler
-result = crawler.run(url="https://example.com")
-
-print("Crawl result:", result)
-```
-
-In this example, we define hooks to handle driver initialization and custom headers before fetching the URL.
-
-## Using CSS Selectors 🎯
-
-CSS selectors allow you to target specific elements on a web page for extraction. This can be useful for scraping structured content, such as articles or product details.
-
-Here's an example of using a CSS selector:
-
-```python
-from crawl4ai import WebCrawler
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-crawler.warmup()
-
-# Run the crawler with a CSS selector to extract only H2 tags
-result = crawler.run(url="https://www.nbcnews.com/business", css_selector="h2")
-
-print("Extracted H2 tags:", result.extracted_content)
-```
-
-In this example, we use the `css_selector` parameter to extract only the H2 tags from the web page.
-
----
-
-With these advanced features, you can leverage Crawl4AI to perform sophisticated web crawling and data extraction tasks. Whether you need to take screenshots, extract specific elements, customize the crawling process, or set custom headers, Crawl4AI provides the flexibility and power to meet your needs. Happy crawling! 🕷️🚀
diff --git a/docs/md _sync/full_details/crawl_request_parameters.md b/docs/md _sync/full_details/crawl_request_parameters.md
deleted file mode 100644
index c32743c6..00000000
--- a/docs/md _sync/full_details/crawl_request_parameters.md
+++ /dev/null
@@ -1,130 +0,0 @@
-# Crawl Request Parameters
-
-The `run` function in Crawl4AI is designed to be highly configurable, allowing you to customize the crawling and extraction process to suit your needs. Below are the parameters you can use with the `run` function, along with their descriptions, possible values, and examples.
-
-## Parameters
-
-### url (str)
-**Description:** The URL of the webpage to crawl.
-**Required:** Yes
-**Example:**
-```python
-url = "https://www.nbcnews.com/business"
-```
-
-### word_count_threshold (int)
-**Description:** The minimum number of words a block must contain to be considered meaningful. The default value is `5`.
-**Required:** No
-**Default Value:** `5`
-**Example:**
-```python
-word_count_threshold = 10
-```
-
-### extraction_strategy (ExtractionStrategy)
-**Description:** The strategy to use for extracting content from the HTML. It must be an instance of `ExtractionStrategy`. If not provided, the default is `NoExtractionStrategy`.
-**Required:** No
-**Default Value:** `NoExtractionStrategy()`
-**Example:**
-```python
-extraction_strategy = CosineStrategy(semantic_filter="finance")
-```
-
-### chunking_strategy (ChunkingStrategy)
-**Description:** The strategy to use for chunking the text before processing. It must be an instance of `ChunkingStrategy`. The default value is `RegexChunking()`.
-**Required:** No
-**Default Value:** `RegexChunking()`
-**Example:**
-```python
-chunking_strategy = NlpSentenceChunking()
-```
-
-### bypass_cache (bool)
-**Description:** Whether to force a fresh crawl even if the URL has been previously crawled. The default value is `False`.
-**Required:** No
-**Default Value:** `False`
-**Example:**
-```python
-bypass_cache = True
-```
-
-### css_selector (str)
-**Description:** The CSS selector to target specific parts of the HTML for extraction. If not provided, the entire HTML will be processed.
-**Required:** No
-**Default Value:** `None`
-**Example:**
-```python
-css_selector = "div.article-content"
-```
-
-### screenshot (bool)
-**Description:** Whether to take screenshots of the page. The default value is `False`.
-**Required:** No
-**Default Value:** `False`
-**Example:**
-```python
-screenshot = True
-```
-
-### user_agent (str)
-**Description:** The user agent to use for the HTTP requests. If not provided, a default user agent will be used.
-**Required:** No
-**Default Value:** `None`
-**Example:**
-```python
-user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
-```
-
-### verbose (bool)
-**Description:** Whether to enable verbose logging. The default value is `True`.
-**Required:** No
-**Default Value:** `True`
-**Example:**
-```python
-verbose = True
-```
-
-### **kwargs
-Additional keyword arguments that can be passed to customize the crawling process further. Some notable options include:
-
-- **only_text (bool):** Whether to extract only text content, excluding HTML tags. Default is `False`.
-
-**Example:**
-```python
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- css_selector="p",
- only_text=True
-)
-```
-
-## Example Usage
-
-Here's an example of how to use the `run` function with various parameters:
-
-```python
-from crawl4ai import WebCrawler
-from crawl4ai.extraction_strategy import CosineStrategy
-from crawl4ai.chunking_strategy import NlpSentenceChunking
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-
-# Run the crawler with custom parameters
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- word_count_threshold=10,
- extraction_strategy=CosineStrategy(semantic_filter="finance"),
- chunking_strategy=NlpSentenceChunking(),
- bypass_cache=True,
- css_selector="div.article-content",
- screenshot=True,
- user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3",
- verbose=True,
- only_text=True
-)
-
-print(result)
-```
-
-This example demonstrates how to configure various parameters to customize the crawling and extraction process using Crawl4AI.
diff --git a/docs/md _sync/full_details/crawl_result_class.md b/docs/md _sync/full_details/crawl_result_class.md
deleted file mode 100644
index f66e16dc..00000000
--- a/docs/md _sync/full_details/crawl_result_class.md
+++ /dev/null
@@ -1,120 +0,0 @@
-# Crawl Result
-
-The `CrawlResult` class is the heart of Crawl4AI's output, encapsulating all the data extracted from a crawling session. This class contains various fields that store the results of the web crawling and extraction process. Let's break down each field and see what it holds. 🎉
-
-## Class Definition
-
-```python
-class CrawlResult(BaseModel):
- url: str
- html: str
- success: bool
- cleaned_html: Optional[str] = None
- media: Dict[str, List[Dict]] = {}
- links: Dict[str, List[Dict]] = {}
- screenshot: Optional[str] = None
- markdown: Optional[str] = None
- extracted_content: Optional[str] = None
- metadata: Optional[dict] = None
- error_message: Optional[str] = None
-```
-
-## Fields Explanation
-
-### `url: str`
-The URL that was crawled. This field simply stores the URL of the web page that was processed.
-
-### `html: str`
-The raw HTML content of the web page. This is the unprocessed HTML source as retrieved by the crawler.
-
-### `success: bool`
-A flag indicating whether the crawling and extraction were successful. If any error occurs during the process, this will be `False`.
-
-### `cleaned_html: Optional[str]`
-The cleaned HTML content of the web page. This field holds the HTML after removing unwanted tags like `
diff --git a/docs/md _sync/introduction.md b/docs/md _sync/introduction.md
deleted file mode 100644
index 6d1ad56b..00000000
--- a/docs/md _sync/introduction.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Introduction
-
-Welcome to the documentation for Crawl4AI v0.2.5! 🕷️🤖
-
-Crawl4AI is designed to simplify the process of crawling web pages and extracting useful information for large language models (LLMs) and AI applications. Whether you're using it as a REST API, a Python library, or through a Google Colab notebook, Crawl4AI provides powerful features to make web data extraction easier and more efficient.
-
-## Key Features ✨
-
-- **🆓 Completely Free and Open-Source**: Crawl4AI is free to use and open-source, making it accessible for everyone.
-- **🤖 LLM-Friendly Output Formats**: Supports JSON, cleaned HTML, and markdown formats.
-- **🌍 Concurrent Crawling**: Crawl multiple URLs simultaneously to save time.
-- **🎨 Media Extraction**: Extract all media tags including images, audio, and video.
-- **🔗 Link Extraction**: Extract all external and internal links from web pages.
-- **📚 Metadata Extraction**: Extract metadata from web pages for additional context.
-- **🔄 Custom Hooks**: Define custom hooks for authentication, headers, and page modifications before crawling.
-- **🕵️ User Agent Support**: Customize the user agent for HTTP requests.
-- **🖼️ Screenshot Capability**: Take screenshots of web pages during crawling.
-- **📜 JavaScript Execution**: Execute custom JavaScripts before crawling.
-- **📚 Advanced Chunking and Extraction Strategies**: Utilize topic-based, regex, sentence chunking, cosine clustering, and LLM extraction strategies.
-- **🎯 CSS Selector Support**: Extract specific content using CSS selectors.
-- **📝 Instruction/Keyword Refinement**: Pass instructions or keywords to refine the extraction process.
-
-Check the [Changelog](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md) for more details.
-
-## Power and Simplicity of Crawl4AI 🚀
-
-Crawl4AI provides an easy way to crawl and extract data from web pages without installing any library. You can use the REST API on our server or run the local server on your machine. For more advanced control, use the Python library to customize your crawling and extraction strategies.
-
-Explore the documentation to learn more about the features, installation process, usage examples, and how to contribute to Crawl4AI. Let's make the web more accessible and useful for AI applications! 💪🌐🤖
diff --git a/docs/md _sync/quickstart.md b/docs/md _sync/quickstart.md
deleted file mode 100644
index a0c1a2c7..00000000
--- a/docs/md _sync/quickstart.md
+++ /dev/null
@@ -1,204 +0,0 @@
-# Quick Start Guide 🚀
-
-Welcome to the Crawl4AI Quickstart Guide! In this tutorial, we'll walk you through the basic usage of Crawl4AI with a friendly and humorous tone. We'll cover everything from basic usage to advanced features like chunking and extraction strategies. Let's dive in! 🌟
-
-## Getting Started 🛠️
-
-First, let's create an instance of `WebCrawler` and call the `warmup()` function. This might take a few seconds the first time you run Crawl4AI, as it loads the required model files.
-
-```python
-from crawl4ai import WebCrawler
-
-def create_crawler():
- crawler = WebCrawler(verbose=True)
- crawler.warmup()
- return crawler
-
-crawler = create_crawler()
-```
-
-### Basic Usage
-
-Simply provide a URL and let Crawl4AI do the magic!
-
-```python
-result = crawler.run(url="https://www.nbcnews.com/business")
-print(f"Basic crawl result: {result}")
-```
-
-### Taking Screenshots 📸
-
-Let's take a screenshot of the page!
-
-```python
-result = crawler.run(url="https://www.nbcnews.com/business", screenshot=True)
-with open("screenshot.png", "wb") as f:
- f.write(base64.b64decode(result.screenshot))
-print("Screenshot saved to 'screenshot.png'!")
-```
-
-### Understanding Parameters 🧠
-
-By default, Crawl4AI caches the results of your crawls. This means that subsequent crawls of the same URL will be much faster! Let's see this in action.
-
-First crawl (caches the result):
-```python
-result = crawler.run(url="https://www.nbcnews.com/business")
-print(f"First crawl result: {result}")
-```
-
-Force to crawl again:
-```python
-result = crawler.run(url="https://www.nbcnews.com/business", bypass_cache=True)
-print(f"Second crawl result: {result}")
-```
-
-### Adding a Chunking Strategy 🧩
-
-Let's add a chunking strategy: `RegexChunking`! This strategy splits the text based on a given regex pattern.
-
-```python
-from crawl4ai.chunking_strategy import RegexChunking
-
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- chunking_strategy=RegexChunking(patterns=["\n\n"])
-)
-print(f"RegexChunking result: {result}")
-```
-
-You can also use `NlpSentenceChunking` which splits the text into sentences using NLP techniques.
-
-```python
-from crawl4ai.chunking_strategy import NlpSentenceChunking
-
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- chunking_strategy=NlpSentenceChunking()
-)
-print(f"NlpSentenceChunking result: {result}")
-```
-
-### Adding an Extraction Strategy 🧠
-
-Let's get smarter with an extraction strategy: `CosineStrategy`! This strategy uses cosine similarity to extract semantically similar blocks of text.
-
-```python
-from crawl4ai.extraction_strategy import CosineStrategy
-
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- extraction_strategy=CosineStrategy(
- word_count_threshold=10,
- max_dist=0.2,
- linkage_method="ward",
- top_k=3
- )
-)
-print(f"CosineStrategy result: {result}")
-```
-
-You can also pass other parameters like `semantic_filter` to extract specific content.
-
-```python
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- extraction_strategy=CosineStrategy(
- semantic_filter="inflation rent prices"
- )
-)
-print(f"CosineStrategy result with semantic filter: {result}")
-```
-
-### Using LLMExtractionStrategy 🤖
-
-Time to bring in the big guns: `LLMExtractionStrategy` without instructions! This strategy uses a large language model to extract relevant information from the web page.
-
-```python
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-import os
-
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- extraction_strategy=LLMExtractionStrategy(
- provider="openai/gpt-4o",
- api_token=os.getenv('OPENAI_API_KEY')
- )
-)
-print(f"LLMExtractionStrategy (no instructions) result: {result}")
-```
-
-You can also provide specific instructions to guide the extraction.
-
-```python
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- extraction_strategy=LLMExtractionStrategy(
- provider="openai/gpt-4o",
- api_token=os.getenv('OPENAI_API_KEY'),
- instruction="I am interested in only financial news"
- )
-)
-print(f"LLMExtractionStrategy (with instructions) result: {result}")
-```
-
-### Targeted Extraction 🎯
-
-Let's use a CSS selector to extract only H2 tags!
-
-```python
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- css_selector="h2"
-)
-print(f"CSS Selector (H2 tags) result: {result}")
-```
-
-### Interactive Extraction 🖱️
-
-Passing JavaScript code to click the 'Load More' button!
-
-```python
-js_code = """
-const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
-loadMoreButton && loadMoreButton.click();
-"""
-
-result = crawler.run(
- url="https://www.nbcnews.com/business",
- js=js_code
-)
-print(f"JavaScript Code (Load More button) result: {result}")
-```
-
-### Using Crawler Hooks 🔗
-
-Let's see how we can customize the crawler using hooks!
-
-```python
-import time
-
-from crawl4ai.web_crawler import WebCrawler
-from crawl4ai.crawler_strategy import *
-
-def delay(driver):
- print("Delaying for 5 seconds...")
- time.sleep(5)
- print("Resuming...")
-
-def create_crawler():
- crawler_strategy = LocalSeleniumCrawlerStrategy(verbose=True)
- crawler_strategy.set_hook('after_get_url', delay)
- crawler = WebCrawler(verbose=True, crawler_strategy=crawler_strategy)
- crawler.warmup()
- return crawler
-
-crawler = create_crawler()
-result = crawler.run(url="https://www.nbcnews.com/business", bypass_cache=True)
-```
-
-check [Hooks](examples/hooks_auth.md) for more examples.
-
-## Congratulations! 🎉
-
-You've made it through the Crawl4AI Quickstart Guide! Now go forth and crawl the web like a pro! 🕸️
diff --git a/docs/md/api/core_classes_and_functions.md b/docs/md/api/core_classes_and_functions.md
deleted file mode 100644
index 198fd427..00000000
--- a/docs/md/api/core_classes_and_functions.md
+++ /dev/null
@@ -1,141 +0,0 @@
-# Core Classes and Functions
-
-## Overview
-
-In this section, we will delve into the core classes and functions that make up the Crawl4AI library. This includes the `WebCrawler` class, various `CrawlerStrategy` classes, `ChunkingStrategy` classes, and `ExtractionStrategy` classes. Understanding these core components will help you leverage the full power of Crawl4AI for your web crawling and data extraction needs.
-
-## WebCrawler Class
-
-The `WebCrawler` class is the main class you'll interact with. It provides the interface for crawling web pages and extracting data.
-
-### Initialization
-
-```python
-from crawl4ai import WebCrawler
-
-# Create an instance of WebCrawler
-crawler = WebCrawler()
-```
-
-### Methods
-
-- **`warmup()`**: Prepares the crawler for use, such as loading necessary models.
-- **`run(url: str, **kwargs)`**: Runs the crawler on the specified URL with optional parameters for customization.
-
-```python
-crawler.warmup()
-result = crawler.run(url="https://www.nbcnews.com/business")
-print(result)
-```
-
-## CrawlerStrategy Classes
-
-The `CrawlerStrategy` classes define how the web crawling is executed. The base class is `CrawlerStrategy`, which is extended by specific implementations like `LocalSeleniumCrawlerStrategy`.
-
-### CrawlerStrategy Base Class
-
-An abstract base class that defines the interface for different crawler strategies.
-
-```python
-from abc import ABC, abstractmethod
-
-class CrawlerStrategy(ABC):
- @abstractmethod
- def crawl(self, url: str, **kwargs) -> str:
- pass
-
- @abstractmethod
- def take_screenshot(self, save_path: str):
- pass
-
- @abstractmethod
- def update_user_agent(self, user_agent: str):
- pass
-
- @abstractmethod
- def set_hook(self, hook_type: str, hook: Callable):
- pass
-```
-
-### LocalSeleniumCrawlerStrategy Class
-
-A concrete implementation of `CrawlerStrategy` that uses Selenium to crawl web pages.
-
-#### Initialization
-
-```python
-from crawl4ai.crawler_strategy import LocalSeleniumCrawlerStrategy
-
-strategy = LocalSeleniumCrawlerStrategy(js_code=["console.log('Hello, world!');"])
-```
-
-#### Methods
-
-- **`crawl(url: str, **kwargs)`**: Crawls the specified URL.
-- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
-- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
-- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
-
-```python
-result = strategy.crawl("https://www.example.com")
-strategy.take_screenshot("screenshot.png")
-strategy.update_user_agent("Mozilla/5.0")
-strategy.set_hook("before_get_url", lambda: print("About to get URL"))
-```
-
-## ChunkingStrategy Classes
-
-The `ChunkingStrategy` classes define how the text from a web page is divided into chunks. Here are a few examples:
-
-### RegexChunking Class
-
-Splits text using regular expressions.
-
-```python
-from crawl4ai.chunking_strategy import RegexChunking
-
-chunker = RegexChunking(patterns=[r'\n\n'])
-chunks = chunker.chunk("This is a sample text. It will be split into chunks.")
-```
-
-### NlpSentenceChunking Class
-
-Uses NLP to split text into sentences.
-
-```python
-from crawl4ai.chunking_strategy import NlpSentenceChunking
-
-chunker = NlpSentenceChunking()
-chunks = chunker.chunk("This is a sample text. It will be split into sentences.")
-```
-
-## ExtractionStrategy Classes
-
-The `ExtractionStrategy` classes define how meaningful content is extracted from the chunks. Here are a few examples:
-
-### CosineStrategy Class
-
-Clusters text chunks based on cosine similarity.
-
-```python
-from crawl4ai.extraction_strategy import CosineStrategy
-
-extractor = CosineStrategy(semantic_filter="finance", word_count_threshold=10)
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-### LLMExtractionStrategy Class
-
-Uses a Language Model to extract meaningful blocks from HTML.
-
-```python
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-
-extractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token', instruction='Extract only news about AI.')
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-## Conclusion
-
-By understanding these core classes and functions, you can customize and extend Crawl4AI to suit your specific web crawling and data extraction needs. Happy crawling! 🕷️🤖
-
diff --git a/docs/md/api/detailed_api_documentation.md b/docs/md/api/detailed_api_documentation.md
deleted file mode 100644
index aaf0e34f..00000000
--- a/docs/md/api/detailed_api_documentation.md
+++ /dev/null
@@ -1,338 +0,0 @@
-# Detailed API Documentation
-
-## Overview
-
-This section provides comprehensive documentation for the Crawl4AI API, covering all classes, methods, and their parameters. This guide will help you understand how to utilize the API to its full potential, enabling efficient web crawling and data extraction.
-
-## WebCrawler Class
-
-The `WebCrawler` class is the primary interface for crawling web pages and extracting data.
-
-### Initialization
-
-```python
-from crawl4ai import WebCrawler
-
-crawler = WebCrawler()
-```
-
-### Methods
-
-#### `warmup()`
-
-Prepares the crawler for use, such as loading necessary models.
-
-```python
-crawler.warmup()
-```
-
-#### `run(url: str, **kwargs) -> CrawlResult`
-
-Crawls the specified URL and returns the result.
-
-- **Parameters:**
- - `url` (str): The URL to crawl.
- - `**kwargs`: Additional parameters for customization.
-
-- **Returns:**
- - `CrawlResult`: An object containing the crawl result.
-
-- **Example:**
-
-```python
-result = crawler.run(url="https://www.nbcnews.com/business")
-print(result)
-```
-
-### CrawlResult Class
-
-Represents the result of a crawl operation.
-
-- **Attributes:**
- - `url` (str): The URL of the crawled page.
- - `html` (str): The raw HTML of the page.
- - `success` (bool): Whether the crawl was successful.
- - `cleaned_html` (Optional[str]): The cleaned HTML.
- - `media` (Dict[str, List[Dict]]): Media tags in the page (images, audio, video).
- - `links` (Dict[str, List[Dict]]): Links in the page (external, internal).
- - `screenshot` (Optional[str]): Base64 encoded screenshot.
- - `markdown` (Optional[str]): Extracted content in Markdown format.
- - `extracted_content` (Optional[str]): Extracted meaningful content.
- - `metadata` (Optional[dict]): Metadata from the page.
- - `error_message` (Optional[str]): Error message if any.
-
-## CrawlerStrategy Classes
-
-The `CrawlerStrategy` classes define how the web crawling is executed.
-
-### CrawlerStrategy Base Class
-
-An abstract base class for different crawler strategies.
-
-#### Methods
-
-- **`crawl(url: str, **kwargs) -> str`**: Crawls the specified URL.
-- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
-- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
-- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
-
-### LocalSeleniumCrawlerStrategy Class
-
-Uses Selenium to crawl web pages.
-
-#### Initialization
-
-```python
-from crawl4ai.crawler_strategy import LocalSeleniumCrawlerStrategy
-
-strategy = LocalSeleniumCrawlerStrategy(js_code=["console.log('Hello, world!');"])
-```
-
-#### Methods
-
-- **`crawl(url: str, **kwargs)`**: Crawls the specified URL.
-- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
-- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
-- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
-
-#### Example
-
-```python
-result = strategy.crawl("https://www.example.com")
-strategy.take_screenshot("screenshot.png")
-strategy.update_user_agent("Mozilla/5.0")
-strategy.set_hook("before_get_url", lambda: print("About to get URL"))
-```
-
-## ChunkingStrategy Classes
-
-The `ChunkingStrategy` classes define how the text from a web page is divided into chunks.
-
-### RegexChunking Class
-
-Splits text using regular expressions.
-
-#### Initialization
-
-```python
-from crawl4ai.chunking_strategy import RegexChunking
-
-chunker = RegexChunking(patterns=[r'\n\n'])
-```
-
-#### Methods
-
-- **`chunk(text: str) -> List[str]`**: Splits the text into chunks.
-
-#### Example
-
-```python
-chunks = chunker.chunk("This is a sample text. It will be split into chunks.")
-```
-
-### NlpSentenceChunking Class
-
-Uses NLP to split text into sentences.
-
-#### Initialization
-
-```python
-from crawl4ai.chunking_strategy import NlpSentenceChunking
-
-chunker = NlpSentenceChunking()
-```
-
-#### Methods
-
-- **`chunk(text: str) -> List[str]`**: Splits the text into sentences.
-
-#### Example
-
-```python
-chunks = chunker.chunk("This is a sample text. It will be split into sentences.")
-```
-
-### TopicSegmentationChunking Class
-
-Uses the TextTiling algorithm to segment text into topics.
-
-#### Initialization
-
-```python
-from crawl4ai.chunking_strategy import TopicSegmentationChunking
-
-chunker = TopicSegmentationChunking(num_keywords=3)
-```
-
-#### Methods
-
-- **`chunk(text: str) -> List[str]`**: Splits the text into topic-based segments.
-
-#### Example
-
-```python
-chunks = chunker.chunk("This is a sample text. It will be split into topic-based segments.")
-```
-
-### FixedLengthWordChunking Class
-
-Splits text into chunks of fixed length based on the number of words.
-
-#### Initialization
-
-```python
-from crawl4ai.chunking_strategy import FixedLengthWordChunking
-
-chunker = FixedLengthWordChunking(chunk_size=100)
-```
-
-#### Methods
-
-- **`chunk(text: str) -> List[str]`**: Splits the text into fixed-length word chunks.
-
-#### Example
-
-```python
-chunks = chunker.chunk("This is a sample text. It will be split into fixed-length word chunks.")
-```
-
-### SlidingWindowChunking Class
-
-Uses a sliding window approach to chunk text.
-
-#### Initialization
-
-```python
-from crawl4ai.chunking_strategy import SlidingWindowChunking
-
-chunker = SlidingWindowChunking(window_size=100, step=50)
-```
-
-#### Methods
-
-- **`chunk(text: str) -> List[str]`**: Splits the text using a sliding window approach.
-
-#### Example
-
-```python
-chunks = chunker.chunk("This is a sample text. It will be split using a sliding window approach.")
-```
-
-## ExtractionStrategy Classes
-
-The `ExtractionStrategy` classes define how meaningful content is extracted from the chunks.
-
-### NoExtractionStrategy Class
-
-Returns the entire HTML content without any modification.
-
-#### Initialization
-
-```python
-from crawl4ai.extraction_strategy import NoExtractionStrategy
-
-extractor = NoExtractionStrategy()
-```
-
-#### Methods
-
-- **`extract(url: str, html: str) -> str`**: Returns the HTML content.
-
-#### Example
-
-```python
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-### LLMExtractionStrategy Class
-
-Uses a Language Model to extract meaningful blocks from HTML.
-
-#### Initialization
-
-```python
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-
-extractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token', instruction='Extract only news about AI.')
-```
-
-#### Methods
-
-- **`extract(url: str, html: str) -> str`**: Extracts meaningful content using the LLM.
-
-#### Example
-
-```python
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-### CosineStrategy Class
-
-Clusters text chunks based on cosine similarity.
-
-#### Initialization
-
-```python
-from crawl4ai.extraction_strategy import CosineStrategy
-
-extractor = CosineStrategy(semantic_filter="finance", word_count_threshold=10)
-```
-
-#### Methods
-
-- **`extract(url: str, html: str) -> str`**: Extracts clusters of text based on cosine similarity.
-
-#### Example
-
-```python
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-### TopicExtractionStrategy Class
-
-Uses the TextTiling algorithm to segment HTML content into topics and extract keywords.
-
-#### Initialization
-
-```python
-from crawl4ai.extraction_strategy import TopicExtractionStrategy
-
-extractor = TopicExtractionStrategy(num_keywords=3)
-```
-
-#### Methods
-
-- **`extract(url: str, html: str) -> str`**: Extracts topic-based segments and keywords.
-
-#### Example
-
-```python
-extracted_content = extractor.extract(url="https://www.example.com", html="...")
-```
-
-## Parameters
-
-Here are the common parameters used across various classes and methods:
-
-- **`url`** (str): The URL to crawl.
-- **`html`** (str): The HTML content of the page.
-- **`user_agent`** (str): The user agent for the HTTP requests.
-- **`patterns`** (list): A list of regular expression patterns for chunking.
-- **`num_keywords`** (int): Number of keywords for topic extraction.
-- **`chunk_size`** (int): Number of words in each chunk.
-- **`window_size`** (int): Number of words in the sliding window.
-- **`step`** (int): Step size for the sliding window.
-- **`semantic_filter`** (str): Keywords for filtering relevant documents.
-- **`word_count_threshold`** (int): Minimum number of words per cluster.
-- **`max_dist`** (float): Maximum cophenetic distance for clustering.
-- **`linkage_method`** (str): Linkage method for hierarchical clustering.
-- **`top_k`** (int): Number of top categories to extract.
-- **`provider`** (
-
-str): Provider for language model completions.
-- **`api_token`** (str): API token for the provider.
-- **`instruction`** (str): Instruction to guide the LLM extraction.
-
-## Conclusion
-
-This detailed API documentation provides a thorough understanding of the classes, methods, and parameters in the Crawl4AI library. With this knowledge, you can effectively use the API to perform advanced web crawling and data extraction tasks.
\ No newline at end of file
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-/*!
- * @preserve
- * Dank Mono (v1.000)
- * This font is subject to its EULA. https://dank.sh
- * © 2018–2020 Phil Plückthun. All Rights Reserved.
- */
-
-@font-face{
- font-family: "dm";
- font-weight: normal;
- font-style: normal;
- unicode-range: U+0000-007F;
- src: local('☺'),
- url(data:font/woff2;charset=utf-8;base64,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) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: normal;
- font-style: normal;
- unicode-range: U+0080-1AFF;
- src: local('☺'),
- 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) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: normal;
- font-style: normal;
- unicode-range: U+1B00-218F;
- src: local('☺'),
- url(data:font/woff2;charset=utf-8;base64,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) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: normal;
- font-style: normal;
- unicode-range: U+2190-21FF;
- src: local('☺'),
- url(data:font/woff2;charset=utf-8;base64,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) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: normal;
- font-style: normal;
- unicode-range: U+F8FF-10FFFF;
- src: local('☺'),
- url(data:font/woff2;charset=utf-8;base64,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) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: normal;
- font-style: italic;
- unicode-range: U+0000-007F;
- src: local('☺'),
- 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) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: normal;
- font-style: italic;
- unicode-range: U+0080-1AFF;
- src: local('☺'),
- 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) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: normal;
- font-style: italic;
- unicode-range: U+1B00-218F;
- src: local('☺'),
- url(data:font/woff2;charset=utf-8;base64,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) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: normal;
- font-style: italic;
- unicode-range: U+2190-21FF;
- src: local('☺'),
- url(data:font/woff2;charset=utf-8;base64,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) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: normal;
- font-style: italic;
- unicode-range: U+F8FF-10FFFF;
- src: local('☺'),
- url(data:font/woff2;charset=utf-8;base64,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) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: bold;
- font-style: normal;
- unicode-range: U+0000-007F;
- src: local('☺'),
- 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) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: bold;
- font-style: normal;
- unicode-range: U+0080-1AFF;
- src: local('☺'),
- 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2la6Vwk/ThgMwbFTzZ3IkyTIKMxSgMf8gDJEMKkQHMHIoWKX1eoQ0Z++61qBtEdyScGI27boqbrmRCl3SSsjZmVI19KhMOGUmT1hcQtHiaAEnqOwv2lTdBsr93JLdV8NXhruDImO49FJYdJ/I297fYgeF2TUC5PPLfmF+PGP71M/5AdRVbayB6lDwYhE8tS/VHvjpzi9CG0uL+LlAu7ar344Xr/k5Iiu9nFcOC8ZhOR9YVL3gdN4ZJqdEnICmagN4YdHXAXe+keSxS2SZWZIWaykaNRn7HYarvx4U30vmC4sQyscpDiIVIAGrbOUjeBRLGVnKAUF6Jf9PwnToFxAE3sQHIWDcCPMrRLfS01+s136FDZW/jBPkcE+FU9rYV1JhTFoiY50NCfvPggmwA7+//Q0cdlNETgyaMQK4JJ942+UfDI/rI3R+tolOF4nodFOaPb0hA0FDFmQatvEcE8RUUM+JJGhMYTrTi88BHTQwmFaUz0wwzr9Tnn1bMAOiI3MjvP1wACGRxSbEhqBpUUgCqhpsUCZVssIlseQaJExRclMbot5iB2EexKqp7Kp1L7+es1HNlpQbSb0Fo7YDXQNxG9fNLiLGyp7k5Mqb43qnAvx9UWHe78lx7CpxBhNOmMnkJ+ARhSKvEF6cNS4Iw8emZ8QL2eoOPkwNxGadmqj2AsAlh0cM41bmx/ehhNzqNj2em621m2Bejtf+Vrhh7SBTHaZ/gtgDSK2xcLzZFmRUEZoKJHQXfM2M80dXFEjhydlZhngwJIb9MhvdXSXu/tRn8oHb73H8QnftkvPyWkAoxl9NUyaXro+X5HRfy0ax2Y6Sh8lNf8zkfoeSHojoiXxOIoiabScwbhkO06XsTL/AJZkE+4vr+yCpeJQi5+XzmOCPaX0zZb/2fPumUn5ZjZi+CtF6KGFWa92K4atn38JyIqfjkwI2RdDlYo4DIwXF8q/Odc7jXcmfzew/8NJ/u4WqFWkDT6ePiyeKVvQLNkUR2MOHLiha887JzhdSMnpeBeSYYkcOLpbDj4rzbtnsQ4OUxJ4dM3hvYOVgvK6h06kyfBerix2Ski5SJJ3Cotpz6jJunPhvtoxm1hu2Z4l5XfYHTr+2lBe2OVx3k5vylrioaVKdv4jPKi7tsophXCgWX+iSjIHX9ZWtLiYUzM6EjBXnDBcVMuGmvZoupYZCpC2K1a9jTF+13sN+D7XeeTWBG4NY6AfzijSfaHULSyBw+LRcc7PGgNDos708Wv7Rl68QcNLL+FLF6u7MhrsLkmAr+VwLt6htD/CbSwBZnEPmvMrFHNwn8/u7aDEvuG/P4EHsbzEXCz7+qd1l+nKdWGMd70RMA2xiNrIyaZcuGK+40Jj9U4KHyS/2Wm9no+i7ImGaqDoDhC7qJrDuQRQVKOTd9D5DG1NKUyVw3Sbs9F3xtiTQYyjhWMPhBxl3XPSqQOja2R1EY/o1ziMTSj+EAKLs6t0IPU/Rzu0t8EtdEkpwthXthUyIO2zs4wx631sPzpvqjQZFMy7Oct6yQGCfFvIsFZCQlpSb1X3zPsd7rLX9hYpra5OZhIPWgj3GbdgbHNVJ86A/ybpekhJaMUjBJDZ0FqwwbuOgYdNLCQV0wlvhZO/3XvgomJioWw5VAA4e/LygDi/1BPXV1fa6fgpu0vDYyZDrYlgD11w/hXvFMt9pbbmqwYj7VzETl4pAKrKQc+jIbRuD+hI93uebPXTJ7vDwtPTI8MnMWff2uKlzLA6HwWK16Us5bCmfixjlodDCkQZbpJ/efvECcV0tmDdbadB3e2w+FKSYGFfKKMQjL7Gmv+xT66yQSRzv0dbAl7ELgkaeii6znyLiuCEFiBvvXy546rW+Hm0vyN5TU92BuHGewSKeBC/2XNtjQTw1zINx4yhhkbgILFyi1IndL4QDYr4xBTCJBE8Lrhi1cc2Q6JkEHbK0LGtawZ4iastC0iwozPAp774Vqgt9o2tx0IiSrOytkAICiX9sPdSbwgm1uyy5UmtaoKvuA+XIW9MUyTSFosRqcXUhYUotuJApvkaN6eSaPoUyWclsHrzmVHWrWF5rATIhkaoIXLspgcDG78k2Gwk5lBTSe9uEPocQ5PyAGQUgFaQOtKs2M1pLRHhFDCZiucFLJLbU/j9VEg7FAYiAYQyYQSMvgsio6H9rL1/Cp2zUVvWCE0c6BjvO6epa0NbLXbdebUltX75gYer2VVpK5vPyCxdxRm0osnAYsI0GXkwXZ+tapg9unVqxeWtTS3075dSjxrzCsrLC6sZaxWm4ILk44byM0ipUswoqwQpzOUgHfQXvquh5eOwHXnQ0WsCjmYR3Z8JbMtRXWRId1fQ6MT5fMWlbBrlVh9hih1lCQ87/A0Z9G28WjFDmtPV57CxbzDGRJGNH22qu1EamXTAximjLBKFdYnbQNIoWb+UpRAsmUeHETohmNwgCLRAiCcJggPBJReOg4RTSD49Dch/xnca2s0ged5odVoNBEpeqdGmEDMvQHcwSqJD3eFZSlzTPIIKYuilj9JsXGw2V1issPCIoTjKDj90+cv1AvY+y3pt7g18MTSEoB/ogi19RNfekessajIvj2QbvgzBM5t9jD4eCRhGJRZpE/IthTEHKHPdo7RY1ibFbWsYht0Vx8WvnXSeD0th+2GUGEx35IACJF0wVHQYhGZbc2Ib4xh8rFDM0mrgIWeANQm4KWjyqEQGiL6Zotz5KCEj0Cvruzv1aG0eBb30btb9xtMZn527iQJwPoACjfkKT9o3ZbEAzJB6bEKpPlM4QQMnCxKjwEfu6E7aKGbTT/NfzRWyg3MdAPBjHd8IvxOMwjAHFd9LwMaOntsvNBpwHSIlTlDxOBjygeIAAqYrvbEMUw7dmS8TrEKJlM7FMEoEnCUHDhhQ0YPMpUHIA+59Oct7MzUOTpyPYXddChDm3GekoLh35hTElEirl1At2Bst3eUxPxCzs9W2gQyJKLs+Wmwz4dlLpCZwUS1DSIUlM8BLFjA/OIxnket+uD1mwQeZ+Q+zgswISSb/0f+OxlghiSzrIXwS+pCpmiFBvhj5i4aHfITjxTnzCBpdShUGEm4IZHyQ26c6IlHrTxy8/eB7WIfFQ8fI//Ngf518hOKlF5tXtIzB/gpgk3/cNcpoykbgLKPzaY6mV8xB+CcFNcmpDpLuOZ62Pk3Eb4SExpHAsMSQ+jH82RR6QdEwPCHu1r1AZmrG2EbubreJuchf7upUf/icZniNgWYKv1JMonaFk+hZDarWuAMYIYBuDmQF7pD+VzzevnrVeeXjeZZeHLEaDgVHNMWzsKpSdaArU04W4Esdu32qK42Sv3WQQakPiuNKioAGlHCooJrdygt7vBImwG2L1tuDCWV7RyX7AHTz9y7MS4fHUS5SjRTQoKkezgN5PaKYrsJZP8Fo+JwJq/zQxC7t8+4XRENJHNU5qlRdLdAfio4mtvo2teET0yMcjhfU65f4kh3nTYk3R+VIAe0UD5yvh4sG1OXi5cM6MKPgvhmD5iMrN0qNg+Vk9YPiHoZ73aXLmRyqe1JDqpSTbRwN6kX9YFIWIdWnwFr1KWKuPpTlRflpYnvJyexBzygc/gLKVctqXOAtq2GHtivUOu/iusCb5CQHLJfMEIAl7HM3mo3gbytc8wJIEoruCnPlkACfLnhrzmrW46WHPvvQXx4t0ipzbJ0H2sIou+LigIP+obgBuYWhnyl8BjK7UbF2sEpxUmaOcd6fcBDYMrWKCXWXhdibBDOg+ML1LOjf8gC4dsT1uDIOyqIzZbDwtgLhPq9pWfBzb/HVZoqbleyp8gNhwJOGimnPGPm3akRd2NEq7SxXf54riw4NlUwK0SfvCEru9JYhRsoEjkAVw1Asa0ZtH1BJHq+Kp76GphslniE/qpdCT6RUuA2gKe2nseLsl1sygB9PMgY//4UhXUIdvOkohGm5GEVpz8LSuXE9xUuRcpxYW4dwkz+vXNPVsm3vgRvLr0hWT1XL3yNjjCp5zQGK2w/QvjVOFVdmHa302XmQSvGLwxlKUpMGbVaxKJy8u5eIoq+HpkpkOI4S85ibQfcmW3/049n8NefxE9k+PXe9s6zT6q2HGM2pm0PqInAx7rSfb9/pBRxTBf2wgMPercuvPfrti+OytCZlbMAEpZtC3st6nguAHUwFjHXEjrfNRpPZ9SLRwrYBS0gNcx1GsECSU1MwzBEQq+yNnixYS6THF6tcwOqZWP4cxttfjoBCU+F3Okv0s/TloBdAypvV0LGAaTl7/7WW5Z32cv7dTwWIbzxLVxmtndzL9KYmC8Uz/cpl+YZV7dpnWU9tKZXkDQ4GafmKk0gNu5W9zu1B0zuPHR4CxTIRm9v0p0sRU2VMlTi7hM8CwcOvH97mCpY3PsysvTB4osrSeFpHZVWSyiEzYU7uOd19izZciL3FRNEke+NFwdj1iPiUSkpjerAOjVikArgH0wc9Ox6MGiXCEnAYqu7DTJCJzKEeMTEo4uuvN5KgphxDGhV7cL0ojAQvOGeiiZqK+yoK4xb6pi+l1orhHFyOG+yTBf2wkMPepcupF3+0Zg4/DONGodkSFfEKy9CodXW9Venjj2QjgaYQlasqZyYobyXA4avgfxCF45ufJf3Hsur2U6MqNyqIbCbwdJcD2lS0mMrPWuDv2S8q9dp/D9JVa02yVDuLKoI27+gWR90ISsj1rjkz+BxRQHDf8F8JjD1Rdtwc1yW1dQg7hkwRebnVn575wP4+F5w/w8Y6phLHDtj04Vxe1sMW3v4xlElSqrA60Yw2BekCVmwP/W5h2xda/1cUZZMuwYVu3LXj6HN1gz2zCLrfxGzf5cM/EKx85GEvhEUXu3Wmwhqy+ETZd9wB/IeQO2/LgXGN/Zgb1sgUsH8Z+sGo3cYJI2zQJ8qdUFDarf5bGKNi8Xt6F+DDSLvtr8t+cDIVddB9SurRd9d6S/Cb4YX659pMff/ja6aWYOmB+L0cbPOQZhB2KV8Xen5y48W6o34ztHpjkh591GE0zHYkrD34umXuaEbjaCrYPs97gAJ6CHVtofD2F+YoPQLiC2+fPQEVXuOj5W888dpwUW1Y0RSfjFHjVDsdZKlGUKi9W7nvHf4yoMJKmklbxFFpRwWg4Z6qYwQcxVhZHgR+4rndwIAKDXwzTQgWYJeAtL3G+yECgn6IMw1oB4zg7yFgUCFT7CbzkYnLmKCJgiT66ixGxVpbxiGCGqLZusnCg1xanQlvYwHp8O7npYs72cNfHgv3mK40+6XbtVMo06VVp6PRZR4w9nbyR4k9bLYUTHZBcWFs20nwgn1tRTcZcCmqeSz6YYjmuhyzydrOcgaP7lwlvL+TOMYTcqXeqn5gAhbySIctWLBOjz5EnT0z09Ue07hBFTaJmsjdzzGpx8lOjy4t7/LdTloHplEJKH13Ntcwp/YA8PeL5keXAZER3/iu+jbbnaLOJLB5JjyiML3VV2WLCgIGkCuStLkEfCM3SoNiBtgRtAkAcdAfAPHqt/JINXejbLmG5gzHNDJLZlXHsx82cVMdmYF6vZgYMqrUFlGeewzobf4AKzCZ0sGTcDU38GzXCwv9rB0XPammMKbOmPHVxPMRqQ4wiCuJBFrOgawfaIQmE3S6StsrCIc8WlBjZ7C3TRZQy58sou0rL4Q71/2Ix+Ru/NA+mFpMrzmWzEspi8O+ZuIORxvIYSSGdSRsQP+42QRE4Buo9DHQFTZZ/ITaoe8RKU4DZa/drM0ITlHgEKcymsX46eoMt6lwx2dfxD3pAENKlYXbCAqwKfW65R5OJTGQWm+FgD4aJZDCnRG46JMNXFPH1Ad9toTKmi6cgcsTjkPRynr306GtNUoRc5eG5Z79CPhG/bSY84NHaDb4jbqTK/co1LGWfV/D57xzRn6r8us6E0miBoUz671q4RfINRX89DqHM2LLDVo6xV9VPRuay8A5/924oLmtN+5npQ8zxCdjtOZOl4QrB2tIu/ZklFqsFlu+GYCgO+MaSrYNOmLFIViarpoD0rtz4LIYBBTsGwLLlvH4TwmLh7VXi/v7cT32gsXzVpI3q+fPqpYDmM/YswrnvQg8athFuzm6jXAd6KzWjQotfA+FxfVNos0TrmxmI/6d3i+jTfM3xpoM4U4HW36XlWjkgZpwGmB6DxW4racYRda2jVVduQ27RmuPMFppQtUD7RFnfOtGkOnJA53DbVdbCdp5px9KkJ3EZmTdWjZdT1XZLcokINxKY916vsMS6SG/Uzt0Wo2frIsm09dSCaUAVgb0d0Id20OKUgmBgpy0BDaY7awcrC29Yr0Nfeclvb08pYVwyeb5HCNmfbSqnwuguNUNDA4L+sirbbLzOoprDP94rdiOfivCQFGkGcAkrb3eFSK79QG7owGF/HUw4gdWzulGEE60kPgATYOmQE97Rt/bf7o/w+3fFbVAvUdJBnT5xhWQ1WStur7kiHC2Wih7iUUQnFMsErIJsPor4Eo9vMXOXo25eERZkPE6NGiUPTxcRyvlbj5LGzSWdwu7/p3yor7Y0PSoXQ0ttxPGJkE7mBiuw9FHDhNJ5MHdCGIAbBJktD0EYbHZWtyb8i+FlM2tn68f44FxJy4LW3EDfEfrwAcgI8w8khYSNOrfN5byT3Bg2b38HsFrOsyGKwuRI1oZVCPnLXkndeRvpCb0sNQzieWFB+0Ipz75wKHl2af8VWhO1Sn08gI6SssxhCLWnSCKpxsIvbK06j4STfRrz3kSg2xoiM++N6fP6kQ3O0gPllMh5rEk9L4Tg06OvVGiLiiBEPHIk+dtZlF578YNz+ozPpFTzsOGL/uBeM9YRWXUwE/SSsq+8LsinRPw5FJ8IUTdVyffKUhU+hstOAgERrztdPEtR4udQ5wtAdvIPhnTjxn20JKjQkm6/H31ZtLflQXxazffrdEVJsf4OiUzAehnKBQm1hbmjG3R9a3DvUhWGtvR2GEtvtyZ8jxU+jMAo7xYiHFwLvT/Slk6vK5XxKfFzSzjZYOUqeVM1WtDPOuNz7eVWULXvylc/o10n2p3tLspmmfnypcbLDe8jycPi7QSUREAu8teQ92byWAf5evg++ituqsLI3H5ubWXrJEfJoVnF57q2QKJ/tkKBhERw8/2FL6cSKtSUs9MPFybDDj62xiB0xqMwa5VWnJGLsCL1/ykKPSaUHg5oYGLuFB+hSj388ebP95OjbhU96rkeHW52r6J1zuykN678yIHNMXlqJ4Gbd5TM7wB0wVM392roaAUby7mB0ZoK4nJ76ELAkbTo4pnbjWF/70j++HWdsBtSaOI6oVgvEHsvVEfssfH/urlpikYvgEA8BFlKAJ0kyC7FfiLwo1F8sTOAHKqvPAwXsTzbet4O4PmjdiyvbVOu+wrOK88dJ1ex7U6ugg3m+h66MGcFzc6vnbgbBuoP6NOuC+c6Yck5lD2uNeV1fMZvI+bNb1RWQKjvy6ha99Fz7lsR6EDoxVrBOmY4tBlALaUbkUwkcMgdeSjl1CU46sxIHhB3pb4tVzBqufGJcsQR6vgoloxMzoIfWGB0DG9wiWmubn+qHb6xiJmIITv63kf0pMAZAr8GRltdAnqcpFvUNbmiCcR9pDpw6Qa7SNKFe6oqeGs82UKiED+AmQ6J3P6SjH5xNwrnge7dKUXpQ85bU85DoMbf7o/j6QRj1on8JgGeEpOvecdgjw2e/tZhmT3812FX49t9oUfBPQru9HTZfgYgXux6tHmTeAqxAj5/DiVxwoXrHdSxUm0iVo8GD2XNOEZ002M23obgSu5fGotc0owi0qwlhGOwUKAWzaaUl9Cavu3J5rGYo+62pEYZqbNgcdM5D87VtRXXroAwUNqPO36wEM+30K3cj6xuxKdN/NP1uBJI8cppkzWtV5euTTGGA7Kb0YNCXF5IsUcn/xt2zgrRTQ+Lb1jvQlSltDMyBEG2jkObwAa5rKnPhSOki2DM4P0X43PIaTD6apWrqm/BHYFiDkPwr57oXP2E9KP5N/USnjG3HgcL642QYGFbvUIV8adC17kztnffysnJ1dzpziCowwk2bybGsdI3I7UKv9usa4P2FQ0T1Q6yKULgetzwD3VOp4w3pf/3QxH5+ZVBdraEP+uz4QLk1wAHRtEv/Ijrblm5aFixbOCwLYUzmV15nQuUXgYTlID9sXzsP/m3w7QbhtbMjO+hDdzrnWQfHKpxOY/uFH3inESuyuBE9q6A4CMTDKgJwqLO/ZZ9hA9pXx2MXLFEHYiY1T2wW5yCv8vJTTKEA7KeaUf27OwzDJm7RcTRw+KbNrgQqZV2cgw3CjiGVg7GKxp7XLKGpA1Ea2u0nwxXW9eVLR3SBN462tXLn8LPu/Po+PgdPCNVaGvUJTjE47cbzkUb/BrCe4CWhL8fI2Joi7QXo5igJqhbMkwbbpBLbwxnboSOBCRsctRwUn0fRTUNMeT7m4KWF2kq01d7mND9MjZ+spKyxmxsILMuP3uxqTdhOJmHaWgxk+vWHTqF/nEp/qUqqOtNEgrrHMp+JGY8hNZ64wMZRneGLNpWFm5ts95Kbs63Q6Oz+lWeFSrIZ0J0binjeLJzmNLMzo71sXQcud/TEzlzZVSUKPMln5LfaXNiTuFaGDu3TRIypyhTzJTJ5Ady6p5dGChS09sH0kC9Erm74pqVEAZudl7Mlpx5Hp1v48ndFxV2iltqmNdFVZFy2mztiMeGKwOjs/G/ZTpiqBScm+DDtdig+a8u3zj6arlPBrXLIXNPeO83HaimDqqsla3Ewfv9u58/xRb5F9mOGJPQNc/CvBirxMehAcpDJ+uvB84VO5QxYlL3Qjl0DgYTX3/BieydAcMOT84YiNz8WUf5rDYTA4tWlxSZN1LVSKwVJuVAqHGASrsxq55up77JN1wfTQgbVAbv1AVPfSS50T0o5PBqypeP/uaIGXS/BdhspLaNPhYo4ydEI9ek8i32Hq7hMFbuDGYL4aHq/95IF/QkQbB47rqpisxrA9/yvfu7ECMaN5tT7zdVa5X/9yV+FkkXJcN0NimU8yobeHGF5ZNPmCpgxk29C+NnjtbGMF8GU6eNrETC39vUo0wbngldjY3cmW8MSgi5qlAhakXHSaeLXcPlUJhrQ5bakGMBP/nFiYjrSM24qXu77arKgGS4DJimdkw48kanS27HqGAmXIK79ht/PjR2gGR9v7IZyx+HnNEzncNUJ27JiX3WmEuHRgXQtbv5A6e/7spE8io2cREmK0WXm5GyJSEiC7rEWFAVJsklMfvg+sofKroKC6pSRtZgUxyswTwSOUiNz7FL/njMpUQHYF/oJkxzlewLSFhdMQ24n+Vjj2/V+TjVazeF3qK/Re75QF7fgTYvVtUoay2M5Tdm35w5ygawvHz+9+d1OvOPlfVXWCmWr25WAheGcvKfrHSoUqIytf3bgotme1Q4bA/mYLc8BnVc9izvitoYjRKuq5vvzfx8O/nQ4TMVVlTcKw6tEDeIwbiBq7QMtG6XolPVRqGCZQEb/ZC+i/nkh/RlyuEVsSA2VPfVbRhF5FvjTRBLbda6/PZnxr5g6opWXDWS3VSddiUi10WhO487TqAXm2LXsgQvzXPy7xWwnp2AdJyFguGaFhuPMgkKQPVwwZQVt1m0XU28bZrJJ+GnRTt+Kf+pLHbGeVAqSq6ac9bZlsSTPYnKzBE3KO5Vv39kT8ExG8/QxLUDog/O3OAEj8CGnfnuWqCSYXmiHpy9xpfpuCYLtGbsa7bxF23kuC++242fv/3d+6W/SVN9sLhM3L9qcZ6LLRnPGdrTs/RCqG9R1z+SXcnBteH0sFfjBh6e54QFYwF3m68d5sOksxGgPWaHX56tBkNr+YVXUa/oJblEUJT/cfwcyzH0KZEFKBKXvea8nSIf6Gg+T8cFtMwKFvV3c8e1d29syrrfjxufzJLNjRnFk9r6eNOE6+etZfvztFlESa3ZRhdgm16XK5rtizss2B03WXjn4Qkjgrf5O2uiiXokAl1ng85NjDYyu8r2ekFj7Vq+5KnTuFWFScM4XXKiNxx0cYOD0T5cgoga77bdU5PJtb+n2VQzm4rG7XcM7x6BAnzHfYDvuWefaqpbmnHnO1inhpTau/b7zfoWSRoSWI8pjq+rlcN02XZHidBZD8sFy5hMrfPNedKuDP2rgjJ9pzcgi4Yn+ya9FZQTVAq6lvn4YO6ZE9YDQS6hTD2SuRc8hHyW0y40DZiHN/Yb8PoPwWtFqQwNLCnKl6VKZbindqCBRqX4E3RoH3vGPQPbojEDRUhNGR0ZYYKi1LtMgk1S9viG73cEboHzZm3uBXWF8whxE2X9/g9zB8U6b2SBn0MQtw5+vukcEX9M/fsbyXVFiP1jjomssJOC7B+l/ptY6YyE8oGyrLokZ4U+UBgUS/KkSVBkoR/24f350t3e8DTVs2+UtTXN2eGPvhwfp0EWwfiLhx89+GTvkT75Ln9vkNcQvYeIRg6v/bB5kIxvJq4zufn++kcH6ON4gcOMQNbOv08G9wEYwya89e4Nm7zpYRegmWp6SOtqradsDpHyRMb7oZdy09PUmVsTKZFljwfO12fUqiXzOcWhEvkXtIQzGgWmdrBAwhfrRH2idKYAirx5lSCPOQCS3LhVgG5uozK0GvKYGUUpY3VA5ov1l7BB+YHrn2lWYEIM/z0OHinc0wpRk3Ccj35FubB1Bqgk68Z+7U2sNG/BaktxaFApylggnh3sSbGXiuMJ1+2U7VNi2o0fO+zLWArvs+DTbwPhVJJWquo97pnfvPw4M6z/BXt3xeIzg4GdayBxnla3IYoZDJwFdcDs48gIDvJUXX9tQNRcYUFpaadVBOPCMG2hS+oFNQ2VrS7XSYMAyWnJ0pvPeXCu9q8hwyUp87Kk1DIfd/qD+WU4RxRzbPJ/YFeU+7FtBPLpEwocNfZrjVPOmxe6QV2+NtUYBtO7CXXRCW3IF23ouVLx0yNLM++B1U9pU79LWZqtvDlJvMiUQ+3bR8Hzne2t+ekQILi2daLpDemc+3wbss5/9BYREsk7MBgUnHBu+Z8HyEXAFta7fDwjaLmiQB4W5oeoCb6CfBUzRE1khkVK/TwaNCEFfcQf68LUNiu1IlN1AXMS2iiP/6S4CUMGtfI8xniOLtZghjgk6Zm+BA336Fy2KXO5BpFBlMHaPvAn0KfeVPiOhg6iOuZSnBshYqKHBu1Zno6Q3K8SJAJdnI3xM/tbpg9tnVSoHM15T2cacplZzWe0dvXI5SVJ5+q2PTG6rI8D2OUHU424I8a01MEWyY1yva/rj94/VclTQTsZwfmqGKJ9G9G6kEbEhF1lR0WcN1uWDSa/aPrtlxzkc8paMzNLkAtGxrs/dmEOaQ1LS1DRwRVID2pYKW+OV3DMyhm2HEVV4aqRZAnZvWiG4xCNzWK6BwdMI8azxqp2uPPuF2+/1h6lWz5nbvzqxaGUNc9cPnMGD95RtM0Vg3PFMNkG8da6+wct9/qbxrKMLsmpoDWOoshA2MEVw1ZLR3gAE3qIOOjKqlMnYiaK3/f/WQRO9MdmhZWoBop8tUa0Y4sheXszbxsO8XIb2a5ZiZMMavtmu3IRcx+an0z00ll7yo0WVYozYIVPhthW/JS3J4vy2OfHCiWz+bsMw71X0N6MhGhqk7so5rwXL10WW+KlFVNTxPmUk/WDGUcz9zgPWJKgF766ZYR3ijtC+el77Mtdoo4LTiu+KtnSs4djt0pG2a2gDby8NvDbDusE/7SWb98JfgtS7mj4pu4cJSngkO9FN9AzgvZtoj0r+ffJe/SdlRhMJqEITJFs7jutA1BrIHd0o8uYvJ66o3rcCPCXBS1Ahy3zzWCU2Qpggufd33BHN5Z4oXemkxGNgTxverakgMvqVmp1LssI4mePBpjpw13iroZZ2fMQCkASLtyCmncCZxEdH5FtlqSCYfjTPFt2rSxtvuMatit4VoS82CbgfUf3ooGPyU0nP1zT9AD8s9C3ulVGcHhfRhlRiFcAylELOUxl0turGm0tePza7O/ubyfsGUPZvlsYlfBCqB4UYkAqHoqOBPMC8Z0714OAtisSxRd8ftD02885UAbIkDYpuDYgZ3R2KCkcDMV5k0WQS/HAjnFYTBWSiI+xTussWtHsMdTfCbYCcap1f5A/5ozo0LV1mI1nagi/JwS85DxigzHSYxxI3iXCps/djRodPh34u1SziQBhbv0/fPndH6+XTUi5EyS8OMygim0RAo3DHFSIEymOdict6WBTOD6q0qWt3XNquxzm4Z2Z9ad1MEVRONwWKTx4p/XpZBRMt6h0adJCtsnObpPgYzMEDyZRRBHVnDr8IvWhqmpLZ584lSMroLrjAV6qJNPXiXjeLAowhuNtQP6zWZNwqGXgrNejYrExu/lnTugoUBt8Wmugq8Kc3mdMYwpB+chT88cIb5nkwkaQikLubHHujTney0mRfkEtbkRU/wq6R7yfDYhOjxKUYmOP7bGP2T3YMgRFfdzsz5+3dYfFKNps0ZbmLRAvCJufRlGvgNucZllxwYfMTpjNMjtlsncnllT9hmYCFWtWylBqzpbBCl8M/KhLhhe/RE7vIXktIaE5qO105QahVnJlotbcKLESWN+brqdV5iZ03HuopO/Cy/YnM4P6yMddP3gCg1mEkdVwI54kP5Un6lJ7Jj8xK6Y44bTuXHLHTYLECipLrs/O7bFcjZyRwT+JlTnPGprWUXZtecjy8PAXL0HkNS7VZluZIT0FpO97HuBiudfy2Ig0DwrHMnJyIWdlPxAWTvq2IGgvgmGDWnABBkpi1z16rMcS2UxXmtjLMkCTpRI4YolXWZJFKoYIpJH5yV9aK53NeRzrG9Abw/oOS8wYRqPLJNKFE4S9YAJIzU498rBG3f6LOVBY0lVYrV4yhxRmU9iEoasnYuZp0yVvxRdKmM4QphRdK3VA6wku8zW4LFhc6182RdFWrCaI59Po4Peu5oFTrhetpsFonufrJVQNQvlIiS3Z5wnAwS2hgnJo1TPgxnhwoK0mxS5XdlzlO4pOrhS7559/GLSHij4MoMzXA/W75wYjxNQPKTQnquErf2r2KGhVn+bm4XlzEun3bVI1K9Tdzz73f7B2W4aE6TpABiRAAoiTF4WI0v1CsE6QhfK7RQArQeSAh4j4J33R77906lHVzLPRZgs23/OzngrmgOUqEciDUZ9nZjB9WeiFPxkDtJsH2pK9JWeuXw+JjxADw3a727cEgN7OU1chdTk526OuLxRbxLykWZkEWJRvWYT6vuInC4aBlQz6eU256aAug9lznAQyyGM+CymjgTf5yLh8kVvc5T4PecurvijcSRfd7eYe7rka21dbh+vrRGd6pQN+vaIijGx8ppU4pNsQTpwgkUzyWUABDTzgNWPyhG50p3s95E2v+rhgIbaz7nZjD/RsFTXX2sGOdqJTvWZD18hKR/qXJlE/MiMNkrc5b5vqSJYUQsdgxWny93/hV/LfGXrIgpIEHeb3e66cb0b6/Xr2Y8yQHhlQUyLTAqZibbchQbIB0SqhuFJQMqTH0C0HCspvFwTwDHSdZ8Fz4HnwAnTOybyI7iVUizseDi6eIsVKsAwoV6FSlWo1VjCoVUfmtDNwwFnnnHfBRZestIqKmkaLVm0go3YdOml10dMxMetmaQm09OjVp5+NlZ2DMywug4YMG+F2xWiLocZjjJfPuAmTpnqORablyVegMDov5oZiJXFYlttuicI6T8IxZpQ31KpTr0FjeAZB3nNYsxYxUSdbEw7IT/lBYM4tAQQShIxNbGYLCMhKVrGG1axlHUeSIhUZkAAESHcf95tB9fzDoQdvIQHw7L1dwj+gb2KG47UO3o0FFuYBqqsP9OUtNB+xGSaNNeSI2k/US2LDJFKCY5a1VN+j806gaxRuxP4LxicsTL52p402WC+dUiY+6IhCBQ5YLUmXNrqwzGYaNnMIetssbURG8VlmbT1t5P7Ka70esFw2lg3ojqnb+EGVe2kIYc9cJtPKC+rnB7meOx7CkGznDc/Yz8q+waTLLMMuAoMUOiZfBh/JbRauIHucv2O8bEOj7xhzO3upzzmAvKIXXB2kDabYdK62zNHyl6uyef8KIs9DO3c9RuyT8T1uVLRPvYVwJYatIGDHgyEpjuBpz7SPJPuQ4Y55+BI9/2tlgLjkiXp7GroQ9vdM5nJNbzqowRV2Kd9Pc7Z0oVEPHHqmc7fDZviYHmbzn/Pn6VEAVAtDQ/l7HES7ugBgROsatFf9pUAEgS2NUFBKIzQAyr9hdUcMY7kpVs9/LqbyUY1IpDjJL3mNyCDzvIGBUBLkUjKhnihgvPBfKMelBOoJbG2FildTp42IxLoeyzti5Mmpf9+siE+bGBeaKzA2lc/ONfDrnxZCAQA=) format('woff2');
-}
-@font-face{
- font-family: "dm";
- font-weight: bold;
- font-style: normal;
- unicode-range: U+1B00-218F;
- src: local('☺'),
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diff --git a/docs/md/assets/highlight.css b/docs/md/assets/highlight.css
deleted file mode 100644
index e69de29b..00000000
diff --git a/docs/md/assets/highlight.min.js b/docs/md/assets/highlight.min.js
deleted file mode 100644
index f43ba9aa..00000000
--- a/docs/md/assets/highlight.min.js
+++ /dev/null
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diff --git a/docs/md/changelog.md b/docs/md/changelog.md
deleted file mode 100644
index 03e490f6..00000000
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+++ /dev/null
@@ -1,102 +0,0 @@
-# Changelog
-
-## [v0.2.77] - 2024-08-04
-
-Significant improvements in text processing and performance:
-
-- 🚀 **Dependency reduction**: Removed dependency on spaCy model for text chunk labeling in cosine extraction strategy.
-- 🤖 **Transformer upgrade**: Implemented text sequence classification using a transformer model for labeling text chunks.
-- ⚡ **Performance enhancement**: Improved model loading speed due to removal of spaCy dependency.
-- 🔧 **Future-proofing**: Laid groundwork for potential complete removal of spaCy dependency in future versions.
-
-These changes address issue #68 and provide a foundation for faster, more efficient text processing in Crawl4AI.
-
-## [v0.2.76] - 2024-08-02
-
-Major improvements in functionality, performance, and cross-platform compatibility! 🚀
-
-- 🐳 **Docker enhancements**: Significantly improved Dockerfile for easy installation on Linux, Mac, and Windows.
-- 🌐 **Official Docker Hub image**: Launched our first official image on Docker Hub for streamlined deployment.
-- 🔧 **Selenium upgrade**: Removed dependency on ChromeDriver, now using Selenium's built-in capabilities for better compatibility.
-- 🖼️ **Image description**: Implemented ability to generate textual descriptions for extracted images from web pages.
-- ⚡ **Performance boost**: Various improvements to enhance overall speed and performance.
-
-A big shoutout to our amazing community contributors:
-- [@aravindkarnam](https://github.com/aravindkarnam) for developing the textual description extraction feature.
-- [@FractalMind](https://github.com/FractalMind) for creating the first official Docker Hub image and fixing Dockerfile errors.
-- [@ketonkss4](https://github.com/ketonkss4) for identifying Selenium's new capabilities, helping us reduce dependencies.
-
-Your contributions are driving Crawl4AI forward! 🙌
-
-## [v0.2.75] - 2024-07-19
-
-Minor improvements for a more maintainable codebase:
-
-- 🔄 Fixed typos in `chunking_strategy.py` and `crawler_strategy.py` to improve code readability
-- 🔄 Removed `.test_pads/` directory from `.gitignore` to keep our repository clean and organized
-
-These changes may seem small, but they contribute to a more stable and sustainable codebase. By fixing typos and updating our `.gitignore` settings, we're ensuring that our code is easier to maintain and scale in the long run.
-
-
-## v0.2.74 - 2024-07-08
-A slew of exciting updates to improve the crawler's stability and robustness! 🎉
-
-- 💻 **UTF encoding fix**: Resolved the Windows \"charmap\" error by adding UTF encoding.
-- 🛡️ **Error handling**: Implemented MaxRetryError exception handling in LocalSeleniumCrawlerStrategy.
-- 🧹 **Input sanitization**: Improved input sanitization and handled encoding issues in LLMExtractionStrategy.
-- 🚮 **Database cleanup**: Removed existing database file and initialized a new one.
-
-## [v0.2.73] - 2024-07-03
-
-💡 In this release, we've bumped the version to v0.2.73 and refreshed our documentation to ensure you have the best experience with our project.
-
-* Supporting website need "with-head" mode to crawl the website with head.
-* Fixing the installation issues for setup.py and dockerfile.
-* Resolve multiple issues.
-
-## [v0.2.72] - 2024-06-30
-
-This release brings exciting updates and improvements to our project! 🎉
-
-* 📚 **Documentation Updates**: Our documentation has been revamped to reflect the latest changes and additions.
-* 🚀 **New Modes in setup.py**: We've added support for three new modes in setup.py: default, torch, and transformers. This enhances the project's flexibility and usability.
-* 🐳 **Docker File Updates**: The Docker file has been updated to ensure seamless compatibility with the new modes and improvements.
-* 🕷️ **Temporary Solution for Headless Crawling**: We've implemented a temporary solution to overcome issues with crawling websites in headless mode.
-
-These changes aim to improve the overall user experience, provide more flexibility, and enhance the project's performance. We're thrilled to share these updates with you and look forward to continuing to evolve and improve our project!
-
-## [0.2.71] - 2024-06-26
-
-**Improved Error Handling and Performance** 🚧
-
-* 🚫 Refactored `crawler_strategy.py` to handle exceptions and provide better error messages, making it more robust and reliable.
-* 💻 Optimized the `get_content_of_website_optimized` function in `utils.py` for improved performance, reducing potential bottlenecks.
-* 💻 Updated `utils.py` with the latest changes, ensuring consistency and accuracy.
-* 🚫 Migrated to `ChromeDriverManager` to resolve Chrome driver download issues, providing a smoother user experience.
-
-These changes focus on refining the existing codebase, resulting in a more stable, efficient, and user-friendly experience. With these improvements, you can expect fewer errors and better performance in the crawler strategy and utility functions.
-
-## [0.2.71] - 2024-06-25
-### Fixed
-- Speed up twice the extraction function.
-
-## [0.2.6] - 2024-06-22
-### Fixed
-- Fix issue #19: Update Dockerfile to ensure compatibility across multiple platforms.
-
-## [0.2.5] - 2024-06-18
-### Added
-- Added five important hooks to the crawler:
- - on_driver_created: Called when the driver is ready for initializations.
- - before_get_url: Called right before Selenium fetches the URL.
- - after_get_url: Called after Selenium fetches the URL.
- - before_return_html: Called when the data is parsed and ready.
- - on_user_agent_updated: Called when the user changes the user_agent, causing the driver to reinitialize.
-- Added an example in `quickstart.py` in the example folder under the docs.
-- Enhancement issue #24: Replaced inline HTML tags (e.g., DEL, INS, SUB, ABBR) with textual format for better context handling in LLM.
-- Maintaining the semantic context of inline tags (e.g., abbreviation, DEL, INS) for improved LLM-friendliness.
-- Updated Dockerfile to ensure compatibility across multiple platforms (Hopefully!).
-
-## [0.2.4] - 2024-06-17
-### Fixed
-- Fix issue #22: Use MD5 hash for caching HTML files to handle long URLs
diff --git a/docs/md/contact.md b/docs/md/contact.md
deleted file mode 100644
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--- a/docs/md/contact.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# Contact
-If you have any questions, suggestions, or feedback, please feel free to reach out to us:
-
-- GitHub: [unclecode](https://github.com/unclecode)
-- Twitter: [@unclecode](https://twitter.com/unclecode)
-- Website: [crawl4ai.com](https://crawl4ai.com)
-
-
-## Contributing 🤝
-
-We welcome contributions from the open-source community to help improve Crawl4AI and make it even more valuable for AI enthusiasts and developers. To contribute, please follow these steps:
-
-1. Fork the repository.
-2. Create a new branch for your feature or bug fix.
-3. Make your changes and commit them with descriptive messages.
-4. Push your changes to your forked repository.
-5. Submit a pull request to the main repository.
-
-For more information on contributing, please see our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTING.md).
-
-## License 📄
-
-Crawl4AI is released under the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE).
-
-Let's work together to make the web more accessible and useful for AI applications! 💪🌐🤖
diff --git a/docs/md/demo.md b/docs/md/demo.md
deleted file mode 100644
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--- a/docs/md/demo.md
+++ /dev/null
@@ -1,231 +0,0 @@
-# Interactive Demo for Crowler
-
-
-
-
-
-
- Response
-
-
- - Markdown
- - Cleaned HTML
- - Media
- - Extracted Content
- - Screenshot
- - Python Code
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/docs/md/examples/index.md b/docs/md/examples/index.md
deleted file mode 100644
index a6634159..00000000
--- a/docs/md/examples/index.md
+++ /dev/null
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-# Examples
-
-Welcome to the examples section of Crawl4AI documentation! In this section, you will find practical examples demonstrating how to use Crawl4AI for various web crawling and data extraction tasks. Each example is designed to showcase different features and capabilities of the library.
-
-## Examples Index
-
-### [LLM Extraction](llm_extraction.md)
-
-This example demonstrates how to use Crawl4AI to extract information using Large Language Models (LLMs). You will learn how to configure the `LLMExtractionStrategy` to get structured data from web pages.
-
-### [JSON CSS Extraction](json_css_extraction.md)
-
-This example demonstrates how to use Crawl4AI to extract structured data without using LLM, and just focusing on page structure. You will learn how to use the `JsonCssExtractionStrategy` to extract data using CSS selectors.
-
-### [JS Execution & CSS Filtering](js_execution_css_filtering.md)
-
-Learn how to execute custom JavaScript code and filter data using CSS selectors. This example shows how to perform complex web interactions and extract specific content from web pages.
-
-### [Hooks & Auth](hooks_auth.md)
-
-This example covers the use of custom hooks for authentication and other pre-crawling tasks. You will see how to set up hooks to modify headers, authenticate sessions, and perform other preparatory actions before crawling.
-
-### [Summarization](summarization.md)
-
-Discover how to use Crawl4AI to summarize web page content. This example demonstrates the summarization capabilities of the library, helping you extract concise information from lengthy web pages.
-
-### [Research Assistant](research_assistant.md)
-
-In this example, Crawl4AI is used as a research assistant to gather and organize information from multiple sources. You will learn how to use various extraction and chunking strategies to compile a comprehensive report.
-
----
-
-Each example includes detailed explanations and code snippets to help you understand and implement the features in your projects. Click on the links to explore each example and start making the most of Crawl4AI!
diff --git a/docs/md/examples/js_execution_css_filtering.md b/docs/md/examples/js_execution_css_filtering.md
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+++ /dev/null
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-# JS Execution & CSS Filtering with AsyncWebCrawler
-
-In this example, we'll demonstrate how to use Crawl4AI's AsyncWebCrawler to execute JavaScript, filter data with CSS selectors, and use a cosine similarity strategy to extract relevant content. This approach is particularly useful when you need to interact with dynamic content on web pages, such as clicking "Load More" buttons.
-
-## Example: Extracting Structured Data Asynchronously
-
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler
-from crawl4ai.chunking_strategy import RegexChunking
-from crawl4ai.extraction_strategy import CosineStrategy
-from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
-
-async def main():
- # Define the JavaScript code to click the "Load More" button
- js_code = """
- const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
- if (loadMoreButton) {
- loadMoreButton.click();
- // Wait for new content to load
- await new Promise(resolve => setTimeout(resolve, 2000));
- }
- """
-
- # Define a wait_for function to ensure content is loaded
- wait_for = """
- () => {
- const articles = document.querySelectorAll('article.tease-card');
- return articles.length > 10;
- }
- """
-
- async with AsyncWebCrawler(verbose=True) as crawler:
- # Run the crawler with keyword filtering and CSS selector
- result = await crawler.arun(
- url="https://www.nbcnews.com/business",
- js_code=js_code,
- wait_for=wait_for,
- css_selector="article.tease-card",
- extraction_strategy=CosineStrategy(
- semantic_filter="technology",
- ),
- chunking_strategy=RegexChunking(),
- )
-
- # Display the extracted result
- print(result.extracted_content)
-
-# Run the async function
-asyncio.run(main())
-```
-
-### Explanation
-
-1. **Asynchronous Execution**: We use `AsyncWebCrawler` with async/await syntax for non-blocking execution.
-
-2. **JavaScript Execution**: The `js_code` variable contains JavaScript code that simulates clicking a "Load More" button and waits for new content to load.
-
-3. **Wait Condition**: The `wait_for` function ensures that the page has loaded more than 10 articles before proceeding with the extraction.
-
-4. **CSS Selector**: The `css_selector="article.tease-card"` parameter ensures that only article cards are extracted from the web page.
-
-5. **Extraction Strategy**: The `CosineStrategy` is used with a semantic filter for "technology" to extract relevant content based on cosine similarity.
-
-6. **Chunking Strategy**: We use `RegexChunking()` to split the content into manageable chunks for processing.
-
-## Advanced Usage: Custom Session and Multiple Requests
-
-For more complex scenarios where you need to maintain state across multiple requests or execute additional JavaScript after the initial page load, you can use a custom session:
-
-```python
-async def advanced_crawl():
- async with AsyncWebCrawler(verbose=True) as crawler:
- # Initial crawl with custom session
- result1 = await crawler.arun(
- url="https://www.nbcnews.com/business",
- js_code=js_code,
- wait_for=wait_for,
- css_selector="article.tease-card",
- session_id="business_session"
- )
-
- # Execute additional JavaScript in the same session
- result2 = await crawler.crawler_strategy.execute_js(
- session_id="business_session",
- js_code="window.scrollTo(0, document.body.scrollHeight);",
- wait_for_js="() => window.innerHeight + window.scrollY >= document.body.offsetHeight"
- )
-
- # Process results
- print("Initial crawl result:", result1.extracted_content)
- print("Additional JS execution result:", result2.html)
-
-asyncio.run(advanced_crawl())
-```
-
-This advanced example demonstrates how to:
-1. Use a custom session to maintain state across requests.
-2. Execute additional JavaScript after the initial page load.
-3. Wait for specific conditions using JavaScript functions.
-
-## Try It Yourself
-
-These examples demonstrate the power and flexibility of Crawl4AI's AsyncWebCrawler in handling complex web interactions and extracting meaningful data asynchronously. You can customize the JavaScript code, CSS selectors, extraction strategies, and waiting conditions to suit your specific requirements.
\ No newline at end of file
diff --git a/docs/md/examples/research_assistant.md b/docs/md/examples/research_assistant.md
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index 61886f9e..00000000
--- a/docs/md/examples/research_assistant.md
+++ /dev/null
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-# Research Assistant Example with AsyncWebCrawler
-
-This example demonstrates how to build an advanced research assistant using `Chainlit`, `Crawl4AI`'s `AsyncWebCrawler`, and various AI services. The assistant can crawl web pages asynchronously, answer questions based on the crawled content, and handle audio inputs.
-
-## Step-by-Step Guide
-
-1. **Install Required Packages**
-
- Ensure you have the necessary packages installed:
-
- ```bash
- pip install chainlit groq openai crawl4ai
- ```
-
-2. **Import Libraries**
-
- ```python
- import os
- import time
- import asyncio
- from openai import AsyncOpenAI
- import chainlit as cl
- import re
- from io import BytesIO
- from chainlit.element import ElementBased
- from groq import Groq
- from crawl4ai import AsyncWebCrawler
- from crawl4ai.extraction_strategy import NoExtractionStrategy
- from crawl4ai.chunking_strategy import RegexChunking
-
- client = AsyncOpenAI(base_url="https://api.groq.com/openai/v1", api_key=os.getenv("GROQ_API_KEY"))
-
- # Instrument the OpenAI client
- cl.instrument_openai()
- ```
-
-3. **Set Configuration**
-
- ```python
- settings = {
- "model": "llama3-8b-8192",
- "temperature": 0.5,
- "max_tokens": 500,
- "top_p": 1,
- "frequency_penalty": 0,
- "presence_penalty": 0,
- }
- ```
-
-4. **Define Utility Functions**
-
- ```python
- def extract_urls(text):
- url_pattern = re.compile(r'(https?://\S+)')
- return url_pattern.findall(text)
-
- async def crawl_urls(urls):
- async with AsyncWebCrawler(verbose=True) as crawler:
- results = await crawler.arun_many(
- urls=urls,
- word_count_threshold=10,
- extraction_strategy=NoExtractionStrategy(),
- chunking_strategy=RegexChunking(),
- bypass_cache=True
- )
- return [result.markdown for result in results if result.success]
- ```
-
-5. **Initialize Chat Start Event**
-
- ```python
- @cl.on_chat_start
- async def on_chat_start():
- cl.user_session.set("session", {
- "history": [],
- "context": {}
- })
- await cl.Message(content="Welcome to the chat! How can I assist you today?").send()
- ```
-
-6. **Handle Incoming Messages**
-
- ```python
- @cl.on_message
- async def on_message(message: cl.Message):
- user_session = cl.user_session.get("session")
-
- # Extract URLs from the user's message
- urls = extract_urls(message.content)
-
- if urls:
- crawled_contents = await crawl_urls(urls)
- for url, content in zip(urls, crawled_contents):
- ref_number = f"REF_{len(user_session['context']) + 1}"
- user_session["context"][ref_number] = {
- "url": url,
- "content": content
- }
-
- user_session["history"].append({
- "role": "user",
- "content": message.content
- })
-
- # Create a system message that includes the context
- context_messages = [
- f'\n{data["content"]}\n '
- for ref, data in user_session["context"].items()
- ]
- system_message = {
- "role": "system",
- "content": (
- "You are a helpful bot. Use the following context for answering questions. "
- "Refer to the sources using the REF number in square brackets, e.g., [1], only if the source is given in the appendices below.\n\n"
- "If the question requires any information from the provided appendices or context, refer to the sources. "
- "If not, there is no need to add a references section. "
- "At the end of your response, provide a reference section listing the URLs and their REF numbers only if sources from the appendices were used.\n\n"
- "\n\n".join(context_messages)
- ) if context_messages else "You are a helpful assistant."
- }
-
- msg = cl.Message(content="")
- await msg.send()
-
- # Get response from the LLM
- stream = await client.chat.completions.create(
- messages=[system_message, *user_session["history"]],
- stream=True,
- **settings
- )
-
- assistant_response = ""
- async for part in stream:
- if token := part.choices[0].delta.content:
- assistant_response += token
- await msg.stream_token(token)
-
- # Add assistant message to the history
- user_session["history"].append({
- "role": "assistant",
- "content": assistant_response
- })
- await msg.update()
-
- # Append the reference section to the assistant's response
- if user_session["context"]:
- reference_section = "\n\nReferences:\n"
- for ref, data in user_session["context"].items():
- reference_section += f"[{ref.split('_')[1]}]: {data['url']}\n"
- msg.content += reference_section
- await msg.update()
- ```
-
-7. **Handle Audio Input**
-
- ```python
- @cl.on_audio_chunk
- async def on_audio_chunk(chunk: cl.AudioChunk):
- if chunk.isStart:
- buffer = BytesIO()
- buffer.name = f"input_audio.{chunk.mimeType.split('/')[1]}"
- cl.user_session.set("audio_buffer", buffer)
- cl.user_session.set("audio_mime_type", chunk.mimeType)
- cl.user_session.get("audio_buffer").write(chunk.data)
-
- @cl.step(type="tool")
- async def speech_to_text(audio_file):
- response = await client.audio.transcriptions.create(
- model="whisper-large-v3", file=audio_file
- )
- return response.text
-
- @cl.on_audio_end
- async def on_audio_end(elements: list[ElementBased]):
- audio_buffer: BytesIO = cl.user_session.get("audio_buffer")
- audio_buffer.seek(0)
- audio_file = audio_buffer.read()
- audio_mime_type: str = cl.user_session.get("audio_mime_type")
-
- start_time = time.time()
- transcription = await speech_to_text((audio_buffer.name, audio_file, audio_mime_type))
- end_time = time.time()
- print(f"Transcription took {end_time - start_time} seconds")
-
- user_msg = cl.Message(author="You", type="user_message", content=transcription)
- await user_msg.send()
- await on_message(user_msg)
- ```
-
-8. **Run the Chat Application**
-
- ```python
- if __name__ == "__main__":
- from chainlit.cli import run_chainlit
- run_chainlit(__file__)
- ```
-
-## Explanation
-
-- **Libraries and Configuration**: We import necessary libraries, including `AsyncWebCrawler` from `crawl4ai`.
-- **Utility Functions**:
- - `extract_urls`: Uses regex to find URLs in messages.
- - `crawl_urls`: An asynchronous function that uses `AsyncWebCrawler` to fetch content from multiple URLs concurrently.
-- **Chat Start Event**: Initializes the chat session and sends a welcome message.
-- **Message Handling**:
- - Extracts URLs from user messages.
- - Asynchronously crawls the URLs using `AsyncWebCrawler`.
- - Updates chat history and context with crawled content.
- - Generates a response using the LLM, incorporating the crawled context.
-- **Audio Handling**: Captures, buffers, and transcribes audio input, then processes the transcription as text.
-- **Running the Application**: Starts the Chainlit server for interaction with the assistant.
-
-## Key Improvements
-
-1. **Asynchronous Web Crawling**: Using `AsyncWebCrawler` allows for efficient, concurrent crawling of multiple URLs.
-2. **Improved Context Management**: The assistant now maintains a context of crawled content, allowing for more informed responses.
-3. **Dynamic Reference System**: The assistant can refer to specific sources in its responses and provide a reference section.
-4. **Seamless Audio Integration**: The ability to handle audio inputs makes the assistant more versatile and user-friendly.
-
-This updated Research Assistant showcases how to create a powerful, interactive tool that can efficiently fetch and process web content, handle various input types, and provide informed responses based on the gathered information.
\ No newline at end of file
diff --git a/docs/md/examples/summarization.md b/docs/md/examples/summarization.md
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index 12c18471..00000000
--- a/docs/md/examples/summarization.md
+++ /dev/null
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-# Summarization Example with AsyncWebCrawler
-
-This example demonstrates how to use Crawl4AI's `AsyncWebCrawler` to extract a summary from a web page asynchronously. The goal is to obtain the title, a detailed summary, a brief summary, and a list of keywords from the given page.
-
-## Step-by-Step Guide
-
-1. **Import Necessary Modules**
-
- First, import the necessary modules and classes:
-
- ```python
- import os
- import json
- import asyncio
- from crawl4ai import AsyncWebCrawler
- from crawl4ai.extraction_strategy import LLMExtractionStrategy
- from crawl4ai.chunking_strategy import RegexChunking
- from pydantic import BaseModel, Field
- ```
-
-2. **Define the URL to be Crawled**
-
- Set the URL of the web page you want to summarize:
-
- ```python
- url = 'https://marketplace.visualstudio.com/items?itemName=Unclecode.groqopilot'
- ```
-
-3. **Define the Data Model**
-
- Use Pydantic to define the structure of the extracted data:
-
- ```python
- class PageSummary(BaseModel):
- title: str = Field(..., description="Title of the page.")
- summary: str = Field(..., description="Summary of the page.")
- brief_summary: str = Field(..., description="Brief summary of the page.")
- keywords: list = Field(..., description="Keywords assigned to the page.")
- ```
-
-4. **Create the Extraction Strategy**
-
- Set up the `LLMExtractionStrategy` with the necessary parameters:
-
- ```python
- extraction_strategy = LLMExtractionStrategy(
- provider="openai/gpt-4o",
- api_token=os.getenv('OPENAI_API_KEY'),
- schema=PageSummary.model_json_schema(),
- extraction_type="schema",
- apply_chunking=False,
- instruction=(
- "From the crawled content, extract the following details: "
- "1. Title of the page "
- "2. Summary of the page, which is a detailed summary "
- "3. Brief summary of the page, which is a paragraph text "
- "4. Keywords assigned to the page, which is a list of keywords. "
- 'The extracted JSON format should look like this: '
- '{ "title": "Page Title", "summary": "Detailed summary of the page.", '
- '"brief_summary": "Brief summary in a paragraph.", "keywords": ["keyword1", "keyword2", "keyword3"] }'
- )
- )
- ```
-
-5. **Define the Async Crawl Function**
-
- Create an asynchronous function to run the crawler:
-
- ```python
- async def crawl_and_summarize(url):
- async with AsyncWebCrawler(verbose=True) as crawler:
- result = await crawler.arun(
- url=url,
- word_count_threshold=1,
- extraction_strategy=extraction_strategy,
- chunking_strategy=RegexChunking(),
- bypass_cache=True,
- )
- return result
- ```
-
-6. **Run the Crawler and Process Results**
-
- Use asyncio to run the crawler and process the results:
-
- ```python
- async def main():
- result = await crawl_and_summarize(url)
-
- if result.success:
- page_summary = json.loads(result.extracted_content)
- print("Extracted Page Summary:")
- print(json.dumps(page_summary, indent=2))
-
- # Save the extracted data
- with open(".data/page_summary.json", "w", encoding="utf-8") as f:
- json.dump(page_summary, f, indent=2)
- print("Page summary saved to .data/page_summary.json")
- else:
- print(f"Failed to crawl and summarize the page. Error: {result.error_message}")
-
- # Run the async main function
- asyncio.run(main())
- ```
-
-## Explanation
-
-- **Importing Modules**: We import the necessary modules, including `AsyncWebCrawler` and `LLMExtractionStrategy` from Crawl4AI.
-- **URL Definition**: We set the URL of the web page to crawl and summarize.
-- **Data Model Definition**: We define the structure of the data to extract using Pydantic's `BaseModel`.
-- **Extraction Strategy Setup**: We create an instance of `LLMExtractionStrategy` with the schema and detailed instructions for the extraction process.
-- **Async Crawl Function**: We define an asynchronous function `crawl_and_summarize` that uses `AsyncWebCrawler` to perform the crawling and extraction.
-- **Main Execution**: In the `main` function, we run the crawler, process the results, and save the extracted data.
-
-## Advanced Usage: Crawling Multiple URLs
-
-To demonstrate the power of `AsyncWebCrawler`, here's how you can summarize multiple pages concurrently:
-
-```python
-async def crawl_multiple_urls(urls):
- async with AsyncWebCrawler(verbose=True) as crawler:
- tasks = [crawler.arun(
- url=url,
- word_count_threshold=1,
- extraction_strategy=extraction_strategy,
- chunking_strategy=RegexChunking(),
- bypass_cache=True
- ) for url in urls]
- results = await asyncio.gather(*tasks)
- return results
-
-async def main():
- urls = [
- 'https://marketplace.visualstudio.com/items?itemName=Unclecode.groqopilot',
- 'https://marketplace.visualstudio.com/items?itemName=GitHub.copilot',
- 'https://marketplace.visualstudio.com/items?itemName=ms-python.python'
- ]
- results = await crawl_multiple_urls(urls)
-
- for i, result in enumerate(results):
- if result.success:
- page_summary = json.loads(result.extracted_content)
- print(f"\nSummary for URL {i+1}:")
- print(json.dumps(page_summary, indent=2))
- else:
- print(f"\nFailed to summarize URL {i+1}. Error: {result.error_message}")
-
-asyncio.run(main())
-```
-
-This advanced example shows how to use `AsyncWebCrawler` to efficiently summarize multiple web pages concurrently, significantly reducing the total processing time compared to sequential crawling.
-
-By leveraging the asynchronous capabilities of Crawl4AI, you can perform advanced web crawling and data extraction tasks with improved efficiency and scalability.
\ No newline at end of file
diff --git a/docs/md/full_details/advanced_features.md b/docs/md/full_details/advanced_features.md
deleted file mode 100644
index 944f6e0d..00000000
--- a/docs/md/full_details/advanced_features.md
+++ /dev/null
@@ -1,138 +0,0 @@
-# Advanced Features
-
-Crawl4AI offers a range of advanced features that allow you to fine-tune your web crawling and data extraction process. This section will cover some of these advanced features, including taking screenshots, extracting media and links, customizing the user agent, using custom hooks, and leveraging CSS selectors.
-
-## Taking Screenshots 📸
-
-One of the cool features of Crawl4AI is the ability to take screenshots of the web pages you're crawling. This can be particularly useful for visual verification or for capturing the state of dynamic content.
-
-Here's how you can take a screenshot:
-
-```python
-from crawl4ai import WebCrawler
-import base64
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-crawler.warmup()
-
-# Run the crawler with the screenshot parameter
-result = crawler.run(url="https://www.nbcnews.com/business", screenshot=True)
-
-# Save the screenshot to a file
-with open("screenshot.png", "wb") as f:
- f.write(base64.b64decode(result.screenshot))
-
-print("Screenshot saved to 'screenshot.png'!")
-```
-
-In this example, we create a `WebCrawler` instance, warm it up, and then run it with the `screenshot` parameter set to `True`. The screenshot is saved as a base64 encoded string in the result, which we then decode and save as a PNG file.
-
-## Extracting Media and Links 🎨🔗
-
-Crawl4AI can extract all media tags (images, audio, and video) and links (both internal and external) from a web page. This feature is useful for collecting multimedia content or analyzing link structures.
-
-Here's an example:
-
-```python
-from crawl4ai import WebCrawler
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-crawler.warmup()
-
-# Run the crawler
-result = crawler.run(url="https://www.nbcnews.com/business")
-
-print("Extracted media:", result.media)
-print("Extracted links:", result.links)
-```
-
-In this example, the `result` object contains dictionaries for media and links, which you can access and use as needed.
-
-## Customizing the User Agent 🕵️♂️
-
-Crawl4AI allows you to set a custom user agent for your HTTP requests. This can help you avoid detection by web servers or simulate different browsing environments.
-
-Here's how to set a custom user agent:
-
-```python
-from crawl4ai import WebCrawler
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-crawler.warmup()
-
-# Run the crawler with a custom user agent
-result = crawler.run(url="https://www.nbcnews.com/business", user_agent="Mozilla/5.0 (compatible; MyCrawler/1.0)")
-
-print("Crawl result:", result)
-```
-
-In this example, we specify a custom user agent string when running the crawler.
-
-## Using Custom Hooks 🪝
-
-Hooks are a powerful feature in Crawl4AI that allow you to customize the crawling process at various stages. You can define hooks for actions such as driver initialization, before and after URL fetching, and before returning the HTML.
-
-Here's an example of using hooks:
-
-```python
-from crawl4ai import WebCrawler
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support.ui import WebDriverWait
-from selenium.webdriver.support import expected_conditions as EC
-
-# Define the hooks
-def on_driver_created(driver):
- driver.maximize_window()
- driver.get('https://example.com/login')
- WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.NAME, 'username'))).send_keys('testuser')
- driver.find_element(By.NAME, 'password').send_keys('password123')
- driver.find_element(By.NAME, 'login').click()
- return driver
-
-def before_get_url(driver):
- driver.execute_cdp_cmd('Network.setExtraHTTPHeaders', {'headers': {'X-Test-Header': 'test'}})
- return driver
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-crawler.warmup()
-
-# Set the hooks
-crawler.set_hook('on_driver_created', on_driver_created)
-crawler.set_hook('before_get_url', before_get_url)
-
-# Run the crawler
-result = crawler.run(url="https://example.com")
-
-print("Crawl result:", result)
-```
-
-In this example, we define hooks to handle driver initialization and custom headers before fetching the URL.
-
-## Using CSS Selectors 🎯
-
-CSS selectors allow you to target specific elements on a web page for extraction. This can be useful for scraping structured content, such as articles or product details.
-
-Here's an example of using a CSS selector:
-
-```python
-from crawl4ai import WebCrawler
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-crawler.warmup()
-
-# Run the crawler with a CSS selector to extract only H2 tags
-result = crawler.run(url="https://www.nbcnews.com/business", css_selector="h2")
-
-print("Extracted H2 tags:", result.extracted_content)
-```
-
-In this example, we use the `css_selector` parameter to extract only the H2 tags from the web page.
-
----
-
-With these advanced features, you can leverage Crawl4AI to perform sophisticated web crawling and data extraction tasks. Whether you need to take screenshots, extract specific elements, customize the crawling process, or set custom headers, Crawl4AI provides the flexibility and power to meet your needs. Happy crawling! 🕷️🚀
diff --git a/docs/md/full_details/chunking_strategies.md b/docs/md/full_details/chunking_strategies.md
deleted file mode 100644
index f429310f..00000000
--- a/docs/md/full_details/chunking_strategies.md
+++ /dev/null
@@ -1,133 +0,0 @@
-## Chunking Strategies 📚
-
-Crawl4AI provides several powerful chunking strategies to divide text into manageable parts for further processing. Each strategy has unique characteristics and is suitable for different scenarios. Let's explore them one by one.
-
-### RegexChunking
-
-`RegexChunking` splits text using regular expressions. This is ideal for creating chunks based on specific patterns like paragraphs or sentences.
-
-#### When to Use
-- Great for structured text with consistent delimiters.
-- Suitable for documents where specific patterns (e.g., double newlines, periods) indicate logical chunks.
-
-#### Parameters
-- `patterns` (list, optional): Regular expressions used to split the text. Default is to split by double newlines (`['\n\n']`).
-
-#### Example
-```python
-from crawl4ai.chunking_strategy import RegexChunking
-
-# Define patterns for splitting text
-patterns = [r'\n\n', r'\. ']
-chunker = RegexChunking(patterns=patterns)
-
-# Sample text
-text = "This is a sample text. It will be split into chunks.\n\nThis is another paragraph."
-
-# Chunk the text
-chunks = chunker.chunk(text)
-print(chunks)
-```
-
-### NlpSentenceChunking
-
-`NlpSentenceChunking` uses NLP models to split text into sentences, ensuring accurate sentence boundaries.
-
-#### When to Use
-- Ideal for texts where sentence boundaries are crucial.
-- Useful for creating chunks that preserve grammatical structures.
-
-#### Parameters
-- None.
-
-#### Example
-```python
-from crawl4ai.chunking_strategy import NlpSentenceChunking
-
-chunker = NlpSentenceChunking()
-
-# Sample text
-text = "This is a sample text. It will be split into sentences. Here's another sentence."
-
-# Chunk the text
-chunks = chunker.chunk(text)
-print(chunks)
-```
-
-### TopicSegmentationChunking
-
-`TopicSegmentationChunking` employs the TextTiling algorithm to segment text into topic-based chunks. This method identifies thematic boundaries.
-
-#### When to Use
-- Perfect for long documents with distinct topics.
-- Useful when preserving topic continuity is more important than maintaining text order.
-
-#### Parameters
-- `num_keywords` (int, optional): Number of keywords for each topic segment. Default is `3`.
-
-#### Example
-```python
-from crawl4ai.chunking_strategy import TopicSegmentationChunking
-
-chunker = TopicSegmentationChunking(num_keywords=3)
-
-# Sample text
-text = "This document contains several topics. Topic one discusses AI. Topic two covers machine learning."
-
-# Chunk the text
-chunks = chunker.chunk(text)
-print(chunks)
-```
-
-### FixedLengthWordChunking
-
-`FixedLengthWordChunking` splits text into chunks based on a fixed number of words. This ensures each chunk has approximately the same length.
-
-#### When to Use
-- Suitable for processing large texts where uniform chunk size is important.
-- Useful when the number of words per chunk needs to be controlled.
-
-#### Parameters
-- `chunk_size` (int, optional): Number of words per chunk. Default is `100`.
-
-#### Example
-```python
-from crawl4ai.chunking_strategy import FixedLengthWordChunking
-
-chunker = FixedLengthWordChunking(chunk_size=10)
-
-# Sample text
-text = "This is a sample text. It will be split into chunks of fixed length."
-
-# Chunk the text
-chunks = chunker.chunk(text)
-print(chunks)
-```
-
-### SlidingWindowChunking
-
-`SlidingWindowChunking` uses a sliding window approach to create overlapping chunks. Each chunk has a fixed length, and the window slides by a specified step size.
-
-#### When to Use
-- Ideal for creating overlapping chunks to preserve context.
-- Useful for tasks where context from adjacent chunks is needed.
-
-#### Parameters
-- `window_size` (int, optional): Number of words in each chunk. Default is `100`.
-- `step` (int, optional): Number of words to slide the window. Default is `50`.
-
-#### Example
-```python
-from crawl4ai.chunking_strategy import SlidingWindowChunking
-
-chunker = SlidingWindowChunking(window_size=10, step=5)
-
-# Sample text
-text = "This is a sample text. It will be split using a sliding window approach to preserve context."
-
-# Chunk the text
-chunks = chunker.chunk(text)
-print(chunks)
-```
-
-With these chunking strategies, you can choose the best method to divide your text based on your specific needs. Whether you need precise sentence boundaries, topic-based segmentation, or uniform chunk sizes, Crawl4AI has you covered. Happy chunking! 📝✨
diff --git a/docs/md/full_details/crawl_request_parameters.md b/docs/md/full_details/crawl_request_parameters.md
deleted file mode 100644
index 896097de..00000000
--- a/docs/md/full_details/crawl_request_parameters.md
+++ /dev/null
@@ -1,179 +0,0 @@
-# Crawl Request Parameters for AsyncWebCrawler
-
-The `arun` method in Crawl4AI's `AsyncWebCrawler` is designed to be highly configurable, allowing you to customize the crawling and extraction process to suit your needs. Below are the parameters you can use with the `arun` method, along with their descriptions, possible values, and examples.
-
-## Parameters
-
-### url (str)
-**Description:** The URL of the webpage to crawl.
-**Required:** Yes
-**Example:**
-```python
-url = "https://www.nbcnews.com/business"
-```
-
-### word_count_threshold (int)
-**Description:** The minimum number of words a block must contain to be considered meaningful. The default value is defined by `MIN_WORD_THRESHOLD`.
-**Required:** No
-**Default Value:** `MIN_WORD_THRESHOLD`
-**Example:**
-```python
-word_count_threshold = 10
-```
-
-### extraction_strategy (ExtractionStrategy)
-**Description:** The strategy to use for extracting content from the HTML. It must be an instance of `ExtractionStrategy`. If not provided, the default is `NoExtractionStrategy`.
-**Required:** No
-**Default Value:** `NoExtractionStrategy()`
-**Example:**
-```python
-extraction_strategy = CosineStrategy(semantic_filter="finance")
-```
-
-### chunking_strategy (ChunkingStrategy)
-**Description:** The strategy to use for chunking the text before processing. It must be an instance of `ChunkingStrategy`. The default value is `RegexChunking()`.
-**Required:** No
-**Default Value:** `RegexChunking()`
-**Example:**
-```python
-chunking_strategy = NlpSentenceChunking()
-```
-
-### bypass_cache (bool)
-**Description:** Whether to force a fresh crawl even if the URL has been previously crawled. The default value is `False`.
-**Required:** No
-**Default Value:** `False`
-**Example:**
-```python
-bypass_cache = True
-```
-
-### css_selector (str)
-**Description:** The CSS selector to target specific parts of the HTML for extraction. If not provided, the entire HTML will be processed.
-**Required:** No
-**Default Value:** `None`
-**Example:**
-```python
-css_selector = "div.article-content"
-```
-
-### screenshot (bool)
-**Description:** Whether to take screenshots of the page. The default value is `False`.
-**Required:** No
-**Default Value:** `False`
-**Example:**
-```python
-screenshot = True
-```
-
-### user_agent (str)
-**Description:** The user agent to use for the HTTP requests. If not provided, a default user agent will be used.
-**Required:** No
-**Default Value:** `None`
-**Example:**
-```python
-user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
-```
-
-### verbose (bool)
-**Description:** Whether to enable verbose logging. The default value is `True`.
-**Required:** No
-**Default Value:** `True`
-**Example:**
-```python
-verbose = True
-```
-
-### **kwargs
-Additional keyword arguments that can be passed to customize the crawling process further. Some notable options include:
-
-- **only_text (bool):** Whether to extract only text content, excluding HTML tags. Default is `False`.
-- **session_id (str):** A unique identifier for the crawling session. This is useful for maintaining state across multiple requests.
-- **js_code (str or list):** JavaScript code to be executed on the page before extraction.
-- **wait_for (str):** A CSS selector or JavaScript function to wait for before considering the page load complete.
-
-**Example:**
-```python
-result = await crawler.arun(
- url="https://www.nbcnews.com/business",
- css_selector="p",
- only_text=True,
- session_id="unique_session_123",
- js_code="window.scrollTo(0, document.body.scrollHeight);",
- wait_for="article.main-article"
-)
-```
-
-## Example Usage
-
-Here's an example of how to use the `arun` method with various parameters:
-
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler
-from crawl4ai.extraction_strategy import CosineStrategy
-from crawl4ai.chunking_strategy import NlpSentenceChunking
-
-async def main():
- # Create the AsyncWebCrawler instance
- async with AsyncWebCrawler(verbose=True) as crawler:
- # Run the crawler with custom parameters
- result = await crawler.arun(
- url="https://www.nbcnews.com/business",
- word_count_threshold=10,
- extraction_strategy=CosineStrategy(semantic_filter="finance"),
- chunking_strategy=NlpSentenceChunking(),
- bypass_cache=True,
- css_selector="div.article-content",
- screenshot=True,
- user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3",
- verbose=True,
- only_text=True,
- session_id="business_news_session",
- js_code="window.scrollTo(0, document.body.scrollHeight);",
- wait_for="footer"
- )
-
- print(result)
-
-# Run the async function
-asyncio.run(main())
-```
-
-This example demonstrates how to configure various parameters to customize the crawling and extraction process using the asynchronous version of Crawl4AI.
-
-## Additional Asynchronous Methods
-
-The `AsyncWebCrawler` class also provides other useful asynchronous methods:
-
-### arun_many
-**Description:** Crawl multiple URLs concurrently.
-**Example:**
-```python
-urls = ["https://example1.com", "https://example2.com", "https://example3.com"]
-results = await crawler.arun_many(urls, word_count_threshold=10, bypass_cache=True)
-```
-
-### aclear_cache
-**Description:** Clear the crawler's cache.
-**Example:**
-```python
-await crawler.aclear_cache()
-```
-
-### aflush_cache
-**Description:** Completely flush the crawler's cache.
-**Example:**
-```python
-await crawler.aflush_cache()
-```
-
-### aget_cache_size
-**Description:** Get the current size of the cache.
-**Example:**
-```python
-cache_size = await crawler.aget_cache_size()
-print(f"Current cache size: {cache_size}")
-```
-
-These asynchronous methods allow for efficient and flexible use of the AsyncWebCrawler in various scenarios.
\ No newline at end of file
diff --git a/docs/md/full_details/crawl_result_class.md b/docs/md/full_details/crawl_result_class.md
deleted file mode 100644
index e2c545fe..00000000
--- a/docs/md/full_details/crawl_result_class.md
+++ /dev/null
@@ -1,104 +0,0 @@
-# Crawl Result
-
-The `CrawlResult` class is the heart of Crawl4AI's output, encapsulating all the data extracted from a crawling session. This class contains various fields that store the results of the web crawling and extraction process. Let's break down each field and see what it holds. 🎉
-
-## Class Definition
-
-```python
-from pydantic import BaseModel
-from typing import Dict, List, Optional
-
-class CrawlResult(BaseModel):
- url: str
- html: str
- success: bool
- cleaned_html: Optional[str] = None
- media: Dict[str, List[Dict]] = {}
- links: Dict[str, List[Dict]] = {}
- screenshot: Optional[str] = None
- markdown: Optional[str] = None
- extracted_content: Optional[str] = None
- metadata: Optional[dict] = None
- error_message: Optional[str] = None
- session_id: Optional[str] = None
- responser_headers: Optional[dict] = None
- status_code: Optional[int] = None
-```
-
-## Fields Explanation
-
-### `url: str`
-The URL that was crawled. This field simply stores the URL of the web page that was processed.
-
-### `html: str`
-The raw HTML content of the web page. This is the unprocessed HTML source as retrieved by the crawler.
-
-### `success: bool`
-A flag indicating whether the crawling and extraction were successful. If any error occurs during the process, this will be `False`.
-
-### `cleaned_html: Optional[str]`
-The cleaned HTML content of the web page. This field holds the HTML after removing unwanted tags like `
diff --git a/docs/md/introduction.md b/docs/md/introduction.md
deleted file mode 100644
index 6d1ad56b..00000000
--- a/docs/md/introduction.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Introduction
-
-Welcome to the documentation for Crawl4AI v0.2.5! 🕷️🤖
-
-Crawl4AI is designed to simplify the process of crawling web pages and extracting useful information for large language models (LLMs) and AI applications. Whether you're using it as a REST API, a Python library, or through a Google Colab notebook, Crawl4AI provides powerful features to make web data extraction easier and more efficient.
-
-## Key Features ✨
-
-- **🆓 Completely Free and Open-Source**: Crawl4AI is free to use and open-source, making it accessible for everyone.
-- **🤖 LLM-Friendly Output Formats**: Supports JSON, cleaned HTML, and markdown formats.
-- **🌍 Concurrent Crawling**: Crawl multiple URLs simultaneously to save time.
-- **🎨 Media Extraction**: Extract all media tags including images, audio, and video.
-- **🔗 Link Extraction**: Extract all external and internal links from web pages.
-- **📚 Metadata Extraction**: Extract metadata from web pages for additional context.
-- **🔄 Custom Hooks**: Define custom hooks for authentication, headers, and page modifications before crawling.
-- **🕵️ User Agent Support**: Customize the user agent for HTTP requests.
-- **🖼️ Screenshot Capability**: Take screenshots of web pages during crawling.
-- **📜 JavaScript Execution**: Execute custom JavaScripts before crawling.
-- **📚 Advanced Chunking and Extraction Strategies**: Utilize topic-based, regex, sentence chunking, cosine clustering, and LLM extraction strategies.
-- **🎯 CSS Selector Support**: Extract specific content using CSS selectors.
-- **📝 Instruction/Keyword Refinement**: Pass instructions or keywords to refine the extraction process.
-
-Check the [Changelog](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md) for more details.
-
-## Power and Simplicity of Crawl4AI 🚀
-
-Crawl4AI provides an easy way to crawl and extract data from web pages without installing any library. You can use the REST API on our server or run the local server on your machine. For more advanced control, use the Python library to customize your crawling and extraction strategies.
-
-Explore the documentation to learn more about the features, installation process, usage examples, and how to contribute to Crawl4AI. Let's make the web more accessible and useful for AI applications! 💪🌐🤖
diff --git a/docs/md/quickstart.md b/docs/md/quickstart.md
deleted file mode 100644
index 39ab6367..00000000
--- a/docs/md/quickstart.md
+++ /dev/null
@@ -1,285 +0,0 @@
-# Quick Start Guide 🚀
-
-Welcome to the Crawl4AI Quickstart Guide! In this tutorial, we'll walk you through the basic usage of Crawl4AI with a friendly and humorous tone. We'll cover everything from basic usage to advanced features like chunking and extraction strategies, all with the power of asynchronous programming. Let's dive in! 🌟
-
-## Getting Started 🛠️
-
-First, let's import the necessary modules and create an instance of `AsyncWebCrawler`. We'll use an async context manager, which handles the setup and teardown of the crawler for us.
-
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler
-
-async def main():
- async with AsyncWebCrawler(verbose=True) as crawler:
- # We'll add our crawling code here
- pass
-
-if __name__ == "__main__":
- asyncio.run(main())
-```
-
-### Basic Usage
-
-Simply provide a URL and let Crawl4AI do the magic!
-
-```python
-async def main():
- async with AsyncWebCrawler(verbose=True) as crawler:
- result = await crawler.arun(url="https://www.nbcnews.com/business")
- print(f"Basic crawl result: {result.markdown[:500]}") # Print first 500 characters
-
-asyncio.run(main())
-```
-
-### Taking Screenshots 📸
-
-Let's take a screenshot of the page!
-
-```python
-import base64
-
-async def main():
- async with AsyncWebCrawler(verbose=True) as crawler:
- result = await crawler.arun(url="https://www.nbcnews.com/business", screenshot=True)
- with open("screenshot.png", "wb") as f:
- f.write(base64.b64decode(result.screenshot))
- print("Screenshot saved to 'screenshot.png'!")
-
-asyncio.run(main())
-```
-
-### Understanding Parameters 🧠
-
-By default, Crawl4AI caches the results of your crawls. This means that subsequent crawls of the same URL will be much faster! Let's see this in action.
-
-```python
-async def main():
- async with AsyncWebCrawler(verbose=True) as crawler:
- # First crawl (caches the result)
- result1 = await crawler.arun(url="https://www.nbcnews.com/business")
- print(f"First crawl result: {result1.markdown[:100]}...")
-
- # Force to crawl again
- result2 = await crawler.arun(url="https://www.nbcnews.com/business", bypass_cache=True)
- print(f"Second crawl result: {result2.markdown[:100]}...")
-
-asyncio.run(main())
-```
-
-### Adding a Chunking Strategy 🧩
-
-Let's add a chunking strategy: `RegexChunking`! This strategy splits the text based on a given regex pattern.
-
-```python
-from crawl4ai.chunking_strategy import RegexChunking
-
-async def main():
- async with AsyncWebCrawler(verbose=True) as crawler:
- result = await crawler.arun(
- url="https://www.nbcnews.com/business",
- chunking_strategy=RegexChunking(patterns=["\n\n"])
- )
- print(f"RegexChunking result: {result.extracted_content[:200]}...")
-
-asyncio.run(main())
-```
-
-### Adding an Extraction Strategy 🧠
-
-Let's get smarter with an extraction strategy: `JsonCssExtractionStrategy`! This strategy extracts structured data from HTML using CSS selectors.
-
-```python
-from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
-import json
-
-async def main():
- schema = {
- "name": "News Articles",
- "baseSelector": "article.tease-card",
- "fields": [
- {
- "name": "title",
- "selector": "h2",
- "type": "text",
- },
- {
- "name": "summary",
- "selector": "div.tease-card__info",
- "type": "text",
- }
- ],
- }
-
- async with AsyncWebCrawler(verbose=True) as crawler:
- result = await crawler.arun(
- url="https://www.nbcnews.com/business",
- extraction_strategy=JsonCssExtractionStrategy(schema, verbose=True)
- )
- extracted_data = json.loads(result.extracted_content)
- print(f"Extracted {len(extracted_data)} articles")
- print(json.dumps(extracted_data[0], indent=2))
-
-asyncio.run(main())
-```
-
-### Using LLMExtractionStrategy 🤖
-
-Time to bring in the big guns: `LLMExtractionStrategy`! This strategy uses a large language model to extract relevant information from the web page.
-
-```python
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-import os
-from pydantic import BaseModel, Field
-
-class OpenAIModelFee(BaseModel):
- model_name: str = Field(..., description="Name of the OpenAI model.")
- input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
- output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
-
-async def main():
- if not os.getenv("OPENAI_API_KEY"):
- print("OpenAI API key not found. Skipping this example.")
- return
-
- async with AsyncWebCrawler(verbose=True) as crawler:
- result = await crawler.arun(
- url="https://openai.com/api/pricing/",
- word_count_threshold=1,
- extraction_strategy=LLMExtractionStrategy(
- provider="openai/gpt-4o",
- api_token=os.getenv("OPENAI_API_KEY"),
- schema=OpenAIModelFee.schema(),
- extraction_type="schema",
- instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens.
- Do not miss any models in the entire content. One extracted model JSON format should look like this:
- {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}.""",
- ),
- bypass_cache=True,
- )
- print(result.extracted_content)
-
-asyncio.run(main())
-```
-
-### Interactive Extraction 🖱️
-
-Let's use JavaScript to interact with the page before extraction!
-
-```python
-async def main():
- js_code = """
- const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
- loadMoreButton && loadMoreButton.click();
- """
-
- wait_for = """() => {
- return Array.from(document.querySelectorAll('article.tease-card')).length > 10;
- }"""
-
- async with AsyncWebCrawler(verbose=True) as crawler:
- result = await crawler.arun(
- url="https://www.nbcnews.com/business",
- js_code=js_code,
- wait_for=wait_for,
- css_selector="article.tease-card",
- bypass_cache=True,
- )
- print(f"JavaScript interaction result: {result.extracted_content[:500]}")
-
-asyncio.run(main())
-```
-
-### Advanced Session-Based Crawling with Dynamic Content 🔄
-
-In modern web applications, content is often loaded dynamically without changing the URL. This is common in single-page applications (SPAs) or websites using infinite scrolling. Traditional crawling methods that rely on URL changes won't work here. That's where Crawl4AI's advanced session-based crawling comes in handy!
-
-Here's what makes this approach powerful:
-
-1. **Session Preservation**: By using a `session_id`, we can maintain the state of our crawling session across multiple interactions with the page. This is crucial for navigating through dynamically loaded content.
-
-2. **Asynchronous JavaScript Execution**: We can execute custom JavaScript to trigger content loading or navigation. In this example, we'll click a "Load More" button to fetch the next page of commits.
-
-3. **Dynamic Content Waiting**: The `wait_for` parameter allows us to specify a condition that must be met before considering the page load complete. This ensures we don't extract data before the new content is fully loaded.
-
-Let's see how this works with a real-world example: crawling multiple pages of commits on a GitHub repository. The URL doesn't change as we load more commits, so we'll use these advanced techniques to navigate and extract data.
-
-```python
-import json
-from bs4 import BeautifulSoup
-from crawl4ai import AsyncWebCrawler
-from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
-
-async def main():
- async with AsyncWebCrawler(verbose=True) as crawler:
- url = "https://github.com/microsoft/TypeScript/commits/main"
- session_id = "typescript_commits_session"
- all_commits = []
-
- js_next_page = """
- const button = document.querySelector('a[data-testid="pagination-next-button"]');
- if (button) button.click();
- """
-
- wait_for = """() => {
- const commits = document.querySelectorAll('li.Box-sc-g0xbh4-0 h4');
- if (commits.length === 0) return false;
- const firstCommit = commits[0].textContent.trim();
- return firstCommit !== window.lastCommit;
- }"""
-
- schema = {
- "name": "Commit Extractor",
- "baseSelector": "li.Box-sc-g0xbh4-0",
- "fields": [
- {
- "name": "title",
- "selector": "h4.markdown-title",
- "type": "text",
- "transform": "strip",
- },
- ],
- }
- extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
-
- for page in range(3): # Crawl 3 pages
- result = await crawler.arun(
- url=url,
- session_id=session_id,
- css_selector="li.Box-sc-g0xbh4-0",
- extraction_strategy=extraction_strategy,
- js_code=js_next_page if page > 0 else None,
- wait_for=wait_for if page > 0 else None,
- js_only=page > 0,
- bypass_cache=True,
- headless=False,
- )
-
- assert result.success, f"Failed to crawl page {page + 1}"
-
- commits = json.loads(result.extracted_content)
- all_commits.extend(commits)
-
- print(f"Page {page + 1}: Found {len(commits)} commits")
-
- await crawler.crawler_strategy.kill_session(session_id)
- print(f"Successfully crawled {len(all_commits)} commits across 3 pages")
-
-asyncio.run(main())
-```
-
-In this example, we're crawling multiple pages of commits from a GitHub repository. The URL doesn't change as we load more commits, so we use JavaScript to click the "Load More" button and a `wait_for` condition to ensure the new content is loaded before extraction. This powerful combination allows us to navigate and extract data from complex, dynamically-loaded web applications with ease!
-
-## Congratulations! 🎉
-
-You've made it through the Crawl4AI Quickstart Guide! Now go forth and crawl the web asynchronously like a pro! 🕸️
-
-Remember, these are just a few examples of what Crawl4AI can do. For more advanced usage, check out our other documentation pages:
-
-- [LLM Extraction](examples/llm_extraction.md)
-- [JS Execution & CSS Filtering](examples/js_execution_css_filtering.md)
-- [Hooks & Auth](examples/hooks_auth.md)
-- [Summarization](examples/summarization.md)
-- [Research Assistant](examples/research_assistant.md)
-
-Happy crawling! 🚀
\ No newline at end of file
diff --git a/docs/md_v2/advanced/content-processing.md b/docs/md_v2/advanced/content-processing.md
new file mode 100644
index 00000000..71a32438
--- /dev/null
+++ b/docs/md_v2/advanced/content-processing.md
@@ -0,0 +1,223 @@
+# Content Processing
+
+Crawl4AI provides powerful content processing capabilities that help you extract clean, relevant content from web pages. This guide covers content cleaning, media handling, link analysis, and metadata extraction.
+
+## Content Cleaning
+
+### Understanding Clean Content
+When crawling web pages, you often encounter a lot of noise - advertisements, navigation menus, footers, popups, and other irrelevant content. Crawl4AI automatically cleans this noise using several approaches:
+
+1. **Basic Cleaning**: Removes unwanted HTML elements and attributes
+2. **Content Relevance**: Identifies and preserves meaningful content blocks
+3. **Layout Analysis**: Understands page structure to identify main content areas
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ word_count_threshold=10, # Remove blocks with fewer words
+ excluded_tags=['form', 'nav'], # Remove specific HTML tags
+ remove_overlay_elements=True # Remove popups/modals
+)
+
+# Get clean content
+print(result.cleaned_html) # Cleaned HTML
+print(result.markdown) # Clean markdown version
+```
+
+### Fit Markdown: Smart Content Extraction
+One of Crawl4AI's most powerful features is `fit_markdown`. This feature uses advanced heuristics to identify and extract the main content from a webpage while excluding irrelevant elements.
+
+#### How Fit Markdown Works
+- Analyzes content density and distribution
+- Identifies content patterns and structures
+- Removes boilerplate content (headers, footers, sidebars)
+- Preserves the most relevant content blocks
+- Maintains content hierarchy and formatting
+
+#### Perfect For:
+- Blog posts and articles
+- News content
+- Documentation pages
+- Any page with a clear main content area
+
+#### Not Recommended For:
+- E-commerce product listings
+- Search results pages
+- Social media feeds
+- Pages with multiple equal-weight content sections
+
+```python
+result = await crawler.arun(url="https://example.com")
+
+# Get the most relevant content
+main_content = result.fit_markdown
+
+# Compare with regular markdown
+all_content = result.markdown
+
+print(f"Fit Markdown Length: {len(main_content)}")
+print(f"Regular Markdown Length: {len(all_content)}")
+```
+
+#### Example Use Case
+```python
+async def extract_article_content(url: str) -> str:
+ """Extract main article content from a blog or news site."""
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(url=url)
+
+ # fit_markdown will focus on the article content,
+ # excluding navigation, ads, and other distractions
+ return result.fit_markdown
+```
+
+## Media Processing
+
+Crawl4AI provides comprehensive media extraction and analysis capabilities. It automatically detects and processes various types of media elements while maintaining their context and relevance.
+
+### Image Processing
+The library handles various image scenarios, including:
+- Regular images
+- Lazy-loaded images
+- Background images
+- Responsive images
+- Image metadata and context
+
+```python
+result = await crawler.arun(url="https://example.com")
+
+for image in result.media["images"]:
+ # Each image includes rich metadata
+ print(f"Source: {image['src']}")
+ print(f"Alt text: {image['alt']}")
+ print(f"Description: {image['desc']}")
+ print(f"Context: {image['context']}") # Surrounding text
+ print(f"Relevance score: {image['score']}") # 0-10 score
+```
+
+### Handling Lazy-Loaded Content
+Crawl4aai already handles lazy loading for media elements. You can also customize the wait time for lazy-loaded content:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ wait_for="css:img[data-src]", # Wait for lazy images
+ delay_before_return_html=2.0 # Additional wait time
+)
+```
+
+### Video and Audio Content
+The library extracts video and audio elements with their metadata:
+
+```python
+# Process videos
+for video in result.media["videos"]:
+ print(f"Video source: {video['src']}")
+ print(f"Type: {video['type']}")
+ print(f"Duration: {video.get('duration')}")
+ print(f"Thumbnail: {video.get('poster')}")
+
+# Process audio
+for audio in result.media["audios"]:
+ print(f"Audio source: {audio['src']}")
+ print(f"Type: {audio['type']}")
+ print(f"Duration: {audio.get('duration')}")
+```
+
+## Link Analysis
+
+Crawl4AI provides sophisticated link analysis capabilities, helping you understand the relationship between pages and identify important navigation patterns.
+
+### Link Classification
+The library automatically categorizes links into:
+- Internal links (same domain)
+- External links (different domains)
+- Social media links
+- Navigation links
+- Content links
+
+```python
+result = await crawler.arun(url="https://example.com")
+
+# Analyze internal links
+for link in result.links["internal"]:
+ print(f"Internal: {link['href']}")
+ print(f"Link text: {link['text']}")
+ print(f"Context: {link['context']}") # Surrounding text
+ print(f"Type: {link['type']}") # nav, content, etc.
+
+# Analyze external links
+for link in result.links["external"]:
+ print(f"External: {link['href']}")
+ print(f"Domain: {link['domain']}")
+ print(f"Type: {link['type']}")
+```
+
+### Smart Link Filtering
+Control which links are included in the results:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ exclude_external_links=True, # Remove external links
+ exclude_social_media_links=True, # Remove social media links
+ exclude_social_media_domains=[ # Custom social media domains
+ "facebook.com", "twitter.com", "instagram.com"
+ ],
+ exclude_domains=["ads.example.com"] # Exclude specific domains
+)
+```
+
+## Metadata Extraction
+
+Crawl4AI automatically extracts and processes page metadata, providing valuable information about the content:
+
+```python
+result = await crawler.arun(url="https://example.com")
+
+metadata = result.metadata
+print(f"Title: {metadata['title']}")
+print(f"Description: {metadata['description']}")
+print(f"Keywords: {metadata['keywords']}")
+print(f"Author: {metadata['author']}")
+print(f"Published Date: {metadata['published_date']}")
+print(f"Modified Date: {metadata['modified_date']}")
+print(f"Language: {metadata['language']}")
+```
+
+## Best Practices
+
+1. **Use Fit Markdown for Articles**
+ ```python
+ # Perfect for blog posts, news articles, documentation
+ content = result.fit_markdown
+ ```
+
+2. **Handle Media Appropriately**
+ ```python
+ # Filter by relevance score
+ relevant_images = [
+ img for img in result.media["images"]
+ if img['score'] > 5
+ ]
+ ```
+
+3. **Combine Link Analysis with Content**
+ ```python
+ # Get content links with context
+ content_links = [
+ link for link in result.links["internal"]
+ if link['type'] == 'content'
+ ]
+ ```
+
+4. **Clean Content with Purpose**
+ ```python
+ # Customize cleaning based on your needs
+ result = await crawler.arun(
+ url=url,
+ word_count_threshold=20, # Adjust based on content type
+ keep_data_attributes=False, # Remove data attributes
+ process_iframes=True # Include iframe content
+ )
+ ```
\ No newline at end of file
diff --git a/docs/md/examples/hooks_auth.md b/docs/md_v2/advanced/hooks-auth.md
similarity index 100%
rename from docs/md/examples/hooks_auth.md
rename to docs/md_v2/advanced/hooks-auth.md
diff --git a/docs/md_v2/advanced/magic-mode.md b/docs/md_v2/advanced/magic-mode.md
new file mode 100644
index 00000000..16c7229e
--- /dev/null
+++ b/docs/md_v2/advanced/magic-mode.md
@@ -0,0 +1,52 @@
+# Magic Mode & Anti-Bot Protection
+
+Crawl4AI provides powerful anti-detection capabilities, with Magic Mode being the simplest and most comprehensive solution.
+
+## Magic Mode
+
+The easiest way to bypass anti-bot protections:
+
+```python
+async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ magic=True # Enables all anti-detection features
+ )
+```
+
+Magic Mode automatically:
+- Masks browser automation signals
+- Simulates human-like behavior
+- Overrides navigator properties
+- Handles cookie consent popups
+- Manages browser fingerprinting
+- Randomizes timing patterns
+
+## Manual Anti-Bot Options
+
+While Magic Mode is recommended, you can also configure individual anti-detection features:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ simulate_user=True, # Simulate human behavior
+ override_navigator=True # Mask automation signals
+)
+```
+
+Note: When `magic=True` is used, you don't need to set these individual options.
+
+## Example: Handling Protected Sites
+
+```python
+async def crawl_protected_site(url: str):
+ async with AsyncWebCrawler(headless=True) as crawler:
+ result = await crawler.arun(
+ url=url,
+ magic=True,
+ remove_overlay_elements=True, # Remove popups/modals
+ page_timeout=60000 # Increased timeout for protection checks
+ )
+
+ return result.markdown if result.success else None
+```
diff --git a/docs/md_v2/advanced/proxy-security.md b/docs/md_v2/advanced/proxy-security.md
new file mode 100644
index 00000000..c7602531
--- /dev/null
+++ b/docs/md_v2/advanced/proxy-security.md
@@ -0,0 +1,84 @@
+# Proxy & Security
+
+Configure proxy settings and enhance security features in Crawl4AI for reliable data extraction.
+
+## Basic Proxy Setup
+
+Simple proxy configuration:
+
+```python
+# Using proxy URL
+async with AsyncWebCrawler(
+ proxy="http://proxy.example.com:8080"
+) as crawler:
+ result = await crawler.arun(url="https://example.com")
+
+# Using SOCKS proxy
+async with AsyncWebCrawler(
+ proxy="socks5://proxy.example.com:1080"
+) as crawler:
+ result = await crawler.arun(url="https://example.com")
+```
+
+## Authenticated Proxy
+
+Use proxy with authentication:
+
+```python
+proxy_config = {
+ "server": "http://proxy.example.com:8080",
+ "username": "user",
+ "password": "pass"
+}
+
+async with AsyncWebCrawler(proxy_config=proxy_config) as crawler:
+ result = await crawler.arun(url="https://example.com")
+```
+
+## Rotating Proxies
+
+Example using a proxy rotation service:
+
+```python
+async def get_next_proxy():
+ # Your proxy rotation logic here
+ return {"server": "http://next.proxy.com:8080"}
+
+async with AsyncWebCrawler() as crawler:
+ # Update proxy for each request
+ for url in urls:
+ proxy = await get_next_proxy()
+ crawler.update_proxy(proxy)
+ result = await crawler.arun(url=url)
+```
+
+## Custom Headers
+
+Add security-related headers:
+
+```python
+headers = {
+ "X-Forwarded-For": "203.0.113.195",
+ "Accept-Language": "en-US,en;q=0.9",
+ "Cache-Control": "no-cache",
+ "Pragma": "no-cache"
+}
+
+async with AsyncWebCrawler(headers=headers) as crawler:
+ result = await crawler.arun(url="https://example.com")
+```
+
+## Combining with Magic Mode
+
+For maximum protection, combine proxy with Magic Mode:
+
+```python
+async with AsyncWebCrawler(
+ proxy="http://proxy.example.com:8080",
+ headers={"Accept-Language": "en-US"}
+) as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ magic=True # Enable all anti-detection features
+ )
+```
\ No newline at end of file
diff --git a/docs/md/full_details/session_based_crawling.md b/docs/md_v2/advanced/session-management-advanced.md
similarity index 100%
rename from docs/md/full_details/session_based_crawling.md
rename to docs/md_v2/advanced/session-management-advanced.md
diff --git a/docs/md_v2/advanced/session-management.md b/docs/md_v2/advanced/session-management.md
new file mode 100644
index 00000000..c38ed852
--- /dev/null
+++ b/docs/md_v2/advanced/session-management.md
@@ -0,0 +1,133 @@
+# Session Management
+
+Session management in Crawl4AI allows you to maintain state across multiple requests and handle complex multi-page crawling tasks, particularly useful for dynamic websites.
+
+## Basic Session Usage
+
+Use `session_id` to maintain state between requests:
+
+```python
+async with AsyncWebCrawler() as crawler:
+ session_id = "my_session"
+
+ # First request
+ result1 = await crawler.arun(
+ url="https://example.com/page1",
+ session_id=session_id
+ )
+
+ # Subsequent request using same session
+ result2 = await crawler.arun(
+ url="https://example.com/page2",
+ session_id=session_id
+ )
+
+ # Clean up when done
+ await crawler.crawler_strategy.kill_session(session_id)
+```
+
+## Dynamic Content with Sessions
+
+Here's a real-world example of crawling GitHub commits across multiple pages:
+
+```python
+async def crawl_dynamic_content():
+ async with AsyncWebCrawler(verbose=True) as crawler:
+ url = "https://github.com/microsoft/TypeScript/commits/main"
+ session_id = "typescript_commits_session"
+ all_commits = []
+
+ # Define navigation JavaScript
+ js_next_page = """
+ const button = document.querySelector('a[data-testid="pagination-next-button"]');
+ if (button) button.click();
+ """
+
+ # Define wait condition
+ wait_for = """() => {
+ const commits = document.querySelectorAll('li.Box-sc-g0xbh4-0 h4');
+ if (commits.length === 0) return false;
+ const firstCommit = commits[0].textContent.trim();
+ return firstCommit !== window.firstCommit;
+ }"""
+
+ # Define extraction schema
+ schema = {
+ "name": "Commit Extractor",
+ "baseSelector": "li.Box-sc-g0xbh4-0",
+ "fields": [
+ {
+ "name": "title",
+ "selector": "h4.markdown-title",
+ "type": "text",
+ "transform": "strip",
+ },
+ ],
+ }
+ extraction_strategy = JsonCssExtractionStrategy(schema)
+
+ # Crawl multiple pages
+ for page in range(3):
+ result = await crawler.arun(
+ url=url,
+ session_id=session_id,
+ extraction_strategy=extraction_strategy,
+ js_code=js_next_page if page > 0 else None,
+ wait_for=wait_for if page > 0 else None,
+ js_only=page > 0,
+ bypass_cache=True
+ )
+
+ if result.success:
+ commits = json.loads(result.extracted_content)
+ all_commits.extend(commits)
+ print(f"Page {page + 1}: Found {len(commits)} commits")
+
+ # Clean up session
+ await crawler.crawler_strategy.kill_session(session_id)
+ return all_commits
+```
+
+## Session Best Practices
+
+1. **Session Naming**:
+```python
+# Use descriptive session IDs
+session_id = "login_flow_session"
+session_id = "product_catalog_session"
+```
+
+2. **Resource Management**:
+```python
+try:
+ # Your crawling code
+ pass
+finally:
+ # Always clean up sessions
+ await crawler.crawler_strategy.kill_session(session_id)
+```
+
+3. **State Management**:
+```python
+# First page: login
+result = await crawler.arun(
+ url="https://example.com/login",
+ session_id=session_id,
+ js_code="document.querySelector('form').submit();"
+)
+
+# Second page: verify login success
+result = await crawler.arun(
+ url="https://example.com/dashboard",
+ session_id=session_id,
+ wait_for="css:.user-profile" # Wait for authenticated content
+)
+```
+
+## Common Use Cases
+
+1. **Authentication Flows**
+2. **Pagination Handling**
+3. **Form Submissions**
+4. **Multi-step Processes**
+5. **Dynamic Content Navigation**
diff --git a/docs/md_v2/api/arun.md b/docs/md_v2/api/arun.md
new file mode 100644
index 00000000..9ef73aef
--- /dev/null
+++ b/docs/md_v2/api/arun.md
@@ -0,0 +1,226 @@
+# Complete Parameter Guide for arun()
+
+The following parameters can be passed to the `arun()` method. They are organized by their primary usage context and functionality.
+
+## Core Parameters
+
+```python
+await crawler.arun(
+ url="https://example.com", # Required: URL to crawl
+ verbose=True, # Enable detailed logging
+ bypass_cache=False, # Skip cache for this request
+ warmup=True # Whether to run warmup check
+)
+```
+
+## Content Processing Parameters
+
+### Text Processing
+```python
+await crawler.arun(
+ word_count_threshold=10, # Minimum words per content block
+ image_description_min_word_threshold=5, # Minimum words for image descriptions
+ only_text=False, # Extract only text content
+ excluded_tags=['form', 'nav'], # HTML tags to exclude
+ keep_data_attributes=False, # Preserve data-* attributes
+)
+```
+
+### Content Selection
+```python
+await crawler.arun(
+ css_selector=".main-content", # CSS selector for content extraction
+ remove_forms=True, # Remove all form elements
+ remove_overlay_elements=True, # Remove popups/modals/overlays
+)
+```
+
+### Link Handling
+```python
+await crawler.arun(
+ exclude_external_links=True, # Remove external links
+ exclude_social_media_links=True, # Remove social media links
+ exclude_external_images=True, # Remove external images
+ exclude_domains=["ads.example.com"], # Specific domains to exclude
+ social_media_domains=[ # Additional social media domains
+ "facebook.com",
+ "twitter.com",
+ "instagram.com"
+ ]
+)
+```
+
+## Browser Control Parameters
+
+### Basic Browser Settings
+```python
+await crawler.arun(
+ headless=True, # Run browser in headless mode
+ browser_type="chromium", # Browser engine: "chromium", "firefox", "webkit"
+ page_timeout=60000, # Page load timeout in milliseconds
+ user_agent="custom-agent", # Custom user agent
+)
+```
+
+### Navigation and Waiting
+```python
+await crawler.arun(
+ wait_for="css:.dynamic-content", # Wait for element/condition
+ delay_before_return_html=2.0, # Wait before returning HTML (seconds)
+)
+```
+
+### JavaScript Execution
+```python
+await crawler.arun(
+ js_code=[ # JavaScript to execute (string or list)
+ "window.scrollTo(0, document.body.scrollHeight);",
+ "document.querySelector('.load-more').click();"
+ ],
+ js_only=False, # Only execute JavaScript without reloading page
+)
+```
+
+### Anti-Bot Features
+```python
+await crawler.arun(
+ magic=True, # Enable all anti-detection features
+ simulate_user=True, # Simulate human behavior
+ override_navigator=True # Override navigator properties
+)
+```
+
+### Session Management
+```python
+await crawler.arun(
+ session_id="my_session", # Session identifier for persistent browsing
+)
+```
+
+### Screenshot Options
+```python
+await crawler.arun(
+ screenshot=True, # Take page screenshot
+ screenshot_wait_for=2.0, # Wait before screenshot (seconds)
+)
+```
+
+### Proxy Configuration
+```python
+await crawler.arun(
+ proxy="http://proxy.example.com:8080", # Simple proxy URL
+ proxy_config={ # Advanced proxy settings
+ "server": "http://proxy.example.com:8080",
+ "username": "user",
+ "password": "pass"
+ }
+)
+```
+
+## Content Extraction Parameters
+
+### Extraction Strategy
+```python
+await crawler.arun(
+ extraction_strategy=LLMExtractionStrategy(
+ provider="ollama/llama2",
+ schema=MySchema.schema(),
+ instruction="Extract specific data"
+ )
+)
+```
+
+### Chunking Strategy
+```python
+await crawler.arun(
+ chunking_strategy=RegexChunking(
+ patterns=[r'\n\n', r'\.\s+']
+ )
+)
+```
+
+### HTML to Text Options
+```python
+await crawler.arun(
+ html2text={
+ "ignore_links": False,
+ "ignore_images": False,
+ "escape_dot": False,
+ "body_width": 0,
+ "protect_links": True,
+ "unicode_snob": True
+ }
+)
+```
+
+## Debug Options
+```python
+await crawler.arun(
+ log_console=True, # Log browser console messages
+)
+```
+
+## Parameter Interactions and Notes
+
+1. **Magic Mode Combinations**
+ ```python
+ # Full anti-detection setup
+ await crawler.arun(
+ magic=True,
+ headless=False,
+ simulate_user=True,
+ override_navigator=True
+ )
+ ```
+
+2. **Dynamic Content Handling**
+ ```python
+ # Handle lazy-loaded content
+ await crawler.arun(
+ js_code="window.scrollTo(0, document.body.scrollHeight);",
+ wait_for="css:.lazy-content",
+ delay_before_return_html=2.0
+ )
+ ```
+
+3. **Content Extraction Pipeline**
+ ```python
+ # Complete extraction setup
+ await crawler.arun(
+ css_selector=".main-content",
+ word_count_threshold=20,
+ extraction_strategy=my_strategy,
+ chunking_strategy=my_chunking,
+ process_iframes=True,
+ remove_overlay_elements=True
+ )
+ ```
+
+## Best Practices
+
+1. **Performance Optimization**
+ ```python
+ await crawler.arun(
+ bypass_cache=False, # Use cache when possible
+ word_count_threshold=10, # Filter out noise
+ process_iframes=False # Skip iframes if not needed
+ )
+ ```
+
+2. **Reliable Scraping**
+ ```python
+ await crawler.arun(
+ magic=True, # Enable anti-detection
+ delay_before_return_html=1.0, # Wait for dynamic content
+ page_timeout=60000 # Longer timeout for slow pages
+ )
+ ```
+
+3. **Clean Content**
+ ```python
+ await crawler.arun(
+ remove_overlay_elements=True, # Remove popups
+ excluded_tags=['nav', 'aside'],# Remove unnecessary elements
+ keep_data_attributes=False # Remove data attributes
+ )
+ ```
\ No newline at end of file
diff --git a/docs/md_v2/api/async-webcrawler.md b/docs/md_v2/api/async-webcrawler.md
new file mode 100644
index 00000000..be956101
--- /dev/null
+++ b/docs/md_v2/api/async-webcrawler.md
@@ -0,0 +1,320 @@
+# AsyncWebCrawler
+
+The `AsyncWebCrawler` class is the main interface for web crawling operations. It provides asynchronous web crawling capabilities with extensive configuration options.
+
+## Constructor
+
+```python
+AsyncWebCrawler(
+ # Browser Settings
+ browser_type: str = "chromium", # Options: "chromium", "firefox", "webkit"
+ headless: bool = True, # Run browser in headless mode
+ verbose: bool = False, # Enable verbose logging
+
+ # Cache Settings
+ always_by_pass_cache: bool = False, # Always bypass cache
+ base_directory: str = str(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home())), # Base directory for cache
+
+ # Network Settings
+ proxy: str = None, # Simple proxy URL
+ proxy_config: Dict = None, # Advanced proxy configuration
+
+ # Browser Behavior
+ sleep_on_close: bool = False, # Wait before closing browser
+
+ # Custom Settings
+ user_agent: str = None, # Custom user agent
+ headers: Dict[str, str] = {}, # Custom HTTP headers
+ js_code: Union[str, List[str]] = None, # Default JavaScript to execute
+)
+```
+
+### Parameters in Detail
+
+#### Browser Settings
+
+- **browser_type** (str, optional)
+ - Default: `"chromium"`
+ - Options: `"chromium"`, `"firefox"`, `"webkit"`
+ - Controls which browser engine to use
+ ```python
+ # Example: Using Firefox
+ crawler = AsyncWebCrawler(browser_type="firefox")
+ ```
+
+- **headless** (bool, optional)
+ - Default: `True`
+ - When `True`, browser runs without GUI
+ - Set to `False` for debugging
+ ```python
+ # Visible browser for debugging
+ crawler = AsyncWebCrawler(headless=False)
+ ```
+
+- **verbose** (bool, optional)
+ - Default: `False`
+ - Enables detailed logging
+ ```python
+ # Enable detailed logging
+ crawler = AsyncWebCrawler(verbose=True)
+ ```
+
+#### Cache Settings
+
+- **always_by_pass_cache** (bool, optional)
+ - Default: `False`
+ - When `True`, always fetches fresh content
+ ```python
+ # Always fetch fresh content
+ crawler = AsyncWebCrawler(always_by_pass_cache=True)
+ ```
+
+- **base_directory** (str, optional)
+ - Default: User's home directory
+ - Base path for cache storage
+ ```python
+ # Custom cache directory
+ crawler = AsyncWebCrawler(base_directory="/path/to/cache")
+ ```
+
+#### Network Settings
+
+- **proxy** (str, optional)
+ - Simple proxy URL
+ ```python
+ # Using simple proxy
+ crawler = AsyncWebCrawler(proxy="http://proxy.example.com:8080")
+ ```
+
+- **proxy_config** (Dict, optional)
+ - Advanced proxy configuration with authentication
+ ```python
+ # Advanced proxy with auth
+ crawler = AsyncWebCrawler(proxy_config={
+ "server": "http://proxy.example.com:8080",
+ "username": "user",
+ "password": "pass"
+ })
+ ```
+
+#### Browser Behavior
+
+- **sleep_on_close** (bool, optional)
+ - Default: `False`
+ - Adds delay before closing browser
+ ```python
+ # Wait before closing
+ crawler = AsyncWebCrawler(sleep_on_close=True)
+ ```
+
+#### Custom Settings
+
+- **user_agent** (str, optional)
+ - Custom user agent string
+ ```python
+ # Custom user agent
+ crawler = AsyncWebCrawler(
+ user_agent="Mozilla/5.0 (Custom Agent) Chrome/90.0"
+ )
+ ```
+
+- **headers** (Dict[str, str], optional)
+ - Custom HTTP headers
+ ```python
+ # Custom headers
+ crawler = AsyncWebCrawler(
+ headers={
+ "Accept-Language": "en-US",
+ "Custom-Header": "Value"
+ }
+ )
+ ```
+
+- **js_code** (Union[str, List[str]], optional)
+ - Default JavaScript to execute on each page
+ ```python
+ # Default JavaScript
+ crawler = AsyncWebCrawler(
+ js_code=[
+ "window.scrollTo(0, document.body.scrollHeight);",
+ "document.querySelector('.load-more').click();"
+ ]
+ )
+ ```
+
+## Methods
+
+### arun()
+
+The primary method for crawling web pages.
+
+```python
+async def arun(
+ # Required
+ url: str, # URL to crawl
+
+ # Content Selection
+ css_selector: str = None, # CSS selector for content
+ word_count_threshold: int = 10, # Minimum words per block
+
+ # Cache Control
+ bypass_cache: bool = False, # Bypass cache for this request
+
+ # Session Management
+ session_id: str = None, # Session identifier
+
+ # Screenshot Options
+ screenshot: bool = False, # Take screenshot
+ screenshot_wait_for: float = None, # Wait before screenshot
+
+ # Content Processing
+ process_iframes: bool = False, # Process iframe content
+ remove_overlay_elements: bool = False, # Remove popups/modals
+
+ # Anti-Bot Settings
+ simulate_user: bool = False, # Simulate human behavior
+ override_navigator: bool = False, # Override navigator properties
+ magic: bool = False, # Enable all anti-detection
+
+ # Content Filtering
+ excluded_tags: List[str] = None, # HTML tags to exclude
+ exclude_external_links: bool = False, # Remove external links
+ exclude_social_media_links: bool = False, # Remove social media links
+
+ # JavaScript Handling
+ js_code: Union[str, List[str]] = None, # JavaScript to execute
+ wait_for: str = None, # Wait condition
+
+ # Page Loading
+ page_timeout: int = 60000, # Page load timeout (ms)
+ delay_before_return_html: float = None, # Wait before return
+
+ # Extraction
+ extraction_strategy: ExtractionStrategy = None # Extraction strategy
+) -> CrawlResult:
+```
+
+### Usage Examples
+
+#### Basic Crawling
+```python
+async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(url="https://example.com")
+```
+
+#### Advanced Crawling
+```python
+async with AsyncWebCrawler(
+ browser_type="firefox",
+ verbose=True,
+ headers={"Custom-Header": "Value"}
+) as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ css_selector=".main-content",
+ word_count_threshold=20,
+ process_iframes=True,
+ magic=True,
+ wait_for="css:.dynamic-content",
+ screenshot=True
+ )
+```
+
+#### Session Management
+```python
+async with AsyncWebCrawler() as crawler:
+ # First request
+ result1 = await crawler.arun(
+ url="https://example.com/login",
+ session_id="my_session"
+ )
+
+ # Subsequent request using same session
+ result2 = await crawler.arun(
+ url="https://example.com/protected",
+ session_id="my_session"
+ )
+```
+
+## Context Manager
+
+AsyncWebCrawler implements the async context manager protocol:
+
+```python
+async def __aenter__(self) -> 'AsyncWebCrawler':
+ # Initialize browser and resources
+ return self
+
+async def __aexit__(self, *args):
+ # Cleanup resources
+ pass
+```
+
+Always use AsyncWebCrawler with async context manager:
+```python
+async with AsyncWebCrawler() as crawler:
+ # Your crawling code here
+ pass
+```
+
+## Best Practices
+
+1. **Resource Management**
+```python
+# Always use context manager
+async with AsyncWebCrawler() as crawler:
+ # Crawler will be properly cleaned up
+ pass
+```
+
+2. **Error Handling**
+```python
+try:
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(url="https://example.com")
+ if not result.success:
+ print(f"Crawl failed: {result.error_message}")
+except Exception as e:
+ print(f"Error: {str(e)}")
+```
+
+3. **Performance Optimization**
+```python
+# Enable caching for better performance
+crawler = AsyncWebCrawler(
+ always_by_pass_cache=False,
+ verbose=True
+)
+```
+
+4. **Anti-Detection**
+```python
+# Maximum stealth
+crawler = AsyncWebCrawler(
+ headless=True,
+ user_agent="Mozilla/5.0...",
+ headers={"Accept-Language": "en-US"}
+)
+result = await crawler.arun(
+ url="https://example.com",
+ magic=True,
+ simulate_user=True
+)
+```
+
+## Note on Browser Types
+
+Each browser type has its characteristics:
+
+- **chromium**: Best overall compatibility
+- **firefox**: Good for specific use cases
+- **webkit**: Lighter weight, good for basic crawling
+
+Choose based on your specific needs:
+```python
+# High compatibility
+crawler = AsyncWebCrawler(browser_type="chromium")
+
+# Memory efficient
+crawler = AsyncWebCrawler(browser_type="webkit")
+```
\ No newline at end of file
diff --git a/docs/md_v2/api/crawl-result.md b/docs/md_v2/api/crawl-result.md
new file mode 100644
index 00000000..06998af3
--- /dev/null
+++ b/docs/md_v2/api/crawl-result.md
@@ -0,0 +1,301 @@
+# CrawlResult
+
+The `CrawlResult` class represents the result of a web crawling operation. It provides access to various forms of extracted content and metadata from the crawled webpage.
+
+## Class Definition
+
+```python
+class CrawlResult(BaseModel):
+ """Result of a web crawling operation."""
+
+ # Basic Information
+ url: str # Crawled URL
+ success: bool # Whether crawl succeeded
+ status_code: Optional[int] = None # HTTP status code
+ error_message: Optional[str] = None # Error message if failed
+
+ # Content
+ html: str # Raw HTML content
+ cleaned_html: Optional[str] = None # Cleaned HTML
+ fit_html: Optional[str] = None # Most relevant HTML content
+ markdown: Optional[str] = None # HTML converted to markdown
+ fit_markdown: Optional[str] = None # Most relevant markdown content
+
+ # Extracted Data
+ extracted_content: Optional[str] = None # Content from extraction strategy
+ media: Dict[str, List[Dict]] = {} # Extracted media information
+ links: Dict[str, List[Dict]] = {} # Extracted links
+ metadata: Optional[dict] = None # Page metadata
+
+ # Additional Data
+ screenshot: Optional[str] = None # Base64 encoded screenshot
+ session_id: Optional[str] = None # Session identifier
+ response_headers: Optional[dict] = None # HTTP response headers
+```
+
+## Properties and Their Data Structures
+
+### Basic Information
+
+```python
+# Access basic information
+result = await crawler.arun(url="https://example.com")
+
+print(result.url) # "https://example.com"
+print(result.success) # True/False
+print(result.status_code) # 200, 404, etc.
+print(result.error_message) # Error details if failed
+```
+
+### Content Properties
+
+#### HTML Content
+```python
+# Raw HTML
+html_content = result.html
+
+# Cleaned HTML (removed ads, popups, etc.)
+clean_content = result.cleaned_html
+
+# Most relevant HTML content
+main_content = result.fit_html
+```
+
+#### Markdown Content
+```python
+# Full markdown version
+markdown_content = result.markdown
+
+# Most relevant markdown content
+main_content = result.fit_markdown
+```
+
+### Media Content
+
+The media dictionary contains organized media elements:
+
+```python
+# Structure
+media = {
+ "images": [
+ {
+ "src": str, # Image URL
+ "alt": str, # Alt text
+ "desc": str, # Contextual description
+ "score": float, # Relevance score (0-10)
+ "type": str, # "image"
+ "width": int, # Image width (if available)
+ "height": int, # Image height (if available)
+ "context": str, # Surrounding text
+ "lazy": bool # Whether image was lazy-loaded
+ }
+ ],
+ "videos": [
+ {
+ "src": str, # Video URL
+ "type": str, # "video"
+ "title": str, # Video title
+ "poster": str, # Thumbnail URL
+ "duration": str, # Video duration
+ "description": str # Video description
+ }
+ ],
+ "audios": [
+ {
+ "src": str, # Audio URL
+ "type": str, # "audio"
+ "title": str, # Audio title
+ "duration": str, # Audio duration
+ "description": str # Audio description
+ }
+ ]
+}
+
+# Example usage
+for image in result.media["images"]:
+ if image["score"] > 5: # High-relevance images
+ print(f"High-quality image: {image['src']}")
+ print(f"Context: {image['context']}")
+```
+
+### Link Analysis
+
+The links dictionary organizes discovered links:
+
+```python
+# Structure
+links = {
+ "internal": [
+ {
+ "href": str, # URL
+ "text": str, # Link text
+ "title": str, # Title attribute
+ "type": str, # Link type (nav, content, etc.)
+ "context": str, # Surrounding text
+ "score": float # Relevance score
+ }
+ ],
+ "external": [
+ {
+ "href": str, # External URL
+ "text": str, # Link text
+ "title": str, # Title attribute
+ "domain": str, # Domain name
+ "type": str, # Link type
+ "context": str # Surrounding text
+ }
+ ]
+}
+
+# Example usage
+for link in result.links["internal"]:
+ print(f"Internal link: {link['href']}")
+ print(f"Context: {link['context']}")
+```
+
+### Metadata
+
+The metadata dictionary contains page information:
+
+```python
+# Structure
+metadata = {
+ "title": str, # Page title
+ "description": str, # Meta description
+ "keywords": List[str], # Meta keywords
+ "author": str, # Author information
+ "published_date": str, # Publication date
+ "modified_date": str, # Last modified date
+ "language": str, # Page language
+ "canonical_url": str, # Canonical URL
+ "og_data": Dict, # Open Graph data
+ "twitter_data": Dict # Twitter card data
+}
+
+# Example usage
+if result.metadata:
+ print(f"Title: {result.metadata['title']}")
+ print(f"Author: {result.metadata.get('author', 'Unknown')}")
+```
+
+### Extracted Content
+
+Content from extraction strategies:
+
+```python
+# For LLM or CSS extraction strategies
+if result.extracted_content:
+ structured_data = json.loads(result.extracted_content)
+ print(structured_data)
+```
+
+### Screenshot
+
+Base64 encoded screenshot:
+
+```python
+# Save screenshot if available
+if result.screenshot:
+ import base64
+
+ # Decode and save
+ with open("screenshot.png", "wb") as f:
+ f.write(base64.b64decode(result.screenshot))
+```
+
+## Usage Examples
+
+### Basic Content Access
+```python
+async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(url="https://example.com")
+
+ if result.success:
+ # Get clean content
+ print(result.fit_markdown)
+
+ # Process images
+ for image in result.media["images"]:
+ if image["score"] > 7:
+ print(f"High-quality image: {image['src']}")
+```
+
+### Complete Data Processing
+```python
+async def process_webpage(url: str) -> Dict:
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(url=url)
+
+ if not result.success:
+ raise Exception(f"Crawl failed: {result.error_message}")
+
+ return {
+ "content": result.fit_markdown,
+ "images": [
+ img for img in result.media["images"]
+ if img["score"] > 5
+ ],
+ "internal_links": [
+ link["href"] for link in result.links["internal"]
+ ],
+ "metadata": result.metadata,
+ "status": result.status_code
+ }
+```
+
+### Error Handling
+```python
+async def safe_crawl(url: str) -> Dict:
+ async with AsyncWebCrawler() as crawler:
+ try:
+ result = await crawler.arun(url=url)
+
+ if not result.success:
+ return {
+ "success": False,
+ "error": result.error_message,
+ "status": result.status_code
+ }
+
+ return {
+ "success": True,
+ "content": result.fit_markdown,
+ "status": result.status_code
+ }
+
+ except Exception as e:
+ return {
+ "success": False,
+ "error": str(e),
+ "status": None
+ }
+```
+
+## Best Practices
+
+1. **Always Check Success**
+```python
+if not result.success:
+ print(f"Error: {result.error_message}")
+ return
+```
+
+2. **Use fit_markdown for Articles**
+```python
+# Better for article content
+content = result.fit_markdown if result.fit_markdown else result.markdown
+```
+
+3. **Filter Media by Score**
+```python
+relevant_images = [
+ img for img in result.media["images"]
+ if img["score"] > 5
+]
+```
+
+4. **Handle Missing Data**
+```python
+metadata = result.metadata or {}
+title = metadata.get('title', 'Unknown Title')
+```
\ No newline at end of file
diff --git a/docs/md_v2/api/parameters.md b/docs/md_v2/api/parameters.md
new file mode 100644
index 00000000..6c7960d2
--- /dev/null
+++ b/docs/md_v2/api/parameters.md
@@ -0,0 +1,35 @@
+# Parameter Reference Table
+
+| File Name | Parameter Name | Code Usage | Strategy/Class | Description |
+|-----------|---------------|------------|----------------|-------------|
+| async_crawler_strategy.py | user_agent | `kwargs.get("user_agent")` | AsyncPlaywrightCrawlerStrategy | User agent string for browser identification |
+| async_crawler_strategy.py | proxy | `kwargs.get("proxy")` | AsyncPlaywrightCrawlerStrategy | Proxy server configuration for network requests |
+| async_crawler_strategy.py | proxy_config | `kwargs.get("proxy_config")` | AsyncPlaywrightCrawlerStrategy | Detailed proxy configuration including auth |
+| async_crawler_strategy.py | headless | `kwargs.get("headless", True)` | AsyncPlaywrightCrawlerStrategy | Whether to run browser in headless mode |
+| async_crawler_strategy.py | browser_type | `kwargs.get("browser_type", "chromium")` | AsyncPlaywrightCrawlerStrategy | Type of browser to use (chromium/firefox/webkit) |
+| async_crawler_strategy.py | headers | `kwargs.get("headers", {})` | AsyncPlaywrightCrawlerStrategy | Custom HTTP headers for requests |
+| async_crawler_strategy.py | verbose | `kwargs.get("verbose", False)` | AsyncPlaywrightCrawlerStrategy | Enable detailed logging output |
+| async_crawler_strategy.py | sleep_on_close | `kwargs.get("sleep_on_close", False)` | AsyncPlaywrightCrawlerStrategy | Add delay before closing browser |
+| async_crawler_strategy.py | use_managed_browser | `kwargs.get("use_managed_browser", False)` | AsyncPlaywrightCrawlerStrategy | Use managed browser instance |
+| async_crawler_strategy.py | user_data_dir | `kwargs.get("user_data_dir", None)` | AsyncPlaywrightCrawlerStrategy | Custom directory for browser profile data |
+| async_crawler_strategy.py | session_id | `kwargs.get("session_id")` | AsyncPlaywrightCrawlerStrategy | Unique identifier for browser session |
+| async_crawler_strategy.py | override_navigator | `kwargs.get("override_navigator", False)` | AsyncPlaywrightCrawlerStrategy | Override browser navigator properties |
+| async_crawler_strategy.py | simulate_user | `kwargs.get("simulate_user", False)` | AsyncPlaywrightCrawlerStrategy | Simulate human-like behavior |
+| async_crawler_strategy.py | magic | `kwargs.get("magic", False)` | AsyncPlaywrightCrawlerStrategy | Enable advanced anti-detection features |
+| async_crawler_strategy.py | log_console | `kwargs.get("log_console", False)` | AsyncPlaywrightCrawlerStrategy | Log browser console messages |
+| async_crawler_strategy.py | js_only | `kwargs.get("js_only", False)` | AsyncPlaywrightCrawlerStrategy | Only execute JavaScript without page load |
+| async_crawler_strategy.py | page_timeout | `kwargs.get("page_timeout", 60000)` | AsyncPlaywrightCrawlerStrategy | Timeout for page load in milliseconds |
+| async_crawler_strategy.py | ignore_body_visibility | `kwargs.get("ignore_body_visibility", True)` | AsyncPlaywrightCrawlerStrategy | Process page even if body is hidden |
+| async_crawler_strategy.py | js_code | `kwargs.get("js_code", kwargs.get("js", self.js_code))` | AsyncPlaywrightCrawlerStrategy | Custom JavaScript code to execute |
+| async_crawler_strategy.py | wait_for | `kwargs.get("wait_for")` | AsyncPlaywrightCrawlerStrategy | Wait for specific element/condition |
+| async_crawler_strategy.py | process_iframes | `kwargs.get("process_iframes", False)` | AsyncPlaywrightCrawlerStrategy | Extract content from iframes |
+| async_crawler_strategy.py | delay_before_return_html | `kwargs.get("delay_before_return_html")` | AsyncPlaywrightCrawlerStrategy | Additional delay before returning HTML |
+| async_crawler_strategy.py | remove_overlay_elements | `kwargs.get("remove_overlay_elements", False)` | AsyncPlaywrightCrawlerStrategy | Remove pop-ups and overlay elements |
+| async_crawler_strategy.py | screenshot | `kwargs.get("screenshot")` | AsyncPlaywrightCrawlerStrategy | Take page screenshot |
+| async_crawler_strategy.py | screenshot_wait_for | `kwargs.get("screenshot_wait_for")` | AsyncPlaywrightCrawlerStrategy | Wait before taking screenshot |
+| async_crawler_strategy.py | semaphore_count | `kwargs.get("semaphore_count", 5)` | AsyncPlaywrightCrawlerStrategy | Concurrent request limit |
+| async_webcrawler.py | verbose | `kwargs.get("verbose", False)` | AsyncWebCrawler | Enable detailed logging |
+| async_webcrawler.py | warmup | `kwargs.get("warmup", True)` | AsyncWebCrawler | Initialize crawler with warmup request |
+| async_webcrawler.py | session_id | `kwargs.get("session_id", None)` | AsyncWebCrawler | Session identifier for browser reuse |
+| async_webcrawler.py | only_text | `kwargs.get("only_text", False)` | AsyncWebCrawler | Extract only text content |
+| async_webcrawler.py | bypass_cache | `kwargs.get("bypass_cache", False)` | AsyncWebCrawler | Skip cache and force fresh crawl |
\ No newline at end of file
diff --git a/docs/md_v2/api/strategies.md b/docs/md_v2/api/strategies.md
new file mode 100644
index 00000000..f0f8f57c
--- /dev/null
+++ b/docs/md_v2/api/strategies.md
@@ -0,0 +1,255 @@
+# Extraction & Chunking Strategies API
+
+This documentation covers the API reference for extraction and chunking strategies in Crawl4AI.
+
+## Extraction Strategies
+
+All extraction strategies inherit from the base `ExtractionStrategy` class and implement two key methods:
+- `extract(url: str, html: str) -> List[Dict[str, Any]]`
+- `run(url: str, sections: List[str]) -> List[Dict[str, Any]]`
+
+### LLMExtractionStrategy
+
+Used for extracting structured data using Language Models.
+
+```python
+LLMExtractionStrategy(
+ # Required Parameters
+ provider: str = DEFAULT_PROVIDER, # LLM provider (e.g., "ollama/llama2")
+ api_token: Optional[str] = None, # API token
+
+ # Extraction Configuration
+ instruction: str = None, # Custom extraction instruction
+ schema: Dict = None, # Pydantic model schema for structured data
+ extraction_type: str = "block", # "block" or "schema"
+
+ # Chunking Parameters
+ chunk_token_threshold: int = 4000, # Maximum tokens per chunk
+ overlap_rate: float = 0.1, # Overlap between chunks
+ word_token_rate: float = 0.75, # Word to token conversion rate
+ apply_chunking: bool = True, # Enable/disable chunking
+
+ # API Configuration
+ base_url: str = None, # Base URL for API
+ extra_args: Dict = {}, # Additional provider arguments
+ verbose: bool = False # Enable verbose logging
+)
+```
+
+### CosineStrategy
+
+Used for content similarity-based extraction and clustering.
+
+```python
+CosineStrategy(
+ # Content Filtering
+ semantic_filter: str = None, # Topic/keyword filter
+ word_count_threshold: int = 10, # Minimum words per cluster
+ sim_threshold: float = 0.3, # Similarity threshold
+
+ # Clustering Parameters
+ max_dist: float = 0.2, # Maximum cluster distance
+ linkage_method: str = 'ward', # Clustering method
+ top_k: int = 3, # Top clusters to return
+
+ # Model Configuration
+ model_name: str = 'sentence-transformers/all-MiniLM-L6-v2', # Embedding model
+
+ verbose: bool = False # Enable verbose logging
+)
+```
+
+### JsonCssExtractionStrategy
+
+Used for CSS selector-based structured data extraction.
+
+```python
+JsonCssExtractionStrategy(
+ schema: Dict[str, Any], # Extraction schema
+ verbose: bool = False # Enable verbose logging
+)
+
+# Schema Structure
+schema = {
+ "name": str, # Schema name
+ "baseSelector": str, # Base CSS selector
+ "fields": [ # List of fields to extract
+ {
+ "name": str, # Field name
+ "selector": str, # CSS selector
+ "type": str, # Field type: "text", "attribute", "html", "regex"
+ "attribute": str, # For type="attribute"
+ "pattern": str, # For type="regex"
+ "transform": str, # Optional: "lowercase", "uppercase", "strip"
+ "default": Any # Default value if extraction fails
+ }
+ ]
+}
+```
+
+## Chunking Strategies
+
+All chunking strategies inherit from `ChunkingStrategy` and implement the `chunk(text: str) -> list` method.
+
+### RegexChunking
+
+Splits text based on regex patterns.
+
+```python
+RegexChunking(
+ patterns: List[str] = None # Regex patterns for splitting
+ # Default: [r'\n\n']
+)
+```
+
+### SlidingWindowChunking
+
+Creates overlapping chunks with a sliding window approach.
+
+```python
+SlidingWindowChunking(
+ window_size: int = 100, # Window size in words
+ step: int = 50 # Step size between windows
+)
+```
+
+### OverlappingWindowChunking
+
+Creates chunks with specified overlap.
+
+```python
+OverlappingWindowChunking(
+ window_size: int = 1000, # Chunk size in words
+ overlap: int = 100 # Overlap size in words
+)
+```
+
+## Usage Examples
+
+### LLM Extraction
+
+```python
+from pydantic import BaseModel
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+# Define schema
+class Article(BaseModel):
+ title: str
+ content: str
+ author: str
+
+# Create strategy
+strategy = LLMExtractionStrategy(
+ provider="ollama/llama2",
+ schema=Article.schema(),
+ instruction="Extract article details"
+)
+
+# Use with crawler
+result = await crawler.arun(
+ url="https://example.com/article",
+ extraction_strategy=strategy
+)
+
+# Access extracted data
+data = json.loads(result.extracted_content)
+```
+
+### CSS Extraction
+
+```python
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+# Define schema
+schema = {
+ "name": "Product List",
+ "baseSelector": ".product-card",
+ "fields": [
+ {
+ "name": "title",
+ "selector": "h2.title",
+ "type": "text"
+ },
+ {
+ "name": "price",
+ "selector": ".price",
+ "type": "text",
+ "transform": "strip"
+ },
+ {
+ "name": "image",
+ "selector": "img",
+ "type": "attribute",
+ "attribute": "src"
+ }
+ ]
+}
+
+# Create and use strategy
+strategy = JsonCssExtractionStrategy(schema)
+result = await crawler.arun(
+ url="https://example.com/products",
+ extraction_strategy=strategy
+)
+```
+
+### Content Chunking
+
+```python
+from crawl4ai.chunking_strategy import OverlappingWindowChunking
+
+# Create chunking strategy
+chunker = OverlappingWindowChunking(
+ window_size=500, # 500 words per chunk
+ overlap=50 # 50 words overlap
+)
+
+# Use with extraction strategy
+strategy = LLMExtractionStrategy(
+ provider="ollama/llama2",
+ chunking_strategy=chunker
+)
+
+result = await crawler.arun(
+ url="https://example.com/long-article",
+ extraction_strategy=strategy
+)
+```
+
+## Best Practices
+
+1. **Choose the Right Strategy**
+ - Use `LLMExtractionStrategy` for complex, unstructured content
+ - Use `JsonCssExtractionStrategy` for well-structured HTML
+ - Use `CosineStrategy` for content similarity and clustering
+
+2. **Optimize Chunking**
+ ```python
+ # For long documents
+ strategy = LLMExtractionStrategy(
+ chunk_token_threshold=2000, # Smaller chunks
+ overlap_rate=0.1 # 10% overlap
+ )
+ ```
+
+3. **Handle Errors**
+ ```python
+ try:
+ result = await crawler.arun(
+ url="https://example.com",
+ extraction_strategy=strategy
+ )
+ if result.success:
+ content = json.loads(result.extracted_content)
+ except Exception as e:
+ print(f"Extraction failed: {e}")
+ ```
+
+4. **Monitor Performance**
+ ```python
+ strategy = CosineStrategy(
+ verbose=True, # Enable logging
+ word_count_threshold=20, # Filter short content
+ top_k=5 # Limit results
+ )
+ ```
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index f103474f..68a93f5d 100644
--- a/docs/md _sync/assets/styles.css
+++ b/docs/md_v2/assets/styles.css
@@ -150,4 +150,11 @@ strong,
.tab-content pre {
margin: 0;
max-height: 300px; overflow: auto; border:none;
+}
+
+ol li::before {
+ content: counters(item, ".") ". ";
+ counter-increment: item;
+ /* float: left; */
+ /* padding-right: 5px; */
}
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diff --git a/docs/md_v2/basic/browser-config.md b/docs/md_v2/basic/browser-config.md
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@@ -0,0 +1,208 @@
+# Browser Configuration
+
+Crawl4AI supports multiple browser engines and offers extensive configuration options for browser behavior.
+
+## Browser Types
+
+Choose from three browser engines:
+
+```python
+# Chromium (default)
+async with AsyncWebCrawler(browser_type="chromium") as crawler:
+ result = await crawler.arun(url="https://example.com")
+
+# Firefox
+async with AsyncWebCrawler(browser_type="firefox") as crawler:
+ result = await crawler.arun(url="https://example.com")
+
+# WebKit
+async with AsyncWebCrawler(browser_type="webkit") as crawler:
+ result = await crawler.arun(url="https://example.com")
+```
+
+## Basic Configuration
+
+Common browser settings:
+
+```python
+async with AsyncWebCrawler(
+ headless=True, # Run in headless mode (no GUI)
+ verbose=True, # Enable detailed logging
+ sleep_on_close=False # No delay when closing browser
+) as crawler:
+ result = await crawler.arun(url="https://example.com")
+```
+
+## Identity Management
+
+Control how your crawler appears to websites:
+
+```python
+# Custom user agent
+async with AsyncWebCrawler(
+ user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
+) as crawler:
+ result = await crawler.arun(url="https://example.com")
+
+# Custom headers
+headers = {
+ "Accept-Language": "en-US,en;q=0.9",
+ "Cache-Control": "no-cache"
+}
+async with AsyncWebCrawler(headers=headers) as crawler:
+ result = await crawler.arun(url="https://example.com")
+```
+
+## Screenshot Capabilities
+
+Capture page screenshots with enhanced error handling:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ screenshot=True, # Enable screenshot
+ screenshot_wait_for=2.0 # Wait 2 seconds before capture
+)
+
+if result.screenshot: # Base64 encoded image
+ import base64
+ with open("screenshot.png", "wb") as f:
+ f.write(base64.b64decode(result.screenshot))
+```
+
+## Timeouts and Waiting
+
+Control page loading behavior:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ page_timeout=60000, # Page load timeout (ms)
+ delay_before_return_html=2.0, # Wait before content capture
+ wait_for="css:.dynamic-content" # Wait for specific element
+)
+```
+
+## JavaScript Execution
+
+Execute custom JavaScript before crawling:
+
+```python
+# Single JavaScript command
+result = await crawler.arun(
+ url="https://example.com",
+ js_code="window.scrollTo(0, document.body.scrollHeight);"
+)
+
+# Multiple commands
+js_commands = [
+ "window.scrollTo(0, document.body.scrollHeight);",
+ "document.querySelector('.load-more').click();"
+]
+result = await crawler.arun(
+ url="https://example.com",
+ js_code=js_commands
+)
+```
+
+## Proxy Configuration
+
+Use proxies for enhanced access:
+
+```python
+# Simple proxy
+async with AsyncWebCrawler(
+ proxy="http://proxy.example.com:8080"
+) as crawler:
+ result = await crawler.arun(url="https://example.com")
+
+# Proxy with authentication
+proxy_config = {
+ "server": "http://proxy.example.com:8080",
+ "username": "user",
+ "password": "pass"
+}
+async with AsyncWebCrawler(proxy_config=proxy_config) as crawler:
+ result = await crawler.arun(url="https://example.com")
+```
+
+## Anti-Detection Features
+
+Enable stealth features to avoid bot detection:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ simulate_user=True, # Simulate human behavior
+ override_navigator=True, # Mask automation signals
+ magic=True # Enable all anti-detection features
+)
+```
+
+## Handling Dynamic Content
+
+Configure browser to handle dynamic content:
+
+```python
+# Wait for dynamic content
+result = await crawler.arun(
+ url="https://example.com",
+ wait_for="js:() => document.querySelector('.content').children.length > 10",
+ process_iframes=True # Process iframe content
+)
+
+# Handle lazy-loaded images
+result = await crawler.arun(
+ url="https://example.com",
+ js_code="window.scrollTo(0, document.body.scrollHeight);",
+ delay_before_return_html=2.0 # Wait for images to load
+)
+```
+
+## Comprehensive Example
+
+Here's how to combine various browser configurations:
+
+```python
+async def crawl_with_advanced_config(url: str):
+ async with AsyncWebCrawler(
+ # Browser setup
+ browser_type="chromium",
+ headless=True,
+ verbose=True,
+
+ # Identity
+ user_agent="Custom User Agent",
+ headers={"Accept-Language": "en-US"},
+
+ # Proxy setup
+ proxy="http://proxy.example.com:8080"
+ ) as crawler:
+ result = await crawler.arun(
+ url=url,
+ # Content handling
+ process_iframes=True,
+ screenshot=True,
+
+ # Timing
+ page_timeout=60000,
+ delay_before_return_html=2.0,
+
+ # Anti-detection
+ magic=True,
+ simulate_user=True,
+
+ # Dynamic content
+ js_code=[
+ "window.scrollTo(0, document.body.scrollHeight);",
+ "document.querySelector('.load-more')?.click();"
+ ],
+ wait_for="css:.dynamic-content"
+ )
+
+ return {
+ "content": result.markdown,
+ "screenshot": result.screenshot,
+ "success": result.success
+ }
+```
\ No newline at end of file
diff --git a/docs/md_v2/basic/content-selection.md b/docs/md_v2/basic/content-selection.md
new file mode 100644
index 00000000..f5f7397b
--- /dev/null
+++ b/docs/md_v2/basic/content-selection.md
@@ -0,0 +1,199 @@
+# Content Selection
+
+Crawl4AI provides multiple ways to select and filter specific content from webpages. Learn how to precisely target the content you need.
+
+## CSS Selectors
+
+The simplest way to extract specific content:
+
+```python
+# Extract specific content using CSS selector
+result = await crawler.arun(
+ url="https://example.com",
+ css_selector=".main-article" # Target main article content
+)
+
+# Multiple selectors
+result = await crawler.arun(
+ url="https://example.com",
+ css_selector="article h1, article .content" # Target heading and content
+)
+```
+
+## Content Filtering
+
+Control what content is included or excluded:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ # Content thresholds
+ word_count_threshold=10, # Minimum words per block
+
+ # Tag exclusions
+ excluded_tags=['form', 'header', 'footer', 'nav'],
+
+ # Link filtering
+ exclude_external_links=True, # Remove external links
+ exclude_social_media_links=True, # Remove social media links
+
+ # Media filtering
+ exclude_external_images=True # Remove external images
+)
+```
+
+## Iframe Content
+
+Process content inside iframes:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ process_iframes=True, # Extract iframe content
+ remove_overlay_elements=True # Remove popups/modals that might block iframes
+)
+```
+
+## Structured Content Selection
+
+### Using LLMs for Smart Selection
+
+Use LLMs to intelligently extract specific types of content:
+
+```python
+from pydantic import BaseModel
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+class ArticleContent(BaseModel):
+ title: str
+ main_points: List[str]
+ conclusion: str
+
+strategy = LLMExtractionStrategy(
+ provider="ollama/nemotron", # Works with any supported LLM
+ schema=ArticleContent.schema(),
+ instruction="Extract the main article title, key points, and conclusion"
+)
+
+result = await crawler.arun(
+ url="https://example.com",
+ extraction_strategy=strategy
+)
+article = json.loads(result.extracted_content)
+```
+
+### Pattern-Based Selection
+
+For repeated content patterns (like product listings, news feeds):
+
+```python
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+schema = {
+ "name": "News Articles",
+ "baseSelector": "article.news-item", # Repeated element
+ "fields": [
+ {"name": "headline", "selector": "h2", "type": "text"},
+ {"name": "summary", "selector": ".summary", "type": "text"},
+ {"name": "category", "selector": ".category", "type": "text"},
+ {
+ "name": "metadata",
+ "type": "nested",
+ "fields": [
+ {"name": "author", "selector": ".author", "type": "text"},
+ {"name": "date", "selector": ".date", "type": "text"}
+ ]
+ }
+ ]
+}
+
+strategy = JsonCssExtractionStrategy(schema)
+result = await crawler.arun(
+ url="https://example.com",
+ extraction_strategy=strategy
+)
+articles = json.loads(result.extracted_content)
+```
+
+## Domain-Based Filtering
+
+Control content based on domains:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ exclude_domains=["ads.com", "tracker.com"],
+ exclude_social_media_domains=["facebook.com", "twitter.com"], # Custom social media domains to exclude
+ exclude_social_media_links=True
+)
+```
+
+## Media Selection
+
+Select specific types of media:
+
+```python
+result = await crawler.arun(url="https://example.com")
+
+# Access different media types
+images = result.media["images"] # List of image details
+videos = result.media["videos"] # List of video details
+audios = result.media["audios"] # List of audio details
+
+# Image with metadata
+for image in images:
+ print(f"URL: {image['src']}")
+ print(f"Alt text: {image['alt']}")
+ print(f"Description: {image['desc']}")
+ print(f"Relevance score: {image['score']}")
+```
+
+## Comprehensive Example
+
+Here's how to combine different selection methods:
+
+```python
+async def extract_article_content(url: str):
+ # Define structured extraction
+ article_schema = {
+ "name": "Article",
+ "baseSelector": "article.main",
+ "fields": [
+ {"name": "title", "selector": "h1", "type": "text"},
+ {"name": "content", "selector": ".content", "type": "text"}
+ ]
+ }
+
+ # Define LLM extraction
+ class ArticleAnalysis(BaseModel):
+ key_points: List[str]
+ sentiment: str
+ category: str
+
+ async with AsyncWebCrawler() as crawler:
+ # Get structured content
+ pattern_result = await crawler.arun(
+ url=url,
+ extraction_strategy=JsonCssExtractionStrategy(article_schema),
+ word_count_threshold=10,
+ excluded_tags=['nav', 'footer'],
+ exclude_external_links=True
+ )
+
+ # Get semantic analysis
+ analysis_result = await crawler.arun(
+ url=url,
+ extraction_strategy=LLMExtractionStrategy(
+ provider="ollama/nemotron",
+ schema=ArticleAnalysis.schema(),
+ instruction="Analyze the article content"
+ )
+ )
+
+ # Combine results
+ return {
+ "article": json.loads(pattern_result.extracted_content),
+ "analysis": json.loads(analysis_result.extracted_content),
+ "media": pattern_result.media
+ }
+```
\ No newline at end of file
diff --git a/docs/md_v2/basic/docker-deploymeny.md b/docs/md_v2/basic/docker-deploymeny.md
new file mode 100644
index 00000000..cc11d0d9
--- /dev/null
+++ b/docs/md_v2/basic/docker-deploymeny.md
@@ -0,0 +1,459 @@
+# Docker Deployment
+
+Crawl4AI provides official Docker images for easy deployment and scalability. This guide covers installation, configuration, and usage of Crawl4AI in Docker environments.
+
+## Quick Start 🚀
+
+Pull and run the basic version:
+
+```bash
+docker pull unclecode/crawl4ai:basic
+docker run -p 11235:11235 unclecode/crawl4ai:basic
+```
+
+Test the deployment:
+```python
+import requests
+
+# Test health endpoint
+health = requests.get("http://localhost:11235/health")
+print("Health check:", health.json())
+
+# Test basic crawl
+response = requests.post(
+ "http://localhost:11235/crawl",
+ json={
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 10
+ }
+)
+task_id = response.json()["task_id"]
+print("Task ID:", task_id)
+```
+
+## Available Images 🏷️
+
+- `unclecode/crawl4ai:basic` - Basic web crawling capabilities
+- `unclecode/crawl4ai:all` - Full installation with all features
+- `unclecode/crawl4ai:gpu` - GPU-enabled version for ML features
+
+## Configuration Options 🔧
+
+### Environment Variables
+
+```bash
+docker run -p 11235:11235 \
+ -e MAX_CONCURRENT_TASKS=5 \
+ -e OPENAI_API_KEY=your_key \
+ unclecode/crawl4ai:all
+```
+
+### Volume Mounting
+
+Mount a directory for persistent data:
+```bash
+docker run -p 11235:11235 \
+ -v $(pwd)/data:/app/data \
+ unclecode/crawl4ai:all
+```
+
+### Resource Limits
+
+Control container resources:
+```bash
+docker run -p 11235:11235 \
+ --memory=4g \
+ --cpus=2 \
+ unclecode/crawl4ai:all
+```
+
+## Usage Examples 📝
+
+### Basic Crawling
+
+```python
+request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 10
+}
+
+response = requests.post("http://localhost:11235/crawl", json=request)
+task_id = response.json()["task_id"]
+
+# Get results
+result = requests.get(f"http://localhost:11235/task/{task_id}")
+```
+
+### Structured Data Extraction
+
+```python
+schema = {
+ "name": "Crypto Prices",
+ "baseSelector": ".cds-tableRow-t45thuk",
+ "fields": [
+ {
+ "name": "crypto",
+ "selector": "td:nth-child(1) h2",
+ "type": "text",
+ },
+ {
+ "name": "price",
+ "selector": "td:nth-child(2)",
+ "type": "text",
+ }
+ ],
+}
+
+request = {
+ "urls": "https://www.coinbase.com/explore",
+ "extraction_config": {
+ "type": "json_css",
+ "params": {"schema": schema}
+ }
+}
+```
+
+### Dynamic Content Handling
+
+```python
+request = {
+ "urls": "https://www.nbcnews.com/business",
+ "js_code": [
+ "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
+ ],
+ "wait_for": "article.tease-card:nth-child(10)"
+}
+```
+
+### AI-Powered Extraction (Full Version)
+
+```python
+request = {
+ "urls": "https://www.nbcnews.com/business",
+ "extraction_config": {
+ "type": "cosine",
+ "params": {
+ "semantic_filter": "business finance economy",
+ "word_count_threshold": 10,
+ "max_dist": 0.2,
+ "top_k": 3
+ }
+ }
+}
+```
+
+## Platform-Specific Instructions 💻
+
+### macOS
+```bash
+docker pull unclecode/crawl4ai:basic
+docker run -p 11235:11235 unclecode/crawl4ai:basic
+```
+
+### Ubuntu
+```bash
+# Basic version
+docker pull unclecode/crawl4ai:basic
+docker run -p 11235:11235 unclecode/crawl4ai:basic
+
+# With GPU support
+docker pull unclecode/crawl4ai:gpu
+docker run --gpus all -p 11235:11235 unclecode/crawl4ai:gpu
+```
+
+### Windows (PowerShell)
+```powershell
+docker pull unclecode/crawl4ai:basic
+docker run -p 11235:11235 unclecode/crawl4ai:basic
+```
+
+## Testing 🧪
+
+Save this as `test_docker.py`:
+
+```python
+import requests
+import json
+import time
+import sys
+
+class Crawl4AiTester:
+ def __init__(self, base_url: str = "http://localhost:11235"):
+ self.base_url = base_url
+
+ def submit_and_wait(self, request_data: dict, timeout: int = 300) -> dict:
+ # Submit crawl job
+ response = requests.post(f"{self.base_url}/crawl", json=request_data)
+ task_id = response.json()["task_id"]
+ print(f"Task ID: {task_id}")
+
+ # Poll for result
+ start_time = time.time()
+ while True:
+ if time.time() - start_time > timeout:
+ raise TimeoutError(f"Task {task_id} timeout")
+
+ result = requests.get(f"{self.base_url}/task/{task_id}")
+ status = result.json()
+
+ if status["status"] == "completed":
+ return status
+
+ time.sleep(2)
+
+def test_deployment():
+ tester = Crawl4AiTester()
+
+ # Test basic crawl
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 10
+ }
+
+ result = tester.submit_and_wait(request)
+ print("Basic crawl successful!")
+ print(f"Content length: {len(result['result']['markdown'])}")
+
+if __name__ == "__main__":
+ test_deployment()
+```
+
+## Advanced Configuration ⚙️
+
+### Crawler Parameters
+
+The `crawler_params` field allows you to configure the browser instance and crawling behavior. Here are key parameters you can use:
+
+```python
+request = {
+ "urls": "https://example.com",
+ "crawler_params": {
+ # Browser Configuration
+ "headless": True, # Run in headless mode
+ "browser_type": "chromium", # chromium/firefox/webkit
+ "user_agent": "custom-agent", # Custom user agent
+ "proxy": "http://proxy:8080", # Proxy configuration
+
+ # Performance & Behavior
+ "page_timeout": 30000, # Page load timeout (ms)
+ "verbose": True, # Enable detailed logging
+ "semaphore_count": 5, # Concurrent request limit
+
+ # Anti-Detection Features
+ "simulate_user": True, # Simulate human behavior
+ "magic": True, # Advanced anti-detection
+ "override_navigator": True, # Override navigator properties
+
+ # Session Management
+ "user_data_dir": "./browser-data", # Browser profile location
+ "use_managed_browser": True, # Use persistent browser
+ }
+}
+```
+
+### Extra Parameters
+
+The `extra` field allows passing additional parameters directly to the crawler's `arun` function:
+
+```python
+request = {
+ "urls": "https://example.com",
+ "extra": {
+ "word_count_threshold": 10, # Min words per block
+ "only_text": True, # Extract only text
+ "bypass_cache": True, # Force fresh crawl
+ "process_iframes": True, # Include iframe content
+ }
+}
+```
+
+### Complete Examples
+
+1. **Advanced News Crawling**
+```python
+request = {
+ "urls": "https://www.nbcnews.com/business",
+ "crawler_params": {
+ "headless": True,
+ "page_timeout": 30000,
+ "remove_overlay_elements": True # Remove popups
+ },
+ "extra": {
+ "word_count_threshold": 50, # Longer content blocks
+ "bypass_cache": True # Fresh content
+ },
+ "css_selector": ".article-body"
+}
+```
+
+2. **Anti-Detection Configuration**
+```python
+request = {
+ "urls": "https://example.com",
+ "crawler_params": {
+ "simulate_user": True,
+ "magic": True,
+ "override_navigator": True,
+ "user_agent": "Mozilla/5.0 ...",
+ "headers": {
+ "Accept-Language": "en-US,en;q=0.9"
+ }
+ }
+}
+```
+
+3. **LLM Extraction with Custom Parameters**
+```python
+request = {
+ "urls": "https://openai.com/pricing",
+ "extraction_config": {
+ "type": "llm",
+ "params": {
+ "provider": "openai/gpt-4",
+ "schema": pricing_schema
+ }
+ },
+ "crawler_params": {
+ "verbose": True,
+ "page_timeout": 60000
+ },
+ "extra": {
+ "word_count_threshold": 1,
+ "only_text": True
+ }
+}
+```
+
+4. **Session-Based Dynamic Content**
+```python
+request = {
+ "urls": "https://example.com",
+ "crawler_params": {
+ "session_id": "dynamic_session",
+ "headless": False,
+ "page_timeout": 60000
+ },
+ "js_code": ["window.scrollTo(0, document.body.scrollHeight);"],
+ "wait_for": "js:() => document.querySelectorAll('.item').length > 10",
+ "extra": {
+ "delay_before_return_html": 2.0
+ }
+}
+```
+
+5. **Screenshot with Custom Timing**
+```python
+request = {
+ "urls": "https://example.com",
+ "screenshot": True,
+ "crawler_params": {
+ "headless": True,
+ "screenshot_wait_for": ".main-content"
+ },
+ "extra": {
+ "delay_before_return_html": 3.0
+ }
+}
+```
+
+### Parameter Reference Table
+
+| Category | Parameter | Type | Description |
+|----------|-----------|------|-------------|
+| Browser | headless | bool | Run browser in headless mode |
+| Browser | browser_type | str | Browser engine selection |
+| Browser | user_agent | str | Custom user agent string |
+| Network | proxy | str | Proxy server URL |
+| Network | headers | dict | Custom HTTP headers |
+| Timing | page_timeout | int | Page load timeout (ms) |
+| Timing | delay_before_return_html | float | Wait before capture |
+| Anti-Detection | simulate_user | bool | Human behavior simulation |
+| Anti-Detection | magic | bool | Advanced protection |
+| Session | session_id | str | Browser session ID |
+| Session | user_data_dir | str | Profile directory |
+| Content | word_count_threshold | int | Minimum words per block |
+| Content | only_text | bool | Text-only extraction |
+| Content | process_iframes | bool | Include iframe content |
+| Debug | verbose | bool | Detailed logging |
+| Debug | log_console | bool | Browser console logs |
+
+## Troubleshooting 🔍
+
+### Common Issues
+
+1. **Connection Refused**
+ ```
+ Error: Connection refused at localhost:11235
+ ```
+ Solution: Ensure the container is running and ports are properly mapped.
+
+2. **Resource Limits**
+ ```
+ Error: No available slots
+ ```
+ Solution: Increase MAX_CONCURRENT_TASKS or container resources.
+
+3. **GPU Access**
+ ```
+ Error: GPU not found
+ ```
+ Solution: Ensure proper NVIDIA drivers and use `--gpus all` flag.
+
+### Debug Mode
+
+Access container for debugging:
+```bash
+docker run -it --entrypoint /bin/bash unclecode/crawl4ai:all
+```
+
+View container logs:
+```bash
+docker logs [container_id]
+```
+
+## Best Practices 🌟
+
+1. **Resource Management**
+ - Set appropriate memory and CPU limits
+ - Monitor resource usage via health endpoint
+ - Use basic version for simple crawling tasks
+
+2. **Scaling**
+ - Use multiple containers for high load
+ - Implement proper load balancing
+ - Monitor performance metrics
+
+3. **Security**
+ - Use environment variables for sensitive data
+ - Implement proper network isolation
+ - Regular security updates
+
+## API Reference 📚
+
+### Health Check
+```http
+GET /health
+```
+
+### Submit Crawl Task
+```http
+POST /crawl
+Content-Type: application/json
+
+{
+ "urls": "string or array",
+ "extraction_config": {
+ "type": "basic|llm|cosine|json_css",
+ "params": {}
+ },
+ "priority": 1-10,
+ "ttl": 3600
+}
+```
+
+### Get Task Status
+```http
+GET /task/{task_id}
+```
+
+For more details, visit the [official documentation](https://crawl4ai.com/mkdocs/).
\ No newline at end of file
diff --git a/docs/md/installation.md b/docs/md_v2/basic/installation.md
similarity index 100%
rename from docs/md/installation.md
rename to docs/md_v2/basic/installation.md
diff --git a/docs/md_v2/basic/output-formats.md b/docs/md_v2/basic/output-formats.md
new file mode 100644
index 00000000..0d25e884
--- /dev/null
+++ b/docs/md_v2/basic/output-formats.md
@@ -0,0 +1,195 @@
+# Output Formats
+
+Crawl4AI provides multiple output formats to suit different needs, from raw HTML to structured data using LLM or pattern-based extraction.
+
+## Basic Formats
+
+```python
+result = await crawler.arun(url="https://example.com")
+
+# Access different formats
+raw_html = result.html # Original HTML
+clean_html = result.cleaned_html # Sanitized HTML
+markdown = result.markdown # Standard markdown
+fit_md = result.fit_markdown # Most relevant content in markdown
+```
+
+## Raw HTML
+
+Original, unmodified HTML from the webpage. Useful when you need to:
+- Preserve the exact page structure
+- Process HTML with your own tools
+- Debug page issues
+
+```python
+result = await crawler.arun(url="https://example.com")
+print(result.html) # Complete HTML including headers, scripts, etc.
+```
+
+## Cleaned HTML
+
+Sanitized HTML with unnecessary elements removed. Automatically:
+- Removes scripts and styles
+- Cleans up formatting
+- Preserves semantic structure
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ excluded_tags=['form', 'header', 'footer'], # Additional tags to remove
+ keep_data_attributes=False # Remove data-* attributes
+)
+print(result.cleaned_html)
+```
+
+## Standard Markdown
+
+HTML converted to clean markdown format. Great for:
+- Content analysis
+- Documentation
+- Readability
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ include_links_on_markdown=True # Include links in markdown
+)
+print(result.markdown)
+```
+
+## Fit Markdown
+
+Most relevant content extracted and converted to markdown. Ideal for:
+- Article extraction
+- Main content focus
+- Removing boilerplate
+
+```python
+result = await crawler.arun(url="https://example.com")
+print(result.fit_markdown) # Only the main content
+```
+
+## Structured Data Extraction
+
+Crawl4AI offers two powerful approaches for structured data extraction:
+
+### 1. LLM-Based Extraction
+
+Use any LLM (OpenAI, HuggingFace, Ollama, etc.) to extract structured data with high accuracy:
+
+```python
+from pydantic import BaseModel
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+class KnowledgeGraph(BaseModel):
+ entities: List[dict]
+ relationships: List[dict]
+
+strategy = LLMExtractionStrategy(
+ provider="ollama/nemotron", # or "huggingface/...", "ollama/..."
+ api_token="your-token", # not needed for Ollama
+ schema=KnowledgeGraph.schema(),
+ instruction="Extract entities and relationships from the content"
+)
+
+result = await crawler.arun(
+ url="https://example.com",
+ extraction_strategy=strategy
+)
+knowledge_graph = json.loads(result.extracted_content)
+```
+
+### 2. Pattern-Based Extraction
+
+For pages with repetitive patterns (e.g., product listings, article feeds), use JsonCssExtractionStrategy:
+
+```python
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+schema = {
+ "name": "Product Listing",
+ "baseSelector": ".product-card", # Repeated element
+ "fields": [
+ {"name": "title", "selector": "h2", "type": "text"},
+ {"name": "price", "selector": ".price", "type": "text"},
+ {"name": "description", "selector": ".desc", "type": "text"}
+ ]
+}
+
+strategy = JsonCssExtractionStrategy(schema)
+result = await crawler.arun(
+ url="https://example.com",
+ extraction_strategy=strategy
+)
+products = json.loads(result.extracted_content)
+```
+
+## Content Customization
+
+### HTML to Text Options
+
+Configure markdown conversion:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ html2text={
+ "escape_dot": False,
+ "body_width": 0,
+ "protect_links": True,
+ "unicode_snob": True
+ }
+)
+```
+
+### Content Filters
+
+Control what content is included:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ word_count_threshold=10, # Minimum words per block
+ exclude_external_links=True, # Remove external links
+ exclude_external_images=True, # Remove external images
+ excluded_tags=['form', 'nav'] # Remove specific HTML tags
+)
+```
+
+## Comprehensive Example
+
+Here's how to use multiple output formats together:
+
+```python
+async def crawl_content(url: str):
+ async with AsyncWebCrawler() as crawler:
+ # Extract main content with fit markdown
+ result = await crawler.arun(
+ url=url,
+ word_count_threshold=10,
+ exclude_external_links=True
+ )
+
+ # Get structured data using LLM
+ llm_result = await crawler.arun(
+ url=url,
+ extraction_strategy=LLMExtractionStrategy(
+ provider="ollama/nemotron",
+ schema=YourSchema.schema(),
+ instruction="Extract key information"
+ )
+ )
+
+ # Get repeated patterns (if any)
+ pattern_result = await crawler.arun(
+ url=url,
+ extraction_strategy=JsonCssExtractionStrategy(your_schema)
+ )
+
+ return {
+ "main_content": result.fit_markdown,
+ "structured_data": json.loads(llm_result.extracted_content),
+ "pattern_data": json.loads(pattern_result.extracted_content),
+ "media": result.media
+ }
+```
\ No newline at end of file
diff --git a/docs/md_v2/basic/page-interaction.md b/docs/md_v2/basic/page-interaction.md
new file mode 100644
index 00000000..7555f225
--- /dev/null
+++ b/docs/md_v2/basic/page-interaction.md
@@ -0,0 +1,207 @@
+# Page Interaction
+
+Crawl4AI provides powerful features for interacting with dynamic webpages, handling JavaScript execution, and managing page events.
+
+## JavaScript Execution
+
+### Basic Execution
+
+```python
+# Single JavaScript command
+result = await crawler.arun(
+ url="https://example.com",
+ js_code="window.scrollTo(0, document.body.scrollHeight);"
+)
+
+# Multiple commands
+js_commands = [
+ "window.scrollTo(0, document.body.scrollHeight);",
+ "document.querySelector('.load-more').click();",
+ "document.querySelector('#consent-button').click();"
+]
+result = await crawler.arun(
+ url="https://example.com",
+ js_code=js_commands
+)
+```
+
+## Wait Conditions
+
+### CSS-Based Waiting
+
+Wait for elements to appear:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ wait_for="css:.dynamic-content" # Wait for element with class 'dynamic-content'
+)
+```
+
+### JavaScript-Based Waiting
+
+Wait for custom conditions:
+
+```python
+# Wait for number of elements
+wait_condition = """() => {
+ return document.querySelectorAll('.item').length > 10;
+}"""
+
+result = await crawler.arun(
+ url="https://example.com",
+ wait_for=f"js:{wait_condition}"
+)
+
+# Wait for dynamic content to load
+wait_for_content = """() => {
+ const content = document.querySelector('.content');
+ return content && content.innerText.length > 100;
+}"""
+
+result = await crawler.arun(
+ url="https://example.com",
+ wait_for=f"js:{wait_for_content}"
+)
+```
+
+## Handling Dynamic Content
+
+### Load More Content
+
+Handle infinite scroll or load more buttons:
+
+```python
+# Scroll and wait pattern
+result = await crawler.arun(
+ url="https://example.com",
+ js_code=[
+ # Scroll to bottom
+ "window.scrollTo(0, document.body.scrollHeight);",
+ # Click load more if exists
+ "const loadMore = document.querySelector('.load-more'); if(loadMore) loadMore.click();"
+ ],
+ # Wait for new content
+ wait_for="js:() => document.querySelectorAll('.item').length > previousCount"
+)
+```
+
+### Form Interaction
+
+Handle forms and inputs:
+
+```python
+js_form_interaction = """
+ // Fill form fields
+ document.querySelector('#search').value = 'search term';
+ // Submit form
+ document.querySelector('form').submit();
+"""
+
+result = await crawler.arun(
+ url="https://example.com",
+ js_code=js_form_interaction,
+ wait_for="css:.results" # Wait for results to load
+)
+```
+
+## Timing Control
+
+### Delays and Timeouts
+
+Control timing of interactions:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ page_timeout=60000, # Page load timeout (ms)
+ delay_before_return_html=2.0, # Wait before capturing content
+)
+```
+
+## Complex Interactions Example
+
+Here's an example of handling a dynamic page with multiple interactions:
+
+```python
+async def crawl_dynamic_content():
+ async with AsyncWebCrawler() as crawler:
+ # Initial page load
+ result = await crawler.arun(
+ url="https://example.com",
+ # Handle cookie consent
+ js_code="document.querySelector('.cookie-accept')?.click();",
+ wait_for="css:.main-content"
+ )
+
+ # Load more content
+ session_id = "dynamic_session" # Keep session for multiple interactions
+
+ for page in range(3): # Load 3 pages of content
+ result = await crawler.arun(
+ url="https://example.com",
+ session_id=session_id,
+ js_code=[
+ # Scroll to bottom
+ "window.scrollTo(0, document.body.scrollHeight);",
+ # Store current item count
+ "window.previousCount = document.querySelectorAll('.item').length;",
+ # Click load more
+ "document.querySelector('.load-more')?.click();"
+ ],
+ # Wait for new items
+ wait_for="""() => {
+ const currentCount = document.querySelectorAll('.item').length;
+ return currentCount > window.previousCount;
+ }""",
+ # Only execute JS without reloading page
+ js_only=True if page > 0 else False
+ )
+
+ # Process content after each load
+ print(f"Page {page + 1} items:", len(result.cleaned_html))
+
+ # Clean up session
+ await crawler.crawler_strategy.kill_session(session_id)
+```
+
+## Using with Extraction Strategies
+
+Combine page interaction with structured extraction:
+
+```python
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy, LLMExtractionStrategy
+
+# Pattern-based extraction after interaction
+schema = {
+ "name": "Dynamic Items",
+ "baseSelector": ".item",
+ "fields": [
+ {"name": "title", "selector": "h2", "type": "text"},
+ {"name": "description", "selector": ".desc", "type": "text"}
+ ]
+}
+
+result = await crawler.arun(
+ url="https://example.com",
+ js_code="window.scrollTo(0, document.body.scrollHeight);",
+ wait_for="css:.item:nth-child(10)", # Wait for 10 items
+ extraction_strategy=JsonCssExtractionStrategy(schema)
+)
+
+# Or use LLM to analyze dynamic content
+class ContentAnalysis(BaseModel):
+ topics: List[str]
+ summary: str
+
+result = await crawler.arun(
+ url="https://example.com",
+ js_code="document.querySelector('.show-more').click();",
+ wait_for="css:.full-content",
+ extraction_strategy=LLMExtractionStrategy(
+ provider="ollama/nemotron",
+ schema=ContentAnalysis.schema(),
+ instruction="Analyze the full content"
+ )
+)
+```
\ No newline at end of file
diff --git a/docs/md_v2/basic/quickstart.md b/docs/md_v2/basic/quickstart.md
new file mode 100644
index 00000000..f4904915
--- /dev/null
+++ b/docs/md_v2/basic/quickstart.md
@@ -0,0 +1,297 @@
+# Quick Start Guide 🚀
+
+Welcome to the Crawl4AI Quickstart Guide! In this tutorial, we'll walk you through the basic usage of Crawl4AI with a friendly and humorous tone. We'll cover everything from basic usage to advanced features like chunking and extraction strategies, all with the power of asynchronous programming. Let's dive in! 🌟
+
+## Getting Started 🛠️
+
+First, let's import the necessary modules and create an instance of `AsyncWebCrawler`. We'll use an async context manager, which handles the setup and teardown of the crawler for us.
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
+
+async def main():
+ async with AsyncWebCrawler(verbose=True) as crawler:
+ # We'll add our crawling code here
+ pass
+
+if __name__ == "__main__":
+ asyncio.run(main())
+```
+
+### Basic Usage
+
+Simply provide a URL and let Crawl4AI do the magic!
+
+```python
+async def main():
+ async with AsyncWebCrawler(verbose=True) as crawler:
+ result = await crawler.arun(url="https://www.nbcnews.com/business")
+ print(f"Basic crawl result: {result.markdown[:500]}") # Print first 500 characters
+
+asyncio.run(main())
+```
+
+### Taking Screenshots 📸
+
+Capture screenshots of web pages easily:
+
+```python
+async def capture_and_save_screenshot(url: str, output_path: str):
+ async with AsyncWebCrawler(verbose=True) as crawler:
+ result = await crawler.arun(
+ url=url,
+ screenshot=True,
+ bypass_cache=True
+ )
+
+ if result.success and result.screenshot:
+ import base64
+ screenshot_data = base64.b64decode(result.screenshot)
+ with open(output_path, 'wb') as f:
+ f.write(screenshot_data)
+ print(f"Screenshot saved successfully to {output_path}")
+ else:
+ print("Failed to capture screenshot")
+```
+
+### Browser Selection 🌐
+
+Crawl4AI supports multiple browser engines. Here's how to use different browsers:
+
+```python
+# Use Firefox
+async with AsyncWebCrawler(browser_type="firefox", verbose=True, headless=True) as crawler:
+ result = await crawler.arun(url="https://www.example.com", bypass_cache=True)
+
+# Use WebKit
+async with AsyncWebCrawler(browser_type="webkit", verbose=True, headless=True) as crawler:
+ result = await crawler.arun(url="https://www.example.com", bypass_cache=True)
+
+# Use Chromium (default)
+async with AsyncWebCrawler(verbose=True, headless=True) as crawler:
+ result = await crawler.arun(url="https://www.example.com", bypass_cache=True)
+```
+
+### User Simulation 🎭
+
+Simulate real user behavior to avoid detection:
+
+```python
+async with AsyncWebCrawler(verbose=True, headless=True) as crawler:
+ result = await crawler.arun(
+ url="YOUR-URL-HERE",
+ bypass_cache=True,
+ simulate_user=True, # Causes random mouse movements and clicks
+ override_navigator=True # Makes the browser appear more like a real user
+ )
+```
+
+### Understanding Parameters 🧠
+
+By default, Crawl4AI caches the results of your crawls. This means that subsequent crawls of the same URL will be much faster! Let's see this in action.
+
+```python
+async def main():
+ async with AsyncWebCrawler(verbose=True) as crawler:
+ # First crawl (caches the result)
+ result1 = await crawler.arun(url="https://www.nbcnews.com/business")
+ print(f"First crawl result: {result1.markdown[:100]}...")
+
+ # Force to crawl again
+ result2 = await crawler.arun(url="https://www.nbcnews.com/business", bypass_cache=True)
+ print(f"Second crawl result: {result2.markdown[:100]}...")
+
+asyncio.run(main())
+```
+
+### Adding a Chunking Strategy 🧩
+
+Let's add a chunking strategy: `RegexChunking`! This strategy splits the text based on a given regex pattern.
+
+```python
+from crawl4ai.chunking_strategy import RegexChunking
+
+async def main():
+ async with AsyncWebCrawler(verbose=True) as crawler:
+ result = await crawler.arun(
+ url="https://www.nbcnews.com/business",
+ chunking_strategy=RegexChunking(patterns=["\n\n"])
+ )
+ print(f"RegexChunking result: {result.extracted_content[:200]}...")
+
+asyncio.run(main())
+```
+
+### Using LLMExtractionStrategy with Different Providers 🤖
+
+Crawl4AI supports multiple LLM providers for extraction:
+
+```python
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+from pydantic import BaseModel, Field
+
+class OpenAIModelFee(BaseModel):
+ model_name: str = Field(..., description="Name of the OpenAI model.")
+ input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
+ output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
+
+# OpenAI
+await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
+
+# Hugging Face
+await extract_structured_data_using_llm(
+ "huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct",
+ os.getenv("HUGGINGFACE_API_KEY")
+)
+
+# Ollama
+await extract_structured_data_using_llm("ollama/llama3.2")
+
+# With custom headers
+custom_headers = {
+ "Authorization": "Bearer your-custom-token",
+ "X-Custom-Header": "Some-Value"
+}
+await extract_structured_data_using_llm(extra_headers=custom_headers)
+```
+
+### Knowledge Graph Generation 🕸️
+
+Generate knowledge graphs from web content:
+
+```python
+from pydantic import BaseModel
+from typing import List
+
+class Entity(BaseModel):
+ name: str
+ description: str
+
+class Relationship(BaseModel):
+ entity1: Entity
+ entity2: Entity
+ description: str
+ relation_type: str
+
+class KnowledgeGraph(BaseModel):
+ entities: List[Entity]
+ relationships: List[Relationship]
+
+extraction_strategy = LLMExtractionStrategy(
+ provider='openai/gpt-4o-mini',
+ api_token=os.getenv('OPENAI_API_KEY'),
+ schema=KnowledgeGraph.model_json_schema(),
+ extraction_type="schema",
+ instruction="Extract entities and relationships from the given text."
+)
+
+async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://paulgraham.com/love.html",
+ bypass_cache=True,
+ extraction_strategy=extraction_strategy
+ )
+```
+
+### Advanced Session-Based Crawling with Dynamic Content 🔄
+
+For modern web applications with dynamic content loading, here's how to handle pagination and content updates:
+
+```python
+async def crawl_dynamic_content():
+ async with AsyncWebCrawler(verbose=True) as crawler:
+ url = "https://github.com/microsoft/TypeScript/commits/main"
+ session_id = "typescript_commits_session"
+
+ js_next_page = """
+ const button = document.querySelector('a[data-testid="pagination-next-button"]');
+ if (button) button.click();
+ """
+
+ wait_for = """() => {
+ const commits = document.querySelectorAll('li.Box-sc-g0xbh4-0 h4');
+ if (commits.length === 0) return false;
+ const firstCommit = commits[0].textContent.trim();
+ return firstCommit !== window.firstCommit;
+ }"""
+
+ schema = {
+ "name": "Commit Extractor",
+ "baseSelector": "li.Box-sc-g0xbh4-0",
+ "fields": [
+ {
+ "name": "title",
+ "selector": "h4.markdown-title",
+ "type": "text",
+ "transform": "strip",
+ },
+ ],
+ }
+ extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+ for page in range(3): # Crawl 3 pages
+ result = await crawler.arun(
+ url=url,
+ session_id=session_id,
+ css_selector="li.Box-sc-g0xbh4-0",
+ extraction_strategy=extraction_strategy,
+ js_code=js_next_page if page > 0 else None,
+ wait_for=wait_for if page > 0 else None,
+ js_only=page > 0,
+ bypass_cache=True,
+ headless=False,
+ )
+
+ await crawler.crawler_strategy.kill_session(session_id)
+```
+
+### Handling Overlays and Fitting Content 📏
+
+Remove overlay elements and fit content appropriately:
+
+```python
+async with AsyncWebCrawler(headless=False) as crawler:
+ result = await crawler.arun(
+ url="your-url-here",
+ bypass_cache=True,
+ word_count_threshold=10,
+ remove_overlay_elements=True,
+ screenshot=True
+ )
+```
+
+## Performance Comparison 🏎️
+
+Crawl4AI offers impressive performance compared to other solutions:
+
+```python
+# Firecrawl comparison
+from firecrawl import FirecrawlApp
+app = FirecrawlApp(api_key=os.environ['FIRECRAWL_API_KEY'])
+start = time.time()
+scrape_status = app.scrape_url(
+ 'https://www.nbcnews.com/business',
+ params={'formats': ['markdown', 'html']}
+)
+end = time.time()
+
+# Crawl4AI comparison
+async with AsyncWebCrawler() as crawler:
+ start = time.time()
+ result = await crawler.arun(
+ url="https://www.nbcnews.com/business",
+ word_count_threshold=0,
+ bypass_cache=True,
+ verbose=False,
+ )
+ end = time.time()
+```
+
+Note: Performance comparisons should be conducted in environments with stable and fast internet connections for accurate results.
+
+## Congratulations! 🎉
+
+You've made it through the updated Crawl4AI Quickstart Guide! Now you're equipped with even more powerful features to crawl the web asynchronously like a pro! 🕸️
+
+Happy crawling! 🚀
\ No newline at end of file
diff --git a/docs/md_v2/basic/simple-crawling.md b/docs/md_v2/basic/simple-crawling.md
new file mode 100644
index 00000000..097d5e61
--- /dev/null
+++ b/docs/md_v2/basic/simple-crawling.md
@@ -0,0 +1,120 @@
+# Simple Crawling
+
+This guide covers the basics of web crawling with Crawl4AI. You'll learn how to set up a crawler, make your first request, and understand the response.
+
+## Basic Usage
+
+Here's the simplest way to crawl a webpage:
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
+
+async def main():
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(url="https://example.com")
+ print(result.markdown) # Print clean markdown content
+
+if __name__ == "__main__":
+ asyncio.run(main())
+```
+
+## Understanding the Response
+
+The `arun()` method returns a `CrawlResult` object with several useful properties. Here's a quick overview (see [CrawlResult](../api/crawl-result.md) for complete details):
+
+```python
+result = await crawler.arun(url="https://example.com")
+
+# Different content formats
+print(result.html) # Raw HTML
+print(result.cleaned_html) # Cleaned HTML
+print(result.markdown) # Markdown version
+print(result.fit_markdown) # Most relevant content in markdown
+
+# Check success status
+print(result.success) # True if crawl succeeded
+print(result.status_code) # HTTP status code (e.g., 200, 404)
+
+# Access extracted media and links
+print(result.media) # Dictionary of found media (images, videos, audio)
+print(result.links) # Dictionary of internal and external links
+```
+
+## Adding Basic Options
+
+Customize your crawl with these common options:
+
+```python
+result = await crawler.arun(
+ url="https://example.com",
+ word_count_threshold=10, # Minimum words per content block
+ exclude_external_links=True, # Remove external links
+ remove_overlay_elements=True, # Remove popups/modals
+ process_iframes=True # Process iframe content
+)
+```
+
+## Handling Errors
+
+Always check if the crawl was successful:
+
+```python
+result = await crawler.arun(url="https://example.com")
+if not result.success:
+ print(f"Crawl failed: {result.error_message}")
+ print(f"Status code: {result.status_code}")
+```
+
+## Logging and Debugging
+
+Enable verbose mode for detailed logging:
+
+```python
+async with AsyncWebCrawler(verbose=True) as crawler:
+ result = await crawler.arun(url="https://example.com")
+```
+
+## Complete Example
+
+Here's a more comprehensive example showing common usage patterns:
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
+
+async def main():
+ async with AsyncWebCrawler(verbose=True) as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ # Content filtering
+ word_count_threshold=10,
+ excluded_tags=['form', 'header'],
+ exclude_external_links=True,
+
+ # Content processing
+ process_iframes=True,
+ remove_overlay_elements=True,
+
+ # Cache control
+ bypass_cache=False # Use cache if available
+ )
+
+ if result.success:
+ # Print clean content
+ print("Content:", result.markdown[:500]) # First 500 chars
+
+ # Process images
+ for image in result.media["images"]:
+ print(f"Found image: {image['src']}")
+
+ # Process links
+ for link in result.links["internal"]:
+ print(f"Internal link: {link['href']}")
+
+ else:
+ print(f"Crawl failed: {result.error_message}")
+
+if __name__ == "__main__":
+ asyncio.run(main())
+```
diff --git a/docs/md _sync/full_details/chunking_strategies.md b/docs/md_v2/extraction/chunking.md
similarity index 100%
rename from docs/md _sync/full_details/chunking_strategies.md
rename to docs/md_v2/extraction/chunking.md
diff --git a/docs/md_v2/extraction/cosine.md b/docs/md_v2/extraction/cosine.md
new file mode 100644
index 00000000..9ce49e40
--- /dev/null
+++ b/docs/md_v2/extraction/cosine.md
@@ -0,0 +1,222 @@
+# Cosine Strategy
+
+The Cosine Strategy in Crawl4AI uses similarity-based clustering to identify and extract relevant content sections from web pages. This strategy is particularly useful when you need to find and extract content based on semantic similarity rather than structural patterns.
+
+## How It Works
+
+The Cosine Strategy:
+1. Breaks down page content into meaningful chunks
+2. Converts text into vector representations
+3. Calculates similarity between chunks
+4. Clusters similar content together
+5. Ranks and filters content based on relevance
+
+## Basic Usage
+
+```python
+from crawl4ai.extraction_strategy import CosineStrategy
+
+strategy = CosineStrategy(
+ semantic_filter="product reviews", # Target content type
+ word_count_threshold=10, # Minimum words per cluster
+ sim_threshold=0.3 # Similarity threshold
+)
+
+async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com/reviews",
+ extraction_strategy=strategy
+ )
+
+ content = result.extracted_content
+```
+
+## Configuration Options
+
+### Core Parameters
+
+```python
+CosineStrategy(
+ # Content Filtering
+ semantic_filter: str = None, # Keywords/topic for content filtering
+ word_count_threshold: int = 10, # Minimum words per cluster
+ sim_threshold: float = 0.3, # Similarity threshold (0.0 to 1.0)
+
+ # Clustering Parameters
+ max_dist: float = 0.2, # Maximum distance for clustering
+ linkage_method: str = 'ward', # Clustering linkage method
+ top_k: int = 3, # Number of top categories to extract
+
+ # Model Configuration
+ model_name: str = 'sentence-transformers/all-MiniLM-L6-v2', # Embedding model
+
+ verbose: bool = False # Enable logging
+)
+```
+
+### Parameter Details
+
+1. **semantic_filter**
+ - Sets the target topic or content type
+ - Use keywords relevant to your desired content
+ - Example: "technical specifications", "user reviews", "pricing information"
+
+2. **sim_threshold**
+ - Controls how similar content must be to be grouped together
+ - Higher values (e.g., 0.8) mean stricter matching
+ - Lower values (e.g., 0.3) allow more variation
+ ```python
+ # Strict matching
+ strategy = CosineStrategy(sim_threshold=0.8)
+
+ # Loose matching
+ strategy = CosineStrategy(sim_threshold=0.3)
+ ```
+
+3. **word_count_threshold**
+ - Filters out short content blocks
+ - Helps eliminate noise and irrelevant content
+ ```python
+ # Only consider substantial paragraphs
+ strategy = CosineStrategy(word_count_threshold=50)
+ ```
+
+4. **top_k**
+ - Number of top content clusters to return
+ - Higher values return more diverse content
+ ```python
+ # Get top 5 most relevant content clusters
+ strategy = CosineStrategy(top_k=5)
+ ```
+
+## Use Cases
+
+### 1. Article Content Extraction
+```python
+strategy = CosineStrategy(
+ semantic_filter="main article content",
+ word_count_threshold=100, # Longer blocks for articles
+ top_k=1 # Usually want single main content
+)
+
+result = await crawler.arun(
+ url="https://example.com/blog/post",
+ extraction_strategy=strategy
+)
+```
+
+### 2. Product Review Analysis
+```python
+strategy = CosineStrategy(
+ semantic_filter="customer reviews and ratings",
+ word_count_threshold=20, # Reviews can be shorter
+ top_k=10, # Get multiple reviews
+ sim_threshold=0.4 # Allow variety in review content
+)
+```
+
+### 3. Technical Documentation
+```python
+strategy = CosineStrategy(
+ semantic_filter="technical specifications documentation",
+ word_count_threshold=30,
+ sim_threshold=0.6, # Stricter matching for technical content
+ max_dist=0.3 # Allow related technical sections
+)
+```
+
+## Advanced Features
+
+### Custom Clustering
+```python
+strategy = CosineStrategy(
+ linkage_method='complete', # Alternative clustering method
+ max_dist=0.4, # Larger clusters
+ model_name='sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2' # Multilingual support
+)
+```
+
+### Content Filtering Pipeline
+```python
+strategy = CosineStrategy(
+ semantic_filter="pricing plans features",
+ word_count_threshold=15,
+ sim_threshold=0.5,
+ top_k=3
+)
+
+async def extract_pricing_features(url: str):
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url=url,
+ extraction_strategy=strategy
+ )
+
+ if result.success:
+ content = json.loads(result.extracted_content)
+ return {
+ 'pricing_features': content,
+ 'clusters': len(content),
+ 'similarity_scores': [item['score'] for item in content]
+ }
+```
+
+## Best Practices
+
+1. **Adjust Thresholds Iteratively**
+ - Start with default values
+ - Adjust based on results
+ - Monitor clustering quality
+
+2. **Choose Appropriate Word Count Thresholds**
+ - Higher for articles (100+)
+ - Lower for reviews/comments (20+)
+ - Medium for product descriptions (50+)
+
+3. **Optimize Performance**
+ ```python
+ strategy = CosineStrategy(
+ word_count_threshold=10, # Filter early
+ top_k=5, # Limit results
+ verbose=True # Monitor performance
+ )
+ ```
+
+4. **Handle Different Content Types**
+ ```python
+ # For mixed content pages
+ strategy = CosineStrategy(
+ semantic_filter="product features",
+ sim_threshold=0.4, # More flexible matching
+ max_dist=0.3, # Larger clusters
+ top_k=3 # Multiple relevant sections
+ )
+ ```
+
+## Error Handling
+
+```python
+try:
+ result = await crawler.arun(
+ url="https://example.com",
+ extraction_strategy=strategy
+ )
+
+ if result.success:
+ content = json.loads(result.extracted_content)
+ if not content:
+ print("No relevant content found")
+ else:
+ print(f"Extraction failed: {result.error_message}")
+
+except Exception as e:
+ print(f"Error during extraction: {str(e)}")
+```
+
+The Cosine Strategy is particularly effective when:
+- Content structure is inconsistent
+- You need semantic understanding
+- You want to find similar content blocks
+- Structure-based extraction (CSS/XPath) isn't reliable
+
+It works well with other strategies and can be used as a pre-processing step for LLM-based extraction.
\ No newline at end of file
diff --git a/docs/md/full_details/advanced_jsoncss_extraction.md b/docs/md_v2/extraction/css-advanced.md
similarity index 100%
rename from docs/md/full_details/advanced_jsoncss_extraction.md
rename to docs/md_v2/extraction/css-advanced.md
diff --git a/docs/md/examples/json_css_extraction.md b/docs/md_v2/extraction/css.md
similarity index 98%
rename from docs/md/examples/json_css_extraction.md
rename to docs/md_v2/extraction/css.md
index 933d1f68..3b5075a6 100644
--- a/docs/md/examples/json_css_extraction.md
+++ b/docs/md_v2/extraction/css.md
@@ -139,4 +139,4 @@ This advanced example demonstrates how to:
By mastering the `JsonCssExtractionStrategy`, you can efficiently extract structured data from a wide variety of web pages, making it a valuable tool in your web scraping toolkit.
-For more details on schema definitions and advanced extraction strategies, check out the[Advanced JsonCssExtraction](../full_details/advanced_jsoncss_extraction.md).
\ No newline at end of file
+For more details on schema definitions and advanced extraction strategies, check out the[Advanced JsonCssExtraction](./css-advanced.md).
\ No newline at end of file
diff --git a/docs/md/examples/llm_extraction.md b/docs/md_v2/extraction/llm.md
similarity index 98%
rename from docs/md/examples/llm_extraction.md
rename to docs/md_v2/extraction/llm.md
index f4bd74c8..ca562147 100644
--- a/docs/md/examples/llm_extraction.md
+++ b/docs/md_v2/extraction/llm.md
@@ -27,7 +27,7 @@ async def extract_openai_fees():
url=url,
word_count_threshold=1,
extraction_strategy=LLMExtractionStrategy(
- provider="openai/gpt-4o",
+ provider="openai/gpt-4o", # Or use ollama like provider="ollama/nemotron"
api_token=os.getenv('OPENAI_API_KEY'),
schema=OpenAIModelFee.model_json_schema(),
extraction_type="schema",
diff --git a/docs/md_v2/extraction/overview.md b/docs/md_v2/extraction/overview.md
new file mode 100644
index 00000000..53a8b87d
--- /dev/null
+++ b/docs/md_v2/extraction/overview.md
@@ -0,0 +1,197 @@
+# Extraction Strategies Overview
+
+Crawl4AI provides powerful extraction strategies to help you get structured data from web pages. Each strategy is designed for specific use cases and offers different approaches to data extraction.
+
+## Available Strategies
+
+### [LLM-Based Extraction](llm.md)
+
+`LLMExtractionStrategy` uses Language Models to extract structured data from web content. This approach is highly flexible and can understand content semantically.
+
+```python
+from pydantic import BaseModel
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+class Product(BaseModel):
+ name: str
+ price: float
+ description: str
+
+strategy = LLMExtractionStrategy(
+ provider="ollama/llama2",
+ schema=Product.schema(),
+ instruction="Extract product details from the page"
+)
+
+result = await crawler.arun(
+ url="https://example.com/product",
+ extraction_strategy=strategy
+)
+```
+
+**Best for:**
+- Complex data structures
+- Content requiring interpretation
+- Flexible content formats
+- Natural language processing
+
+### [CSS-Based Extraction](css.md)
+
+`JsonCssExtractionStrategy` extracts data using CSS selectors. This is fast, reliable, and perfect for consistently structured pages.
+
+```python
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+schema = {
+ "name": "Product Listing",
+ "baseSelector": ".product-card",
+ "fields": [
+ {"name": "title", "selector": "h2", "type": "text"},
+ {"name": "price", "selector": ".price", "type": "text"},
+ {"name": "image", "selector": "img", "type": "attribute", "attribute": "src"}
+ ]
+}
+
+strategy = JsonCssExtractionStrategy(schema)
+
+result = await crawler.arun(
+ url="https://example.com/products",
+ extraction_strategy=strategy
+)
+```
+
+**Best for:**
+- E-commerce product listings
+- News article collections
+- Structured content pages
+- High-performance needs
+
+### [Cosine Strategy](cosine.md)
+
+`CosineStrategy` uses similarity-based clustering to identify and extract relevant content sections.
+
+```python
+from crawl4ai.extraction_strategy import CosineStrategy
+
+strategy = CosineStrategy(
+ semantic_filter="product reviews", # Content focus
+ word_count_threshold=10, # Minimum words per cluster
+ sim_threshold=0.3, # Similarity threshold
+ max_dist=0.2, # Maximum cluster distance
+ top_k=3 # Number of top clusters to extract
+)
+
+result = await crawler.arun(
+ url="https://example.com/reviews",
+ extraction_strategy=strategy
+)
+```
+
+**Best for:**
+- Content similarity analysis
+- Topic clustering
+- Relevant content extraction
+- Pattern recognition in text
+
+## Strategy Selection Guide
+
+Choose your strategy based on these factors:
+
+1. **Content Structure**
+ - Well-structured HTML → Use CSS Strategy
+ - Natural language text → Use LLM Strategy
+ - Mixed/Complex content → Use Cosine Strategy
+
+2. **Performance Requirements**
+ - Fastest: CSS Strategy
+ - Moderate: Cosine Strategy
+ - Variable: LLM Strategy (depends on provider)
+
+3. **Accuracy Needs**
+ - Highest structure accuracy: CSS Strategy
+ - Best semantic understanding: LLM Strategy
+ - Best content relevance: Cosine Strategy
+
+## Combining Strategies
+
+You can combine strategies for more powerful extraction:
+
+```python
+# First use CSS strategy for initial structure
+css_result = await crawler.arun(
+ url="https://example.com",
+ extraction_strategy=css_strategy
+)
+
+# Then use LLM for semantic analysis
+llm_result = await crawler.arun(
+ url="https://example.com",
+ extraction_strategy=llm_strategy
+)
+```
+
+## Common Use Cases
+
+1. **E-commerce Scraping**
+ ```python
+ # CSS Strategy for product listings
+ schema = {
+ "name": "Products",
+ "baseSelector": ".product",
+ "fields": [
+ {"name": "name", "selector": ".title", "type": "text"},
+ {"name": "price", "selector": ".price", "type": "text"}
+ ]
+ }
+ ```
+
+2. **News Article Extraction**
+ ```python
+ # LLM Strategy for article content
+ class Article(BaseModel):
+ title: str
+ content: str
+ author: str
+ date: str
+
+ strategy = LLMExtractionStrategy(
+ provider="ollama/llama2",
+ schema=Article.schema()
+ )
+ ```
+
+3. **Content Analysis**
+ ```python
+ # Cosine Strategy for topic analysis
+ strategy = CosineStrategy(
+ semantic_filter="technology trends",
+ top_k=5
+ )
+ ```
+
+## Best Practices
+
+1. **Choose the Right Strategy**
+ - Start with CSS for structured data
+ - Use LLM for complex interpretation
+ - Try Cosine for content relevance
+
+2. **Optimize Performance**
+ - Cache LLM results
+ - Keep CSS selectors specific
+ - Tune similarity thresholds
+
+3. **Handle Errors**
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ extraction_strategy=strategy
+ )
+
+ if not result.success:
+ print(f"Extraction failed: {result.error_message}")
+ else:
+ data = json.loads(result.extracted_content)
+ ```
+
+Each strategy has its strengths and optimal use cases. Explore the detailed documentation for each strategy to learn more about their specific features and configurations.
\ No newline at end of file
diff --git a/docs/md_v2/index.md b/docs/md_v2/index.md
new file mode 100644
index 00000000..65ea6da8
--- /dev/null
+++ b/docs/md_v2/index.md
@@ -0,0 +1,113 @@
+# Crawl4AI
+
+Welcome to the official documentation for Crawl4AI! 🕷️🤖 Crawl4AI is an open-source Python library designed to simplify web crawling and extract useful information from web pages. This documentation will guide you through the features, usage, and customization of Crawl4AI.
+
+## Introduction
+
+Crawl4AI has one clear task: to make crawling and data extraction from web pages easy and efficient, especially for large language models (LLMs) and AI applications. Whether you are using it as a REST API or a Python library, Crawl4AI offers a robust and flexible solution with full asynchronous support.
+
+## Quick Start
+
+Here's a quick example to show you how easy it is to use Crawl4AI with its asynchronous capabilities:
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
+
+async def main():
+ # Create an instance of AsyncWebCrawler
+ async with AsyncWebCrawler(verbose=True) as crawler:
+ # Run the crawler on a URL
+ result = await crawler.arun(url="https://www.nbcnews.com/business")
+
+ # Print the extracted content
+ print(result.markdown)
+
+# Run the async main function
+asyncio.run(main())
+```
+
+## Key Features ✨
+
+- 🆓 Completely free and open-source
+- 🚀 Blazing fast performance, outperforming many paid services
+- 🤖 LLM-friendly output formats (JSON, cleaned HTML, markdown)
+- 📄 Fit markdown generation for extracting main article content.
+- 🌐 Multi-browser support (Chromium, Firefox, WebKit)
+- 🌍 Supports crawling multiple URLs simultaneously
+- 🎨 Extracts and returns all media tags (Images, Audio, and Video)
+- 🔗 Extracts all external and internal links
+- 📚 Extracts metadata from the page
+- 🔄 Custom hooks for authentication, headers, and page modifications
+- 🕵️ User-agent customization
+- 🖼️ Takes screenshots of pages with enhanced error handling
+- 📜 Executes multiple custom JavaScripts before crawling
+- 📊 Generates structured output without LLM using JsonCssExtractionStrategy
+- 📚 Various chunking strategies: topic-based, regex, sentence, and more
+- 🧠 Advanced extraction strategies: cosine clustering, LLM, and more
+- 🎯 CSS selector support for precise data extraction
+- 📝 Passes instructions/keywords to refine extraction
+- 🔒 Proxy support with authentication for enhanced access
+- 🔄 Session management for complex multi-page crawling
+- 🌐 Asynchronous architecture for improved performance
+- 🖼️ Improved image processing with lazy-loading detection
+- 🕰️ Enhanced handling of delayed content loading
+- 🔑 Custom headers support for LLM interactions
+- 🖼️ iframe content extraction for comprehensive analysis
+- ⏱️ Flexible timeout and delayed content retrieval options
+
+## Documentation Structure
+
+Our documentation is organized into several sections:
+
+### Basic Usage
+- [Installation](basic/installation.md)
+- [Quick Start](basic/quickstart.md)
+- [Simple Crawling](basic/simple-crawling.md)
+- [Browser Configuration](basic/browser-config.md)
+- [Content Selection](basic/content-selection.md)
+- [Output Formats](basic/output-formats.md)
+- [Page Interaction](basic/page-interaction.md)
+
+### Advanced Features
+- [Magic Mode](advanced/magic-mode.md)
+- [Session Management](advanced/session-management.md)
+- [Hooks & Authentication](advanced/hooks-auth.md)
+- [Proxy & Security](advanced/proxy-security.md)
+- [Content Processing](advanced/content-processing.md)
+
+### Extraction & Processing
+- [Extraction Strategies Overview](extraction/overview.md)
+- [LLM Integration](extraction/llm.md)
+- [CSS-Based Extraction](extraction/css.md)
+- [Cosine Strategy](extraction/cosine.md)
+- [Chunking Strategies](extraction/chunking.md)
+
+### API Reference
+- [AsyncWebCrawler](api/async-webcrawler.md)
+- [CrawlResult](api/crawl-result.md)
+- [Extraction Strategies](api/strategies.md)
+- [arun() Method Parameters](api/arun.md)
+
+### Examples
+- Coming soon!
+
+## Getting Started
+
+1. Install Crawl4AI:
+```bash
+pip install crawl4ai
+```
+
+2. Check out our [Quick Start Guide](basic/quickstart.md) to begin crawling web pages.
+
+3. Explore our [examples](https://github.com/unclecode/crawl4ai/tree/main/docs/examples) to see Crawl4AI in action.
+
+## Support
+
+For questions, suggestions, or issues:
+- GitHub Issues: [Report a Bug](https://github.com/unclecode/crawl4ai/issues)
+- Twitter: [@unclecode](https://twitter.com/unclecode)
+- Website: [crawl4ai.com](https://crawl4ai.com)
+
+Happy Crawling! 🕸️🚀
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diff --git a/docs/md_v2/tutorial/episode_01_Introduction_to_Crawl4AI_and_Basic_Installation.md b/docs/md_v2/tutorial/episode_01_Introduction_to_Crawl4AI_and_Basic_Installation.md
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+# Crawl4AI
+
+## Episode 1: Introduction to Crawl4AI and Basic Installation
+
+### Quick Intro
+Walk through installation from PyPI, setup, and verification. Show how to install with options like `torch` or `transformer` for advanced capabilities.
+
+Here's a condensed outline of the **Installation and Setup** video content:
+
+---
+
+1) **Introduction to Crawl4AI**: Briefly explain that Crawl4AI is a powerful tool for web scraping, data extraction, and content processing, with customizable options for various needs.
+
+2) **Installation Overview**:
+
+ - **Basic Install**: Run `pip install crawl4ai` and `playwright install` (to set up browser dependencies).
+
+ - **Optional Advanced Installs**:
+ - `pip install crawl4ai[torch]` - Adds PyTorch for clustering.
+ - `pip install crawl4ai[transformer]` - Adds support for LLM-based extraction.
+ - `pip install crawl4ai[all]` - Installs all features for complete functionality.
+
+3) **Verifying the Installation**:
+
+ - Walk through a simple test script to confirm the setup:
+ ```python
+ import asyncio
+ from crawl4ai import AsyncWebCrawler
+
+ async def main():
+ async with AsyncWebCrawler(verbose=True) as crawler:
+ result = await crawler.arun(url="https://www.example.com")
+ print(result.markdown[:500]) # Show first 500 characters
+
+ asyncio.run(main())
+ ```
+ - Explain that this script initializes the crawler and runs it on a test URL, displaying part of the extracted content to verify functionality.
+
+4) **Important Tips**:
+
+ - **Run** `playwright install` **after installation** to set up dependencies.
+ - **For full performance** on text-related tasks, run `crawl4ai-download-models` after installing with `[torch]`, `[transformer]`, or `[all]` options.
+ - If you encounter issues, refer to the documentation or GitHub issues.
+
+5) **Wrap Up**:
+
+ - Introduce the next topic in the series, which will cover Crawl4AI's browser configuration options (like choosing between `chromium`, `firefox`, and `webkit`).
+
+---
+
+This structure provides a concise, effective guide to get viewers up and running with Crawl4AI in minutes.
\ No newline at end of file
diff --git a/docs/md_v2/tutorial/episode_02_Overview_of_Advanced_Features.md b/docs/md_v2/tutorial/episode_02_Overview_of_Advanced_Features.md
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+# Crawl4AI
+
+## Episode 2: Overview of Advanced Features
+
+### Quick Intro
+A general overview of advanced features like hooks, CSS selectors, and JSON CSS extraction.
+
+Here's a condensed outline for an **Overview of Advanced Features** video covering Crawl4AI's powerful customization and extraction options:
+
+---
+
+### **Overview of Advanced Features**
+
+1) **Introduction to Advanced Features**:
+
+ - Briefly introduce Crawl4AI’s advanced tools, which let users go beyond basic crawling to customize and fine-tune their scraping workflows.
+
+2) **Taking Screenshots**:
+
+ - Explain the screenshot capability for capturing page state and verifying content.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://www.example.com", screenshot=True)
+ ```
+ - Mention that screenshots are saved as a base64 string in `result`, allowing easy decoding and saving.
+
+3) **Media and Link Extraction**:
+
+ - Demonstrate how to pull all media (images, videos) and links (internal and external) from a page for deeper analysis or content gathering.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://www.example.com")
+ print("Media:", result.media)
+ print("Links:", result.links)
+ ```
+
+4) **Custom User Agent**:
+
+ - Show how to set a custom user agent to disguise the crawler or simulate specific devices/browsers.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://www.example.com", user_agent="Mozilla/5.0 (compatible; MyCrawler/1.0)")
+ ```
+
+5) **Custom Hooks for Enhanced Control**:
+
+ - Briefly cover how to use hooks, which allow custom actions like setting headers or handling login during the crawl.
+ - **Example**: Setting a custom header with `before_get_url` hook.
+ ```python
+ async def before_get_url(page):
+ await page.set_extra_http_headers({"X-Test-Header": "test"})
+ ```
+
+6) **CSS Selectors for Targeted Extraction**:
+
+ - Explain the use of CSS selectors to extract specific elements, ideal for structured data like articles or product details.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://www.example.com", css_selector="h2")
+ print("H2 Tags:", result.extracted_content)
+ ```
+
+7) **Crawling Inside Iframes**:
+
+ - Mention how enabling `process_iframes=True` allows extracting content within iframes, useful for sites with embedded content or ads.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://www.example.com", process_iframes=True)
+ ```
+
+8) **Wrap-Up**:
+
+ - Summarize these advanced features and how they allow users to customize every part of their web scraping experience.
+ - Tease upcoming videos where each feature will be explored in detail.
+
+---
+
+This covers each advanced feature with a brief example, providing a useful overview to prepare viewers for the more in-depth videos.
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diff --git a/docs/md_v2/tutorial/episode_03_Browser_Configurations_&_Headless_Crawling.md b/docs/md_v2/tutorial/episode_03_Browser_Configurations_&_Headless_Crawling.md
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+# Crawl4AI
+
+## Episode 3: Browser Configurations & Headless Crawling
+
+### Quick Intro
+Explain browser options (`chromium`, `firefox`, `webkit`) and settings for headless mode, caching, and verbose logging.
+
+Here’s a streamlined outline for the **Browser Configurations & Headless Crawling** video:
+
+---
+
+### **Browser Configurations & Headless Crawling**
+
+1) **Overview of Browser Options**:
+
+ - Crawl4AI supports three browser engines:
+ - **Chromium** (default) - Highly compatible.
+ - **Firefox** - Great for specialized use cases.
+ - **Webkit** - Lightweight, ideal for basic needs.
+ - **Example**:
+ ```python
+ # Using Chromium (default)
+ crawler = AsyncWebCrawler(browser_type="chromium")
+
+ # Using Firefox
+ crawler = AsyncWebCrawler(browser_type="firefox")
+
+ # Using WebKit
+ crawler = AsyncWebCrawler(browser_type="webkit")
+ ```
+
+2) **Headless Mode**:
+
+ - Headless mode runs the browser without a visible GUI, making it faster and less resource-intensive.
+ - To enable or disable:
+ ```python
+ # Headless mode (default is True)
+ crawler = AsyncWebCrawler(headless=True)
+
+ # Disable headless mode for debugging
+ crawler = AsyncWebCrawler(headless=False)
+ ```
+
+3) **Verbose Logging**:
+ - Use `verbose=True` to get detailed logs for each action, useful for debugging:
+ ```python
+ crawler = AsyncWebCrawler(verbose=True)
+ ```
+
+4) **Running a Basic Crawl with Configuration**:
+ - Example of a simple crawl with custom browser settings:
+ ```python
+ async with AsyncWebCrawler(browser_type="firefox", headless=True, verbose=True) as crawler:
+ result = await crawler.arun(url="https://www.example.com")
+ print(result.markdown[:500]) # Show first 500 characters
+ ```
+ - This example uses Firefox in headless mode with logging enabled, demonstrating the flexibility of Crawl4AI’s setup.
+
+5) **Recap & Next Steps**:
+ - Recap the power of selecting different browsers and running headless mode for speed and efficiency.
+ - Tease the next video: **Proxy & Security Settings** for navigating blocked or restricted content and protecting IP identity.
+
+---
+
+This breakdown covers browser configuration essentials in Crawl4AI, providing users with practical steps to optimize their scraping setup.
\ No newline at end of file
diff --git a/docs/md_v2/tutorial/episode_04_Advanced_Proxy_and_Security_Settings.md b/docs/md_v2/tutorial/episode_04_Advanced_Proxy_and_Security_Settings.md
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+# Crawl4AI
+
+## Episode 4: Advanced Proxy and Security Settings
+
+### Quick Intro
+Showcase proxy configurations (HTTP, SOCKS5, authenticated proxies). Demo: Use rotating proxies and set custom headers to avoid IP blocking and enhance security.
+
+Here’s a focused outline for the **Proxy and Security Settings** video:
+
+---
+
+### **Proxy & Security Settings**
+
+1) **Why Use Proxies in Web Crawling**:
+
+ - Proxies are essential for bypassing IP-based restrictions, improving anonymity, and managing rate limits.
+ - Crawl4AI supports simple proxies, authenticated proxies, and proxy rotation for robust web scraping.
+
+2) **Basic Proxy Setup**:
+
+ - **Using a Simple Proxy**:
+ ```python
+ # HTTP proxy
+ crawler = AsyncWebCrawler(proxy="http://proxy.example.com:8080")
+
+ # SOCKS proxy
+ crawler = AsyncWebCrawler(proxy="socks5://proxy.example.com:1080")
+ ```
+
+3) **Authenticated Proxies**:
+
+ - Use `proxy_config` for proxies requiring a username and password:
+ ```python
+ proxy_config = {
+ "server": "http://proxy.example.com:8080",
+ "username": "user",
+ "password": "pass"
+ }
+ crawler = AsyncWebCrawler(proxy_config=proxy_config)
+ ```
+
+4) **Rotating Proxies**:
+
+ - Rotating proxies helps avoid IP bans by switching IP addresses for each request:
+ ```python
+ async def get_next_proxy():
+ # Define proxy rotation logic here
+ return {"server": "http://next.proxy.com:8080"}
+
+ async with AsyncWebCrawler() as crawler:
+ for url in urls:
+ proxy = await get_next_proxy()
+ crawler.update_proxy(proxy)
+ result = await crawler.arun(url=url)
+ ```
+ - This setup periodically switches the proxy for enhanced security and access.
+
+5) **Custom Headers for Additional Security**:
+
+ - Set custom headers to mask the crawler’s identity and avoid detection:
+ ```python
+ headers = {
+ "X-Forwarded-For": "203.0.113.195",
+ "Accept-Language": "en-US,en;q=0.9",
+ "Cache-Control": "no-cache",
+ "Pragma": "no-cache"
+ }
+ crawler = AsyncWebCrawler(headers=headers)
+ ```
+
+6) **Combining Proxies with Magic Mode for Anti-Bot Protection**:
+
+ - For sites with aggressive bot detection, combine `proxy` settings with `magic=True`:
+ ```python
+ async with AsyncWebCrawler(proxy="http://proxy.example.com:8080", headers={"Accept-Language": "en-US"}) as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ magic=True # Enables anti-detection features
+ )
+ ```
+ - **Magic Mode** automatically enables user simulation, random timing, and browser property masking.
+
+7) **Wrap Up & Next Steps**:
+
+ - Summarize the importance of proxies and anti-detection in accessing restricted content and avoiding bans.
+ - Tease the next video: **JavaScript Execution and Handling Dynamic Content** for working with interactive and dynamically loaded pages.
+
+---
+
+This outline provides a practical guide to setting up proxies and security configurations, empowering users to navigate restricted sites while staying undetected.
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diff --git a/docs/md_v2/tutorial/episode_05_JavaScript_Execution_and_Dynamic_Content_Handling.md b/docs/md_v2/tutorial/episode_05_JavaScript_Execution_and_Dynamic_Content_Handling.md
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+# Crawl4AI
+
+## Episode 5: JavaScript Execution and Dynamic Content Handling
+
+### Quick Intro
+Explain JavaScript code injection with examples (e.g., simulating scrolling, clicking ‘load more’). Demo: Extract content from a page that uses dynamic loading with lazy-loaded images.
+
+Here’s a focused outline for the **JavaScript Execution and Dynamic Content Handling** video:
+
+---
+
+### **JavaScript Execution & Dynamic Content Handling**
+
+1) **Why JavaScript Execution Matters**:
+
+ - Many modern websites load content dynamically via JavaScript, requiring special handling to access all elements.
+ - Crawl4AI can execute JavaScript on pages, enabling it to interact with elements like “load more” buttons, infinite scrolls, and content that appears only after certain actions.
+
+2) **Basic JavaScript Execution**:
+
+ - Use `js_code` to execute JavaScript commands on a page:
+ ```python
+ # Scroll to bottom of the page
+ result = await crawler.arun(
+ url="https://example.com",
+ js_code="window.scrollTo(0, document.body.scrollHeight);"
+ )
+ ```
+ - This command scrolls to the bottom, triggering any lazy-loaded or dynamically added content.
+
+3) **Multiple Commands & Simulating Clicks**:
+
+ - Combine multiple JavaScript commands to interact with elements like “load more” buttons:
+ ```python
+ js_commands = [
+ "window.scrollTo(0, document.body.scrollHeight);",
+ "document.querySelector('.load-more').click();"
+ ]
+ result = await crawler.arun(
+ url="https://example.com",
+ js_code=js_commands
+ )
+ ```
+ - This script scrolls down and then clicks the “load more” button, useful for loading additional content blocks.
+
+4) **Waiting for Dynamic Content**:
+
+ - Use `wait_for` to ensure the page loads specific elements before proceeding:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ js_code="window.scrollTo(0, document.body.scrollHeight);",
+ wait_for="css:.dynamic-content" # Wait for elements with class `.dynamic-content`
+ )
+ ```
+ - This example waits until elements with `.dynamic-content` are loaded, helping to capture content that appears after JavaScript actions.
+
+5) **Handling Complex Dynamic Content (e.g., Infinite Scroll)**:
+
+ - Combine JavaScript execution with conditional waiting to handle infinite scrolls or paginated content:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ js_code=[
+ "window.scrollTo(0, document.body.scrollHeight);",
+ "const loadMore = document.querySelector('.load-more'); if (loadMore) loadMore.click();"
+ ],
+ wait_for="js:() => document.querySelectorAll('.item').length > 10" # Wait until 10 items are loaded
+ )
+ ```
+ - This example scrolls and clicks "load more" repeatedly, waiting each time for a specified number of items to load.
+
+6) **Complete Example: Dynamic Content Handling with Extraction**:
+
+ - Full example demonstrating a dynamic load and content extraction in one process:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ js_code=[
+ "window.scrollTo(0, document.body.scrollHeight);",
+ "document.querySelector('.load-more').click();"
+ ],
+ wait_for="css:.main-content",
+ css_selector=".main-content"
+ )
+ print(result.markdown[:500]) # Output the main content extracted
+ ```
+
+7) **Wrap Up & Next Steps**:
+
+ - Recap how JavaScript execution allows access to dynamic content, enabling powerful interactions.
+ - Tease the next video: **Content Cleaning and Fit Markdown** to show how Crawl4AI can extract only the most relevant content from complex pages.
+
+---
+
+This outline explains how to handle dynamic content and JavaScript-based interactions effectively, enabling users to scrape and interact with complex, modern websites.
\ No newline at end of file
diff --git a/docs/md_v2/tutorial/episode_06_Magic_Mode_and_Anti-Bot_Protection.md b/docs/md_v2/tutorial/episode_06_Magic_Mode_and_Anti-Bot_Protection.md
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+# Crawl4AI
+
+## Episode 6: Magic Mode and Anti-Bot Protection
+
+### Quick Intro
+Highlight `Magic Mode` and anti-bot features like user simulation, navigator overrides, and timing randomization. Demo: Access a site with anti-bot protection and show how `Magic Mode` seamlessly handles it.
+
+Here’s a concise outline for the **Magic Mode and Anti-Bot Protection** video:
+
+---
+
+### **Magic Mode & Anti-Bot Protection**
+
+1) **Why Anti-Bot Protection is Important**:
+
+ - Many websites use bot detection mechanisms to block automated scraping. Crawl4AI’s anti-detection features help avoid IP bans, CAPTCHAs, and access restrictions.
+ - **Magic Mode** is a one-step solution to enable a range of anti-bot features without complex configuration.
+
+2) **Enabling Magic Mode**:
+
+ - Simply set `magic=True` to activate Crawl4AI’s full anti-bot suite:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ magic=True # Enables all anti-detection features
+ )
+ ```
+ - This enables a blend of stealth techniques, including masking automation signals, randomizing timings, and simulating real user behavior.
+
+3) **What Magic Mode Does Behind the Scenes**:
+
+ - **User Simulation**: Mimics human actions like mouse movements and scrolling.
+ - **Navigator Overrides**: Hides signals that indicate an automated browser.
+ - **Timing Randomization**: Adds random delays to simulate natural interaction patterns.
+ - **Cookie Handling**: Accepts and manages cookies dynamically to avoid triggers from cookie pop-ups.
+
+4) **Manual Anti-Bot Options (If Not Using Magic Mode)**:
+
+ - For granular control, you can configure individual settings without Magic Mode:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ simulate_user=True, # Enables human-like behavior
+ override_navigator=True # Hides automation fingerprints
+ )
+ ```
+ - **Use Cases**: This approach allows more specific adjustments when certain anti-bot features are needed but others are not.
+
+5) **Combining Proxies with Magic Mode**:
+
+ - To avoid rate limits or IP blocks, combine Magic Mode with a proxy:
+ ```python
+ async with AsyncWebCrawler(
+ proxy="http://proxy.example.com:8080",
+ headers={"Accept-Language": "en-US"}
+ ) as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ magic=True # Full anti-detection
+ )
+ ```
+ - This setup maximizes stealth by pairing anti-bot detection with IP obfuscation.
+
+6) **Example of Anti-Bot Protection in Action**:
+
+ - Full example with Magic Mode and proxies to scrape a protected page:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com/protected-content",
+ magic=True,
+ proxy="http://proxy.example.com:8080",
+ wait_for="css:.content-loaded" # Wait for the main content to load
+ )
+ print(result.markdown[:500]) # Display first 500 characters of the content
+ ```
+ - This example ensures seamless access to protected content by combining anti-detection and waiting for full content load.
+
+7) **Wrap Up & Next Steps**:
+
+ - Recap the power of Magic Mode and anti-bot features for handling restricted websites.
+ - Tease the next video: **Content Cleaning and Fit Markdown** to show how to extract clean and focused content from a page.
+
+---
+
+This outline shows users how to easily avoid bot detection and access restricted content, demonstrating both the power and simplicity of Magic Mode in Crawl4AI.
\ No newline at end of file
diff --git a/docs/md_v2/tutorial/episode_07_Content_Cleaning_and_Fit_Markdown.md b/docs/md_v2/tutorial/episode_07_Content_Cleaning_and_Fit_Markdown.md
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@@ -0,0 +1,89 @@
+# Crawl4AI
+
+## Episode 7: Content Cleaning and Fit Markdown
+
+### Quick Intro
+Explain content cleaning options, including `fit_markdown` to keep only the most relevant content. Demo: Extract and compare regular vs. fit markdown from a news site or blog.
+
+Here’s a streamlined outline for the **Content Cleaning and Fit Markdown** video:
+
+---
+
+### **Content Cleaning & Fit Markdown**
+
+1) **Overview of Content Cleaning in Crawl4AI**:
+
+ - Explain that web pages often include extra elements like ads, navigation bars, footers, and popups.
+ - Crawl4AI’s content cleaning features help extract only the main content, reducing noise and enhancing readability.
+
+2) **Basic Content Cleaning Options**:
+
+ - **Removing Unwanted Elements**: Exclude specific HTML tags, like forms or navigation bars:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ word_count_threshold=10, # Filter out blocks with fewer than 10 words
+ excluded_tags=['form', 'nav'], # Exclude specific tags
+ remove_overlay_elements=True # Remove popups and modals
+ )
+ ```
+ - This example extracts content while excluding forms, navigation, and modal overlays, ensuring clean results.
+
+3) **Fit Markdown for Main Content Extraction**:
+
+ - **What is Fit Markdown**: Uses advanced analysis to identify the most relevant content (ideal for articles, blogs, and documentation).
+ - **How it Works**: Analyzes content density, removes boilerplate elements, and maintains formatting for a clear output.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://example.com")
+ main_content = result.fit_markdown # Extracted main content
+ print(main_content[:500]) # Display first 500 characters
+ ```
+ - Fit Markdown is especially helpful for long-form content like news articles or blog posts.
+
+4) **Comparing Fit Markdown with Regular Markdown**:
+
+ - **Fit Markdown** returns the primary content without extraneous elements.
+ - **Regular Markdown** includes all extracted text in markdown format.
+ - Example to show the difference:
+ ```python
+ all_content = result.markdown # Full markdown
+ main_content = result.fit_markdown # Only the main content
+
+ print(f"All Content Length: {len(all_content)}")
+ print(f"Main Content Length: {len(main_content)}")
+ ```
+ - This comparison shows the effectiveness of Fit Markdown in focusing on essential content.
+
+5) **Media and Metadata Handling with Content Cleaning**:
+
+ - **Media Extraction**: Crawl4AI captures images and videos with metadata like alt text, descriptions, and relevance scores:
+ ```python
+ for image in result.media["images"]:
+ print(f"Source: {image['src']}, Alt Text: {image['alt']}, Relevance Score: {image['score']}")
+ ```
+ - **Use Case**: Useful for saving only relevant images or videos from an article or content-heavy page.
+
+6) **Example of Clean Content Extraction in Action**:
+
+ - Full example extracting cleaned content and Fit Markdown:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ word_count_threshold=10,
+ excluded_tags=['nav', 'footer'],
+ remove_overlay_elements=True
+ )
+ print(result.fit_markdown[:500]) # Show main content
+ ```
+ - This example demonstrates content cleaning with settings for filtering noise and focusing on the core text.
+
+7) **Wrap Up & Next Steps**:
+
+ - Summarize the power of Crawl4AI’s content cleaning features and Fit Markdown for capturing clean, relevant content.
+ - Tease the next video: **Link Analysis and Smart Filtering** to focus on analyzing and filtering links within crawled pages.
+
+---
+
+This outline covers Crawl4AI’s content cleaning features and the unique benefits of Fit Markdown, showing users how to retrieve focused, high-quality content from web pages.
\ No newline at end of file
diff --git a/docs/md_v2/tutorial/episode_08_Media_Handling_Images_Videos_and_Audio.md b/docs/md_v2/tutorial/episode_08_Media_Handling_Images_Videos_and_Audio.md
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--- /dev/null
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+# Crawl4AI
+
+## Episode 8: Media Handling: Images, Videos, and Audio
+
+### Quick Intro
+Showcase Crawl4AI’s media extraction capabilities, including lazy-loaded media and metadata. Demo: Crawl a multimedia page, extract images, and show metadata (alt text, context, relevance score).
+
+Here’s a clear and focused outline for the **Media Handling: Images, Videos, and Audio** video:
+
+---
+
+### **Media Handling: Images, Videos, and Audio**
+
+1) **Overview of Media Extraction in Crawl4AI**:
+
+ - Crawl4AI can detect and extract different types of media (images, videos, and audio) along with useful metadata.
+ - This functionality is essential for gathering visual content from multimedia-heavy pages like e-commerce sites, news articles, and social media feeds.
+
+2) **Image Extraction and Metadata**:
+
+ - Crawl4AI captures images with detailed metadata, including:
+ - **Source URL**: The direct URL to the image.
+ - **Alt Text**: Image description if available.
+ - **Relevance Score**: A score (0–10) indicating how relevant the image is to the main content.
+ - **Context**: Text surrounding the image on the page.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://example.com")
+
+ for image in result.media["images"]:
+ print(f"Source: {image['src']}")
+ print(f"Alt Text: {image['alt']}")
+ print(f"Relevance Score: {image['score']}")
+ print(f"Context: {image['context']}")
+ ```
+ - This example shows how to access each image’s metadata, making it easy to filter for the most relevant visuals.
+
+3) **Handling Lazy-Loaded Images**:
+
+ - Crawl4AI automatically supports lazy-loaded images, which are commonly used to optimize webpage loading.
+ - **Example with Wait for Lazy-Loaded Content**:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ wait_for="css:img[data-src]", # Wait for lazy-loaded images
+ delay_before_return_html=2.0 # Allow extra time for images to load
+ )
+ ```
+ - This setup waits for lazy-loaded images to appear, ensuring they are fully captured.
+
+4) **Video Extraction and Metadata**:
+
+ - Crawl4AI captures video elements, including:
+ - **Source URL**: The video’s direct URL.
+ - **Type**: Format of the video (e.g., MP4).
+ - **Thumbnail**: A poster or thumbnail image if available.
+ - **Duration**: Video length, if metadata is provided.
+ - **Example**:
+ ```python
+ for video in result.media["videos"]:
+ print(f"Video Source: {video['src']}")
+ print(f"Type: {video['type']}")
+ print(f"Thumbnail: {video.get('poster')}")
+ print(f"Duration: {video.get('duration')}")
+ ```
+ - This allows users to gather video content and relevant details for further processing or analysis.
+
+5) **Audio Extraction and Metadata**:
+
+ - Audio elements can also be extracted, with metadata like:
+ - **Source URL**: The audio file’s direct URL.
+ - **Type**: Format of the audio file (e.g., MP3).
+ - **Duration**: Length of the audio, if available.
+ - **Example**:
+ ```python
+ for audio in result.media["audios"]:
+ print(f"Audio Source: {audio['src']}")
+ print(f"Type: {audio['type']}")
+ print(f"Duration: {audio.get('duration')}")
+ ```
+ - Useful for sites with podcasts, sound bites, or other audio content.
+
+6) **Filtering Media by Relevance**:
+
+ - Use metadata like relevance score to filter only the most useful media content:
+ ```python
+ relevant_images = [img for img in result.media["images"] if img['score'] > 5]
+ ```
+ - This is especially helpful for content-heavy pages where you only want media directly related to the main content.
+
+7) **Example: Full Media Extraction with Content Filtering**:
+
+ - Full example extracting images, videos, and audio along with filtering by relevance:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ word_count_threshold=10, # Filter content blocks for relevance
+ exclude_external_images=True # Only keep internal images
+ )
+
+ # Display media summaries
+ print(f"Relevant Images: {len(relevant_images)}")
+ print(f"Videos: {len(result.media['videos'])}")
+ print(f"Audio Clips: {len(result.media['audios'])}")
+ ```
+ - This example shows how to capture and filter various media types, focusing on what’s most relevant.
+
+8) **Wrap Up & Next Steps**:
+
+ - Recap the comprehensive media extraction capabilities, emphasizing how metadata helps users focus on relevant content.
+ - Tease the next video: **Link Analysis and Smart Filtering** to explore how Crawl4AI handles internal, external, and social media links for more focused data gathering.
+
+---
+
+This outline provides users with a complete guide to handling images, videos, and audio in Crawl4AI, using metadata to enhance relevance and precision in multimedia extraction.
diff --git a/docs/md_v2/tutorial/episode_09_Link_Analysis_and_Smart_Filtering.md b/docs/md_v2/tutorial/episode_09_Link_Analysis_and_Smart_Filtering.md
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--- /dev/null
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+# Crawl4AI
+
+## Episode 9: Link Analysis and Smart Filtering
+
+### Quick Intro
+Walk through internal and external link classification, social media link filtering, and custom domain exclusion. Demo: Analyze links on a website, focusing on internal navigation vs. external or ad links.
+
+Here’s a focused outline for the **Link Analysis and Smart Filtering** video:
+
+---
+
+### **Link Analysis & Smart Filtering**
+
+1) **Importance of Link Analysis in Web Crawling**:
+
+ - Explain that web pages often contain numerous links, including internal links, external links, social media links, and ads.
+ - Crawl4AI’s link analysis and filtering options help extract only relevant links, enabling more targeted and efficient crawls.
+
+2) **Automatic Link Classification**:
+
+ - Crawl4AI categorizes links automatically into internal, external, and social media links.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://example.com")
+
+ # Access internal and external links
+ internal_links = result.links["internal"]
+ external_links = result.links["external"]
+
+ # Print first few links for each type
+ print("Internal Links:", internal_links[:3])
+ print("External Links:", external_links[:3])
+ ```
+
+3) **Filtering Out Unwanted Links**:
+
+ - **Exclude External Links**: Remove all links pointing to external sites.
+ - **Exclude Social Media Links**: Filter out social media domains like Facebook or Twitter.
+ - **Example**:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ exclude_external_links=True, # Remove external links
+ exclude_social_media_links=True # Remove social media links
+ )
+ ```
+
+4) **Custom Domain Filtering**:
+
+ - **Exclude Specific Domains**: Filter links from particular domains, e.g., ad sites.
+ - **Custom Social Media Domains**: Add additional social media domains if needed.
+ - **Example**:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ exclude_domains=["ads.com", "trackers.com"],
+ exclude_social_media_domains=["facebook.com", "linkedin.com"]
+ )
+ ```
+
+5) **Accessing Link Context and Metadata**:
+
+ - Crawl4AI provides additional metadata for each link, including its text, type (e.g., navigation or content), and surrounding context.
+ - **Example**:
+ ```python
+ for link in result.links["internal"]:
+ print(f"Link: {link['href']}, Text: {link['text']}, Context: {link['context']}")
+ ```
+ - **Use Case**: Helps users understand the relevance of links based on where they are placed on the page (e.g., navigation vs. article content).
+
+6) **Example of Comprehensive Link Filtering and Analysis**:
+
+ - Full example combining link filtering, metadata access, and contextual information:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ exclude_external_links=True,
+ exclude_social_media_links=True,
+ exclude_domains=["ads.com"],
+ css_selector=".main-content" # Focus only on main content area
+ )
+ for link in result.links["internal"]:
+ print(f"Internal Link: {link['href']}, Text: {link['text']}, Context: {link['context']}")
+ ```
+ - This example filters unnecessary links, keeping only internal and relevant links from the main content area.
+
+7) **Wrap Up & Next Steps**:
+
+ - Summarize the benefits of link filtering for efficient crawling and relevant content extraction.
+ - Tease the next video: **Custom Headers, Identity Management, and User Simulation** to explain how to configure identity settings and simulate user behavior for stealthier crawls.
+
+---
+
+This outline provides a practical overview of Crawl4AI’s link analysis and filtering features, helping users target only essential links while eliminating distractions.
\ No newline at end of file
diff --git a/docs/md_v2/tutorial/episode_10_Custom_Headers,_Identity,_and_User_Simulation.md b/docs/md_v2/tutorial/episode_10_Custom_Headers,_Identity,_and_User_Simulation.md
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+# Crawl4AI
+
+## Episode 10: Custom Headers, Identity, and User Simulation
+
+### Quick Intro
+Teach how to use custom headers, user-agent strings, and simulate real user interactions. Demo: Set custom user-agent and headers to access a site that blocks typical crawlers.
+
+Here’s a concise outline for the **Custom Headers, Identity Management, and User Simulation** video:
+
+---
+
+### **Custom Headers, Identity Management, & User Simulation**
+
+1) **Why Customize Headers and Identity in Crawling**:
+
+ - Websites often track request headers and browser properties to detect bots. Customizing headers and managing identity help make requests appear more human, improving access to restricted sites.
+
+2) **Setting Custom Headers**:
+
+ - Customize HTTP headers to mimic genuine browser requests or meet site-specific requirements:
+ ```python
+ headers = {
+ "Accept-Language": "en-US,en;q=0.9",
+ "X-Requested-With": "XMLHttpRequest",
+ "Cache-Control": "no-cache"
+ }
+ crawler = AsyncWebCrawler(headers=headers)
+ ```
+ - **Use Case**: Customize the `Accept-Language` header to simulate local user settings, or `Cache-Control` to bypass cache for fresh content.
+
+3) **Setting a Custom User Agent**:
+
+ - Some websites block requests from common crawler user agents. Setting a custom user agent string helps bypass these restrictions:
+ ```python
+ crawler = AsyncWebCrawler(
+ user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+ )
+ ```
+ - **Tip**: Use user-agent strings from popular browsers (e.g., Chrome, Firefox) to improve access and reduce detection risks.
+
+4) **User Simulation for Human-like Behavior**:
+
+ - Enable `simulate_user=True` to mimic natural user interactions, such as random timing and simulated mouse movements:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ simulate_user=True # Simulates human-like behavior
+ )
+ ```
+ - **Behavioral Effects**: Adds subtle variations in interactions, making the crawler harder to detect on bot-protected sites.
+
+5) **Navigator Overrides and Magic Mode for Full Identity Masking**:
+
+ - Use `override_navigator=True` to mask automation indicators like `navigator.webdriver`, which websites check to detect bots:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ override_navigator=True # Masks bot-related signals
+ )
+ ```
+ - **Combining with Magic Mode**: For a complete anti-bot setup, combine these identity options with `magic=True` for maximum protection:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ magic=True, # Enables all anti-bot detection features
+ user_agent="Custom-Agent", # Custom agent with Magic Mode
+ )
+ ```
+ - This setup includes all anti-detection techniques like navigator masking, random timing, and user simulation.
+
+6) **Example: Comprehensive Setup for Identity Management**:
+
+ - A full example combining custom headers, user-agent, and user simulation for a realistic browsing profile:
+ ```python
+ async with AsyncWebCrawler(
+ headers={"Accept-Language": "en-US", "Cache-Control": "no-cache"},
+ user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) Chrome/91.0",
+ simulate_user=True
+ ) as crawler:
+ result = await crawler.arun(url="https://example.com/secure-page")
+ print(result.markdown[:500]) # Display extracted content
+ ```
+ - This example enables detailed customization for evading detection and accessing protected pages smoothly.
+
+7) **Wrap Up & Next Steps**:
+
+ - Recap the value of headers, user-agent customization, and simulation in bypassing bot detection.
+ - Tease the next video: **Extraction Strategies: JSON CSS, LLM, and Cosine** to dive into structured data extraction methods for high-quality content retrieval.
+
+---
+
+This outline equips users with tools for managing crawler identity and human-like behavior, essential for accessing bot-protected or restricted websites.
\ No newline at end of file
diff --git a/docs/md_v2/tutorial/episode_11_1_Extraction_Strategies_JSON_CSS.md b/docs/md_v2/tutorial/episode_11_1_Extraction_Strategies_JSON_CSS.md
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+Here’s a detailed outline for the **JSON-CSS Extraction Strategy** video, covering all key aspects and supported structures in Crawl4AI:
+
+---
+
+### **10.1 JSON-CSS Extraction Strategy**
+
+#### **1. Introduction to JSON-CSS Extraction**
+ - JSON-CSS Extraction is used for pulling structured data from pages with repeated patterns, like product listings, article feeds, or directories.
+ - This strategy allows defining a schema with CSS selectors and data fields, making it easy to capture nested, list-based, or singular elements.
+
+#### **2. Basic Schema Structure**
+ - **Schema Fields**: The schema has two main components:
+ - `baseSelector`: A CSS selector to locate the main elements you want to extract (e.g., each article or product block).
+ - `fields`: Defines the data fields for each element, supporting various data types and structures.
+
+#### **3. Simple Field Extraction**
+ - **Example HTML**:
+ ```html
+
+ Sample Product
+ $19.99
+ This is a sample product.
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".product",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text"},
+ {"name": "price", "selector": ".price", "type": "text"},
+ {"name": "description", "selector": ".description", "type": "text"}
+ ]
+ }
+ ```
+ - **Explanation**: Each field captures text content from specified CSS selectors within each `.product` element.
+
+#### **4. Supported Field Types: Text, Attribute, HTML, Regex**
+ - **Field Type Options**:
+ - `text`: Extracts visible text.
+ - `attribute`: Captures an HTML attribute (e.g., `src`, `href`).
+ - `html`: Extracts the raw HTML of an element.
+ - `regex`: Allows regex patterns to extract part of the text.
+
+ - **Example HTML** (including an image):
+ ```html
+
+ Sample Product
+ 
+ $19.99
+ Limited time offer.
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".product",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text"},
+ {"name": "image_url", "selector": ".product-image", "type": "attribute", "attribute": "src"},
+ {"name": "price", "selector": ".price", "type": "regex", "pattern": r"\$(\d+\.\d+)"},
+ {"name": "description_html", "selector": ".description", "type": "html"}
+ ]
+ }
+ ```
+ - **Explanation**:
+ - `attribute`: Extracts the `src` attribute from `.product-image`.
+ - `regex`: Extracts the numeric part from `$19.99`.
+ - `html`: Retrieves the full HTML of the description element.
+
+#### **5. Nested Field Extraction**
+ - **Use Case**: Useful when content contains sub-elements, such as an article with author details within it.
+ - **Example HTML**:
+ ```html
+
+ Sample Article
+
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".article",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text"},
+ {"name": "author", "type": "nested", "selector": ".author", "fields": [
+ {"name": "name", "selector": ".name", "type": "text"},
+ {"name": "bio", "selector": ".bio", "type": "text"}
+ ]}
+ ]
+ }
+ ```
+ - **Explanation**:
+ - `nested`: Extracts `name` and `bio` within `.author`, grouping the author details in a single `author` object.
+
+#### **6. List and Nested List Extraction**
+ - **List**: Extracts multiple elements matching the selector as a list.
+ - **Nested List**: Allows lists within lists, useful for items with sub-lists (e.g., specifications for each product).
+ - **Example HTML**:
+ ```html
+
+ Product with Features
+
+ - Feature 1
+ - Feature 2
+ - Feature 3
+
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".product",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text"},
+ {"name": "features", "type": "list", "selector": ".features .feature", "fields": [
+ {"name": "feature", "type": "text"}
+ ]}
+ ]
+ }
+ ```
+ - **Explanation**:
+ - `list`: Captures each `.feature` item within `.features`, outputting an array of features under the `features` field.
+
+#### **7. Transformations for Field Values**
+ - Transformations allow you to modify extracted values (e.g., converting to lowercase).
+ - Supported transformations: `lowercase`, `uppercase`, `strip`.
+ - **Example HTML**:
+ ```html
+
+ Special Product
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".product",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text", "transform": "uppercase"}
+ ]
+ }
+ ```
+ - **Explanation**: The `transform` property changes the `title` to uppercase, useful for standardized outputs.
+
+#### **8. Full JSON-CSS Extraction Example**
+ - Combining all elements in a single schema example for a comprehensive crawl:
+ - **Example HTML**:
+ ```html
+
+ Featured Product
+ 
+ $99.99
+ Best product of the year.
+
+ - Durable
+ - Eco-friendly
+
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".product",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text", "transform": "uppercase"},
+ {"name": "image_url", "selector": ".product-image", "type": "attribute", "attribute": "src"},
+ {"name": "price", "selector": ".price", "type": "regex", "pattern": r"\$(\d+\.\d+)"},
+ {"name": "description", "selector": ".description", "type": "html"},
+ {"name": "features", "type": "list", "selector": ".features .feature", "fields": [
+ {"name": "feature", "type": "text"}
+ ]}
+ ]
+ }
+ ```
+ - **Explanation**: This schema captures and transforms each aspect of the product, illustrating the JSON-CSS strategy’s versatility for structured extraction.
+
+#### **9. Wrap Up & Next Steps**
+ - Summarize JSON-CSS Extraction’s flexibility for structured, pattern-based extraction.
+ - Tease the next video: **10.2 LLM Extraction Strategy**, focusing on using language models to extract data based on intelligent content analysis.
+
+---
+
+This outline covers each JSON-CSS Extraction option in Crawl4AI, with practical examples and schema configurations, making it a thorough guide for users.
diff --git a/docs/md_v2/tutorial/episode_11_2_Extraction_Strategies_LLM.md b/docs/md_v2/tutorial/episode_11_2_Extraction_Strategies_LLM.md
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+# Crawl4AI
+
+## Episode 11: Extraction Strategies: JSON CSS, LLM, and Cosine
+
+### Quick Intro
+Introduce JSON CSS Extraction Strategy for structured data, LLM Extraction Strategy for intelligent parsing, and Cosine Strategy for clustering similar content. Demo: Use JSON CSS to scrape product details from an e-commerce site.
+
+Here’s a comprehensive outline for the **LLM Extraction Strategy** video, covering key details and example applications.
+
+---
+
+### **10.2 LLM Extraction Strategy**
+
+#### **1. Introduction to LLM Extraction Strategy**
+ - The LLM Extraction Strategy leverages language models to interpret and extract structured data from complex web content.
+ - Unlike traditional CSS selectors, this strategy uses natural language instructions and schemas to guide the extraction, ideal for unstructured or diverse content.
+ - Supports **OpenAI**, **Azure OpenAI**, **HuggingFace**, and **Ollama** models, enabling flexibility with both proprietary and open-source providers.
+
+#### **2. Key Components of LLM Extraction Strategy**
+ - **Provider**: Specifies the LLM provider (e.g., OpenAI, HuggingFace, Azure).
+ - **API Token**: Required for most providers, except Ollama (local LLM model).
+ - **Instruction**: Custom extraction instructions sent to the model, providing flexibility in how the data is structured and extracted.
+ - **Schema**: Optional, defines structured fields to organize extracted data into JSON format.
+ - **Extraction Type**: Supports `"block"` for simpler text blocks or `"schema"` when a structured output format is required.
+ - **Chunking Parameters**: Breaks down large documents, with options to adjust chunk size and overlap rate for more accurate extraction across lengthy texts.
+
+#### **3. Basic Extraction Example: OpenAI Model Pricing**
+ - **Goal**: Extract model names and their input and output fees from the OpenAI pricing page.
+ - **Schema Definition**:
+ - **Model Name**: Text for model identification.
+ - **Input Fee**: Token cost for input processing.
+ - **Output Fee**: Token cost for output generation.
+
+ - **Schema**:
+ ```python
+ class OpenAIModelFee(BaseModel):
+ model_name: str = Field(..., description="Name of the OpenAI model.")
+ input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
+ output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
+ ```
+
+ - **Example Code**:
+ ```python
+ async def extract_openai_pricing():
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://openai.com/api/pricing/",
+ extraction_strategy=LLMExtractionStrategy(
+ provider="openai/gpt-4o",
+ api_token=os.getenv("OPENAI_API_KEY"),
+ schema=OpenAIModelFee.schema(),
+ extraction_type="schema",
+ instruction="Extract model names and fees for input and output tokens from the page."
+ ),
+ bypass_cache=True
+ )
+ print(result.extracted_content)
+ ```
+
+ - **Explanation**:
+ - The extraction strategy combines a schema and detailed instruction to guide the LLM in capturing structured data.
+ - Each model’s name, input fee, and output fee are extracted in a JSON format.
+
+#### **4. Knowledge Graph Extraction Example**
+ - **Goal**: Extract entities and their relationships from a document for use in a knowledge graph.
+ - **Schema Definition**:
+ - **Entities**: Individual items with descriptions (e.g., people, organizations).
+ - **Relationships**: Connections between entities, including descriptions and relationship types.
+
+ - **Schema**:
+ ```python
+ class Entity(BaseModel):
+ name: str
+ description: str
+
+ class Relationship(BaseModel):
+ entity1: Entity
+ entity2: Entity
+ description: str
+ relation_type: str
+
+ class KnowledgeGraph(BaseModel):
+ entities: List[Entity]
+ relationships: List[Relationship]
+ ```
+
+ - **Example Code**:
+ ```python
+ async def extract_knowledge_graph():
+ extraction_strategy = LLMExtractionStrategy(
+ provider="azure/gpt-4o-mini",
+ api_token=os.getenv("AZURE_API_KEY"),
+ schema=KnowledgeGraph.schema(),
+ extraction_type="schema",
+ instruction="Extract entities and relationships from the content to build a knowledge graph."
+ )
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com/some-article",
+ extraction_strategy=extraction_strategy,
+ bypass_cache=True
+ )
+ print(result.extracted_content)
+ ```
+
+ - **Explanation**:
+ - In this setup, the LLM extracts entities and their relationships based on the schema and instruction.
+ - The schema organizes results into a JSON-based knowledge graph format.
+
+#### **5. Key Settings in LLM Extraction**
+ - **Chunking Options**:
+ - For long pages, set `chunk_token_threshold` to specify maximum token count per section.
+ - Adjust `overlap_rate` to control the overlap between chunks, useful for contextual consistency.
+ - **Example**:
+ ```python
+ extraction_strategy = LLMExtractionStrategy(
+ provider="openai/gpt-4",
+ api_token=os.getenv("OPENAI_API_KEY"),
+ chunk_token_threshold=3000,
+ overlap_rate=0.2, # 20% overlap between chunks
+ instruction="Extract key insights and relationships."
+ )
+ ```
+ - This setup ensures that longer texts are divided into manageable chunks with slight overlap, enhancing the quality of extraction.
+
+#### **6. Flexible Provider Options for LLM Extraction**
+ - **Using Proprietary Models**: OpenAI, Azure, and HuggingFace provide robust language models, often suited for complex or detailed extractions.
+ - **Using Open-Source Models**: Ollama and other open-source models can be deployed locally, suitable for offline or cost-effective extraction.
+ - **Example Call**:
+ ```python
+ await extract_structured_data_using_llm("huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct", os.getenv("HUGGINGFACE_API_KEY"))
+ await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
+ await extract_structured_data_using_llm("ollama/llama3.2")
+ ```
+
+#### **7. Complete Example of LLM Extraction Setup**
+ - Code to run both the OpenAI pricing and Knowledge Graph extractions, using various providers:
+ ```python
+ async def main():
+ await extract_openai_pricing()
+ await extract_knowledge_graph()
+
+ if __name__ == "__main__":
+ asyncio.run(main())
+ ```
+
+#### **8. Wrap Up & Next Steps**
+ - Recap the power of LLM extraction for handling unstructured or complex data extraction tasks.
+ - Tease the next video: **10.3 Cosine Similarity Strategy** for clustering similar content based on semantic similarity.
+
+---
+
+This outline explains LLM Extraction in Crawl4AI, with examples showing how to extract structured data using custom schemas and instructions. It demonstrates flexibility with multiple providers, ensuring practical application for different use cases.
diff --git a/docs/md_v2/tutorial/episode_11_3_Extraction_Strategies_Cosine.md b/docs/md_v2/tutorial/episode_11_3_Extraction_Strategies_Cosine.md
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+# Crawl4AI
+
+## Episode 11: Extraction Strategies: JSON CSS, LLM, and Cosine
+
+### Quick Intro
+Introduce JSON CSS Extraction Strategy for structured data, LLM Extraction Strategy for intelligent parsing, and Cosine Strategy for clustering similar content. Demo: Use JSON CSS to scrape product details from an e-commerce site.
+
+Here’s a structured outline for the **Cosine Similarity Strategy** video, covering key concepts, configuration, and a practical example.
+
+---
+
+### **10.3 Cosine Similarity Strategy**
+
+#### **1. Introduction to Cosine Similarity Strategy**
+ - The Cosine Similarity Strategy clusters content by semantic similarity, offering an efficient alternative to LLM-based extraction, especially when speed is a priority.
+ - Ideal for grouping similar sections of text, this strategy is well-suited for pages with content sections that may need to be classified or tagged, like news articles, product descriptions, or reviews.
+
+#### **2. Key Configuration Options**
+ - **semantic_filter**: A keyword-based filter to focus on relevant content.
+ - **word_count_threshold**: Minimum number of words per cluster, filtering out shorter, less meaningful clusters.
+ - **max_dist**: Maximum allowable distance between elements in clusters, impacting cluster tightness.
+ - **linkage_method**: Method for hierarchical clustering, such as `'ward'` (for well-separated clusters).
+ - **top_k**: Specifies the number of top categories for each cluster.
+ - **model_name**: Defines the model for embeddings, such as `sentence-transformers/all-MiniLM-L6-v2`.
+ - **sim_threshold**: Minimum similarity threshold for filtering, allowing control over cluster relevance.
+
+#### **3. How Cosine Similarity Clustering Works**
+ - **Step 1**: Embeddings are generated for each text section, transforming them into vectors that capture semantic meaning.
+ - **Step 2**: Hierarchical clustering groups similar sections based on cosine similarity, forming clusters with related content.
+ - **Step 3**: Clusters are filtered based on word count, removing those below the `word_count_threshold`.
+ - **Step 4**: Each cluster is then categorized with tags, if enabled, providing context to each grouped content section.
+
+#### **4. Example Use Case: Clustering Blog Article Sections**
+ - **Goal**: Group related sections of a blog or news page to identify distinct topics or discussion areas.
+ - **Example HTML Sections**:
+ ```text
+ "The economy is showing signs of recovery, with markets up this quarter.",
+ "In the sports world, several major teams are preparing for the upcoming season.",
+ "New advancements in AI technology are reshaping the tech landscape.",
+ "Market analysts are optimistic about continued growth in tech stocks."
+ ```
+
+ - **Code Setup**:
+ ```python
+ async def extract_blog_sections():
+ extraction_strategy = CosineStrategy(
+ word_count_threshold=15,
+ max_dist=0.3,
+ sim_threshold=0.2,
+ model_name="sentence-transformers/all-MiniLM-L6-v2",
+ top_k=2
+ )
+ async with AsyncWebCrawler() as crawler:
+ url = "https://example.com/blog-page"
+ result = await crawler.arun(
+ url=url,
+ extraction_strategy=extraction_strategy,
+ bypass_cache=True
+ )
+ print(result.extracted_content)
+ ```
+
+ - **Explanation**:
+ - **word_count_threshold**: Ensures only clusters with meaningful content are included.
+ - **sim_threshold**: Filters out clusters with low similarity, focusing on closely related sections.
+ - **top_k**: Selects top tags, useful for identifying main topics.
+
+#### **5. Applying Semantic Filtering with Cosine Similarity**
+ - **Semantic Filter**: Filters sections based on relevance to a specific keyword, such as “technology” for tech articles.
+ - **Example Code**:
+ ```python
+ extraction_strategy = CosineStrategy(
+ semantic_filter="technology",
+ word_count_threshold=10,
+ max_dist=0.25,
+ model_name="sentence-transformers/all-MiniLM-L6-v2"
+ )
+ ```
+ - **Explanation**:
+ - **semantic_filter**: Only sections with high similarity to the “technology” keyword will be included in the clustering, making it easy to focus on specific topics within a mixed-content page.
+
+#### **6. Clustering Product Reviews by Similarity**
+ - **Goal**: Organize product reviews by themes, such as “price,” “quality,” or “durability.”
+ - **Example Reviews**:
+ ```text
+ "The quality of this product is outstanding and well worth the price.",
+ "I found the product to be durable but a bit overpriced.",
+ "Great value for the money and long-lasting.",
+ "The build quality is good, but I expected a lower price point."
+ ```
+
+ - **Code Setup**:
+ ```python
+ async def extract_product_reviews():
+ extraction_strategy = CosineStrategy(
+ word_count_threshold=20,
+ max_dist=0.35,
+ sim_threshold=0.25,
+ model_name="sentence-transformers/all-MiniLM-L6-v2"
+ )
+ async with AsyncWebCrawler() as crawler:
+ url = "https://example.com/product-reviews"
+ result = await crawler.arun(
+ url=url,
+ extraction_strategy=extraction_strategy,
+ bypass_cache=True
+ )
+ print(result.extracted_content)
+ ```
+
+ - **Explanation**:
+ - This configuration clusters similar reviews, grouping feedback by common themes, helping businesses understand customer sentiments around particular product aspects.
+
+#### **7. Performance Advantages of Cosine Strategy**
+ - **Speed**: The Cosine Similarity Strategy is faster than LLM-based extraction, as it doesn’t rely on API calls to external LLMs.
+ - **Local Processing**: The strategy runs locally with pre-trained sentence embeddings, ideal for high-throughput scenarios where cost and latency are concerns.
+ - **Comparison**: With a well-optimized local model, this method can perform clustering on large datasets quickly, making it suitable for tasks requiring rapid, repeated analysis.
+
+#### **8. Full Code Example for Clustering News Articles**
+ - **Code**:
+ ```python
+ async def main():
+ await extract_blog_sections()
+ await extract_product_reviews()
+
+ if __name__ == "__main__":
+ asyncio.run(main())
+ ```
+
+#### **9. Wrap Up & Next Steps**
+ - Recap the efficiency and effectiveness of Cosine Similarity for clustering related content quickly.
+ - Close with a reminder of Crawl4AI’s flexibility across extraction strategies, and prompt users to experiment with different settings to optimize clustering for their specific content.
+
+---
+
+This outline covers Cosine Similarity Strategy’s speed and effectiveness, providing examples that showcase its potential for clustering various content types efficiently.
diff --git a/docs/md_v2/tutorial/episode_12_Session-Based_Crawling_for_Dynamic_Websites.md b/docs/md_v2/tutorial/episode_12_Session-Based_Crawling_for_Dynamic_Websites.md
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+# Crawl4AI
+
+## Episode 12: Session-Based Crawling for Dynamic Websites
+
+### Quick Intro
+Show session management for handling websites with multiple pages or actions (like “load more” buttons). Demo: Crawl a paginated content page, persisting session data across multiple requests.
+
+Here’s a detailed outline for the **Session-Based Crawling for Dynamic Websites** video, explaining why sessions are necessary, how to use them, and providing practical examples and a visual diagram to illustrate the concept.
+
+---
+
+### **11. Session-Based Crawling for Dynamic Websites**
+
+#### **1. Introduction to Session-Based Crawling**
+ - **What is Session-Based Crawling**: Session-based crawling maintains a continuous browsing session across multiple page states, allowing the crawler to interact with a page and retrieve content that loads dynamically or based on user interactions.
+ - **Why It’s Needed**:
+ - In static pages, all content is available directly from a single URL.
+ - In dynamic websites, content often loads progressively or based on user actions (e.g., clicking “load more,” submitting forms, scrolling).
+ - Session-based crawling helps simulate user actions, capturing content that is otherwise hidden until specific actions are taken.
+
+#### **2. Conceptual Diagram for Session-Based Crawling**
+
+ ```mermaid
+ graph TD
+ Start[Start Session] --> S1[Initial State (S1)]
+ S1 -->|Crawl| Content1[Extract Content S1]
+ S1 -->|Action: Click Load More| S2[State S2]
+ S2 -->|Crawl| Content2[Extract Content S2]
+ S2 -->|Action: Scroll Down| S3[State S3]
+ S3 -->|Crawl| Content3[Extract Content S3]
+ S3 -->|Action: Submit Form| S4[Final State]
+ S4 -->|Crawl| Content4[Extract Content S4]
+ Content4 --> End[End Session]
+ ```
+
+ - **Explanation of Diagram**:
+ - **Start**: Initializes the session and opens the starting URL.
+ - **State Transitions**: Each action (e.g., clicking “load more,” scrolling) transitions to a new state, where additional content becomes available.
+ - **Session Persistence**: Keeps the same browsing session active, preserving the state and allowing for a sequence of actions to unfold.
+ - **End**: After reaching the final state, the session ends, and all accumulated content has been extracted.
+
+#### **3. Key Components of Session-Based Crawling in Crawl4AI**
+ - **Session ID**: A unique identifier to maintain the state across requests, allowing the crawler to “remember” previous actions.
+ - **JavaScript Execution**: Executes JavaScript commands (e.g., clicks, scrolls) to simulate interactions.
+ - **Wait Conditions**: Ensures the crawler waits for content to load in each state before moving on.
+ - **Sequential State Transitions**: By defining actions and wait conditions between states, the crawler can navigate through the page as a user would.
+
+#### **4. Basic Session Example: Multi-Step Content Loading**
+ - **Goal**: Crawl an article feed that requires several “load more” clicks to display additional content.
+ - **Code**:
+ ```python
+ async def crawl_article_feed():
+ async with AsyncWebCrawler() as crawler:
+ session_id = "feed_session"
+
+ for page in range(3):
+ result = await crawler.arun(
+ url="https://example.com/articles",
+ session_id=session_id,
+ js_code="document.querySelector('.load-more-button').click();" if page > 0 else None,
+ wait_for="css:.article",
+ css_selector=".article" # Target article elements
+ )
+ print(f"Page {page + 1}: Extracted {len(result.extracted_content)} articles")
+ ```
+ - **Explanation**:
+ - **session_id**: Ensures all requests share the same browsing state.
+ - **js_code**: Clicks the “load more” button after the initial page load, expanding content on each iteration.
+ - **wait_for**: Ensures articles have loaded after each click before extraction.
+
+#### **5. Advanced Example: E-Commerce Product Search with Filter Selection**
+ - **Goal**: Interact with filters on an e-commerce page to extract products based on selected criteria.
+ - **Example Steps**:
+ 1. **State 1**: Load the main product page.
+ 2. **State 2**: Apply a filter (e.g., “On Sale”) by selecting a checkbox.
+ 3. **State 3**: Scroll to load additional products and capture updated results.
+
+ - **Code**:
+ ```python
+ async def extract_filtered_products():
+ async with AsyncWebCrawler() as crawler:
+ session_id = "product_session"
+
+ # Step 1: Open product page
+ result = await crawler.arun(
+ url="https://example.com/products",
+ session_id=session_id,
+ wait_for="css:.product-item"
+ )
+
+ # Step 2: Apply filter (e.g., "On Sale")
+ result = await crawler.arun(
+ url="https://example.com/products",
+ session_id=session_id,
+ js_code="document.querySelector('#sale-filter-checkbox').click();",
+ wait_for="css:.product-item"
+ )
+
+ # Step 3: Scroll to load additional products
+ for _ in range(2): # Scroll down twice
+ result = await crawler.arun(
+ url="https://example.com/products",
+ session_id=session_id,
+ js_code="window.scrollTo(0, document.body.scrollHeight);",
+ wait_for="css:.product-item"
+ )
+ print(f"Loaded {len(result.extracted_content)} products after scroll")
+ ```
+ - **Explanation**:
+ - **State Persistence**: Each action (filter selection and scroll) builds on the previous session state.
+ - **Multiple Interactions**: Combines clicking a filter with scrolling, demonstrating how the session preserves these actions.
+
+#### **6. Key Benefits of Session-Based Crawling**
+ - **Accessing Hidden Content**: Retrieves data that loads only after user actions.
+ - **Simulating User Behavior**: Handles interactive elements such as “load more” buttons, dropdowns, and filters.
+ - **Maintaining Continuity Across States**: Enables a sequential process, moving logically from one state to the next, capturing all desired content without reloading the initial state each time.
+
+#### **7. Additional Configuration Tips**
+ - **Manage Session End**: Always conclude the session after the final state to release resources.
+ - **Optimize with Wait Conditions**: Use `wait_for` to ensure complete loading before each extraction.
+ - **Handling Errors in Session-Based Crawling**: Include error handling for interactions that may fail, ensuring robustness across state transitions.
+
+#### **8. Complete Code Example: Multi-Step Session Workflow**
+ - **Example**:
+ ```python
+ async def main():
+ await crawl_article_feed()
+ await extract_filtered_products()
+
+ if __name__ == "__main__":
+ asyncio.run(main())
+ ```
+
+#### **9. Wrap Up & Next Steps**
+ - Recap the usefulness of session-based crawling for dynamic content extraction.
+ - Tease the next video: **Hooks and Custom Workflow with AsyncWebCrawler** to cover advanced customization options for further control over the crawling process.
+
+---
+
+This outline covers session-based crawling from both a conceptual and practical perspective, helping users understand its importance, configure it effectively, and use it to handle complex dynamic content.
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diff --git a/docs/md_v2/tutorial/episode_13_Chunking_Strategies_for_Large_Text_Processing.md b/docs/md_v2/tutorial/episode_13_Chunking_Strategies_for_Large_Text_Processing.md
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+# Crawl4AI
+
+## Episode 13: Chunking Strategies for Large Text Processing
+
+### Quick Intro
+Explain Regex, NLP, and Fixed-Length chunking, and when to use each. Demo: Chunk a large article or document for processing by topics or sentences.
+
+Here’s a structured outline for the **Chunking Strategies for Large Text Processing** video, emphasizing how chunking works within extraction and why it’s crucial for effective data aggregation.
+
+Here’s a structured outline for the **Chunking Strategies for Large Text Processing** video, explaining each strategy, when to use it, and providing examples to illustrate.
+
+---
+
+### **12. Chunking Strategies for Large Text Processing**
+
+#### **1. Introduction to Chunking in Crawl4AI**
+ - **What is Chunking**: Chunking is the process of dividing large text into manageable sections or “chunks,” enabling efficient processing in extraction tasks.
+ - **Why It’s Needed**:
+ - When processing large text, feeding it directly into an extraction function (like `F(x)`) can overwhelm memory or token limits.
+ - Chunking breaks down `x` (the text) into smaller pieces, which are processed sequentially or in parallel by the extraction function, with the final result being an aggregation of all chunks’ processed output.
+
+#### **2. Key Chunking Strategies and Use Cases**
+ - Crawl4AI offers various chunking strategies to suit different text structures, chunk sizes, and processing requirements.
+ - **Choosing a Strategy**: Select based on the type of text (e.g., articles, transcripts) and extraction needs (e.g., simple splitting or context-sensitive processing).
+
+#### **3. Strategy 1: Regex-Based Chunking**
+ - **Description**: Uses regular expressions to split text based on specified patterns (e.g., paragraphs or section breaks).
+ - **Use Case**: Ideal for dividing text by paragraphs or larger logical blocks where sections are clearly separated by line breaks or punctuation.
+ - **Example**:
+ - **Pattern**: `r'\n\n'` for double line breaks.
+ ```python
+ chunker = RegexChunking(patterns=[r'\n\n'])
+ text_chunks = chunker.chunk(long_text)
+ print(text_chunks) # Output: List of paragraphs
+ ```
+ - **Pros**: Flexible for pattern-based chunking.
+ - **Cons**: Limited to text with consistent formatting.
+
+#### **4. Strategy 2: NLP Sentence-Based Chunking**
+ - **Description**: Uses NLP to split text by sentences, ensuring grammatically complete segments.
+ - **Use Case**: Useful for extracting individual statements, such as in news articles, quotes, or legal text.
+ - **Example**:
+ ```python
+ chunker = NlpSentenceChunking()
+ sentence_chunks = chunker.chunk(long_text)
+ print(sentence_chunks) # Output: List of sentences
+ ```
+ - **Pros**: Maintains sentence structure, ideal for tasks needing semantic completeness.
+ - **Cons**: May create very small chunks, which could limit contextual extraction.
+
+#### **5. Strategy 3: Topic-Based Segmentation Using TextTiling**
+ - **Description**: Segments text into topics using TextTiling, identifying topic shifts and key segments.
+ - **Use Case**: Ideal for long articles, reports, or essays where each section covers a different topic.
+ - **Example**:
+ ```python
+ chunker = TopicSegmentationChunking(num_keywords=3)
+ topic_chunks = chunker.chunk_with_topics(long_text)
+ print(topic_chunks) # Output: List of topic segments with keywords
+ ```
+ - **Pros**: Groups related content, preserving topical coherence.
+ - **Cons**: Depends on identifiable topic shifts, which may not be present in all texts.
+
+#### **6. Strategy 4: Fixed-Length Word Chunking**
+ - **Description**: Splits text into chunks based on a fixed number of words.
+ - **Use Case**: Ideal for text where exact segment size is required, such as processing word-limited documents for LLMs.
+ - **Example**:
+ ```python
+ chunker = FixedLengthWordChunking(chunk_size=100)
+ word_chunks = chunker.chunk(long_text)
+ print(word_chunks) # Output: List of 100-word chunks
+ ```
+ - **Pros**: Ensures uniform chunk sizes, suitable for token-based extraction limits.
+ - **Cons**: May split sentences, affecting semantic coherence.
+
+#### **7. Strategy 5: Sliding Window Chunking**
+ - **Description**: Uses a fixed window size with a step, creating overlapping chunks to maintain context.
+ - **Use Case**: Useful for maintaining context across sections, as with documents where context is needed for neighboring sections.
+ - **Example**:
+ ```python
+ chunker = SlidingWindowChunking(window_size=100, step=50)
+ window_chunks = chunker.chunk(long_text)
+ print(window_chunks) # Output: List of overlapping word chunks
+ ```
+ - **Pros**: Retains context across adjacent chunks, ideal for complex semantic extraction.
+ - **Cons**: Overlap increases data size, potentially impacting processing time.
+
+#### **8. Strategy 6: Overlapping Window Chunking**
+ - **Description**: Similar to sliding windows but with a defined overlap, allowing chunks to share content at the edges.
+ - **Use Case**: Suitable for handling long texts with essential overlapping information, like research articles or medical records.
+ - **Example**:
+ ```python
+ chunker = OverlappingWindowChunking(window_size=1000, overlap=100)
+ overlap_chunks = chunker.chunk(long_text)
+ print(overlap_chunks) # Output: List of overlapping chunks with defined overlap
+ ```
+ - **Pros**: Allows controlled overlap for consistent content coverage across chunks.
+ - **Cons**: Redundant data in overlapping areas may increase computation.
+
+#### **9. Practical Example: Using Chunking with an Extraction Strategy**
+ - **Goal**: Combine chunking with an extraction strategy to process large text effectively.
+ - **Example Code**:
+ ```python
+ from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+ async def extract_large_text():
+ # Initialize chunker and extraction strategy
+ chunker = FixedLengthWordChunking(chunk_size=200)
+ extraction_strategy = LLMExtractionStrategy(provider="openai/gpt-4", api_token="your_api_token")
+
+ # Split text into chunks
+ text_chunks = chunker.chunk(large_text)
+
+ async with AsyncWebCrawler() as crawler:
+ for chunk in text_chunks:
+ result = await crawler.arun(
+ url="https://example.com",
+ extraction_strategy=extraction_strategy,
+ content=chunk
+ )
+ print(result.extracted_content)
+ ```
+
+ - **Explanation**:
+ - `chunker.chunk()`: Divides the `large_text` into smaller segments based on the chosen strategy.
+ - `extraction_strategy`: Processes each chunk separately, and results are then aggregated to form the final output.
+
+#### **10. Choosing the Right Chunking Strategy**
+ - **Text Structure**: If text has clear sections (e.g., paragraphs, topics), use Regex or Topic Segmentation.
+ - **Extraction Needs**: If context is crucial, consider Sliding or Overlapping Window Chunking.
+ - **Processing Constraints**: For word-limited extractions (e.g., LLMs with token limits), Fixed-Length Word Chunking is often most effective.
+
+#### **11. Wrap Up & Next Steps**
+ - Recap the benefits of each chunking strategy and when to use them in extraction workflows.
+ - Tease the next video: **Hooks and Custom Workflow with AsyncWebCrawler**, focusing on customizing crawler behavior with hooks for a fine-tuned extraction process.
+
+---
+
+This outline provides a complete understanding of chunking strategies, explaining each method’s strengths and best-use scenarios to help users process large texts effectively in Crawl4AI.
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diff --git a/docs/md_v2/tutorial/episode_14_Hooks_and_Custom_Workflow_with_AsyncWebCrawler.md b/docs/md_v2/tutorial/episode_14_Hooks_and_Custom_Workflow_with_AsyncWebCrawler.md
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+# Crawl4AI
+
+## Episode 14: Hooks and Custom Workflow with AsyncWebCrawler
+
+### Quick Intro
+Cover hooks (`on_browser_created`, `before_goto`, `after_goto`) to add custom workflows. Demo: Use hooks to add custom cookies or headers, log HTML, or trigger specific events on page load.
+
+Here’s a detailed outline for the **Hooks and Custom Workflow with AsyncWebCrawler** video, covering each hook’s purpose, usage, and example implementations.
+
+---
+
+### **13. Hooks and Custom Workflow with AsyncWebCrawler**
+
+#### **1. Introduction to Hooks in Crawl4AI**
+ - **What are Hooks**: Hooks are customizable entry points in the crawling process that allow users to inject custom actions or logic at specific stages.
+ - **Why Use Hooks**:
+ - They enable fine-grained control over the crawling workflow.
+ - Useful for performing additional tasks (e.g., logging, modifying headers) dynamically during the crawl.
+ - Hooks provide the flexibility to adapt the crawler to complex site structures or unique project needs.
+
+#### **2. Overview of Available Hooks**
+ - Crawl4AI offers seven key hooks to modify and control different stages in the crawling lifecycle:
+ - `on_browser_created`
+ - `on_user_agent_updated`
+ - `on_execution_started`
+ - `before_goto`
+ - `after_goto`
+ - `before_return_html`
+ - `before_retrieve_html`
+
+#### **3. Hook-by-Hook Explanation and Examples**
+
+---
+
+##### **Hook 1: `on_browser_created`**
+ - **Purpose**: Triggered right after the browser instance is created.
+ - **Use Case**:
+ - Initializing browser-specific settings or performing setup actions.
+ - Configuring browser extensions or scripts before any page is opened.
+ - **Example**:
+ ```python
+ async def log_browser_creation(browser):
+ print("Browser instance created:", browser)
+
+ crawler.crawler_strategy.set_hook('on_browser_created', log_browser_creation)
+ ```
+ - **Explanation**: This hook logs the browser creation event, useful for tracking when a new browser instance starts.
+
+---
+
+##### **Hook 2: `on_user_agent_updated`**
+ - **Purpose**: Called whenever the user agent string is updated.
+ - **Use Case**:
+ - Modifying the user agent based on page requirements, e.g., changing to a mobile user agent for mobile-only pages.
+ - **Example**:
+ ```python
+ def update_user_agent(user_agent):
+ print(f"User Agent Updated: {user_agent}")
+
+ crawler.crawler_strategy.set_hook('on_user_agent_updated', update_user_agent)
+ crawler.update_user_agent("Mozilla/5.0 (iPhone; CPU iPhone OS 14_0 like Mac OS X)")
+ ```
+ - **Explanation**: This hook provides a callback every time the user agent changes, helpful for debugging or dynamically altering user agent settings based on conditions.
+
+---
+
+##### **Hook 3: `on_execution_started`**
+ - **Purpose**: Called right before the crawler begins any interaction (e.g., JavaScript execution, clicks).
+ - **Use Case**:
+ - Performing setup actions, such as inserting cookies or initiating custom scripts.
+ - **Example**:
+ ```python
+ async def log_execution_start(page):
+ print("Execution started on page:", page.url)
+
+ crawler.crawler_strategy.set_hook('on_execution_started', log_execution_start)
+ ```
+ - **Explanation**: Logs the start of any major interaction on the page, ideal for cases where you want to monitor each interaction.
+
+---
+
+##### **Hook 4: `before_goto`**
+ - **Purpose**: Triggered before navigating to a new URL with `page.goto()`.
+ - **Use Case**:
+ - Modifying request headers or setting up conditions right before the page loads.
+ - Adding headers or dynamically adjusting options for specific URLs.
+ - **Example**:
+ ```python
+ async def modify_headers_before_goto(page):
+ await page.set_extra_http_headers({"X-Custom-Header": "CustomValue"})
+ print("Custom headers set before navigation")
+
+ crawler.crawler_strategy.set_hook('before_goto', modify_headers_before_goto)
+ ```
+ - **Explanation**: This hook allows injecting headers or altering settings based on the page’s needs, particularly useful for pages with custom requirements.
+
+---
+
+##### **Hook 5: `after_goto`**
+ - **Purpose**: Executed immediately after a page has loaded (after `page.goto()`).
+ - **Use Case**:
+ - Checking the loaded page state, modifying the DOM, or performing post-navigation actions (e.g., scrolling).
+ - **Example**:
+ ```python
+ async def post_navigation_scroll(page):
+ await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
+ print("Scrolled to the bottom after navigation")
+
+ crawler.crawler_strategy.set_hook('after_goto', post_navigation_scroll)
+ ```
+ - **Explanation**: This hook scrolls to the bottom of the page after loading, which can help load dynamically added content like infinite scroll elements.
+
+---
+
+##### **Hook 6: `before_return_html`**
+ - **Purpose**: Called right before HTML content is retrieved and returned.
+ - **Use Case**:
+ - Removing overlays or cleaning up the page for a cleaner HTML extraction.
+ - **Example**:
+ ```python
+ async def remove_advertisements(page, html):
+ await page.evaluate("document.querySelectorAll('.ad-banner').forEach(el => el.remove());")
+ print("Advertisements removed before returning HTML")
+
+ crawler.crawler_strategy.set_hook('before_return_html', remove_advertisements)
+ ```
+ - **Explanation**: The hook removes ad banners from the HTML before it’s retrieved, ensuring a cleaner data extraction.
+
+---
+
+##### **Hook 7: `before_retrieve_html`**
+ - **Purpose**: Runs right before Crawl4AI initiates HTML retrieval.
+ - **Use Case**:
+ - Finalizing any page adjustments (e.g., setting timers, waiting for specific elements).
+ - **Example**:
+ ```python
+ async def wait_for_content_before_retrieve(page):
+ await page.wait_for_selector('.main-content')
+ print("Main content loaded, ready to retrieve HTML")
+
+ crawler.crawler_strategy.set_hook('before_retrieve_html', wait_for_content_before_retrieve)
+ ```
+ - **Explanation**: This hook waits for the main content to load before retrieving the HTML, ensuring that all essential content is captured.
+
+#### **4. Setting Hooks in Crawl4AI**
+ - **How to Set Hooks**:
+ - Use `set_hook` to define a custom function for each hook.
+ - Each hook function can be asynchronous (useful for actions like waiting or retrieving async data).
+ - **Example Setup**:
+ ```python
+ crawler.crawler_strategy.set_hook('on_browser_created', log_browser_creation)
+ crawler.crawler_strategy.set_hook('before_goto', modify_headers_before_goto)
+ crawler.crawler_strategy.set_hook('after_goto', post_navigation_scroll)
+ ```
+
+#### **5. Complete Example: Using Hooks for a Customized Crawl Workflow**
+ - **Goal**: Log each key step, set custom headers before navigation, and clean up the page before retrieving HTML.
+ - **Example Code**:
+ ```python
+ async def custom_crawl():
+ async with AsyncWebCrawler() as crawler:
+ # Set hooks for custom workflow
+ crawler.crawler_strategy.set_hook('on_browser_created', log_browser_creation)
+ crawler.crawler_strategy.set_hook('before_goto', modify_headers_before_goto)
+ crawler.crawler_strategy.set_hook('after_goto', post_navigation_scroll)
+ crawler.crawler_strategy.set_hook('before_return_html', remove_advertisements)
+
+ # Perform the crawl
+ url = "https://example.com"
+ result = await crawler.arun(url=url)
+ print(result.html) # Display or process HTML
+ ```
+
+#### **6. Benefits of Using Hooks in Custom Crawling Workflows**
+ - **Enhanced Control**: Hooks offer precise control over each stage, allowing adjustments based on content and structure.
+ - **Efficient Modifications**: Avoid reloading or restarting the session; hooks can alter actions dynamically.
+ - **Context-Sensitive Actions**: Hooks enable custom logic tailored to specific pages or sections, maximizing extraction quality.
+
+#### **7. Wrap Up & Next Steps**
+ - Recap how hooks empower customized workflows in Crawl4AI, enabling flexibility at every stage.
+ - Tease the next video: **Automating Post-Processing with Crawl4AI**, covering automated steps after data extraction.
+
+---
+
+This outline provides a thorough understanding of hooks, their practical applications, and examples for customizing the crawling workflow in Crawl4AI.
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+# Crawl4AI
+
+## Episode 1: Introduction to Crawl4AI and Basic Installation
+
+### Quick Intro
+Walk through installation from PyPI, setup, and verification. Show how to install with options like `torch` or `transformer` for advanced capabilities.
+
+Here's a condensed outline of the **Installation and Setup** video content:
+
+---
+
+1) **Introduction to Crawl4AI**:
+
+ - Briefly explain that Crawl4AI is a powerful tool for web scraping, data extraction, and content processing, with customizable options for various needs.
+
+2) **Installation Overview**:
+
+ - **Basic Install**: Run `pip install crawl4ai` and `playwright install` (to set up browser dependencies).
+ - **Optional Advanced Installs**:
+ - `pip install crawl4ai[torch]` - Adds PyTorch for clustering.
+ - `pip install crawl4ai[transformer]` - Adds support for LLM-based extraction.
+ - `pip install crawl4ai[all]` - Installs all features for complete functionality.
+
+3) **Verifying the Installation**:
+
+ - Walk through a simple test script to confirm the setup:
+ ```python
+ import asyncio
+ from crawl4ai import AsyncWebCrawler
+
+ async def main():
+ async with AsyncWebCrawler(verbose=True) as crawler:
+ result = await crawler.arun(url="https://www.example.com")
+ print(result.markdown[:500]) # Show first 500 characters
+
+ asyncio.run(main())
+ ```
+ - Explain that this script initializes the crawler and runs it on a test URL, displaying part of the extracted content to verify functionality.
+
+4) **Important Tips**:
+
+ - **Run** `playwright install` **after installation** to set up dependencies.
+ - **For full performance** on text-related tasks, run `crawl4ai-download-models` after installing with `[torch]`, `[transformer]`, or `[all]` options.
+ - If you encounter issues, refer to the documentation or GitHub issues.
+
+5) **Wrap Up**:
+
+ - Introduce the next topic in the series, which will cover Crawl4AI's browser configuration options (like choosing between `chromium`, `firefox`, and `webkit`).
+
+---
+
+This structure provides a concise, effective guide to get viewers up and running with Crawl4AI in minutes.# Crawl4AI
+
+## Episode 2: Overview of Advanced Features
+
+### Quick Intro
+A general overview of advanced features like hooks, CSS selectors, and JSON CSS extraction.
+
+Here's a condensed outline for an **Overview of Advanced Features** video covering Crawl4AI's powerful customization and extraction options:
+
+---
+
+### **Overview of Advanced Features**
+
+1) **Introduction to Advanced Features**:
+
+ - Briefly introduce Crawl4AI’s advanced tools, which let users go beyond basic crawling to customize and fine-tune their scraping workflows.
+
+2) **Taking Screenshots**:
+
+ - Explain the screenshot capability for capturing page state and verifying content.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://www.example.com", screenshot=True)
+ ```
+ - Mention that screenshots are saved as a base64 string in `result`, allowing easy decoding and saving.
+
+3) **Media and Link Extraction**:
+
+ - Demonstrate how to pull all media (images, videos) and links (internal and external) from a page for deeper analysis or content gathering.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://www.example.com")
+ print("Media:", result.media)
+ print("Links:", result.links)
+ ```
+
+4) **Custom User Agent**:
+
+ - Show how to set a custom user agent to disguise the crawler or simulate specific devices/browsers.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://www.example.com", user_agent="Mozilla/5.0 (compatible; MyCrawler/1.0)")
+ ```
+
+5) **Custom Hooks for Enhanced Control**:
+
+ - Briefly cover how to use hooks, which allow custom actions like setting headers or handling login during the crawl.
+ - **Example**: Setting a custom header with `before_get_url` hook.
+ ```python
+ async def before_get_url(page):
+ await page.set_extra_http_headers({"X-Test-Header": "test"})
+ ```
+
+6) **CSS Selectors for Targeted Extraction**:
+
+ - Explain the use of CSS selectors to extract specific elements, ideal for structured data like articles or product details.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://www.example.com", css_selector="h2")
+ print("H2 Tags:", result.extracted_content)
+ ```
+
+7) **Crawling Inside Iframes**:
+
+ - Mention how enabling `process_iframes=True` allows extracting content within iframes, useful for sites with embedded content or ads.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://www.example.com", process_iframes=True)
+ ```
+
+8) **Wrap-Up**:
+
+ - Summarize these advanced features and how they allow users to customize every part of their web scraping experience.
+ - Tease upcoming videos where each feature will be explored in detail.
+
+---
+
+This covers each advanced feature with a brief example, providing a useful overview to prepare viewers for the more in-depth videos.# Crawl4AI
+
+## Episode 3: Browser Configurations & Headless Crawling
+
+### Quick Intro
+Explain browser options (`chromium`, `firefox`, `webkit`) and settings for headless mode, caching, and verbose logging.
+
+Here’s a streamlined outline for the **Browser Configurations & Headless Crawling** video:
+
+---
+
+### **Browser Configurations & Headless Crawling**
+
+1) **Overview of Browser Options**:
+
+ - Crawl4AI supports three browser engines:
+ - **Chromium** (default) - Highly compatible.
+ - **Firefox** - Great for specialized use cases.
+ - **Webkit** - Lightweight, ideal for basic needs.
+ - **Example**:
+ ```python
+ # Using Chromium (default)
+ crawler = AsyncWebCrawler(browser_type="chromium")
+
+ # Using Firefox
+ crawler = AsyncWebCrawler(browser_type="firefox")
+
+ # Using WebKit
+ crawler = AsyncWebCrawler(browser_type="webkit")
+ ```
+
+2) **Headless Mode**:
+
+ - Headless mode runs the browser without a visible GUI, making it faster and less resource-intensive.
+ - To enable or disable:
+ ```python
+ # Headless mode (default is True)
+ crawler = AsyncWebCrawler(headless=True)
+
+ # Disable headless mode for debugging
+ crawler = AsyncWebCrawler(headless=False)
+ ```
+
+3) **Verbose Logging**:
+
+ - Use `verbose=True` to get detailed logs for each action, useful for debugging:
+ ```python
+ crawler = AsyncWebCrawler(verbose=True)
+ ```
+
+4) **Running a Basic Crawl with Configuration**:
+
+ - Example of a simple crawl with custom browser settings:
+ ```python
+ async with AsyncWebCrawler(browser_type="firefox", headless=True, verbose=True) as crawler:
+ result = await crawler.arun(url="https://www.example.com")
+ print(result.markdown[:500]) # Show first 500 characters
+ ```
+ - This example uses Firefox in headless mode with logging enabled, demonstrating the flexibility of Crawl4AI’s setup.
+
+5) **Recap & Next Steps**:
+
+ - Recap the power of selecting different browsers and running headless mode for speed and efficiency.
+ - Tease the next video: **Proxy & Security Settings** for navigating blocked or restricted content and protecting IP identity.
+
+---
+
+This breakdown covers browser configuration essentials in Crawl4AI, providing users with practical steps to optimize their scraping setup.# Crawl4AI
+
+## Episode 4: Advanced Proxy and Security Settings
+
+### Quick Intro
+Showcase proxy configurations (HTTP, SOCKS5, authenticated proxies). Demo: Use rotating proxies and set custom headers to avoid IP blocking and enhance security.
+
+Here’s a focused outline for the **Proxy and Security Settings** video:
+
+---
+
+### **Proxy & Security Settings**
+
+1) **Why Use Proxies in Web Crawling**:
+
+ - Proxies are essential for bypassing IP-based restrictions, improving anonymity, and managing rate limits.
+ - Crawl4AI supports simple proxies, authenticated proxies, and proxy rotation for robust web scraping.
+
+2) **Basic Proxy Setup**:
+
+ - **Using a Simple Proxy**:
+ ```python
+ # HTTP proxy
+ crawler = AsyncWebCrawler(proxy="http://proxy.example.com:8080")
+
+ # SOCKS proxy
+ crawler = AsyncWebCrawler(proxy="socks5://proxy.example.com:1080")
+ ```
+
+3) **Authenticated Proxies**:
+
+ - Use `proxy_config` for proxies requiring a username and password:
+ ```python
+ proxy_config = {
+ "server": "http://proxy.example.com:8080",
+ "username": "user",
+ "password": "pass"
+ }
+ crawler = AsyncWebCrawler(proxy_config=proxy_config)
+ ```
+
+4) **Rotating Proxies**:
+
+ - Rotating proxies helps avoid IP bans by switching IP addresses for each request:
+ ```python
+ async def get_next_proxy():
+ # Define proxy rotation logic here
+ return {"server": "http://next.proxy.com:8080"}
+
+ async with AsyncWebCrawler() as crawler:
+ for url in urls:
+ proxy = await get_next_proxy()
+ crawler.update_proxy(proxy)
+ result = await crawler.arun(url=url)
+ ```
+ - This setup periodically switches the proxy for enhanced security and access.
+
+5) **Custom Headers for Additional Security**:
+
+ - Set custom headers to mask the crawler’s identity and avoid detection:
+ ```python
+ headers = {
+ "X-Forwarded-For": "203.0.113.195",
+ "Accept-Language": "en-US,en;q=0.9",
+ "Cache-Control": "no-cache",
+ "Pragma": "no-cache"
+ }
+ crawler = AsyncWebCrawler(headers=headers)
+ ```
+
+6) **Combining Proxies with Magic Mode for Anti-Bot Protection**:
+
+ - For sites with aggressive bot detection, combine `proxy` settings with `magic=True`:
+ ```python
+ async with AsyncWebCrawler(proxy="http://proxy.example.com:8080", headers={"Accept-Language": "en-US"}) as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ magic=True # Enables anti-detection features
+ )
+ ```
+ - **Magic Mode** automatically enables user simulation, random timing, and browser property masking.
+
+7) **Wrap Up & Next Steps**:
+
+ - Summarize the importance of proxies and anti-detection in accessing restricted content and avoiding bans.
+ - Tease the next video: **JavaScript Execution and Handling Dynamic Content** for working with interactive and dynamically loaded pages.
+
+---
+
+This outline provides a practical guide to setting up proxies and security configurations, empowering users to navigate restricted sites while staying undetected.# Crawl4AI
+
+## Episode 5: JavaScript Execution and Dynamic Content Handling
+
+### Quick Intro
+Explain JavaScript code injection with examples (e.g., simulating scrolling, clicking ‘load more’). Demo: Extract content from a page that uses dynamic loading with lazy-loaded images.
+
+Here’s a focused outline for the **JavaScript Execution and Dynamic Content Handling** video:
+
+---
+
+### **JavaScript Execution & Dynamic Content Handling**
+
+1) **Why JavaScript Execution Matters**:
+
+ - Many modern websites load content dynamically via JavaScript, requiring special handling to access all elements.
+ - Crawl4AI can execute JavaScript on pages, enabling it to interact with elements like “load more” buttons, infinite scrolls, and content that appears only after certain actions.
+
+2) **Basic JavaScript Execution**:
+
+ - Use `js_code` to execute JavaScript commands on a page:
+ ```python
+ # Scroll to bottom of the page
+ result = await crawler.arun(
+ url="https://example.com",
+ js_code="window.scrollTo(0, document.body.scrollHeight);"
+ )
+ ```
+ - This command scrolls to the bottom, triggering any lazy-loaded or dynamically added content.
+
+3) **Multiple Commands & Simulating Clicks**:
+
+ - Combine multiple JavaScript commands to interact with elements like “load more” buttons:
+ ```python
+ js_commands = [
+ "window.scrollTo(0, document.body.scrollHeight);",
+ "document.querySelector('.load-more').click();"
+ ]
+ result = await crawler.arun(
+ url="https://example.com",
+ js_code=js_commands
+ )
+ ```
+ - This script scrolls down and then clicks the “load more” button, useful for loading additional content blocks.
+
+4) **Waiting for Dynamic Content**:
+
+ - Use `wait_for` to ensure the page loads specific elements before proceeding:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ js_code="window.scrollTo(0, document.body.scrollHeight);",
+ wait_for="css:.dynamic-content" # Wait for elements with class `.dynamic-content`
+ )
+ ```
+ - This example waits until elements with `.dynamic-content` are loaded, helping to capture content that appears after JavaScript actions.
+
+5) **Handling Complex Dynamic Content (e.g., Infinite Scroll)**:
+
+ - Combine JavaScript execution with conditional waiting to handle infinite scrolls or paginated content:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ js_code=[
+ "window.scrollTo(0, document.body.scrollHeight);",
+ "const loadMore = document.querySelector('.load-more'); if (loadMore) loadMore.click();"
+ ],
+ wait_for="js:() => document.querySelectorAll('.item').length > 10" # Wait until 10 items are loaded
+ )
+ ```
+ - This example scrolls and clicks "load more" repeatedly, waiting each time for a specified number of items to load.
+
+6) **Complete Example: Dynamic Content Handling with Extraction**:
+
+ - Full example demonstrating a dynamic load and content extraction in one process:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ js_code=[
+ "window.scrollTo(0, document.body.scrollHeight);",
+ "document.querySelector('.load-more').click();"
+ ],
+ wait_for="css:.main-content",
+ css_selector=".main-content"
+ )
+ print(result.markdown[:500]) # Output the main content extracted
+ ```
+
+7) **Wrap Up & Next Steps**:
+
+ - Recap how JavaScript execution allows access to dynamic content, enabling powerful interactions.
+ - Tease the next video: **Content Cleaning and Fit Markdown** to show how Crawl4AI can extract only the most relevant content from complex pages.
+
+---
+
+This outline explains how to handle dynamic content and JavaScript-based interactions effectively, enabling users to scrape and interact with complex, modern websites.# Crawl4AI
+
+## Episode 6: Magic Mode and Anti-Bot Protection
+
+### Quick Intro
+Highlight `Magic Mode` and anti-bot features like user simulation, navigator overrides, and timing randomization. Demo: Access a site with anti-bot protection and show how `Magic Mode` seamlessly handles it.
+
+Here’s a concise outline for the **Magic Mode and Anti-Bot Protection** video:
+
+---
+
+### **Magic Mode & Anti-Bot Protection**
+
+1) **Why Anti-Bot Protection is Important**:
+
+ - Many websites use bot detection mechanisms to block automated scraping. Crawl4AI’s anti-detection features help avoid IP bans, CAPTCHAs, and access restrictions.
+ - **Magic Mode** is a one-step solution to enable a range of anti-bot features without complex configuration.
+
+2) **Enabling Magic Mode**:
+
+ - Simply set `magic=True` to activate Crawl4AI’s full anti-bot suite:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ magic=True # Enables all anti-detection features
+ )
+ ```
+ - This enables a blend of stealth techniques, including masking automation signals, randomizing timings, and simulating real user behavior.
+
+3) **What Magic Mode Does Behind the Scenes**:
+
+ - **User Simulation**: Mimics human actions like mouse movements and scrolling.
+ - **Navigator Overrides**: Hides signals that indicate an automated browser.
+ - **Timing Randomization**: Adds random delays to simulate natural interaction patterns.
+ - **Cookie Handling**: Accepts and manages cookies dynamically to avoid triggers from cookie pop-ups.
+
+4) **Manual Anti-Bot Options (If Not Using Magic Mode)**:
+
+ - For granular control, you can configure individual settings without Magic Mode:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ simulate_user=True, # Enables human-like behavior
+ override_navigator=True # Hides automation fingerprints
+ )
+ ```
+ - **Use Cases**: This approach allows more specific adjustments when certain anti-bot features are needed but others are not.
+
+5) **Combining Proxies with Magic Mode**:
+
+ - To avoid rate limits or IP blocks, combine Magic Mode with a proxy:
+ ```python
+ async with AsyncWebCrawler(
+ proxy="http://proxy.example.com:8080",
+ headers={"Accept-Language": "en-US"}
+ ) as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ magic=True # Full anti-detection
+ )
+ ```
+ - This setup maximizes stealth by pairing anti-bot detection with IP obfuscation.
+
+6) **Example of Anti-Bot Protection in Action**:
+
+ - Full example with Magic Mode and proxies to scrape a protected page:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com/protected-content",
+ magic=True,
+ proxy="http://proxy.example.com:8080",
+ wait_for="css:.content-loaded" # Wait for the main content to load
+ )
+ print(result.markdown[:500]) # Display first 500 characters of the content
+ ```
+ - This example ensures seamless access to protected content by combining anti-detection and waiting for full content load.
+
+7) **Wrap Up & Next Steps**:
+
+ - Recap the power of Magic Mode and anti-bot features for handling restricted websites.
+ - Tease the next video: **Content Cleaning and Fit Markdown** to show how to extract clean and focused content from a page.
+
+---
+
+This outline shows users how to easily avoid bot detection and access restricted content, demonstrating both the power and simplicity of Magic Mode in Crawl4AI.# Crawl4AI
+
+## Episode 7: Content Cleaning and Fit Markdown
+
+### Quick Intro
+Explain content cleaning options, including `fit_markdown` to keep only the most relevant content. Demo: Extract and compare regular vs. fit markdown from a news site or blog.
+
+Here’s a streamlined outline for the **Content Cleaning and Fit Markdown** video:
+
+---
+
+### **Content Cleaning & Fit Markdown**
+
+1) **Overview of Content Cleaning in Crawl4AI**:
+
+ - Explain that web pages often include extra elements like ads, navigation bars, footers, and popups.
+ - Crawl4AI’s content cleaning features help extract only the main content, reducing noise and enhancing readability.
+
+2) **Basic Content Cleaning Options**:
+
+ - **Removing Unwanted Elements**: Exclude specific HTML tags, like forms or navigation bars:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ word_count_threshold=10, # Filter out blocks with fewer than 10 words
+ excluded_tags=['form', 'nav'], # Exclude specific tags
+ remove_overlay_elements=True # Remove popups and modals
+ )
+ ```
+ - This example extracts content while excluding forms, navigation, and modal overlays, ensuring clean results.
+
+3) **Fit Markdown for Main Content Extraction**:
+
+ - **What is Fit Markdown**: Uses advanced analysis to identify the most relevant content (ideal for articles, blogs, and documentation).
+ - **How it Works**: Analyzes content density, removes boilerplate elements, and maintains formatting for a clear output.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://example.com")
+ main_content = result.fit_markdown # Extracted main content
+ print(main_content[:500]) # Display first 500 characters
+ ```
+ - Fit Markdown is especially helpful for long-form content like news articles or blog posts.
+
+4) **Comparing Fit Markdown with Regular Markdown**:
+
+ - **Fit Markdown** returns the primary content without extraneous elements.
+ - **Regular Markdown** includes all extracted text in markdown format.
+ - Example to show the difference:
+ ```python
+ all_content = result.markdown # Full markdown
+ main_content = result.fit_markdown # Only the main content
+
+ print(f"All Content Length: {len(all_content)}")
+ print(f"Main Content Length: {len(main_content)}")
+ ```
+ - This comparison shows the effectiveness of Fit Markdown in focusing on essential content.
+
+5) **Media and Metadata Handling with Content Cleaning**:
+
+ - **Media Extraction**: Crawl4AI captures images and videos with metadata like alt text, descriptions, and relevance scores:
+ ```python
+ for image in result.media["images"]:
+ print(f"Source: {image['src']}, Alt Text: {image['alt']}, Relevance Score: {image['score']}")
+ ```
+ - **Use Case**: Useful for saving only relevant images or videos from an article or content-heavy page.
+
+6) **Example of Clean Content Extraction in Action**:
+
+ - Full example extracting cleaned content and Fit Markdown:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ word_count_threshold=10,
+ excluded_tags=['nav', 'footer'],
+ remove_overlay_elements=True
+ )
+ print(result.fit_markdown[:500]) # Show main content
+ ```
+ - This example demonstrates content cleaning with settings for filtering noise and focusing on the core text.
+
+7) **Wrap Up & Next Steps**:
+
+ - Summarize the power of Crawl4AI’s content cleaning features and Fit Markdown for capturing clean, relevant content.
+ - Tease the next video: **Link Analysis and Smart Filtering** to focus on analyzing and filtering links within crawled pages.
+
+---
+
+This outline covers Crawl4AI’s content cleaning features and the unique benefits of Fit Markdown, showing users how to retrieve focused, high-quality content from web pages.# Crawl4AI
+
+## Episode 8: Media Handling: Images, Videos, and Audio
+
+### Quick Intro
+Showcase Crawl4AI’s media extraction capabilities, including lazy-loaded media and metadata. Demo: Crawl a multimedia page, extract images, and show metadata (alt text, context, relevance score).
+
+Here’s a clear and focused outline for the **Media Handling: Images, Videos, and Audio** video:
+
+---
+
+### **Media Handling: Images, Videos, and Audio**
+
+1) **Overview of Media Extraction in Crawl4AI**:
+
+ - Crawl4AI can detect and extract different types of media (images, videos, and audio) along with useful metadata.
+ - This functionality is essential for gathering visual content from multimedia-heavy pages like e-commerce sites, news articles, and social media feeds.
+
+2) **Image Extraction and Metadata**:
+
+ - Crawl4AI captures images with detailed metadata, including:
+ - **Source URL**: The direct URL to the image.
+ - **Alt Text**: Image description if available.
+ - **Relevance Score**: A score (0–10) indicating how relevant the image is to the main content.
+ - **Context**: Text surrounding the image on the page.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://example.com")
+
+ for image in result.media["images"]:
+ print(f"Source: {image['src']}")
+ print(f"Alt Text: {image['alt']}")
+ print(f"Relevance Score: {image['score']}")
+ print(f"Context: {image['context']}")
+ ```
+ - This example shows how to access each image’s metadata, making it easy to filter for the most relevant visuals.
+
+3) **Handling Lazy-Loaded Images**:
+
+ - Crawl4AI automatically supports lazy-loaded images, which are commonly used to optimize webpage loading.
+ - **Example with Wait for Lazy-Loaded Content**:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ wait_for="css:img[data-src]", # Wait for lazy-loaded images
+ delay_before_return_html=2.0 # Allow extra time for images to load
+ )
+ ```
+ - This setup waits for lazy-loaded images to appear, ensuring they are fully captured.
+
+4) **Video Extraction and Metadata**:
+
+ - Crawl4AI captures video elements, including:
+ - **Source URL**: The video’s direct URL.
+ - **Type**: Format of the video (e.g., MP4).
+ - **Thumbnail**: A poster or thumbnail image if available.
+ - **Duration**: Video length, if metadata is provided.
+ - **Example**:
+ ```python
+ for video in result.media["videos"]:
+ print(f"Video Source: {video['src']}")
+ print(f"Type: {video['type']}")
+ print(f"Thumbnail: {video.get('poster')}")
+ print(f"Duration: {video.get('duration')}")
+ ```
+ - This allows users to gather video content and relevant details for further processing or analysis.
+
+5) **Audio Extraction and Metadata**:
+
+ - Audio elements can also be extracted, with metadata like:
+ - **Source URL**: The audio file’s direct URL.
+ - **Type**: Format of the audio file (e.g., MP3).
+ - **Duration**: Length of the audio, if available.
+ - **Example**:
+ ```python
+ for audio in result.media["audios"]:
+ print(f"Audio Source: {audio['src']}")
+ print(f"Type: {audio['type']}")
+ print(f"Duration: {audio.get('duration')}")
+ ```
+ - Useful for sites with podcasts, sound bites, or other audio content.
+
+6) **Filtering Media by Relevance**:
+
+ - Use metadata like relevance score to filter only the most useful media content:
+ ```python
+ relevant_images = [img for img in result.media["images"] if img['score'] > 5]
+ ```
+ - This is especially helpful for content-heavy pages where you only want media directly related to the main content.
+
+7) **Example: Full Media Extraction with Content Filtering**:
+
+ - Full example extracting images, videos, and audio along with filtering by relevance:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ word_count_threshold=10, # Filter content blocks for relevance
+ exclude_external_images=True # Only keep internal images
+ )
+
+ # Display media summaries
+ print(f"Relevant Images: {len(relevant_images)}")
+ print(f"Videos: {len(result.media['videos'])}")
+ print(f"Audio Clips: {len(result.media['audios'])}")
+ ```
+ - This example shows how to capture and filter various media types, focusing on what’s most relevant.
+
+8) **Wrap Up & Next Steps**:
+
+ - Recap the comprehensive media extraction capabilities, emphasizing how metadata helps users focus on relevant content.
+ - Tease the next video: **Link Analysis and Smart Filtering** to explore how Crawl4AI handles internal, external, and social media links for more focused data gathering.
+
+---
+
+This outline provides users with a complete guide to handling images, videos, and audio in Crawl4AI, using metadata to enhance relevance and precision in multimedia extraction.# Crawl4AI
+
+## Episode 9: Link Analysis and Smart Filtering
+
+### Quick Intro
+Walk through internal and external link classification, social media link filtering, and custom domain exclusion. Demo: Analyze links on a website, focusing on internal navigation vs. external or ad links.
+
+Here’s a focused outline for the **Link Analysis and Smart Filtering** video:
+
+---
+
+### **Link Analysis & Smart Filtering**
+
+1) **Importance of Link Analysis in Web Crawling**:
+
+ - Explain that web pages often contain numerous links, including internal links, external links, social media links, and ads.
+ - Crawl4AI’s link analysis and filtering options help extract only relevant links, enabling more targeted and efficient crawls.
+
+2) **Automatic Link Classification**:
+
+ - Crawl4AI categorizes links automatically into internal, external, and social media links.
+ - **Example**:
+ ```python
+ result = await crawler.arun(url="https://example.com")
+
+ # Access internal and external links
+ internal_links = result.links["internal"]
+ external_links = result.links["external"]
+
+ # Print first few links for each type
+ print("Internal Links:", internal_links[:3])
+ print("External Links:", external_links[:3])
+ ```
+
+3) **Filtering Out Unwanted Links**:
+
+ - **Exclude External Links**: Remove all links pointing to external sites.
+ - **Exclude Social Media Links**: Filter out social media domains like Facebook or Twitter.
+ - **Example**:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ exclude_external_links=True, # Remove external links
+ exclude_social_media_links=True # Remove social media links
+ )
+ ```
+
+4) **Custom Domain Filtering**:
+
+ - **Exclude Specific Domains**: Filter links from particular domains, e.g., ad sites.
+ - **Custom Social Media Domains**: Add additional social media domains if needed.
+ - **Example**:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ exclude_domains=["ads.com", "trackers.com"],
+ exclude_social_media_domains=["facebook.com", "linkedin.com"]
+ )
+ ```
+
+5) **Accessing Link Context and Metadata**:
+
+ - Crawl4AI provides additional metadata for each link, including its text, type (e.g., navigation or content), and surrounding context.
+ - **Example**:
+ ```python
+ for link in result.links["internal"]:
+ print(f"Link: {link['href']}, Text: {link['text']}, Context: {link['context']}")
+ ```
+ - **Use Case**: Helps users understand the relevance of links based on where they are placed on the page (e.g., navigation vs. article content).
+
+6) **Example of Comprehensive Link Filtering and Analysis**:
+
+ - Full example combining link filtering, metadata access, and contextual information:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ exclude_external_links=True,
+ exclude_social_media_links=True,
+ exclude_domains=["ads.com"],
+ css_selector=".main-content" # Focus only on main content area
+ )
+ for link in result.links["internal"]:
+ print(f"Internal Link: {link['href']}, Text: {link['text']}, Context: {link['context']}")
+ ```
+ - This example filters unnecessary links, keeping only internal and relevant links from the main content area.
+
+7) **Wrap Up & Next Steps**:
+
+ - Summarize the benefits of link filtering for efficient crawling and relevant content extraction.
+ - Tease the next video: **Custom Headers, Identity Management, and User Simulation** to explain how to configure identity settings and simulate user behavior for stealthier crawls.
+
+---
+
+This outline provides a practical overview of Crawl4AI’s link analysis and filtering features, helping users target only essential links while eliminating distractions.# Crawl4AI
+
+## Episode 10: Custom Headers, Identity, and User Simulation
+
+### Quick Intro
+Teach how to use custom headers, user-agent strings, and simulate real user interactions. Demo: Set custom user-agent and headers to access a site that blocks typical crawlers.
+
+Here’s a concise outline for the **Custom Headers, Identity Management, and User Simulation** video:
+
+---
+
+### **Custom Headers, Identity Management, & User Simulation**
+
+1) **Why Customize Headers and Identity in Crawling**:
+
+ - Websites often track request headers and browser properties to detect bots. Customizing headers and managing identity help make requests appear more human, improving access to restricted sites.
+
+2) **Setting Custom Headers**:
+
+ - Customize HTTP headers to mimic genuine browser requests or meet site-specific requirements:
+ ```python
+ headers = {
+ "Accept-Language": "en-US,en;q=0.9",
+ "X-Requested-With": "XMLHttpRequest",
+ "Cache-Control": "no-cache"
+ }
+ crawler = AsyncWebCrawler(headers=headers)
+ ```
+ - **Use Case**: Customize the `Accept-Language` header to simulate local user settings, or `Cache-Control` to bypass cache for fresh content.
+
+3) **Setting a Custom User Agent**:
+
+ - Some websites block requests from common crawler user agents. Setting a custom user agent string helps bypass these restrictions:
+ ```python
+ crawler = AsyncWebCrawler(
+ user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+ )
+ ```
+ - **Tip**: Use user-agent strings from popular browsers (e.g., Chrome, Firefox) to improve access and reduce detection risks.
+
+4) **User Simulation for Human-like Behavior**:
+
+ - Enable `simulate_user=True` to mimic natural user interactions, such as random timing and simulated mouse movements:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ simulate_user=True # Simulates human-like behavior
+ )
+ ```
+ - **Behavioral Effects**: Adds subtle variations in interactions, making the crawler harder to detect on bot-protected sites.
+
+5) **Navigator Overrides and Magic Mode for Full Identity Masking**:
+
+ - Use `override_navigator=True` to mask automation indicators like `navigator.webdriver`, which websites check to detect bots:
+ ```python
+ result = await crawler.arun(
+ url="https://example.com",
+ override_navigator=True # Masks bot-related signals
+ )
+ ```
+ - **Combining with Magic Mode**: For a complete anti-bot setup, combine these identity options with `magic=True` for maximum protection:
+ ```python
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com",
+ magic=True, # Enables all anti-bot detection features
+ user_agent="Custom-Agent", # Custom agent with Magic Mode
+ )
+ ```
+ - This setup includes all anti-detection techniques like navigator masking, random timing, and user simulation.
+
+6) **Example: Comprehensive Setup for Identity Management**:
+
+ - A full example combining custom headers, user-agent, and user simulation for a realistic browsing profile:
+ ```python
+ async with AsyncWebCrawler(
+ headers={"Accept-Language": "en-US", "Cache-Control": "no-cache"},
+ user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) Chrome/91.0",
+ ) as crawler:
+ result = await crawler.arun(
+ url="https://example.com/secure-page",
+ simulate_user=True
+ )
+ print(result.markdown[:500]) # Display extracted content
+ ```
+ - This example enables detailed customization for evading detection and accessing protected pages smoothly.
+
+7) **Wrap Up & Next Steps**:
+
+ - Recap the value of headers, user-agent customization, and simulation in bypassing bot detection.
+ - Tease the next video: **Extraction Strategies: JSON CSS, LLM, and Cosine** to dive into structured data extraction methods for high-quality content retrieval.
+
+---
+
+This outline equips users with tools for managing crawler identity and human-like behavior, essential for accessing bot-protected or restricted websites.Here’s a detailed outline for the **JSON-CSS Extraction Strategy** video, covering all key aspects and supported structures in Crawl4AI:
+
+---
+
+### **10.1 JSON-CSS Extraction Strategy**
+
+#### **1. Introduction to JSON-CSS Extraction**
+ - JSON-CSS Extraction is used for pulling structured data from pages with repeated patterns, like product listings, article feeds, or directories.
+ - This strategy allows defining a schema with CSS selectors and data fields, making it easy to capture nested, list-based, or singular elements.
+
+#### **2. Basic Schema Structure**
+ - **Schema Fields**: The schema has two main components:
+ - `baseSelector`: A CSS selector to locate the main elements you want to extract (e.g., each article or product block).
+ - `fields`: Defines the data fields for each element, supporting various data types and structures.
+
+#### **3. Simple Field Extraction**
+ - **Example HTML**:
+ ```html
+
+ Sample Product
+ $19.99
+ This is a sample product.
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".product",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text"},
+ {"name": "price", "selector": ".price", "type": "text"},
+ {"name": "description", "selector": ".description", "type": "text"}
+ ]
+ }
+ ```
+ - **Explanation**: Each field captures text content from specified CSS selectors within each `.product` element.
+
+#### **4. Supported Field Types: Text, Attribute, HTML, Regex**
+ - **Field Type Options**:
+ - `text`: Extracts visible text.
+ - `attribute`: Captures an HTML attribute (e.g., `src`, `href`).
+ - `html`: Extracts the raw HTML of an element.
+ - `regex`: Allows regex patterns to extract part of the text.
+
+ - **Example HTML** (including an image):
+ ```html
+
+ Sample Product
+
+ $19.99
+ Limited time offer.
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".product",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text"},
+ {"name": "image_url", "selector": ".product-image", "type": "attribute", "attribute": "src"},
+ {"name": "price", "selector": ".price", "type": "regex", "pattern": r"\$(\d+\.\d+)"},
+ {"name": "description_html", "selector": ".description", "type": "html"}
+ ]
+ }
+ ```
+ - **Explanation**:
+ - `attribute`: Extracts the `src` attribute from `.product-image`.
+ - `regex`: Extracts the numeric part from `$19.99`.
+ - `html`: Retrieves the full HTML of the description element.
+
+#### **5. Nested Field Extraction**
+ - **Use Case**: Useful when content contains sub-elements, such as an article with author details within it.
+ - **Example HTML**:
+ ```html
+
+ Sample Article
+
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".article",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text"},
+ {"name": "author", "type": "nested", "selector": ".author", "fields": [
+ {"name": "name", "selector": ".name", "type": "text"},
+ {"name": "bio", "selector": ".bio", "type": "text"}
+ ]}
+ ]
+ }
+ ```
+ - **Explanation**:
+ - `nested`: Extracts `name` and `bio` within `.author`, grouping the author details in a single `author` object.
+
+#### **6. List and Nested List Extraction**
+ - **List**: Extracts multiple elements matching the selector as a list.
+ - **Nested List**: Allows lists within lists, useful for items with sub-lists (e.g., specifications for each product).
+ - **Example HTML**:
+ ```html
+
+ Product with Features
+
+ - Feature 1
+ - Feature 2
+ - Feature 3
+
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".product",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text"},
+ {"name": "features", "type": "list", "selector": ".features .feature", "fields": [
+ {"name": "feature", "type": "text"}
+ ]}
+ ]
+ }
+ ```
+ - **Explanation**:
+ - `list`: Captures each `.feature` item within `.features`, outputting an array of features under the `features` field.
+
+#### **7. Transformations for Field Values**
+ - Transformations allow you to modify extracted values (e.g., converting to lowercase).
+ - Supported transformations: `lowercase`, `uppercase`, `strip`.
+ - **Example HTML**:
+ ```html
+
+ Special Product
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".product",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text", "transform": "uppercase"}
+ ]
+ }
+ ```
+ - **Explanation**: The `transform` property changes the `title` to uppercase, useful for standardized outputs.
+
+#### **8. Full JSON-CSS Extraction Example**
+ - Combining all elements in a single schema example for a comprehensive crawl:
+ - **Example HTML**:
+ ```html
+
+ Featured Product
+
+ $99.99
+ Best product of the year.
+
+ - Durable
+ - Eco-friendly
+
+
+ ```
+ - **Schema**:
+ ```python
+ schema = {
+ "baseSelector": ".product",
+ "fields": [
+ {"name": "title", "selector": ".title", "type": "text", "transform": "uppercase"},
+ {"name": "image_url", "selector": ".product-image", "type": "attribute", "attribute": "src"},
+ {"name": "price", "selector": ".price", "type": "regex", "pattern": r"\$(\d+\.\d+)"},
+ {"name": "description", "selector": ".description", "type": "html"},
+ {"name": "features", "type": "list", "selector": ".features .feature", "fields": [
+ {"name": "feature", "type": "text"}
+ ]}
+ ]
+ }
+ ```
+ - **Explanation**: This schema captures and transforms each aspect of the product, illustrating the JSON-CSS strategy’s versatility for structured extraction.
+
+#### **9. Wrap Up & Next Steps**
+ - Summarize JSON-CSS Extraction’s flexibility for structured, pattern-based extraction.
+ - Tease the next video: **10.2 LLM Extraction Strategy**, focusing on using language models to extract data based on intelligent content analysis.
+
+---
+
+This outline covers each JSON-CSS Extraction option in Crawl4AI, with practical examples and schema configurations, making it a thorough guide for users.# Crawl4AI
+
+## Episode 11: Extraction Strategies: JSON CSS, LLM, and Cosine
+
+### Quick Intro
+Introduce JSON CSS Extraction Strategy for structured data, LLM Extraction Strategy for intelligent parsing, and Cosine Strategy for clustering similar content. Demo: Use JSON CSS to scrape product details from an e-commerce site.
+
+Here’s a comprehensive outline for the **LLM Extraction Strategy** video, covering key details and example applications.
+
+---
+
+### **10.2 LLM Extraction Strategy**
+
+#### **1. Introduction to LLM Extraction Strategy**
+ - The LLM Extraction Strategy leverages language models to interpret and extract structured data from complex web content.
+ - Unlike traditional CSS selectors, this strategy uses natural language instructions and schemas to guide the extraction, ideal for unstructured or diverse content.
+ - Supports **OpenAI**, **Azure OpenAI**, **HuggingFace**, and **Ollama** models, enabling flexibility with both proprietary and open-source providers.
+
+#### **2. Key Components of LLM Extraction Strategy**
+ - **Provider**: Specifies the LLM provider (e.g., OpenAI, HuggingFace, Azure).
+ - **API Token**: Required for most providers, except Ollama (local LLM model).
+ - **Instruction**: Custom extraction instructions sent to the model, providing flexibility in how the data is structured and extracted.
+ - **Schema**: Optional, defines structured fields to organize extracted data into JSON format.
+ - **Extraction Type**: Supports `"block"` for simpler text blocks or `"schema"` when a structured output format is required.
+ - **Chunking Parameters**: Breaks down large documents, with options to adjust chunk size and overlap rate for more accurate extraction across lengthy texts.
+
+#### **3. Basic Extraction Example: OpenAI Model Pricing**
+ - **Goal**: Extract model names and their input and output fees from the OpenAI pricing page.
+ - **Schema Definition**:
+ - **Model Name**: Text for model identification.
+ - **Input Fee**: Token cost for input processing.
+ - **Output Fee**: Token cost for output generation.
+
+ - **Schema**:
+ ```python
+ class OpenAIModelFee(BaseModel):
+ model_name: str = Field(..., description="Name of the OpenAI model.")
+ input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
+ output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
+ ```
+
+ - **Example Code**:
+ ```python
+ async def extract_openai_pricing():
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://openai.com/api/pricing/",
+ extraction_strategy=LLMExtractionStrategy(
+ provider="openai/gpt-4o",
+ api_token=os.getenv("OPENAI_API_KEY"),
+ schema=OpenAIModelFee.schema(),
+ extraction_type="schema",
+ instruction="Extract model names and fees for input and output tokens from the page."
+ ),
+ bypass_cache=True
+ )
+ print(result.extracted_content)
+ ```
+
+ - **Explanation**:
+ - The extraction strategy combines a schema and detailed instruction to guide the LLM in capturing structured data.
+ - Each model’s name, input fee, and output fee are extracted in a JSON format.
+
+#### **4. Knowledge Graph Extraction Example**
+ - **Goal**: Extract entities and their relationships from a document for use in a knowledge graph.
+ - **Schema Definition**:
+ - **Entities**: Individual items with descriptions (e.g., people, organizations).
+ - **Relationships**: Connections between entities, including descriptions and relationship types.
+
+ - **Schema**:
+ ```python
+ class Entity(BaseModel):
+ name: str
+ description: str
+
+ class Relationship(BaseModel):
+ entity1: Entity
+ entity2: Entity
+ description: str
+ relation_type: str
+
+ class KnowledgeGraph(BaseModel):
+ entities: List[Entity]
+ relationships: List[Relationship]
+ ```
+
+ - **Example Code**:
+ ```python
+ async def extract_knowledge_graph():
+ extraction_strategy = LLMExtractionStrategy(
+ provider="azure/gpt-4o-mini",
+ api_token=os.getenv("AZURE_API_KEY"),
+ schema=KnowledgeGraph.schema(),
+ extraction_type="schema",
+ instruction="Extract entities and relationships from the content to build a knowledge graph."
+ )
+ async with AsyncWebCrawler() as crawler:
+ result = await crawler.arun(
+ url="https://example.com/some-article",
+ extraction_strategy=extraction_strategy,
+ bypass_cache=True
+ )
+ print(result.extracted_content)
+ ```
+
+ - **Explanation**:
+ - In this setup, the LLM extracts entities and their relationships based on the schema and instruction.
+ - The schema organizes results into a JSON-based knowledge graph format.
+
+#### **5. Key Settings in LLM Extraction**
+ - **Chunking Options**:
+ - For long pages, set `chunk_token_threshold` to specify maximum token count per section.
+ - Adjust `overlap_rate` to control the overlap between chunks, useful for contextual consistency.
+ - **Example**:
+ ```python
+ extraction_strategy = LLMExtractionStrategy(
+ provider="openai/gpt-4",
+ api_token=os.getenv("OPENAI_API_KEY"),
+ chunk_token_threshold=3000,
+ overlap_rate=0.2, # 20% overlap between chunks
+ instruction="Extract key insights and relationships."
+ )
+ ```
+ - This setup ensures that longer texts are divided into manageable chunks with slight overlap, enhancing the quality of extraction.
+
+#### **6. Flexible Provider Options for LLM Extraction**
+ - **Using Proprietary Models**: OpenAI, Azure, and HuggingFace provide robust language models, often suited for complex or detailed extractions.
+ - **Using Open-Source Models**: Ollama and other open-source models can be deployed locally, suitable for offline or cost-effective extraction.
+ - **Example Call**:
+ ```python
+ await extract_structured_data_using_llm("huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct", os.getenv("HUGGINGFACE_API_KEY"))
+ await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
+ await extract_structured_data_using_llm("ollama/llama3.2")
+ ```
+
+#### **7. Complete Example of LLM Extraction Setup**
+ - Code to run both the OpenAI pricing and Knowledge Graph extractions, using various providers:
+ ```python
+ async def main():
+ await extract_openai_pricing()
+ await extract_knowledge_graph()
+
+ if __name__ == "__main__":
+ asyncio.run(main())
+ ```
+
+#### **8. Wrap Up & Next Steps**
+ - Recap the power of LLM extraction for handling unstructured or complex data extraction tasks.
+ - Tease the next video: **10.3 Cosine Similarity Strategy** for clustering similar content based on semantic similarity.
+
+---
+
+This outline explains LLM Extraction in Crawl4AI, with examples showing how to extract structured data using custom schemas and instructions. It demonstrates flexibility with multiple providers, ensuring practical application for different use cases.# Crawl4AI
+
+## Episode 11: Extraction Strategies: JSON CSS, LLM, and Cosine
+
+### Quick Intro
+Introduce JSON CSS Extraction Strategy for structured data, LLM Extraction Strategy for intelligent parsing, and Cosine Strategy for clustering similar content. Demo: Use JSON CSS to scrape product details from an e-commerce site.
+
+Here’s a structured outline for the **Cosine Similarity Strategy** video, covering key concepts, configuration, and a practical example.
+
+---
+
+### **10.3 Cosine Similarity Strategy**
+
+#### **1. Introduction to Cosine Similarity Strategy**
+ - The Cosine Similarity Strategy clusters content by semantic similarity, offering an efficient alternative to LLM-based extraction, especially when speed is a priority.
+ - Ideal for grouping similar sections of text, this strategy is well-suited for pages with content sections that may need to be classified or tagged, like news articles, product descriptions, or reviews.
+
+#### **2. Key Configuration Options**
+ - **semantic_filter**: A keyword-based filter to focus on relevant content.
+ - **word_count_threshold**: Minimum number of words per cluster, filtering out shorter, less meaningful clusters.
+ - **max_dist**: Maximum allowable distance between elements in clusters, impacting cluster tightness.
+ - **linkage_method**: Method for hierarchical clustering, such as `'ward'` (for well-separated clusters).
+ - **top_k**: Specifies the number of top categories for each cluster.
+ - **model_name**: Defines the model for embeddings, such as `sentence-transformers/all-MiniLM-L6-v2`.
+ - **sim_threshold**: Minimum similarity threshold for filtering, allowing control over cluster relevance.
+
+#### **3. How Cosine Similarity Clustering Works**
+ - **Step 1**: Embeddings are generated for each text section, transforming them into vectors that capture semantic meaning.
+ - **Step 2**: Hierarchical clustering groups similar sections based on cosine similarity, forming clusters with related content.
+ - **Step 3**: Clusters are filtered based on word count, removing those below the `word_count_threshold`.
+ - **Step 4**: Each cluster is then categorized with tags, if enabled, providing context to each grouped content section.
+
+#### **4. Example Use Case: Clustering Blog Article Sections**
+ - **Goal**: Group related sections of a blog or news page to identify distinct topics or discussion areas.
+ - **Example HTML Sections**:
+ ```text
+ "The economy is showing signs of recovery, with markets up this quarter.",
+ "In the sports world, several major teams are preparing for the upcoming season.",
+ "New advancements in AI technology are reshaping the tech landscape.",
+ "Market analysts are optimistic about continued growth in tech stocks."
+ ```
+
+ - **Code Setup**:
+ ```python
+ async def extract_blog_sections():
+ extraction_strategy = CosineStrategy(
+ word_count_threshold=15,
+ max_dist=0.3,
+ sim_threshold=0.2,
+ model_name="sentence-transformers/all-MiniLM-L6-v2",
+ top_k=2
+ )
+ async with AsyncWebCrawler() as crawler:
+ url = "https://example.com/blog-page"
+ result = await crawler.arun(
+ url=url,
+ extraction_strategy=extraction_strategy,
+ bypass_cache=True
+ )
+ print(result.extracted_content)
+ ```
+
+ - **Explanation**:
+ - **word_count_threshold**: Ensures only clusters with meaningful content are included.
+ - **sim_threshold**: Filters out clusters with low similarity, focusing on closely related sections.
+ - **top_k**: Selects top tags, useful for identifying main topics.
+
+#### **5. Applying Semantic Filtering with Cosine Similarity**
+ - **Semantic Filter**: Filters sections based on relevance to a specific keyword, such as “technology” for tech articles.
+ - **Example Code**:
+ ```python
+ extraction_strategy = CosineStrategy(
+ semantic_filter="technology",
+ word_count_threshold=10,
+ max_dist=0.25,
+ model_name="sentence-transformers/all-MiniLM-L6-v2"
+ )
+ ```
+ - **Explanation**:
+ - **semantic_filter**: Only sections with high similarity to the “technology” keyword will be included in the clustering, making it easy to focus on specific topics within a mixed-content page.
+
+#### **6. Clustering Product Reviews by Similarity**
+ - **Goal**: Organize product reviews by themes, such as “price,” “quality,” or “durability.”
+ - **Example Reviews**:
+ ```text
+ "The quality of this product is outstanding and well worth the price.",
+ "I found the product to be durable but a bit overpriced.",
+ "Great value for the money and long-lasting.",
+ "The build quality is good, but I expected a lower price point."
+ ```
+
+ - **Code Setup**:
+ ```python
+ async def extract_product_reviews():
+ extraction_strategy = CosineStrategy(
+ word_count_threshold=20,
+ max_dist=0.35,
+ sim_threshold=0.25,
+ model_name="sentence-transformers/all-MiniLM-L6-v2"
+ )
+ async with AsyncWebCrawler() as crawler:
+ url = "https://example.com/product-reviews"
+ result = await crawler.arun(
+ url=url,
+ extraction_strategy=extraction_strategy,
+ bypass_cache=True
+ )
+ print(result.extracted_content)
+ ```
+
+ - **Explanation**:
+ - This configuration clusters similar reviews, grouping feedback by common themes, helping businesses understand customer sentiments around particular product aspects.
+
+#### **7. Performance Advantages of Cosine Strategy**
+ - **Speed**: The Cosine Similarity Strategy is faster than LLM-based extraction, as it doesn’t rely on API calls to external LLMs.
+ - **Local Processing**: The strategy runs locally with pre-trained sentence embeddings, ideal for high-throughput scenarios where cost and latency are concerns.
+ - **Comparison**: With a well-optimized local model, this method can perform clustering on large datasets quickly, making it suitable for tasks requiring rapid, repeated analysis.
+
+#### **8. Full Code Example for Clustering News Articles**
+ - **Code**:
+ ```python
+ async def main():
+ await extract_blog_sections()
+ await extract_product_reviews()
+
+ if __name__ == "__main__":
+ asyncio.run(main())
+ ```
+
+#### **9. Wrap Up & Next Steps**
+ - Recap the efficiency and effectiveness of Cosine Similarity for clustering related content quickly.
+ - Close with a reminder of Crawl4AI’s flexibility across extraction strategies, and prompt users to experiment with different settings to optimize clustering for their specific content.
+
+---
+
+This outline covers Cosine Similarity Strategy’s speed and effectiveness, providing examples that showcase its potential for clustering various content types efficiently.# Crawl4AI
+
+## Episode 12: Session-Based Crawling for Dynamic Websites
+
+### Quick Intro
+Show session management for handling websites with multiple pages or actions (like “load more” buttons). Demo: Crawl a paginated content page, persisting session data across multiple requests.
+
+Here’s a detailed outline for the **Session-Based Crawling for Dynamic Websites** video, explaining why sessions are necessary, how to use them, and providing practical examples and a visual diagram to illustrate the concept.
+
+---
+
+### **11. Session-Based Crawling for Dynamic Websites**
+
+#### **1. Introduction to Session-Based Crawling**
+ - **What is Session-Based Crawling**: Session-based crawling maintains a continuous browsing session across multiple page states, allowing the crawler to interact with a page and retrieve content that loads dynamically or based on user interactions.
+ - **Why It’s Needed**:
+ - In static pages, all content is available directly from a single URL.
+ - In dynamic websites, content often loads progressively or based on user actions (e.g., clicking “load more,” submitting forms, scrolling).
+ - Session-based crawling helps simulate user actions, capturing content that is otherwise hidden until specific actions are taken.
+
+#### **2. Conceptual Diagram for Session-Based Crawling**
+
+ ```mermaid
+ graph TD
+ Start[Start Session] --> S1[Initial State (S1)]
+ S1 -->|Crawl| Content1[Extract Content S1]
+ S1 -->|Action: Click Load More| S2[State S2]
+ S2 -->|Crawl| Content2[Extract Content S2]
+ S2 -->|Action: Scroll Down| S3[State S3]
+ S3 -->|Crawl| Content3[Extract Content S3]
+ S3 -->|Action: Submit Form| S4[Final State]
+ S4 -->|Crawl| Content4[Extract Content S4]
+ Content4 --> End[End Session]
+ ```
+
+ - **Explanation of Diagram**:
+ - **Start**: Initializes the session and opens the starting URL.
+ - **State Transitions**: Each action (e.g., clicking “load more,” scrolling) transitions to a new state, where additional content becomes available.
+ - **Session Persistence**: Keeps the same browsing session active, preserving the state and allowing for a sequence of actions to unfold.
+ - **End**: After reaching the final state, the session ends, and all accumulated content has been extracted.
+
+#### **3. Key Components of Session-Based Crawling in Crawl4AI**
+ - **Session ID**: A unique identifier to maintain the state across requests, allowing the crawler to “remember” previous actions.
+ - **JavaScript Execution**: Executes JavaScript commands (e.g., clicks, scrolls) to simulate interactions.
+ - **Wait Conditions**: Ensures the crawler waits for content to load in each state before moving on.
+ - **Sequential State Transitions**: By defining actions and wait conditions between states, the crawler can navigate through the page as a user would.
+
+#### **4. Basic Session Example: Multi-Step Content Loading**
+ - **Goal**: Crawl an article feed that requires several “load more” clicks to display additional content.
+ - **Code**:
+ ```python
+ async def crawl_article_feed():
+ async with AsyncWebCrawler() as crawler:
+ session_id = "feed_session"
+
+ for page in range(3):
+ result = await crawler.arun(
+ url="https://example.com/articles",
+ session_id=session_id,
+ js_code="document.querySelector('.load-more-button').click();" if page > 0 else None,
+ wait_for="css:.article",
+ css_selector=".article" # Target article elements
+ )
+ print(f"Page {page + 1}: Extracted {len(result.extracted_content)} articles")
+ ```
+ - **Explanation**:
+ - **session_id**: Ensures all requests share the same browsing state.
+ - **js_code**: Clicks the “load more” button after the initial page load, expanding content on each iteration.
+ - **wait_for**: Ensures articles have loaded after each click before extraction.
+
+#### **5. Advanced Example: E-Commerce Product Search with Filter Selection**
+ - **Goal**: Interact with filters on an e-commerce page to extract products based on selected criteria.
+ - **Example Steps**:
+ 1. **State 1**: Load the main product page.
+ 2. **State 2**: Apply a filter (e.g., “On Sale”) by selecting a checkbox.
+ 3. **State 3**: Scroll to load additional products and capture updated results.
+
+ - **Code**:
+ ```python
+ async def extract_filtered_products():
+ async with AsyncWebCrawler() as crawler:
+ session_id = "product_session"
+
+ # Step 1: Open product page
+ result = await crawler.arun(
+ url="https://example.com/products",
+ session_id=session_id,
+ wait_for="css:.product-item"
+ )
+
+ # Step 2: Apply filter (e.g., "On Sale")
+ result = await crawler.arun(
+ url="https://example.com/products",
+ session_id=session_id,
+ js_code="document.querySelector('#sale-filter-checkbox').click();",
+ wait_for="css:.product-item"
+ )
+
+ # Step 3: Scroll to load additional products
+ for _ in range(2): # Scroll down twice
+ result = await crawler.arun(
+ url="https://example.com/products",
+ session_id=session_id,
+ js_code="window.scrollTo(0, document.body.scrollHeight);",
+ wait_for="css:.product-item"
+ )
+ print(f"Loaded {len(result.extracted_content)} products after scroll")
+ ```
+ - **Explanation**:
+ - **State Persistence**: Each action (filter selection and scroll) builds on the previous session state.
+ - **Multiple Interactions**: Combines clicking a filter with scrolling, demonstrating how the session preserves these actions.
+
+#### **6. Key Benefits of Session-Based Crawling**
+ - **Accessing Hidden Content**: Retrieves data that loads only after user actions.
+ - **Simulating User Behavior**: Handles interactive elements such as “load more” buttons, dropdowns, and filters.
+ - **Maintaining Continuity Across States**: Enables a sequential process, moving logically from one state to the next, capturing all desired content without reloading the initial state each time.
+
+#### **7. Additional Configuration Tips**
+ - **Manage Session End**: Always conclude the session after the final state to release resources.
+ - **Optimize with Wait Conditions**: Use `wait_for` to ensure complete loading before each extraction.
+ - **Handling Errors in Session-Based Crawling**: Include error handling for interactions that may fail, ensuring robustness across state transitions.
+
+#### **8. Complete Code Example: Multi-Step Session Workflow**
+ - **Example**:
+ ```python
+ async def main():
+ await crawl_article_feed()
+ await extract_filtered_products()
+
+ if __name__ == "__main__":
+ asyncio.run(main())
+ ```
+
+#### **9. Wrap Up & Next Steps**
+ - Recap the usefulness of session-based crawling for dynamic content extraction.
+ - Tease the next video: **Hooks and Custom Workflow with AsyncWebCrawler** to cover advanced customization options for further control over the crawling process.
+
+---
+
+This outline covers session-based crawling from both a conceptual and practical perspective, helping users understand its importance, configure it effectively, and use it to handle complex dynamic content.# Crawl4AI
+
+## Episode 13: Chunking Strategies for Large Text Processing
+
+### Quick Intro
+Explain Regex, NLP, and Fixed-Length chunking, and when to use each. Demo: Chunk a large article or document for processing by topics or sentences.
+
+Here’s a structured outline for the **Chunking Strategies for Large Text Processing** video, emphasizing how chunking works within extraction and why it’s crucial for effective data aggregation.
+
+Here’s a structured outline for the **Chunking Strategies for Large Text Processing** video, explaining each strategy, when to use it, and providing examples to illustrate.
+
+---
+
+### **12. Chunking Strategies for Large Text Processing**
+
+#### **1. Introduction to Chunking in Crawl4AI**
+ - **What is Chunking**: Chunking is the process of dividing large text into manageable sections or “chunks,” enabling efficient processing in extraction tasks.
+ - **Why It’s Needed**:
+ - When processing large text, feeding it directly into an extraction function (like `F(x)`) can overwhelm memory or token limits.
+ - Chunking breaks down `x` (the text) into smaller pieces, which are processed sequentially or in parallel by the extraction function, with the final result being an aggregation of all chunks’ processed output.
+
+#### **2. Key Chunking Strategies and Use Cases**
+ - Crawl4AI offers various chunking strategies to suit different text structures, chunk sizes, and processing requirements.
+ - **Choosing a Strategy**: Select based on the type of text (e.g., articles, transcripts) and extraction needs (e.g., simple splitting or context-sensitive processing).
+
+#### **3. Strategy 1: Regex-Based Chunking**
+ - **Description**: Uses regular expressions to split text based on specified patterns (e.g., paragraphs or section breaks).
+ - **Use Case**: Ideal for dividing text by paragraphs or larger logical blocks where sections are clearly separated by line breaks or punctuation.
+ - **Example**:
+ - **Pattern**: `r'\n\n'` for double line breaks.
+ ```python
+ chunker = RegexChunking(patterns=[r'\n\n'])
+ text_chunks = chunker.chunk(long_text)
+ print(text_chunks) # Output: List of paragraphs
+ ```
+ - **Pros**: Flexible for pattern-based chunking.
+ - **Cons**: Limited to text with consistent formatting.
+
+#### **4. Strategy 2: NLP Sentence-Based Chunking**
+ - **Description**: Uses NLP to split text by sentences, ensuring grammatically complete segments.
+ - **Use Case**: Useful for extracting individual statements, such as in news articles, quotes, or legal text.
+ - **Example**:
+ ```python
+ chunker = NlpSentenceChunking()
+ sentence_chunks = chunker.chunk(long_text)
+ print(sentence_chunks) # Output: List of sentences
+ ```
+ - **Pros**: Maintains sentence structure, ideal for tasks needing semantic completeness.
+ - **Cons**: May create very small chunks, which could limit contextual extraction.
+
+#### **5. Strategy 3: Topic-Based Segmentation Using TextTiling**
+ - **Description**: Segments text into topics using TextTiling, identifying topic shifts and key segments.
+ - **Use Case**: Ideal for long articles, reports, or essays where each section covers a different topic.
+ - **Example**:
+ ```python
+ chunker = TopicSegmentationChunking(num_keywords=3)
+ topic_chunks = chunker.chunk_with_topics(long_text)
+ print(topic_chunks) # Output: List of topic segments with keywords
+ ```
+ - **Pros**: Groups related content, preserving topical coherence.
+ - **Cons**: Depends on identifiable topic shifts, which may not be present in all texts.
+
+#### **6. Strategy 4: Fixed-Length Word Chunking**
+ - **Description**: Splits text into chunks based on a fixed number of words.
+ - **Use Case**: Ideal for text where exact segment size is required, such as processing word-limited documents for LLMs.
+ - **Example**:
+ ```python
+ chunker = FixedLengthWordChunking(chunk_size=100)
+ word_chunks = chunker.chunk(long_text)
+ print(word_chunks) # Output: List of 100-word chunks
+ ```
+ - **Pros**: Ensures uniform chunk sizes, suitable for token-based extraction limits.
+ - **Cons**: May split sentences, affecting semantic coherence.
+
+#### **7. Strategy 5: Sliding Window Chunking**
+ - **Description**: Uses a fixed window size with a step, creating overlapping chunks to maintain context.
+ - **Use Case**: Useful for maintaining context across sections, as with documents where context is needed for neighboring sections.
+ - **Example**:
+ ```python
+ chunker = SlidingWindowChunking(window_size=100, step=50)
+ window_chunks = chunker.chunk(long_text)
+ print(window_chunks) # Output: List of overlapping word chunks
+ ```
+ - **Pros**: Retains context across adjacent chunks, ideal for complex semantic extraction.
+ - **Cons**: Overlap increases data size, potentially impacting processing time.
+
+#### **8. Strategy 6: Overlapping Window Chunking**
+ - **Description**: Similar to sliding windows but with a defined overlap, allowing chunks to share content at the edges.
+ - **Use Case**: Suitable for handling long texts with essential overlapping information, like research articles or medical records.
+ - **Example**:
+ ```python
+ chunker = OverlappingWindowChunking(window_size=1000, overlap=100)
+ overlap_chunks = chunker.chunk(long_text)
+ print(overlap_chunks) # Output: List of overlapping chunks with defined overlap
+ ```
+ - **Pros**: Allows controlled overlap for consistent content coverage across chunks.
+ - **Cons**: Redundant data in overlapping areas may increase computation.
+
+#### **9. Practical Example: Using Chunking with an Extraction Strategy**
+ - **Goal**: Combine chunking with an extraction strategy to process large text effectively.
+ - **Example Code**:
+ ```python
+ from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+ async def extract_large_text():
+ # Initialize chunker and extraction strategy
+ chunker = FixedLengthWordChunking(chunk_size=200)
+ extraction_strategy = LLMExtractionStrategy(provider="openai/gpt-4", api_token="your_api_token")
+
+ # Split text into chunks
+ text_chunks = chunker.chunk(large_text)
+
+ async with AsyncWebCrawler() as crawler:
+ for chunk in text_chunks:
+ result = await crawler.arun(
+ url="https://example.com",
+ extraction_strategy=extraction_strategy,
+ content=chunk
+ )
+ print(result.extracted_content)
+ ```
+
+ - **Explanation**:
+ - `chunker.chunk()`: Divides the `large_text` into smaller segments based on the chosen strategy.
+ - `extraction_strategy`: Processes each chunk separately, and results are then aggregated to form the final output.
+
+#### **10. Choosing the Right Chunking Strategy**
+ - **Text Structure**: If text has clear sections (e.g., paragraphs, topics), use Regex or Topic Segmentation.
+ - **Extraction Needs**: If context is crucial, consider Sliding or Overlapping Window Chunking.
+ - **Processing Constraints**: For word-limited extractions (e.g., LLMs with token limits), Fixed-Length Word Chunking is often most effective.
+
+#### **11. Wrap Up & Next Steps**
+ - Recap the benefits of each chunking strategy and when to use them in extraction workflows.
+ - Tease the next video: **Hooks and Custom Workflow with AsyncWebCrawler**, focusing on customizing crawler behavior with hooks for a fine-tuned extraction process.
+
+---
+
+This outline provides a complete understanding of chunking strategies, explaining each method’s strengths and best-use scenarios to help users process large texts effectively in Crawl4AI.# Crawl4AI
+
+## Episode 14: Hooks and Custom Workflow with AsyncWebCrawler
+
+### Quick Intro
+Cover hooks (`on_browser_created`, `before_goto`, `after_goto`) to add custom workflows. Demo: Use hooks to add custom cookies or headers, log HTML, or trigger specific events on page load.
+
+Here’s a detailed outline for the **Hooks and Custom Workflow with AsyncWebCrawler** video, covering each hook’s purpose, usage, and example implementations.
+
+---
+
+### **13. Hooks and Custom Workflow with AsyncWebCrawler**
+
+#### **1. Introduction to Hooks in Crawl4AI**
+ - **What are Hooks**: Hooks are customizable entry points in the crawling process that allow users to inject custom actions or logic at specific stages.
+ - **Why Use Hooks**:
+ - They enable fine-grained control over the crawling workflow.
+ - Useful for performing additional tasks (e.g., logging, modifying headers) dynamically during the crawl.
+ - Hooks provide the flexibility to adapt the crawler to complex site structures or unique project needs.
+
+#### **2. Overview of Available Hooks**
+ - Crawl4AI offers seven key hooks to modify and control different stages in the crawling lifecycle:
+ - `on_browser_created`
+ - `on_user_agent_updated`
+ - `on_execution_started`
+ - `before_goto`
+ - `after_goto`
+ - `before_return_html`
+ - `before_retrieve_html`
+
+#### **3. Hook-by-Hook Explanation and Examples**
+
+---
+
+##### **Hook 1: `on_browser_created`**
+ - **Purpose**: Triggered right after the browser instance is created.
+ - **Use Case**:
+ - Initializing browser-specific settings or performing setup actions.
+ - Configuring browser extensions or scripts before any page is opened.
+ - **Example**:
+ ```python
+ async def log_browser_creation(browser):
+ print("Browser instance created:", browser)
+
+ crawler.crawler_strategy.set_hook('on_browser_created', log_browser_creation)
+ ```
+ - **Explanation**: This hook logs the browser creation event, useful for tracking when a new browser instance starts.
+
+---
+
+##### **Hook 2: `on_user_agent_updated`**
+ - **Purpose**: Called whenever the user agent string is updated.
+ - **Use Case**:
+ - Modifying the user agent based on page requirements, e.g., changing to a mobile user agent for mobile-only pages.
+ - **Example**:
+ ```python
+ def update_user_agent(user_agent):
+ print(f"User Agent Updated: {user_agent}")
+
+ crawler.crawler_strategy.set_hook('on_user_agent_updated', update_user_agent)
+ crawler.update_user_agent("Mozilla/5.0 (iPhone; CPU iPhone OS 14_0 like Mac OS X)")
+ ```
+ - **Explanation**: This hook provides a callback every time the user agent changes, helpful for debugging or dynamically altering user agent settings based on conditions.
+
+---
+
+##### **Hook 3: `on_execution_started`**
+ - **Purpose**: Called right before the crawler begins any interaction (e.g., JavaScript execution, clicks).
+ - **Use Case**:
+ - Performing setup actions, such as inserting cookies or initiating custom scripts.
+ - **Example**:
+ ```python
+ async def log_execution_start(page):
+ print("Execution started on page:", page.url)
+
+ crawler.crawler_strategy.set_hook('on_execution_started', log_execution_start)
+ ```
+ - **Explanation**: Logs the start of any major interaction on the page, ideal for cases where you want to monitor each interaction.
+
+---
+
+##### **Hook 4: `before_goto`**
+ - **Purpose**: Triggered before navigating to a new URL with `page.goto()`.
+ - **Use Case**:
+ - Modifying request headers or setting up conditions right before the page loads.
+ - Adding headers or dynamically adjusting options for specific URLs.
+ - **Example**:
+ ```python
+ async def modify_headers_before_goto(page):
+ await page.set_extra_http_headers({"X-Custom-Header": "CustomValue"})
+ print("Custom headers set before navigation")
+
+ crawler.crawler_strategy.set_hook('before_goto', modify_headers_before_goto)
+ ```
+ - **Explanation**: This hook allows injecting headers or altering settings based on the page’s needs, particularly useful for pages with custom requirements.
+
+---
+
+##### **Hook 5: `after_goto`**
+ - **Purpose**: Executed immediately after a page has loaded (after `page.goto()`).
+ - **Use Case**:
+ - Checking the loaded page state, modifying the DOM, or performing post-navigation actions (e.g., scrolling).
+ - **Example**:
+ ```python
+ async def post_navigation_scroll(page):
+ await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
+ print("Scrolled to the bottom after navigation")
+
+ crawler.crawler_strategy.set_hook('after_goto', post_navigation_scroll)
+ ```
+ - **Explanation**: This hook scrolls to the bottom of the page after loading, which can help load dynamically added content like infinite scroll elements.
+
+---
+
+##### **Hook 6: `before_return_html`**
+ - **Purpose**: Called right before HTML content is retrieved and returned.
+ - **Use Case**:
+ - Removing overlays or cleaning up the page for a cleaner HTML extraction.
+ - **Example**:
+ ```python
+ async def remove_advertisements(page, html):
+ await page.evaluate("document.querySelectorAll('.ad-banner').forEach(el => el.remove());")
+ print("Advertisements removed before returning HTML")
+
+ crawler.crawler_strategy.set_hook('before_return_html', remove_advertisements)
+ ```
+ - **Explanation**: The hook removes ad banners from the HTML before it’s retrieved, ensuring a cleaner data extraction.
+
+---
+
+##### **Hook 7: `before_retrieve_html`**
+ - **Purpose**: Runs right before Crawl4AI initiates HTML retrieval.
+ - **Use Case**:
+ - Finalizing any page adjustments (e.g., setting timers, waiting for specific elements).
+ - **Example**:
+ ```python
+ async def wait_for_content_before_retrieve(page):
+ await page.wait_for_selector('.main-content')
+ print("Main content loaded, ready to retrieve HTML")
+
+ crawler.crawler_strategy.set_hook('before_retrieve_html', wait_for_content_before_retrieve)
+ ```
+ - **Explanation**: This hook waits for the main content to load before retrieving the HTML, ensuring that all essential content is captured.
+
+#### **4. Setting Hooks in Crawl4AI**
+ - **How to Set Hooks**:
+ - Use `set_hook` to define a custom function for each hook.
+ - Each hook function can be asynchronous (useful for actions like waiting or retrieving async data).
+ - **Example Setup**:
+ ```python
+ crawler.crawler_strategy.set_hook('on_browser_created', log_browser_creation)
+ crawler.crawler_strategy.set_hook('before_goto', modify_headers_before_goto)
+ crawler.crawler_strategy.set_hook('after_goto', post_navigation_scroll)
+ ```
+
+#### **5. Complete Example: Using Hooks for a Customized Crawl Workflow**
+ - **Goal**: Log each key step, set custom headers before navigation, and clean up the page before retrieving HTML.
+ - **Example Code**:
+ ```python
+ async def custom_crawl():
+ async with AsyncWebCrawler() as crawler:
+ # Set hooks for custom workflow
+ crawler.crawler_strategy.set_hook('on_browser_created', log_browser_creation)
+ crawler.crawler_strategy.set_hook('before_goto', modify_headers_before_goto)
+ crawler.crawler_strategy.set_hook('after_goto', post_navigation_scroll)
+ crawler.crawler_strategy.set_hook('before_return_html', remove_advertisements)
+
+ # Perform the crawl
+ url = "https://example.com"
+ result = await crawler.arun(url=url)
+ print(result.html) # Display or process HTML
+ ```
+
+#### **6. Benefits of Using Hooks in Custom Crawling Workflows**
+ - **Enhanced Control**: Hooks offer precise control over each stage, allowing adjustments based on content and structure.
+ - **Efficient Modifications**: Avoid reloading or restarting the session; hooks can alter actions dynamically.
+ - **Context-Sensitive Actions**: Hooks enable custom logic tailored to specific pages or sections, maximizing extraction quality.
+
+#### **7. Wrap Up & Next Steps**
+ - Recap how hooks empower customized workflows in Crawl4AI, enabling flexibility at every stage.
+ - Tease the next video: **Automating Post-Processing with Crawl4AI**, covering automated steps after data extraction.
+
+---
+
+This outline provides a thorough understanding of hooks, their practical applications, and examples for customizing the crawling workflow in Crawl4AI.
\ No newline at end of file
diff --git a/docs/notebooks/Crawl4AI_v0.3.72_Release_Announcement.ipynb b/docs/notebooks/Crawl4AI_v0.3.72_Release_Announcement.ipynb
new file mode 100644
index 00000000..053bc6c5
--- /dev/null
+++ b/docs/notebooks/Crawl4AI_v0.3.72_Release_Announcement.ipynb
@@ -0,0 +1,235 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 🚀 Crawl4AI v0.3.72 Release Announcement\n",
+ "\n",
+ "Welcome to the new release of **Crawl4AI v0.3.72**! This notebook highlights the latest features and demonstrates how they work in real-time. Follow along to see each feature in action!\n",
+ "\n",
+ "### What’s New?\n",
+ "- ✨ `Fit Markdown`: Extracts only the main content from articles and blogs\n",
+ "- 🛡️ **Magic Mode**: Comprehensive anti-bot detection bypass\n",
+ "- 🌐 **Multi-browser support**: Switch between Chromium, Firefox, WebKit\n",
+ "- 🔍 **Knowledge Graph Extraction**: Generate structured graphs of entities & relationships from any URL\n",
+ "- 🤖 **Crawl4AI GPT Assistant**: Chat directly with our AI assistant for help, code generation, and faster learning (available [here](https://tinyurl.com/your-gpt-assistant-link))\n",
+ "\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 📥 Setup\n",
+ "To start, we'll install `Crawl4AI` along with Playwright and `nest_asyncio` to ensure compatibility with Colab’s asynchronous environment."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install Crawl4AI and dependencies\n",
+ "!pip install crawl4ai\n",
+ "!playwright install\n",
+ "!pip install nest_asyncio"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import nest_asyncio and apply it to allow asyncio in Colab\n",
+ "import nest_asyncio\n",
+ "nest_asyncio.apply()\n",
+ "\n",
+ "print('Setup complete!')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "## ✨ Feature 1: `Fit Markdown`\n",
+ "Extracts only the main content from articles and blog pages, removing sidebars, ads, and other distractions.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import asyncio\n",
+ "from crawl4ai import AsyncWebCrawler\n",
+ "\n",
+ "async def fit_markdown_demo():\n",
+ " async with AsyncWebCrawler() as crawler:\n",
+ " result = await crawler.arun(url=\"https://janineintheworld.com/places-to-visit-in-central-mexico\")\n",
+ " print(result.fit_markdown) # Shows main content in Markdown format\n",
+ "\n",
+ "# Run the demo\n",
+ "await fit_markdown_demo()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "## 🛡️ Feature 2: Magic Mode\n",
+ "Magic Mode bypasses anti-bot detection to make crawling more reliable on protected websites.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "async def magic_mode_demo():\n",
+ " async with AsyncWebCrawler() as crawler: # Enables anti-bot detection bypass\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://www.reuters.com/markets/us/global-markets-view-usa-pix-2024-08-29/\",\n",
+ " magic=True # Enables magic mode\n",
+ " )\n",
+ " print(result.markdown) # Shows the full content in Markdown format\n",
+ "\n",
+ "# Run the demo\n",
+ "await magic_mode_demo()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "## 🌐 Feature 3: Multi-Browser Support\n",
+ "Crawl4AI now supports Chromium, Firefox, and WebKit. Here’s how to specify Firefox for a crawl.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "async def multi_browser_demo():\n",
+ " async with AsyncWebCrawler(browser_type=\"firefox\") as crawler: # Using Firefox instead of default Chromium\n",
+ " result = await crawler.arun(url=\"https://crawl4i.com\")\n",
+ " print(result.markdown) # Shows content extracted using Firefox\n",
+ "\n",
+ "# Run the demo\n",
+ "await multi_browser_demo()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "## ✨ Put them all together\n",
+ "\n",
+ "Let's combine all the features to extract the main content from a blog post, bypass anti-bot detection, and generate a knowledge graph from the extracted content."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from crawl4ai.extraction_strategy import LLMExtractionStrategy\n",
+ "from pydantic import BaseModel\n",
+ "import json, os\n",
+ "from typing import List\n",
+ "\n",
+ "# Define classes for the knowledge graph structure\n",
+ "class Landmark(BaseModel):\n",
+ " name: str\n",
+ " description: str\n",
+ " activities: list[str] # E.g., visiting, sightseeing, relaxing\n",
+ "\n",
+ "class City(BaseModel):\n",
+ " name: str\n",
+ " description: str\n",
+ " landmarks: list[Landmark]\n",
+ " cultural_highlights: list[str] # E.g., food, music, traditional crafts\n",
+ "\n",
+ "class TravelKnowledgeGraph(BaseModel):\n",
+ " cities: list[City] # Central Mexican cities to visit\n",
+ "\n",
+ "async def combined_demo():\n",
+ " # Define the knowledge graph extraction strategy\n",
+ " strategy = LLMExtractionStrategy(\n",
+ " # provider=\"ollama/nemotron\",\n",
+ " provider='openai/gpt-4o-mini', # Or any other provider, including Ollama and open source models\n",
+ " pi_token=os.getenv('OPENAI_API_KEY'), # In case of Ollama just pass \"no-token\"\n",
+ " schema=TravelKnowledgeGraph.schema(),\n",
+ " instruction=(\n",
+ " \"Extract cities, landmarks, and cultural highlights for places to visit in Central Mexico. \"\n",
+ " \"For each city, list main landmarks with descriptions and activities, as well as cultural highlights.\"\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " # Set up the AsyncWebCrawler with multi-browser support, Magic Mode, and Fit Markdown\n",
+ " async with AsyncWebCrawler(browser_type=\"firefox\") as crawler:\n",
+ " result = await crawler.arun(\n",
+ " url=\"https://janineintheworld.com/places-to-visit-in-central-mexico\",\n",
+ " extraction_strategy=strategy,\n",
+ " bypass_cache=True,\n",
+ " magic=True\n",
+ " )\n",
+ " \n",
+ " # Display main article content in Fit Markdown format\n",
+ " print(\"Extracted Main Content:\\n\", result.fit_markdown)\n",
+ " \n",
+ " # Display extracted knowledge graph of cities, landmarks, and cultural highlights\n",
+ " if result.extracted_content:\n",
+ " travel_graph = json.loads(result.extracted_content)\n",
+ " print(\"\\nExtracted Knowledge Graph:\\n\", json.dumps(travel_graph, indent=2))\n",
+ "\n",
+ "# Run the combined demo\n",
+ "await combined_demo()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "## 🤖 Crawl4AI GPT Assistant\n",
+ "Chat with the Crawl4AI GPT Assistant for code generation, support, and learning Crawl4AI faster. Try it out [here](https://tinyurl.com/crawl4ai-gpt)!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python",
+ "version": "3.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/main.py b/main.py
index 71d5eeee..853cd0b7 100644
--- a/main.py
+++ b/main.py
@@ -1,10 +1,6 @@
-import os
-import importlib
-import asyncio
-from functools import lru_cache
-import logging
-logging.basicConfig(level=logging.DEBUG)
-
+import asyncio, os
+from fastapi import FastAPI, HTTPException, BackgroundTasks, Request
+from fastapi.responses import JSONResponse
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
@@ -15,101 +11,303 @@ from starlette.middleware.base import BaseHTTPMiddleware
from starlette.responses import FileResponse
from fastapi.responses import RedirectResponse
-from pydantic import BaseModel, HttpUrl
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from typing import List, Optional
-
-from crawl4ai.web_crawler import WebCrawler
-from crawl4ai.database import get_total_count, clear_db
-
+from pydantic import BaseModel, HttpUrl, Field
+from typing import Optional, List, Dict, Any, Union
+import psutil
import time
-from slowapi import Limiter, _rate_limit_exceeded_handler
-from slowapi.util import get_remote_address
-from slowapi.errors import RateLimitExceeded
+import uuid
+from collections import defaultdict
+from urllib.parse import urlparse
+import math
+import logging
+from enum import Enum
+from dataclasses import dataclass
+import json
+from crawl4ai import AsyncWebCrawler, CrawlResult
+from crawl4ai.extraction_strategy import (
+ LLMExtractionStrategy,
+ CosineStrategy,
+ JsonCssExtractionStrategy,
+)
-# load .env file
-from dotenv import load_dotenv
-load_dotenv()
-
-# Configuration
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
-MAX_CONCURRENT_REQUESTS = 10 # Adjust this to change the maximum concurrent requests
-current_requests = 0
-lock = asyncio.Lock()
-app = FastAPI()
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
-# Initialize rate limiter
-def rate_limit_key_func(request: Request):
- access_token = request.headers.get("access-token")
- if access_token == os.environ.get('ACCESS_TOKEN'):
- return None
- return get_remote_address(request)
+class TaskStatus(str, Enum):
+ PENDING = "pending"
+ PROCESSING = "processing"
+ COMPLETED = "completed"
+ FAILED = "failed"
-limiter = Limiter(key_func=rate_limit_key_func)
-app.state.limiter = limiter
+class CrawlerType(str, Enum):
+ BASIC = "basic"
+ LLM = "llm"
+ COSINE = "cosine"
+ JSON_CSS = "json_css"
-# Dictionary to store last request times for each client
-last_request_times = {}
-last_rate_limit = {}
+class ExtractionConfig(BaseModel):
+ type: CrawlerType
+ params: Dict[str, Any] = {}
+class CrawlRequest(BaseModel):
+ urls: Union[HttpUrl, List[HttpUrl]]
+ extraction_config: Optional[ExtractionConfig] = None
+ crawler_params: Dict[str, Any] = {}
+ priority: int = Field(default=5, ge=1, le=10)
+ ttl: Optional[int] = 3600
+ js_code: Optional[List[str]] = None
+ wait_for: Optional[str] = None
+ css_selector: Optional[str] = None
+ screenshot: bool = False
+ magic: bool = False
+ extra: Optional[Dict[str, Any]] = {}
-def get_rate_limit():
- limit = os.environ.get('ACCESS_PER_MIN', "5")
- return f"{limit}/minute"
+@dataclass
+class TaskInfo:
+ id: str
+ status: TaskStatus
+ result: Optional[Union[CrawlResult, List[CrawlResult]]] = None
+ error: Optional[str] = None
+ created_at: float = time.time()
+ ttl: int = 3600
-# Custom rate limit exceeded handler
-async def custom_rate_limit_exceeded_handler(request: Request, exc: RateLimitExceeded) -> JSONResponse:
- if request.client.host not in last_rate_limit or time.time() - last_rate_limit[request.client.host] > 60:
- last_rate_limit[request.client.host] = time.time()
- retry_after = 60 - (time.time() - last_rate_limit[request.client.host])
- reset_at = time.time() + retry_after
- return JSONResponse(
- status_code=429,
- content={
- "detail": "Rate limit exceeded",
- "limit": str(exc.limit.limit),
- "retry_after": retry_after,
- 'reset_at': reset_at,
- "message": f"You have exceeded the rate limit of {exc.limit.limit}."
- }
- )
-
-app.add_exception_handler(RateLimitExceeded, custom_rate_limit_exceeded_handler)
+class ResourceMonitor:
+ def __init__(self, max_concurrent_tasks: int = 10):
+ self.max_concurrent_tasks = max_concurrent_tasks
+ self.memory_threshold = 0.85
+ self.cpu_threshold = 0.90
+ self._last_check = 0
+ self._check_interval = 1 # seconds
+ self._last_available_slots = max_concurrent_tasks
+ async def get_available_slots(self) -> int:
+ current_time = time.time()
+ if current_time - self._last_check < self._check_interval:
+ return self._last_available_slots
-# Middleware for token-based bypass and per-request limit
-class RateLimitMiddleware(BaseHTTPMiddleware):
- async def dispatch(self, request: Request, call_next):
- SPAN = int(os.environ.get('ACCESS_TIME_SPAN', 10))
- access_token = request.headers.get("access-token")
- if access_token == os.environ.get('ACCESS_TOKEN'):
- return await call_next(request)
-
- path = request.url.path
- if path in ["/crawl", "/old"]:
- client_ip = request.client.host
+ mem_usage = psutil.virtual_memory().percent / 100
+ cpu_usage = psutil.cpu_percent() / 100
+
+ memory_factor = max(0, (self.memory_threshold - mem_usage) / self.memory_threshold)
+ cpu_factor = max(0, (self.cpu_threshold - cpu_usage) / self.cpu_threshold)
+
+ self._last_available_slots = math.floor(
+ self.max_concurrent_tasks * min(memory_factor, cpu_factor)
+ )
+ self._last_check = current_time
+
+ return self._last_available_slots
+
+class TaskManager:
+ def __init__(self, cleanup_interval: int = 300):
+ self.tasks: Dict[str, TaskInfo] = {}
+ self.high_priority = asyncio.PriorityQueue()
+ self.low_priority = asyncio.PriorityQueue()
+ self.cleanup_interval = cleanup_interval
+ self.cleanup_task = None
+
+ async def start(self):
+ self.cleanup_task = asyncio.create_task(self._cleanup_loop())
+
+ async def stop(self):
+ if self.cleanup_task:
+ self.cleanup_task.cancel()
+ try:
+ await self.cleanup_task
+ except asyncio.CancelledError:
+ pass
+
+ async def add_task(self, task_id: str, priority: int, ttl: int) -> None:
+ task_info = TaskInfo(id=task_id, status=TaskStatus.PENDING, ttl=ttl)
+ self.tasks[task_id] = task_info
+ queue = self.high_priority if priority > 5 else self.low_priority
+ await queue.put((-priority, task_id)) # Negative for proper priority ordering
+
+ async def get_next_task(self) -> Optional[str]:
+ try:
+ # Try high priority first
+ _, task_id = await asyncio.wait_for(self.high_priority.get(), timeout=0.1)
+ return task_id
+ except asyncio.TimeoutError:
+ try:
+ # Then try low priority
+ _, task_id = await asyncio.wait_for(self.low_priority.get(), timeout=0.1)
+ return task_id
+ except asyncio.TimeoutError:
+ return None
+
+ def update_task(self, task_id: str, status: TaskStatus, result: Any = None, error: str = None):
+ if task_id in self.tasks:
+ task_info = self.tasks[task_id]
+ task_info.status = status
+ task_info.result = result
+ task_info.error = error
+
+ def get_task(self, task_id: str) -> Optional[TaskInfo]:
+ return self.tasks.get(task_id)
+
+ async def _cleanup_loop(self):
+ while True:
+ try:
+ await asyncio.sleep(self.cleanup_interval)
+ current_time = time.time()
+ expired_tasks = [
+ task_id
+ for task_id, task in self.tasks.items()
+ if current_time - task.created_at > task.ttl
+ and task.status in [TaskStatus.COMPLETED, TaskStatus.FAILED]
+ ]
+ for task_id in expired_tasks:
+ del self.tasks[task_id]
+ except Exception as e:
+ logger.error(f"Error in cleanup loop: {e}")
+
+class CrawlerPool:
+ def __init__(self, max_size: int = 10):
+ self.max_size = max_size
+ self.active_crawlers: Dict[AsyncWebCrawler, float] = {}
+ self._lock = asyncio.Lock()
+
+ async def acquire(self, **kwargs) -> AsyncWebCrawler:
+ async with self._lock:
+ # Clean up inactive crawlers
current_time = time.time()
-
- # Check time since last request
- if client_ip in last_request_times:
- time_since_last_request = current_time - last_request_times[client_ip]
- if time_since_last_request < SPAN:
- return JSONResponse(
- status_code=429,
- content={
- "detail": "Too many requests",
- "message": "Rate limit exceeded. Please wait 10 seconds between requests.",
- "retry_after": max(0, SPAN - time_since_last_request),
- "reset_at": current_time + max(0, SPAN - time_since_last_request),
- }
- )
-
- last_request_times[client_ip] = current_time
+ inactive = [
+ crawler
+ for crawler, last_used in self.active_crawlers.items()
+ if current_time - last_used > 600 # 10 minutes timeout
+ ]
+ for crawler in inactive:
+ await crawler.__aexit__(None, None, None)
+ del self.active_crawlers[crawler]
- return await call_next(request)
+ # Create new crawler if needed
+ if len(self.active_crawlers) < self.max_size:
+ crawler = AsyncWebCrawler(**kwargs)
+ await crawler.__aenter__()
+ self.active_crawlers[crawler] = current_time
+ return crawler
-app.add_middleware(RateLimitMiddleware)
+ # Reuse least recently used crawler
+ crawler = min(self.active_crawlers.items(), key=lambda x: x[1])[0]
+ self.active_crawlers[crawler] = current_time
+ return crawler
+
+ async def release(self, crawler: AsyncWebCrawler):
+ async with self._lock:
+ if crawler in self.active_crawlers:
+ self.active_crawlers[crawler] = time.time()
+
+ async def cleanup(self):
+ async with self._lock:
+ for crawler in list(self.active_crawlers.keys()):
+ await crawler.__aexit__(None, None, None)
+ self.active_crawlers.clear()
+
+class CrawlerService:
+ def __init__(self, max_concurrent_tasks: int = 10):
+ self.resource_monitor = ResourceMonitor(max_concurrent_tasks)
+ self.task_manager = TaskManager()
+ self.crawler_pool = CrawlerPool(max_concurrent_tasks)
+ self._processing_task = None
+
+ async def start(self):
+ await self.task_manager.start()
+ self._processing_task = asyncio.create_task(self._process_queue())
+
+ async def stop(self):
+ if self._processing_task:
+ self._processing_task.cancel()
+ try:
+ await self._processing_task
+ except asyncio.CancelledError:
+ pass
+ await self.task_manager.stop()
+ await self.crawler_pool.cleanup()
+
+ def _create_extraction_strategy(self, config: ExtractionConfig):
+ if not config:
+ return None
+
+ if config.type == CrawlerType.LLM:
+ return LLMExtractionStrategy(**config.params)
+ elif config.type == CrawlerType.COSINE:
+ return CosineStrategy(**config.params)
+ elif config.type == CrawlerType.JSON_CSS:
+ return JsonCssExtractionStrategy(**config.params)
+ return None
+
+ async def submit_task(self, request: CrawlRequest) -> str:
+ task_id = str(uuid.uuid4())
+ await self.task_manager.add_task(task_id, request.priority, request.ttl or 3600)
+
+ # Store request data with task
+ self.task_manager.tasks[task_id].request = request
+
+ return task_id
+
+ async def _process_queue(self):
+ while True:
+ try:
+ available_slots = await self.resource_monitor.get_available_slots()
+ if False and available_slots <= 0:
+ await asyncio.sleep(1)
+ continue
+
+ task_id = await self.task_manager.get_next_task()
+ if not task_id:
+ await asyncio.sleep(1)
+ continue
+
+ task_info = self.task_manager.get_task(task_id)
+ if not task_info:
+ continue
+
+ request = task_info.request
+ self.task_manager.update_task(task_id, TaskStatus.PROCESSING)
+
+ try:
+ crawler = await self.crawler_pool.acquire(**request.crawler_params)
+
+ extraction_strategy = self._create_extraction_strategy(request.extraction_config)
+
+ if isinstance(request.urls, list):
+ results = await crawler.arun_many(
+ urls=[str(url) for url in request.urls],
+ extraction_strategy=extraction_strategy,
+ js_code=request.js_code,
+ wait_for=request.wait_for,
+ css_selector=request.css_selector,
+ screenshot=request.screenshot,
+ magic=request.magic,
+ **request.extra,
+ )
+ else:
+ results = await crawler.arun(
+ url=str(request.urls),
+ extraction_strategy=extraction_strategy,
+ js_code=request.js_code,
+ wait_for=request.wait_for,
+ css_selector=request.css_selector,
+ screenshot=request.screenshot,
+ magic=request.magic,
+ **request.extra,
+ )
+
+ await self.crawler_pool.release(crawler)
+ self.task_manager.update_task(task_id, TaskStatus.COMPLETED, results)
+
+ except Exception as e:
+ logger.error(f"Error processing task {task_id}: {str(e)}")
+ self.task_manager.update_task(task_id, TaskStatus.FAILED, error=str(e))
+
+ except Exception as e:
+ logger.error(f"Error in queue processing: {str(e)}")
+ await asyncio.sleep(1)
+
+app = FastAPI(title="Crawl4AI API")
# CORS configuration
origins = ["*"] # Allow all origins
@@ -127,128 +325,59 @@ app.mount("/mkdocs", StaticFiles(directory="site", html=True), name="mkdocs")
site_templates = Jinja2Templates(directory=__location__ + "/site")
templates = Jinja2Templates(directory=__location__ + "/pages")
-@lru_cache()
-def get_crawler():
- # Initialize and return a WebCrawler instance
- crawler = WebCrawler(verbose = True)
- crawler.warmup()
- return crawler
+crawler_service = CrawlerService()
-class CrawlRequest(BaseModel):
- urls: List[str]
- include_raw_html: Optional[bool] = False
- bypass_cache: bool = False
- extract_blocks: bool = True
- word_count_threshold: Optional[int] = 5
- extraction_strategy: Optional[str] = "NoExtractionStrategy"
- extraction_strategy_args: Optional[dict] = {}
- chunking_strategy: Optional[str] = "RegexChunking"
- chunking_strategy_args: Optional[dict] = {}
- css_selector: Optional[str] = None
- screenshot: Optional[bool] = False
- user_agent: Optional[str] = None
- verbose: Optional[bool] = True
+@app.on_event("startup")
+async def startup_event():
+ await crawler_service.start()
+
+@app.on_event("shutdown")
+async def shutdown_event():
+ await crawler_service.stop()
@app.get("/")
def read_root():
return RedirectResponse(url="/mkdocs")
-@app.get("/old", response_class=HTMLResponse)
-@limiter.limit(get_rate_limit())
-async def read_index(request: Request):
- partials_dir = os.path.join(__location__, "pages", "partial")
- partials = {}
-
- for filename in os.listdir(partials_dir):
- if filename.endswith(".html"):
- with open(os.path.join(partials_dir, filename), "r", encoding="utf8") as file:
- partials[filename[:-5]] = file.read()
-
- return templates.TemplateResponse("index.html", {"request": request, **partials})
-
-@app.get("/total-count")
-async def get_total_url_count():
- count = get_total_count()
- return JSONResponse(content={"count": count})
-
-@app.get("/clear-db")
-async def clear_database():
- # clear_db()
- return JSONResponse(content={"message": "Database cleared."})
-
-def import_strategy(module_name: str, class_name: str, *args, **kwargs):
- try:
- module = importlib.import_module(module_name)
- strategy_class = getattr(module, class_name)
- return strategy_class(*args, **kwargs)
- except ImportError:
- print("ImportError: Module not found.")
- raise HTTPException(status_code=400, detail=f"Module {module_name} not found.")
- except AttributeError:
- print("AttributeError: Class not found.")
- raise HTTPException(status_code=400, detail=f"Class {class_name} not found in {module_name}.")
@app.post("/crawl")
-@limiter.limit(get_rate_limit())
-async def crawl_urls(crawl_request: CrawlRequest, request: Request):
- logging.debug(f"[LOG] Crawl request for URL: {crawl_request.urls}")
- global current_requests
- async with lock:
- if current_requests >= MAX_CONCURRENT_REQUESTS:
- raise HTTPException(status_code=429, detail="Too many requests - please try again later.")
- current_requests += 1
+async def crawl(request: CrawlRequest) -> Dict[str, str]:
+ task_id = await crawler_service.submit_task(request)
+ return {"task_id": task_id}
- try:
- logging.debug("[LOG] Loading extraction and chunking strategies...")
- crawl_request.extraction_strategy_args['verbose'] = True
- crawl_request.chunking_strategy_args['verbose'] = True
-
- extraction_strategy = import_strategy("crawl4ai.extraction_strategy", crawl_request.extraction_strategy, **crawl_request.extraction_strategy_args)
- chunking_strategy = import_strategy("crawl4ai.chunking_strategy", crawl_request.chunking_strategy, **crawl_request.chunking_strategy_args)
+@app.get("/task/{task_id}")
+async def get_task_status(task_id: str):
+ task_info = crawler_service.task_manager.get_task(task_id)
+ if not task_info:
+ raise HTTPException(status_code=404, detail="Task not found")
- # Use ThreadPoolExecutor to run the synchronous WebCrawler in async manner
- logging.debug("[LOG] Running the WebCrawler...")
- with ThreadPoolExecutor() as executor:
- loop = asyncio.get_event_loop()
- futures = [
- loop.run_in_executor(
- executor,
- get_crawler().run,
- str(url),
- crawl_request.word_count_threshold,
- extraction_strategy,
- chunking_strategy,
- crawl_request.bypass_cache,
- crawl_request.css_selector,
- crawl_request.screenshot,
- crawl_request.user_agent,
- crawl_request.verbose
- )
- for url in crawl_request.urls
- ]
- results = await asyncio.gather(*futures)
+ response = {
+ "status": task_info.status,
+ "created_at": task_info.created_at,
+ }
- # if include_raw_html is False, remove the raw HTML content from the results
- if not crawl_request.include_raw_html:
- for result in results:
- result.html = None
+ if task_info.status == TaskStatus.COMPLETED:
+ # Convert CrawlResult to dict for JSON response
+ if isinstance(task_info.result, list):
+ response["results"] = [result.dict() for result in task_info.result]
+ else:
+ response["result"] = task_info.result.dict()
+ elif task_info.status == TaskStatus.FAILED:
+ response["error"] = task_info.error
- return {"results": [result.model_dump() for result in results]}
- finally:
- async with lock:
- current_requests -= 1
-
-@app.get("/strategies/extraction", response_class=JSONResponse)
-async def get_extraction_strategies():
- with open(f"{__location__}/docs/extraction_strategies.json", "r") as file:
- return JSONResponse(content=file.read())
-
-@app.get("/strategies/chunking", response_class=JSONResponse)
-async def get_chunking_strategies():
- with open(f"{__location__}/docs/chunking_strategies.json", "r") as file:
- return JSONResponse(content=file.read())
+ return response
+@app.get("/health")
+async def health_check():
+ available_slots = await crawler_service.resource_monitor.get_available_slots()
+ memory = psutil.virtual_memory()
+ return {
+ "status": "healthy",
+ "available_slots": available_slots,
+ "memory_usage": memory.percent,
+ "cpu_usage": psutil.cpu_percent(),
+ }
if __name__ == "__main__":
import uvicorn
- uvicorn.run(app, host="0.0.0.0", port=8888)
+ uvicorn.run(app, host="0.0.0.0", port=11235)
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index 66bc3473..b09cb9eb 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,36 +1,80 @@
site_name: Crawl4AI Documentation
-docs_dir: docs/md
+site_description: 🔥🕷️ Crawl4AI, Open-source LLM Friendly Web Crawler & Scrapper
+site_url: https://docs.crawl4ai.com
+repo_url: https://github.com/unclecode/crawl4ai
+repo_name: unclecode/crawl4ai
+docs_dir: docs/md_v2
+
nav:
- - Home: index.md
- - First Steps:
- - Introduction: introduction.md
- - Installation: installation.md
- - Quick Start: quickstart.md
- - Examples:
- - Intro: examples/index.md
- - Structured Data Extraction: examples/json_css_extraction.md
- - LLM Extraction: examples/llm_extraction.md
- - JS Execution & CSS Filtering: examples/js_execution_css_filtering.md
- - Hooks & Auth: examples/hooks_auth.md
- - Summarization: examples/summarization.md
- - Research Assistant: examples/research_assistant.md
- - Full Details of Using Crawler:
- - Crawl Request Parameters: full_details/crawl_request_parameters.md
- - Crawl Result Class: full_details/crawl_result_class.md
- - Session Based Crawling: full_details/session_based_crawling.md
- - Advanced Features: full_details/advanced_features.md
- - Advanced JsonCssExtraction: full_details/advanced_jsoncss_extraction.md
- - Chunking Strategies: full_details/chunking_strategies.md
- - Extraction Strategies: full_details/extraction_strategies.md
- - Miscellaneous:
- - Change Log: changelog.md
- - Contact: contact.md
+ - Home: 'index.md'
+ - 'Installation': 'basic/installation.md'
+ - 'Docker Deplotment': 'basic/docker-deploymeny.md'
+ - 'Quick Start': 'basic/quickstart.md'
+
+ - Basic:
+ - 'Simple Crawling': 'basic/simple-crawling.md'
+ - 'Output Formats': 'basic/output-formats.md'
+ - 'Browser Configuration': 'basic/browser-config.md'
+ - 'Page Interaction': 'basic/page-interaction.md'
+ - 'Content Selection': 'basic/content-selection.md'
+
+ - Advanced:
+ - 'Content Processing': 'advanced/content-processing.md'
+ - 'Magic Mode': 'advanced/magic-mode.md'
+ - 'Hooks & Auth': 'advanced/hooks-auth.md'
+ - 'Proxy & Security': 'advanced/proxy-security.md'
+ - 'Session Management': 'advanced/session-management.md'
+ - 'Session Management (Advanced)': 'advanced/session-management-advanced.md'
+
+ - Extraction:
+ - 'Overview': 'extraction/overview.md'
+ - 'LLM Strategy': 'extraction/llm.md'
+ - 'Json-CSS Extractor Basic': 'extraction/css.md'
+ - 'Json-CSS Extractor Advanced': 'extraction/css-advanced.md'
+ - 'Cosine Strategy': 'extraction/cosine.md'
+ - 'Chunking': 'extraction/chunking.md'
+
+ - API Reference:
+ - 'Parameters Table': 'api/parameters.md'
+ - 'AsyncWebCrawler': 'api/async-webcrawler.md'
+ - 'AsyncWebCrawler.arun()': 'api/arun.md'
+ - 'CrawlResult': 'api/crawl-result.md'
+ - 'Strategies': 'api/strategies.md'
+
+ - Tutorial:
+ - '1. Getting Started': 'tutorial/episode_01_Introduction_to_Crawl4AI_and_Basic_Installation.md'
+ - '2. Advanced Features': 'tutorial/episode_02_Overview_of_Advanced_Features.md'
+ - '3. Browser Setup': 'tutorial/episode_03_Browser_Configurations_&_Headless_Crawling.md'
+ - '4. Proxy Settings': 'tutorial/episode_04_Advanced_Proxy_and_Security_Settings.md'
+ - '5. Dynamic Content': 'tutorial/episode_05_JavaScript_Execution_and_Dynamic_Content_Handling.md'
+ - '6. Magic Mode': 'tutorial/episode_06_Magic_Mode_and_Anti-Bot_Protection.md'
+ - '7. Content Cleaning': 'tutorial/episode_07_Content_Cleaning_and_Fit_Markdown.md'
+ - '8. Media Handling': 'tutorial/episode_08_Media_Handling:_Images,_Videos,_and_Audio.md'
+ - '9. Link Analysis': 'tutorial/episode_09_Link_Analysis_and_Smart_Filtering.md'
+ - '10. User Simulation': 'tutorial/episode_10_Custom_Headers,_Identity,_and_User_Simulation.md'
+ - '11.1. JSON CSS': 'tutorial/episode_11_1_Extraction_Strategies:_JSON_CSS.md'
+ - '11.2. LLM Strategy': 'tutorial/episode_11_2_Extraction_Strategies:_LLM.md'
+ - '11.3. Cosine Strategy': 'tutorial/episode_11_3_Extraction_Strategies:_Cosine.md'
+ - '12. Session Crawling': 'tutorial/episode_12_Session-Based_Crawling_for_Dynamic_Websites.md'
+ - '13. Text Chunking': 'tutorial/episode_13_Chunking_Strategies_for_Large_Text_Processing.md'
+ - '14. Custom Workflows': 'tutorial/episode_14_Hooks_and_Custom_Workflow_with_AsyncWebCrawler.md'
+
theme:
name: terminal
palette: dark
-# Add the css/extra.css
+markdown_extensions:
+ - pymdownx.highlight:
+ anchor_linenums: true
+ - pymdownx.inlinehilite
+ - pymdownx.snippets
+ - pymdownx.superfences
+ - admonition
+ - pymdownx.details
+ - attr_list
+ - tables
+
extra_css:
- assets/styles.css
- assets/highlight.css
@@ -38,4 +82,4 @@ extra_css:
extra_javascript:
- assets/highlight.min.js
- - assets/highlight_init.js
+ - assets/highlight_init.js
\ No newline at end of file
diff --git a/requirements-dev.txt b/requirements-dev.txt
index f2578c6a..7bc121a4 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,2 +1,5 @@
-r requirements.txt
-pytest
\ No newline at end of file
+pytest
+pytest-asyncio
+selenium
+setuptools
diff --git a/requirements.txt b/requirements.txt
index 7d21f5ae..9a942958 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,11 @@
-aiosqlite==0.20.0
-html2text==2024.2.26
-lxml==5.3.0
-litellm==1.48.0
-numpy>=1.26.0,<2.1.1
-pillow==10.4.0
-playwright==1.47.0
-python-dotenv==1.0.1
-requests>=2.26.0,<2.32.3
-beautifulsoup4==4.12.3
\ No newline at end of file
+aiosqlite~=0.20
+html2text~=2024.2
+lxml~=5.3
+litellm~=1.48
+numpy>=1.26.0,<3
+pillow~=10.4
+playwright>=1.47,<1.48
+python-dotenv~=1.0
+requests~=2.26
+beautifulsoup4~=4.12
+playwright_stealth~=1.0
diff --git a/setup.py b/setup.py
index b827e6af..90063212 100644
--- a/setup.py
+++ b/setup.py
@@ -8,7 +8,7 @@ import sys
# Create the .crawl4ai folder in the user's home directory if it doesn't exist
# If the folder already exists, remove the cache folder
-crawl4ai_folder = Path.home() / ".crawl4ai"
+crawl4ai_folder = os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()) / ".crawl4ai"
cache_folder = crawl4ai_folder / "cache"
if cache_folder.exists():
@@ -23,7 +23,7 @@ with open(os.path.join(__location__, "requirements.txt")) as f:
requirements = f.read().splitlines()
# Read version from __init__.py
-with open("crawl4ai/__init__.py") as f:
+with open("crawl4ai/_version.py") as f:
for line in f:
if line.startswith("__version__"):
version = line.split("=")[1].strip().strip('"')
@@ -31,9 +31,11 @@ with open("crawl4ai/__init__.py") as f:
# Define the requirements for different environments
default_requirements = requirements
-torch_requirements = ["torch", "nltk", "spacy", "scikit-learn"]
-transformer_requirements = ["transformers", "tokenizers", "onnxruntime"]
-cosine_similarity_requirements = ["torch", "transformers", "nltk", "spacy"]
+# torch_requirements = ["torch", "nltk", "spacy", "scikit-learn"]
+# transformer_requirements = ["transformers", "tokenizers", "onnxruntime"]
+torch_requirements = ["torch", "nltk", "scikit-learn"]
+transformer_requirements = ["transformers", "tokenizers"]
+cosine_similarity_requirements = ["torch", "transformers", "nltk" ]
sync_requirements = ["selenium"]
def install_playwright():
diff --git a/tests/test_docker.py b/tests/test_docker.py
new file mode 100644
index 00000000..c22acd55
--- /dev/null
+++ b/tests/test_docker.py
@@ -0,0 +1,300 @@
+import requests
+import json
+import time
+import sys
+import base64
+import os
+from typing import Dict, Any
+
+class Crawl4AiTester:
+ def __init__(self, base_url: str = "http://localhost:11235"):
+ self.base_url = base_url
+
+ def submit_and_wait(self, request_data: Dict[str, Any], timeout: int = 300) -> Dict[str, Any]:
+ # Submit crawl job
+ response = requests.post(f"{self.base_url}/crawl", json=request_data)
+ task_id = response.json()["task_id"]
+ print(f"Task ID: {task_id}")
+
+ # Poll for result
+ start_time = time.time()
+ while True:
+ if time.time() - start_time > timeout:
+ raise TimeoutError(f"Task {task_id} did not complete within {timeout} seconds")
+
+ result = requests.get(f"{self.base_url}/task/{task_id}")
+ status = result.json()
+
+ if status["status"] == "failed":
+ print("Task failed:", status.get("error"))
+ raise Exception(f"Task failed: {status.get('error')}")
+
+ if status["status"] == "completed":
+ return status
+
+ time.sleep(2)
+
+def test_docker_deployment(version="basic"):
+ tester = Crawl4AiTester()
+ print(f"Testing Crawl4AI Docker {version} version")
+
+ # Health check with timeout and retry
+ max_retries = 5
+ for i in range(max_retries):
+ try:
+ health = requests.get(f"{tester.base_url}/health", timeout=10)
+ print("Health check:", health.json())
+ break
+ except requests.exceptions.RequestException as e:
+ if i == max_retries - 1:
+ print(f"Failed to connect after {max_retries} attempts")
+ sys.exit(1)
+ print(f"Waiting for service to start (attempt {i+1}/{max_retries})...")
+ time.sleep(5)
+
+ # Test cases based on version
+ test_basic_crawl(tester)
+
+ # if version in ["full", "transformer"]:
+ # test_cosine_extraction(tester)
+
+ # test_js_execution(tester)
+ # test_css_selector(tester)
+ # test_structured_extraction(tester)
+ # test_llm_extraction(tester)
+ # test_llm_with_ollama(tester)
+ # test_screenshot(tester)
+
+
+def test_basic_crawl(tester: Crawl4AiTester):
+ print("\n=== Testing Basic Crawl ===")
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 10
+ }
+
+ result = tester.submit_and_wait(request)
+ print(f"Basic crawl result length: {len(result['result']['markdown'])}")
+ assert result["result"]["success"]
+ assert len(result["result"]["markdown"]) > 0
+
+def test_js_execution(tester: Crawl4AiTester):
+ print("\n=== Testing JS Execution ===")
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 8,
+ "js_code": [
+ "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
+ ],
+ "wait_for": "article.tease-card:nth-child(10)",
+ "crawler_params": {
+ "headless": True
+ }
+ }
+
+ result = tester.submit_and_wait(request)
+ print(f"JS execution result length: {len(result['result']['markdown'])}")
+ assert result["result"]["success"]
+
+def test_css_selector(tester: Crawl4AiTester):
+ print("\n=== Testing CSS Selector ===")
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 7,
+ "css_selector": ".wide-tease-item__description",
+ "crawler_params": {
+ "headless": True
+ },
+ "extra": {"word_count_threshold": 10}
+
+ }
+
+ result = tester.submit_and_wait(request)
+ print(f"CSS selector result length: {len(result['result']['markdown'])}")
+ assert result["result"]["success"]
+
+def test_structured_extraction(tester: Crawl4AiTester):
+ print("\n=== Testing Structured Extraction ===")
+ schema = {
+ "name": "Coinbase Crypto Prices",
+ "baseSelector": ".cds-tableRow-t45thuk",
+ "fields": [
+ {
+ "name": "crypto",
+ "selector": "td:nth-child(1) h2",
+ "type": "text",
+ },
+ {
+ "name": "symbol",
+ "selector": "td:nth-child(1) p",
+ "type": "text",
+ },
+ {
+ "name": "price",
+ "selector": "td:nth-child(2)",
+ "type": "text",
+ }
+ ],
+ }
+
+ request = {
+ "urls": "https://www.coinbase.com/explore",
+ "priority": 9,
+ "extraction_config": {
+ "type": "json_css",
+ "params": {
+ "schema": schema
+ }
+ }
+ }
+
+ result = tester.submit_and_wait(request)
+ extracted = json.loads(result["result"]["extracted_content"])
+ print(f"Extracted {len(extracted)} items")
+ print("Sample item:", json.dumps(extracted[0], indent=2))
+ assert result["result"]["success"]
+ assert len(extracted) > 0
+
+def test_llm_extraction(tester: Crawl4AiTester):
+ print("\n=== Testing LLM Extraction ===")
+ schema = {
+ "type": "object",
+ "properties": {
+ "model_name": {
+ "type": "string",
+ "description": "Name of the OpenAI model."
+ },
+ "input_fee": {
+ "type": "string",
+ "description": "Fee for input token for the OpenAI model."
+ },
+ "output_fee": {
+ "type": "string",
+ "description": "Fee for output token for the OpenAI model."
+ }
+ },
+ "required": ["model_name", "input_fee", "output_fee"]
+ }
+
+ request = {
+ "urls": "https://openai.com/api/pricing",
+ "priority": 8,
+ "extraction_config": {
+ "type": "llm",
+ "params": {
+ "provider": "openai/gpt-4o-mini",
+ "api_token": os.getenv("OPENAI_API_KEY"),
+ "schema": schema,
+ "extraction_type": "schema",
+ "instruction": """From the crawled content, extract all mentioned model names along with their fees for input and output tokens."""
+ }
+ },
+ "crawler_params": {"word_count_threshold": 1}
+ }
+
+ try:
+ result = tester.submit_and_wait(request)
+ extracted = json.loads(result["result"]["extracted_content"])
+ print(f"Extracted {len(extracted)} model pricing entries")
+ print("Sample entry:", json.dumps(extracted[0], indent=2))
+ assert result["result"]["success"]
+ except Exception as e:
+ print(f"LLM extraction test failed (might be due to missing API key): {str(e)}")
+
+def test_llm_with_ollama(tester: Crawl4AiTester):
+ print("\n=== Testing LLM with Ollama ===")
+ schema = {
+ "type": "object",
+ "properties": {
+ "article_title": {
+ "type": "string",
+ "description": "The main title of the news article"
+ },
+ "summary": {
+ "type": "string",
+ "description": "A brief summary of the article content"
+ },
+ "main_topics": {
+ "type": "array",
+ "items": {"type": "string"},
+ "description": "Main topics or themes discussed in the article"
+ }
+ }
+ }
+
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 8,
+ "extraction_config": {
+ "type": "llm",
+ "params": {
+ "provider": "ollama/llama2",
+ "schema": schema,
+ "extraction_type": "schema",
+ "instruction": "Extract the main article information including title, summary, and main topics."
+ }
+ },
+ "extra": {"word_count_threshold": 1},
+ "crawler_params": {"verbose": True}
+ }
+
+ try:
+ result = tester.submit_and_wait(request)
+ extracted = json.loads(result["result"]["extracted_content"])
+ print("Extracted content:", json.dumps(extracted, indent=2))
+ assert result["result"]["success"]
+ except Exception as e:
+ print(f"Ollama extraction test failed: {str(e)}")
+
+def test_cosine_extraction(tester: Crawl4AiTester):
+ print("\n=== Testing Cosine Extraction ===")
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 8,
+ "extraction_config": {
+ "type": "cosine",
+ "params": {
+ "semantic_filter": "business finance economy",
+ "word_count_threshold": 10,
+ "max_dist": 0.2,
+ "top_k": 3
+ }
+ }
+ }
+
+ try:
+ result = tester.submit_and_wait(request)
+ extracted = json.loads(result["result"]["extracted_content"])
+ print(f"Extracted {len(extracted)} text clusters")
+ print("First cluster tags:", extracted[0]["tags"])
+ assert result["result"]["success"]
+ except Exception as e:
+ print(f"Cosine extraction test failed: {str(e)}")
+
+def test_screenshot(tester: Crawl4AiTester):
+ print("\n=== Testing Screenshot ===")
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 5,
+ "screenshot": True,
+ "crawler_params": {
+ "headless": True
+ }
+ }
+
+ result = tester.submit_and_wait(request)
+ print("Screenshot captured:", bool(result["result"]["screenshot"]))
+
+ if result["result"]["screenshot"]:
+ # Save screenshot
+ screenshot_data = base64.b64decode(result["result"]["screenshot"])
+ with open("test_screenshot.jpg", "wb") as f:
+ f.write(screenshot_data)
+ print("Screenshot saved as test_screenshot.jpg")
+
+ assert result["result"]["success"]
+
+if __name__ == "__main__":
+ version = sys.argv[1] if len(sys.argv) > 1 else "basic"
+ # version = "full"
+ test_docker_deployment(version)
\ No newline at end of file
diff --git a/tests/test_main.py b/tests/test_main.py
new file mode 100644
index 00000000..19f938c8
--- /dev/null
+++ b/tests/test_main.py
@@ -0,0 +1,281 @@
+import asyncio
+import aiohttp
+import json
+import time
+import os
+from typing import Optional, Dict, Any
+from pydantic import BaseModel, HttpUrl
+
+class NBCNewsAPITest:
+ def __init__(self, base_url: str = "http://localhost:8000"):
+ self.base_url = base_url
+ self.session = None
+
+ async def __aenter__(self):
+ self.session = aiohttp.ClientSession()
+ return self
+
+ async def __aexit__(self, exc_type, exc_val, exc_tb):
+ if self.session:
+ await self.session.close()
+
+ async def submit_crawl(self, request_data: Dict[str, Any]) -> str:
+ async with self.session.post(f"{self.base_url}/crawl", json=request_data) as response:
+ result = await response.json()
+ return result["task_id"]
+
+ async def get_task_status(self, task_id: str) -> Dict[str, Any]:
+ async with self.session.get(f"{self.base_url}/task/{task_id}") as response:
+ return await response.json()
+
+ async def wait_for_task(self, task_id: str, timeout: int = 300, poll_interval: int = 2) -> Dict[str, Any]:
+ start_time = time.time()
+ while True:
+ if time.time() - start_time > timeout:
+ raise TimeoutError(f"Task {task_id} did not complete within {timeout} seconds")
+
+ status = await self.get_task_status(task_id)
+ if status["status"] in ["completed", "failed"]:
+ return status
+
+ await asyncio.sleep(poll_interval)
+
+ async def check_health(self) -> Dict[str, Any]:
+ async with self.session.get(f"{self.base_url}/health") as response:
+ return await response.json()
+
+async def test_basic_crawl():
+ print("\n=== Testing Basic Crawl ===")
+ async with NBCNewsAPITest() as api:
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 10
+ }
+ task_id = await api.submit_crawl(request)
+ result = await api.wait_for_task(task_id)
+ print(f"Basic crawl result length: {len(result['result']['markdown'])}")
+ assert result["status"] == "completed"
+ assert "result" in result
+ assert result["result"]["success"]
+
+async def test_js_execution():
+ print("\n=== Testing JS Execution ===")
+ async with NBCNewsAPITest() as api:
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 8,
+ "js_code": [
+ "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
+ ],
+ "wait_for": "article.tease-card:nth-child(10)",
+ "crawler_params": {
+ "headless": True
+ }
+ }
+ task_id = await api.submit_crawl(request)
+ result = await api.wait_for_task(task_id)
+ print(f"JS execution result length: {len(result['result']['markdown'])}")
+ assert result["status"] == "completed"
+ assert result["result"]["success"]
+
+async def test_css_selector():
+ print("\n=== Testing CSS Selector ===")
+ async with NBCNewsAPITest() as api:
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 7,
+ "css_selector": ".wide-tease-item__description"
+ }
+ task_id = await api.submit_crawl(request)
+ result = await api.wait_for_task(task_id)
+ print(f"CSS selector result length: {len(result['result']['markdown'])}")
+ assert result["status"] == "completed"
+ assert result["result"]["success"]
+
+async def test_structured_extraction():
+ print("\n=== Testing Structured Extraction ===")
+ async with NBCNewsAPITest() as api:
+ schema = {
+ "name": "NBC News Articles",
+ "baseSelector": "article.tease-card",
+ "fields": [
+ {
+ "name": "title",
+ "selector": "h2",
+ "type": "text"
+ },
+ {
+ "name": "description",
+ "selector": ".tease-card__description",
+ "type": "text"
+ },
+ {
+ "name": "link",
+ "selector": "a",
+ "type": "attribute",
+ "attribute": "href"
+ }
+ ]
+ }
+
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 9,
+ "extraction_config": {
+ "type": "json_css",
+ "params": {
+ "schema": schema
+ }
+ }
+ }
+ task_id = await api.submit_crawl(request)
+ result = await api.wait_for_task(task_id)
+ extracted = json.loads(result["result"]["extracted_content"])
+ print(f"Extracted {len(extracted)} articles")
+ assert result["status"] == "completed"
+ assert result["result"]["success"]
+ assert len(extracted) > 0
+
+async def test_batch_crawl():
+ print("\n=== Testing Batch Crawl ===")
+ async with NBCNewsAPITest() as api:
+ request = {
+ "urls": [
+ "https://www.nbcnews.com/business",
+ "https://www.nbcnews.com/business/consumer",
+ "https://www.nbcnews.com/business/economy"
+ ],
+ "priority": 6,
+ "crawler_params": {
+ "headless": True
+ }
+ }
+ task_id = await api.submit_crawl(request)
+ result = await api.wait_for_task(task_id)
+ print(f"Batch crawl completed, got {len(result['results'])} results")
+ assert result["status"] == "completed"
+ assert "results" in result
+ assert len(result["results"]) == 3
+
+async def test_llm_extraction():
+ print("\n=== Testing LLM Extraction with Ollama ===")
+ async with NBCNewsAPITest() as api:
+ schema = {
+ "type": "object",
+ "properties": {
+ "article_title": {
+ "type": "string",
+ "description": "The main title of the news article"
+ },
+ "summary": {
+ "type": "string",
+ "description": "A brief summary of the article content"
+ },
+ "main_topics": {
+ "type": "array",
+ "items": {"type": "string"},
+ "description": "Main topics or themes discussed in the article"
+ }
+ },
+ "required": ["article_title", "summary", "main_topics"]
+ }
+
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 8,
+ "extraction_config": {
+ "type": "llm",
+ "params": {
+ "provider": "openai/gpt-4o-mini",
+ "api_key": os.getenv("OLLAMA_API_KEY"),
+ "schema": schema,
+ "extraction_type": "schema",
+ "instruction": """Extract the main article information including title, a brief summary, and main topics discussed.
+ Focus on the primary business news article on the page."""
+ }
+ },
+ "crawler_params": {
+ "headless": True,
+ "word_count_threshold": 1
+ }
+ }
+
+ task_id = await api.submit_crawl(request)
+ result = await api.wait_for_task(task_id)
+
+ if result["status"] == "completed":
+ extracted = json.loads(result["result"]["extracted_content"])
+ print(f"Extracted article analysis:")
+ print(json.dumps(extracted, indent=2))
+
+ assert result["status"] == "completed"
+ assert result["result"]["success"]
+
+async def test_screenshot():
+ print("\n=== Testing Screenshot ===")
+ async with NBCNewsAPITest() as api:
+ request = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 5,
+ "screenshot": True,
+ "crawler_params": {
+ "headless": True
+ }
+ }
+ task_id = await api.submit_crawl(request)
+ result = await api.wait_for_task(task_id)
+ print("Screenshot captured:", bool(result["result"]["screenshot"]))
+ assert result["status"] == "completed"
+ assert result["result"]["success"]
+ assert result["result"]["screenshot"] is not None
+
+async def test_priority_handling():
+ print("\n=== Testing Priority Handling ===")
+ async with NBCNewsAPITest() as api:
+ # Submit low priority task first
+ low_priority = {
+ "urls": "https://www.nbcnews.com/business",
+ "priority": 1,
+ "crawler_params": {"headless": True}
+ }
+ low_task_id = await api.submit_crawl(low_priority)
+
+ # Submit high priority task
+ high_priority = {
+ "urls": "https://www.nbcnews.com/business/consumer",
+ "priority": 10,
+ "crawler_params": {"headless": True}
+ }
+ high_task_id = await api.submit_crawl(high_priority)
+
+ # Get both results
+ high_result = await api.wait_for_task(high_task_id)
+ low_result = await api.wait_for_task(low_task_id)
+
+ print("Both tasks completed")
+ assert high_result["status"] == "completed"
+ assert low_result["status"] == "completed"
+
+async def main():
+ try:
+ # Start with health check
+ async with NBCNewsAPITest() as api:
+ health = await api.check_health()
+ print("Server health:", health)
+
+ # Run all tests
+ # await test_basic_crawl()
+ # await test_js_execution()
+ # await test_css_selector()
+ # await test_structured_extraction()
+ await test_llm_extraction()
+ # await test_batch_crawl()
+ # await test_screenshot()
+ # await test_priority_handling()
+
+ except Exception as e:
+ print(f"Test failed: {str(e)}")
+ raise
+
+if __name__ == "__main__":
+ asyncio.run(main())
\ No newline at end of file