Files
crawl4ai/crawler/utils.py
2024-05-09 19:10:25 +08:00

400 lines
14 KiB
Python

import requests
from bs4 import BeautifulSoup, Comment, element, Tag, NavigableString
import html2text
import json
import re
import os
import litellm
from litellm import completion, batch_completion
from .prompts import PROMPT_EXTRACT_BLOCKS
from .config import *
import re
import html
def beautify_html(escaped_html):
"""
Beautifies an escaped HTML string.
Parameters:
escaped_html (str): A string containing escaped HTML.
Returns:
str: A beautifully formatted HTML string.
"""
# Unescape the HTML string
unescaped_html = html.unescape(escaped_html)
# Use BeautifulSoup to parse and prettify the HTML
soup = BeautifulSoup(unescaped_html, 'html.parser')
pretty_html = soup.prettify()
return pretty_html
def split_and_parse_json_objects(json_string):
"""
Splits a JSON string which is a list of objects and tries to parse each object.
Parameters:
json_string (str): A string representation of a list of JSON objects, e.g., '[{...}, {...}, ...]'.
Returns:
tuple: A tuple containing two lists:
- First list contains all successfully parsed JSON objects.
- Second list contains the string representations of all segments that couldn't be parsed.
"""
# Trim the leading '[' and trailing ']'
if json_string.startswith('[') and json_string.endswith(']'):
json_string = json_string[1:-1].strip()
# Split the string into segments that look like individual JSON objects
segments = []
depth = 0
start_index = 0
for i, char in enumerate(json_string):
if char == '{':
if depth == 0:
start_index = i
depth += 1
elif char == '}':
depth -= 1
if depth == 0:
segments.append(json_string[start_index:i+1])
# Try parsing each segment
parsed_objects = []
unparsed_segments = []
for segment in segments:
try:
obj = json.loads(segment)
parsed_objects.append(obj)
except json.JSONDecodeError:
unparsed_segments.append(segment)
return parsed_objects, unparsed_segments
def sanitize_html(html):
# Replace all weird and special characters with an empty string
sanitized_html = re.sub(r'[^\w\s.,;:!?=\[\]{}()<>\/\\\-"]', '', html)
# Escape all double and single quotes
sanitized_html = sanitized_html.replace('"', '\\"').replace("'", "\\'")
return sanitized_html
def escape_json_string(s):
"""
Escapes characters in a string to be JSON safe.
Parameters:
s (str): The input string to be escaped.
Returns:
str: The escaped string, safe for JSON encoding.
"""
# Replace problematic backslash first
s = s.replace('\\', '\\\\')
# Replace the double quote
s = s.replace('"', '\\"')
# Escape control characters
s = s.replace('\b', '\\b')
s = s.replace('\f', '\\f')
s = s.replace('\n', '\\n')
s = s.replace('\r', '\\r')
s = s.replace('\t', '\\t')
# Additional problematic characters
# Unicode control characters
s = re.sub(r'[\x00-\x1f\x7f-\x9f]', lambda x: '\\u{:04x}'.format(ord(x.group())), s)
return s
def get_content_of_website(html, word_count_threshold = MIN_WORD_THRESHOLD):
try:
# Parse HTML content with BeautifulSoup
soup = BeautifulSoup(html, 'html.parser')
# Get the content within the <body> tag
body = soup.body
# Remove script, style, and other tags that don't carry useful content from body
for tag in body.find_all(['script', 'style', 'link', 'meta', 'noscript']):
tag.decompose()
# Remove all attributes from remaining tags in body, except for img tags
for tag in body.find_all():
if tag.name != 'img':
tag.attrs = {}
# Replace images with their alt text or remove them if no alt text is available
for img in body.find_all('img'):
alt_text = img.get('alt')
if alt_text:
img.replace_with(soup.new_string(alt_text))
else:
img.decompose()
# Recursively remove empty elements, their parent elements, and elements with word count below threshold
def remove_empty_and_low_word_count_elements(node):
for child in node.contents:
if isinstance(child, element.Tag):
remove_empty_and_low_word_count_elements(child)
word_count = len(child.get_text(strip=True).split())
if (len(child.contents) == 0 and not child.get_text(strip=True)) or word_count < word_count_threshold:
child.decompose()
return node
body = remove_empty_and_low_word_count_elements(body)
def remove_small_text_tags(body: Tag, word_count_threshold: int = MIN_WORD_THRESHOLD):
# We'll use a list to collect all tags that don't meet the word count requirement
tags_to_remove = []
# Traverse all tags in the body
for tag in body.find_all(True): # True here means all tags
# Check if the tag contains text and if it's not just whitespace
if tag.string and tag.string.strip():
# Split the text by spaces and count the words
word_count = len(tag.string.strip().split())
# If the word count is less than the threshold, mark the tag for removal
if word_count < word_count_threshold:
tags_to_remove.append(tag)
# Remove all marked tags from the tree
for tag in tags_to_remove:
tag.decompose() # or tag.extract() to remove and get the element
return body
# Remove small text tags
body = remove_small_text_tags(body, word_count_threshold)
def is_empty_or_whitespace(tag: Tag):
if isinstance(tag, NavigableString):
return not tag.strip()
# Check if the tag itself is empty or all its children are empty/whitespace
if not tag.contents:
return True
return all(is_empty_or_whitespace(child) for child in tag.contents)
def remove_empty_tags(body: Tag):
# Continue processing until no more changes are made
changes = True
while changes:
changes = False
# Collect all tags that are empty or contain only whitespace
empty_tags = [tag for tag in body.find_all(True) if is_empty_or_whitespace(tag)]
for tag in empty_tags:
# If a tag is empty, decompose it
tag.decompose()
changes = True # Mark that a change was made
return body
# Remove empty tags
body = remove_empty_tags(body)
# Flatten nested elements with only one child of the same type
def flatten_nested_elements(node):
for child in node.contents:
if isinstance(child, element.Tag):
flatten_nested_elements(child)
if len(child.contents) == 1 and child.contents[0].name == child.name:
# print('Flattening:', child.name)
child_content = child.contents[0]
child.replace_with(child_content)
return node
body = flatten_nested_elements(body)
# Remove comments
for comment in soup.find_all(text=lambda text: isinstance(text, Comment)):
comment.extract()
# Remove consecutive empty newlines and replace multiple spaces with a single space
cleaned_html = str(body).replace('\n\n', '\n').replace(' ', ' ')
# Sanitize the cleaned HTML content
cleaned_html = sanitize_html(cleaned_html)
# sanitized_html = escape_json_string(cleaned_html)
# Convert cleaned HTML to Markdown
h = html2text.HTML2Text()
h.ignore_links = True
markdown = h.handle(cleaned_html)
# Return the Markdown content
return{
'markdown': markdown,
'cleaned_html': cleaned_html,
'success': True
}
except Exception as e:
print('Error processing HTML content:', str(e))
return None
# Example usage
# word_count_threshold = 5 # Adjust this value according to your desired threshold
# markdown_content = get_content_of_website(word_count_threshold)
# print(markdown_content)
def extract_xml_tags(string):
tags = re.findall(r'<(\w+)>', string)
return list(set(tags))
def extract_xml_data(tags, string):
data = {}
for tag in tags:
pattern = f"<{tag}>(.*?)</{tag}>"
match = re.search(pattern, string, re.DOTALL)
if match:
data[tag] = match.group(1).strip()
else:
data[tag] = ""
return data
import time
import litellm
# Function to perform the completion with exponential backoff
def perform_completion_with_backoff(provider, prompt_with_variables, api_token):
max_attempts = 3
base_delay = 2 # Base delay in seconds, you can adjust this based on your needs
for attempt in range(max_attempts):
try:
response = completion(
model=provider,
messages=[
{"role": "user", "content": prompt_with_variables}
],
temperature=0.01,
api_key=api_token
)
return response # Return the successful response
except litellm.exceptions.RateLimitError as e:
print("Rate limit error:", str(e))
# Check if we have exhausted our max attempts
if attempt < max_attempts - 1:
# Calculate the delay and wait
delay = base_delay * (2 ** attempt) # Exponential backoff formula
print(f"Waiting for {delay} seconds before retrying...")
time.sleep(delay)
else:
# Return an error response after exhausting all retries
return [{
"index": 0,
"tags": ["error"],
"content": ["Rate limit error. Please try again later."]
}]
def extract_blocks(url, html, provider = DEFAULT_PROVIDER, api_token = None):
# api_token = os.getenv('GROQ_API_KEY', None) if not api_token else api_token
api_token = PROVIDER_MODELS.get(provider, None) if not api_token else api_token
variable_values = {
"URL": url,
"HTML": escape_json_string(sanitize_html(html)),
}
prompt_with_variables = PROMPT_EXTRACT_BLOCKS
for variable in variable_values:
prompt_with_variables = prompt_with_variables.replace(
"{" + variable + "}", variable_values[variable]
)
response = perform_completion_with_backoff(provider, prompt_with_variables, api_token)
# try:
# response = completion(
# model = provider,
# messages = [
# {"role": "user", "content": prompt_with_variables}
# ],
# temperature = 0.01,
# api_key = api_token
# )
# except litellm.exceptions.RateLimitError as e:
# print("Rate limit error:", str(e))
# return [{
# "index": 0,
# "tags": ["error"],
# "content": ["Rate limit error. Please try again later."]
# }]
try:
blocks = extract_xml_data(["blocks"], response.choices[0].message.content)['blocks']
blocks = json.loads(blocks)
## Add error: False to the blocks
for block in blocks:
block['error'] = False
except Exception as e:
print("Error extracting blocks:", str(e))
parsed, unparsed = split_and_parse_json_objects(response.choices[0].message.content)
blocks = parsed
# Append all unparsed segments as onr error block and content is list of unparsed segments
if unparsed:
blocks.append({
"index": 0,
"error": True,
"tags": ["error"],
"content": unparsed
})
return blocks
def extract_blocks_batch(batch_data, provider = "groq/llama3-70b-8192", api_token = None):
api_token = os.getenv('GROQ_API_KEY', None) if not api_token else api_token
messages = []
for url, html in batch_data:
variable_values = {
"URL": url,
"HTML": html,
}
prompt_with_variables = PROMPT_EXTRACT_BLOCKS
for variable in variable_values:
prompt_with_variables = prompt_with_variables.replace(
"{" + variable + "}", variable_values[variable]
)
messages.append([{"role": "user", "content": prompt_with_variables}])
responses = batch_completion(
model = provider,
messages = messages,
temperature = 0.01
)
all_blocks = []
for response in responses:
try:
blocks = extract_xml_data(["blocks"], response.choices[0].message.content)['blocks']
blocks = json.loads(blocks)
except Exception as e:
print("Error extracting blocks:", str(e))
blocks = [{
"index": 0,
"tags": ["error"],
"content": ["Error extracting blocks from the HTML content. Choose another provider/model or try again."],
"questions": ["What went wrong during the block extraction process?"]
}]
all_blocks.append(blocks)
return sum(all_blocks, [])