Update .gitignore to include test_env/ and tmp/ directories

This code change updates the `LLMExtractionStrategy` class to handle schema extraction when the schema is non-empty. Previously, the schema extraction was only triggered when the `extract_type` was set to "schema", regardless of whether a schema was provided. With this update, the schema extraction will only be performed if the `extract_type` is "schema" and a non-empty schema is provided. This ensures that the extraction strategy behaves correctly and avoids unnecessary schema extraction when not needed. Also "numpy" is removed from default installation mode.

.gitignore

@@ -189,4 +189,6 @@ a.txt
 .lambda_function.py
 ec2*
 
-update_changelog.sh
+update_changelog.sh
+test_env/
+tmp/

README.md

@@ -190,33 +190,6 @@ result = crawler.run(
 print(result.extracted_content)
 ```
 
-### Extract Structured Data from Web Pages With Proxy and BaseUrl
-
-```python
-from crawl4ai import WebCrawler
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-
-def create_crawler():
-    crawler = WebCrawler(verbose=True, proxy="http://127.0.0.1:7890")
-    crawler.warmup()
-    return crawler
-
-crawler = create_crawler()
-
-crawler.warmup()
-
-result = crawler.run(
-    url="https://www.nbcnews.com/business",
-    extraction_strategy=LLMExtractionStrategy(
-        provider="openai/gpt-4o",
-        api_token="sk-",
-        base_url="https://api.openai.com/v1"
-    )
-)
-
-print(result.markdown)
-```
-
 ## Documentation 📚
 
 For detailed documentation, including installation instructions, advanced features, and API reference, visit our [Documentation Website](https://crawl4ai.com/mkdocs/).

crawl4ai/crawler_strategy.py

@@ -82,8 +82,6 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
         print("[LOG] 🚀 Initializing LocalSeleniumCrawlerStrategy")
         self.options = Options()
         self.options.headless = True
-        if kwargs.get("proxy"):
-            self.options.add_argument("--proxy-server={}".format(kwargs.get("proxy")))
         if kwargs.get("user_agent"):
             self.options.add_argument("--user-agent=" + kwargs.get("user_agent"))
         else:
@@ -244,7 +242,6 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
                 driver.quit()
             
             # Execute JS code if provided
-            self.js_code = kwargs.get("js_code", self.js_code)
             if self.js_code and type(self.js_code) == str:
                 self.driver.execute_script(self.js_code)
                 # Optionally, wait for some condition after executing the JS code

crawl4ai/extraction_strategy.py

@@ -79,7 +79,6 @@ class LLMExtractionStrategy(ExtractionStrategy):
         self.overlap_rate = kwargs.get("overlap_rate", OVERLAP_RATE)
         self.word_token_rate = kwargs.get("word_token_rate", WORD_TOKEN_RATE)
         self.apply_chunking = kwargs.get("apply_chunking", True)
-        self.base_url = kwargs.get("base_url", None)
         if not self.apply_chunking:
             self.chunk_token_threshold = 1e9
         
@@ -102,7 +101,7 @@ class LLMExtractionStrategy(ExtractionStrategy):
             variable_values["REQUEST"] = self.instruction
             prompt_with_variables = PROMPT_EXTRACT_BLOCKS_WITH_INSTRUCTION
             
-        if self.extract_type == "schema":
+        if self.extract_type == "schema" and self.schema:
             variable_values["SCHEMA"] = json.dumps(self.schema, indent=2)
             prompt_with_variables = PROMPT_EXTRACT_SCHEMA_WITH_INSTRUCTION
 
@@ -111,7 +110,7 @@ class LLMExtractionStrategy(ExtractionStrategy):
                 "{" + variable + "}", variable_values[variable]
             )
         
-        response = perform_completion_with_backoff(self.provider, prompt_with_variables, self.api_token, base_url=self.base_url) # , json_response=self.extract_type == "schema")
+        response = perform_completion_with_backoff(self.provider, prompt_with_variables, self.api_token) # , json_response=self.extract_type == "schema")
         try:
             blocks = extract_xml_data(["blocks"], response.choices[0].message.content)['blocks']
             blocks = json.loads(blocks)

crawl4ai/prompts.py

@@ -29,7 +29,7 @@ To generate the JSON objects:
 
 5. Make sure the generated JSON is complete and parsable, with no errors or omissions.
 
-6. Make sure to escape any special characters in the HTML content, and also single or double quote to avoid JSON parsing issues.
+6. Make sur to escape any special characters in the HTML content, and also single or double quote to avoid JSON parsing issues.
 
 Please provide your output within <blocks> tags, like this:
 
@@ -87,7 +87,7 @@ To generate the JSON objects:
 
 5. Make sure the generated JSON is complete and parsable, with no errors or omissions.
 
-6. Make sure to escape any special characters in the HTML content, and also single or double quote to avoid JSON parsing issues.
+6. Make sur to escape any special characters in the HTML content, and also single or double quote to avoid JSON parsing issues.
 
 7. Never alter the extracted content, just copy and paste it as it is.
 
@@ -142,7 +142,7 @@ To generate the JSON objects:
 
 5. Make sure the generated JSON is complete and parsable, with no errors or omissions.
 
-6. Make sure to escape any special characters in the HTML content, and also single or double quote to avoid JSON parsing issues.
+6. Make sur to escape any special characters in the HTML content, and also single or double quote to avoid JSON parsing issues.
 
 7. Never alter the extracted content, just copy and paste it as it is.
 
@@ -201,4 +201,4 @@ Avoid Common Mistakes:
 - Do not generate the Python coee show me how to do the task, this is your task to extract the information and return it in JSON format.
 
 Result
-Output the final list of JSON objects, wrapped in <blocks>...</blocks> XML tags. Make sure to close the tag properly."""
+Output the final list of JSON objects, wrapped in <blocks>...</blocks> XML tags. Make sure to close the tag properly."""

crawl4ai/utils.py

@@ -634,12 +634,7 @@ def get_content_of_website_optimized(url: str, html: str, word_count_threshold:
         return node
 
     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)
+
     cleaned_html = str(body).replace('\n\n', '\n').replace('  ', ' ')
     cleaned_html = sanitize_html(cleaned_html)
 
@@ -721,7 +716,7 @@ def extract_xml_data(tags, string):
     return data
     
 # Function to perform the completion with exponential backoff
-def perform_completion_with_backoff(provider, prompt_with_variables, api_token, json_response = False, base_url=None):
+def perform_completion_with_backoff(provider, prompt_with_variables, api_token, json_response = False):
     from litellm import completion 
     from litellm.exceptions import RateLimitError
     max_attempts = 3
@@ -740,7 +735,6 @@ def perform_completion_with_backoff(provider, prompt_with_variables, api_token,
                 ],
                 temperature=0.01,
                 api_key=api_token,
-                base_url=base_url,
                 **extra_args
             )
             return response  # Return the successful response
@@ -761,7 +755,7 @@ def perform_completion_with_backoff(provider, prompt_with_variables, api_token,
                     "content": ["Rate limit error. Please try again later."]
                 }]
     
-def extract_blocks(url, html, provider = DEFAULT_PROVIDER, api_token = None, base_url = None):
+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
     
@@ -776,7 +770,7 @@ def extract_blocks(url, html, provider = DEFAULT_PROVIDER, api_token = None, bas
             "{" + variable + "}", variable_values[variable]
         )
         
-    response = perform_completion_with_backoff(provider, prompt_with_variables, api_token, base_url=base_url)
+    response = perform_completion_with_backoff(provider, prompt_with_variables, api_token)
         
     try:
         blocks = extract_xml_data(["blocks"], response.choices[0].message.content)['blocks']
@@ -840,7 +834,6 @@ def extract_blocks_batch(batch_data, provider = "groq/llama3-70b-8192", api_toke
     
     return sum(all_blocks, [])
 
-
 def merge_chunks_based_on_token_threshold(chunks, token_threshold):
     """
     Merges small chunks into larger ones based on the total token threshold.
@@ -870,23 +863,22 @@ def merge_chunks_based_on_token_threshold(chunks, token_threshold):
 
     return merged_sections
 
-def process_sections(url: str, sections: list, provider: str, api_token: str, base_url=None) -> list:
+def process_sections(url: str, sections: list, provider: str, api_token: str) -> list:
     extracted_content = []
     if provider.startswith("groq/"):
         # Sequential processing with a delay
         for section in sections:
-            extracted_content.extend(extract_blocks(url, section, provider, api_token, base_url=base_url))
+            extracted_content.extend(extract_blocks(url, section, provider, api_token))
             time.sleep(0.5)  # 500 ms delay between each processing
     else:
         # Parallel processing using ThreadPoolExecutor
         with ThreadPoolExecutor() as executor:
-            futures = [executor.submit(extract_blocks, url, section, provider, api_token, base_url=base_url) for section in sections]
+            futures = [executor.submit(extract_blocks, url, section, provider, api_token) for section in sections]
             for future in as_completed(futures):
                 extracted_content.extend(future.result())
     
     return extracted_content
 
-
 def wrap_text(draw, text, font, max_width):
     # Wrap the text to fit within the specified width
     lines = []
@@ -898,7 +890,6 @@ def wrap_text(draw, text, font, max_width):
         lines.append(line)
     return '\n'.join(lines)
 
-
 def format_html(html_string):
     soup = BeautifulSoup(html_string, 'html.parser')
     return soup.prettify()

crawl4ai/web_crawler.py

@@ -22,10 +22,9 @@ class WebCrawler:
         crawler_strategy: CrawlerStrategy = None,
         always_by_pass_cache: bool = False,
         verbose: bool = False,
-        proxy: str = None,
     ):
         # self.db_path = db_path
-        self.crawler_strategy = crawler_strategy or LocalSeleniumCrawlerStrategy(verbose=verbose, proxy=proxy)
+        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

setup.py

@@ -19,7 +19,7 @@ with open("requirements.txt") as f:
     requirements = f.read().splitlines()
 
 # Define the requirements for different environments
-default_requirements = [req for req in requirements if not req.startswith(("torch", "transformers", "onnxruntime", "nltk", "spacy", "tokenizers", "scikit-learn", "numpy"))]
+default_requirements = [req for req in requirements if not req.startswith(("torch", "transformers", "onnxruntime", "nltk", "spacy", "tokenizers", "scikit-learn"))]
 torch_requirements = [req for req in requirements if req.startswith(("torch", "nltk", "spacy", "scikit-learn", "numpy"))]
 transformer_requirements = [req for req in requirements if req.startswith(("transformers", "tokenizers", "onnxruntime"))]