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updates for stability and speed
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teknium
parent
f957ec2267
commit
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5 changed files with 447 additions and 131 deletions
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@ -15,7 +15,7 @@ Backend compatibility:
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- Firecrawl: https://docs.firecrawl.dev/introduction
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LLM Processing:
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- Uses Nous Research API with Gemini 2.5 Flash for intelligent content extraction
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- Uses OpenRouter API with Gemini 3 Flash Preview for intelligent content extraction
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- Extracts key excerpts and creates markdown summaries to reduce token usage
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Debug Mode:
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@ -54,14 +54,14 @@ from openai import AsyncOpenAI
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# Initialize Firecrawl client once at module level
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firecrawl_client = Firecrawl(api_key=os.getenv("FIRECRAWL_API_KEY"))
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# Initialize Nous Research API client for LLM processing (async)
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nous_client = AsyncOpenAI(
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api_key=os.getenv("NOUS_API_KEY"),
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base_url="https://inference-api.nousresearch.com/v1"
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# Initialize OpenRouter API client for LLM processing (async)
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summarizer_client = AsyncOpenAI(
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api_key=os.getenv("OPENROUTER_API_KEY"),
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base_url="https://openrouter.ai/api/v1"
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)
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# Configuration for LLM processing
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DEFAULT_SUMMARIZER_MODEL = "gemini-2.5-flash"
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DEFAULT_SUMMARIZER_MODEL = "google/gemini-3-flash-preview"
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DEFAULT_MIN_LENGTH_FOR_SUMMARIZATION = 5000
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# Debug mode configuration
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@ -135,7 +135,7 @@ async def process_content_with_llm(
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"""
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Process web content using LLM to create intelligent summaries with key excerpts.
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This function uses Gemini 2.5 Flash (or specified model) via Nous Research API
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This function uses Gemini 3 Flash Preview (or specified model) via OpenRouter API
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to intelligently extract key information and create markdown summaries,
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significantly reducing token usage while preserving all important information.
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@ -143,7 +143,7 @@ async def process_content_with_llm(
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content (str): The raw content to process
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url (str): The source URL (for context, optional)
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title (str): The page title (for context, optional)
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model (str): The model to use for processing (default: gemini-2.5-flash)
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model (str): The model to use for processing (default: google/gemini-3-flash-preview)
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min_length (int): Minimum content length to trigger processing (default: 5000)
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Returns:
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@ -190,7 +190,7 @@ Create a markdown summary that captures all key information in a well-organized,
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for attempt in range(max_retries):
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try:
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response = await nous_client.chat.completions.create(
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response = await summarizer_client.chat.completions.create(
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model=model,
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messages=[
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{"role": "system", "content": system_prompt},
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@ -399,7 +399,7 @@ async def web_extract_tool(
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urls (List[str]): List of URLs to extract content from
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format (str): Desired output format ("markdown" or "html", optional)
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use_llm_processing (bool): Whether to process content with LLM for summarization (default: True)
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model (str): The model to use for LLM processing (default: gemini-2.5-flash)
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model (str): The model to use for LLM processing (default: google/gemini-3-flash-preview)
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min_length (int): Minimum content length to trigger LLM processing (default: 5000)
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Returns:
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@ -527,57 +527,74 @@ async def web_extract_tool(
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debug_call_data["original_response_size"] = len(json.dumps(response))
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# Process each result with LLM if enabled
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if use_llm_processing and os.getenv("NOUS_API_KEY"):
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print("🧠 Processing extracted content with LLM...")
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if use_llm_processing and os.getenv("OPENROUTER_API_KEY"):
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print("🧠 Processing extracted content with LLM (parallel)...")
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debug_call_data["processing_applied"].append("llm_processing")
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for result in response.get('results', []):
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# Prepare tasks for parallel processing
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async def process_single_result(result):
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"""Process a single result with LLM and return updated result with metrics."""
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url = result.get('url', 'Unknown URL')
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title = result.get('title', '')
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raw_content = result.get('raw_content', '') or result.get('content', '')
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if raw_content:
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original_size = len(raw_content)
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if not raw_content:
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return result, None, "no_content"
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original_size = len(raw_content)
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# Process content with LLM
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processed = await process_content_with_llm(
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raw_content, url, title, model, min_length
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)
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if processed:
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processed_size = len(processed)
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compression_ratio = processed_size / original_size if original_size > 0 else 1.0
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# Process content with LLM
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processed = await process_content_with_llm(
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raw_content, url, title, model, min_length
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)
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# Update result with processed content
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result['content'] = processed
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result['raw_content'] = raw_content
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if processed:
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processed_size = len(processed)
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compression_ratio = processed_size / original_size if original_size > 0 else 1.0
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# Capture compression metrics
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debug_call_data["compression_metrics"].append({
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"url": url,
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"original_size": original_size,
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"processed_size": processed_size,
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"compression_ratio": compression_ratio,
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"model_used": model
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})
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# Replace content with processed version
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result['content'] = processed
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# Keep raw content in separate field for reference
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result['raw_content'] = raw_content
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debug_call_data["pages_processed_with_llm"] += 1
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print(f" 📝 {url} (processed)")
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else:
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debug_call_data["compression_metrics"].append({
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"url": url,
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"original_size": original_size,
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"processed_size": original_size,
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"compression_ratio": 1.0,
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"model_used": None,
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"reason": "content_too_short"
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})
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print(f" 📝 {url} (no processing - content too short)")
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metrics = {
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"url": url,
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"original_size": original_size,
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"processed_size": processed_size,
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"compression_ratio": compression_ratio,
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"model_used": model
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}
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return result, metrics, "processed"
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else:
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metrics = {
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"url": url,
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"original_size": original_size,
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"processed_size": original_size,
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"compression_ratio": 1.0,
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"model_used": None,
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"reason": "content_too_short"
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}
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return result, metrics, "too_short"
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# Run all LLM processing in parallel
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results_list = response.get('results', [])
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tasks = [process_single_result(result) for result in results_list]
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processed_results = await asyncio.gather(*tasks)
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# Collect metrics and print results
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for result, metrics, status in processed_results:
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url = result.get('url', 'Unknown URL')
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if status == "processed":
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debug_call_data["compression_metrics"].append(metrics)
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debug_call_data["pages_processed_with_llm"] += 1
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print(f" 📝 {url} (processed)")
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elif status == "too_short":
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debug_call_data["compression_metrics"].append(metrics)
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print(f" 📝 {url} (no processing - content too short)")
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else:
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print(f" ⚠️ {url} (no content to process)")
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else:
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if use_llm_processing and not os.getenv("NOUS_API_KEY"):
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print("⚠️ LLM processing requested but NOUS_API_KEY not set, returning raw content")
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if use_llm_processing and not os.getenv("OPENROUTER_API_KEY"):
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print("⚠️ LLM processing requested but OPENROUTER_API_KEY not set, returning raw content")
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debug_call_data["processing_applied"].append("llm_processing_unavailable")
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# Print summary of extracted pages for debugging (original behavior)
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@ -646,7 +663,7 @@ async def web_crawl_tool(
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instructions (str): Instructions for what to crawl/extract using LLM intelligence (optional)
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depth (str): Depth of extraction ("basic" or "advanced", default: "basic")
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use_llm_processing (bool): Whether to process content with LLM for summarization (default: True)
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model (str): The model to use for LLM processing (default: gemini-2.5-flash)
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model (str): The model to use for LLM processing (default: google/gemini-3-flash-preview)
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min_length (int): Minimum content length to trigger LLM processing (default: 5000)
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Returns:
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@ -806,57 +823,74 @@ async def web_crawl_tool(
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debug_call_data["original_response_size"] = len(json.dumps(response))
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# Process each result with LLM if enabled
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if use_llm_processing and os.getenv("NOUS_API_KEY"):
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print("🧠 Processing crawled content with LLM...")
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if use_llm_processing and os.getenv("OPENROUTER_API_KEY"):
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print("🧠 Processing crawled content with LLM (parallel)...")
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debug_call_data["processing_applied"].append("llm_processing")
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for result in response.get('results', []):
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# Prepare tasks for parallel processing
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async def process_single_crawl_result(result):
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"""Process a single crawl result with LLM and return updated result with metrics."""
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page_url = result.get('url', 'Unknown URL')
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title = result.get('title', '')
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content = result.get('content', '')
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if content:
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original_size = len(content)
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if not content:
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return result, None, "no_content"
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original_size = len(content)
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# Process content with LLM
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processed = await process_content_with_llm(
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content, page_url, title, model, min_length
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)
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if processed:
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processed_size = len(processed)
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compression_ratio = processed_size / original_size if original_size > 0 else 1.0
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# Process content with LLM
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processed = await process_content_with_llm(
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content, page_url, title, model, min_length
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)
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# Update result with processed content
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result['raw_content'] = content
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result['content'] = processed
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if processed:
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processed_size = len(processed)
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compression_ratio = processed_size / original_size if original_size > 0 else 1.0
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# Capture compression metrics
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debug_call_data["compression_metrics"].append({
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"url": page_url,
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"original_size": original_size,
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"processed_size": processed_size,
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"compression_ratio": compression_ratio,
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"model_used": model
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})
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# Keep original content in raw_content field
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result['raw_content'] = content
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# Replace content with processed version
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result['content'] = processed
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debug_call_data["pages_processed_with_llm"] += 1
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print(f" 🌐 {page_url} (processed)")
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else:
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debug_call_data["compression_metrics"].append({
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"url": page_url,
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"original_size": original_size,
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"processed_size": original_size,
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"compression_ratio": 1.0,
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"model_used": None,
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"reason": "content_too_short"
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})
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print(f" 🌐 {page_url} (no processing - content too short)")
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metrics = {
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"url": page_url,
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"original_size": original_size,
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"processed_size": processed_size,
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"compression_ratio": compression_ratio,
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"model_used": model
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}
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return result, metrics, "processed"
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else:
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metrics = {
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"url": page_url,
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"original_size": original_size,
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"processed_size": original_size,
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"compression_ratio": 1.0,
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"model_used": None,
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"reason": "content_too_short"
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}
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return result, metrics, "too_short"
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# Run all LLM processing in parallel
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results_list = response.get('results', [])
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tasks = [process_single_crawl_result(result) for result in results_list]
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processed_results = await asyncio.gather(*tasks)
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# Collect metrics and print results
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for result, metrics, status in processed_results:
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page_url = result.get('url', 'Unknown URL')
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if status == "processed":
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debug_call_data["compression_metrics"].append(metrics)
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debug_call_data["pages_processed_with_llm"] += 1
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print(f" 🌐 {page_url} (processed)")
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elif status == "too_short":
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debug_call_data["compression_metrics"].append(metrics)
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print(f" 🌐 {page_url} (no processing - content too short)")
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else:
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print(f" ⚠️ {page_url} (no content to process)")
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else:
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if use_llm_processing and not os.getenv("NOUS_API_KEY"):
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print("⚠️ LLM processing requested but NOUS_API_KEY not set, returning raw content")
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if use_llm_processing and not os.getenv("OPENROUTER_API_KEY"):
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print("⚠️ LLM processing requested but OPENROUTER_API_KEY not set, returning raw content")
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debug_call_data["processing_applied"].append("llm_processing_unavailable")
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# Print summary of crawled pages for debugging (original behavior)
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@ -918,7 +952,7 @@ def check_nous_api_key() -> bool:
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Returns:
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bool: True if API key is set, False otherwise
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"""
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return bool(os.getenv("NOUS_API_KEY"))
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return bool(os.getenv("OPENROUTER_API_KEY"))
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def get_debug_session_info() -> Dict[str, Any]:
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@ -967,8 +1001,8 @@ if __name__ == "__main__":
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print("✅ Firecrawl API key found")
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if not nous_available:
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print("❌ NOUS_API_KEY environment variable not set")
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print("Please set your API key: export NOUS_API_KEY='your-key-here'")
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print("❌ OPENROUTER_API_KEY environment variable not set")
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print("Please set your API key: export OPENROUTER_API_KEY='your-key-here'")
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print("Get API key at: https://inference-api.nousresearch.com/")
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print("⚠️ Without Nous API key, LLM content processing will be disabled")
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else:
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@ -980,7 +1014,7 @@ if __name__ == "__main__":
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print("🛠️ Web tools ready for use!")
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if nous_available:
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print("🧠 LLM content processing available with Gemini 2.5 Flash")
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print("🧠 LLM content processing available with Gemini 3 Flash Preview via OpenRouter")
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print(f" Default min length for processing: {DEFAULT_MIN_LENGTH_FOR_SUMMARIZATION} chars")
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# Show debug mode status
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@ -1012,7 +1046,7 @@ if __name__ == "__main__":
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print(" crawl_data = await web_crawl_tool(")
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print(" 'docs.python.org',")
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print(" 'Find key concepts',")
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print(" model='gemini-2.5-flash',")
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print(" model='google/gemini-3-flash-preview',")
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print(" min_length=3000")
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print(" )")
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print("")
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