feat: Add insight generation functionality with compatibility scoring and web search integration
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bolade
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import asyncio
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import logging
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import os
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from crawl4ai import AsyncWebCrawler
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from ddgs import DDGS
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from langgraph.prebuilt import create_react_agent
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from schemas.insight_schema import InsightResponse
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
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)
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logger = logging.getLogger("web_search_agent")
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load_dotenv()
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OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
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if not OPENROUTER_API_KEY:
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logger.warning("OPENROUTER_API_KEY not set. LLM calls will fail if invoked.")
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class QueryProcessor:
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def __init__(self):
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self.llm = ChatOpenAI(
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api_key=OPENROUTER_API_KEY,
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base_url="https://openrouter.ai/api/v1",
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model="openai/gpt-5-nano",
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temperature=0,
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)
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self.agent = create_react_agent(
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model=self.llm,
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tools=[self.web_search],
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response_format=InsightResponse,
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)
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self.ddg_search = DDGS()
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async def crawl(self, url: str):
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"""Tool to search the web using a web crawler. given the url"""
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logger.info(f"\nCrawl tool called with url: {url}")
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async with AsyncWebCrawler() as crawler:
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results = await crawler.arun(url)
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return results.markdown
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def web_search(self, query: str):
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"""Tool to search the web using google, provide the relevant query to get the information"""
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logger.info(f"\nWeb Search Tool Called with query: {query}")
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if query:
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result = self.ddg_search.text(query, max_results=10, backend="google")
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return result
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return "No query provided."
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async def get_investor_insights(
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self,
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investor_name: str,
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investor_website: str = None,
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investor_description: str = None,
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investor_headquarters: str = None,
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investment_thesis: list = None,
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portfolio_highlights: list = None,
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) -> dict:
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"""
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Get investment pattern analysis and market position for an investor.
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Args:
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investor_name: Name of the investor/VC firm
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investor_website: Website URL of the investor
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investor_description: Description of the investor
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investor_headquarters: Headquarters location
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investment_thesis: List of investment thesis statements
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portfolio_highlights: List of notable portfolio companies
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Returns:
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Dictionary with investment_pattern_analysis and market_position
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"""
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logger.info(f"Getting insights for investor: {investor_name}")
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# Build context information
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context_parts = [f'Investment Firm: "{investor_name}"']
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if investor_website:
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context_parts.append(f"Website: {investor_website}")
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if investor_headquarters:
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context_parts.append(f"Location: {investor_headquarters}")
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if investor_description:
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context_parts.append(f"Description: {investor_description}")
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if investment_thesis:
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thesis_str = ", ".join(investment_thesis[:3]) # Limit to first 3
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context_parts.append(f"Investment Focus: {thesis_str}")
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if portfolio_highlights:
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portfolio_str = ", ".join(portfolio_highlights[:5]) # Limit to first 5
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context_parts.append(f"Notable Portfolio Companies: {portfolio_str}")
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context = "\n".join(context_parts)
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prompt = f"""
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Research and analyze the following investment firm:
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{context}
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CRITICAL INSTRUCTIONS:
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- You MUST provide concrete, data-driven insights with specific numbers and percentages
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- Use the web_search tool to find recent news, press releases, and investment databases (Crunchbase, PitchBook, etc.)
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- If you cannot find sufficient data after searching, make reasonable inferences based on available information
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- DO NOT state that data is unavailable or ambiguous - provide the best analysis possible with what you find
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- Focus on ACTIONABLE insights, not disclaimers
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Provide insights in the InsightResponse schema format:
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1. investment_pattern_analysis (MAX 3 SENTENCES):
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- Recent investment activity and trends in the last 12-18 months
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- Investment size ranges, deal frequency, and sector preferences
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- Notable patterns (e.g., "increased AI investments by 40%", "average check size $5-10M")
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- If specific numbers aren't available, provide reasonable estimates based on portfolio and market position
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2. market_position (MAX 3 SENTENCES):
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- Standing in the venture capital market
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- Activity level in specific sectors and notable unicorn investments
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- Deal leadership roles (lead vs co-lead) and market influence
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- Regional or global market presence and competitive positioning
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Use the web_search tool strategically. Search for:
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- "{investor_name}" recent investments 2024 2025
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- "{investor_name}" portfolio Crunchbase
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- "{investor_name}" funding rounds news
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- Specific portfolio companies if mentioned above
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"""
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try:
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result = await self.agent.ainvoke({"messages": [("user", prompt)]})
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# The agent with response_format=InsightResponse returns structured output
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logger.info(f"Raw agent result keys: {result.keys()}")
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# Check if structured_response exists and is an InsightResponse object
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if "structured_response" in result:
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structured = result["structured_response"]
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logger.info(f"Structured response type: {type(structured)}")
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# If it's already an InsightResponse object, convert to dict
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if isinstance(structured, InsightResponse):
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return structured.model_dump()
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# If it's already a dict, return it
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elif isinstance(structured, dict):
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return structured
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# Fallback: shouldn't reach here, but handle it gracefully
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logger.warning("No structured_response found in result, using fallback")
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return {
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"investment_pattern_analysis": "Unable to retrieve investment pattern analysis at this time.",
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"market_position": "Unable to retrieve market position at this time.",
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}
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except Exception as e:
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logger.error(f"Error getting insights for {investor_name}: {e}")
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logger.exception("Full exception details:")
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return {
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"investment_pattern_analysis": "Unable to retrieve investment pattern analysis at this time.",
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"market_position": "Unable to retrieve market position at this time.",
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}
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async def main():
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qp = QueryProcessor()
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result = await qp.agent.ainvoke(
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{"messages": [("user", "Can you tell me about 3T Finance investment company")]}
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)
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final_message = result["messages"][-1].content
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print(final_message)
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if __name__ == "__main__":
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asyncio.run(main())
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