Implement investor processing and querying functionality
- Added InvestorProcessor class for processing CSV data in batches and saving to SQL and vector databases. - Introduced QueryProcessor class for querying investor information from SQL and vector databases. - Integrated OpenAI's ChatGPT for structured output generation. - Implemented data cleaning and control character removal in CSV processing. - Added asynchronous processing capabilities for batch handling. - Established connection to ChromaDB for vector storage of investor descriptions. - Defined structured output schemas using Pydantic for investor data validation. - Enhanced settings management for API key and database configurations.
This commit is contained in:
bolade
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+15
-17
@@ -1,11 +1,12 @@
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import os
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from contextlib import contextmanager
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from typing import Generator
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from typing import Annotated
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from fastapi import Depends
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from sqlalchemy import create_engine
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from sqlalchemy.ext.declarative import declarative_base
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from sqlalchemy.orm import Session, sessionmaker
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from schema import Base
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Base = declarative_base()
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# Database configuration
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DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///investors.db")
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@@ -17,26 +18,23 @@ engine = create_engine(DATABASE_URL, echo=False)
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SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
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def get_db():
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db = SessionLocal()
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try:
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yield db
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finally:
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db.close()
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db_dependency = Annotated[Session, Depends(get_db)]
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def init_database():
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"""Initialize the database by creating all tables"""
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Base.metadata.create_all(bind=engine)
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print("Database initialized successfully!")
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@contextmanager
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def get_session() -> Generator[Session, None, None]:
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"""Get a database session with automatic cleanup"""
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session = SessionLocal()
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try:
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yield session
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session.commit()
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except Exception as e:
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session.rollback()
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raise e
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finally:
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session.close()
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def get_session_sync() -> Session:
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"""Get a database session for synchronous operations"""
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return SessionLocal()
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@@ -0,0 +1,23 @@
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import datetime
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from sqlalchemy import Column, DateTime, Integer, String
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from db.db import Base
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class InvestorTable(Base):
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__tablename__ = "investors"
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id = Column(Integer, primary_key=True, index=True)
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name = Column(String, nullable=False)
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aum = Column(Integer, nullable=False)
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check_size = Column(String, nullable=False)
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sector_focus = Column(String, nullable=False)
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stage_focus = Column(String, nullable=False)
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region = Column(String, nullable=False)
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created_at = Column(DateTime, default=datetime.datetime.now(datetime.UTC))
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updated_at = Column(
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DateTime,
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default=datetime.datetime.now(datetime.UTC),
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onupdate=datetime.datetime.now(datetime.UTC),
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)
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+39
-2
@@ -1,7 +1,44 @@
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from fastapi import FastAPI
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import io
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import pandas as pd
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from db.db import db_dependency, init_database
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from fastapi import FastAPI, File, UploadFile
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from services.openrouter import InvestorProcessor
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from app.services.querying import QueryProcessor
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app = FastAPI()
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init_database()
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@app.get("/")
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def read_root():
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return {"Hello": "World"}
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return {"Hello": "World"}
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@app.post("/parse-csv")
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async def parse_csv(db: db_dependency, file: UploadFile = File(...)):
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# Read uploaded CSV with pandas
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content = await file.read()
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df = pd.read_csv(io.StringIO(content.decode("utf-8")))
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# Process the dataframe
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processor = InvestorProcessor(sql_session=db)
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results = await processor.process_csv(df)
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# Convert Pydantic objects to dictionaries
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return {"results": [r.dict() for r in results]}
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@app.post("/query")
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async def query_investors(db: db_dependency, question: str):
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processor = QueryProcessor(sql_session=db)
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results = processor.process_query(question)
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return {"results": [r.dict() for r in results]}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app="main:app", host="localhost", port=8000, reload=True)
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@@ -0,0 +1,38 @@
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from typing import List
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from pydantic import BaseModel
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class Investor(BaseModel):
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name: str
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aum: int
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check_size: str
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sector_focus: str
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stage_focus: str
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region: str
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investment_thesis: str
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investor_description: str
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class InvestorList(BaseModel):
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investor_list: List[Investor]
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class QueryResponse(BaseModel):
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name: str
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aum: int
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check_size: str
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sector_focus: str
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stage_focus: str
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region: str
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investment_thesis: str
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investor_description: str
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reason: str
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class QueryRequest(BaseModel):
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question: str
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class QueryResponseList(BaseModel):
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responses: List[QueryResponse]
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@@ -1,449 +0,0 @@
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#!/usr/bin/env python3
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"""
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LLM-powered Investor Parser
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A comprehensive parser that processes investor CSV data and saves it to both SQL and vector databases.
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Supports both simple parsing and LLM-enhanced parsing for better data quality.
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Usage:
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python investor_parser.py --help
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python investor_parser.py --file="path/to/csv" --limit=10
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python investor_parser.py --file="path/to/csv" --use-llm --limit=50
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python investor_parser.py --search="bioeconomy circular"
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"""
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import argparse
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import json
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import logging
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import os
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from typing import Any, Dict, Optional
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import chromadb
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import pandas as pd
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from dotenv import load_dotenv
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from openai import OpenAI
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from db import get_session, init_database
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from schema import CSVRow, Investor
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# Load environment variables
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load_dotenv()
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# Configure logging
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
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)
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logger = logging.getLogger(__name__)
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class InvestorParser:
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"""Complete investor parser with optional LLM enhancement"""
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def __init__(self, use_llm: bool = False):
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self.use_llm = use_llm
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# Initialize OpenAI client if using LLM
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if self.use_llm:
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api_key = os.getenv("OPENAI_API_KEY")
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if not api_key:
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logger.warning(
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"OpenAI API key not found. LLM features will be disabled."
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)
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self.use_llm = False
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else:
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self.openai_client = OpenAI(api_key=api_key)
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logger.info("LLM enhancement enabled")
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# Initialize ChromaDB
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self.chroma_client = chromadb.PersistentClient(path="./chroma_db")
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self.collection = self.chroma_client.get_or_create_collection(
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name="investor_descriptions",
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metadata={
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"description": "Investor descriptions and investment thesis focus"
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},
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)
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# Initialize database
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init_database()
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def parse_json_field(self, json_str: str) -> Dict[str, Any]:
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"""Safely parse JSON string with optional LLM assistance"""
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if not json_str or json_str.strip() == "":
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return {}
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try:
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return json.loads(json_str)
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except json.JSONDecodeError as e:
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logger.warning(f"JSON parsing failed: {e}")
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# Use LLM to clean JSON if available
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if self.use_llm:
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return self._llm_clean_json(json_str)
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else:
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return {}
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def _llm_clean_json(self, malformed_json: str) -> Dict[str, Any]:
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"""Use LLM to clean and parse malformed JSON"""
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try:
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prompt = f"""
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The following text appears to be malformed JSON. Please clean it up and return valid JSON.
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If it's not possible to create valid JSON, return an empty object {{}}.
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Original text:
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{malformed_json[:2000]} # Limit length for API
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Return only the cleaned JSON, no explanations:
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"""
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response = self.openai_client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": prompt}],
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temperature=0,
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)
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cleaned_json = response.choices[0].message.content.strip()
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return json.loads(cleaned_json)
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except Exception as e:
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logger.error(f"LLM JSON cleaning failed: {e}")
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return {}
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def extract_structured_data(self, csv_row: CSVRow) -> Dict[str, Any]:
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"""Extract and structure data from CSV row"""
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# Parse the investment firm profile
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profile_data = {}
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if csv_row.investment_firm_profile:
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profile_data = self.parse_json_field(csv_row.investment_firm_profile)
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# Create structured output
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structured_data = {
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"name": csv_row.name,
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"website": csv_row.website or profile_data.get("websiteURL"),
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"investor_description": profile_data.get("investorDescription", ""),
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"investment_thesis_focus": profile_data.get("investmentThesisFocus", []),
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"headquarters": profile_data.get("headquarters", ""),
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"aum_info": profile_data.get("overallAssetsUnderManagement", {}),
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"funds_info": profile_data.get("funds", []),
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"crunchbase_urls": csv_row.crunchbase_linkedin_urls or "",
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"crunchbase_extract": csv_row.crunchbase_firm_extract or "",
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"linkedin_profile": csv_row.linkedin_investment_profile or "",
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"source_truth_profile": csv_row.source_of_truth_profile or "",
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}
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return structured_data
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def enhance_with_llm(self, investor_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Use LLM to enhance and standardize investor data"""
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if not self.use_llm:
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return investor_data
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try:
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# Combine all available text for context
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context_text = " ".join(
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[
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investor_data.get("investor_description", ""),
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investor_data.get("crunchbase_extract", ""),
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investor_data.get("linkedin_profile", ""),
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investor_data.get("source_truth_profile", ""),
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]
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)
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if not context_text.strip():
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return investor_data
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prompt = f"""
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Based on the following information about an investor, please extract and standardize:
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1. A concise investor description (2-3 sentences)
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2. Investment thesis focus areas (list of specific focus areas)
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3. Headquarters location (city, country format)
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Investor: {investor_data["name"]}
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Context: {context_text[:3000]} # Limit for API
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Return in JSON format:
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{{
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"enhanced_description": "concise description here",
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"standardized_focus": ["focus area 1", "focus area 2", ...],
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"standardized_headquarters": "City, Country"
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}}
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"""
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response = self.openai_client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": prompt}],
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temperature=0.3,
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)
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enhanced_data = json.loads(response.choices[0].message.content)
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# Update investor data with enhanced information
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if enhanced_data.get("enhanced_description"):
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investor_data["enhanced_description"] = enhanced_data[
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"enhanced_description"
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]
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if enhanced_data.get("standardized_focus"):
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investor_data["standardized_focus"] = enhanced_data[
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"standardized_focus"
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]
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if enhanced_data.get("standardized_headquarters"):
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investor_data["standardized_headquarters"] = enhanced_data[
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"standardized_headquarters"
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]
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return investor_data
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except Exception as e:
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logger.error(f"LLM enhancement failed for {investor_data['name']}: {e}")
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return investor_data
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def save_to_sql(self, investor_data: Dict[str, Any]) -> int:
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"""Save investor data to SQL database"""
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try:
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with get_session() as session:
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# Check if investor already exists
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existing = (
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session.query(Investor)
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.filter_by(name=investor_data["name"])
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.first()
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)
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if existing:
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logger.info(f"Updating existing investor: {investor_data['name']}")
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investor = existing
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else:
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logger.info(f"Creating new investor: {investor_data['name']}")
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investor = Investor()
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# Map data to investor object
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investor.name = investor_data["name"]
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investor.website = investor_data.get("website")
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investor.investor_description = investor_data.get(
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"enhanced_description"
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) or investor_data.get("investor_description")
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investor.investment_thesis_focus = investor_data.get(
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"standardized_focus"
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) or investor_data.get("investment_thesis_focus")
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investor.headquarters = investor_data.get(
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"standardized_headquarters"
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) or investor_data.get("headquarters")
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# AUM information
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aum_info = investor_data.get("aum_info") or {}
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investor.aum_amount = aum_info.get("aumAmount")
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investor.aum_as_of_date = aum_info.get("asOfDate")
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investor.aum_source_url = aum_info.get("sourceUrl")
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# Fund information
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investor.funds_info = investor_data.get("funds_info", [])
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# Raw data
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investor.crunchbase_urls = investor_data.get("crunchbase_urls")
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investor.crunchbase_extract = investor_data.get("crunchbase_extract")
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investor.linkedin_profile = investor_data.get("linkedin_profile")
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investor.source_truth_profile = investor_data.get(
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"source_truth_profile"
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)
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if not existing:
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session.add(investor)
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session.flush() # Get the ID
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return investor.id
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except Exception as e:
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logger.error(f"Failed to save to SQL: {e}")
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raise
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def save_to_vector_db(self, investor_id: int, investor_data: Dict[str, Any]):
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"""Save investor description and focus to ChromaDB"""
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try:
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# Prepare text for embedding
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description_text = investor_data.get(
|
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"enhanced_description"
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) or investor_data.get("investor_description", "")
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focus_areas = investor_data.get("standardized_focus") or investor_data.get(
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"investment_thesis_focus", []
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)
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if isinstance(focus_areas, list):
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focus_text = " ".join(focus_areas)
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else:
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focus_text = str(focus_areas)
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# Combine description and focus for embedding
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combined_text = f"{description_text} {focus_text}".strip()
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|
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if not combined_text:
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logger.warning(f"No text to embed for investor {investor_data['name']}")
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return
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# Create metadata
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metadata = {
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"investor_id": investor_id,
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"name": investor_data["name"],
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"website": investor_data.get("website") or "",
|
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"headquarters": investor_data.get("standardized_headquarters")
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or investor_data.get("headquarters")
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or "",
|
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"focus_areas_count": len(focus_areas)
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if isinstance(focus_areas, list)
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else 0,
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}
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# Add to ChromaDB
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self.collection.add(
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documents=[combined_text],
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metadatas=[metadata],
|
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ids=[f"investor_{investor_id}"],
|
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)
|
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|
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logger.info(f"Added investor {investor_data['name']} to vector database")
|
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|
||||
except Exception as e:
|
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logger.error(f"Failed to save to vector DB: {e}")
|
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|
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def process_csv_file(self, csv_file_path: str, limit: Optional[int] = None):
|
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"""Process the entire CSV file"""
|
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logger.info(f"Starting to process CSV file: {csv_file_path}")
|
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|
||||
# Read CSV
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||||
df = pd.read_csv(csv_file_path)
|
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logger.info(f"Loaded {len(df)} rows from CSV")
|
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|
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if limit:
|
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df = df.head(limit)
|
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logger.info(f"Processing limited to {limit} rows")
|
||||
|
||||
processed_count = 0
|
||||
error_count = 0
|
||||
|
||||
for index, row in df.iterrows():
|
||||
try:
|
||||
logger.info(f"Processing row {index + 1}/{len(df)}: {row['Name']}")
|
||||
|
||||
# Create CSVRow object
|
||||
csv_row = CSVRow(
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||||
name=row["Name"],
|
||||
website=row.get("Website"),
|
||||
investment_firm_profile=row.get("Investment Firm Profile"),
|
||||
crunchbase_linkedin_urls=row.get("Crunchbase & LinkedIn URLs"),
|
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crunchbase_firm_extract=row.get("Crunchbase Firm Extract"),
|
||||
linkedin_investment_profile=row.get("LinkedIn Investment Profile"),
|
||||
source_of_truth_profile=row.get("Source of Truth Profile"),
|
||||
)
|
||||
|
||||
# Extract structured data
|
||||
structured_data = self.extract_structured_data(csv_row)
|
||||
|
||||
# Enhance with LLM if enabled
|
||||
enhanced_data = self.enhance_with_llm(structured_data)
|
||||
|
||||
# Save to SQL database
|
||||
investor_id = self.save_to_sql(enhanced_data)
|
||||
|
||||
# Save to vector database
|
||||
self.save_to_vector_db(investor_id, enhanced_data)
|
||||
|
||||
processed_count += 1
|
||||
|
||||
# Progress update every 10 rows
|
||||
if (index + 1) % 10 == 0:
|
||||
logger.info(
|
||||
f"Progress: {processed_count} processed, {error_count} errors"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
error_count += 1
|
||||
logger.error(
|
||||
f"Error processing row {index + 1} ({row.get('Name', 'Unknown')}): {e}"
|
||||
)
|
||||
continue
|
||||
|
||||
logger.info(
|
||||
f"Processing complete! Processed: {processed_count}, Errors: {error_count}"
|
||||
)
|
||||
return processed_count, error_count
|
||||
|
||||
def search_investors(self, query: str, limit: int = 10):
|
||||
"""Search investors using vector similarity"""
|
||||
try:
|
||||
results = self.collection.query(query_texts=[query], n_results=limit)
|
||||
|
||||
return results
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Search failed: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def main():
|
||||
"""Main function with command line interface"""
|
||||
parser = argparse.ArgumentParser(description="LLM-powered Investor Parser")
|
||||
parser.add_argument("--file", type=str, help="Path to CSV file to process")
|
||||
parser.add_argument("--limit", type=int, help="Limit number of rows to process")
|
||||
parser.add_argument(
|
||||
"--use-llm",
|
||||
action="store_true",
|
||||
help="Enable LLM enhancement (requires OpenAI API key)",
|
||||
)
|
||||
parser.add_argument("--search", type=str, help="Search query for vector database")
|
||||
parser.add_argument(
|
||||
"--search-limit",
|
||||
type=int,
|
||||
default=10,
|
||||
help="Number of search results to return",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Initialize parser
|
||||
investor_parser = InvestorParser(use_llm=args.use_llm)
|
||||
|
||||
if args.search:
|
||||
# Perform search
|
||||
logger.info(f"Searching for: {args.search}")
|
||||
results = investor_parser.search_investors(args.search, args.search_limit)
|
||||
|
||||
if results and results["documents"][0]:
|
||||
print(f"\nFound {len(results['documents'][0])} similar investors:")
|
||||
for i, (doc, metadata) in enumerate(
|
||||
zip(results["documents"][0], results["metadatas"][0])
|
||||
):
|
||||
print(f"{i + 1}. {metadata['name']}")
|
||||
print(f" Website: {metadata.get('website', 'N/A')}")
|
||||
print(f" HQ: {metadata.get('headquarters', 'N/A')}")
|
||||
print(f" Focus areas: {metadata.get('focus_areas_count', 0)}")
|
||||
print(f" Similarity score: {results['distances'][0][i]:.3f}")
|
||||
print()
|
||||
else:
|
||||
print("No results found.")
|
||||
|
||||
elif args.file:
|
||||
# Process CSV file
|
||||
if not os.path.exists(args.file):
|
||||
logger.error(f"File not found: {args.file}")
|
||||
return
|
||||
|
||||
processed, errors = investor_parser.process_csv_file(args.file, args.limit)
|
||||
|
||||
print("\nProcessing complete!")
|
||||
print(f"Successfully processed: {processed} investors")
|
||||
print(f"Errors encountered: {errors}")
|
||||
|
||||
# Show some search examples
|
||||
print("\nTrying some example searches...")
|
||||
for query in ["bioeconomy", "venture capital", "sustainability"]:
|
||||
results = investor_parser.search_investors(query, 3)
|
||||
if results and results["documents"][0]:
|
||||
print(f"\nTop matches for '{query}':")
|
||||
for i, metadata in enumerate(results["metadatas"][0][:3]):
|
||||
print(f" {i + 1}. {metadata['name']}")
|
||||
|
||||
else:
|
||||
parser.print_help()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+361
-21
@@ -1,28 +1,368 @@
|
||||
import asyncio
|
||||
import csv
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from openai import AsyncOpenAI
|
||||
from pydantic import BaseModel
|
||||
import chromadb
|
||||
import pandas as pd
|
||||
from dotenv import load_dotenv
|
||||
from openai import OpenAI
|
||||
|
||||
from db import get_session, init_database
|
||||
from schema import CSVRow, Investor
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class RowSchema(BaseModel):
|
||||
section: str
|
||||
explanation: str
|
||||
class LLMInvestorParser:
|
||||
def __init__(self):
|
||||
# Initialize OpenAI client
|
||||
self.openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
client = AsyncOpenAI()
|
||||
# Initialize ChromaDB
|
||||
self.chroma_client = chromadb.PersistentClient(path="./chroma_db")
|
||||
self.collection = self.chroma_client.get_or_create_collection(
|
||||
name="investor_descriptions",
|
||||
metadata={
|
||||
"description": "Investor descriptions and investment thesis focus"
|
||||
},
|
||||
)
|
||||
|
||||
async def process_row(row):
|
||||
resp = await client.chat.completions.create(
|
||||
model="gpt-4o-mini",
|
||||
messages=[{"role": "user", "content": f"Extract relevant section:\n{row}"}],
|
||||
response_format={"type": "json_object"} # ensures JSON output
|
||||
)
|
||||
return RowSchema.model_validate_json(resp.choices[0].message.content)
|
||||
# Initialize database
|
||||
init_database()
|
||||
|
||||
async def main():
|
||||
with open("data.csv") as f:
|
||||
reader = csv.DictReader(f)
|
||||
tasks = [process_row(row) for row in reader]
|
||||
return await asyncio.gather(*tasks)
|
||||
def parse_json_field(self, json_str: str) -> Dict[str, Any]:
|
||||
"""Safely parse JSON string with LLM assistance if needed"""
|
||||
if not json_str or json_str.strip() == "":
|
||||
return {}
|
||||
|
||||
results = asyncio.run(main())
|
||||
try:
|
||||
# Try direct JSON parsing first
|
||||
return json.loads(json_str)
|
||||
except json.JSONDecodeError:
|
||||
# If direct parsing fails, use LLM to clean and parse
|
||||
logger.info("Direct JSON parsing failed, using LLM to clean JSON")
|
||||
return self._llm_clean_json(json_str)
|
||||
|
||||
def _llm_clean_json(self, malformed_json: str) -> Dict[str, Any]:
|
||||
"""Use LLM to clean and parse malformed JSON"""
|
||||
try:
|
||||
prompt = f"""
|
||||
The following text appears to be malformed JSON. Please clean it up and return valid JSON.
|
||||
If it's not possible to create valid JSON, return an empty object {{}}.
|
||||
|
||||
Original text:
|
||||
{malformed_json[:2000]} # Limit length for API
|
||||
|
||||
Return only the cleaned JSON, no explanations:
|
||||
"""
|
||||
|
||||
response = self.openai_client.chat.completions.create(
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
cleaned_json = response.choices[0].message.content.strip()
|
||||
return json.loads(cleaned_json)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"LLM JSON cleaning failed: {e}")
|
||||
return {}
|
||||
|
||||
def extract_structured_data(self, csv_row: CSVRow) -> Dict[str, Any]:
|
||||
"""Extract and structure data from CSV row using LLM"""
|
||||
# Parse the investment firm profile
|
||||
profile_data = {}
|
||||
if csv_row.investment_firm_profile:
|
||||
profile_data = self.parse_json_field(csv_row.investment_firm_profile)
|
||||
|
||||
# Create structured output
|
||||
structured_data = {
|
||||
"name": csv_row.name,
|
||||
"website": csv_row.website or profile_data.get("websiteURL"),
|
||||
"investor_description": profile_data.get("investorDescription", ""),
|
||||
"investment_thesis_focus": profile_data.get("investmentThesisFocus", []),
|
||||
"headquarters": profile_data.get("headquarters", ""),
|
||||
"aum_info": profile_data.get("overallAssetsUnderManagement", {}),
|
||||
"funds_info": profile_data.get("funds", []),
|
||||
"crunchbase_urls": csv_row.crunchbase_linkedin_urls or "",
|
||||
"crunchbase_extract": csv_row.crunchbase_firm_extract or "",
|
||||
"linkedin_profile": csv_row.linkedin_investment_profile or "",
|
||||
"source_truth_profile": csv_row.source_of_truth_profile or "",
|
||||
}
|
||||
|
||||
return structured_data
|
||||
|
||||
def enhance_with_llm(self, investor_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Use LLM to enhance and standardize investor data"""
|
||||
try:
|
||||
# Combine all available text for context
|
||||
context_text = " ".join(
|
||||
[
|
||||
investor_data.get("investor_description", ""),
|
||||
investor_data.get("crunchbase_extract", ""),
|
||||
investor_data.get("linkedin_profile", ""),
|
||||
investor_data.get("source_truth_profile", ""),
|
||||
]
|
||||
)
|
||||
|
||||
if not context_text.strip():
|
||||
return investor_data
|
||||
|
||||
prompt = f"""
|
||||
Based on the following information about an investor, please extract and standardize:
|
||||
1. A concise investor description (2-3 sentences)
|
||||
2. Investment thesis focus areas (list of specific focus areas)
|
||||
3. Headquarters location (city, country format)
|
||||
|
||||
Investor: {investor_data["name"]}
|
||||
Context: {context_text[:3000]} # Limit for API
|
||||
|
||||
Return in JSON format:
|
||||
{{
|
||||
"enhanced_description": "concise description here",
|
||||
"standardized_focus": ["focus area 1", "focus area 2", ...],
|
||||
"standardized_headquarters": "City, Country"
|
||||
}}
|
||||
"""
|
||||
|
||||
response = self.openai_client.chat.completions.create(
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
temperature=0.3,
|
||||
)
|
||||
|
||||
enhanced_data = json.loads(response.choices[0].message.content)
|
||||
|
||||
# Update investor data with enhanced information
|
||||
if enhanced_data.get("enhanced_description"):
|
||||
investor_data["enhanced_description"] = enhanced_data[
|
||||
"enhanced_description"
|
||||
]
|
||||
|
||||
if enhanced_data.get("standardized_focus"):
|
||||
investor_data["standardized_focus"] = enhanced_data[
|
||||
"standardized_focus"
|
||||
]
|
||||
|
||||
if enhanced_data.get("standardized_headquarters"):
|
||||
investor_data["standardized_headquarters"] = enhanced_data[
|
||||
"standardized_headquarters"
|
||||
]
|
||||
|
||||
return investor_data
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"LLM enhancement failed for {investor_data['name']}: {e}")
|
||||
return investor_data
|
||||
|
||||
def save_to_sql(self, investor_data: Dict[str, Any]) -> int:
|
||||
"""Save investor data to SQL database"""
|
||||
try:
|
||||
with get_session() as session:
|
||||
# Check if investor already exists
|
||||
existing = (
|
||||
session.query(Investor)
|
||||
.filter_by(name=investor_data["name"])
|
||||
.first()
|
||||
)
|
||||
|
||||
if existing:
|
||||
logger.info(f"Updating existing investor: {investor_data['name']}")
|
||||
investor = existing
|
||||
else:
|
||||
logger.info(f"Creating new investor: {investor_data['name']}")
|
||||
investor = Investor()
|
||||
|
||||
# Map data to investor object
|
||||
investor.name = investor_data["name"]
|
||||
investor.website = investor_data.get("website")
|
||||
investor.investor_description = investor_data.get(
|
||||
"enhanced_description"
|
||||
) or investor_data.get("investor_description")
|
||||
investor.investment_thesis_focus = investor_data.get(
|
||||
"standardized_focus"
|
||||
) or investor_data.get("investment_thesis_focus")
|
||||
investor.headquarters = investor_data.get(
|
||||
"standardized_headquarters"
|
||||
) or investor_data.get("headquarters")
|
||||
|
||||
# AUM information
|
||||
aum_info = investor_data.get("aum_info", {})
|
||||
investor.aum_amount = aum_info.get("aumAmount")
|
||||
investor.aum_as_of_date = aum_info.get("asOfDate")
|
||||
investor.aum_source_url = aum_info.get("sourceUrl")
|
||||
|
||||
# Fund information
|
||||
investor.funds_info = investor_data.get("funds_info", [])
|
||||
|
||||
# Raw data
|
||||
investor.crunchbase_urls = investor_data.get("crunchbase_urls")
|
||||
investor.crunchbase_extract = investor_data.get("crunchbase_extract")
|
||||
investor.linkedin_profile = investor_data.get("linkedin_profile")
|
||||
investor.source_truth_profile = investor_data.get(
|
||||
"source_truth_profile"
|
||||
)
|
||||
|
||||
if not existing:
|
||||
session.add(investor)
|
||||
|
||||
session.flush() # Get the ID
|
||||
return investor.id
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to save to SQL: {e}")
|
||||
raise
|
||||
|
||||
def save_to_vector_db(self, investor_id: int, investor_data: Dict[str, Any]):
|
||||
"""Save investor description and focus to ChromaDB"""
|
||||
try:
|
||||
# Prepare text for embedding
|
||||
description_text = investor_data.get(
|
||||
"enhanced_description"
|
||||
) or investor_data.get("investor_description", "")
|
||||
focus_areas = investor_data.get("standardized_focus") or investor_data.get(
|
||||
"investment_thesis_focus", []
|
||||
)
|
||||
|
||||
if isinstance(focus_areas, list):
|
||||
focus_text = " ".join(focus_areas)
|
||||
else:
|
||||
focus_text = str(focus_areas)
|
||||
|
||||
# Combine description and focus for embedding
|
||||
combined_text = f"{description_text} {focus_text}".strip()
|
||||
|
||||
if not combined_text:
|
||||
logger.warning(f"No text to embed for investor {investor_data['name']}")
|
||||
return
|
||||
|
||||
# Create metadata
|
||||
metadata = {
|
||||
"investor_id": investor_id,
|
||||
"name": investor_data["name"],
|
||||
"website": investor_data.get("website", ""),
|
||||
"headquarters": investor_data.get("standardized_headquarters")
|
||||
or investor_data.get("headquarters", ""),
|
||||
"focus_areas_count": len(focus_areas)
|
||||
if isinstance(focus_areas, list)
|
||||
else 0,
|
||||
}
|
||||
|
||||
# Add to ChromaDB
|
||||
self.collection.add(
|
||||
documents=[combined_text],
|
||||
metadatas=[metadata],
|
||||
ids=[f"investor_{investor_id}"],
|
||||
)
|
||||
|
||||
logger.info(f"Added investor {investor_data['name']} to vector database")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to save to vector DB: {e}")
|
||||
|
||||
def process_csv_file(self, csv_file_path: str, limit: Optional[int] = None):
|
||||
"""Process the entire CSV file"""
|
||||
logger.info(f"Starting to process CSV file: {csv_file_path}")
|
||||
|
||||
# Read CSV
|
||||
df = pd.read_csv(csv_file_path)
|
||||
logger.info(f"Loaded {len(df)} rows from CSV")
|
||||
|
||||
if limit:
|
||||
df = df.head(limit)
|
||||
logger.info(f"Processing limited to {limit} rows")
|
||||
|
||||
processed_count = 0
|
||||
error_count = 0
|
||||
|
||||
for index, row in df.iterrows():
|
||||
try:
|
||||
logger.info(f"Processing row {index + 1}/{len(df)}: {row['Name']}")
|
||||
|
||||
# Create CSVRow object
|
||||
csv_row = CSVRow(
|
||||
name=row["Name"],
|
||||
website=row.get("Website"),
|
||||
investment_firm_profile=row.get("Investment Firm Profile"),
|
||||
crunchbase_linkedin_urls=row.get("Crunchbase & LinkedIn URLs"),
|
||||
crunchbase_firm_extract=row.get("Crunchbase Firm Extract"),
|
||||
linkedin_investment_profile=row.get("LinkedIn Investment Profile"),
|
||||
source_of_truth_profile=row.get("Source of Truth Profile"),
|
||||
)
|
||||
|
||||
# Extract structured data
|
||||
structured_data = self.extract_structured_data(csv_row)
|
||||
|
||||
# Enhance with LLM
|
||||
enhanced_data = self.enhance_with_llm(structured_data)
|
||||
|
||||
# Save to SQL database
|
||||
investor_id = self.save_to_sql(enhanced_data)
|
||||
|
||||
# Save to vector database
|
||||
self.save_to_vector_db(investor_id, enhanced_data)
|
||||
|
||||
processed_count += 1
|
||||
|
||||
# Progress update every 10 rows
|
||||
if (index + 1) % 10 == 0:
|
||||
logger.info(
|
||||
f"Processed {processed_count} rows successfully, {error_count} errors"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
error_count += 1
|
||||
logger.error(
|
||||
f"Error processing row {index + 1} ({row.get('Name', 'Unknown')}): {e}"
|
||||
)
|
||||
continue
|
||||
|
||||
logger.info(
|
||||
f"Processing complete! Processed: {processed_count}, Errors: {error_count}"
|
||||
)
|
||||
return processed_count, error_count
|
||||
|
||||
def search_investors(self, query: str, limit: int = 5):
|
||||
"""Search investors using vector similarity"""
|
||||
try:
|
||||
results = self.collection.query(query_texts=[query], n_results=limit)
|
||||
|
||||
return results
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Search failed: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def main():
|
||||
"""Main function to run the parser"""
|
||||
parser = LLMInvestorParser()
|
||||
|
||||
# Process the CSV file
|
||||
csv_file = "/home/oluwasanmi/Documents/Work/MKD/anton_wireframe/New Excerpt 5 investors - Sheet1 parse.csv"
|
||||
|
||||
# Start with a small sample for testing
|
||||
processed, errors = parser.process_csv_file(csv_file, limit=5)
|
||||
|
||||
print("\nProcessing complete!")
|
||||
print(f"Successfully processed: {processed} investors")
|
||||
print(f"Errors encountered: {errors}")
|
||||
|
||||
# Test search functionality
|
||||
print("\nTesting search functionality...")
|
||||
results = parser.search_investors("bioeconomy circular economy")
|
||||
if results:
|
||||
print(f"Found {len(results['documents'][0])} similar investors")
|
||||
for i, doc in enumerate(results["documents"][0]):
|
||||
print(f" {i + 1}. {results['metadatas'][0][i]['name']}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,178 @@
|
||||
import asyncio
|
||||
from typing import List, Optional
|
||||
|
||||
import chromadb
|
||||
import pandas as pd
|
||||
from db.tables import InvestorTable
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
from langchain_openai import ChatOpenAI
|
||||
from pydantic_schemas import Investor, InvestorList
|
||||
from settings import settings
|
||||
|
||||
# Add these imports for your databases
|
||||
# from sqlalchemy.ext.asyncio import AsyncSession
|
||||
# from your_vector_db import VectorDBClient
|
||||
|
||||
|
||||
class InvestorProcessor:
|
||||
def __init__(
|
||||
self,
|
||||
sql_session: Optional[object] = None,
|
||||
vector_db_client: Optional[object] = None,
|
||||
):
|
||||
self.template = """You are an expert data extraction assistant. Extract investor information from the provided CSV data and return it as a list of structured records.
|
||||
|
||||
Given the following CSV data rows:
|
||||
{question}
|
||||
|
||||
For each row, extract and structure the following fields:
|
||||
- name: The investor's full name
|
||||
- aum: Assets under management (as integer, use 0 if not available)
|
||||
- check_size: Investment check size (as string)
|
||||
- sector_focus: Sector focus (as string)
|
||||
- stage_focus: Investment stage focus (as string)
|
||||
- region: Geographic region (as string)
|
||||
- investment_thesis: Investment thesis (as string)
|
||||
- investor_description: Description of the investor (as string)
|
||||
|
||||
Important:
|
||||
- If a field is not available in the data, use appropriate default values (empty string for text fields, 0 for numbers)
|
||||
- Ensure all text fields are properly escaped and contain no control characters
|
||||
- Return clean, valid JSON only
|
||||
|
||||
Return the data as a structured list of investors."""
|
||||
|
||||
self.prompt = PromptTemplate(
|
||||
template=self.template, input_variables=["question"]
|
||||
)
|
||||
|
||||
self.llm = ChatOpenAI(
|
||||
api_key=settings.OPENROUTER_API_KEY,
|
||||
base_url="https://openrouter.ai/api/v1",
|
||||
model="openai/gpt-oss-120b:fre",
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
self.structured_llm = self.llm.with_structured_output(InvestorList)
|
||||
self.sql_session = sql_session
|
||||
self.vector_db_client = vector_db_client
|
||||
|
||||
self.vector_db_client = chromadb.PersistentClient(path="./chroma_db")
|
||||
self.collection = self.vector_db_client.get_or_create_collection(
|
||||
name="investor_descriptions",
|
||||
metadata={
|
||||
"description": "Investor descriptions and investment thesis focus"
|
||||
},
|
||||
)
|
||||
|
||||
async def _process_batch(self, batch: pd.DataFrame, batch_idx: int) -> List:
|
||||
"""Process a single batch of data"""
|
||||
# Convert batch to string representation - clean the data
|
||||
batch_str = ""
|
||||
for idx, row in batch.iterrows():
|
||||
# Clean values to remove control characters
|
||||
cleaned_row = {}
|
||||
for key, value in row.items():
|
||||
if pd.notna(value):
|
||||
# Convert to string and clean control characters
|
||||
clean_value = (
|
||||
str(value)
|
||||
.replace("\n", " ")
|
||||
.replace("\r", " ")
|
||||
.replace("\t", " ")
|
||||
)
|
||||
# Remove other control characters
|
||||
clean_value = "".join(
|
||||
char
|
||||
for char in clean_value
|
||||
if ord(char) >= 32 or char in ["\n", "\r", "\t"]
|
||||
)
|
||||
cleaned_row[key] = clean_value
|
||||
|
||||
row_str = ", ".join(
|
||||
[f"{key}: {value}" for key, value in cleaned_row.items()]
|
||||
)
|
||||
batch_str += f"Row {idx + 1}: {row_str}\n"
|
||||
|
||||
try:
|
||||
print(f"Processing batch {batch_idx + 1}...")
|
||||
batch_results = await self.structured_llm.ainvoke(batch_str)
|
||||
return batch_results.investor_list
|
||||
except Exception as e:
|
||||
print(f"Error processing batch {batch_idx + 1}: {e}")
|
||||
return []
|
||||
|
||||
async def _save_to_sql(self, investors: List[Investor]) -> None:
|
||||
"""Save investors to SQL database"""
|
||||
if not self.sql_session:
|
||||
return
|
||||
|
||||
# Implement SQL saving logic here
|
||||
for investor in investors:
|
||||
db_investor = InvestorTable(
|
||||
name=investor.name,
|
||||
aum=investor.aum,
|
||||
check_size=investor.check_size,
|
||||
sector_focus=investor.sector_focus,
|
||||
stage_focus=investor.stage_focus,
|
||||
region=investor.region,
|
||||
)
|
||||
self.sql_session.add(db_investor)
|
||||
self.sql_session.commit()
|
||||
|
||||
async def _save_to_vector_db(self, investors: List[Investor]) -> None:
|
||||
"""Save investors to vector database"""
|
||||
if not self.vector_db_client:
|
||||
return
|
||||
|
||||
documents = []
|
||||
metadatas = []
|
||||
ids = []
|
||||
|
||||
for i, investor in enumerate(investors):
|
||||
doc_text = f"{investor.investor_description}\nInvestment Thesis: {investor.investment_thesis}"
|
||||
documents.append(doc_text)
|
||||
metadatas.append({"name": investor.name})
|
||||
ids.append(f"investor_{i}_{investor.name.replace(' ', '_')}")
|
||||
|
||||
if documents:
|
||||
# Use add method with proper parameters
|
||||
self.collection.add(documents=documents, metadatas=metadatas, ids=ids)
|
||||
|
||||
async def process_csv(
|
||||
self, df: pd.DataFrame, batch_size: int = 10, max_concurrent: int = 10
|
||||
) -> List:
|
||||
"""Process CSV data in parallel batches and save to databases"""
|
||||
results = []
|
||||
|
||||
# Create batches
|
||||
batches = []
|
||||
for i in range(0, len(df), batch_size):
|
||||
batch = df.iloc[i : i + batch_size]
|
||||
batches.append((batch, i // batch_size))
|
||||
|
||||
# Process batches with concurrency control
|
||||
semaphore = asyncio.Semaphore(max_concurrent)
|
||||
|
||||
async def process_with_semaphore(batch_data):
|
||||
batch, batch_idx = batch_data
|
||||
async with semaphore:
|
||||
return await self._process_batch(batch, batch_idx)
|
||||
|
||||
# Execute all batches concurrently
|
||||
batch_results = await asyncio.gather(
|
||||
*[process_with_semaphore(batch_data) for batch_data in batches],
|
||||
return_exceptions=True,
|
||||
)
|
||||
|
||||
# Collect results, filtering out exceptions
|
||||
for batch_result in batch_results:
|
||||
if not isinstance(batch_result, Exception):
|
||||
results.extend(batch_result)
|
||||
|
||||
# Save to databases
|
||||
if results:
|
||||
await self._save_to_sql(results)
|
||||
await self._save_to_vector_db(results)
|
||||
|
||||
return results
|
||||
@@ -0,0 +1,61 @@
|
||||
from typing import Optional
|
||||
|
||||
import chromadb
|
||||
from langchain_openai import ChatOpenAI
|
||||
from pydantic_schemas import Investor, InvestorList
|
||||
from settings import settings
|
||||
|
||||
# Add these imports for your databases
|
||||
# from sqlalchemy.ext.asyncio import AsyncSession
|
||||
# from your_vector_db import VectorDBClient
|
||||
|
||||
|
||||
class QueryProcessor:
|
||||
def __init__(
|
||||
self,
|
||||
sql_session: Optional[object] = None,
|
||||
vector_db_client: Optional[object] = None,
|
||||
):
|
||||
self.llm = ChatOpenAI(
|
||||
api_key=settings.OPENROUTER_API_KEY,
|
||||
base_url="https://openrouter.ai/api/v1",
|
||||
model="openai/gpt-oss-120b:free",
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
self.structured_llm = self.llm.with_structured_output(InvestorList)
|
||||
self.sql_session = sql_session
|
||||
self.vector_db_client = vector_db_client
|
||||
|
||||
self.vector_db_client = chromadb.PersistentClient(path="./chroma_db")
|
||||
self.collection = self.vector_db_client.get_or_create_collection(
|
||||
name="investor_descriptions",
|
||||
metadata={
|
||||
"description": "Investor descriptions and investment thesis focus"
|
||||
},
|
||||
)
|
||||
|
||||
def query_sql_database(self, query: str) -> Optional[InvestorList]:
|
||||
"""Query the SQL database for investor information."""
|
||||
if not self.sql_session:
|
||||
return None
|
||||
|
||||
# Implement SQL querying logic here
|
||||
result = self.sql_session.execute(query)
|
||||
investors = result.scalars().all()
|
||||
return InvestorList(investors=investors)
|
||||
|
||||
def query_vector_database(self, query: str) -> Optional[InvestorList]:
|
||||
"""Query the vector database for investor information."""
|
||||
if not self.vector_db_client:
|
||||
return None
|
||||
|
||||
# Implement vector database querying logic here
|
||||
results = self.vector_db_client.query(collection=self.collection, query=query)
|
||||
investors = [Investor(**doc.metadata) for doc in results.documents]
|
||||
return InvestorList(investors=investors)
|
||||
|
||||
def process_query(self, question: str) -> InvestorList:
|
||||
"""Process a query using the LLM and return structured investor data."""
|
||||
response = self.structured_llm.predict(question=question)
|
||||
return response
|
||||
+4
-3
@@ -1,10 +1,11 @@
|
||||
from pydantic_settings import BaseSettings
|
||||
|
||||
|
||||
class Settings(BaseSettings):
|
||||
api_key: str
|
||||
db_url: str
|
||||
OPENROUTER_API_KEY: str
|
||||
|
||||
class Config:
|
||||
env_file = ".env"
|
||||
|
||||
settings = Settings()
|
||||
|
||||
settings = Settings()
|
||||
|
||||
Reference in New Issue
Block a user