bolade
bbf6af58f0
- Added FastAPI application with a simple root endpoint. - Developed LLMInvestorParser class for processing investor data from CSV files. - Integrated OpenAI API for LLM enhancements and JSON cleaning. - Implemented structured data extraction and saving to SQL database. - Added functionality to save investor descriptions to ChromaDB for vector similarity search. - Created command-line interface for processing files and searching investors. - Added schema definitions for Investor and related data models using SQLAlchemy and Pydantic. - Implemented logging for better traceability and error handling. - Included requirements.txt for dependency management.
261 lines
9.5 KiB
Python
261 lines
9.5 KiB
Python
import json
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import logging
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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 db import get_session, init_database
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from schema import CSVRow, Investor
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class SimpleInvestorParser:
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"""Simplified parser that works without OpenAI API for testing"""
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def __init__(self):
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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"""
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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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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 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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"investor_description"
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)
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investor.investment_thesis_focus = investor_data.get(
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"investment_thesis_focus"
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)
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investor.headquarters = 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("investor_description", "")
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focus_areas = investor_data.get("investment_thesis_focus", [])
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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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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("headquarters") 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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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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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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if limit:
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df = df.head(limit)
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logger.info(f"Processing limited to {limit} rows")
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processed_count = 0
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error_count = 0
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for index, row in df.iterrows():
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try:
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logger.info(f"Processing row {index + 1}/{len(df)}: {row['Name']}")
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# Create CSVRow object
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csv_row = CSVRow(
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name=row["Name"],
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website=row.get("Website"),
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investment_firm_profile=row.get("Investment Firm Profile"),
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crunchbase_linkedin_urls=row.get("Crunchbase & LinkedIn URLs"),
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crunchbase_firm_extract=row.get("Crunchbase Firm Extract"),
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linkedin_investment_profile=row.get("LinkedIn Investment Profile"),
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source_of_truth_profile=row.get("Source of Truth Profile"),
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)
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# Extract structured data
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structured_data = self.extract_structured_data(csv_row)
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# Save to SQL database
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investor_id = self.save_to_sql(structured_data)
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# Save to vector database
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self.save_to_vector_db(investor_id, structured_data)
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processed_count += 1
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# Progress update every 10 rows
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if (index + 1) % 10 == 0:
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logger.info(
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f"Processed {processed_count} rows successfully, {error_count} errors"
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)
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except Exception as e:
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error_count += 1
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logger.error(
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f"Error processing row {index + 1} ({row.get('Name', 'Unknown')}): {e}"
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)
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continue
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logger.info(
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f"Processing complete! Processed: {processed_count}, Errors: {error_count}"
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)
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return processed_count, error_count
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def search_investors(self, query: str, limit: int = 5):
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"""Search investors using vector similarity"""
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try:
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results = self.collection.query(query_texts=[query], n_results=limit)
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return results
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except Exception as e:
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logger.error(f"Search failed: {e}")
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return None
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def main():
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"""Main function to run the parser"""
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parser = SimpleInvestorParser()
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# Process the CSV file
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csv_file = "/home/oluwasanmi/Documents/Work/MKD/anton_wireframe/New Excerpt 5 investors - Sheet1 parse.csv"
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# Start with a small sample for testing
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processed, errors = parser.process_csv_file(csv_file, limit=5)
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print("Processing complete!")
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print(f"Successfully processed: {processed} investors")
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print(f"Errors encountered: {errors}")
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# Test search functionality
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print("\nTesting search functionality...")
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results = parser.search_investors("bioeconomy circular economy")
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if results:
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print(f"Found {len(results['documents'][0])} similar investors")
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for i, doc in enumerate(results["documents"][0]):
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print(f" {i + 1}. {results['metadatas'][0][i]['name']}")
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if __name__ == "__main__":
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main()
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