Implement LLM-powered Investor Parser with CSV processing, SQL and vector database integration
- 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.
This commit is contained in:
bolade
@@ -0,0 +1,82 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Quick demonstration of the LLM Investor Parser functionality.
|
||||
This script shows how to use the system programmatically.
|
||||
"""
|
||||
|
||||
from sqlalchemy import func, select
|
||||
|
||||
from db import get_session
|
||||
from investor_parser import InvestorParser
|
||||
from schema import Investor
|
||||
|
||||
|
||||
def main():
|
||||
print("🚀 LLM Investor Parser Demo")
|
||||
print("=" * 50)
|
||||
|
||||
# Initialize parser (without LLM for demo)
|
||||
parser = InvestorParser(use_llm=False)
|
||||
|
||||
# Show current database stats
|
||||
with get_session() as session:
|
||||
count = session.scalar(select(func.count(Investor.id)))
|
||||
print(f"📊 Current database: {count} investors")
|
||||
|
||||
# Demonstrate search functionality
|
||||
print("\n🔍 Search Examples:")
|
||||
|
||||
search_queries = [
|
||||
"circular bioeconomy sustainable",
|
||||
"venture capital early stage",
|
||||
"fintech financial technology",
|
||||
"healthcare biotechnology",
|
||||
"climate sustainability",
|
||||
]
|
||||
|
||||
for query in search_queries:
|
||||
print(f"\n🔎 Searching for: '{query}'")
|
||||
results = parser.search_investors(query, limit=3)
|
||||
|
||||
if results and results["documents"][0]:
|
||||
for i, metadata in enumerate(results["metadatas"][0]):
|
||||
score = results["distances"][0][i]
|
||||
print(f" {i + 1}. {metadata['name']} (score: {score:.3f})")
|
||||
else:
|
||||
print(" No results found")
|
||||
|
||||
# Show detailed investor information
|
||||
print("\n📋 Detailed Investor Sample:")
|
||||
|
||||
with get_session() as session:
|
||||
investor = session.execute(
|
||||
select(Investor).where(Investor.investor_description.isnot(None)).limit(1)
|
||||
).scalar_one_or_none()
|
||||
|
||||
if investor:
|
||||
print(f"\n🏢 {investor.name}")
|
||||
print(f"🌐 Website: {investor.website}")
|
||||
print(f"📍 HQ: {investor.headquarters or 'Not specified'}")
|
||||
print(f"📝 Description: {investor.investor_description[:200]}...")
|
||||
|
||||
if investor.investment_thesis_focus:
|
||||
print(
|
||||
f"\n🎯 Investment Focus ({len(investor.investment_thesis_focus)} areas):"
|
||||
)
|
||||
for i, focus in enumerate(investor.investment_thesis_focus[:3], 1):
|
||||
print(f" {i}. {focus}")
|
||||
if len(investor.investment_thesis_focus) > 3:
|
||||
print(f" ... and {len(investor.investment_thesis_focus) - 3} more")
|
||||
|
||||
if investor.aum_amount:
|
||||
print(f"\n💰 AUM: {investor.aum_amount}")
|
||||
|
||||
print("\n✅ Demo complete!")
|
||||
print("\nTo run the full parser:")
|
||||
print(" python investor_parser.py --file 'your_file.csv' --limit 50")
|
||||
print("\nTo search investors:")
|
||||
print(" python investor_parser.py --search 'your search query'")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,368 @@
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
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 LLMInvestorParser:
|
||||
def __init__(self):
|
||||
# Initialize OpenAI client
|
||||
self.openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
# 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"
|
||||
},
|
||||
)
|
||||
|
||||
# Initialize database
|
||||
init_database()
|
||||
|
||||
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 {}
|
||||
|
||||
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 +1,449 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
LLM-powered Investor Parser
|
||||
|
||||
A comprehensive parser that processes investor CSV data and saves it to both SQL and vector databases.
|
||||
Supports both simple parsing and LLM-enhanced parsing for better data quality.
|
||||
|
||||
Usage:
|
||||
python investor_parser.py --help
|
||||
python investor_parser.py --file="path/to/csv" --limit=10
|
||||
python investor_parser.py --file="path/to/csv" --use-llm --limit=50
|
||||
python investor_parser.py --search="bioeconomy circular"
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
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, format="%(asctime)s - %(levelname)s - %(message)s"
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class InvestorParser:
|
||||
"""Complete investor parser with optional LLM enhancement"""
|
||||
|
||||
def __init__(self, use_llm: bool = False):
|
||||
self.use_llm = use_llm
|
||||
|
||||
# Initialize OpenAI client if using LLM
|
||||
if self.use_llm:
|
||||
api_key = os.getenv("OPENAI_API_KEY")
|
||||
if not api_key:
|
||||
logger.warning(
|
||||
"OpenAI API key not found. LLM features will be disabled."
|
||||
)
|
||||
self.use_llm = False
|
||||
else:
|
||||
self.openai_client = OpenAI(api_key=api_key)
|
||||
logger.info("LLM enhancement enabled")
|
||||
|
||||
# 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"
|
||||
},
|
||||
)
|
||||
|
||||
# Initialize database
|
||||
init_database()
|
||||
|
||||
def parse_json_field(self, json_str: str) -> Dict[str, Any]:
|
||||
"""Safely parse JSON string with optional LLM assistance"""
|
||||
if not json_str or json_str.strip() == "":
|
||||
return {}
|
||||
|
||||
try:
|
||||
return json.loads(json_str)
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning(f"JSON parsing failed: {e}")
|
||||
|
||||
# Use LLM to clean JSON if available
|
||||
if self.use_llm:
|
||||
return self._llm_clean_json(json_str)
|
||||
else:
|
||||
return {}
|
||||
|
||||
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"""
|
||||
# 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"""
|
||||
if not self.use_llm:
|
||||
return 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") or {}
|
||||
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") or "",
|
||||
"headquarters": investor_data.get("standardized_headquarters")
|
||||
or investor_data.get("headquarters")
|
||||
or "",
|
||||
"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 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()
|
||||
@@ -0,0 +1,109 @@
|
||||
from sqlalchemy import Column, Integer, String, Text, DateTime, JSON, Float
|
||||
from sqlalchemy.ext.declarative import declarative_base
|
||||
from sqlalchemy.sql import func
|
||||
from pydantic import BaseModel
|
||||
from typing import List, Optional, Dict, Any
|
||||
import json
|
||||
|
||||
Base = declarative_base()
|
||||
|
||||
class Investor(Base):
|
||||
__tablename__ = 'investors'
|
||||
|
||||
id = Column(Integer, primary_key=True, autoincrement=True)
|
||||
name = Column(String(500), nullable=False)
|
||||
website = Column(String(1000))
|
||||
|
||||
# Core investment information
|
||||
investor_description = Column(Text)
|
||||
investment_thesis_focus = Column(JSON) # List of focus areas
|
||||
headquarters = Column(String(1000))
|
||||
|
||||
# AUM information
|
||||
aum_amount = Column(String(200))
|
||||
aum_as_of_date = Column(String(100))
|
||||
aum_source_url = Column(String(1000))
|
||||
|
||||
# Fund information
|
||||
funds_info = Column(JSON) # Complex fund data
|
||||
|
||||
# Raw data columns for reference
|
||||
crunchbase_urls = Column(Text)
|
||||
crunchbase_extract = Column(Text)
|
||||
linkedin_profile = Column(Text)
|
||||
source_truth_profile = Column(Text)
|
||||
|
||||
# Metadata
|
||||
created_at = Column(DateTime(timezone=True), server_default=func.now())
|
||||
updated_at = Column(DateTime(timezone=True), onupdate=func.now())
|
||||
|
||||
def __repr__(self):
|
||||
return f"<Investor(name='{self.name}', website='{self.website}')>"
|
||||
|
||||
# Pydantic models for data validation and parsing
|
||||
class AUMInfo(BaseModel):
|
||||
aumAmount: Optional[str] = None
|
||||
asOfDate: Optional[str] = None
|
||||
sourceUrl: Optional[str] = None
|
||||
|
||||
class FundInfo(BaseModel):
|
||||
fundName: Optional[str] = None
|
||||
fundSize: Optional[str] = None
|
||||
vintage: Optional[str] = None
|
||||
status: Optional[str] = None
|
||||
description: Optional[str] = None
|
||||
|
||||
class InvestorProfile(BaseModel):
|
||||
websiteURL: Optional[str] = None
|
||||
investorDescription: Optional[str] = None
|
||||
investmentThesisFocus: Optional[List[str]] = None
|
||||
headquarters: Optional[str] = None
|
||||
overallAssetsUnderManagement: Optional[AUMInfo] = None
|
||||
funds: Optional[List[FundInfo]] = None
|
||||
|
||||
class CSVRow(BaseModel):
|
||||
name: str
|
||||
website: Optional[str] = None
|
||||
investment_firm_profile: Optional[str] = None
|
||||
crunchbase_linkedin_urls: Optional[str] = None
|
||||
crunchbase_firm_extract: Optional[str] = None
|
||||
linkedin_investment_profile: Optional[str] = None
|
||||
source_of_truth_profile: Optional[str] = None
|
||||
|
||||
def get_combined_description(self) -> str:
|
||||
"""Combine all description fields for vector embedding"""
|
||||
descriptions = []
|
||||
|
||||
if self.investment_firm_profile:
|
||||
try:
|
||||
profile_data = json.loads(self.investment_firm_profile)
|
||||
if isinstance(profile_data, dict):
|
||||
desc = profile_data.get('investorDescription', '')
|
||||
if desc:
|
||||
descriptions.append(desc)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
if self.crunchbase_firm_extract:
|
||||
descriptions.append(self.crunchbase_firm_extract)
|
||||
|
||||
if self.linkedin_investment_profile:
|
||||
descriptions.append(self.linkedin_investment_profile)
|
||||
|
||||
if self.source_of_truth_profile:
|
||||
descriptions.append(self.source_of_truth_profile)
|
||||
|
||||
return " ".join(descriptions)
|
||||
|
||||
def get_investment_focus(self) -> List[str]:
|
||||
"""Extract investment thesis focus"""
|
||||
if self.investment_firm_profile:
|
||||
try:
|
||||
profile_data = json.loads(self.investment_firm_profile)
|
||||
if isinstance(profile_data, dict):
|
||||
focus = profile_data.get('investmentThesisFocus', [])
|
||||
if isinstance(focus, list):
|
||||
return focus
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
return []
|
||||
@@ -0,0 +1,260 @@
|
||||
import json
|
||||
import logging
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
import chromadb
|
||||
import pandas as pd
|
||||
|
||||
from db import get_session, init_database
|
||||
from schema import CSVRow, Investor
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class SimpleInvestorParser:
|
||||
"""Simplified parser that works without OpenAI API for testing"""
|
||||
|
||||
def __init__(self):
|
||||
# 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"
|
||||
},
|
||||
)
|
||||
|
||||
# Initialize database
|
||||
init_database()
|
||||
|
||||
def parse_json_field(self, json_str: str) -> Dict[str, Any]:
|
||||
"""Safely parse JSON string"""
|
||||
if not json_str or json_str.strip() == "":
|
||||
return {}
|
||||
|
||||
try:
|
||||
return json.loads(json_str)
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning(f"JSON parsing failed: {e}")
|
||||
return {}
|
||||
|
||||
def extract_structured_data(self, csv_row: CSVRow) -> Dict[str, Any]:
|
||||
"""Extract and structure data from CSV row"""
|
||||
# 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 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(
|
||||
"investor_description"
|
||||
)
|
||||
investor.investment_thesis_focus = investor_data.get(
|
||||
"investment_thesis_focus"
|
||||
)
|
||||
investor.headquarters = investor_data.get("headquarters")
|
||||
|
||||
# AUM information
|
||||
aum_info = investor_data.get("aum_info") or {}
|
||||
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("investor_description", "")
|
||||
focus_areas = 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") or "",
|
||||
"headquarters": investor_data.get("headquarters") or "",
|
||||
"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)
|
||||
|
||||
# Save to SQL database
|
||||
investor_id = self.save_to_sql(structured_data)
|
||||
|
||||
# Save to vector database
|
||||
self.save_to_vector_db(investor_id, structured_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 = SimpleInvestorParser()
|
||||
|
||||
# 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("Processing 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()
|
||||
Reference in New Issue
Block a user