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feat: Update query endpoint to return paginated investment responses with fund details

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This commit is contained in:
bolade
2025-10-08 14:19:36 +01:00
parent 58722f1102
commit cefe89bb67
8 changed files with 107 additions and 45 deletions
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app/__pycache__/main.cpython-312.pyc
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app/main.py
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@@ -6,7 +6,7 @@ from dotenv import load_dotenv
from fastapi import FastAPI, File, Form, UploadFile
from pydantic import BaseModel
from routers import companies, investors, projects
from schemas.router_schemas import InvestorList
from schemas.router_schemas import InvestmentResponse, PaginatedResponse
from services.llm_parser import InvestorProcessor
from services.querying import QueryProcessor
@@ -84,11 +84,16 @@ async def parse_csv(
return results
@app.post("/query", response_model=InvestorList, tags=["Querying"])
@app.post(
"/query", response_model=PaginatedResponse[InvestmentResponse], tags=["Querying"]
)
async def query_investors(request: QueryRequest):
"""
Query investors using natural language.
Returns fund-level matches (one row per fund) with investor details.
This ensures only relevant funds are included in the response.
Supports queries like:
- "Show me seed stage investors"
- "Find fintech investors in Silicon Valley"
app/services/__pycache__/querying.cpython-312.pyc
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app/services/compatibility_score.py
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app/services/crm.py
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app/services/insight.py
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app/services/querying.py
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@@ -2,13 +2,18 @@ import os
from typing import List
from db.db import DATABASE_URL, get_db
from db.models import InvestorTable
from db.models import FundTable, InvestorTable
from langchain import hub
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.utilities import SQLDatabase
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from schemas.py_schemas import InvestorData, InvestorList
from schemas.router_schemas import (
CompanyMinimal,
InvestmentResponse,
PaginatedResponse,
SectorMinimal,
)
from sqlalchemy.orm import selectinload
# Connect to SQLite
@@ -21,16 +26,16 @@ class QueryProcessor:
self.llm = ChatOpenAI(
api_key=os.getenv("OPENROUTER_API_KEY"),
base_url="https://openrouter.ai/api/v1",
model="openai/gpt-4o-mini",
model="x-ai/grok-4-fast",
temperature=0,
)
self.toolkit = SQLDatabaseToolkit(db=db, llm=self.llm)
# Update system message to specifically request only investor IDs
# Update system message to specifically request only fund IDs
system_message_updated = (
prompt_template.format(dialect="SQLite", top_k=5)
+ "\n\nIMPORTANT: You must ONLY return the investor IDs (id field) that match the user's criteria. "
+ "\n\nIMPORTANT: You must ONLY return the fund IDs (id field from the funds table) that match the user's criteria. "
+ "Do NOT return any other information, explanations, or data. "
+ "Your response should be ONLY a comma-separated list of numbers representing the investor IDs. "
+ "Your response should be ONLY a comma-separated list of numbers representing the fund IDs. "
+ "Example format: 1, 5, 12, 23"
)
self.agent = create_react_agent(
@@ -39,9 +44,9 @@ class QueryProcessor:
prompt=system_message_updated,
)
def process_query(self, question: str) -> InvestorList:
"""Process a query using the LLM and return investor data."""
# Let the LLM handle all database interactions and filtering to get IDs
def process_query(self, question: str) -> PaginatedResponse[InvestmentResponse]:
"""Process a query using the LLM and return investment response data."""
# Let the LLM handle all database interactions and filtering to get fund IDs
response = self.agent.invoke(
{"messages": [("user", question)]},
)
@@ -51,70 +56,122 @@ class QueryProcessor:
response["messages"][-1].content if response.get("messages") else ""
)
# Extract investor IDs from the AI response
investor_ids = self._extract_investor_ids_from_response(ai_response)
# Extract fund IDs from the AI response
fund_ids = self._extract_fund_ids_from_response(ai_response)
# Fetch full investor data using the IDs
return self._fetch_investors_by_ids(investor_ids)
# Fetch full fund data with investor relationships using the IDs
return self._fetch_funds_by_ids(fund_ids)
def _extract_investor_ids_from_response(self, ai_response: str) -> List[int]:
"""Extract investor IDs from AI response."""
def _extract_fund_ids_from_response(self, ai_response: str) -> List[int]:
"""Extract fund IDs from AI response."""
import re
investor_ids = []
fund_ids = []
try:
# Try multiple patterns to extract IDs from the response
# Pattern 1: Simple numbers (assuming they are IDs)
numbers = re.findall(r"\b\d+\b", ai_response)
investor_ids = [int(num) for num in numbers]
fund_ids = [int(num) for num in numbers]
# Pattern 2: If response contains explicit ID references
id_matches = re.findall(r"\bid[:\s]*(\d+)", ai_response.lower())
if id_matches:
investor_ids = [int(id_str) for id_str in id_matches]
fund_ids = [int(id_str) for id_str in id_matches]
except Exception as e:
print(f"Error extracting IDs from response: {e}")
return []
return investor_ids
return fund_ids
def _fetch_investors_by_ids(self, investor_ids: List[int]) -> InvestorList:
"""Fetch investors with all their relationships from the database using IDs."""
if not investor_ids:
return InvestorList(investors=[])
def _fetch_funds_by_ids(
self, fund_ids: List[int]
) -> PaginatedResponse[InvestmentResponse]:
"""Fetch funds with all their relationships from the database using fund IDs.
Constructs response similar to read_investors but starting from funds."""
if not fund_ids:
return PaginatedResponse(
items=[],
total=0,
page=1,
page_size=len(fund_ids) if fund_ids else 10,
total_pages=0,
)
# Get database session
db_session = next(get_db())
try:
# Build query with all relationships loaded
query = (
db_session.query(InvestorTable)
# Query funds with all necessary relationships loaded
funds = (
db_session.query(FundTable)
.options(
selectinload(InvestorTable.portfolio_companies),
selectinload(InvestorTable.team_members),
selectinload(InvestorTable.sectors),
selectinload(InvestorTable.funds),
selectinload(FundTable.investor).selectinload(
InvestorTable.portfolio_companies
),
selectinload(FundTable.investor).selectinload(
InvestorTable.team_members
),
selectinload(FundTable.investor).selectinload(
InvestorTable.sectors
),
selectinload(FundTable.investment_stages),
selectinload(FundTable.sectors),
)
.filter(InvestorTable.id.in_(investor_ids))
.filter(FundTable.id.in_(fund_ids))
.all()
)
investors = query.all()
# Transform to InvestmentResponse format (one row per fund)
investment_responses = []
for fund in funds:
investor = fund.investor
# Transform to InvestorData format
investor_data_list = []
for investor in investors:
investor_data = InvestorData(
investor=investor,
portfolio_companies=investor.portfolio_companies,
team_members=investor.team_members,
sectors=investor.sectors,
funds=investor.funds,
# Get top 3 portfolio companies (id and name only)
portfolio_companies = [
CompanyMinimal(id=company.id, name=company.name)
for company in investor.portfolio_companies[:3]
]
# Get stage focus as comma-separated string
stage_focus = (
", ".join([stage.name for stage in fund.investment_stages])
if fund.investment_stages
else None
)
investor_data_list.append(investor_data)
return InvestorList(investors=investor_data_list)
# Get top 3 sectors from fund (id and name only)
fund_sectors = [
SectorMinimal(id=sector.id, name=sector.name)
for sector in (fund.sectors[:3] if fund.sectors else [])
]
investment_response = InvestmentResponse(
id=investor.id,
name=f"{investor.name} - {fund.fund_name}"
if fund.fund_name
else investor.name,
aum=investor.aum,
check_size_lower=fund.check_size_lower,
check_size_upper=fund.check_size_upper,
geographic_focus=fund.geographic_focus,
stage_focus=stage_focus,
portfolio_companies=portfolio_companies,
sectors=fund_sectors,
compatibility_score=1.0,
)
investment_responses.append(investment_response)
total_count = len(investment_responses)
total_pages = 1 if total_count > 0 else 0
return PaginatedResponse(
items=investment_responses,
total=total_count,
page=1,
page_size=total_count,
total_pages=total_pages,
)
finally:
db_session.close()
app/services/report_gen.py
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