bolade
117 lines
3.4 KiB
Python
117 lines
3.4 KiB
Python
import io
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import pandas as pd
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from db.db import Base, db_dependency, engine
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from dotenv import load_dotenv
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from fastapi import FastAPI, File, Form, UploadFile
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from pydantic import BaseModel
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from routers import companies, folk_crm, investors, projects
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from schemas.router_schemas import InvestmentResponse, PaginatedResponse
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from services.llm_parser import InvestorProcessor
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from services.querying import QueryProcessor
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load_dotenv()
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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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init_database()
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app = FastAPI()
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# Request models
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class QueryRequest(BaseModel):
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question: str
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class Config:
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json_schema_extra = {
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"example": {
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"question": "Find me deep tech investors that do deals in Europe under 5 million."
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}
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}
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@app.get("/")
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def health():
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return {"Hello": "World"}
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@app.post("/parse-csv", tags=["CSV Upload"], response_model=list[dict])
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async def parse_csv(
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db: db_dependency, file: UploadFile = File(...), is_investor: int = Form(...)
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):
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"""
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Parse and import CSV data into the database.
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**For investors:**
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- Expected columns: Name, Website, Final Investor Profile, Final Profile sourcing
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- Manually parses JSON profiles for efficiency
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- Uses LLM only for currency conversion to USD
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- Handles AUM, fund sizes, and check sizes as integers
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**For companies:**
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- Expected columns: Name, Website, Investor, Final Investor Profile (company profile)
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- 100% manual JSON parsing - no LLM needed
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- Extracts company details, executives, investors, and client categories
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- Automatically links companies to investors in database
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**Benefits:**
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- Fast processing (5-10s per record)
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- Low cost (minimal or no LLM usage)
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- Accurate data extraction
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- Automatic database persistence
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"""
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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()
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if is_investor == 1:
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# Manual parser with LLM currency conversion
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results = await processor.parse_investors(df, save_to_db=True)
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# Results are already dicts from the new parser
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return results
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else:
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# Manual parser for companies (no LLM needed)
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results = await processor.parse_companies(df, save_to_db=True)
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# Results are already dicts from the new parser
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return results
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@app.post(
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"/query", response_model=PaginatedResponse[InvestmentResponse], tags=["Querying"]
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)
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async def query_investors(request: QueryRequest):
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"""
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Query investors using natural language.
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Returns fund-level matches (one row per fund) with investor details.
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This ensures only relevant funds are included in the response.
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Supports queries like:
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- "Show me seed stage investors"
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- "Find fintech investors in Silicon Valley"
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- "Growth stage investors with $5M+ check sizes"
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- "Healthcare investors in Europe"
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"""
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processor = QueryProcessor()
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results = processor.process_query(request.question)
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return results
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app.include_router(investors.router)
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app.include_router(companies.router)
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app.include_router(projects.router)
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app.include_router(folk_crm.router)
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app="main:app", host="0.0.0.0", port=8585, reload=True)
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