bolade
ba0ed169ce
- 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.
45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
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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@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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