api/routes/search.py

from typing import Annotated

from fastapi import APIRouter, UploadFile, File, Body
from fastapi.responses import JSONResponse
import openai
import io
import os
from pypdf import PdfReader
from langchain.schema import Document
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from langchain.text_splitter import SentenceTransformersTokenTextSplitter
from db.vector_store import ToyVectorStore

router = APIRouter()
_chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff", verbose=True)

@router.post("/v1/docs")
async def create_or_update(name: Annotated[str, Body()], file_name: Annotated[str, Body()], file: UploadFile = File(...)):
    """Create or update an existing collection with information from the file 
    `name` of the collection
    `file` to upload.
    `fileName` name of the file.
    """
    
    _db = ToyVectorStore.get_instance().get_collection(name)
    if not _db:
        #todo. fix this to create a collection, may be.
        return JSONResponse(status_code=404, content={})

    async for doc in generate_documents(file, file_name):
        print(doc)
        _db.add_documents([doc])
    #todo return something sensible
    return JSONResponse(status_code=200, content={"name": name})

@router.get("/v1/doc/{name}/answer")
async def answer(name: str, query: str):
    """ Answer a question from the collection
    `name` of the collection.
    `query` to be answered.
    """
    _db = ToyVectorStore.get_instance().get_collection(name)
    print(query)
    docs = _db.similarity_search_with_score(query=query)
    print(docs)
    answer = _chain.run(input_documents=[tup[0] for tup in docs], question=query)
    return JSONResponse(status_code=200, content={"answer": answer, "file_score": [[f"{d[0].metadata['file']} : {d[0].metadata['page']}", d[1]] for d in docs]}) 

async def generate_documents(file: UploadFile, file_name: str):
    num=0
    async for txts in convert_documents(file):
        num += 1
        for txt in txts:
            document = Document(page_content=txt,metadata={"file": file_name, "page": num})
            yield document
 
async def convert_documents(file: UploadFile):
    splitter = SentenceTransformersTokenTextSplitter(chunk_overlap=0)

    #parse pdf document
    if file.content_type == 'application/pdf':
        content = await file.read()
        pdf_reader = PdfReader(io.BytesIO(content))
        try:
            for page in pdf_reader.pages:
                yield splitter.split_text(page.extract_text())
        except Exception as e:
            print(f"Exception {e}")
    elif "text" in file.content_type:
        content = await file.read()
        yield splitter.split_text(content.decode("utf-8"))
    else:
        return