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app.py
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app.py
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from fastapi import FastAPI, Request, Form
from fastapi.responses import HTMLResponse
from fastapi.templating import Jinja2Templates
from fastapi.staticfiles import StaticFiles
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from dotenv import load_dotenv
from src.prompt import *
import os
from langchain_openai import OpenAIEmbeddings
from langchain_community.llms import OpenAI
from langchain_openai import OpenAI
from src.helper import load_pdf, text_split
import os
from langchain_community.llms import OpenAI
from langchain_objectbox.vectorstores import ObjectBox ##vector Database
load_dotenv()
app = FastAPI()
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")
os.environ["OPENAI_API_KEY"]=""
embedding=OpenAIEmbeddings()
extracted_data = load_pdf("data/")
text_chunks = text_split(extracted_data)
vectors = ObjectBox.from_documents(text_chunks, OpenAIEmbeddings(), embedding_dimensions=768)
PROMPT=PromptTemplate(template=prompt_template, input_variables=["context", "question"])
chain_type_kwargs={"prompt": PROMPT}
llm=OpenAI()
qa_chain = RetrievalQA.from_chain_type(llm=OpenAI(),
chain_type="stuff",
retriever=vectors.as_retriever(),
return_source_documents=True,
chain_type_kwargs=chain_type_kwargs)
## Cite sources
def process_llm_response(llm_response):
print(llm_response['result'])
print('\n\nSources:')
for source in llm_response["source_documents"]:
print(source.metadata['source'])
# full example
query = "What are Allergies?"
llm_response = qa_chain.invoke(query)
print(llm_response)
print(process_llm_response(llm_response))
@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
return templates.TemplateResponse("chat.html", {"request": request})
@app.post("/get", response_class=HTMLResponse)
async def chat(msg: str = Form(...)):
input = msg
result = qa_chain({"query": input})
return result["result"]
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8080)