app.py

import os
import json
from langchain_groq import ChatGroq
from langchain_core.prompts import PromptTemplate
from qdrant_client import QdrantClient
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import Qdrant
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.encoders import jsonable_encoder
from fastapi.templating import Jinja2Templates
from fastapi import FastAPI, Request, Form, Response
from langchain_community.embeddings import SentenceTransformerEmbeddings

os.environ["TRANSFORMERS_FORCE_CPU"] = "true"

app = FastAPI()
templates = Jinja2Templates(directory="templates")

config = {
    'max_new_tokens': 1024,
    'context_length': 2048,
    'repetition_penalty': 1.1,
    'temperature': 0.1,
    'top_k': 50,
    'top_p': 0.9,
    'stream': True,
    'threads': int(os.cpu_count() / 2)
}

api_key = os.environ.get("API_KEY")

llm = ChatGroq(
    model="mixtral-8x7b-32768",
    api_key=api_key,
)

print("LLM Initialized....")

prompt_template = """Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Context: {context}
Question: {question}
Only return the helpful answer below and nothing else.
Helpful answer:
"""

embeddings = SentenceTransformerEmbeddings(model_name="BAAI/bge-large-en")

url = os.environ.get("INSTANCE_URL")

client = QdrantClient(
    url=url, prefer_grpc=False
)

db = Qdrant(client=client, embeddings=embeddings, collection_name="patent_database")

prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question'])

retriever = db.as_retriever(search_kwargs={"k": 3})

@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})

@app.post("/get_response")
async def get_response(query: str = Form(...)):
    chain_type_kwargs = {"prompt": prompt}
    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True, chain_type_kwargs=chain_type_kwargs, verbose=True)
    response = qa(query)
    print(response)
    answer = response['result']
    source_document = response['source_documents'][0].page_content
    doc = response['source_documents'][0].metadata['source']
    response_data = jsonable_encoder(json.dumps({"answer": answer, "source_document": source_document, "doc": doc}))

    res = Response(response_data)
    return res