Delete rag.py

rag.py

@@ -1,83 +0,0 @@
-from langchain import PromptTemplate
-from langchain.llms import CTransformers
-from langchain.chains import RetrievalQA
-from langchain.embeddings import SentenceTransformerEmbeddings
-from fastapi import FastAPI, Request, Form, Response
-from fastapi.responses import HTMLResponse
-from fastapi.templating import Jinja2Templates
-from fastapi.staticfiles import StaticFiles
-from fastapi.encoders import jsonable_encoder
-from qdrant_client import QdrantClient
-from langchain.vectorstores import Qdrant
-import os
-import json
-
-app = FastAPI()
-templates = Jinja2Templates(directory="templates")
-app.mount("/static", StaticFiles(directory="static"), name="static")
-
-local_llm = "joshnader/meditron-7b-Q4_K_M-GGUF"
-
-config = {
-'max_new_tokens': 512,
-'context_length': 2048,
-'repetition_penalty': 1.1,
-'temperature': 0.1,
-'top_k': 50,
-'top_p': 0.9,
-'stream': True,
-'threads': int(os.cpu_count() / 4)
-}
-
-
-llm = CTransformers(
-    model=local_llm,
-    model_type="llama",
-    **config
-)
-
-
-
-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="NeuML/pubmedbert-base-embeddings")
-
-url = "http://localhost:6333"
-
-client = QdrantClient(
-    url=url, prefer_grpc=False
-)
-
-db = Qdrant(client=client, embeddings=embeddings, collection_name="vector_db")
-
-prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question'])
-
-retriever = db.as_retriever(search_kwargs={"k":1})
-
-@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