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from helper import download_hugging_face_embeddings
from langchain_pinecone import PineconeVectorStore
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
from langchain.chains import RetrievalQA
from dotenv import load_dotenv
from prompt import prompt_template
import os
load_dotenv()
PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")
PINECONE_API_ENV = os.environ.get("PINECONE_API_ENV")
embeddings = download_hugging_face_embeddings()
index_name = "llm-chatbot"
# Initializing the Pinecone
docsearch = PineconeVectorStore.from_existing_index(index_name, embeddings)
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
chain_type_kwargs = {"prompt": PROMPT}
current_dir = os.getcwd()
llm = OpenAI()
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=docsearch.as_retriever(search_kwargs={"k": 2}),
return_source_documents=True,
chain_type_kwargs=chain_type_kwargs,
verbose=True,
)
def openai_call(input):
result = qa.invoke({"query": input})
return str(result["result"])
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