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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"]) | |