conversation.py

from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.memory import ConversationBufferMemory
import pinecone
import os
from langchain.vectorstores import Chroma
from dotenv import load_dotenv
load_dotenv()

openai_api_key=os.getenv('OPENAI_API_KEY')

def create_conversation(query: str, chat_history: list, collection_name: str) -> tuple:
    try:

        embeddings = OpenAIEmbeddings(
            openai_api_key=openai_api_key
        )
        
        persist_directory = './db_metadata'
        
        db = Chroma(
            collection_name=collection_name,
            persist_directory=persist_directory,
            embedding_function=embeddings
        )
        
        memory = ConversationBufferMemory(
            memory_key='chat_history',
            return_messages=False
        )
        cqa = ConversationalRetrievalChain.from_llm(
            llm=ChatOpenAI(temperature=0.0,
                           openai_api_key=openai_api_key),
            chain_type='stuff',
            retriever=db.as_retriever(),
            memory=memory,
            get_chat_history=lambda h: h,
            verbose=True,
            return_source_documents=False,
        )
        result = cqa({'question': query, 'chat_history': chat_history})
        chat_history.append((query, result['answer']))
        return '', chat_history
    # except Exception as e:
    #     chat_history.append((query, e))
    #     return '', chat_history
    except pinecone.exceptions.PineconeException as pe:
        chat_history.append((query, f"Pinecone Error: {pe}"))
        return '', chat_history
    except Exception as e:
        chat_history.append((query, f"Unexpected Error: {e}"))
        return '', chat_history