utils.py

from langchain_openai import OpenAIEmbeddings
from pinecone import Pinecone
import streamlit as st
from openai import OpenAI
import os
from dotenv import load_dotenv

load_dotenv()

# Initialize OpenAI client
client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))

# Initialize embeddings
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", api_key=os.getenv('OPENAI_API_KEY'))

# Initialize Pinecone
pc = Pinecone(api_key=os.getenv('PINECONE_API_KEY'))

# Check if index exists and connect to it
index_name = "aido-hybrid"
if index_name not in pc.list_indexes().names():
    print("Creating a new Pinecone index...")
    pc.create_index(
        name=index_name,
        dimension=1536,  # dimensionality of text-embedding-ada-002
        metric="cosine"
    )

# Connect to the existing Pinecone index
index = pc.Index(index_name)


def find_match(input):
    # Get embeddings for the input query
    input_em = embeddings.embed_query(input)

    # Query Pinecone
    result = index.query(vector=input_em, top_k=5, include_metadata=True)

    # Return the top 2 matches
    return result['matches'][0]['metadata']['text'] + "\n" + result['matches'][1]['metadata']['text']


def query_refiner(conversation, query):
    # Using the new ChatCompletion API instead of the deprecated Completion API
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system",
             "content": "You are a helpful assistant that refines user queries based on conversation context."},
            {"role": "user",
             "content": f"Given the following user query and conversation log, formulate a question that would be the most relevant to provide the user with an answer from a knowledge base.\n\nCONVERSATION LOG: \n{conversation}\n\nQuery: {query}\n\nRefined Query:"}
        ],
        temperature=0.7,
        max_tokens=256
    )
    return response.choices[0].message.content


def get_conversation_string():
    conversation_string = ""
    for i in range(len(st.session_state['responses']) - 1):
        conversation_string += "Human: " + st.session_state['requests'][i] + "\n"
        conversation_string += "Bot: " + st.session_state['responses'][i + 1] + "\n"
    return conversation_string