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from sentence_transformers import SentenceTransformer
import pinecone
import openai
import streamlit as st
import os
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv("/home/oem/Desktop/TRUEINFO LABS/Quotes_chat/.env")
logo_image = "https://i.ibb.co/vHJZL0y/insightly.png"
st.image(logo_image, width=200)
# Access OpenAI and Pinecone API keys from environment variables
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
openai.api_key = OPENAI_API_KEY
model = SentenceTransformer('all-MiniLM-L6-v2')
pinecone.init(api_key = PINECONE_API_KEY, environment='asia-southeast1-gcp-free')
index = pinecone.Index('langchain-chatbot')
def find_match(input):
input_em = model.encode(input).tolist()
result = index.query(input_em, top_k=2, includeMetadata=True)
return result['matches'][0]['metadata']['text']+"\n"+result['matches'][1]['metadata']['text']
def query_refiner(conversation, query):
response = openai.Completion.create(
model="text-davinci-003",
prompt=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,
top_p=1,
frequency_penalty=0,
presence_penalty=0
)
return response['choices'][0]['text']
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 |