utils.py

from sentence_transformers import SentenceTransformer
import pinecone
import openai
import streamlit as st


openai.api_key = ""
cache_folder = "path/to/your/cache/directory"
model = SentenceTransformer('all-MiniLM-L6-v2', cache_folder=cache_folder)

pinecone.init(
    api_key="0deabe30-31e5-4385-a193-bbeff060b252",  # find at app.pinecone.io
    environment="gcp-starter"  # next to api key in console
)

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

def find_match(input):
    print(input)
    input_em = model.encode(input).tolist()
    result = index.query(input_em, top_k=1, includeMetadata=True)
    print(result)
    return result['matches'][0]['metadata']['text']
    #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=100,
    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

def limit_words(text, max_words=300):
    words = text.split()
    if len(words) > max_words:
        return " ".join(words[:max_words]) + "..."
    return text