chat.py

from time import time, sleep
import datetime
# import dotenv
import os
import openai
import json
import pinecone
from uuid import uuid4
from helper import open_file, save_file
import re
from langchain.memory import VectorStoreRetrieverMemory

## Read the environment variables
# dotenv.load_dotenv('.env')
openai.api_key = os.getenv('OPENAI_API_KEY')
embedding_model = os.getenv('EMBEDDING_ENGINE')
convo_length = int(os.getenv('CONVO_LENGTH_TO_FETCH'))
llm_model = os.getenv('LLM_MODEL')

pinecone_api_key = os.getenv('PINECONE_API_KEY')
pinecone_env = os.getenv('PINECONE_REGION')
pinecone_index = os.getenv('PINECONE_INDEX')
pinecone.init(
    api_key=pinecone_api_key,  
    environment=pinecone_env 
)
vector_db = pinecone.Index(pinecone_index)

def timestamp_to_datetime(unix_time):
    return datetime.datetime.fromtimestamp(unix_time).strftime("%A, %B %d, %Y at %I:%M%p %Z")


def perform_embedding(content):
    content = content.encode(encoding='ASCII',errors='ignore').decode()
    response = openai.Embedding.create(model=embedding_model, input=content)
    vector = response['data'][0]['embedding']
    return vector

def load_conversation(results):
    result = list()
    for m in results['matches']:
        result.append({'time1': m['metadata']['timestring'], 'text': m['metadata']['text']})
    ordered = sorted(result, key=lambda d: d['time1'], reverse = False)
    messages = [i['text'] for i in ordered]
    message_block = '\n'.join(messages).strip()
    return message_block
 

def call_gpt(prompt):
    max_retry = 5
    retry = 0
    prompt = prompt.encode(encoding='ASCII',errors='ignore').decode()
    while True:
        try:
            response = openai.ChatCompletion.create(
                    model=llm_model,
                    temperature=0.9,
               messages=[
                {"role": "user", "content": prompt}
              ]
            )
            
            text = response.choices[0].message.content
            text = re.sub('[\r\n]+', '\n', text)
            text = re.sub('[\t ]+', ' ', text)
            filename = '%s_gpt3.txt' % time()
            if not os.path.exists('gpt3_logs'):
                os.makedirs('gpt3_logs')
            save_file('gpt3_logs/%s' % filename, prompt + '\n\n==========\n\n' + text)
            response.choices[0].message.content = text
            return response
        except Exception as oops:
            retry += 1
            if retry >= max_retry:
                return "GPT3 error: %s" % oops
            print('Error communicating with OpenAI:', oops)
            sleep(1)


def start_game(game_id, user_id, user_input):
    payload = list()

    # Get user input, save it, vectorize it and save to pinecone
    timestamp = time()
    timestring = timestamp_to_datetime(timestamp)   
    unique_id = str(uuid4())
    vector = perform_embedding(user_input)
    metadata = {'speaker': 'USER', 'user_id': user_id, 'game_id': game_id, 'timestring': timestring, 'text': user_input}
    payload.append((unique_id, vector, metadata))
       
    
    # Search for relevant messages and return a response
    results=vector_db.query(vector=vector, top_k=convo_length, include_metadata=True, 
                            filter={
                                    "$and": [{ "user_id": { "$eq": user_id } }, { "game_id": { "$eq": game_id } }]
                             }
                          )
    conversation = load_conversation(results)
                                           

    # Populate prompt
    prompt_text = open_file(f"prompt_{game_id}_{user_id}.txt")
    prompt = open_file('prompt_response.txt').replace('<<PROMPT_VALUE>>', prompt_text).replace('<<CONVERSATION>>', conversation).replace('<<USER_MSG>>', user_input).replace('<<USER_VAL>>', user_id)

    # Generate response, vectorize
    llm_output_msg = call_gpt(prompt)
    llm_output = llm_output_msg.choices[0].message.content
    timestamp_op = time()
    timestring_op = timestamp_to_datetime(timestamp) 
    vector_op = perform_embedding(llm_output)
    unique_id_op = str(uuid4)
    metadata_op = {'speaker': 'BOT', 'user_id': user_id, 'game_id': game_id, 'timestring': timestring, 'text': llm_output}
    payload.append((unique_id_op, vector_op, metadata_op))

    # Upsert into the vector database
    vector_db.upsert(payload)
       
    return(llm_output)
  

def populate_prompt(game_id, splits):
    prompt_text = list()
    idlist = []
    for j in range(int(splits)):
       idlist.append(game_id + "-" + str(j)) 

    results=vector_db.fetch(ids=idlist)  
    for ids in idlist:
        prompt_text.append(results['vectors'][ids]["metadata"]["text"])

    whole_prompt = ' '.join(prompt_text).strip()
    return whole_prompt
    

def initialize_game(game_id, user_id, user_input):
    game_details = get_game_details(game_id)
    whole_prompt = populate_prompt(game_id, game_details["splits"])
    whole_prompt = whole_prompt.replace("<<USER_INPUT_MSG>>", user_input)
       
    llm_prompt_op = call_gpt(whole_prompt)
    #print(llm_prompt_op.choices[0]["message"]["content"])
    fname="prompt_" + game_id + "_" + user_id + ".txt"
    save_file(fname, llm_prompt_op.choices[0]["message"]["content"])
    return llm_prompt_op.choices[0]["message"]["content"]

if __name__ == '__main__':
    print("main")