[Question]: How to handle multipe users, if they send messages to LLM concurrently. about llama_index HOT 14 OPEN

To address the issue of handling concurrent messages from multiple users in your Slack bot integrated with Azure OpenAI (GPT-3.5), you'll need to ensure that each user's request is processed asynchronously and independently. This involves creating a unique session or context for each user's request to prevent the mixing of responses. Here's a more focused approach:

1. Asynchronous Request Handling: Modify your Slack bot to handle incoming messages asynchronously. This can be achieved by setting up an asynchronous web server, such as one using aiohttp, which listens for Slack events and processes each event in a separate asynchronous task.

2. Unique Session for Each User: For every message received from a user, generate a unique session or context. This is essential for ensuring that the responses are user-specific and do not get mixed up between different users.

3. Implementing asyncio for Concurrent Processing: Use asyncio to manage the concurrent processing of user messages. Create an asynchronous function dedicated to handling the logic of processing each user's message, including the interaction with the RAG system and the asChatEngine method.

Here's a simplified code structure to illustrate the concept:

import asyncio
from aiohttp import web
# Assuming a function to handle Slack events exists
from your_slack_bot import handle_slack_event

async def handle_user_request(request):
    user_message = request.get('text')
    user_id = request.get('user')
    # Process the message using a unique session/context
    response = await process_user_message(user_message, user_id)
    # Send the response back to the user in Slack
    await send_response_to_slack(user_id, response)

async def main():
    app = web.Application()
    app.router.add_post('/slack/events', handle_slack_event)
    runner = web.AppRunner(app)
    await runner.setup()
    site = web.TCPSite(runner, 'localhost', 8080)
    await site.start()
    while True:
        await asyncio.sleep(3600)

if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())

In this setup, process_user_message is where you'd integrate your specific logic for interacting with the RAG system and asChatEngine method, ensuring each user's interaction is handled in its own context. This approach will help you manage concurrent messages effectively, with each user receiving a response that's relevant to their query.

To continue the conversation, mention @dosu.

from llama_index.

To effectively manage multiple concurrent user messages in your Slack bot, you should maintain separate instances of ChatMemoryBuffer and ChatEngine for each user. This can be done by mapping user IDs to their respective instances. When a message is received, the bot should check if there's an existing chat context for the user and use it to process the message. If not, it should create a new one. This ensures that each user's conversation is handled independently, preventing the mixing of responses.

Here's how you can implement this in your message handler:

# Dictionary to store ChatMemoryBuffer and ChatEngine instances for each user
user_chat_contexts = {}

@app.message(".*")
def message_handler(message, say, logger):    
    user_id = message['user']  # Extract user ID from the message
    question = message['text']

    # Check if the user already has a chat context, if not, create one
    if user_id not in user_chat_contexts:
        memory = ChatMemoryBuffer.from_defaults(token_limit=3900)
        chat_engine = index.as_chat_engine(
            chat_mode="context",
            memory=memory,
            system_prompt=cfg.prompt
        )
        user_chat_contexts[user_id] = {'memory': memory, 'chat_engine': chat_engine}
    
    chat_context = user_chat_contexts[user_id]
    response = chat_context['chat_engine'].chat(str(question)).response
    say(response)

This code snippet ensures that each user's chat context is stored and retrieved using their unique user ID, allowing for concurrent conversations without overlap.

To continue the conversation, mention @dosu.

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The solution involving separate instances of ChatMemoryBuffer and ChatEngine for each user, while effective for ensuring individualized responses, does raise scalability concerns as the number of concurrent users increases. This is due to the linear growth in memory requirements and computational complexity with each additional user. The CondensePlusContextChatEngine approach, which enriches chat interactions with context and condenses chat history, further compounds these challenges by increasing the computational steps and memory usage required for each interaction.

To mitigate these scalability issues, consider the following strategies:

• Leverage cloud services with dynamic resource allocation to efficiently manage concurrency and scalability.
• Implement caching for frequently accessed data to reduce computational demands.
• Explore sharing components across users, like context retrieval services, to minimize the resource footprint per user.

Additionally, the ChatSummaryMemoryBuffer offers a promising approach to managing chat history scalability by summarizing older messages to maintain a concise history within a token limit. This method helps in reducing the memory footprint and computational load per user, making it a viable strategy for enhancing scalability and efficiency in handling concurrent user messages. Integrating or complementing the CondensePlusContextChatEngine with this buffer could significantly improve the scalability of your Slack bot.

To continue the conversation, mention @dosu.

from llama_index.

Azure OpenAI(GPT3.5)

from llama_index.

I've integrated it with slack,where the slack is sending message to Bot, If 3 users send messages to Bot It is giving the single response to all the users..

from llama_index.

Hello @Varshan2003! 👋 I'm Dosu, a bot here to lend a hand with bugs, answer questions, and guide you towards becoming an amazing contributor to our community. While we're waiting for a human maintainer to join in, I'm here to assist you! I've noticed the issue you're experiencing with handling multiple users sending messages concurrently. I'll be taking a closer look at this and get back to you shortly with some insights.

from llama_index.

What LLM are you using?

from llama_index.

How are you using it? Azure and OpenAI both handle concurrent requests fine for me

from llama_index.

@Varshan2003 but like, concretely with code, how are you doing it?

from llama_index.

I've followed the RAG system,creating index and chating with Index using asChatEngine method..

from llama_index.

@Varshan2003 are you able to share snippets or a template of your code to show how you are handling user requests now?

from llama_index.

from llama_index.core import SimpleDirectoryReader
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SimpleNodeParser
from llama_index.core import ServiceContext,StorageContext
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.core.settings import Settings
from llama_index.core import load_index_from_storage
import os
from llama_index.core.prompts import PromptTemplate
from dotenv import load_dotenv
from llama_index.core.memory import ChatMemoryBuffer
from slack_bolt import App
from slack_bolt.adapter.socket_mode import SocketModeHandler
load_dotenv()  
import csv
import json

api_key = os.getenv("azure_openai_api_key")
azure_endpoint = os.getenv("azure_endpoint")
api_version = os.getenv("api_version")
app = App(token=os.getenv("SLACK_BOT_TOKEN"))

PERSIS_DIR = "./db_fin"

try:
    llm = AzureOpenAI(
        model="gpt-35-turbo",
        deployment_name="gpt-35-turbo-default",
        api_key=api_key,
        azure_endpoint=azure_endpoint,
        api_version=api_version
    )
    Settings.llm = llm
    print("initialized the llm")
except Exception as e:
    print("Error initializing llm:", e)

try:
    embed_model = AzureOpenAIEmbedding(
        model="text-embedding-ada-002",
        deployment_name="text-embedding-ada-002",
        api_key=api_key,
        azure_endpoint=azure_endpoint,
        api_version=api_version
    )
    Settings.embed_model = embed_model
    print("initialized the embed-model")
except Exception as e:
    print("Error initializing embed_model:", e)



def create_index():
    try:
        if not os.path.exists(PERSIS_DIR):
            documents = SimpleDirectoryReader("data").load_data()
            index = VectorStoreIndex.from_documents(documents)
            parser = SimpleNodeParser()
            nodes = parser.get_nodes_from_documents(documents)
            # Initialize service context
            service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)
            storage_context = StorageContext.from_defaults()
            index = VectorStoreIndex(nodes = nodes, service_context=service_context, storage_context=storage_context)
            # Persist the index to directory
            index.storage_context.persist(persist_dir=PERSIS_DIR)
        else:
            # Load the index from directory
            service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)
            storage_context = StorageContext.from_defaults(persist_dir=PERSIS_DIR)
            index = load_index_from_storage(service_context=service_context, storage_context=storage_context)
        return index
    except Exception as e:
        print("Error creating index:", e)
        return None
    



index = create_index() 

memory = ChatMemoryBuffer.from_defaults(token_limit=3900)
chat_engine = index.as_chat_engine(
    chat_mode="context",
    memory=memory,
    system_prompt=cfg.prompt
)

# Message handler for Slack
@app.message(".*")
def message_handler(message, say, logger):    
    print(message)
    question = message['text']
    response = chat_engine.chat(str(question)).response
    say(response)


# Start your app
if __name__ == "__main__":
    SocketModeHandler(app, os.environ["SLACK_APP_TOKEN"]).start()

from llama_index.

@dosu can you look into above?

from llama_index.

@dosu is the above solution scalable?

from llama_index.