hey how can I create seperate context engine in fastapi...

@kapa.ai hey how can I create seperate context engine in fastapi for different users in llamaindex.

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To create separate context engines for different users in LlamaIndex, you can define query engines with necessary filters for each user. Here is an example of how you can do this:

Plain Text

# For Jerry
jerry_query_engine = index.as_query_engine(
    filters=MetadataFilters(
        filters=[
            ExactMatchFilter(
                key="user",
                value="Jerry",
            )
        ]
    ),
    similarity_top_k=3,
)

# For Ravi
ravi_query_engine = index.as_query_engine(
    filters=MetadataFilters(
        filters=[
            ExactMatchFilter(
                key="user",
                value="Ravi",
            )
        ]
    ),
    similarity_top_k=3,
)

In the above code, index.as_query_engine is used to create a query engine for each user with a filter that matches the user's name. The ExactMatchFilter is used to filter the data based on the user's name.

Once you have the query engines, you can use them to create context chat engines for each user. Here is an example of how you can create a context chat engine:

```python
from llama_index.core.chat_engine import ContextChatEngine
from llama_index.core.llms import LLM
from llama_index.core.memory import BaseMemory
from llama_index.core.chat_engine import ChatMessage, MessageRole

Initialize the necessary components

retriever = jerry_query_engine # or ravi_query_engine
llm = LLM() # Initialize your LLM
memory = BaseMemory() # Initialize your memory
prefix_messages = [ChatMessage(role=MessageRole.USER, content="Hello!")]

kkapa.ai

Create the context chat engine

jerry_chat_engine = ContextChatEngine(
retriever=retriever,
llm=llm,
memory=memory,
prefix_messages=prefix_messages,
)

``
In the above code,

ContextChatEngine

 is used to create a chat engine that uses a retriever to retrieve a context, set the context in the system prompt, and then uses an LLM to generate a response, for a fluid chat experience.

Please note that you need to replace

LLM() and BaseMemory() with the actual initialization of your LLM and memory. The prefix_messages is a list of ChatMessage` objects that you want to start the conversation with.

You can repeat the process to create a context chat engine for each user.

For more details, you can refer to the following sources:

Define Query Engines ()
ContextChatEngine ()

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TTech explorer

@kapa.ai what I meant is multiple users may try to interact with chat engine at same time , so how can we isolate eachother so that my chatengine won't get corrupted

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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To handle multiple users interacting with the chat engine at the same time, you need to maintain a separate instance of the chat engine for each user. This can be achieved by storing the chat engines in a dictionary where the key is a unique identifier for each user (like a user ID), and the value is the chat engine instance for that user.

Here's a basic example:

Plain Text

# Initialize a dictionary to store chat engines
chat_engines = {}

# When a new user starts a conversation
user_id = "unique_user_id"  # Replace with actual unique user ID
chat_engines[user_id] = index.as_chat_engine(chat_mode="context")

# To handle a user's message
def handle_message(user_id, message):
    chat_engine = chat_engines[user_id]
    response = chat_engine.chat(message)
    return response

In this example, handle_message is a function that takes a user ID and a message, retrieves the corresponding chat engine, and uses it to generate a response.

This way, each user's conversation is isolated and won't interfere with each other.

Please note that this is a simplified example. In a real-world application, you would need to handle situations like users leaving and rejoining, timeouts, error handling, etc.

kkapa.ai

For more details on how to use the chat engine, you can refer to the following sources:

Chat Engine - Context Mode ()
Chat Engine - Condense Question Mode ()
Chat Engine - ReAct Agent Mode ()

----------------------
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TTech explorer

@kapa.ai how can I avoid deadlock of a server if multiple users try with multiple requests

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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To avoid deadlock when multiple users are making requests to your server, you can use the multiprocessing module in Python, which allows for the creation of separate processes, each with its own Python interpreter. This can help you handle multiple requests concurrently.

In the context of LlamaIndex, you can use the BaseManager python package to provide sequential access to the index using a separate server and locks. This can help handle concurrent uploads and ensure sequential inserts into the index.

Here's a basic example of what your index_server.py might look like:

Plain Text

import os
from multiprocessing import Lock
from multiprocessing.managers import BaseManager
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, Document

os.environ["OPENAI_API_KEY"] = "your key here"

index = None
lock = Lock()

def initialize_index():
    global index
    with lock:
        # same as before ...
        pass

def query_index(query_text):
    global index
    query_engine = index.as_query_engine()
    response = query_engine.query(query_text)
    return str(response)

if __name__ == "__main__":
    # init the global index
    print("initializing index...")
    initialize_index()

In this example, a Lock is used to ensure that only one process can access the index at a time, preventing race conditions. The initialize_index and query_index functions are defined to be used with the BaseManager, which will handle the concurrent requests.

Please note that this is a simplified example. In a real-world application, you would need to handle situations like error handling, timeouts, etc.

kkapa.ai

For more details on how to handle user document uploads, you can refer to the following source:

Advanced Flask - Handling User Document Uploads Part 1 ()

----------------------
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----------------------

TTech explorer

@kapa.ai but my scenario is i have only one index and multiple users will try to query the index and having a deadlock

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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To handle multiple users querying the same index without causing a deadlock, you can use asynchronous programming. In Python, this can be achieved using the asyncio library.

In the context of a FastAPI application, you can use the async keyword to define your endpoint functions. This will allow FastAPI to handle multiple requests concurrently using a single thread, without blocking the server.

Here's a basic example:

Plain Text

from fastapi import FastAPI
from llama_index.core.query_engine import QueryEngine

app = FastAPI()
query_engine = QueryEngine()  # Initialize your query engine

@app.get("/query")
async def query_index(query: str):
    response = query_engine.query(query)
    return response

In this example, the query_index function is defined with the async keyword, which means it's an asynchronous function. When this function is called, it will return a coroutine that FastAPI can run concurrently with other coroutines, allowing it to handle multiple requests at the same time.

Please note that this is a simplified example. In a real-world application, you would need to handle situations like error handling, timeouts, etc.

For more details on how to use FastAPI with asynchronous programming, you can refer to the following source:

FastAPI - Async ()

----------------------
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hey how can I create seperate context engine in fastapi for different users in llamaindex.

Initialize the necessary components

Create the context chat engine