[Question]: "ChatMemoryBuffer" Stores Data in Redis but Fails to Retrieve It with "VectorStoreIndex" Query Engine

[mohilmakwana31]

Question Validation

• [x] I have searched both the documentation and discord for an answer.

Question

I am using the ChatMemoryBuffer with the SimpleChatEngine, and it works as expected. The conversation is stored in Redis cache and can be retrieved correctly.

Sample code :

chat_store = RedisChatStore(redis_url="redis://localhost:6379", ttl=1000)

chat_memory = ChatMemoryBuffer.from_defaults(
    token_limit=30000,
    chat_store=chat_store,
    chat_store_key="roy",
)

chat_engine = SimpleChatEngine(
    memory=memory, llm=Settings.llm, prefix_messages=[]
)
response = chat_engine.chat("Hello.")

In contrast, when using the ChatMemoryBuffer with the VectorStoreIndex query engine, the data is stored in Redis cache but is not retrieved properly.

Here is the code for the VectorStoreIndex setup:

from pinecone import Pinecone
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.pinecone import PineconeVectorStore

pc = Pinecone(api_key=<api_key>)
pinecone_index = pc.Index(<index_name>)

chat_store = RedisChatStore(redis_url="redis://localhost:6379", ttl=1000)

vector_store = PineconeVectorStore(
    pinecone_index=pinecone_index
)

chat_memory = ChatMemoryBuffer.from_defaults(
    token_limit=30000,
    chat_store=chat_store,
    chat_store_key="roy",
)

query_engine = VectorStoreIndex.from_vector_store(vector_store).as_chat_engine(
    similarity_top_k=5,chat_memory = chat_memory
)

My first query :

response = query_engine .query('My name is Roy')
response

Output : Response(response='Hello Roy! How can I assist you today?', source_nodes=[], metadata=None)

Second query :

response = query_engine .query("What's my name?")
print(response)

Output : I am an expert Q&A system.

[dosubot[bot]]

Hey @mohilmakwana31! 👋 I'm here to help you with any bugs, questions, or contributions you have in mind. I'm excited to assist you!

The issue you're encountering with the ChatMemoryBuffer failing to retrieve data properly when using the VectorStoreIndex query engine is likely due to the way embeddings are handled. The embeddings are generated at query time but are not saved, which leads to retrieval issues.

This problem does not occur with the SimpleChatEngine because it likely does not rely on the same mechanism for embedding storage and retrieval.

To address this, you need to ensure that the embeddings are properly stored and retrieved. Here are some steps and configurations you can try:

1. Ensure Embeddings are Stored: Make sure that the embeddings are being stored in the vector store when the index is created. This might involve checking the configuration of your VectorStoreIndex and PineconeVectorStore.

2. Configure Retriever Parameters: Use the retriever_kwargs dictionary within the VectorMemory class to set specific parameters required for proper retrieval. Here is an example:

   class VectorMemory(BaseMemory):
       ...
       retriever_kwargs: Dict[str, Any] = Field(default_factory=dict)
       ...
       def get(
           self, input: Optional[str] = None, initial_token_count: int = 0, **kwargs: Any
       ) -> List[ChatMessage]:
           """Get chat history."""
           if input is None:
               return []
   
           # retrieve from index
           retriever = self.vector_index.as_retriever(**self.retriever_kwargs)
           nodes = retriever.retrieve(input or "")
   
           # retrieve underlying messages
           return [
               ChatMessage.model_validate(sub_dict)
               for node in nodes
               for sub_dict in node.metadata["sub_dicts"]
           ]

3. Direct Query to Vector Store: If the above steps do not resolve the issue, you might need to query the vector store directly to retrieve the necessary information. Here is an example of how you might do this:

   # Query the vector store for a person's information
   query_embedding = embed_model.embed_text("information about person")
   results = index.query(query_embedding, top_k=1)
   
   # The results are a list of tuples, where each tuple contains an embedding and its metadata
   for embedding, metadata in results:
       print(metadata)

These steps should help ensure that the chat memory is properly retrieved when using the VectorStoreIndex query engine ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[mohilmakwana31]

@dosu But issue is when I query with query engine first my redis cache should be checked whether the question is already asked by user or not. If the question is asked it should use the existing answer otherwise it should query vector database and save new question and answer in redis cache and return it to user.

[dosubot[bot]]

To configure the query engine to first check the Redis cache for an existing answer before querying the vector database and then save the new question and answer in Redis if not found, you can follow these steps:

1. Check Redis Cache: First, attempt to retrieve the answer from Redis.
2. Query Vector Database: If the answer is not found in Redis, query the vector database.
3. Save to Redis: Save the new question and answer in Redis for future queries.

Here is a Python code snippet demonstrating this process, incorporating the RedisVectorStore from LlamaIndex:

from redis import Redis
import json
from llama_index.legacy.vector_stores.redis import RedisVectorStore

# Create a Redis client connection
redis_client = Redis.from_url("redis://localhost:6379")

# Initialize the RedisVectorStore
vector_store = RedisVectorStore(
    index_name="my_index",
    index_prefix="llama_index",
    index_args={"algorithm": "HNSW", "m": 16, "ef_construction": 200, "distance_metric": "cosine"},
    redis_url="redis://localhost:6379/",
    overwrite=True
)

def get_answer_from_cache(question):
    cached_answer = redis_client.get(question)
    if cached_answer:
        return json.loads(cached_answer)
    return None

def save_answer_to_cache(question, answer):
    redis_client.set(question, json.dumps(answer))

def query_vector_database(question):
    # Assuming `query_engine` is already initialized as shown in the document
    response = vector_store.query(VectorStoreQuery(query_embedding=question))
    return str(response)

def get_answer(question):
    # Step 1: Check Redis cache
    answer = get_answer_from_cache(question)
    if answer:
        return answer

    # Step 2: Query vector database
    answer = query_vector_database(question)

    # Step 3: Save the new question and answer in Redis
    save_answer_to_cache(question, answer)

    return answer

# Example usage
question = "What did the author learn?"
answer = get_answer(question)
print(answer)

In this code:

• get_answer_from_cache checks if the answer to the question is already in Redis.
• save_answer_to_cache saves the question and answer pair in Redis.
• query_vector_database queries the vector database using the RedisVectorStore from LlamaIndex.
• get_answer orchestrates the process by first checking the cache, then querying the vector database if necessary, and finally saving the result back to Redis ^[1][2].

To continue talking to Dosu, mention @dosu.

[mohilmakwana31]

@dosu Could you explain how I can make this multi-threaded? Specifically, how can I maintain separate chat threads for each user?

[dosubot[bot]]

To make the chat engine multi-threaded and maintain separate chat threads for each user, you can utilize Python's threading module. Here's an example of how you can modify your setup to handle separate chat threads for each user:

1. Modify the SimpleChatEngine Class: Add threading to handle separate chat threads for each user.

import threading
from llama_index.core.chat_engine.simple import SimpleChatEngine
from llama_index.core.memory.chat_memory_buffer import ChatMemoryBuffer
from llama_index.legacy.storage.chat_store.redis_chat_store import RedisChatStore

class MultiThreadedChatEngine(SimpleChatEngine):
    def __init__(self, llm, memory, prefix_messages, callback_manager=None):
        super().__init__(llm, memory, prefix_messages, callback_manager)
        self.user_threads = {}

    def start_user_thread(self, user_id, message, chat_history=None):
        if user_id not in self.user_threads:
            thread = threading.Thread(target=self.chat, args=(message, chat_history))
            self.user_threads[user_id] = thread
            thread.start()
        else:
            # Handle the case where the user already has an active thread
            pass

    @trace_method("chat")
    def chat(self, message, chat_history=None):
        if chat_history is not None:
            self._memory.set(chat_history)
        self._memory.put(ChatMessage(content=message, role="user"))
        initial_token_count = len(
            self._memory.tokenizer_fn(
                " ".join([(m.content or "") for m in self._prefix_messages])
            )
        )
        all_messages = self._prefix_messages + self._memory.get(
            initial_token_count=initial_token_count
        )

        chat_response = self._llm.chat(all_messages)
        ai_message = chat_response.message
        self._memory.put(ai_message)

        return AgentChatResponse(response=str(chat_response.message.content))

# Example usage
chat_store = RedisChatStore(redis_url="redis://localhost:6379", ttl=1000)
chat_memory = ChatMemoryBuffer.from_defaults(token_limit=30000, chat_store=chat_store, chat_store_key="roy")

chat_engine = MultiThreadedChatEngine(llm=Settings.llm, memory=chat_memory, prefix_messages=[])
chat_engine.start_user_thread(user_id="user1", message="Hello!")

2. Integrate with VectorStoreIndex: Ensure that the VectorStoreIndex query engine is also multi-threaded.

from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.pinecone import PineconeVectorStore
from pinecone import Pinecone

# Initialize Pinecone and Redis
pc = Pinecone(api_key=<api_key>)
pinecone_index = pc.Index(<index_name>)
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)

# Initialize Redis chat store and memory buffer
chat_store = RedisChatStore(redis_url="redis://localhost:6379", ttl=1000)
chat_memory = ChatMemoryBuffer.from_defaults(token_limit=30000, chat_store=chat_store, chat_store_key="roy")

# Create the query engine
query_engine = VectorStoreIndex.from_vector_store(vector_store).as_chat_engine(
    similarity_top_k=5, chat_memory=chat_memory
)

# Example usage with threading
def handle_user_query(user_id, query):
    response = query_engine.query(query)
    print(f"User {user_id}: {response}")

user_threads = {}
user_id = "user1"
query = "What's my name?"

if user_id not in user_threads:
    thread = threading.Thread(target=handle_user_query, args=(user_id, query))
    user_threads[user_id] = thread
    thread.start()
else:
    # Handle the case where the user already has an active thread
    pass

In this setup:

• The MultiThreadedChatEngine class extends SimpleChatEngine to handle separate chat threads for each user.
• The start_user_thread method starts a new thread for each user if one does not already exist.
• The handle_user_query function is used to handle user queries in separate threads when using the VectorStoreIndex query engine.

This approach ensures that each user's chat is handled in a separate thread, maintaining separate chat threads for each user ^[1].

To continue talking to Dosu, mention @dosu.

[logan-markewich]

@mohilmakwana31 the query engine/vector store does not use chat history at all. You need to use a chat engine or agent with the index//vector store

[mohilmakwana31]

@logan-markewich , thank you for the information. If possible, could you please share any documentation or reference materials that might assist further?