[Feature Request]: Please add memory to index.as_query_engine()

[USTAADCOM]

Feature Description

I try different RAG techniques but VectorStoreIndex with query engine provide very good results. But now there is a problem which i am facing how we can pass memory with query engine. If someone got any solution please share his/her experience.

Reason

No response

Value of Feature

No response

[dosubot[bot]]

To add memory to index.as_query_engine() in VectorStoreIndex, you can customize the storage context with a specific vector store. Here is an example using the DeepLakeVectorStore:

from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    StorageContext,
)
from llama_index.vector_stores.deeplake import DeepLakeVectorStore

# Construct vector store and customize storage context
storage_context = StorageContext.from_defaults(
    vector_store=DeepLakeVectorStore(dataset_path="<dataset_path>")
)

# Load documents and build index
documents = SimpleDirectoryReader("../paul_graham_essay/data").load_data()
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

# Query index
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")

This example demonstrates how to integrate a custom vector store into the VectorStoreIndex and use it for querying ^[1].

Additionally, the llama_index.core.memory module includes several memory components such as ChatMemoryBuffer, ChatSummaryMemoryBuffer, SimpleComposableMemory, and VectorMemory, which can be used to integrate memory functionalities within the LlamaIndex framework ^[2][3].

To continue talking to Dosu, mention @dosu.

[logan-markewich]

@USTAADCOM This is supported in many ways

We have many chat engines, as well as agents https://docs.llamaindex.ai/en/stable/module_guides/deploying/chat_engines/usage_pattern/#available-chat-modes

I would recommend something like chat_engine = index.as_chat_engine(chat_mode="condense_plus_context", system_prompt="You are a helpful assistant that answers questions about XZY.")

But do check out the docs for this