[Question]: How to save multiple index for different users and maintain uniqueness for each index

[kashiftriffort]

Question Validation

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

Question

I am developing a chatbot application using Flask, and I've successfully created an index for specific documents and implemented a process for fetching questions and answers from storage while working locally. However, I'm facing an issue when it comes to deploying this application on a server. My challenge is identifying a method to uniquely save the index for each user on the server side. This is crucial because there might be situations where multiple users are simultaneously uploading PDFs for Q&A. I need a solution that allows the chatbot to differentiate between these users and provide answers for their unique queries. How can I achieve this user-specific index storage on the server?

[dosubot[bot]]

🤖

Hello,

To uniquely save and maintain multiple indexes for different users on a server-side deployment of a chatbot application using the LlamaIndex data framework, you can create a unique index for each user. This can be done by using the user's unique identifier (like user ID) as part of the index_name parameter when initializing the RedisVectorStore.

Here is an example of how you can do this:

from llama_index.vector_stores.redis import RedisVectorStore

def create_user_index(user_id: str):
    # Create a RedisVectorStore for each user
    vector_store = RedisVectorStore(
        index_name=f"user_{user_id}_index",  # Unique index name for each user
        index_prefix="llama_index",
        index_args={"algorithm": "HNSW", "m": 16, "ef_construction": 200, "distance_metric": "cosine"},
        redis_url="redis://localhost:6379/",
        overwrite=True
    )
    return vector_store

In this example, create_user_index function takes a user_id as an argument and creates a unique RedisVectorStore for that user. The index_name is set to user_{user_id}_index, which ensures that each user has a unique index.

You can then use the add method of the RedisVectorStore to add nodes to the user's unique index, and the query method to query the user's unique index.

Please note that this approach assumes that each user's data is independent and does not need to be queried together. If you need to perform queries across multiple users' data, you would need to use a different approach, such as including the user ID in the metadata of each node and using that as a filter in your queries.

For more details, you can refer to the RedisVectorStore class in the LlamaIndex codebase here.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• [Question]: chat_engine with flask: how to deal with multiple chats at the same time
• llama_index/vector_stores/redis.py

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