rag-redis-multi-modal-multi-vector

Multi-modal LLMs enable visual assistants that can perform question-answering about images.

This template create a visual assistant for slide decks, which often contain visuals such as graphs or figures.

It uses GPT-4V to create image summaries for each slide, embeds the summaries, and stores them in Redis.

Given a question, relevant slides are retrieved and passed to GPT-4V for answer synthesis.

Input

Supply a slide deck as PDF in the /docs directory.

By default, this template has a slide deck about recent earnings from NVIDIA.

Example questions to ask can be:

1/ how much can H100 TensorRT improve LLama2 inference performance?
2/ what is the % change in GPU accelerated applications from 2020 to 2023?

To create an index of the slide deck, run:

poetry install
poetry shell
python ingest.py

Storage

Here is the process the template will use to create an index of the slides (see blog):

• Extract the slides as a collection of images
• Use GPT-4V to summarize each image
• Embed the image summaries using text embeddings with a link to the original images
• Retrieve relevant image based on similarity between the image summary and the user input question
• Pass those images to GPT-4V for answer synthesis

Redis

This template uses Redis to power the MultiVectorRetriever including:

• Redis as the VectorStore (to store + index image summary embeddings)
• Redis as the ByteStore (to store images)

Make sure to deploy a Redis instance either in the cloud (free) or locally with docker.

This will give you an accessible Redis endpoint that you can use as a URL. If deploying locally, simply use redis://localhost:6379.

LLM

The app will retrieve images based on similarity between the text input and the image summary (text), and pass the images to GPT-4V for answer synthesis.

Environment Setup

Set the OPENAI_API_KEY environment variable to access the OpenAI GPT-4V.

Set REDIS_URL environment variable to access your Redis database.

Usage

To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package rag-redis-multi-modal-multi-vector

If you want to add this to an existing project, you can just run:

langchain app add rag-redis-multi-modal-multi-vector

And add the following code to your server.py file:

from rag_redis_multi_modal_multi_vector import chain as rag_redis_multi_modal_chain_mv

add_routes(app, rag_redis_multi_modal_chain_mv, path="/rag-redis-multi-modal-multi-vector")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/rag-redis-multi-modal-multi-vector/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-redis-multi-modal-multi-vector")