langchain/templates/rag-chroma-multi-modal-multi-vector/README.md

rag-chroma-multi-modal-multi-vector

Presentations (slide decks, etc) contain visual content that challenges conventional RAG.

Multi-modal LLMs unlock new ways to build apps over visual content like presentations.

This template performs multi-modal RAG using Chroma with the multi-vector retriever (see blog):

• Extracts the slides as images
• Uses GPT-4V to summarize each image
• Embeds the image summaries with a link to the original images
• Retrieves relevant image based on similarity between the image summary and the user input
• Finally pass those images to GPT-4V for answer synthesis

Storage

We will use Upstash to store the images, which offers Redis with a REST API.

Simply login here and create a database.

This will give you a REST API with:

• UPSTASH_URL
• UPSTASH_TOKEN

Set UPSTASH_URL and UPSTASH_TOKEN as environment variables to access your database.

We will use Chroma to store and index the image summaries, which will be created locally in the template directory.

Input

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

Create your vectorstore (Chroma) and populae Upstash with:

poetry install
python ingest.py

LLM

The app will retrieve images using multi-modal embeddings, and pass them to GPT-4V.

Environment Setup

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

Set UPSTASH_URL and UPSTASH_TOKEN as environment variables to access your 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-chroma-multi-modal-multi-vector

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

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

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

from rag_chroma_multi_modal_multi_vector import chain as rag_chroma_multi_modal_chain_mv

add_routes(app, rag_chroma_multi_modal_chain_mv, path="/rag-chroma-multi-modal-multi-vector")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up 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-chroma-multi-modal-multi-vector/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

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