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rag-gemini-multi-modal

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 multi-modal OpenCLIP embeddings and Google Gemini.

Input​

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

Create your vectorstore with: 

poetry install
python ingest.py

Embeddings​

This template will use OpenCLIP multi-modal embeddings.

You can select different options (see results here).

The first time you run the app, it will automatically download the multimodal embedding model.

By default, LangChain will use an embedding model with reasonably strong performance, ViT-H-14.

You can choose alternative OpenCLIPEmbeddings models in ingest.py:

vectorstore_mmembd = Chroma(
    collection_name="multi-modal-rag",
    persist_directory=str(re_vectorstore_path),
    embedding_function=OpenCLIPEmbeddings(
        model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k"
    ),
)

LLM​

The app will retrieve images using multi-modal embeddings, and pass them to Google Gemini.

Environment Setup​

Set the GOOGLE_API_KEY environment variable to access Gemini.

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-gemini-multi-modal

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

langchain app add rag-gemini-multi-modal

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

from rag_gemini_multi_modal import chain as rag_gemini_multi_modal_chain

add_routes(app, rag_gemini_multi_modal_chain, path="/rag-gemini-multi-modal")

(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-gemini-multi-modal/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

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