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 Chroma.
Given a question, relevat slides are retrieved and passed to GPT-4V for answer synthesis.
Supply a slide deck as pdf in the
By default, this template has a slide deck about Q3 earnings from DataDog, a public techologyy company.
Example questions to ask can be:
How many customers does Datadog have?
What is Datadog platform % Y/Y growth in FY20, FY21, and FY22?
To create an index of the slide deck, run:
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
By default, this will use LocalFileStore to store images and Chroma to store summaries.
For production, it may be desirable to use a remote option such as Redis.
You can set the
local_file_store flag in
ingest.py to switch between the two options.
For Redis, the template will use UpstashRedisByteStore.
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_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.
The app will retrieve images based on similarity between the text input and the image summary, and pass the images to GPT-4V.
OPENAI_API_KEY environment variable to access the OpenAI GPT-4V.
UPSTASH_TOKEN as environment variables to access your database if you use
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
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_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:
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")