# rag-chroma-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 Chroma. Given a question, relevat slides are retrieved and passed to GPT-4V for answer synthesis. ![Diagram illustrating the multi-modal LLM process with a slide deck, captioning, storage, question input, and answer synthesis with year-over-year growth percentages.](https://github.com/langchain-ai/langchain/assets/122662504/5277ef6b-d637-43c7-8dc1-9b1567470503 "Multi-modal LLM Process Diagram") ## Input Supply a slide deck as pdf in the `/docs` directory. 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: ``` poetry install python ingest.py ``` ## Storage Here is the process the template will use to create an index of the slides (see [blog](https://blog.langchain.dev/multi-modal-rag-template/)): * 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](https://python.langchain.com/docs/integrations/stores/file_system) 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 `chain.py` and `ingest.py` to switch between the two options. For Redis, the template will use [UpstashRedisByteStore](https://python.langchain.com/docs/integrations/stores/upstash_redis). We will use Upstash to store the images, which offers Redis with a REST API. Simply login [here](https://upstash.com/) 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. ## LLM The app will retrieve images based on similarity between the text input and the image summary, and pass the images 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 if you use `UpstashRedisByteStore`. ## Usage To use this package, you should first have the LangChain CLI installed: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this as the only package, you can do: ```shell 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: ```shell langchain app add rag-chroma-multi-modal-multi-vector ``` And add the following code to your `server.py` file: ```python 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. You can sign up for LangSmith [here](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](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](http://127.0.0.1:8000/rag-chroma-multi-modal-multi-vector/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-chroma-multi-modal-multi-vector") ```