# rag-multi-modal-local Visual search is a famililar application to many with iPhones or Android devices. It allows user to serch photos using natural language. With the release of open source, multi-modal LLMs it's possible to build this kind of application for yourself for your own private photo collection. This template demonstrates how to perform private visual search and question-answering over a collection of your photos. It uses OpenCLIP embeddings to embed all of the photos and stores them in Chroma. Given a question, relevat photos are retrieved and passed to an open source multi-modal LLM of your choice for answer synthesis. ![mm-local](https://github.com/langchain-ai/langchain/assets/122662504/da543b21-052c-4c43-939e-d4f882a45d75) ## Input Supply a set of photos in the `/docs` directory. By default, this template has a toy collection of 3 food pictures. Example questions to ask can be: ``` What kind of soft serve did I have? ``` In practice, a larger corpus of images can be tested. To create an index of the images, run: ``` poetry install python ingest.py ``` ## Storage This template will use [OpenCLIP](https://github.com/mlfoundations/open_clip) multi-modal embeddings to embed the images. You can select different embedding model options (see results [here](https://github.com/mlfoundations/open_clip/blob/main/docs/openclip_results.csv)). The first time you run the app, it will automatically download the multimodal embedding model. By default, LangChain will use an embedding model with moderate performance but lower memory requirments, `ViT-H-14`. You can choose alternative `OpenCLIPEmbeddings` models in `rag_chroma_multi_modal/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 This template will use [Ollama](https://python.langchain.com/docs/integrations/chat/ollama#multi-modal). Download the latest version of Ollama: https://ollama.ai/ Pull the an open source multi-modal LLM: e.g., https://ollama.ai/library/bakllava ``` ollama pull bakllava ``` The app is by default configured for `bakllava`. But you can change this in `chain.py` and `ingest.py` for different downloaded models. ## 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 ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-chroma-multi-modal ``` And add the following code to your `server.py` file: ```python from rag_chroma_multi_modal import chain as rag_chroma_multi_modal_chain add_routes(app, rag_chroma_multi_modal_chain, path="/rag-chroma-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](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/playground](http://127.0.0.1:8000/rag-chroma-multi-modal/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-chroma-multi-modal") ```