# rag-multi-modal-mv-local Visual search is a famililar application to many with iPhones or Android devices. It allows user to search 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 an open source multi-modal LLM of your choice to create image summaries for each photos, embeds the summaries, and stores them in Chroma. Given a question, relevat photos are retrieved and passed to the multi-modal LLM for answer synthesis. ![Diagram illustrating the visual search process with food pictures, captioning, a database, a question input, and the synthesis of an answer using a multi-modal LLM.](https://github.com/langchain-ai/langchain/assets/122662504/cd9b3d82-9b06-4a39-8490-7482466baf43 "Visual Search Process Diagram") ## Input Supply a set of photos in the `/docs` directory. By default, this template has a toy collection of 3 food pictures. The app will look up and summarize photos based upon provided keywords or questions: ``` What kind of ice cream 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 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/)): * Given a set of images * It uses a local multi-modal LLM ([bakllava](https://ollama.ai/library/bakllava)) to summarize each image * Embeds the image summaries with a link to the original images * Given a user question, it will relevant image(s) based on similarity between the image summary and user input (using Ollama embeddings) * It will pass those images to bakllava 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. ## LLM and Embedding Models We will use [Ollama](https://python.langchain.com/docs/integrations/chat/ollama#multi-modal) for generating image summaries, embeddings, and the final image QA. Download the latest version of Ollama: https://ollama.ai/ Pull an open source multi-modal LLM: e.g., https://ollama.ai/library/bakllava Pull an open source embedding model: e.g., https://ollama.ai/library/llama2:7b ``` ollama pull bakllava ollama pull llama2:7b ``` The app is by default configured for `bakllava`. But you can change this in `chain.py` and `ingest.py` for different downloaded models. The app will retrieve images based on similarity between the text input and the image summary, and pass the images to `bakllava`. ## 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-multi-modal-mv-local ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-multi-modal-mv-local ``` And add the following code to your `server.py` file: ```python from rag_multi_modal_mv_local import chain as rag_multi_modal_mv_local_chain add_routes(app, rag_multi_modal_mv_local_chain, path="/rag-multi-modal-mv-local") ``` (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-multi-modal-mv-local/playground](http://127.0.0.1:8000/rag-multi-modal-mv-local/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-multi-modal-mv-local") ```