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 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.
Supply a set of photos in the
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:
Here is the process the template will use to create an index of the slides (see blog):
- Given a set of images
- It uses a local multi-modal LLM (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 to store images and Chroma to store summaries.
LLM and Embedding Models
We will use Ollama 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
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
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-multi-modal-mv-local
If you want to add this to an existing project, you can just run:
langchain app add rag-multi-modal-mv-local
And add the following code to your
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. 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-multi-modal-mv-local/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-multi-modal-mv-local")