mirror of
https://github.com/hwchase17/langchain
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123 lines
4.4 KiB
Markdown
123 lines
4.4 KiB
Markdown
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# rag-multi-modal-mv-local
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Visual search is a famililar application to many with iPhones or Android devices. It allows user to search photos using natural language.
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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.
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This template demonstrates how to perform private visual search and question-answering over a collection of your photos.
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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.
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Given a question, relevat photos are retrieved and passed to the multi-modal LLM for answer synthesis.
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## Input
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Supply a set of photos in the `/docs` directory.
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By default, this template has a toy collection of 3 food pictures.
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The app will look up and summarize photos based upon provided keywords or questions:
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```
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What kind of ice cream did I have?
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```
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In practice, a larger corpus of images can be tested.
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To create an index of the images, run:
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```
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poetry install
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python ingest.py
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```
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## Storage
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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/)):
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* Given a set of images
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* It uses a local multi-modal LLM ([bakllava](https://ollama.ai/library/bakllava)) to summarize each image
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* Embeds the image summaries with a link to the original images
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* Given a user question, it will relevant image(s) based on similarity between the image summary and user input (using Ollama embeddings)
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* It will pass those images to bakllava for answer synthesis
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By default, this will use [LocalFileStore](https://python.langchain.com/docs/integrations/stores/file_system) to store images and Chroma to store summaries.
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## LLM and Embedding Models
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We will use [Ollama](https://python.langchain.com/docs/integrations/chat/ollama#multi-modal) for generating image summaries, embeddings, and the final image QA.
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Download the latest version of Ollama: https://ollama.ai/
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Pull an open source multi-modal LLM: e.g., https://ollama.ai/library/bakllava
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Pull an open source embedding model: e.g., https://ollama.ai/library/llama2:7b
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```
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ollama pull bakllava
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ollama pull llama2:7b
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```
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The app is by default configured for `bakllava`. But you can change this in `chain.py` and `ingest.py` for different downloaded models.
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The app will retrieve images based on similarity between the text input and the image summary, and pass the images to `bakllava`.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package rag-multi-modal-mv-local
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add rag-multi-modal-mv-local
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```
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And add the following code to your `server.py` file:
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```python
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from rag_multi_modal_mv_local import chain as rag_multi_modal_mv_local_chain
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add_routes(app, rag_multi_modal_mv_local_chain, path="/rag-multi-modal-mv-local")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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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)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-multi-modal-mv-local")
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```
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