mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
a74f3a4979
**Description:** Batch update of alt text and title attributes for images in `md` & `mdx` files across the repo using [alttexter](https://github.com/jonathanalgar/alttexter)/[alttexter-ghclient](https://github.com/jonathanalgar/alttexter-ghclient) (built using LangChain/LangSmith). **Limitation:** cannot update `ipynb` files because of [this issue](https://github.com/langchain-ai/langchain/pull/15357#issuecomment-1885037250). Can revisit when Docusaurus is bumped to v3. I checked all the generated alt texts and titles and didn't find any technical inaccuracies. That's not to say they're _perfect_, but a lot better than what's there currently. [Deployed](https://langchain-819yf1tbk-langchain.vercel.app/docs/modules/model_io/) image example:  You can see LangSmith traces for all the calls out to the LLM in the PRs merged into this one: * https://github.com/jonathanalgar/langchain/pull/6 * https://github.com/jonathanalgar/langchain/pull/4 * https://github.com/jonathanalgar/langchain/pull/3 I didn't add the following files to the PR as the images already have OK alt texts: *27dca2d92f/docs/docs/integrations/providers/argilla.mdx (L3)*27dca2d92f/docs/docs/integrations/providers/apify.mdx (L11)--------- Co-authored-by: github-actions <github-actions@github.com>
123 lines
4.4 KiB
Markdown
123 lines
4.4 KiB
Markdown
|
|
# rag-multi-modal-mv-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 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.
|
|
|
|
|
|
|
|
## 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=<your-api-key>
|
|
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:
|
|
|
|
```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")
|
|
```
|