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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
119 lines
4.1 KiB
Markdown
119 lines
4.1 KiB
Markdown
# RAG - Redis - multi-modal, multi-vector
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`Multi-modal` LLMs enable visual assistants that can perform question-answering about images.
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This template create a visual assistant for slide decks, which often contain visuals such as graphs or figures.
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It uses `GPT-4V` to create image summaries for each slide, embeds the summaries, and stores them in `Redis`.
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Given a question, relevant slides are retrieved and passed to GPT-4V for answer synthesis.
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## Input
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Supply a slide deck as PDF in the `/docs` directory.
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By default, this template has a slide deck about recent earnings from NVIDIA.
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Example questions to ask can be:
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```
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1/ how much can H100 TensorRT improve LLama2 inference performance?
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2/ what is the % change in GPU accelerated applications from 2020 to 2023?
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```
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To create an index of the slide deck, run:
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```
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poetry install
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poetry shell
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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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* Extract the slides as a collection of images
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* Use GPT-4V to summarize each image
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* Embed the image summaries using text embeddings with a link to the original images
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* Retrieve relevant image based on similarity between the image summary and the user input question
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* Pass those images to GPT-4V for answer synthesis
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### Redis
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This template uses [Redis](https://redis.com) to power the [MultiVectorRetriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector) including:
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- Redis as the [VectorStore](https://python.langchain.com/docs/integrations/vectorstores/redis) (to store + index image summary embeddings)
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- Redis as the [ByteStore](https://python.langchain.com/docs/integrations/stores/redis) (to store images)
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Make sure to deploy a Redis instance either in the [cloud](https://redis.com/try-free) (free) or locally with [docker](https://redis.io/docs/install/install-stack/docker/).
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This will give you an accessible Redis endpoint that you can use as a URL. If deploying locally, simply use `redis://localhost:6379`.
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## LLM
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The app will retrieve images based on similarity between the text input and the image summary (text), and pass the images to GPT-4V for answer synthesis.
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI GPT-4V.
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Set `REDIS_URL` environment variable to access your Redis database.
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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-redis-multi-modal-multi-vector
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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-redis-multi-modal-multi-vector
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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_redis_multi_modal_multi_vector import chain as rag_redis_multi_modal_chain_mv
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add_routes(app, rag_redis_multi_modal_chain_mv, path="/rag-redis-multi-modal-multi-vector")
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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-redis-multi-modal-multi-vector/playground](http://127.0.0.1:8000/rag-redis-multi-modal-multi-vector/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-redis-multi-modal-multi-vector")
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```
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