mirror of
https://github.com/hwchase17/langchain
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120 lines
4.1 KiB
Markdown
120 lines
4.1 KiB
Markdown
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# 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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