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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.
133 lines
4.5 KiB
Markdown
133 lines
4.5 KiB
Markdown
# RAG - Chroma multi-modal multi-vector
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`Multi-modal LLMs` enable visual assistants that can perform
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question-answering about images.
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This template create a visual assistant for slide decks,
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which often contain visuals such as graphs or figures.
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It uses `GPT-4V` to create image summaries for each slide,
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embeds the summaries, and stores them in `Chroma`.
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Given a question, relevant slides are retrieved and passed
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to GPT-4V for answer synthesis.
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 "Multi-modal LLM Process Diagram"
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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 Q3 earnings from DataDog, a public technology company.
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Example questions to ask can be:
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```
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How many customers does Datadog have?
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What is Datadog platform % Y/Y growth in FY20, FY21, and FY22?
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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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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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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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For production, it may be desirable to use a remote option such as Redis.
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You can set the `local_file_store` flag in `chain.py` and `ingest.py` to switch between the two options.
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For Redis, the template will use [UpstashRedisByteStore](https://python.langchain.com/docs/integrations/stores/upstash_redis).
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We will use Upstash to store the images, which offers Redis with a REST API.
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Simply login [here](https://upstash.com/) and create a database.
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This will give you a REST API with:
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* `UPSTASH_URL`
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* `UPSTASH_TOKEN`
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Set `UPSTASH_URL` and `UPSTASH_TOKEN` as environment variables to access your database.
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We will use Chroma to store and index the image summaries, which will be created locally in the template directory.
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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, and pass the images to GPT-4V.
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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 `UPSTASH_URL` and `UPSTASH_TOKEN` as environment variables to access your database if you use `UpstashRedisByteStore`.
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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-chroma-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-chroma-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_chroma_multi_modal_multi_vector import chain as rag_chroma_multi_modal_chain_mv
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add_routes(app, rag_chroma_multi_modal_chain_mv, path="/rag-chroma-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-chroma-multi-modal-multi-vector/playground](http://127.0.0.1:8000/rag-chroma-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-chroma-multi-modal-multi-vector")
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```
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