mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +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>
130 lines
4.6 KiB
Markdown
130 lines
4.6 KiB
Markdown
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# rag-chroma-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 Chroma.
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Given a question, relevat 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 Q3 earnings from DataDog, a public techologyy 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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LangSmith is currently in private beta, you can sign up [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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