mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
106 lines
3.1 KiB
Markdown
106 lines
3.1 KiB
Markdown
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# rag-chroma-multi-modal
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Presentations (slide decks, etc) contain visual content that challenges conventional RAG.
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Multi-modal LLMs unlock new ways to build apps over visual content like presentations.
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This template performs multi-modal RAG using Chroma with multi-modal OpenCLIP embeddings and OpenAI GPT-4V.
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## Input
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Supply a slide deck as pdf in the `/docs` directory.
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Create your vectorstore with:
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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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## Embeddings
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This template will use [OpenCLIP](https://github.com/mlfoundations/open_clip) multi-modal embeddings.
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You can select different options (see results [here](https://github.com/mlfoundations/open_clip/blob/main/docs/openclip_results.csv)).
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The first time you run the app, it will automatically download the multimodal embedding model.
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By default, LangChain will use an embedding model with reasonably strong performance, `ViT-H-14`.
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You can choose alternative `OpenCLIPEmbeddings` models in `rag_chroma_multi_modal/ingest.py`:
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```
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vectorstore_mmembd = Chroma(
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collection_name="multi-modal-rag",
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persist_directory=str(re_vectorstore_path),
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embedding_function=OpenCLIPEmbeddings(
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model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k"
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),
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)
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```
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## LLM
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The app will retrieve images using multi-modal embeddings, and pass them 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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## 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
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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
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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 import chain as rag_chroma_multi_modal_chain
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add_routes(app, rag_chroma_multi_modal_chain, path="/rag-chroma-multi-modal")
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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/playground](http://127.0.0.1:8000/rag-chroma-multi-modal/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")
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``` |