mirror of
https://github.com/hwchase17/langchain
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118 lines
3.6 KiB
Markdown
118 lines
3.6 KiB
Markdown
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# rag-gemini-multi-modal
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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 OpenCLIP embeddings to embed all of the slide images and stores them in Chroma.
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Given a question, relevat slides are retrieved and passed to [Google Gemini](https://deepmind.google/technologies/gemini/#introduction) 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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This template will use [OpenCLIP](https://github.com/mlfoundations/open_clip) multi-modal embeddings to embed the images.
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You can select different embedding model 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 moderate performance but lower memory requirments, `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 Google Gemini.
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## Environment Setup
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Set your `GOOGLE_API_KEY` environment variable in order to access Gemini.
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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-gemini-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-gemini-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_gemini_multi_modal import chain as rag_gemini_multi_modal_chain
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add_routes(app, rag_gemini_multi_modal_chain, path="/rag-gemini-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-gemini-multi-modal/playground](http://127.0.0.1:8000/rag-gemini-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-gemini-multi-modal")
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```
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