langchain/templates/rag-gemini-multi-modal/README.md

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# rag-gemini-multi-modal
Multi-modal LLMs enable text-to-image retrieval and question-answering over images.
You can ask questions in natural language about a collection of photos, retrieve relevant ones, and have a multi-modal LLM answer questions about the retrieved images.
This template performs text-to-image retrieval for question-answering about a slide deck, which often contains visual elements that are not captured in standard RAG.
This will use OpenCLIP embeddings and [Google Gemini](https://deepmind.google/technologies/gemini/#introduction) for answer synthesis.
## Input
Supply a slide deck as pdf in the `/docs` directory.
By default, this template has a slide deck about Q3 earnings from DataDog, a public techologyy company.
Example questions to ask can be:
```
How many customers does Datadog have?
What is Datadog platform % Y/Y growth in FY20, FY21, and FY22?
```
To create an index of the slide deck, run:
```
poetry install
python ingest.py
```
## Storage
This template will use [OpenCLIP](https://github.com/mlfoundations/open_clip) multi-modal embeddings to embed the images.
You can select different embedding model options (see results [here](https://github.com/mlfoundations/open_clip/blob/main/docs/openclip_results.csv)).
The first time you run the app, it will automatically download the multimodal embedding model.
By default, LangChain will use an embedding model with moderate performance but lower memory requirments, `ViT-H-14`.
You can choose alternative `OpenCLIPEmbeddings` models in `rag_chroma_multi_modal/ingest.py`:
```
vectorstore_mmembd = Chroma(
collection_name="multi-modal-rag",
persist_directory=str(re_vectorstore_path),
embedding_function=OpenCLIPEmbeddings(
model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k"
),
)
```
## LLM
The app will retrieve images using multi-modal embeddings, and pass them to Google Gemini.
## Environment Setup
Set your `GOOGLE_API_KEY` environment variable in order to access Gemini.
## Usage
To use this package, you should first have the LangChain CLI installed:
```shell
pip install -U langchain-cli
```
To create a new LangChain project and install this as the only package, you can do:
```shell
langchain app new my-app --package rag-gemini-multi-modal
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add rag-gemini-multi-modal
```
And add the following code to your `server.py` file:
```python
from rag_gemini_multi_modal import chain as rag_gemini_multi_modal_chain
add_routes(app, rag_gemini_multi_modal_chain, path="/rag-gemini-multi-modal")
```
(Optional) Let's now configure LangSmith.
LangSmith will help us trace, monitor and debug LangChain applications.
LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
If you don't have access, you can skip this section
```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
```
If you are inside this directory, then you can spin up a LangServe instance directly by:
```shell
langchain serve
```
This will start the FastAPI app with a server is running locally at
[http://localhost:8000](http://localhost:8000)
We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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)
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-gemini-multi-modal")
```