# 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")
```