# RAG over Tesla 10-K's (with Embedded Tables)

This is a [LlamaIndex](https://www.llamaindex.ai/) project bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).

We use our recursive retriever, combined with an OpenAI Agent, to create a bot capable of tabular/semi-structured/unstructured analysis within complex docs.

This also streams the intermediate results from the agent via a custom Callback handler

## Main Files to Look At

This extends beyond the simple `create-llama` example. To see changes, look at the following files:
- `backend/app/utils/index.py` - contains core logic for constructing + getting agent
- `backend/app/api/routers/chat.py` - contains implementation of chat endpoint

## Getting Started

First, startup the backend as described in the [backend README](./backend/README.md).

Second, run the development server of the frontend as described in the [frontend README](./frontend/README.md).

Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.

## Learn More

To learn more about LlamaIndex, take a look at the following resources:

- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex (Python features).
- [LlamaIndexTS Documentation](https://ts.llamaindex.ai) - learn about LlamaIndex (Typescript features).

You can check out [the LlamaIndexTS GitHub repository](https://github.com/run-llama/LlamaIndexTS) - your feedback and contributions are welcome!