Scaffold LLM Agent 💻

This project provides a scaffold LLM agent to help you get started with Llamaindex, Trulens, and Streamlit. It uses advanced RAG techniques (sentence-window and automerging retrieval) to create a custom agent and Trulens to evaluate the RAG. It can run either locally or in a containerized version.

Getting Started

Prerequisites

• Python 3.7 or higher
• Docker (for containerized version)

Installation

1. Fork this repo.
2. Copy the .env.example to .env, then replace the placeholder with your own OpenAI API key.

Local Installation

1. Install the requirements: pip install -r requirements.txt
2. Run the application: streamlit run chatbot.py --server.port=8502

Note: The port 8501 is defaulted to Trulens dashboard, so we use 8502 for this application.

Containerized Installation

1. Run the application: docker-compose up

Usage

This scaffold is a Streamlit version of the popular course by DeepLearning.ai - Building and Evaluating Advanced RAG

In the course, the instructors build a RAG using the eBook "How to build a career in AI". It's also included here in the /data directory.

To start working on your own,

1. Add your data to the /data directory (note: there's many ways to access your data through Llamahub and the SampleDirectoryReader documentation)
2. Update the load_data() function with the new path.
3. Choose the type of RAG to run. By default, it's using automerging - but you can replace it with the sentence retrieval function on line 80 in chatbot.py
4. Update system_prompt.py with your custom prompt!

Debugging

By default, the RAG is cached by streamlit, as well as saved in their respective diretory. For example, auto-merging creates a new directory called merging_index with the index_store.json along with other documents. If behaviour is outdated or unexpected, delete the directory and re-run.

License

This project is licensed under the MIT License.