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https://github.com/tade0726/ato_chatbot

Australian Tax Office (ATO) chatbot using LlamaIndex RAG and OpenAI. Features automated documentation processing with ZenML pipelines, Qdrant vector storage, and Streamlit interface. Built for accurate tax information retrieval and natural language query processing.
https://github.com/tade0726/ato_chatbot

llamaindex llm rag streamlit

Last synced: 27 days ago
JSON representation

Host: GitHub
URL: https://github.com/tade0726/ato_chatbot
Owner: tade0726
License: apache-2.0
Created: 2024-11-07T13:50:11.000Z (3 months ago)
Default Branch: main
Last Pushed: 2024-12-12T07:05:17.000Z (about 2 months ago)
Last Synced: 2024-12-12T08:19:13.497Z (about 2 months ago)
Topics: llamaindex, llm, rag, streamlit
Language: Python
Homepage: https://ato-chat.streamlit.app/
Size: 804 KB
Stars: 10
Watchers: 3
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

# ATO Chatbot

A RAG-based chatbot system for Australian Taxation Office (ATO) information retrieval and assistance, powered by data sourced from ato.gov.au.

## Live Demo
https://ato-chat.streamlit.app/

![Chat Interface Screenshot](./docs/chat_interface.png)
*Figure 1: Streamlit Chat Interface with example conversation*

## Overview

This project implements a Retrieval-Augmented Generation (RAG) chatbot system specifically designed for ATO-related queries. It consists of two main components:

1. **Data Pipeline & Model Training**: A modular pipeline built with ZenML for data processing and index creation
2. **Interactive Interface**: A Streamlit-based chat interface for user interactions

## Architecture

![System Architecture](./docs/architecture.svg)

## Technology Stack

- **Data Collection & Processing**
- [Firecrawl](https://github.com/brave-experiments/firecrawl) - Web crawling and content extraction
- [ZenML](https://zenml.io/) - MLOps pipeline orchestration
- [Qdrant](https://qdrant.tech/) - Vector database for embeddings storage

- **Machine Learning & AI**
- [OpenAI](https://openai.com/) - Large Language Model API
- [LlamaIndex](https://www.llamaindex.ai/) - RAG framework and indexing

- **Backend & Infrastructure**
- [Docker](https://www.docker.com/) - Containerization
- [MongoDB](https://www.mongodb.com/) - Document storage

- **Frontend**
- [Streamlit](https://streamlit.io/) - Interactive web interface
- [Streamlit-Chat](https://streamlit.io/components) - Chat UI components

## Components

### 1. Data Pipeline

The data pipeline is built using ZenML and consists of several key steps:

1. **Data Collection**: Uses Firecrawl to extract content from ATO pages
2. **Data Cleaning**: Processes and filters the collected data
3. **Index Creation**: Creates embeddings and stores them in Qdrant

![Data Pipeline Flow](./docs/pipeline_flow.png)
*Figure 2: ZenML Pipeline Workflow showing data processing steps*

Key pipeline components:

```
python:src/ato_chatbot/pipelines/simple_index_pipeline.py
```

### 2. Chat Interface

The chat interface is built with Streamlit and implements a 3-step RAG process:

1. **Query Rephrasing**: Improves query understanding
2. **Knowledge Retrieval**: Fetches relevant information from Qdrant
3. **Response Generation**: Uses OpenAI to generate contextual responses

Key interface components:

```
python:src/ato_chatbot/chat_interface.py
```

## Setup

### Prerequisites

- Python 3.12+g
- Docker and Docker Compose
- OpenAI API key

### Installation

1. Clone the repository
2. Install dependencies:

```
uv install
```

3. Start required services:

```bash
make up
```

### Running the Application

1. Train the model:

```bash
make zen_run_simple_index
```

2. Start the chat interface:

```bash
make streamlit
```

## Dependencies

Key dependencies include:

```
toml:pyproject.toml
```

## License

Apache License 2.0