https://github.com/0xchron/rag-chatbot-assistant
Retrieval Augmented Generation (RAG) based chatbot template
https://github.com/0xchron/rag-chatbot-assistant
chatbot embeddings langchain llm retrieval-augmented-generation
Last synced: 3 months ago
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Retrieval Augmented Generation (RAG) based chatbot template
- Host: GitHub
- URL: https://github.com/0xchron/rag-chatbot-assistant
- Owner: 0xChron
- License: mit
- Created: 2025-05-16T16:23:26.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-08-10T16:20:35.000Z (11 months ago)
- Last Synced: 2025-08-10T18:15:54.184Z (11 months ago)
- Topics: chatbot, embeddings, langchain, llm, retrieval-augmented-generation
- Language: Python
- Homepage:
- Size: 16.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# RAG AI Chatbot
A starter template for a Retrieval-Augmented Generation (RAG) chatbot that leverages PDF files as its primary knowledge source. Designed for easy setup, customization, and future expansion. Click the `Use this template` button on the upper right corner of this repository to use this sample template.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Features](#features)
- [Project Structure](#project-structure)
- [Future Enhancements](#future-enhancements)
## Installation
1. Clone the repo:
```
git clone https://github.com/0xChron/rag-chatbot-assistant.git
cd rag-chatbot-assistant
```
2. Create .env file and input your own configuration:
```
cp .env.sample .env
```
3. Create and activate a virtual environment (recommended):
```
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
4. Install dependencies:
```
pip install -r requirements.txt
```
## Usage
1. Create a folder named `data/` and place your PDF documents there.
2. Ingest documents using:
```
python ingest.py
```
3. Start the chatbot:
```
python main.py
```
4. You can modify settings in `config.py` file 👽
## Features
- 📄 Support PDF document ingestion
- 🧠 RAG-based conversational AI
- ⚙️ Configurable via config.py
- 🛠️ Easy to extend for additional file types and features
## Future enhancements
- Additional file types support
- Web-based UI