RAG-test

RAG-test is a demo project that builds a Retrieval-Augmented Generation (RAG) service using NebulaBlock's inference and embedding models. This can serve as a starting point to create your own RAG-powered applications, such as customer service bots, community assistants, or intelligent document Q&A systems.

Features

• Utilizes NebulaBlock's hosted LLM and embedding models
• Easily configurable via environment variables
• Telegram bot integration for interactive Q&A
• Customizable document source via GitHub repo
• Scalable and adaptable to various RAG-based use cases

---

Configuration

Create a .env file in the root directory and fill in the following values:

WORK_DIRECTORY="ragtest"
REPO_URL="https://github.com/Nebula-Block-Data/docs"
TELEGRAM_BOT_TOKEN=""

LLM_API_KEY=""
LLM_MODEL="meta-llama/Llama-3.3-70B-Instruct"
LLM_API_BASE="https://inference.nebulablock.com/v1"

EMBEDDING_API_KEY=""
EMBEDDING_MODEL="togethercomputer/m2-bert-80M-2k-retrieval"
EMBEDDING_API_BASE="https://inference.nebulablock.com/v1"

Replace the API keys and tokens with your actual credentials.

---

Setup & Run

Create Environment and Install Dependencies

# Create a virtual environment
python3 -m venv myenv
source myenv/bin/activate

# Install dependencies
pip install -r requirements.txt

Start the RAG Service and Telegram Bot

# Start the main service in the background
python3 main.py >> server.log 2>&1 &

---

How to Use

1. Launch the service as described above.
2. Open Telegram and find your bot using @your_bot_username.
3. Ask questions — the bot will retrieve relevant info from the specified GitHub repo and generate accurate, contextual answers.

---

Customization Tips

• Change REPO_URL to use a different documentation or knowledge source.
• Swap models by editing LLM_MODEL and EMBEDDING_MODEL in .env.
• You can extend this demo into a full web-based application using Flask, FastAPI, or any frontend framework of your choice.

---

License

MIT License