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title: Beaglemind Rag Poc emoji: 👀 colorFrom: red colorTo: purple sdk: gradio sdk_version: 5.35.0 app_file: app.py pinned: false

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

BeagleMind CLI

An intelligent documentation assistant CLI tool for Beagleboard projects that uses RAG (Retrieval-Augmented Generation) to answer questions about codebases and documentation.

Features

• Multi-backend LLM support: Use both cloud (Groq) and local (Ollama) language models
• Intelligent search: Advanced semantic search with reranking and filtering
• Rich CLI interface: Beautiful command-line interface with syntax highlighting
• Persistent configuration: Save your preferences for seamless usage
• Source attribution: Get references to original documentation and code

Installation

Development Installation

# Clone the repository
git clone https://github.com/beagleboard-gsoc/BeagleMind-RAG-PoC
cd BeagleMind-RAG-PoC

# Install in development mode
pip install -e .

Using pip

pip install beaglemind-cli

Environment Setup

For Groq (Cloud)

Set your Groq API key:

export GROQ_API_KEY="your-api-key-here"

For OpenAI (Cloud)

Set your OpenAI API key:

export OPENAI_API_KEY="your-api-key-here"

For Ollama (Local)

1. Install Ollama: https://ollama.ai
2. Pull a supported model:

   ollama pull qwen3:1.7b
   

3. Ensure Ollama is running:

   ollama serve
   

Quick Start

1. List Available Models

See what language models are available:

# List all models
beaglemind list-models

# List models for specific backend
beaglemind list-models --backend groq
beaglemind list-models --backend ollama

2. Start Chatting

Ask questions about the documentation:

# Simple question
beaglemind chat -p "How do I configure the BeagleY-AI board?"

# With specific model and backend
beaglemind chat -p "Show me GPIO examples" --backend groq --model llama-3.3-70b-versatile

# With sources shown
beaglemind chat -p "What are the pin configurations?" --sources

CLI Commands

beaglemind list-models

List available language models.

Options:

• --backend, -b: Show models for specific backend (groq/ollama)

Examples:

beaglemind list-models
beaglemind list-models --backend groq

beaglemind chat

Chat with BeagleMind using natural language.

Options:

• --prompt, -p: Your question (required)
• --backend, -b: LLM backend (groq/ollama)
• --model, -m: Specific model to use
• --temperature, -t: Response creativity (0.0-1.0)
• --strategy, -s: Search strategy (adaptive/multi_query/context_aware/default)
• --sources: Show source references

Examples:

# Basic usage
beaglemind chat -p "How to flash an image to BeagleY-AI?"

# Advanced usage
beaglemind chat \
  -p "Show me Python GPIO examples" \
  --backend groq \
  --model llama-3.3-70b-versatile \
  --temperature 0.2 \
  --strategy adaptive \
  --sources

# Code-focused questions
beaglemind chat -p "How to implement I2C communication?" --sources

# Documentation questions  
beaglemind chat -p "What are the system requirements?" --strategy context_aware

Interactive Chat Mode

You can start an interactive multi-turn chat session (REPL) that remembers context and lets you toggle features live.

Start it by simply running the chat command without a prompt:

beaglemind chat

Or force it explicitly:

beaglemind chat --interactive

During the session you can use these inline commands (type them as messages):

Command	Description
/help	Show available commands and tips
/sources	Toggle display of source documents for answers
/tools	Enable/disable tool usage (file creation, code analysis, etc.)
/config	Show current backend/model/session settings
/clear	Clear the screen and keep session state
/exit or /quit	End the interactive session

Example interactive flow:

$ beaglemind chat
BeagleMind (1) > How do I configure GPIO?
...answer...
BeagleMind (2) > /sources
✓ Source display: enabled
BeagleMind (3) > Give me a Python example
...answer with sources...
BeagleMind (4) > /tools
✓ Tool usage: disabled
BeagleMind (5) > /exit

Tips:

1. Use /sources when you need provenance; turn it off for faster, cleaner output.
2. Disable tools (/tools) if you want read-only behavior.
3. Ask follow-ups naturally; prior Q&A stays in context for better answers.

Available Models

Groq (Cloud)

• llama-3.3-70b-versatile
• llama-3.1-8b-instant
• gemma2-9b-it
• meta-llama/llama-4-scout-17b-16e-instruct
• meta-llama/llama-4-maverick-17b-128e-instruct

OpenAI (Cloud)

• gpt-4o
• gpt-4o-mini
• gpt-4-turbo
• gpt-3.5-turbo
• o1-preview
• o1-mini

Ollama (Local)

• qwen3:1.7b
• smollm2:360m
• deepseek-r1:1.5b

Tips for Best Results

1. Be specific: "How to configure GPIO pins on BeagleY-AI?" vs "GPIO help"

2. Use technical terms: Include model names, component names, exact error messages

3. Ask follow-up questions: Build on previous responses for deeper understanding

4. Use --sources: See exactly where information comes from

5. Try different strategies: Some work better for different question types

Troubleshooting

"BeagleMind is not initialized"

Run beaglemind init first.

"No API Key" for Groq

Set the GROQ_API_KEY environment variable.

"No API Key" for OpenAI

Set the OPENAI_API_KEY environment variable.

"Service Down" for Ollama

Ensure Ollama is running: ollama serve

"Model not available"

Check beaglemind list-models for available options.

Development

Running from Source

# Make the script executable
chmod +x beaglemind

# Run directly
./beaglemind --help

# Or with Python
python -m src.cli --help

Adding New Models

Edit the model lists in src/cli.py:

GROQ_MODELS = [
    "new-model-name",
    # ... existing models
]

OPENAI_MODELS = [
    "new-openai-model",
    # ... existing models
]

OLLAMA_MODELS = [
    "new-local-model",
    # ... existing models  
]

License

MIT License - see LICENSE file for details.

Support

• GitHub Issues: Create an issue
• Community: BeagleBoard forums