Reachy2 Expert Agent

An intelligent assistant for the Reachy2 robot platform that provides accurate, context-aware responses about robot control and programming.

Features

• Context-aware responses using RAG (Retrieval-Augmented Generation)
• Real-time code generation and execution
• Safety-first approach with built-in guidelines
• Comprehensive documentation search
• Interactive chat interface

Project Structure

reachy2_expert_agent/
├── src/                    # Source code
│   ├── chatbot.py         # Main chatbot application
│   └── utils/             # Core utilities
│       ├── config.py      # Configuration settings
│       ├── rag_utils.py   # RAG pipeline utilities
│       ├── db_utils.py    # Database operations
│       └── embedding_utils.py  # Embedding generation
├── tests/                 # Test suite
│   ├── integration/      # Integration tests
│   ├── unit/            # Unit tests
│   └── conftest.py      # Test configuration
├── tools/                # Utility scripts
│   ├── chunk_documents.py    # Document chunking
│   ├── update_vectordb.py    # Vector database updates
│   ├── scrape_sdk_docs.py    # SDK documentation scraper
│   ├── scrape_vision_docs.py # Vision documentation scraper
│   ├── scrape_tutorials.py   # Tutorials scraper
│   ├── scrape_reachy2_docs.py # Main documentation scraper
│   ├── analyze_coverage.py   # Coverage analysis
│   └── evaluate_retrieval.py # Retrieval evaluation
├── data/                 # Data directory
│   ├── raw_docs/        # Raw documentation
│   ├── external_docs/   # Processed documentation
│   ├── vectorstore/    # Vector database
│   └── cache/          # Cache directory
└── docs/               # Documentation

Setup

Requirements: Python 3.10 or higher is required for this project.

1. Create a Python virtual environment:

   python3.10 -m venv venv
   source venv/bin/activate  # On Unix/macOS
   # or
   .\venv\Scripts\activate  # On Windows

2. Install the package and its dependencies (recommended):

   pip install -e .

   Alternatively, you can install dependencies directly:

   pip install -r requirements.txt

   Note: The initial installation will download the InstructorXL model (~5GB). This may take some time depending on your internet connection and requires sufficient disk space.

   Note: For vision-related functionality (camera, object detection, tracking), you'll need to manually install the pollen-vision package:

   pip install git+https://github.com/pollen-robotics/pollen-vision.git

3. Install in development mode:

   make install-dev

4. Set up environment variables:

   cp .env.example .env

   Edit .env to include your API keys:

   # Required API keys
   MISTRAL_API_KEY=your_mistral_api_key    # Get from https://console.mistral.ai/
   
   # Optional settings
   DEBUG=false                             # Enable for detailed logging

   Note: The Mistral API key is required for the chatbot to function. You can obtain one by:

   1. Creating an account at console.mistral.ai
   2. Generating an API key in your account settings
   3. Copying the key to your .env file

5. Initialize the vector database:

   make refresh

   Note: While make refresh-full is available to include Reachy2 docs, it's recommended to use make refresh as it provides all the necessary documentation for the agent to function properly. The full refresh option is still experimental and may include incomplete documentation.

   If you encounter network issues during the refresh process, the system will automatically retry failed operations. You can also run individual scraping commands separately:

   make clean        # Clean existing data
   make scrape      # Scrape documentation
   make chunk       # Process documents
   make update-db   # Update vector database

Development

1. Run tests:

   make test

2. Run code quality checks:

   make lint
   make format

3. Update documentation:

   make docs

4. Evaluate retrieval performance:

   make evaluate

Usage

Run the chatbot:

python src/chatbot.py

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests and linting
5. Submit a pull request

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Acknowledgments

• Pollen Robotics for the Reachy2 platform
• Contributors and maintainers

Copyright 2024 Pollen Robotics

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.