Time Series RAG

A powerful system for time series similarity search and retrieval augmented generation (RAG). This project provides an efficient way to search for similar time series patterns and augment them with contextual information.

Project Structure

timeseries_rag/
├── .github/                    # GitHub Actions workflows
│   └── workflows/
│       └── azure-deploy.yml    # Azure deployment configuration
├── data/                       # Example datasets
│   ├── financial/             # Financial time series data
│   ├── sensor/                # Sensor readings data
│   ├── synthetic/             # Synthetic patterns
│   └── weather/               # Weather measurements
├── docs/                       # Documentation
│   └── docs/
│       ├── source/            # Sphinx documentation source
│       └── requirements.txt    # Documentation dependencies
├── scripts/                    # Utility scripts
│   └── start.sh               # Azure startup script
├── src/                       # Source code
│   └── timeseries_rag/        # Main package
│       ├── __init__.py        # Package initialization
│       ├── api.py             # FastAPI web application
│       ├── models.py          # Time series embedding models
│       └── rag.py             # RAG system implementation
├── .gitignore                 # Git ignore patterns
├── LICENSE                    # MIT License
├── MANIFEST.in                # Package manifest
├── README.md                  # This file
├── requirements.txt           # Project dependencies
└── setup.py                   # Package setup configuration

Features

Time Series Embedding

• Statistical feature extraction
• Resampling to fixed length
• Efficient dimensionality reduction
• Support for variable-length time series

Similarity Search

• FAISS vector database integration
• Fast nearest neighbor search
• Customizable distance metrics
• Metadata-aware retrieval

Web Interface

• Interactive time series visualization
• Easy data upload and search
• Real-time results display
• Plotly-powered charts

API

• RESTful endpoints
• FastAPI backend
• CORS and iframe support
• Easy integration

Installation

Development Installation

1. Clone the repository:

git clone https://github.com/minesh-1291/timeseries-rag.git
cd timeseries-rag

2. Install in development mode:

pip install -e .

Production Installation

pip install timeseries-rag

Usage

Running the Web Application

Development mode:

python -m timeseries_rag.api

Production mode:

gunicorn timeseries_rag.api:app --workers 4 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000

Access the web interface at:

• Development: http://localhost:8000
• Production: http://your-domain:8000

Python API

from timeseries_rag.models import TimeSeriesEmbedder
from timeseries_rag.rag import TimeSeriesRAG, TimeSeriesDocument
import numpy as np

# Initialize components
embedder = TimeSeriesEmbedder(target_length=256)
rag = TimeSeriesRAG()

# Create some example time series
t = np.linspace(0, 10, 100)
sine_wave = np.sin(t)
noisy_sine = sine_wave + np.random.normal(0, 0.2, size=len(sine_wave))

# Embed and store the first time series
embedding = embedder.embed(sine_wave)
doc = TimeSeriesDocument(
    id="sine_1",
    data=sine_wave,
    metadata={"type": "sine", "frequency": 1.0},
    embedding=embedding
)
rag.add_document(doc)

# Search for similar patterns
query_embedding = embedder.embed(noisy_sine)
results = rag.search(query_embedding, k=5)

# Print results
for result in results:
    print(f"Document ID: {result['id']}")
    print(f"Distance: {result['distance']:.4f}")
    print(f"Metadata: {result['metadata']}")
    print()

Documentation

Full documentation is available at https://minesh-1291.github.io/timeseries-rag/

Deployment

Azure App Service

1. Configure GitHub Actions:

   • Add Azure publish profile as secret
   • Enable GitHub Actions workflow

2. Deploy using GitHub Actions:

   • Push to main branch
   • Monitor deployment in Actions tab

3. Access your application:

   • Web Interface: https://your-app.azurewebsites.net
   • API Documentation: https://your-app.azurewebsites.net/docs

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Citation

If you use this project in your research, please cite:

@software{timeseries_rag2024,
  author = {Jethva, Minesh A.},
  title = {Time Series RAG},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/minesh1291/timeseries-rag}
}