Ryoma is an AI-powered data agent framework designed for data analysis, engineering, and visualization. It targets data professionals, enabling them to interact with data sources using natural language and leverage AI for tasks like SQL generation and data exploration. The framework aims to simplify complex data workflows by integrating various cutting-edge tools.

How It Works

Ryoma integrates Langchain for LLM orchestration, Reflex for building the UI, Apache Arrow for efficient in-memory data handling, and Jupyter AI Magics for enhanced notebook experiences. It also incorporates data discovery (Amundsen), data analysis (Ibis), and feature stores (Feast) to provide a comprehensive data ecosystem. This combination allows for natural language querying of databases, automated SQL generation, and interactive data visualization.

Quick Start & Requirements

• Install via pip: pip install ryoma_ai or pip install ryoma_ai[snowflake] for Snowflake support.
• Requires API keys for supported LLM providers (e.g., OPENAI_API_KEY).
• Documentation: https://ryoma-ai.github.io/ryoma/

Highlighted Details

• Supports multiple data sources including Snowflake, BigQuery, Postgres, MySQL, and various file formats.
• Integrates with Apache Spark and Apache Flink engines.
• Offers a UI called Ryoma Lab, built with Reflex, for interactive data and AI model exploration.
• Provides a SqlAgent for natural language to SQL conversion and execution.

Maintenance & Community

• The project is actively maintained.
• Community links are not explicitly provided in the README.

Licensing & Compatibility

• Licensed under the Apache Software License 2.0.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The framework relies on external LLM providers, requiring API keys and potentially incurring costs. Support for certain data sources like Redshift and DynamoDB is marked as incomplete.