EvaDB is a database system designed to simplify the development of AI-powered applications by integrating AI capabilities directly into SQL queries. It targets software developers who need to incorporate AI functionalities like natural language processing, computer vision, and machine learning into their applications without requiring deep AI expertise. EvaDB aims to democratize AI integration by allowing users to leverage pre-trained models and AutoML frameworks through familiar SQL syntax, accelerating development and reducing complexity.

How It Works

EvaDB functions as an AI-centric query optimizer and engine. It allows users to connect to various data sources (SQL, S3, local files) and then query this data using SQL statements that incorporate AI functions. These functions can call pre-trained models from providers like Hugging Face, OpenAI, and YOLO, or utilize AutoML frameworks such as Ludwig and Scikit-learn. EvaDB's architecture includes an AI-centric query optimizer that generates efficient query plans, leveraging techniques like function result caching, LLM batching, and parallel query processing to accelerate AI workloads.

Quick Start & Requirements

• Install via pip: pip install evadb
• Requires Python 3.8+.
• Supports various data sources (PostgreSQL, S3, local files) and AI models (Hugging Face, OpenAI, YOLO, Ludwig).
• Documentation: evadb.ai/docs
• Getting Started Guide: evadb.readthedocs.io/en/stable/source/overview/getting-started.html

Highlighted Details

• SQL interface for AI tasks, simplifying complex AI workflows.
• Supports a wide range of data sources and pre-trained AI models.
• AI-centric query optimizations for faster inference (caching, batching, parallel processing).
• Enables custom model integration via CREATE FUNCTION.

Maintenance & Community

• Active community with Slack channel available.
• Public roadmap available for feature prioritization.
• Contributions are welcomed.
• CI Status:
• Documentation Status:

Licensing & Compatibility

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

Limitations & Caveats

The project is actively developed, and while it supports numerous integrations, users should consult the documentation for the latest compatibility and potential limitations of specific AI models or data sources.