This open-source Text2SQL tool translates natural language questions into SQL queries, simplifying database interactions. It leverages graph-powered schema understanding to interpret user prompts, enabling queries in plain English. The project targets developers and power users seeking efficient, natural-language access to databases.

How It Works

The system employs graph-based schema understanding to interpret natural language prompts and generate corresponding SQL. It exposes a REST API for managing database schemas (graphs) and executing queries, with optional integration for the Model Context Protocol (MCP). AI/LLMs, configurable for Azure OpenAI or direct OpenAI usage, power the core Text2SQL conversion.

Quick Start & Requirements

• Primary Install: Docker is recommended for evaluation.

  docker run -p 5000:5000 -it falkordb/queryweaver
  

  Using a .env file for configuration is advised.
• Prerequisites: Python 3.12+, pipenv, Node.js/npm (for source development), FalkorDB instance, and API keys for Azure OpenAI or OpenAI.
• Links: Dockerhub, Swagger UI, FalkorDB Cloud.

Highlighted Details

• REST API: Manages graphs and executes Text2SQL queries, supporting bearer token authentication.
• MCP Support: Optional integration for exposing or consuming Text2SQL services via the Model Context Protocol.
• AI/LLM Flexibility: Supports both Azure OpenAI and OpenAI directly, configurable via environment variables.
• Streaming Responses: Provides intermediate reasoning steps and final SQL in a streaming format.

Maintenance & Community

• Community: Active Discord server available at https://discord.gg/b32KEzMzce.
• CI/CD: GitHub Actions are used for automated testing.

Licensing & Compatibility

• License: GNU Affero General Public License (AGPL).
• Compatibility: AGPL is a strong copyleft license, requiring derivative works to also be AGPL-licensed, which may impact commercial use or integration into proprietary systems.

Limitations & Caveats

• Destructive Operations: Requires explicit handling of confirmation steps for data modification queries (INSERT, UPDATE, DELETE) when automating.
• OAuth Configuration: Proper APP_ENV setting is crucial for secure session handling and avoiding CSRF warnings in production/staging.
• Development Dependencies: Local development from source requires specific Python and Node.js versions and package managers.