Queryweaver

@FalkorDB

About Queryweaver

An open-source Text2SQL tool that transforms natural language into SQL using graph-powered schema understanding. Ask your database questions in plain English, QueryWeaver handles the weaving.

#text2sql #semantic-layer #falkordb

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "queryweaver": {
      "type": "http",
      "url": "https://app.queryweaver.ai/mcp",
      "headers": {
        "Authorization": "Bearer your_token_here"
      }
    }
  },
  "inputs": []
}

Tools

List all available graphs/databases for the authenticated user. Requires authentication.

Return all nodes and edges for the specified database schema. Requires authentication. args: graph_id (str): The ID of the graph to query (the database name).

Query the Database with the given graph_id and chat_data. Requires authentication. Args: graph_id (str): The ID of the graph to query. chat_data (ChatRequest): The chat data containing user queries and context.

Accepts a JSON payload with a database URL and attempts to connect. Supports both PostgreSQL and MySQL databases. Streams progress steps as a sequence of JSON messages separated by a delimiter. Requires authentication.

Overview

What is QueryWeaver?

QueryWeaver is an open-source Text2SQL tool that converts plain-English questions into SQL using graph-powered schema understanding. It helps you ask databases natural-language questions and returns SQL and results.

How to use QueryWeaver?

You can run QueryWeaver via Docker with a single command (docker run -p 5000:5000 -it falkordb/queryweaver) or from source using Python 3.12+ and pipenv. Configuration is provided through environment variables (e.g., Azure OpenAI or OpenAI API keys). QueryWeaver exposes a REST API with endpoints for managing graphs and running Text2SQL queries, and optionally provides MCP endpoints for integration with AI assistants.

Key features of QueryWeaver

Graph-powered schema understanding for Text2SQL
Plain-English to SQL conversion
REST API with Swagger UI documentation
Optional MCP server endpoints
Docker deployment (single command)
Supports Azure OpenAI and OpenAI
OAuth authentication (Google, GitHub)
Streaming responses with reasoning steps

Use cases of QueryWeaver

Natural language database querying without writing SQL
Automating SQL generation for dashboards and reports
Integrating database access into AI assistants and chatbots
Prototyping and data exploration for analysts and developers
Enabling non-technical team members to query databases

FAQ from QueryWeaver

What AI providers does QueryWeaver support?

QueryWeaver supports Azure OpenAI by default. To use OpenAI directly, set the OPENAI_API_KEY environment variable instead of AZURE_API_KEY.

How can I authenticate with the REST API?

All authenticated endpoints require a Bearer token in the Authorization header. In the browser the app uses session cookies and OAuth flows (Google, GitHub); for scripts you can use an API token.

Does QueryWeaver support the Model Context Protocol (MCP)?

Yes, QueryWeaver includes built-in MCP endpoints (list_databases, connect_database, database_schema, query_database). These are enabled by default and can be disabled by setting DISABLE_MCP=true.

What are the system requirements for running from source?

You need Python 3.12+, pipenv, a FalkorDB instance, and Node.js with npm (for the TypeScript frontend).

How does the streaming response work?

The POST /graphs/{graph_id} endpoint streams JSON objects delimited by the boundary string |||FALKORDB_MESSAGE_BOUNDARY||| containing intermediate reasoning steps, follow-up questions, and the final SQL.

Frequently asked questions

What AI providers does QueryWeaver support?

QueryWeaver supports Azure OpenAI by default. To use OpenAI directly, set the `OPENAI_API_KEY` environment variable instead of `AZURE_API_KEY`.

How can I authenticate with the REST API?

All authenticated endpoints require a Bearer token in the `Authorization` header. In the browser the app uses session cookies and OAuth flows (Google, GitHub); for scripts you can use an API token.

Does QueryWeaver support the Model Context Protocol (MCP)?

Yes, QueryWeaver includes built-in MCP endpoints (`list_databases`, `connect_database`, `database_schema`, `query_database`). These are enabled by default and can be disabled by setting `DISABLE_MCP=true`.

What are the system requirements for running from source?

You need Python 3.12+, pipenv, a FalkorDB instance, and Node.js with npm (for the TypeScript frontend).

How does the streaming response work?

The `POST /graphs/{graph_id}` endpoint streams JSON objects delimited by the boundary string `|||FALKORDB_MESSAGE_BOUNDARY|||` containing intermediate reasoning steps, follow-up questions, and the final SQL.

Comments

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