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<h1>QueryWeaver</h1>

REST API · MCP · Graph-powered

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.

Connect and ask questions:

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Get Started

Docker

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

Launch: http://localhost:5000

---

Use an .env file (Recommended)

Create a local .env by copying .env.example and passing it to Docker. This is the simplest way to provide all required configuration:

cp .env.example .env
edit .env to set your values, then:
docker run -p 5000:5000 --env-file .env falkordb/queryweaver

Alternative: Pass individual environment variables

If you prefer to pass variables on the command line, use -e flags (less convenient for many variables):

docker run -p 5000:5000 -it \
  -e APP_ENV=production \
  -e FASTAPI_SECRET_KEY=your_super_secret_key_here \
  -e GOOGLE_CLIENT_ID=your_google_client_id \
  -e GOOGLE_CLIENT_SECRET=your_google_client_secret \
  -e GITHUB_CLIENT_ID=your_github_client_id \
  -e GITHUB_CLIENT_SECRET=your_github_client_secret \
  -e AZURE_API_KEY=your_azure_api_key \
  falkordb/queryweaver

> Note: To use OpenAI directly instead of Azure OpenAI, replace AZURE_API_KEY with OPENAI_API_KEY in the above command.

> For a full list of configuration options, consult .env.example.

MCP server: host or connect (optional)

QueryWeaver includes optional support for the Model Context Protocol (MCP). You can either have QueryWeaver expose an MCP-compatible HTTP surface (so other services can call QueryWeaver as an MCP server), or configure QueryWeaver to call an external MCP server for model/context services.

What QueryWeaver provides
- The app registers MCP operations focused on Text2SQL flows:
- list_databases
- connect_database
- database_schema
- query_database

- To disable the built-in MCP endpoints set DISABLE_MCP=true in your .env or environment (default: MCP enabled).
- Configuration

- DISABLE_MCP — disable QueryWeaver's built-in MCP HTTP surface. Set to true to disable. Default: false (MCP enabled).

Examples

Disable the built-in MCP when running with Docker:

docker run -p 5000:5000 -it --env DISABLE_MCP=true falkordb/queryweaver

Server Configuration

Below is a minimal example mcp.json client configuration that targets a local QueryWeaver instance exposing the MCP HTTP surface at /mcp.

{
   "servers": {
      "queryweaver": {
         "type": "http",
         "url": "http://127.0.0.1:5000/mcp",
         "headers": {
            "Authorization": "Bearer your_token_here"
         }
      }
   },
   "inputs": []
}

REST API

API Documentation

Swagger UI: https://app.queryweaver.ai/docs

OpenAPI JSON: https://app.queryweaver.ai/openapi.json

Overview

QueryWeaver exposes a small REST API for managing graphs (database schemas) and running Text2SQL queries. All endpoints that modify or access user-scoped data require authentication via a bearer token. In the browser the app uses session cookies and OAuth flows; for CLI and scripts you can use an API token (see tokens routes or the web UI to create one).

Core endpoints
- GET /graphs — list available graphs for the authenticated user
- GET /graphs/{graph_id}/data — return nodes/links (tables, columns, foreign keys) for the graph
- POST /graphs — upload or create a graph (JSON payload or file upload)
- POST /graphs/{graph_id} — run a Text2SQL chat query against the named graph (streaming response)

Authentication
- Add an Authorization header: Authorization: Bearer <API_TOKEN>

Examples

1) List graphs (GET)

curl example:

curl -s -H "Authorization: Bearer $TOKEN" \
   https://app.queryweaver.ai/graphs

Python example:

import requests
resp = requests.get('https://app.queryweaver.ai/graphs', headers={'Authorization': f'Bearer {TOKEN}'})
print(resp.json())

2) Get graph schema (GET)

curl example:

curl -s -H "Authorization: Bearer $TOKEN" \
   https://app.queryweaver.ai/graphs/my_database/data

Python example:

resp = requests.get('https://app.queryweaver.ai/graphs/my_database/data', headers={'Authorization': f'Bearer {TOKEN}'})
print(resp.json())

3) Load a graph (POST) — JSON payload

curl -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
   -d '{"database": "my_database", "tables": [...]}' \
   https://app.queryweaver.ai/graphs

Or upload a file (multipart/form-data):

curl -H "Authorization: Bearer $TOKEN" -F "file=@schema.json" \
   https://app.queryweaver.ai/graphs

4) Query a graph (POST) — run a chat-based Text2SQL request

The POST /graphs/{graph_id} endpoint accepts a JSON body with at least a chat field (an array of messages). The endpoint streams processing steps and the final SQL back as server-sent-message chunks delimited by a special boundary used by the frontend. For simple scripting you can call it and read the final JSON object from the streamed messages.

Example payload:

{
   "chat": ["How many users signed up last month?"],
   "result": [],
   "instructions": "Prefer PostgreSQL compatible SQL"
}

curl example (simple, collects whole response):

curl -s -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
   -d '{"chat": ["Count orders last week"]}' \
   https://app.queryweaver.ai/graphs/my_database

Python example (stream-aware):

```python
import requests
import json

url = 'https://app.queryweaver.ai/graphs/my_database'
headers = {'Authorization': f'Bearer {TOKEN}', 'Content-Type': 'application/json'}
with requests.post(url, headers=headers, json={"chat": ["Count orders last week"]}, stream=True) as r:
# The server yields JSON objects delimited by a message boundary string
boundary = '|||FALKORDB_MESSAGE_BOUNDARY|||'
buffer = ''
for chunk in r.iter_content(decode_unicode=True, chunk_size=1024):
buffer += chunk
while boundary in buffer:
part, buffer = buffer.split(boundary, 1)
if not part.strip():
continue
obj = json.loads(part)
print('STREAM:', obj)

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