Get Started

Docker

💡 Recommended for evaluation purposes (Local Python or Node are not required)

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.

Memory TTL (optional)

QueryWeaver stores per-user conversation memory in FalkorDB. By default these graphs persist indefinitely. Set MEMORY_TTL_SECONDS to apply a Redis TTL (in seconds) so idle memory graphs are automatically cleaned up.

# Expire memory graphs after 1 week of inactivity
MEMORY_TTL_SECONDS=604800

The TTL is refreshed on every user interaction, so active users keep their memory.

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

Calling the built-in MCP endpoints (example)

• The MCP surface is exposed as HTTP endpoints.

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):

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)

Notes & tips
- Graph IDs are namespaced per-user. When calling the API directly use the plain graph id (the server will namespace by the authenticated user). For uploaded files the `database` field determines the saved graph id.
- The streaming response includes intermediate reasoning steps, follow-up questions (if the query is ambiguous or off-topic), and the final SQL. The frontend expects the boundary string `|||FALKORDB_MESSAGE_BOUNDARY|||` between messages.
- For destructive SQL (INSERT/UPDATE/DELETE etc) the service will include a confirmation step in the stream; the frontend handles this flow. If you automate destructive operations, ensure you handle confirmation properly (see the `ConfirmRequest` model in the code).


## Development

Follow these steps to run and develop QueryWeaver from source.

### Prerequisites

- Python 3.12+
- pipenv
- A FalkorDB instance (local or remote)
- Node.js and npm (for the React frontend)

### Install and configure

Quickstart (recommended for development):

```bash
# Clone the repo
git clone https://github.com/FalkorDB/QueryWeaver.git
cd QueryWeaver

# Install dependencies (backend + frontend) and start the dev server
make install
make run-dev

If you prefer to set up manually or need a custom environment, use Pipenv:

# Install Python (backend) and frontend dependencies
pipenv sync --dev

# Create a local environment file
cp .env.example .env
# Edit .env with your values (set APP_ENV=development for local development)

Run the app locally

pipenv run uvicorn api.index:app --host 0.0.0.0 --port 5000 --reload

The server will be available at http://localhost:5000

Alternatively, the repository provides Make targets for running the app:

make run-dev   # development server (reload, debug-friendly)
make run-prod  # production mode (ensure frontend build if needed)

Frontend build (when needed)

The frontend is a modern React + Vite app in app/. Build before production runs or after frontend changes:

make install       # installs backend and frontend deps
make build-prod    # builds the frontend into app/dist/

# or manually
cd app
npm ci
npm run build

OAuth configuration

QueryWeaver supports Google and GitHub OAuth. Create OAuth credentials for each provider and paste the client IDs/secrets into your .env file.

• Google: set authorized origin and callback http://localhost:5000/login/google/authorized
• GitHub: set homepage and callback http://localhost:5000/login/github/authorized

Environment-specific OAuth settings

For production/staging deployments, set APP_ENV=production or APP_ENV=staging in your environment to enable secure session cookies (HTTPS-only). This prevents OAuth CSRF state mismatch errors.

# For production/staging (enables HTTPS-only session cookies)
APP_ENV=production

# For development (allows HTTP session cookies)
APP_ENV=development

Important: If you're getting "mismatching_state: CSRF Warning!" errors on staging/production, ensure APP_ENV is set to production or staging to enable secure session handling.

AI/LLM configuration

QueryWeaver uses AI models for Text2SQL conversion and supports both Azure OpenAI and OpenAI directly.

Default: Azure OpenAI

By default, QueryWeaver is configured to use Azure OpenAI. You need to set all three Azure credentials:

AZURE_API_KEY=your_azure_api_key
AZURE_API_BASE=https://your-resource.openai.azure.com/
AZURE_API_VERSION=2024-12-01-preview

Alternative: OpenAI directly

To use OpenAI directly instead of Azure, simply set the OPENAI_API_KEY environment variable:

OPENAI_API_KEY=your_openai_api_key

When OPENAI_API_KEY is provided, QueryWeaver automatically switches to use OpenAI's models:

• Embedding model: openai/text-embedding-ada-002
• Completion model: openai/gpt-4.1

This configuration is handled automatically in api/config.py - you only need to provide the appropriate API key.

Docker examples with AI configuration

Using Azure OpenAI:

docker run -p 5000:5000 -it \
  -e FASTAPI_SECRET_KEY=your_secret_key \
  -e AZURE_API_KEY=your_azure_api_key \
  -e AZURE_API_BASE=https://your-resource.openai.azure.com/ \
  -e AZURE_API_VERSION=2024-12-01-preview \
  falkordb/queryweaver

Using OpenAI directly:

docker run -p 5000:5000 -it \
  -e FASTAPI_SECRET_KEY=your_secret_key \
  -e OPENAI_API_KEY=your_openai_api_key \
  falkordb/queryweaver

Testing

Quick note: many tests require FalkorDB to be available. Use the included helper to run a test DB in Docker if needed.

Prerequisites

• Install dev dependencies: pipenv sync --dev
• Start FalkorDB (see make docker-falkordb)
• Install Playwright browsers: pipenv run playwright install

Quick commands

Recommended: prepare the development/test environment using the Make helper (installs dependencies and Playwright browsers):

# Prepare development/test environment (installs deps and Playwright browsers)
make setup-dev

Alternatively, you can run the E2E-specific setup script and then run tests manually:

# Prepare E2E test environment (installs browsers and other setup)
./setup_e2e_tests.sh

# Run all tests
make test

# Run unit tests only (faster)
make test-unit

# Run E2E tests (headless)
make test-e2e

# Run E2E tests with a visible browser for debugging
make test-e2e-headed

Test types

• Unit tests: focus on individual modules and utilities. Run with make test-unit or pipenv run pytest tests/ -k "not e2e".
• End-to-end (E2E) tests: run via Playwright and exercise UI flows, OAuth, file uploads, schema processing, chat queries, and API endpoints. Use make test-e2e.

See tests/e2e/README.md for full E2E test instructions.

CI/CD

GitHub Actions run unit and E2E tests on pushes and pull requests. Failures capture screenshots and artifacts for debugging.

Troubleshooting

• FalkorDB connection issues: start the DB helper make docker-falkordb or check network/host settings.
• Playwright/browser failures: install browsers with pipenv run playwright install and ensure system deps are present.
• Missing environment variables: copy .env.example and fill required values.
• OAuth "mismatching_state: CSRF Warning!" errors: Set APP_ENV=production (or staging) in your environment for HTTPS deployments, or APP_ENV=development for HTTP development environments. This ensures session cookies are configured correctly for your deployment type.

Project layout (high level)

• api/ – FastAPI backend
• app/ – React + Vite frontend
• tests/ – unit and E2E tests

License

Licensed under the GNU Affero General Public License (AGPL). See LICENSE.

Copyright FalkorDB Ltd. 2025