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: QueryWeaver supports multiple AI providers. You can use OPENAI_API_KEY, GEMINI_API_KEY, ANTHROPIC_API_KEY, or AZURE_API_KEY. See the AI/LLM configuration section for details.

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+
• uv (Python package manager)
• A FalkorDB instance (local or remote)
• Node.js and npm (for the React frontend)

Install and configure

Quickstart (recommended for development):

# 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 uv:

# Install Python (backend) and frontend dependencies
uv sync

# 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

uv 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 supports multiple AI providers. Set one API key and QueryWeaver auto-detects which provider to use.

Priority order: Ollama > OpenAI > Gemini > Anthropic > Cohere > Azure (default)

Provider	API Key	Default Models
Ollama	OLLAMA_MODEL	ollama/<your-model>, ollama/nomic-embed-text
OpenAI	OPENAI_API_KEY	openai/gpt-4.1, openai/text-embedding-ada-002
Google Gemini	GEMINI_API_KEY	gemini/gemini-3-pro-preview, gemini/gemini-embedding-001
Anthropic	ANTHROPIC_API_KEY	anthropic/claude-sonnet-4-5-20250929, voyage/voyage-3*
Cohere	COHERE_API_KEY	cohere/command-a-03-2025, cohere/embed-v4.0
Azure OpenAI	AZURE_API_KEY	azure/gpt-4.1, azure/text-embedding-ada-002

* Anthropic has no native embeddings. You must set VOYAGE_API_KEY or EMBEDDING_MODEL for embeddings, otherwise startup will fail with an error.

Optional: Override default models

COMPLETION_MODEL=gemini/gemini-3-pro-preview
EMBEDDING_MODEL=gemini/gemini-embedding-001

Both must match your API key's provider.

Docker examples with AI configuration

Using OpenAI:

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

Using Google Gemini:

docker run -p 5000:5000 -it \
  -e FASTAPI_SECRET_KEY=your_secret_key \
  -e GEMINI_API_KEY=your_gemini_api_key \
  falkordb/queryweaver

Using Anthropic:

docker run -p 5000:5000 -it \
  -e FASTAPI_SECRET_KEY=your_secret_key \
  -e ANTHROPIC_API_KEY=your_anthropic_api_key \
  falkordb/queryweaver

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

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: uv sync
• Start FalkorDB (see make docker-falkordb)
• Install Playwright browsers: uv 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 uv run python -m 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 uv 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