Python Service Quickstart

CyborgDB can be deployed as a Python service using pip installation. This approach provides a lightweight alternative to Docker, allowing you to run the CyborgDB service directly in your Python environment with full control over dependencies and configuration.

Looking to use Docker? Check out our Docker Quickstart Guide.

Overview

The Python service is ideal for developers who prefer managing Python environments directly, need custom dependency management, or want to integrate CyborgDB service into existing Python applications. It provides the same REST API functionality as the Docker version.

1. Get an API Key

   To use CyborgDB, you need an API key. You can get one from the CyborgDB Admin Dashboard.

   Make sure to keep your API key secure and do not share it publicly.

2. Check Prerequisites

   Ensure you have the required Python version and environment tools:

   • Python: 3.9, 3.10, 3.11, 3.12, or 3.13
   • Package Manager: conda (recommended) or pip with virtual environment

   While conda is mentioned in the build instructions, the wheel works with any Python environment manager including pip, pipenv, poetry, etc.
3. Choose Your Database Backend (Optional)

   For quick evaluation and testing, you can skip database setup entirely - the service will automatically use a built-in standalone instance.

   For production deployments or if you have existing database infrastructure, CyborgDB service supports two external database backends:

   • PostgreSQL: Compatible with your existing PostgreSQL infrastructure
     • Managed services: AWS RDS, Azure Database for PostgreSQL, Google Cloud SQL, DigitalOcean Managed Databases
     • Self-hosted PostgreSQL instances
   • Redis: Compatible with your existing Redis infrastructure
     • Managed services: AWS ElastiCache, Azure Cache for Redis, Google Cloud Memorystore, Redis Cloud
     • Self-hosted Redis instances

   All backends provide identical CyborgDB functionality. For more info, refer to this guide.

   Standalone is for evaluation and testing only - For production use, configure an external PostgreSQL or Redis database.
4. Install CyborgDB Service

   Install the CyborgDB service package using conda and pip:

   Conda Environment (Recommended)

   # Create conda environment
   conda create -n cyborgdb-service python=3.12
   conda activate cyborgdb-service
   
   # Install the service
   pip install cyborgdb-service
   
   # If you want to use automatic embedding generation, install with:
   pip install cyborgdb-service[embeddings]
   

   Using conda or a virtual environment is strongly recommended to avoid dependency conflicts with other Python packages.
5. Configure Environment Variables

   Set the required environment variables for your deployment:

   Quick Start (Standalone)

   Linux/macOS

   # Minimal configuration - uses built-in standalone
   export CYBORGDB_API_KEY=cyborg_your_api_key_here
   # No database configuration needed - standalone starts automatically
   

   Standalone is for evaluation and testing only. For production, use PostgreSQL or Redis.

   Using a .env file is the most convenient method as it persists your configuration and keeps sensitive data out of your shell history.

   For more information on environment variables, refer to this guide.

6. Start the Service

   Run the CyborgDB service using the installed command:

   cyborgdb-service
   

   The service will start and display startup information including:

   • Database connection status
   • Server URL (default: http://localhost:8000)
   • Available API endpoints

   For additional configuration options, run cyborgdb-service --help to see all available command-line arguments and connection string examples.
7. Verify Installation

   Once the service is running, verify it’s working correctly:

   Health Check:

   curl http://localhost:8000/v1/health
   

   API Documentation: Navigate to http://localhost:8000/docs to explore the interactive API documentation.

   You should see a response indicating the service is healthy and ready to accept requests.

8. Advanced Configuration

   For production deployments, consider these additional configurations:

   Environment Variables

   Variable	Description	Required	Example
   CYBORGDB_API_KEY	Your CyborgDB API key	✅	cyborg_abc123...
   CYBORGDB_DB_TYPE	Database backend type	⚠️ Optional (defaults to standalone)	postgres or redis
   CYBORGDB_CONNECTION_STRING	Database connection details	⚠️ Optional (defaults to standalone)	See connection formats
   CYBORGDB_VERSION	Service version (optional)	❌	0.11.1
   PORT	Service port	❌	8000
   SSL_CERT_PATH	Path to SSL certificate file	❌	/etc/ssl/certs/server.crt
   SSL_KEY_PATH	Path to SSL private key file	❌	/etc/ssl/private/server.key

   If CYBORGDB_DB_TYPE and CYBORGDB_CONNECTION_STRING are not set, the service uses a built-in standalone database for evaluation. For production, configure an external database. For more information, refer to this guide.
9. Next Steps

   Now that your CyborgDB service is running, you can interact with it using client SDKs:

   Install Client SDKs:

   Python

   pip install cyborgdb
   

   REST API Reference

   Learn how to use the REST API for direct integration

   Python SDK Reference

   Learn how to use the Python SDK for direct integration

   JS/TS SDK Reference

   Learn how to use the JavaScript/TypeScript SDK for direct integration

   Go SDK Reference

   Learn how to use the Go SDK for direct integration

Comparison with Docker Service

Aspect	Python Service	Docker Service
Installation	pip install	docker run
Dependencies	Managed by pip/conda	Bundled in container
Resource Usage	Lower overhead	Higher overhead
Environment Isolation	Python virtualenv	Container isolation
Deployment Complexity	Simple Python deployment	Container orchestration
Configuration	Environment variables/files	Environment variables
Best For	Development, Python-heavy workflows	Production, cloud deployment

Both approaches provide identical CyborgDB functionality. Choose based on your deployment preferences and infrastructure requirements.