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CyborgDB seamlessly integrates with popular AI and machine learning frameworks, enabling you to leverage confidential vector search within your existing workflows. These integrations provide native support for CyborgDB’s encrypted vector operations while maintaining compatibility with familiar APIs and patterns.

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Available Integrations

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🦜🔗 LangChain

The LangChain integration provides a CyborgVectorStore class that implements the standard VectorStore interface, enabling confidential vector search for RAG pipelines and LLM applications. This integration is available for cyborgdb-core (Embedded), cyborgdb (Python SDK), and cyborgdb (JS/TS SDK), and includes:
  • Full VectorStore API compatibility (add_texts, similarity_search, etc.)
  • Async operations support
  • Configurable distance metrics (cosine, euclidean, squared_euclidean)
  • Document ID tracking and management
  • Max marginal relevance search (Embedded and JS/TS)
See the LangChain Integration Guide for detailed usage instructions.

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Getting Started

Each integration includes detailed installation instructions and usage examples. Choose the integration that matches your current tech stack to get started with confidential vector search in minutes. For deployment-specific guidance, see our Deployment Models Guide to understand the differences between cyborgdb and cyborgdb-core.