AI & Machine Learning

Discover how to leverage SerenDB for AI and machine learning applications, from building AI agents to implementing vector search and semantic similarity.

Overview

SerenDB provides powerful features for AI and ML workloads, including:

• Vector Search: Utilize pgvector extension for semantic search and similarity matching

• AI Agent Development: Build sophisticated AI agents with database integration

• Model Context Protocol (MCP): Connect AI models directly to your SerenDB databases

• Database Versioning: Manage different versions of your datasets for ML experiments

• Scale with AI Workloads: Leverage SerenDB's autoscaling for variable AI/ML workloads

Popular Integrations

AI Frameworks

• LangChain: Build LLM-powered applications with SerenDB as your data store

• LlamaIndex: Create sophisticated RAG (Retrieval-Augmented Generation) systems

• Semantic Kernel: Integrate with Microsoft's AI orchestration framework

Development Environments

• Google Colab: Connect SerenDB to your Jupyter notebooks

• Azure Notebooks: Seamlessly integrate with Azure's ML environment

Serverless Functions

• Inngest: Build durable workflows with AI and database integration

AI-Specific Rules & SDKs

Learn how to optimize your development workflow with SerenDB-specific rules for:

• Cursor AI editor

• GitHub Copilot

• Other AI coding assistants

These rules help AI assistants understand SerenDB's APIs, best practices, and common patterns.

Getting Started

1. Start with AI Concepts to understand SerenDB's AI capabilities

2. Explore Vector Search Optimization for performance tuning

3. Try Building AI Applications for hands-on tutorials

4. Review AI Agent Tools for advanced integration patterns

Resources

• Learn about Database Versioning for AI

• Understand how to Scale with SerenDB

• Connect MCP Clients to SerenDB