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Readme

ai-database

AI-native database abstraction with hybrid vector search capabilities for synthetic data, tool-calling, and RAG applications.

Features

Hybrid vector search optimized for AI workloads
Synthetic data generation and management
Tool-calling interface compatible with major AI SDKs
Built-in support for RAG (Retrieval Augmented Generation)
Seamless integration with mdxdb for document storage

Installation

npm install ai-database
# or
pnpm add ai-database
# or
yarn add ai-database

Quick Start

import { createDatabase } from 'ai-database'

// Initialize database with vector search capabilities
const db = createDatabase({
  namespace: 'my-app',
  vectorSearch: true
})

// Store documents with embeddings
await db.collection('documents').store({
  content: 'Example document',
  embeddings: [0.1, 0.2, 0.3]
})

// Perform hybrid search
const results = await db.collection('documents').search({
  query: 'example',
  vector: [0.1, 0.2, 0.3],
  threshold: 0.8
})

Tool Integration

ai-database exports AI-compatible tools that work with any LLM supporting function calling:

import { tools } from 'ai-database'

// Use with any AI SDK (Vercel AI, LangChain, etc)
const searchTool = tools.vectorSearch({
  collection: 'documents',
  namespace: 'my-app'
})

Integration with AI Primitives

ai-database is designed to work seamlessly with other AI Primitives packages:

ai-functions: Provides database operations as callable AI functions
ai-workflows: Enables database integration in AI workflow definitions
ai-agents: Offers database access tools for AI agents

API Reference

[API documentation link]

Dependencies

Built on top of mdxdb for robust document storage and vector search capabilities.