akyn-ai

Turn any data source into an MCP server in 5 minutes.

Build knowledge bases that AI assistants like Claude and Cursor can query directly. No infrastructure needed.

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What is this?

This SDK lets you create MCP (Model Context Protocol) servers from any data source. Your docs, PDFs, websites, or any text can become a queryable knowledge base that AI assistants can access directly.

Use cases:
- 📚 Make your documentation searchable by Cursor/Claude
- 🔍 Build RAG (Retrieval-Augmented Generation) pipelines
- 🤖 Create custom AI assistants with domain knowledge
- 📖 Index research papers, guides, or any text content

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Quick Start

Install

npm install akyn-ai

Basic Usage

import { KnowledgeBase } from 'akyn-ai'
// Create a knowledge base

const kb = new KnowledgeBase({

  name: 'my-docs',

  description: 'My project documentation',

})
// Add your content

await kb.addDirectory('./docs')           // Add all docs from a folder

await kb.addFile('./README.md')           // Add a specific file

await kb.addURL('https://docs.example.com') // Scrape a URL

await kb.addText('Important info here')   // Add raw text
// Serve as MCP server

kb.serveStdio()  // For Cursor/Claude Desktop

Connect to Cursor

Add to your .cursor/mcp.json:

{
  "mcpServers": {
    "my-docs": {
      "command": "npx",
      "args": ["ts-node", "./my-kb.ts"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Connect to Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "my-docs": {
      "command": "npx",
      "args": ["ts-node", "/path/to/my-kb.ts"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

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Features

📁 Multi-Source Ingestion

// Files (PDF, DOCX, TXT, Markdown)
await kb.addFile('./guide.pdf')
await kb.addFile('./manual.docx')
// Directories (recursive)

await kb.addDirectory('./docs', {

  recursive: true,

  extensions: ['.md', '.txt', '.pdf'],

})
// URLs

await kb.addURL('https://docs.example.com')

await kb.addURLs([

  'https://example.com/page1',

  'https://example.com/page2',

])
// Raw text

await kb.addText('Custom content here', 'My Notes')

🔍 Smart Chunking

Text is automatically split into optimal chunks for embedding:

const kb = new KnowledgeBase({
  name: 'my-kb',
  chunking: {
    maxSize: 1000,    // Max characters per chunk
    overlap: 200,     // Overlap between chunks for context
  },
})

🧠 Flexible Embeddings

Uses OpenAI by default, but you can bring your own:

import { KnowledgeBase, type EmbeddingsProvider } from 'akyn-ai'
// Use OpenAI (default)

const kb = new KnowledgeBase({ name: 'my-kb' })
// Or customize OpenAI settings

import { OpenAIEmbeddings } from 'akyn-ai'
const kb = new KnowledgeBase({

  name: 'my-kb',

  embeddings: new OpenAIEmbeddings({

    model: 'text-embedding-3-large',  // Better quality

    apiKey: 'sk-...',

  }),

})
// Or bring your own provider

class MyEmbeddings implements EmbeddingsProvider {

  readonly dimensions = 384

  

  async embed(text: string) {

    // Your embedding logic here

    return { embedding: [...], tokenCount: 100 }

  }

  

  async embedBatch(texts: string[]) {

    return Promise.all(texts.map(t => this.embed(t)))

  }

}
const kb = new KnowledgeBase({

  name: 'my-kb',

  embeddings: new MyEmbeddings(),

})

💾 Vector Stores

In-Memory (Default)

Perfect for development and small datasets:

import { InMemoryVectorStore } from 'akyn-ai'
const kb = new KnowledgeBase({

  name: 'my-kb',

  vectorStore: new InMemoryVectorStore({

    persistPath: './kb-data.json',  // Optional: save to disk

  }),

})

Qdrant

For production workloads, use Qdrant - a high-performance vector database:

import { KnowledgeBase, QdrantVectorStore } from 'akyn-ai'
const kb = new KnowledgeBase({

  name: 'my-kb',

  vectorStore: new QdrantVectorStore(),  // That's it!

})

Local Setup (Docker)

# Start Qdrant with one command
docker run -p 6333:6333 qdrant/qdrant
With persistent storage
docker run -p 6333:6333 -v ./qdrant_data:/qdrant/storage qdrant/qdrant

Qdrant Cloud

For managed hosting, use Qdrant Cloud:

const kb = new KnowledgeBase({
  name: 'my-kb',
  vectorStore: new QdrantVectorStore({
    url: 'https://your-cluster.cloud.qdrant.io',
    apiKey: process.env.QDRANT_API_KEY,
    collection: 'my-docs',  // Optional: defaults to 'akyn_documents'
  }),
})

| Option | Type | Default | Description |
|--------|------|---------|-------------|
| url | string | http://localhost:6333 | Qdrant server URL |
| apiKey | string | - | API key (required for Qdrant Cloud) |
| collection | string | akyn_documents | Collection name |
| dimensions | number | auto-detected | Vector dimensions |

Custom Vector Store

Implement the VectorStore interface for other databases (Pinecone, Weaviate, etc.):

import type { VectorStore } from 'akyn-ai'
class MyVectorStore implements VectorStore {

  async add(document) { / ... / }

  async addBatch(documents) { / ... / }

  async search(embedding, options) { / ... / }

  async delete(id) { / ... / }

  async clear() { / ... / }

  async count() { / ... / }

}

🌐 Multiple Transport Options

// Stdio (for Cursor/Claude Desktop)
kb.serveStdio()
// HTTP (for web clients)

await kb.serveHttp({ port: 3000 })

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CLI Usage

You can also use the CLI without writing code:

# Index a directory
npx akyn-ai --dir ./docs --name "My Docs"
Use a config file
npx akyn-ai --config ./kb-config.json
Run as HTTP server
npx akyn-ai --dir ./docs --http 3000

Config File Format

{
  "name": "My Knowledge Base",
  "description": "Project documentation",
  "sources": [
    { "type": "directory", "path": "./docs" },
    { "type": "file", "path": "./README.md" },
    { "type": "url", "url": "https://docs.example.com" }
  ]
}

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API Reference

KnowledgeBase

Main class for creating and managing knowledge bases.

const kb = new KnowledgeBase({
  name: string,           // Required: Name of the knowledge base
  description?: string,   // Optional: Description
  version?: string,       // Optional: Version (default: '1.0.0')
  embeddings?: EmbeddingsProvider,  // Optional: Custom embeddings
  vectorStore?: VectorStore,        // Optional: Custom vector store
  chunking?: ChunkOptions,          // Optional: Chunking settings
  retrieval?: RetrievalOptions,     // Optional: Retrieval settings
})

Retrieval Options

Control how many results are returned and their minimum quality. These options are configured in your code (not exposed to AI agents), giving you full control over retrieval behavior.

const kb = new KnowledgeBase({
  name: 'my-kb',
  retrieval: {
    topK: 10,         // Return up to 10 chunks per query
    threshold: 0.5,   // Only return chunks with similarity score >= 0.5
  },
})

| Option | Type | Default | Description |
|--------|------|---------|-------------|
| topK | number | 5 | Maximum number of chunks to retrieve per query |
| threshold | number | 0 | Minimum similarity score (0-1). Set to 0 to return all results, or higher (e.g. 0.5, 0.7) to filter out less relevant chunks |

Methods

| Method | Description |
|--------|-------------|
| addText(text, name?) | Add raw text content |
| addFile(path, name?) | Add a file (PDF, DOCX, TXT, MD) |
| addDirectory(path, options?) | Add all files from a directory |
| addURL(url, name?) | Add content from a URL |
| addURLs(urls) | Add multiple URLs |
| query(question, options?) | Query the knowledge base |
| listSources() | List all indexed sources |
| serveStdio(options?) | Start stdio MCP server |
| serveHttp(options?) | Start HTTP MCP server |

HTTP Server Options

await kb.serveHttp({
  port: 3000,           // Port to listen on (default: 3000)
  host: '0.0.0.0',      // Host to bind to (default: '0.0.0.0')
  cors: true,           // Enable CORS (default: true)
  corsOrigin: '',      // CORS origin (default: '')
  debug: false,         // Enable debug logging (default: false)
})

Utilities

The SDK also exports utilities you can use independently:

import {
  // Text processing
  normalizeText,
  chunkText,
  extractTextFromHTML,
  stripMarkdown,
  
  // File loading
  loadFile,
  loadDirectory,
  loadURL,
  
  // Embeddings
  OpenAIEmbeddings,
  cosineSimilarity,
  
  // Vector stores
  InMemoryVectorStore,
  QdrantVectorStore,
} from 'akyn-ai'

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MCP Tools

When connected via MCP, your knowledge base exposes these tools:

query

Search the knowledge base with a natural language question.

{
  "name": "query",
  "arguments": {
    "question": "How do I authenticate?"
  }
}

| Parameter | Type | Description |
|-----------|------|-------------|
| question | string | The question to search for |

> Note: The number of results and similarity threshold are configured via the retrieval option when creating the KnowledgeBase. See Retrieval Options.

list_sources

List all indexed sources in the knowledge base.

{
  "name": "list_sources",
  "arguments": {}
}

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Examples

See the examples directory for more:

- Basic usage
- HTTP server
- Custom embeddings
- Qdrant vector store

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Requirements

- Node.js 18+
- OpenAI API key (or custom embeddings provider)

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Want Managed Hosting?

Building something bigger? Check out Akyn for:

- ☁️ Hosted knowledge bases
- 👥 Team collaboration
- 📊 Usage analytics
- 💰 Monetization (charge for queries)
- 🔐 API key management

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Contributing

Contributions welcome! Please read our contributing guidelines first.

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License

MIT © Akyn AI