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Akyn AI

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Knowledge bases for AI agents via MCP

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#automation#analytics#scraping#database#documents#devops#git#memory

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.

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

Features

📁 Multi-Source Ingestion

Copy & paste — that's it
// 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:

Copy & paste — that's it
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:

Copy & paste — that's it
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:

Copy & paste — that's it
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:

Copy & paste — that's it
import { KnowledgeBase, QdrantVectorStore } from 'akyn-ai'

const kb = new KnowledgeBase({
 name: 'my-kb',
 vectorStore: new QdrantVectorStore(), // That's it!
})

Local Setup (Docker)

Copy & paste — that's it
# 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:

Copy & paste — that's it
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.):

Copy & paste — that's it
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

Copy & paste — that's it
// Stdio (for Cursor/Claude Desktop)
kb.serveStdio()

// HTTP (for web clients)
await kb.serveHttp({ port: 3000 })

API Reference

KnowledgeBase

Main class for creating and managing knowledge bases.

Copy & paste — that's it
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.

Copy & paste — that's it
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

Copy & paste — that's it
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:

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

MCP Tools

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

query

Search the knowledge base with a natural language question.

Copy & paste — that's it
{
 "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.

Copy & paste — that's it
{
 "name": "list_sources",
 "arguments": {}
}

Examples

See the examples directory for more:

Want Managed Hosting?

Building something bigger? Check out Akyn for:

  • ☁️ Hosted knowledge bases

  • 👥 Team collaboration

  • 📊 Usage analytics

  • 💰 Monetization (charge for queries)

  • 🔐 API key management

Contributing

Contributions welcome! Please read our contributing guidelines first.

License

MIT © Akyn AI