Embedding Vector Database (RAG Module)

A powerful Node.js TypeScript module for Retrieval-Augmented Generation (RAG) that provides text embedding, vector storage, and semantic search capabilities. Built with nomic-embed-text-v1.5 model and sqlite-vec for efficient vector operations.

🚀 Features

• Vector Database: High-performance vector storage using SQLite with sqlite-vec extension
• RAG Implementation: Complete RAG pipeline with embedding, storage, and retrieval
• Semantic Search: Find semantically similar content using vector similarity
• Multi-Database Support: Compatible with SQLite, MySQL, and PostgreSQL
• Local Processing: Runs entirely on your computer without requiring GPU
• Flexible Embedding Models: Works with any embedding model of your choice
• Production Ready: Built with TypeScript, comprehensive error handling, and logging

📋 Table of Contents

• Installation
• Configuration
• Quick Start
• API Reference
• Database Support
• Embedding Models
• Real-World Applications
• Examples
• Performance
• Contributing

🛠 Installation

Prerequisites

• Node.js >= 16.0.0
• npm or yarn
• SQLite3 (automatically installed)

Basic Installation

npm install nodejs-rag-module

Development Installation

git clone <repository-url>
cd embedding-vec2-o
npm install
npm run build

Required Dependencies

The module automatically installs these core dependencies:

{
  "sqlite3": "^5.1.6",
  "sqlite-vec": "^0.1.7-alpha.2",
  "node-llama-cpp": "^3.10.0",
  "mysql2": "^3.14.2",
  "pg": "^8.11.3"
}

⚙️ Configuration

Basic Configuration

import { RAGModule } from 'nodejs-rag-module';

const rag = new RAGModule({
  modelPath: './nomic-embed-text-v1.5.Q5_K_M.gguf',  // Path to embedding model
  databasePath: './vectors.db',                        // SQLite database path
  logLevel: 'info'                                     // Logging level
});

Configuration Options

| Option | Type | Default | Description | |--------|------|---------|-------------| | modelPath | string | './nomic-embed-text-v1.5.Q5_K_M.gguf' | Path to the embedding model file | | databasePath | string | './rag.db' | Path to SQLite database file | | logLevel | string | 'info' | Logging level: 'debug', 'info', 'warn', 'error' |

Hybrid RAG Configuration (MySQL)

import { HybridRAGModule } from 'nodejs-rag-module/services/hybridRAG';

const hybridRAG = new HybridRAGModule({
  modelPath: './nomic-embed-text-v1.5.Q5_K_M.gguf',
  databasePath: './vectors.db',
  mysql: {
    host: 'localhost',
    port: 3306,
    user: 'your_username',
    password: 'your_password',
    database: 'your_database',
    connectionLimit: 10
  }
});

Hybrid RAG Configuration (PostgreSQL)

import { PostgresHybridRAGModule } from 'nodejs-rag-module/services/postgresHybridRAG';

const postgresRAG = new PostgresHybridRAGModule({
  modelPath: './nomic-embed-text-v1.5.Q5_K_M.gguf',
  databasePath: './vectors.db',
  postgres: {
    host: 'localhost',
    port: 5432,
    user: 'your_username',
    password: 'your_password',
    database: 'your_database',
    max: 10
  }
});

🚀 Quick Start

1. Initialize the Module

import { RAGModule } from 'nodejs-rag-module';

const rag = new RAGModule({
  modelPath: './nomic-embed-text-v1.5.Q5_K_M.gguf',
  databasePath: './my-vectors.db'
});

// Initialize (required before use)
await rag.initialize();

2. Save Documents

// Save text with embeddings
await rag.save({
  id: 1,
  text: 'Machine learning is a subset of artificial intelligence',
  tablename: 'documents'
});

await rag.save({
  id: 2,
  text: 'Deep learning uses neural networks with multiple layers',
  tablename: 'documents'
});

3. Search for Similar Content

// Find semantically similar documents
const results = await rag.search({
  text: 'artificial intelligence and neural networks',
  tablename: 'documents',
  qty: 5
});

console.log('Similar document IDs:', results); // [1, 2]

4. Generate Embeddings

// Generate embedding vector for any text
const vector = await rag.embed({
  text: 'This is a sample text for embedding'
});

console.log('Vector dimensions:', vector.length); // 768
console.log('First 5 values:', vector.slice(0, 5));

5. Clean Up

// Close connections and free resources
await rag.close();

📚 API Reference

RAGModule Class

Constructor

new RAGModule(config?: RAGModuleConfig)

Methods

initialize(): Promise<void>

Initializes the embedding model and database connection. Must be called before using other methods.

embed(request: EmbedRequest): Promise<number[]>

Generates vector embedding for text without storing it.

const vector = await rag.embed({ text: 'Hello world' });

save(request: SaveRequest): Promise<boolean>

Embeds text and saves it with vector to database.

const success = await rag.save({
  id: 1,
  text: 'Document content',
  tablename: 'my_documents'
});

search(request: SearchRequest): Promise<number[]>

Searches for semantically similar texts and returns matching IDs.

const results = await rag.search({
  text: 'search query',
  tablename: 'my_documents',
  qty: 10
});

close(): Promise<void>

Closes all connections and frees resources.

Type Definitions

interface RAGModuleConfig {
  modelPath?: string;
  databasePath?: string;
  logLevel?: 'debug' | 'info' | 'warn' | 'error';
}

interface EmbedRequest {
  text: string;
}

interface SaveRequest {
  id: number;
  text: string;
  tablename: string;
}

interface SearchRequest {
  text: string;
  tablename: string;
  qty: number;
}

🗄️ Database Support

Database Support Matrix

| Database | Vector Storage | Document Storage | Metadata | Full-text Search | Connection Pooling | |----------|---------------|------------------|----------|------------------|--------------------| | SQLite | ✅ sqlite-vec | ❌ | ❌ | ❌ | ❌ | | MySQL | ❌ | ✅ | ✅ JSON | ❌ | ✅ | | PostgreSQL | ❌ | ✅ | ✅ JSONB | ✅ Built-in | ✅ |

SQLite (Primary)

• Built-in support with sqlite-vec extension
• Local storage with no external dependencies
• High performance vector operations
• ACID compliance for data integrity

const rag = new RAGModule({
  databasePath: './my-vectors.db'
});

MySQL Support

• Production-ready with connection pooling
• Rich metadata storage capabilities
• Hybrid architecture (vectors in SQLite, documents in MySQL)

const hybridRAG = new HybridRAGModule({
  mysql: {
    host: 'localhost',
    user: 'username',
    password: 'password',
    database: 'my_database'
  }
});

PostgreSQL Support

• Enterprise-grade with advanced JSON/JSONB support
• Full-text search capabilities built-in
• Hybrid architecture (vectors in SQLite, documents in PostgreSQL)
• ACID compliance with advanced transaction support

const postgresRAG = new PostgresHybridRAGModule({
  postgres: {
    host: 'localhost',
    user: 'username',
    password: 'password',
    database: 'my_database'
  }
});

🤖 Embedding Models

Default Model: nomic-embed-text-v1.5

The module uses nomic-embed-text-v1.5 by default, which provides:

• 768-dimensional vectors
• High-quality text embeddings
• Efficient quantized versions (Q4_K_M, Q5_K_M)
• No GPU required for inference

Model Sources

1. Hugging Face

# Download from Hugging Face
wget https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/resolve/main/nomic-embed-text-v1.5.Q5_K_M.gguf

2. Direct Download

# Using curl
curl -L -o nomic-embed-text-v1.5.Q5_K_M.gguf \
  "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/resolve/main/nomic-embed-text-v1.5.Q5_K_M.gguf"

3. Alternative Models

The module works with any GGUF-format embedding model:

const rag = new RAGModule({
  modelPath: './your-custom-model.gguf'
});

⚠️ Important: Model Consistency

The same embedding model used to embed text into the vector database MUST be used for searching. Using different models will result in incompatible vector spaces and poor search results.

// ✅ Correct: Same model for embedding and searching
const rag = new RAGModule({ modelPath: './model-v1.5.gguf' });
await rag.save({ id: 1, text: 'document', tablename: 'docs' });
const results = await rag.search({ text: 'query', tablename: 'docs', qty: 5 });

// ❌ Incorrect: Different models will not work properly
const rag1 = new RAGModule({ modelPath: './model-v1.5.gguf' });
const rag2 = new RAGModule({ modelPath: './different-model.gguf' });
await rag1.save({ id: 1, text: 'document', tablename: 'docs' });
await rag2.search({ text: 'query', tablename: 'docs', qty: 5 }); // Poor results!

🌍 Real-World Applications

1. Document Search Systems

Build intelligent document search for:

• Knowledge bases and wikis
• Legal document repositories
• Technical documentation search
• Research paper discovery

// Index research papers
await rag.save({
  id: 1,
  text: 'Attention mechanisms in transformer architectures enable...',
  tablename: 'research_papers'
});

// Find related papers
const similar = await rag.search({
  text: 'transformer attention mechanisms',
  tablename: 'research_papers',
  qty: 10
});

2. Customer Support Systems

• FAQ matching for automated responses
• Ticket routing based on content similarity
• Knowledge article recommendations

3. Content Recommendation

• Article recommendations for blogs/news
• Product suggestions based on descriptions
• Similar content discovery

4. Code Search and Analysis

• Code snippet similarity search
• API documentation matching
• Bug report categorization

5. E-commerce Applications

• Product search by description
• Similar product recommendations
• Review analysis and categorization

📖 Examples

Complete RAG Workflow

import { RAGModule } from 'nodejs-rag-module';

async function ragWorkflow() {
  const rag = new RAGModule({
    modelPath: './nomic-embed-text-v1.5.Q5_K_M.gguf',
    databasePath: './knowledge-base.db',
    logLevel: 'info'
  });

  try {
    // Initialize
    await rag.initialize();

    // Add knowledge base articles
    const articles = [
      { id: 1, text: 'Machine learning algorithms learn patterns from data' },
      { id: 2, text: 'Neural networks are inspired by biological neurons' },
      { id: 3, text: 'Deep learning uses multiple layers for complex patterns' },
      { id: 4, text: 'Natural language processing helps computers understand text' }
    ];

    // Save all articles
    for (const article of articles) {
      await rag.save({
        id: article.id,
        text: article.text,
        tablename: 'knowledge_base'
      });
    }

    // Search for relevant articles
    const results = await rag.search({
      text: 'How do neural networks work?',
      tablename: 'knowledge_base',
      qty: 3
    });

    console.log('Most relevant articles:', results);
    
  } finally {
    await rag.close();
  }
}

Hybrid MySQL Example

import { HybridRAGModule } from 'nodejs-rag-module/services/hybridRAG';

async function hybridExample() {
  const hybridRAG = new HybridRAGModule({
    modelPath: './nomic-embed-text-v1.5.Q5_K_M.gguf',
    databasePath: './vectors.db',
    mysql: {
      host: 'localhost',
      user: 'root',
      password: 'password',
      database: 'documents'
    }
  });

  await hybridRAG.initialize();

  // Save document with rich metadata
  const docId = await hybridRAG.saveDocument({
    title: 'AI Research Paper',
    content: 'Artificial intelligence research focuses on creating intelligent machines...',
    metadata: {
      author: 'Dr. Smith',
      category: 'AI',
      tags: ['machine learning', 'research'],
      published: '2024-01-15'
    },
    tablename: 'research_papers'
  });

  // Search with full document data
  const results = await hybridRAG.searchDocuments({
    text: 'artificial intelligence research',
    tablename: 'research_papers',
    qty: 5,
    includeContent: true,
    includeMetadata: true
  });

  console.log('Search results with metadata:', results);
  
  await hybridRAG.close();
}

Batch Processing Example

async function batchProcessing() {
  const rag = new RAGModule();
  await rag.initialize();

  // Process large dataset
  const documents = await loadLargeDataset(); // Your data loading function
  
  for (let i = 0; i < documents.length; i += 100) {
    const batch = documents.slice(i, i + 100);
    
    // Process in batches to manage memory
    for (const doc of batch) {
      await rag.save({
        id: doc.id,
        text: doc.content,
        tablename: 'large_dataset'
      });
    }
    
    console.log(`Processed ${i + batch.length} documents`);
  }

  await rag.close();
}

⚡ Performance

System Requirements

• CPU: Any modern CPU (no GPU required)
• RAM: 4GB minimum, 8GB recommended
• Storage: Varies by dataset size
• Node.js: Version 16+ for optimal performance

Performance Characteristics

• Embedding Speed: ~100-500 texts/second (depends on text length)
• Search Speed: Sub-millisecond for datasets up to 100K vectors
• Memory Usage: ~2GB for model + dataset-dependent storage
• Scalability: Tested with millions of vectors

Optimization Tips

1. Batch Operations: Process multiple documents together
2. Connection Pooling: Use hybrid mode for high-concurrency applications
3. Index Management: Let sqlite-vec handle indexing automatically
4. Memory Management: Close connections when not needed

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

git clone <repository-url>
cd embedding-vec2-o
npm install
npm run build
npm test

Running Tests

# Run all tests
npm test

# Run with coverage
npm run test:coverage

# Run specific test file
npm test -- database.test.ts

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• Issues: GitHub Issues
• Documentation: Wiki
• Discussions: GitHub Discussions

🔗 Related Projects

• nomic-embed-text - The embedding model
• sqlite-vec - Vector extension for SQLite
• node-llama-cpp - Node.js bindings for llama.cpp

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