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Build searchable knowledge bases by scraping developer documentation and creating AI-powered vector embeddings for semantic search.

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Dev Knowledge

Build knowledge base that scrapes developer documentation and makes it searchable using AI embeddings.

πŸš€ What it does

Turns documentation websites into a searchable database:

  • Scrapes Node.js, TypeScript, Python, and JavaScript docs
  • Converts HTML to clean Markdown
  • Creates vector embeddings for semantic understanding
  • Stores everything in a local SQLite database
  • Searches using natural language queries

πŸ“¦ Installation

npm install
npm run build

🎯 Usage

Scraping and Indexing

npx tsx src/index.ts
# or
node dist/index.js      # after building

This will:

  1. Initialize the embedding encoder
  2. Scrape configured documentation sources
  3. Convert HTML to Markdown
  4. Generate vector embeddings
  5. Store in SQLite database

βš™οΈ Configuration

Edit src/Processor.ts to modify scraping sources:

export const scraperSchema: ScrapeSchema[] = [
  {
    url: ['https://nodejs.org/docs/latest-v24.x/api/'],
    parse: (content: string): string | null => {
      // Custom parsing logic
    }
  }
  // Add more sources...
]

πŸ—οΈ Project Structure

src/
β”œβ”€β”€ core/                    # Core functionality
β”‚   β”œβ”€β”€ Database.ts          # SQLite + sqlite-vec vector storage
β”‚   └── embedding/           # Vector operations
β”‚       β”œβ”€β”€ Encoder.ts       # Text to vector conversion
β”‚       └── Decoder.ts       # Vector similarity search
β”œβ”€β”€ interfaces/              # TypeScript type definitions
β”œβ”€β”€ utils/                   # Utility classes
β”‚   β”œβ”€β”€ Scraper.ts           # Web scraping with memory management
β”‚   β”œβ”€β”€ Logger.ts            # Logging utilities
β”‚   └── Generator.ts         # ID generation
β”œβ”€β”€ index.ts                 # Main entry point
└── Processor.ts             # HTML to Markdown + embedding pipeline

πŸ“š Dependencies

  • @neabyte/fetch - HTTP client with retry logic
  • @xenova/transformers - Vector embeddings
  • better-sqlite3 - SQLite database
  • sqlite-vec - Vector similarity search
  • turndown - HTML to Markdown conversion
  • jsdom - DOM parsing

πŸ—„οΈ Database Schema

CREATE TABLE embedding (
  id TEXT PRIMARY KEY,
  source TEXT NOT NULL,
  content TEXT NOT NULL,
  vector BLOB NOT NULL,
  timestamp INTEGER NOT NULL
)

πŸ“„ License

This project is licensed under the MIT license. See the LICENSE file for more info.

About

Build searchable knowledge bases by scraping developer documentation and creating AI-powered vector embeddings for semantic search.

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