Oak Semantic Search

Advanced AI-powered semantic search for source code, leveraging AST-aware chunking and vector embeddings.

🎯 Overview

Oak Semantic Search goes beyond traditional keyword search by understanding the structure and meaning of your code. It uses oak-core to intelligently chunk source code into meaningful units (like functions, classes, and documentation) and indexes them using state-of-the-art embedding models and vector databases.

✨ Features

AST-Aware Chunking: Intelligently splits code based on logical boundaries (Definitions, Statements, etc.) rather than simple line counts.
Embedding Integration: Built-in support for fastembed to generate high-quality vector representations of code.
Vector DB Support: Designed to work with vectordb (LanceDB) for efficient similarity search.
Contextual Search: Find code by describing its functionality in natural language.
Role-Based Indexing: Prioritizes definitions and documentation for better search relevance.

🚀 Quick Start

Basic usage of the SemanticSearcher:

use oak_semantic_search::SemanticSearcher;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let searcher = SemanticSearcher::new("./my_vector_db").await?;
    
    // Indexing code is handled via the index_code method
    // searcher.index_code::<MyLanguage>(&root, source, "my_project_table").await?;
    
    // Search using natural language
    let results = searcher.search("how to handle user authentication", 5).await?;
    for result in results {
        println!("Found: {}", result);
    }
    
    Ok(())
}

📋 Examples

Intelligent Code Chunking

The library uses a ChunkCollector to extract meaningful pieces of code:

// Internally, it identifies nodes with roles like:
// - UniversalElementRole::Definition
// - UniversalElementRole::Statement
// - UniversalElementRole::Documentation

🔧 Advanced Features

Custom Embedding Models

Oak Semantic Search leverages fastembed, allowing you to choose from various pre-trained models optimized for code or general text.

Integration with MCP

The library implements the SemanticSearch trait, making it compatible with the Model Context Protocol (MCP) for AI agent integration.

🏗️ Integration

Oak MCP: Powers the semantic search tool in AI-assisted coding environments.
Documentation Portals: Enhances documentation with "search by meaning" capabilities.
Code Discovery: Helps developers find relevant code patterns in large monorepos.

📊 Performance

Fast Indexing: Concurrent embedding generation for high throughput.
Scalable Search: Vector-based retrieval remains fast even with millions of code chunks.
Efficient Storage: Optimized vector storage with minimal disk footprint.

🤝 Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

Oak Semantic Search - Understanding the meaning behind the code 🚀

oak-semantic-search 0.0.2