Claude Context with LanceDB

Overview

Claude Context is an AI-powered semantic code search system built on LanceDB's local vector database with optional enterprise-grade Milvus/Zilliz Cloud support. It provides zero-configuration semantic code navigation through the Model Context Protocol.

Features

Semantic Code Search

• Natural language code queries
• Find code by functionality
• Discover similar implementations
• Search across programming languages
• Context-aware results

Local Vector Storage

• LanceDB: Embedded, zero-config vector database
• No external dependencies required
• Fast local indexing
• Incremental updates
• Persistent storage

Enterprise Option

• Milvus/Zilliz Cloud: Production-scale deployments
• Distributed architecture
• Advanced indexing strategies
• High availability
• Team collaboration

Code Understanding

• Function and method detection
• Class hierarchy analysis
• Import and dependency tracking
• Documentation extraction
• Code pattern recognition

Supported Languages

• Python
• JavaScript/TypeScript
• Java
• C++/C
• Go
• Rust
• Ruby
• PHP
• And more

Use Cases

• Codebase exploration
• Finding implementation examples
• Understanding unfamiliar code
• Refactoring assistance
• Code review
• Documentation generation
• Technical onboarding
• Architecture analysis

Zero-Config Setup

# Install and run
npx claude-context

# Automatically indexes current directory
# No configuration needed

Advanced Configuration

LanceDB (Default)

{
  "vectorDb": "lancedb",
  "storagePath": ".claude-context",
  "embeddingModel": "local"
}

Milvus/Zilliz Cloud

{
  "vectorDb": "milvus",
  "endpoint": "https://your-cluster.zillizcloud.com",
  "token": "your-api-token"
}

Indexing Process

1. Code Parsing: Extract functions, classes, methods
2. Embedding Generation: Create semantic vectors
3. Vector Storage: Store in LanceDB or Milvus
4. Index Building: Optimize for fast retrieval

Query Examples

• "Find functions that handle user authentication"
• "Show me database connection code"
• "Where is error handling implemented?"
• "Find API endpoint definitions"
• "Show similar code to this function"

Performance

LanceDB

• Instant startup
• No server required
• Sub-second queries
• Minimal memory usage

Milvus

• Billions of vectors
• Millisecond latency
• Horizontal scaling
• Enterprise features

Technical Implementation

Built with TypeScript, leveraging LanceDB's columnar format and Apache Arrow for efficient vector operations. Optional Milvus integration for production deployments.

Integration

Compatible with Claude Desktop, Cursor, VS Code, and other MCP clients. Works as a standalone tool or integrated into development workflows.