VectorCodeLens
Semantic codebase analysis platform using vector embeddings and LLMs for intelligent code understanding.
VectorCodeLens is a sophisticated TypeScript application that revolutionizes how developers understand and query codebases. By combining vector embeddings, large language models, and the Model Context Protocol (MCP), it provides intelligent semantic analysis that goes beyond traditional static code analysis. The platform implements a progressive enhancement strategy, gracefully degrading functionality when external AI services are unavailable while maintaining core capabilities. It features modular architecture with clear separation between scanning, analysis, storage, and querying components, making it both maintainable and extensible. Key innovations include intelligent code chunking with structural awareness, semantic search powered by vector similarity, and natural language querying capabilities that allow developers to ask questions about their codebase in plain English. The system automatically discovers patterns, relationships, and architectural insights that would be difficult to identify manually.
Key Metrics
Features
Semantic Code Analysis
Understand code structure, patterns, and relationships using advanced LLM analysis and vector embeddings.
Natural Language Queries
Ask questions about your codebase in plain English and get precise, contextual answers with code references.
Vector-Powered Search
Semantic search capabilities using Qdrant vector database for finding similar code patterns and concepts.
Progressive Enhancement
Graceful degradation ensuring core functionality works even when external AI services are unavailable.
MCP Integration
Full Model Context Protocol implementation enabling seamless integration with AI development tools.
Intelligent Chunking
Structure-aware code segmentation that preserves context and relationships for better analysis accuracy.