A semantic code search tool for intelligent, cross-repo context retrieval.
Convert source code to LLM ready knowledge base
Python application to index code locally supporting ollama and voyageai to power semantic searching a large codebase
An AI-powered system for intelligent code search, moving beyond keywords to semantic understanding. It offers multi-dimensional search capabilities across files, classes/interfaces, and methods, each with optimized AI-generated embeddings. Get precise, context-aware results to natural language queries quickly and efficiently.
A very simple setup with pgvector, sentencetransformer, and MCP Python SDK, just to bootstrap indexing code files to facilitate RAG-based search for AI coding agents.
Intelligent code indexing and retrieval system for Ruby on Rails projects with MCP integration
Code indexing examples for converting source code into structured repository maps optimized for semantic search LLM understanding.
🔍 Convert source code into a searchable knowledge base with automatic chunking and intelligent search, now enhanced with MCP support for better integration.
Add a description, image, and links to the code-indexing topic page so that developers can more easily learn about it.
To associate your repository with the code-indexing topic, visit your repo's landing page and select "manage topics."