Search enterprise codebases using semantic AI to find relevant code snippets across local projects and Git repositories based on natural language queries.
Create a vector index for semantic code search by generating embeddings with tree-sitter and Jina AI, enabling efficient and accurate querying of source code files.
Identify design patterns for programming problems using semantic search. Describe your challenge in natural language to get pattern recommendations with implementation examples.
Discover design patterns for programming problems using semantic search. Describe your challenge in natural language to receive pattern recommendations with implementation examples.
A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.
Enables AI agents to perform semantic search over codebases by converting natural language queries into efficient search patterns like grep and ripgrep. It utilizes LLMs to verify relevance and find code snippets that traditional keyword-based searches might miss.