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.
Index project files for semantic search by generating vector embeddings, supporting providers like OpenAI, Azure, and Gemini, with options to specify paths and force re-indexing.
Perform semantic code searches with metadata extraction and AST-aware chunking. Search within specified folders, file types, and exclude patterns to retrieve relevant code snippets efficiently.
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.
Enables semantic search over local notes and documents using natural language queries. Supports multiple file types (Markdown, Python, HTML, JSON, CSV, text) with fast local embeddings and persistent ChromaDB vector storage.