1.0.0

• Semantic search over your files: meaning-based retrieval powered by AI embeddings and a vector database, not keyword matching.
• Go library, injectable by design: compose an Engine from an embedder, a metadata store, a vector store, and strategies; bring your own implementation of any interface.
• Multi-format indexing: built-in strategies for PDF, Markdown, source code, DOCX, and plain text, each parsed and chunked to fit its structure.
• Structure-aware chunking: headings, functions/classes, and document sections become titled chunks, sharpening retrieval relevance.
• Source code understanding: per-definition chunks for Go, JavaScript, TypeScript, Python, PHP, Java, Rust, C/C++, C#, and more, titled with their nesting path.
• Pluggable embedders: configurable OpenAI-compatible embedding API that works with local servers like LM Studio and Ollama, or any hosted model.
• Swappable storage: SQLite metadata store and sqlite-vec vector store included; point them at separate databases, or implement your own backend.
• Exact vector search: k-nearest-neighbor (kNN) search over embeddings for precise, full-recall results.
• Incremental delta indexing: re-running only re-embeds changed files, detected by content hash.
• Pure-Go format parsing: no external binaries; PDF via WebAssembly, code via a pure-Go lexer, DOCX via the standard library.
• A self-hostable backend for meaning-based document search: a building block for retrieval-augmented generation (RAG) and NotebookLM-style search over your own content.