Provides a hybrid search engine for Obsidian vaults, enabling LLM agents to query notes with BM25 keyword and vector semantic search, metadata filtering, and sibling-document retrieval.
Enables LLMs to search and retrieve information from large technical documentation (OpenAPI specs, markdown) via intelligent chunking and semantic search.
Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
Enables agentic document retrieval over markdown, CSV, and JSONL using BM25 and tree navigation, without vector databases or embeddings, allowing AI agents to search, browse, and retrieve structured document sections.