Enables fast, token-efficient access to large documentation files in llms.txt format through semantic search. Solves token limit issues by searching first and retrieving only relevant sections instead of dumping entire documentation.
Enables vectorless RAG by letting LLM clients like Claude and Cursor parse documents, inspect outlines, and retrieve specific sections with heading breadcrumbs. Supports PDF, Word, HTML, and PowerPoint without LLM calls or heavy ML models.
Provides token-efficient semantic search and document retrieval by indexing PDFs, text, and markdown files into local notebooks using ChromaDB. It enables AI agents to query relevant passages from large documents through local embedding models like Hugging Face or Ollama.
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.
Enables LLMs to search and retrieve information from large technical documentation (OpenAPI specs, markdown) via intelligent chunking and semantic search.
