Enables LLMs to search and retrieve information from large technical documentation (OpenAPI specs, markdown) via intelligent chunking and semantic search.
Enables semantic search through markdown documentation in code repositories using AI embeddings. Provides intelligent document chunking and similarity-based search to help users find relevant documentation based on meaning rather than just keywords.
Aggregates documentation from multiple sources (llms.txt format or web scraping) and provides semantic search capabilities using vector embeddings and hybrid search for each documentation source.
Provides token-efficient document search and retrieval for LLMs by returning relevant document sections within specified token budgets. It utilizes section-aware parsing and Bloom filter elimination to offer high-speed, zero-dependency access to large documents.