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.
Enables AI assistants to crawl, index, and retrieve information from technical documentation using semantic search, with optional knowledge graph validation for code hallucination detection.
Crawls documentation websites and provides semantic search capabilities over the content through vector embeddings, enabling natural language queries of technical documentation.
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 ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
