Provides AI agents with instant, structured access to electronic component datasheets, pinouts, and electrical specifications without requiring PDF uploads. It enables seamless part searching, design validation, and side-by-side component comparisons across major hardware providers.
Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.
Indexes local documents (PDF, Word, Markdown, text) into a SQLite database for AI agents to search and retrieve bounded, source-located passages. Runs fully locally with optional OCR, preserving privacy.
Enables intelligent search and question-answering over PDF documents using semantic similarity and keyword search. Supports OCR for scanned PDFs, persistent vector storage with ChromaDB, and maintains source tracking with page numbers.
Enables local indexing and semantic search of PDF documents (like AGLC4 style guide) with OCR support, allowing LLM tools to query PDF content and retrieve relevant text snippets with context.
