Universal AI Indexer

Universal AI Indexer is a comprehensive Retrieval-Augmented Generation (RAG) solution built with a focus on data privacy and versatility. While many AI tools require sending sensitive data to external APIs, our indexer operates 100% locally on your own infrastructure. The core of the project is a modular "Adapter" architecture that intelligently parses and chunks various file formats, including Python source code, Markdown documentation, SQL scripts, and plain text files. It uses a sophisticated hybrid search engine that fuses BM25 textual matching with semantic vector similarity via Reciprocal Rank Fusion (RRF), ensuring that users find exactly what they need regardless of whether they use technical keywords or natural language descriptions. Key features include a modern, high-performance Web UI built with FastAPI, a powerful CLI for automated indexing tasks, and support for local LLMs like Llama 3 via Ollama. This ensures a premium user experience while maintaining the "Data Sovereignty" principle—your private code and documents never leave your machine. The roadmap includes expansion into multi-modal support