Scaling RAG: Hybrid Search and Hierarchical Chunking for 780k Pages

I built a custom desktop-server search engine designed to help me instantly find and manage documents within my 40GB PDF library.

Technical Overview:

• The Interface: A Windows application where I can search and browse through the results easily.
• The Search Brain: A backend powered by FastAPI that uses “hybrid search”
• Data Processing: Python and Bash scripts that handle the heavy lifting, such as pulling Markdown and generating page thumbnails from every file.
• Annotation AI: vLLM based LLM server that extract metadata.
• The Future: I am currently adding a RAG (Retrieval-Augmented Generation) feature so I can ask the AI questions directly about the content of my documents.