Extract structured text from a PDF, page by page (PyMuPDF). Returns JSON: page count, document metadata, and each page's text with 1-based page numbers — use pages='1-3' or '1,5' to limit output.

Extract embedded images from a PDF as base64 PNG (PyMuPDF). Each image carries page, width, height and byte size. Set include_data=false to inventory images without transferring payloads; oversized images return metadata only.

Extract tabular data from a PDF (Camelot lattice/stream with a PyMuPDF fallback; flavor='auto' picks the first strategy that finds tables). Returns JSON with rows, columns and CSV per table.

Index a PDF into the local RAG store: PyMuPDF text -> LangChain chunking -> local fastembed embeddings -> FAISS. Call once per document, then use search_context for queries. Re-indexing a changed file replaces its chunks (set replace=false to append).

Semantic search over indexed documents (FAISS + local embeddings). Returns the top-k chunks ranked by cosine similarity with source file and page provenance — feed these to your answer generation. Optional source_filter narrows hits to one document.

Report vector store status: documents, chunk count, dimension, FAISS index type.

Drop every indexed document and clear the persisted vector store.