DeepTutor v1.4.12 Release Notes

Release Date: 2026.06.24

A knowledge-base release on top of v1.4.11: a new LightRAG Server
retrieval engine, a lightweight PyMuPDF4LLM parsing engine, and a FAISS vector
backend that makes large-KB retrieval dramatically faster. Drop-in — existing
knowledge bases keep working untouched.

What's New

LightRAG Server retrieval engine

Point a knowledge base at a standalone LightRAG server you run yourself, and
DeepTutor offloads retrieval to it over HTTP — no local index is built. Each KB
stores its own server URL (plus optional API key) at connect time, so one server
workspace maps to one KB, and queries run in any of LightRAG's modes
(naive/local/global/hybrid/mix). It needs no install and is always available in the
engine picker.

PyMuPDF4LLM document parsing

A fifth parsing engine joins Text-only, MinerU, Docling, and markitdown. PyMuPDF4LLM
rides on PyMuPDF (already bundled), making it the lightest image-capable option — no
model downloads, no CUDA, runs on low-end and GPU-less machines — turning PDFs and
e-books into Markdown and extracting images. Install with
pip install deeptutor[parse-pymupdf4llm]; engines that need packages can now be
installed in the background straight from Settings → Document Parsing.

FAISS vector retrieval

The default LlamaIndex engine now retrieves through FAISS instead of re-parsing
the entire vector store on every query, so search on large knowledge bases is
dramatically faster. faiss-cpu ships as a core dependency with a graceful fallback
to the previous store, so there's nothing new to install.

Upgrade Notes

Drop-in from v1.4.11: pip install -U deeptutor; Docker users pull
ghcr.io/hkuds/deeptutor:latest. No migrations. Existing LlamaIndex knowledge bases
keep loading and querying as-is — re-index a KB to move it onto the faster FAISS
backend. The new engines are opt-in: choose LightRAG Server or PyMuPDF4LLM when
creating a knowledge base or in Settings.

Full Changelog: v1.4.11...v1.4.12