Changelog

Append-only log of major changes to LEANN (new features, breaking changes, important fixes). Newest entries at the bottom.

2026-03-05: IVF backend incremental update support

Added leann-backend-ivf with FAISS IndexIVFFlat + DirectMap.Hashtable.
IVF supports in-place add_vectors and remove_ids without full rebuild.
leann build is now idempotent: re-running on an existing index does incremental update (add new, remove deleted, re-index modified files).
Fixed incremental build chunking inconsistency and shared metadata dict bug.
Fixed IVF incremental update duplicate chunks from stale passages.jsonl.

Added leann_build MCP tool: build or incrementally update indexes directly from Claude Code.
Added leann_status MCP tool: inspect index details (backend, embedding model, chunk/file count, size).
leann_search now uses --json output with file paths always included, formatted as markdown code blocks.
Fixed float32 JSON serialization bug in leann search --json.
Cleaned up MCP tool descriptions (concise, no emoji).

Rewrote docs/roadmap.md with current P0/P1 priorities from GitHub issue #237.
Added docs/ultimate_goal.md — long-term vision (personal data platform, best code retrieval MCP, multimodal, local-first).
Added self-contained documentation principle and dev doc maintenance rules to CLAUDE.md.

Add leann-backend-flashlib-ivf, a GPU IVF-Flat (inverted file) approximate-NN backend built on FlashLib (flash_ivf_flat, Triton/CuteDSL) — the GPU counterpart of the FAISS ivf backend. Registered as backend name flashlib_ivf; install via uv sync --extra flashlib-ivf or pip install leann-backend-flashlib-ivf. Shares the nlist/nprobe recall knobs with the ivf backend, so the two are drop-in comparable. Requires a CUDA GPU at build (k-means) and search.
Add benchmarks/flashlib_ivf_vs_faiss_ivf.py: head-to-head flashlib_ivf (GPU) vs ivf (FAISS, CPU) at matched nlist across an nprobe sweep (build time, single-query latency, batched throughput, recall@k vs exact ground truth). On an NVIDIA H200 at 1M x 768 vectors (nlist=4096, 8 CPU threads): ~13x faster build and, at nprobe=32, ~6.5x lower single-query latency / ~75x higher batched throughput at comparable recall (GPU latency stays ~flat while CPU grows linearly with nprobe).
Docs: docs/flashlib_backend_guide.md gains a flashlib_ivf section.