a2e5f5294b
* Drop Python 3.9 support, require Python 3.10+ Python 3.9 reached end-of-life and the codebase uses PEP 604 union type syntax (str | None) which requires Python 3.10+. Changes: - Remove Python 3.9 from CI build matrix - Update requires-python to >=3.10 in all pyproject.toml files - Update classifiers to reflect supported Python versions (3.10-3.13) * Remove macos-13 from CI build matrix GitHub Actions deprecated macos-13 runner (brownout started Sept 2025, fully retired Dec 2025). See: https://github.blog/changelog/2025-09-19-github-actions-macos-13-runner-image-is-closing-down/ * Add macos-15-large for Intel Mac builds Replace deprecated macos-13 with macos-15-large (x86_64 Intel) to continue supporting Intel Mac users. * Set MACOSX_DEPLOYMENT_TARGET=13.x for Intel builds Intel Mac wheels (macos-15-large) now target macOS 13.0/13.3 for backward compatibility, allowing macOS 13/14/15 Intel users to install pre-built wheels. * Remove Intel Mac builds (macos-15-large requires paid plan) Intel Mac users can build from source. This avoids: - Paid GitHub Actions runners (macos-15-large) - Complex cross-compilation setup * Add macos-15-intel for Intel Mac builds (free runner) Use macos-15-intel (free standard runner) instead of macos-15-large (paid). This provides Intel Mac wheel support until Aug 2027. - MACOSX_DEPLOYMENT_TARGET=13.0 for backward compatibility - Replaces deprecated macos-13 runner * Add macOS 26 (beta) to build matrix Add macos-26 (arm64) runner to the build matrix for testing future macOS compatibility. This is currently a beta runner that helps ensure wheels work on upcoming macOS versions. * Fix macos-15-intel deployment target The macos-15-intel runner runs macOS 15.7, so Homebrew libraries are built for macOS 14+. Setting MACOSX_DEPLOYMENT_TARGET=13.0 causes delocate to fail because system libraries require newer macOS. Fix by setting deployment target to 15.0 for macos-15-intel, matching the actual OS version. Intel Mac users will need macOS 15+. * Exclude macos-15-intel + Python 3.13 (no PyTorch wheels available)
LEANN - The smallest vector index in the world
LEANN is a revolutionary vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using 97% less storage than traditional solutions without accuracy loss.
Installation
# Default installation (includes both HNSW and DiskANN backends)
uv pip install leann
Quick Start
from leann import LeannBuilder, LeannSearcher, LeannChat
from pathlib import Path
INDEX_PATH = str(Path("./").resolve() / "demo.leann")
# Build an index (choose backend: "hnsw" or "diskann")
builder = LeannBuilder(backend_name="hnsw") # or "diskann" for large-scale deployments
builder.add_text("LEANN saves 97% storage compared to traditional vector databases.")
builder.add_text("Tung Tung Tung Sahur called—they need their banana‑crocodile hybrid back")
builder.build_index(INDEX_PATH)
# Search
searcher = LeannSearcher(INDEX_PATH)
results = searcher.search("fantastical AI-generated creatures", top_k=1)
# Chat with your data
chat = LeannChat(INDEX_PATH, llm_config={"type": "hf", "model": "Qwen/Qwen3-0.6B"})
response = chat.ask("How much storage does LEANN save?", top_k=1)
License
MIT License