54df6310c5
- Fix OpenMP library linking in DiskANN CMake configuration - Add timeout protection for HuggingFace model loading to prevent hangs - Improve embedding server process termination with better timeouts - Make DiskANN backend default enabled alongside HNSW - Update documentation to reflect both backends included by default
1.2 KiB
1.2 KiB
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