| Name | leann JSON |
| Version |
0.3.0
JSON |
| download |
| home_page | None |
| Summary | LEANN - The smallest vector index in the world. RAG Everything with LEANN! |
| upload_time | 2025-08-16 06:12:00 |
| maintainer | None |
| docs_url | None |
| author | LEANN Team |
| requires_python | >=3.9 |
| license | MIT |
| keywords |
vector-database
rag
embeddings
search
ai
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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
```bash
# Default installation (includes both HNSW and DiskANN backends)
uv pip install leann
```
## Quick Start
```python
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
Raw data
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