📌 What LEANN Is

LEANN (Low-storage Efficient ANN) is an open-source retrieval-augmented generation (RAG) tool and vector search index designed for private, efficient, on-device semantic search. It uses a graph-based selective recomputation strategy that enables indexing millions of documents using about 97% less storage than traditional vector databases — all with fast and accurate search on your laptop or local server.

👍 Pros & 👎 Cons

👍 Pros

✔️ Extreme storage efficiency — Up to ~97% less storage than traditional vector DBs like FAISS.
✔️ 100% private & on-device — Your data never leaves your machine.
✔️ Multi-source RAG support — Index files, emails, chats, browser history, codebases, and more.
✔️ Compatible with many LLMs — Works with OpenAI, HuggingFace, Ollama, and other OpenAI-compatible APIs.
✔️ CLI + Python API — Flexible for developers and power users.

👎 Cons

⚠️ Setup complexity — Installing dependencies (like DiskANN) and tuning graph parameters requires technical knowledge.
⚠️ Local hardware limits — On-device performance depends on your machine’s CPU/GPU.
⚠️ Still maturing — Feature expansion depends on community and roadmap updates.

📌 How It Can Be Used

🧠 Typical Use Cases

Personal semantic search — Search across your documents without any cloud.
RAG for LLMs — Support intelligent generative AI services with efficient document retrieval.
Private knowledge base — Build a private LLM knowledge layer from emails, chats, PDFs, code, browser history, etc.
Codebase search — AST-aware code search for better developer assistance.
Agent memory store — Efficient on-device memory store for agents and workflows.

📋 Steps to Use LEANN

1) Install LEANN

git clone https://github.com/yichuan-w/LEANN.git leann
cd leann
uv venv
source .venv/bin/activate
uv pip install leann

(optional: build advanced backends like DiskANN for speed).

2) Build an Index

Python example:

from leann import LeannBuilder
builder = LeannBuilder(backend_name="hnsw")
builder.add_text("text goes here")
builder.build_index("myindex.leann")

3) Search / Chat

from leann import LeannSearcher
searcher = LeannSearcher("myindex.leann")
results = searcher.search("your query", top_k=5)

4) Interactive RAG

leann ask myindex --interactive

🎯 Benefits of Using LEANN

✨ Massive storage savings — Keep huge datasets locally.
🔍 Private semantic search — No data exposure to cloud services.
⚡ Fast retrieval — Efficient graph search with recomputed embeddings.
🛠️ Flexible tooling — CLI + Python API for diverse workflows.

🔁 Alternatives

Tool	What It Focuses On
FAISS	Traditional fast vector search (higher storage)
Milvus	Scalable vector database suited for cloud deployments
Pinecone	Hosted vector DB service (cloud, not private)
Qdrant	Vector DB with payload filtering
Weaviate	Hybrid cloud & local vector search
LlamaIndex	RAG orchestration layer with multiple storage backends

LEANN stands out for storage efficiency + privacy + on-device RAG.

LEANN

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