UC Berkeley 开源向量索引 LEANN：将 60M 文本块的 RAG 索引从 201 GB 压缩到 6 GB

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Home Editors Pick Agentic AIMeet LEANN: An Open-Source Vector Index that Shrinks a 60M-Chunk RAG Index...

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Meet LEANN: An Open-Source Vector Index that Shrinks a 60M-Chunk RAG Index from 201 GB to 6 GB

Asif Razzaq

October 27, 2026

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UC Berkeley’s Sky Computing Lab built LEANN, an open-source vector index that stores almost no embeddings. The LEANN vector index recomputes embeddings at query time instead of saving them to disk. The repo benchmark shows a 60 million chunk Wikipedia index dropping from 201 GB to 6 GB. The research paper won the Best Research Paper award at MLSys 2026. So, it brings semantic search and RAG over personal data, on a laptop.

TL;DR

Size: 6 GB for 60M chunks (repo); 4 GB in the paper’s 76 GB Wikipedia setup. Default embedder: Contriever (110M params). It is an index, not an LLM, so no context length applies.
Runs on: laptops and workstations. The paper tested an RTX 4090 box with 32 GB RAM and an M1 Ultra Mac. A CPU-only Linux install exists, with no published CPU latency.
Performance: matches HNSW downstream accuracy at 90% recall, using under 5% of raw-data storage.
Best: 25.5% NQ accuracy, identical to HNSW, using 4 GB instead of 188 GB.
Worst: HotpotQA retrieval takes 44.80 s on the Mac, 50.1% of total pipeline time.

What is LEANN?

LEANN is a low-storage approximate nearest neighbor (ANN) index built on top of FAISS. It targets one problem: vector indexes are often bigger than the data they index. The research paper shows a 76 GB corpus needs 173 GB of embeddings plus a 15 GB HNSW graph. That more than doubles the raw data.

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LEANN keeps a pruned graph, drops the full vectors, and rebuilds them on demand. The code is MIT licensed and installs with

uv pip install leann

How does LEANN recompute embeddings without stalling search?

Graph indexes like HNSW visit only a small set of nodes per query. LEANN exploits that. It stores the graph plus a compact product quantization (PQ) table. At query time, it re-embeds only nodes on the search path.

3 techniques keep this practical:

Two-level search: cheap PQ distances rank neighbors first. Only top candidates get exact recomputation.
Dynamic batching: candidates from several hops are grouped, for example 64 at once, for GPU efficiency.
High-degree preserving pruning: most nodes keep few edges, while the top 2% “hub” nodes keep more.

In ablations, two-level search gives an average 1.4x speedup. Adding batching raises it to 1.8x. Embedding recomputation still accounts for about 76% of query latency.

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How does LEANN perform on benchmarks?

The research team tested NQ, TriviaQA, GPQA and HotpotQA against a 60M-passage RPJ-Wiki datastore. Latency is measured at 90% Recall@3. All figures below are self-reported in the research paper.

On NQ, with Qwen3-4B on an RTX 4090, LEANN and HNSW both reach 25.5% downstream accuracy. LEANN uses 4 GB of storage. HNSW uses 188 GB. BM25 scores 18.3%, and PQ compression scores 17.9%.

The cost is search time. LEANN retrieval takes 2.48 s on NQ, versus 0.05 s for HNSW. Generation takes about 20 s, so end-to-end latency rises from 20.95 s to 23.34 s.

Across datasets on the RTX 4090, LEANN retrieval ranges from 1.12 s (GPQA) to 7.12 s (HotpotQA). The IVF-based recomputation baseline, inspired by EdgeRAG, is up to 200x slower. LEANN improves exact match by up to 11.8% over BM25.

The GitHub repo reports similar savings on personal data. 400K chat messages shrink from 1.8 GB to 64 MB. 38K browser entries shrink from 130 MB to 6.4 MB.

LEANN vs other vector indexes

Feature	LEANN	HNSW (FAISS)	DiskANN	IVF-Recompute (EdgeRAG-style)	PQ compression
Approach	Pruned graph, recompute on demand	Graph plus all vectors in RAM	Graph and vectors on SSD, PQ in RAM	IVF clusters, recompute on demand	Compressed vectors plus graph
Storage, 76 GB corpus	4 GB	188 GB	270 GB	Below 5% of raw data	20 GB
NQ retrieval at 90% recall (RTX 4090)	2.48 s	0.05 s	0.03 s	307.61 s	Fails to reach 90%
HotpotQA retrieval at 90% recall (RTX 4090)	7.12 s	0.05 s	0.11 s	429.46 s	Fails to reach 90%
NQ downstream accuracy (Qwen3-4B)	25.5%	25.5%	Not disclosed	Not disclosed	17.9%
Fits 32 GB RAM workstation	Yes	No (OOM)	Yes	Yes	Yes
License	MIT	MIT	MIT	Not disclosed	Varies by implementation

Storage, latency and accuracy figures come from the LEANN paper v2, Tables 1 and 2 and Section 7.2 (self-reported). License and design rows link to each project’s own source.

Where can developers use LEANN?

The repo ships ready apps for documents, Apple Mail, Chrome history, WeChat, iMessage, and ChatGPT or Claude exports. MCP readers pull live data from Slack and Twitter bookmarks. LEANN also runs as a semantic search MCP server for Claude Code.

On 30 SWE-Bench Pro tasks from ContextBench, the team reports 24.2% initial relevant-code recall versus 11.4% for BM25. The agent also used 8.4% fewer tokens.

HNSW is the default backend, with DiskANN for faster search. Generation works with Ollama, Hugging Face, Anthropic, or any OpenAI-compatible API. It supports Ubuntu, Arch, WSL, macOS and Windows. Some apps, like Apple Mail search, are macOS only.

Key Takeaways

LEANN stores a pruned graph and recomputes embeddings at query time.
60M chunks: 201 GB down to 6 GB in the repo benchmark.
Matches HNSW accuracy (25.5% on NQ) at 90% recall.
Search costs seconds: 2.48 s vs 0.05 s for HNSW on NQ.
MIT licensed and an MLSys 2026 Best Research Paper.

Check out the Paper, GitHub Repo and PyPI package. Also,feel free to follow us on**Twitterand don’t forget to join our150k+ML SubRedditand Subscribe toour Newsletter. Wait! are you on telegram?now you can join us on telegram as well.**

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Asif Razzaq

Website|+ posts Bio ⮌

Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.