LEANN：将百万文档RAG系统装进笔记本电脑，存储减少97%

Introduction

In the era of artificial intelligence, powerful retrieval systems often come with significant trade-offs: massive cloud storage costs, privacy concerns, and computational overhead that excludes personal devices. LEANN (Lightweight Embedding Architecture for Neural Networks) presents a paradigm shift. It is an innovative vector database designed to democratize personal AI by transforming your standard laptop into a capable Retrieval-Augmented Generation (RAG) system. LEANN can index and search through millions of documents while reducing storage footprint by an astonishing 97% compared to conventional solutions, all without compromising search accuracy.

在人工智能时代，强大的检索系统往往伴随着显著的权衡：巨大的云存储成本、隐私问题以及将个人设备排除在外的计算开销。LEANN（轻量级神经网络嵌入架构）代表了一种范式转变。它是一款创新的向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.，旨在通过将你的标准笔记本电脑转变为功能强大的检索增强生成（RAG）系统，使个人AI大众化。LEANN可以索引和搜索数百万份文档，同时与传统解决方案相比，将存储占用空间减少惊人的97%，且不损害搜索准确性。

Core Innovation: On-Demand Embedding Computation

The secret behind LEANN's efficiency lies in its fundamental architectural departure from traditional vector databases. Instead of pre-computing and storing dense vector embeddings for every document—a process that consumes vast amounts of storage—LEANN employs a graph-based selective recomputation strategy coupled with higher-order preserving pruning.

Think of your data as a sophisticated graph where nodes represent text chunks and edges represent semantic relationships. LEANN stores this lightweight graph structure and a powerful language model. When a search query is received, it intelligently traverses this graph, recomputing embeddings only for the most relevant nodes on the fly. This "compute-when-needed" approach eliminates the need for a massive, static embedding storage layer.

LEANN高效背后的秘诀在于其从根本上与传统向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.不同的架构。LEANN没有为每个文档预计算并存储密集的向量嵌入（这一过程会消耗大量存储空间），而是采用了基于图的选择性重计算LEANN的核心技术，通过图结构动态选择需要重新计算的嵌入向量，避免存储所有嵌入。策略，并结合了高阶保持剪枝在图优化过程中保留重要高阶连接关系的剪枝技术，确保搜索准确性不受影响。。

你可以将你的数据想象成一个复杂的图，其中节点代表文本块，边代表语义关系。LEANN存储这个轻量级的图结构和一个强大的语言模型。当收到搜索查询时，它会智能地遍历这个图，并仅为最相关的节点动态重计算嵌入。这种"按需计算"的方法消除了对庞大、静态的嵌入存储层的需求。

Key Features and Benefits

Uncompromising Privacy

Your data never leaves your laptop. LEANN operates entirely locally, with no calls to external APIs (like OpenAI), no cloud services, and no ambiguous Terms of Service governing your personal information.

你的数据永远不会离开你的笔记本电脑。LEANN完全在本地运行，不调用外部API（如OpenAI），不使用云服务，也没有管理你个人信息的模糊服务条款。

Privacy: Your data never leaves your device. No OpenAI, no cloud, no "Terms of Service". (隐私：你的数据绝不会离开你的设备。不使用OpenAI，不使用云服务，也没有“服务条款”。)

Radical Storage Efficiency

By replacing gigabyte-heavy embedding storage with a compact graph and model, LEANN achieves dramatic space savings. Intelligent graph pruning and efficient Compressed Sparse Row (CSR) storage formats further minimize overhead. The result is a system that is consistently lightweight on both disk and memory.

通过用紧凑的图和模型取代占用数GB空间的嵌入存储，LEANN实现了巨大的空间节省。智能的图剪枝和高效的压缩稀疏行（CSR）存储格式进一步减少了开销。其结果是一个在磁盘和内存上都始终保持轻量级的系统。

Lightweight: Graph-based recomputation eliminates heavy embedding storage, while smart pruning and CSR format minimize graph overhead. Always less storage, lower memory. (轻量级：基于图的重计算消除了繁重的嵌入存储，而智能图剪枝和CSR格式压缩稀疏行格式，用于高效存储稀疏图数据，最小化存储开销。最大限度地减少了图存储开销。始终减少存储空间，降低内存使用。)

Portable Personal AI Memory

Your entire indexed knowledge base—your emails, documents, notes—becomes portable. Transfer it between your devices at minimal cost, carrying your personalized AI context with you wherever you go.

你整个被索引的知识库——你的电子邮件、文档、笔记——变得可移植。以极低的成本在设备间传输，无论你走到哪里，都能随身携带你个性化的AI上下文。

Portable: Transfer your entire knowledge base between devices (and even to others) at minimal cost—your personal AI memory travels with you. (便携：以最低的成本在设备之间（甚至与其他设备之间）传输您的整个知识库——你的个人AI记忆随身携带。)

Robust Scalability for Personal Data

Personal data is often messy, unstructured, and constantly growing. LEANN is built to handle this chaos gracefully, easily scaling to manage expanding personal datasets and the growing memory generated by AI agents, scenarios that often overwhelm traditional vector databases.

个人数据通常是混乱的、非结构化的且不断增长的。LEANN旨在优雅地处理这种混乱，轻松扩展以管理不断增长的个人数据集和AI代理生成的内存，这些场景常常会让传统向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.不堪重负。

Scalability: Easily handle messy personal data that would crash traditional vector databases, effortlessly managing growing personalized data and agent-generated memory. (可扩展性：轻松处理会导致传统向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.崩溃的混乱个人数据，轻松管理不断增长的个性化数据和代理生成的内存。)

Accuracy Without the Bloat

This is the cornerstone of LEANN's value proposition. It delivers search result quality on par with heavyweight, storage-intensive solutions. You get the accuracy without the associated storage cost.

这是LEANN价值主张的基石。它提供的搜索结果质量与重量级、存储密集型的解决方案不相上下。你在获得高准确性的同时，无需承担相关的存储成本。

No Loss in Accuracy: Reduce storage by 97% while maintaining the same search quality as heavyweight solutions. (不损失准确性：在保持与重量级解决方案相同搜索质量的同时，减少97%的存储空间。)

Transform Your Laptop into an AI Powerhouse

With LEANN, your laptop gains the capability to perform semantic search across a vast, unified knowledge base compiled from your digital life:

Local Filesystems: Index and search documents, notes, and media files. (本地文件系统：索引和搜索文档、笔记和媒体文件。)
Communication: Email archives, chat histories (e.g., WeChat, iMessage). (通信：电子邮件存档、聊天记录（如微信、iMessage）。)
Agent Memory: Context from AI chats (e.g., ChatGPT, Claude history). (代理内存：来自AI聊天的上下文（如ChatGPT、Claude历史记录）。)
Real-time Streams: Data from platforms like Slack and Twitter/X. (实时数据流：来自Slack和Twitter/X等平台的数据。)
Code Repositories: Enable semantic understanding of your codebase. (代码库：实现对代码库的语义理解。)
External Knowledge: Integrate massive external datasets (e.g., 60 million documents). (外部知识：集成海量外部数据集（例如6000万份文档）。)

All processing occurs locally, incurring no cloud fees and ensuring complete data privacy.

通过LEANN，你的笔记本电脑获得了对来自你数字生活的、庞大的统一知识库进行语义搜索基于语义理解而非关键词匹配的搜索技术，能理解查询意图和内容含义。的能力：

本地文件系统：索引和搜索文档、笔记和媒体文件。

通信：电子邮件存档、聊天记录（如微信、iMessage）。

代理内存：来自AI聊天的上下文（如ChatGPT、Claude历史记录）。

实时数据流：来自Slack和Twitter/X等平台的数据。

代码库：实现对代码库的语义理解。

外部知识：集成海量外部数据集（例如6000万份文档）。

所有处理都在本地进行，不产生云费用，并确保数据完全私有。

Seamless Integration with Claude Code

It's worth noting that Claude Code's native functionality is limited to basic grep-style keyword searches. LEANN acts as a plug-and-play semantic search service compatible with the Model Context Protocol (MCP). It integrates directly with Claude Code, unlocking intelligent retrieval capabilities without requiring any change to your existing workflow.

值得注意的是，Claude Code的原生功能仅限于基本的grep式关键词搜索。LEANN作为一个即插即用的语义搜索基于语义理解而非关键词匹配的搜索技术，能理解查询意图和内容含义。服务，与模型上下文协议（MCP）兼容。它直接与Claude Code集成，无需改变现有工作流程即可解锁智能检索功能。

The Proof is in the Numbers: Dramatic Efficiency Gains

The performance metrics speak volumes. To index 60 million text chunks, a traditional vector database might require approximately 201 GB of space just for the embeddings. LEANN accomplishes the same task using only about 6 GB—a 97% reduction. This efficiency breakthrough is what makes it feasible to store a comprehensive index of everything from your email to your browsing history directly on your laptop's drive.

性能指标说明了一切。要索引6000万个文本块，一个传统的向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.可能仅嵌入就需要大约201 GB的空间。LEANN完成同样的任务仅需约6 GB——减少了97%。正是这种效率上的突破，使得将涵盖从电子邮件到浏览历史记录的所有内容的综合索引直接存储在笔记本电脑驱动器上成为可能。

For detailed benchmark comparisons across different applications and datasets, please refer to the project's official documentation.

有关不同应用程序和数据集的详细基准测试比较，请参阅项目的官方文档。

Conclusion

LEANN represents a significant step towards truly personal and private artificial intelligence. By rethinking the core architecture of vector search—shifting from storing embeddings to storing the capability to regenerate them intelligently—it breaks down the barriers of cost, privacy, and hardware requirements. It empowers individuals to own and operate sophisticated AI retrieval systems on their personal computers, turning every laptop into a potent, private, and portable AI assistant.

LEANN代表了迈向真正个人化和私有化人工智能的重要一步。通过重新思考向量搜索的核心架构——从存储嵌入转向存储智能再生嵌入的能力——它打破了成本、隐私和硬件要求的壁垒。它使个人能够在自己的个人电脑上拥有和运行复杂的AI检索系统，将每一台笔记本电脑转变为强大的、私有的、可便携的AI助手。