diff --git a/site/zh/getstarted/install-overview.md b/site/zh/getstarted/install-overview.md
index 3d9e5f828..1fc6709db 100644
--- a/site/zh/getstarted/install-overview.md
+++ b/site/zh/getstarted/install-overview.md
@@ -1,63 +1,62 @@
---
id: install-overview.md
-summary: Milvus is a highly performant, scalable vector database. It supports use cases of a wide range of sizes, from demos running locally in Jupyter Notebooks to massive-scale Kubernetes clusters handling tens of billions of vectors. Currently, there are three Milvus deployment options_ Milvus Lite, Milvus Standalone, and Milvus Distributed.
-title: Overview of Milvus Deployment Options
+summary: Milvus 是一个高性能、可扩展的向量数据库。它支持各种大小的用例,从在 Jupyter Notebooks 中本地运行的演示到处理数百亿向量的大规模 Kubernetes 集群。目前,Milvus 有三种部署选项:Milvus Lite、Milvus Standalone 和 Milvus Distributed。
+title: Milvus 部署选项概述
---
-# Overview of Milvus Deployment Options
+# Milvus 部署选项概述
-Milvus is a highly performant, scalable vector database. It supports use cases of a wide range of sizes, from demos running locally in Jupyter Notebooks to massive-scale Kubernetes clusters handling tens of billions of vectors. Currently, there are three Milvus deployment options: Milvus Lite, Milvus Standalone, and Milvus Distributed.
+Milvus 是一个高性能、可扩展的向量数据库。它支持各种大小的用例,从在 Jupyter Notebooks 中本地运行的演示到处理数百亿向量的大规模 Kubernetes 集群。目前,Milvus 有三种部署选项:Milvus Lite、Milvus Standalone 和 Milvus Distributed。
## Milvus Lite
-[Milvus Lite](https://milvus.io/docs/milvus_lite.md) is a Python library that can be imported into your applications. As a lightweight version of Milvus, it is ideal for quick prototyping in Jupyter Notebooks or running on smart devices with limited resources. Milvus Lite supports the same APIs as other Milvus deployments. The client-side code interacting with Milvus Lite can also work with Milvus instances in other deployment modes.
+[Milvus Lite](https://milvus.io/docs/milvus_lite.md) 是一个可以导入到您的应用程序中的 Python 库。作为 Milvus 的轻量级版本,它非常适合在 Jupyter Notebooks 中快速进行原型设计或在资源有限的智能设备上运行。Milvus Lite 支持与其他 Milvus 部署相同的 API。与 Milvus Lite 交互的客户端代码也可以与其他部署模式下的 Milvus 实例一起使用。
-To integrate Milvus Lite into your applications, run `pip install pymilvus` to install it and use the `MilvusClient("./demo.db")` statement to instantiate a vector database with a local file that persists all your data. For more details, refer to [Run Milvus Lite](https://milvus.io/docs/milvus_lite.md).
+要将 Milvus Lite 集成到您的应用程序中,请运行 `pip install pymilvus` 进行安装,并使用 `MilvusClient("./demo.db")` 语句使用保存所有数据的本地文件实例化矢量数据库。有关更多详细信息,请参阅 [运行 Milvus Lite](https://milvus.io/docs/milvus_lite.md)。
## Milvus Standalone
-Milvus Standalone is a single-machine server deployment. All components of Milvus Standalone are packed into a single [Docker image](https://milvus.io/docs/install_standalone-docker.md), making deployment convenient. If you have a production workload but prefer not to use Kubernetes, running Milvus Standalone on a single machine with sufficient memory is a good option. Additionally, Milvus Standalone supports high availability through master-slave replication.
+Milvus Standalone 是单机服务器部署。Milvus Standalone 的所有组件都打包到单个 [Docker 镜像](https://milvus.io/docs/install_standalone-docker.md) 中,部署起来非常方便。如果您有生产工作负载但不想使用 Kubernetes,那么在具有足够内存的单台机器上运行 Milvus Standalone 是一个不错的选择。此外,Milvus Standalone 通过主从复制支持高可用性。
## Milvus Distributed
-Milvus Distributed can be deployed on [Kubernetes](https://milvus.io/docs/install_cluster-milvusoperator.md) clusters. This deployment features a cloud-native architecture, where ingestion load and search queries are separately handled by isolated nodes, allowing redundancy for critical components. It offers the highest scalability and availability, as well as the flexibility in customizing the allocated resources in each component. Milvus Distributed is the top choice for enterprise users running large-scale vector search systems in production.
+Milvus Distributed 可以部署在 [Kubernetes](https://milvus.io/docs/install_cluster-milvusoperator.md) 集群上。此部署具有云原生架构,其中提取负载和搜索查询由独立节点分别处理,从而为关键组件提供冗余。它提供最高的可扩展性和可用性,以及自定义每个组件中分配的资源的灵活性。 Milvus Distributed 是企业用户在生产中运行大规模向量搜索系统的首选。
-## Choose the Right Deployment for Your Use Case
+## 为您的用例选择正确的部署
-The selection of a deployment mode typically depends on the development stage of your application:
+部署模式的选择通常取决于应用程序的开发阶段:
-- **For Quick Prototyping**
+- **用于快速原型设计**
- If you would like to quickly build something as a prototype or for learning purposes, such as Retrieval Augmented Generation (RAG) demos, AI chatbots, multi-modality search, Milvus Lite itself or a combination of Milvus Lite and Milvus Standalone is suitable. You can use Milvus Lite in notebooks for rapid prototyping and explore various approaches such as different chunking strategies in RAG. You may want to deploy the application built with Milvus Lite in a small-scale production to serve real users, or validating the idea on larger datasets, say more than a few millions of vectors. Milvus Standalone is appropriate. The application logic for Milvus Lite can still be shared as all Milvus deployments have the same client side API. The data stored in Milvus Lite can also be ported to Milvus Standalone with a command line tool.
+如果您想快速构建某些东西作为原型或用于学习目的,例如检索增强生成 (RAG) 演示、AI 聊天机器人、多模态搜索,Milvus Lite 本身或 Milvus Lite 和 Milvus Standalone 的组合都适用。您可以在笔记本中使用 Milvus Lite 进行快速原型设计,并探索各种方法,例如 RAG 中的不同分块策略。您可能希望在小规模生产中部署使用 Milvus Lite 构建的应用程序以服务于真实用户,或者在更大的数据集上验证该想法,比如几百万个向量。Milvus Standalone 是合适的。Milvus Lite 的应用程序逻辑仍然可以共享,因为所有 Milvus 部署都具有相同的客户端 API。 Milvus Lite 中存储的数据也可以通过命令行工具移植到 Milvus Standalone。
-- **Small-Scale Production Deployment**
+- **小规模生产部署**
- For early-stage production, when the project is still seeking product-market fit and agility is more important than scalability, Milvus Standalone is the best choice. It can still scale up to 100M vectors given enough machine resource, while requiring much less DevOps than maintaining a K8s cluster.
+对于早期生产,当项目仍在寻求产品与市场的契合度,敏捷性比可扩展性更重要时,Milvus Standalone 是最佳选择。在有足够的机器资源的情况下,它仍然可以扩展到 100M 个向量,同时所需的 DevOps 比维护 K8s 集群要少得多。
-- **Large-Scale Production Deployment**
+- **大规模生产部署**
- As your business is rapidly growing and the data scale exceeds the capacity of a single server, it's time to consider Milvus Distributed. You can keep using Milvus Standalone for dev or staging environment for its convenience, and operate the K8s cluster that runs Milvus Distributed. This can sustain you towards tens of billions of vectors, as well as providing flexibility on tailoring the node size for your particular workload, such as high-read, infrequent write or high-write, low read cases.
+随着您的业务快速增长,数据规模超出了单个服务器的容量,是时候考虑 Milvus Distributed 了。您可以继续使用 Milvus Standalone 作为开发或预发布环境,以方便使用,并操作运行 Milvus Distributed 的 K8s 集群。这可以支持您处理数百亿个向量,并可以灵活地根据特定工作负载(例如高读取、不频繁写入或高写入、低读取的情况)定制节点大小。
-- **Local Search on Edge Devices**
+- **边缘设备上的本地搜索**
- For searching through private or sensitive on edge devices, you can deploy Milvus Lite on the device without relying on a cloud-based service to do text or image search. This is suitable for cases such as proprietary document search, or on-device object detection.
+如需在边缘设备上搜索私人或敏感信息,您可以在设备上部署 Milvus Lite,而无需依赖基于云的服务进行文本或图像搜索。这适用于专有文档搜索或设备上对象检测等情况。
-The choice of Milvus deployment mode depends on your project's stage and scale. Milvus provides a flexible and powerful solution for various needs, from rapid prototyping to large-scale enterprise deployment.
+Milvus 部署模式的选择取决于您项目的阶段和规模。Milvus 为各种需求提供了灵活而强大的解决方案,从快速原型设计到大规模企业部署。
-- **Milvus Lite** is recommended for smaller datasets, up to a few million vectors.
-- **Milvus Standalone** is suitable for medium-sized datasets, scaling up to 100 million vectors.
-- **Milvus Distributed** is designed for large-scale deployments, capable of handling datasets from 100 million up to tens of billions of vectors.
+- **Milvus Lite** 建议用于较小的数据集,最多几百万个向量。
+- **Milvus Standalone** 适用于中型数据集,可扩展至 1 亿个向量。
+- **Milvus Distributed** 专为大规模部署而设计,能够处理从 1 亿到数百亿向量的数据集。
-![Select deployment option for your use case](../../../assets/select-deployment-option.png)
+![为您的用例选择部署选项](../../../assets/select-deployment-option.png)
-## Comparison on functionalities
+## 功能比较
| Feature | Milvus Lite | Milvus Standalone | Milvus Distributed |
-|--------------------------|---------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|
+| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| SDK / Client Lirary | Python
gRPC | Python
Go
Java
Node.js
C#
RESTful | Python
Java
Go
Node.js
C#
RESTful |
| Data types | Dense Vector
Sparse Vector
Binary Vector
Boolean
Integer
Floating Point
VarChar
Array
JSON | Dense Vector
Sparse Vector
Binary Vector
Boolean
Integer
Floating Point
VarChar
Array
JSON | Dense Vector
Sparse Vector
Binary Vector
Boolean
Integer
Floating Point
VarChar
Array
JSON |
| Search capabilities | Vector Search (ANN Search)
Metadata Filtering
Range Search
Scalar Query
Get Entities by Primary Key
Hybrid Search | Vector Search (ANN Search)
Metadata Filtering
Range Search
Scalar Query
Get Entities by Primary Key
Hybrid Search | Vector Search (ANN Search)
Metadata Filtering
Range Search
Scalar Query
Get Entities by Primary Key
Hybrid Search |
| CRUD operations | ✔️ | ✔️ | ✔️ |
| Advanced data management | N/A | Access Control
Partition
Partition Key | Access Control
Partition
Partition Key
Physical Resource Grouping |
| Consistency Levels | Strong | Strong
Bounded Staleness
Session
Eventual | Strong
Bounded Staleness
Session
Eventual |
-
diff --git a/site/zh/getstarted/install_SDKs/install-go.md b/site/zh/getstarted/install_SDKs/install-go.md
index 5ccff3c81..c83e893d3 100644
--- a/site/zh/getstarted/install_SDKs/install-go.md
+++ b/site/zh/getstarted/install_SDKs/install-go.md
@@ -1,38 +1,36 @@
---
id: install-go.md
-label: Install GO SDK
+label: 安装 GO SDK
related_key: SDK
-summary: Learn how to install the GO SDK of Milvus.
-title: Install Milvus Go SDK
+summary: 了解如何安装 Milvus 的 GO SDK。
+title: 安装 Milvus Go SDK
---
-# Install Milvus Go SDK
+# 安装 Milvus Go SDK
-This topic describes how to install Milvus Go SDK for Milvus.
+本主题介绍如何为 Milvus 安装 Milvus Go SDK。
-Current version of Milvus supports SDKs in Python, Node.js, GO, and Java.
+当前版本的 Milvus 支持 Python、Node.js、GO 和 Java 中的 SDK。
-## Requirement
+## 要求
-GO (1.17 or later) is required.
+需要 GO(1.17 或更高版本)。
-## Install Milvus GO SDK
+## 安装 Milvus GO SDK
-Install the Milvus GO SDK and dependencies via `go get`.
+通过 `go get` 安装 Milvus GO SDK 和依赖项。
```bash
$ go get -u github.com/milvus-io/milvus-sdk-go/v2
```
-## What's next
-
-Having installed Milvus GO SDK, you can:
-
-- Learn the basic operations of Milvus:
- - [Manage Collections](manage-collections.md)
- - [Manage Partitions](manage-partitions.md)
- - [Insert, Upsert & Delete](insert-update-delete.md)
- - [Single-Vector Search](single-vector-search.md)
- - [Hybrid Search](multi-vector-search.md)
+## 下一步
+安装 Milvus GO SDK 后,您可以:
+- 了解 Milvus 的基本操作:
+- [管理集合](manage-collections.md)
+- [管理分区](manage-partitions.md)
+- [插入、更新插入和删除](insert-update-delete.md)
+- [单向量搜索](single-vector-search.md)
+- [混合搜索](multi-vector-search.md)
\ No newline at end of file
diff --git a/site/zh/getstarted/install_SDKs/install-java.md b/site/zh/getstarted/install_SDKs/install-java.md
index d67f65e5e..333785ecc 100644
--- a/site/zh/getstarted/install_SDKs/install-java.md
+++ b/site/zh/getstarted/install_SDKs/install-java.md
@@ -1,33 +1,33 @@
---
id: install-java.md
-label: Install Java SDK
+label: 安装 Java SDK
related_key: SDK
-summary: Learn how to install the Java SDK of Milvus.
-title: Install Milvus Java SDK
+summary: 了解如何安装 Milvus 的 Java SDK。
+title: 安装 Milvus Java SDK
---
-# Install Milvus Java SDK
+# 安装 Milvus Java SDK
-This topic describes how to install Milvus Java SDK for Milvus.
+本主题介绍如何为 Milvus 安装 Milvus Java SDK。
-Current version of Milvus supports SDKs in Python, Node.js, GO, and Java.
+当前版本的 Milvus 支持 Python、Node.js、GO 和 Java 中的 SDK。
-## Requirement
+## 要求
-- Java (8 or later)
-- Apache Maven or Gradle/Grails
+- Java(8 或更高版本)
+- Apache Maven 或 Gradle/Grails
-## Install Milvus Java SDK
+## 安装 Milvus Java SDK
-Run the following command to install Milvus Java SDK.
+运行以下命令安装 Milvus Java SDK。
- Apache Maven
```xml
Try Zilliz Cloud for free! The easiest way to experience Milvus!
+免費試用 Zilliz Cloud! +
免费试用 Zilliz Cloud!体验 Milvus 的最简单方式!
Install Milvus
+ +安装 Milvus
-Learn how to install Milvus using either Docker Compose or on Kubernetes.
-了解如何使用 Docker Compose 或在 Kubernetes 上安装 Milvus。 +
Quick Start
+ +快速启动
-Learn how to quickly run Milvus with sample code.
- +通过示例代码了解如何快速运行 Milvus。
+
-
+
Bootcamp
- Learn how to build vector similarity search applications with Milvus. + 学习如何使用 Milvus 构建矢量相似性搜索应用程序。
- + +Deploy
+部署
+- [配置 Milvus](configure-docker.md +- [管理依赖关系](deploy_s3.md) +- [在云上部署](eks.md) +- [扩展 Milvus 集群](scaleout.md) +- [监控和警报](monitor_overview.md) + -Learn
+学习
-- [System Configuration](system_configuration.md) -- [Architecture Overview](architecture_overview.md) -- [Vector Index](index.md) -- [Similarity Metrics](metric.md) -- [Glossary](glossary.md) - +- [系统配置](system_configuration.md) +- [架构概述](architecture_overview.md) +- [矢量索引](index.md) +- [相似度度量](metric.md) +- [术语表](glossary.md) + + -## What's new in docs +## 新文档 -_Mar 2024 - Milvus 2.4.0 release_ +_2024年3月 - Milvus 2.4.0 发布_ _Mar 2024 - Milvus 2.4.0 release_ -- Added guidance on how to [conduct hybrid search](multi-vector-search.md). -- Added description of [GPU index](gpu_index.md). -- Added guidance on how to [embed your data using PyMilvus](embeddings.md). -- Added descriptions of [Milvus CDC](milvus-cdc-overview.md). +- 添加了关于如何 [进行混合搜索] 的指导(multi-vector-search.md)。 +- 添加了 [GPU 索引] 的说明(gpu_index.md)。 +- 添加了关于如何 [使用 PyMilvus 嵌入数据] 的指导(embeddings.md)。 +- 添加了 [Milvus CDC](milvus-cdc-overview.md) 的说明。 - + -For quick setup, use the [`create_collection()`](https://milvus.io/api-reference/pymilvus/v2.4.x/MilvusClient/Collections/create_collection.md) method of the [`MilvusClient`](https://milvus.io/api-reference/pymilvus/v2.4.x/MilvusClient/Client/MilvusClient.md) class to create a collection with the specified name and dimension. +要快速设置,请使用 [`create_collection()`](https://milvus.io/api-reference/pymilvus/v2.4.x/MilvusClient/Collections/create_collection.md) 类的方法来创建具有指定名称和维度的集合。 [`MilvusClient`](https://milvus.io/api-reference/pymilvus/v2.4.x/MilvusClient/Client/MilvusClient.md) API 端点用于创建具有指定名称和维度的集合。An IndexParams object containing a list of IndexParam objects. | +
Use
+使用
-- [Manage Collections](manage-collections.md) -- [Insert, Upsert, and Delete](insert-update-delete.md) -- [Index Vector Fields](index-vector-fields.md) -- [Single-Vector Search](single-vector-search.md) -- [Get & Scalar Query](get-and-scalar-query.md) -