docs: make Chinese README the default

README.md

@@ -1,3 +1,9 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/google/langextract) · [上游 README](https://github.com/google/langextract/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <p align="center">
   <a href="https://github.com/google/langextract">
     <img src="https://raw.githubusercontent.com/google/langextract/main/docs/_static/logo.svg" alt="LangExtract Logo" width="128" />
@@ -11,48 +17,48 @@
 ![Tests](https://github.com/google/langextract/actions/workflows/ci.yaml/badge.svg)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17015089.svg)](https://doi.org/10.5281/zenodo.17015089)
 
-## Table of Contents
+## 目录
 
-- [Introduction](#introduction)
-- [Why LangExtract?](#why-langextract)
-- [Quick Start](#quick-start)
-- [Installation](#installation)
-- [API Key Setup for Cloud Models](#api-key-setup-for-cloud-models)
-- [Adding Custom Model Providers](#adding-custom-model-providers)
-- [Using OpenAI Models](#using-openai-models)
-- [Using Local LLMs with Ollama](#using-local-llms-with-ollama)
-- [More Examples](#more-examples)
-  - [*Romeo and Juliet* Full Text Extraction](#romeo-and-juliet-full-text-extraction)
-  - [Medication Extraction](#medication-extraction)
-  - [Radiology Report Structuring: RadExtract](#radiology-report-structuring-radextract)
-- [Community Providers](#community-providers)
-- [Contributing](#contributing)
-- [Testing](#testing)
-- [Disclaimer](#disclaimer)
+- [简介](#introduction)
+- [为什么选择 LangExtract？](#why-langextract)
+- [快速入门](#quick-start)
+- [安装](#installation)
+- [云端模型的 API 密钥配置](#api-key-setup-for-cloud-models)
+- [添加自定义模型提供商](#adding-custom-model-providers)
+- [使用 OpenAI 模型](#using-openai-models)
+- [通过 Ollama 使用本地 LLM](#using-local-llms-with-ollama)
+- [更多示例](#more-examples)
+  - [*Romeo and Juliet* 全文抽取](#romeo-and-juliet-full-text-extraction)
+  - [用药信息抽取](#medication-extraction)
+  - [放射学报告结构化：RadExtract](#radiology-report-structuring-radextract)
+- [社区提供商](#community-providers)
+- [贡献](#contributing)
+- [测试](#testing)
+- [免责声明](#disclaimer)
 
-## Introduction
+## 简介
 
-LangExtract is a Python library that uses LLMs to extract structured information from unstructured text documents based on user-defined instructions. It processes materials such as clinical notes or reports, identifying and organizing key details while ensuring the extracted data corresponds to the source text.
+LangExtract 是一个 Python 库，可根据用户定义的指令，使用 LLM 从非结构化文本文档中抽取结构化信息。它可处理临床笔记或报告等材料，识别并整理关键细节，同时确保抽取的数据与源文本相对应。
 
-## Why LangExtract?
+## 为什么选择 LangExtract？
 
-1.  **Precise Source Grounding:** Maps every extraction to its exact location in the source text, enabling visual highlighting for easy traceability and verification.
-2.  **Reliable Structured Outputs:** Enforces a consistent output schema based on your few-shot examples, leveraging controlled generation in supported models like Gemini to guarantee robust, structured results.
-3.  **Optimized for Long Documents:** Overcomes the "needle-in-a-haystack" challenge of large document extraction by using an optimized strategy of text chunking, parallel processing, and multiple passes for higher recall.
-4.  **Interactive Visualization:** Instantly generates a self-contained, interactive HTML file to visualize and review thousands of extracted entities in their original context.
-5.  **Flexible LLM Support:** Supports your preferred models, from cloud-based LLMs like the Google Gemini family to local open-source models via the built-in Ollama interface.
-6.  **Adaptable to Any Domain:** Define extraction tasks for any domain using just a few examples. LangExtract adapts to your needs without requiring any model fine-tuning.
-7.  **Leverages LLM World Knowledge:** Utilize precise prompt wording and few-shot examples to influence how the extraction task may utilize LLM knowledge. The accuracy of any inferred information and its adherence to the task specification are contingent upon the selected LLM, the complexity of the task, the clarity of the prompt instructions, and the nature of the prompt examples.
+1.  **精确的源文本锚定（Source Grounding）：** 将每次抽取映射到源文本中的确切位置，支持可视化高亮，便于追溯与核验。
+2.  **可靠的结构化输出：** 基于你的少样本（few-shot）示例强制执行一致的输出模式（schema），并在 Gemini 等支持的模型中利用受控生成（controlled generation），以保证稳健、结构化的结果。
+3.  **针对长文档优化：** 通过优化的文本分块、并行处理与多轮遍历策略，克服大型文档抽取中的“大海捞针”难题，提高召回率。
+4.  **交互式可视化：** 即时生成自包含的交互式 HTML 文件，在原始上下文中可视化并审阅数千条抽取实体。
+5.  **灵活的 LLM 支持：** 支持你所偏好的模型，从 Google Gemini 系列等云端 LLM，到通过内置 Ollama 接口使用的本地开源模型。
+6.  **适配任意领域：** 仅用少量示例即可为任意领域定义抽取任务。LangExtract 可按需适配，无需对模型进行微调。
+7.  **利用 LLM 世界知识：** 通过精确的提示词措辞与少样本示例，影响抽取任务对 LLM 知识的运用方式。任何推断信息的准确性及其对任务规范的遵循程度，取决于所选 LLM、任务复杂度、提示指令的清晰度以及示例的性质。
 
-## Quick Start
+## 快速入门
 
-> **Note:** Using cloud-hosted models like Gemini requires an API key. See the [API Key Setup](#api-key-setup-for-cloud-models) section for instructions on how to get and configure your key.
+> **注意：** 使用 Gemini 等云端托管模型需要 API 密钥。请参阅 [API 密钥配置](#api-key-setup-for-cloud-models) 一节，了解如何获取并配置密钥。
 
-Extract structured information with just a few lines of code.
+只需几行代码即可抽取结构化信息。
 
-### 1. Define Your Extraction Task
+### 1. 定义抽取任务
 
-First, create a prompt that clearly describes what you want to extract. Then, provide a high-quality example to guide the model.
+首先，编写一段清晰描述你想抽取内容的提示词。然后，提供一个高质量示例以引导模型。
 
 ```python
 import langextract as lx
@@ -89,13 +95,13 @@ examples = [
 ]
 ```
 
-> **Note:** Examples drive model behavior. Each `extraction_text` should ideally be verbatim from the example's `text` (no paraphrasing), listed in order of appearance. LangExtract raises `Prompt alignment` warnings by default if examples don't follow this pattern—resolve these for best results.
+> **注意：** 示例驱动模型行为。每个 `extraction_text` 理想情况下应与示例中的 `text` 逐字一致（不要改写），并按出现顺序列出。若示例未遵循该模式，LangExtract 默认会发出 `Prompt alignment` 警告——请解决这些问题以获得最佳效果。
 >
-> **Grounding:** LLMs may occasionally extract content from few-shot examples rather than the input text. LangExtract automatically detects this: extractions that cannot be located in the source text will have `char_interval = None`. Filter these out with `[e for e in result.extractions if e.char_interval]` to keep only grounded results.
+> **锚定（Grounding）：** LLM 偶尔可能从少样本示例而非输入文本中抽取内容。LangExtract 会自动检测这一点：无法在源文本中定位的抽取结果将带有 `char_interval = None`。使用 `[e for e in result.extractions if e.char_interval]` 过滤这些结果，仅保留已锚定的结果。
 
-### 2. Run the Extraction
+### 2. 运行抽取
 
-Provide your input text and the prompt materials to the `lx.extract` function.
+将你的输入文本与提示材料传入 `lx.extract` 函数。
 
 ```python
 # The input text to be processed
@@ -110,18 +116,16 @@ result = lx.extract(
 )
 ```
 
-For advanced constraints beyond examples, such as enum values on extraction
-attributes, Gemini and OpenAI support `output_schema` with or without
-few-shot examples. See
-[Custom output schemas](docs/examples/output_schema.md).
+对于超出示例范围的高级约束（例如抽取属性上的枚举值），Gemini 与 OpenAI 支持带或不带少样本示例的 `output_schema`。请参阅
+[自定义输出模式](docs/examples/output_schema.md)。
 
-> **Model Selection**: `gemini-3.5-flash` is the recommended default, offering strong extraction quality for LangExtract's schema-constrained workflows. For high-volume or cost-sensitive workloads, consider the current stable Flash-Lite model, `gemini-3.1-flash-lite`; for highly complex tasks requiring deeper reasoning, evaluate a current Gemini Pro model from the official model documentation. For large-scale or production use, a paid Gemini tier is suggested to increase throughput and avoid rate limits. See the [rate-limit documentation](https://ai.google.dev/gemini-api/docs/rate-limits#usage-tiers) for details.
+> **模型选择**：`gemini-3.5-flash` 是推荐的默认选项，在 LangExtract 的模式约束工作流中可提供较强的抽取质量。对于高吞吐量或成本敏感的工作负载，可考虑当前稳定的 Flash-Lite 模型 `gemini-3.1-flash-lite`；对于需要更深推理的高度复杂任务，可评估官方模型文档中的当前 Gemini Pro 模型。对于大规模或生产环境使用，建议使用付费 Gemini 套餐以提高吞吐量并避免速率限制。详情请参阅[速率限制文档](https://ai.google.dev/gemini-api/docs/rate-limits#usage-tiers)。
 >
-> **Model Lifecycle**: Note that Gemini models have a lifecycle with defined retirement dates. Users should consult the [official model version documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions) to stay informed about the latest stable and legacy versions.
+> **模型生命周期**：请注意，Gemini 模型具有定义了退役日期的生命周期。用户应查阅[官方模型版本文档](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions)，以了解最新的稳定版与旧版信息。
 
-### 3. Visualize the Results
+### 3. 可视化结果
 
-The extractions can be saved to a `.jsonl` file, a popular format for working with language model data. LangExtract can then generate an interactive HTML visualization from this file to review the entities in context.
+抽取结果可保存为 `.jsonl` 文件——这是处理语言模型数据的常用格式。LangExtract 随后可基于该文件生成交互式 HTML 可视化，以便在上下文中审阅实体。
 
 ```python
 # Save the results to a JSONL file
@@ -136,15 +140,15 @@ with open("visualization.html", "w") as f:
         f.write(html_content)
 ```
 
-This creates an animated and interactive HTML file:
+这将创建一个带动画效果的交互式 HTML 文件：
 
 ![Romeo and Juliet Basic Visualization ](https://raw.githubusercontent.com/google/langextract/main/docs/_static/romeo_juliet_basic.gif)
 
-> **Note on LLM Knowledge Utilization:** This example demonstrates extractions that stay close to the text evidence - extracting "longing" for Lady Juliet's emotional state and identifying "yearning" from "gazed longingly at the stars." The task could be modified to generate attributes that draw more heavily from the LLM's world knowledge (e.g., adding `"identity": "Capulet family daughter"` or `"literary_context": "tragic heroine"`). The balance between text-evidence and knowledge-inference is controlled by your prompt instructions and example attributes.
+> **关于 LLM 知识运用的说明：** 本示例演示的抽取结果紧贴文本证据——为 Lady Juliet 的情绪状态抽取“longing”，并从“gazed longingly at the stars”中识别“yearning”。该任务可修改为生成更多依赖 LLM 世界知识的属性（例如添加 `"identity": "Capulet family daughter"` 或 `"literary_context": "tragic heroine"`）。文本证据与知识推断之间的平衡，由你的提示指令与示例属性控制。
 
-### Scaling to Longer Documents
+### 扩展到更长文档
 
-For larger texts, you can process entire documents directly from URLs with parallel processing and enhanced sensitivity:
+对于较长的文本，你可以通过并行处理和增强的敏感度，直接从 URL 处理整篇文档：
 
 ```python
 # Process Romeo & Juliet directly from Project Gutenberg
@@ -159,25 +163,23 @@ result = lx.extract(
 )
 ```
 
-This approach can extract hundreds of entities from full novels while maintaining high accuracy. The interactive visualization seamlessly handles large result sets, making it easy to explore hundreds of entities from the output JSONL file. **[See the full *Romeo and Juliet* extraction example →](https://github.com/google/langextract/blob/main/docs/examples/longer_text_example.md)** for detailed results and performance insights.
+这种方法可以从完整小说中抽取数百个实体，同时保持高准确度。交互式可视化可无缝处理大型结果集，便于从输出的 JSONL 文件中探索数百个实体。**[查看完整的 *Romeo and Juliet* 抽取示例 →](https://github.com/google/langextract/blob/main/docs/examples/longer_text_example.md)** for detailed results and performance insights.
 
-### Vertex AI Batch Processing
+### Vertex AI 批处理
 
-Save costs on large-scale tasks by enabling Vertex AI Batch API with
-`language_model_params` that include `vertexai=True`, `project`, `location`,
-and a `batch` config.
+通过启用 Vertex AI Batch API，并配合包含 `vertexai=True`、`project`、`location` 以及 `batch` 配置的 `language_model_params`，可在大规模任务中节省成本。
 
-See an example of the Vertex AI Batch API usage in [this example](docs/examples/batch_api_example.md).
+在 [此示例](docs/examples/batch_api_example.md) 中查看 Vertex AI Batch API 的用法示例。
 
-## Installation
+## 安装
 
-### From PyPI
+### 从 PyPI 安装
 
 ```bash
 pip install langextract
 ```
 
-*Recommended for most users. For isolated environments, consider using a virtual environment:*
+*推荐大多数用户使用。对于隔离环境，建议使用虚拟环境：*
 
 ```bash
 python -m venv langextract_env
@@ -185,11 +187,11 @@ source langextract_env/bin/activate  # On Windows: langextract_env\Scripts\activ
 pip install langextract
 ```
 
-### From Source
+### 从源码安装
 
-LangExtract uses modern Python packaging with `pyproject.toml` for dependency management:
+LangExtract 使用现代 Python 打包方式，并通过 `pyproject.toml` 进行依赖管理：
 
-*Installing with `-e` puts the package in development mode, allowing you to modify the code without reinstalling.*
+*使用 `-e` 安装会将软件包置于开发模式，允许你在不重新安装的情况下修改代码。*
 
 
 ```bash
@@ -213,32 +215,31 @@ docker build -t langextract .
 docker run --rm -e LANGEXTRACT_API_KEY="your-api-key" langextract python your_script.py
 ```
 
-## API Key Setup for Cloud Models
+## 云模型 API 密钥配置
 
-When using LangExtract with cloud-hosted models (like Gemini or OpenAI), you'll need to
-set up an API key. On-device models don't require an API key. For developers
-using local LLMs, LangExtract offers built-in support for Ollama and can be
-extended to other third-party APIs by updating the inference endpoints.
+在 LangExtract 中使用云端托管模型（如 Gemini 或 OpenAI）时，你需要
+配置 API 密钥。本地设备上的模型不需要 API 密钥。对于使用本地 LLM 的开发者，
+LangExtract 内置支持 Ollama，并可通过更新推理端点扩展至其他第三方 API。
 
-### API Key Sources
+### API 密钥来源
 
-Get API keys from:
+从以下来源获取 API 密钥：
 
 *   [AI Studio](https://aistudio.google.com/app/apikey) for Gemini models
 *   [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/sdks/overview) for enterprise use
 *   [OpenAI Platform](https://platform.openai.com/api-keys) for OpenAI models
 
-### Setting up API key in your environment
+### 在环境中配置 API 密钥
 
-**Option 1: Environment Variable**
+**选项 1：环境变量**
 
 ```bash
 export LANGEXTRACT_API_KEY="your-api-key-here"
 ```
 
-**Option 2: .env File (Recommended)**
+**选项 2：.env 文件（推荐）**
 
-Add your API key to a `.env` file:
+将 API 密钥添加到 `.env` 文件中：
 
 ```bash
 # Add API key to .env file
@@ -250,7 +251,7 @@ EOF
 echo '.env' >> .gitignore
 ```
 
-In your Python code:
+在 Python 代码中：
 ```python
 import langextract as lx
 
@@ -262,9 +263,9 @@ result = lx.extract(
 )
 ```
 
-**Option 3: Direct API Key (Not Recommended for Production)**
+**选项 3：直接传入 API 密钥（不建议用于生产环境）**
 
-You can also provide the API key directly in your code, though this is not recommended for production use:
+你也可以在代码中直接提供 API 密钥，但不建议在生产环境中这样做：
 
 ```python
 result = lx.extract(
@@ -276,9 +277,9 @@ result = lx.extract(
 )
 ```
 
-**Option 4: Vertex AI (Service Accounts)**
+**选项 4：Vertex AI（服务账号）**
 
-Use [Vertex AI](https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform) for authentication with service accounts:
+使用 [Vertex AI](https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform) for authentication with service accounts:
 
 ```python
 result = lx.extract(
@@ -294,25 +295,25 @@ result = lx.extract(
 )
 ```
 
-## Adding Custom Model Providers
+## 添加自定义模型提供方
 
-LangExtract supports custom LLM providers via a lightweight plugin system. You can add support for new models without changing core code.
+LangExtract 通过轻量级插件系统支持自定义 LLM 提供方。你可以在不修改核心代码的情况下添加对新模型的支持。
 
-- Add new model support independently of the core library
-- Distribute your provider as a separate Python package
-- Keep custom dependencies isolated
-- Override or extend built-in providers via priority-based resolution
+- 独立于核心库添加新模型支持
+- 将你的提供方作为独立的 Python 软件包分发
+- 隔离自定义依赖
+- 通过基于优先级的解析覆盖或扩展内置提供方
 
-See the detailed guide in [Provider System Documentation](langextract/providers/README.md) to learn how to:
+请参阅 [Provider System Documentation（提供方系统文档）](langextract/providers/README.md) 中的详细指南，了解如何：
 
-- Register a provider with `@router.register(...)` from `langextract.providers`
-- Publish an entry point for discovery
-- Optionally provide a schema with `get_schema_class()` for structured output
-- Integrate with the factory via `create_model(...)`
+- 使用 `@router.register(...)` 从 `langextract.providers` 注册提供方
+- 发布用于发现的入口点（entry point）
+- 可选地使用 `get_schema_class()` 提供结构化输出的 schema
+- 通过 `create_model(...)` 与工厂（factory）集成
 
-## Using OpenAI Models
+## 使用 OpenAI 模型
 
-LangExtract supports OpenAI models (requires optional dependency: `pip install langextract[openai]`):
+LangExtract 支持 OpenAI 模型（需要可选依赖：`pip install langextract[openai]`）：
 
 ```python
 import langextract as lx
@@ -327,15 +328,13 @@ result = lx.extract(
 )
 ```
 
-The OpenAI provider uses structured outputs or JSON mode and auto-determines
-fence behavior — leave `fence_output` and `use_schema_constraints` unset.
-`output_schema` is also supported for OpenAI models that support structured
-outputs; provide a LangExtract output-envelope JSON schema, preferably with the
-`lx.schema` helpers.
+OpenAI 提供方使用结构化输出（structured outputs）或 JSON 模式，并自动确定
+围栏（fence）行为——请将 `fence_output` 和 `use_schema_constraints` 留空。
+对于支持结构化输出的 OpenAI 模型，还支持 `output_schema`；请提供 LangExtract 输出封装（output-envelope）JSON schema，最好配合
+`lx.schema` 辅助工具使用。
 
-For large, non-latency-sensitive OpenAI workloads, enable the OpenAI Batch API
-with `language_model_params`. Batch mode is opt-in and falls back to realtime
-calls when the prompt count is below the configured threshold.
+对于大规模、对延迟不敏感的 OpenAI 工作负载，可通过 `language_model_params` 启用 OpenAI Batch API。
+批处理模式为可选启用，当提示（prompt）数量低于配置阈值时会回退到实时调用。
 
 ```python
 result = lx.extract(
@@ -353,7 +352,7 @@ result = lx.extract(
 )
 ```
 
-For OpenAI-compatible endpoints or non-GPT model IDs (which skip auto-routing), use `ModelConfig` with an explicit provider:
+对于 OpenAI 兼容端点或非 GPT 模型 ID（会跳过自动路由），请使用 `ModelConfig` 并显式指定提供方：
 
 ```python
 from langextract.factory import ModelConfig
@@ -370,8 +369,8 @@ result = lx.extract(
 )
 ```
 
-## Using Local LLMs with Ollama
-LangExtract supports local inference using Ollama, allowing you to run models without API keys:
+## 使用 Ollama 运行本地 LLM
+LangExtract 支持通过 Ollama 进行本地推理，无需 API 密钥即可运行模型：
 
 ```python
 import langextract as lx
@@ -385,56 +384,52 @@ result = lx.extract(
 )
 ```
 
-The Ollama provider exposes `FormatModeSchema` for JSON mode. Leave `fence_output`
-and `use_schema_constraints` unset so the factory auto-configures from the provider's
-schema. Ollama does not currently support `output_schema`.
+Ollama 提供方为 JSON 模式公开了 `FormatModeSchema`。请将 `fence_output`
+和 `use_schema_constraints` 留空，以便工厂根据提供方的
+schema 自动配置。Ollama 目前不支持 `output_schema`。
 
-**Quick setup:** Install Ollama from [ollama.com](https://ollama.com/), run `ollama pull gemma2:2b`, then `ollama serve`.
+**快速设置：** 从 [ollama.com](https://ollama.com/), 安装 Ollama，运行 `ollama pull gemma2:2b`，然后执行 `ollama serve`。
 
-For detailed installation, Docker setup, and examples, see [`examples/ollama/`](examples/ollama/).
+有关详细安装、Docker 配置和示例，请参阅 [`examples/ollama/`](examples/ollama/)。
 
-## More Examples
+## 更多示例
 
-Additional examples of LangExtract in action:
+LangExtract 的更多实际应用示例：
 
-### *Romeo and Juliet* Full Text Extraction
+### *Romeo and Juliet* 全文抽取
 
-LangExtract can process complete documents directly from URLs. This example demonstrates extraction from the full text of *Romeo and Juliet* from Project Gutenberg (147,843 characters), showing parallel processing, sequential extraction passes, and performance optimization for long document processing.
+LangExtract 可直接从 URL 处理完整文档。本示例演示从 Project Gutenberg 上的 *Romeo and Juliet* 全文（147,843 个字符）中进行抽取，展示并行处理、顺序抽取轮次以及长文档处理的性能优化。
 
-**[View *Romeo and Juliet* Full Text Example →](https://github.com/google/langextract/blob/main/docs/examples/longer_text_example.md)**
+**[查看 *Romeo and Juliet* 全文示例 →](https://github.com/google/langextract/blob/main/docs/examples/longer_text_example.md)**
 
-### Medication Extraction
+### 药物提取
 
-> **Disclaimer:** This demonstration is for illustrative purposes of LangExtract's baseline capability only. It does not represent a finished or approved product, is not intended to diagnose or suggest treatment of any disease or condition, and should not be used for medical advice.
+> **免责声明：** 本演示仅用于说明 LangExtract 的基线能力，不代表成品或已获批产品，不用于诊断或建议任何疾病或病症的治疗，且不应作为医疗建议使用。
 
-LangExtract excels at extracting structured medical information from clinical text. These examples demonstrate both basic entity recognition (medication names, dosages, routes) and relationship extraction (connecting medications to their attributes), showing LangExtract's effectiveness for healthcare applications.
+LangExtract 擅长从临床文本中提取结构化医疗信息。以下示例展示了基础实体识别（药物名称、剂量、给药途径）与关系提取（将药物与其属性关联），体现了 LangExtract 在医疗场景中的有效性。
 
-**[View Medication Examples →](https://github.com/google/langextract/blob/main/docs/examples/medication_examples.md)**
+**[查看药物提取示例 →](https://github.com/google/langextract/blob/main/docs/examples/medication_examples.md)**
 
-### Radiology Report Structuring: RadExtract
+### 放射学报告结构化：RadExtract
 
-Explore RadExtract, a live interactive demo on HuggingFace Spaces that shows how LangExtract can automatically structure radiology reports. Try it directly in your browser with no setup required.
+探索 RadExtract——HuggingFace Spaces 上的实时交互式演示，展示 LangExtract 如何自动结构化放射学报告。无需任何配置，直接在浏览器中试用。
 
-**[View RadExtract Demo →](https://huggingface.co/spaces/google/radextract)**
+**[查看 RadExtract 演示 →](https://huggingface.co/spaces/google/radextract)**
 
-## Community Providers
+## 社区提供商（Community Providers）
 
-Extend LangExtract with custom model providers! Check out our [Community Provider Plugins](COMMUNITY_PROVIDERS.md) registry to discover providers created by the community or add your own.
+使用自定义模型提供商扩展 LangExtract！查看我们的[社区提供商插件](COMMUNITY_PROVIDERS.md)注册表，发现社区创建的提供商或添加你自己的。
 
-For detailed instructions on creating a provider plugin, see the [Custom Provider Plugin Example](examples/custom_provider_plugin/).
+有关创建提供商插件的详细说明，请参阅[自定义提供商插件示例](examples/custom_provider_plugin/)。
 
-## Contributing
+## 贡献
 
-Contributions are welcome! See [CONTRIBUTING.md](https://github.com/google/langextract/blob/main/CONTRIBUTING.md) to get started
-with development, testing, and pull requests. You must sign a
-[Contributor License Agreement](https://cla.developers.google.com/about)
-before submitting patches.
+欢迎贡献！请参阅 [CONTRIBUTING.md](https://github.com/google/langextract/blob/main/CONTRIBUTING.md) 了解如何开始开发、测试和提交拉取请求。提交补丁前，你必须签署[贡献者许可协议](https://cla.developers.google.com/about)
+（Contributor License Agreement）。
 
+## 测试
 
-
-## Testing
-
-To run tests locally from the source:
+从源码在本地运行测试：
 
 ```bash
 # Clone the repository
@@ -448,28 +443,28 @@ pip install -e ".[test]"
 pytest tests
 ```
 
-Or reproduce the full CI matrix locally with tox:
+或使用 tox 在本地复现完整 CI 矩阵：
 
 ```bash
 tox  # runs pylint + pytest on Python 3.10 and 3.11
 ```
 
-### Ollama Integration Testing
+### Ollama 集成测试
 
-If you have Ollama installed locally, you can run integration tests:
+若本地已安装 Ollama，可运行集成测试：
 
 ```bash
 # Test Ollama integration (requires Ollama running with gemma2:2b model)
 tox -e ollama-integration
 ```
 
-This test will automatically detect if Ollama is available and run real inference tests.
+该测试会自动检测 Ollama 是否可用，并运行真实推理测试。
 
-## Development
+## 开发
 
-### Code Formatting
+### 代码格式化
 
-This project uses automated formatting tools to maintain consistent code style:
+本项目使用自动化格式化工具以保持一致的代码风格：
 
 ```bash
 # Auto-format all code
@@ -480,32 +475,30 @@ isort langextract tests --profile google --line-length 80
 pyink langextract tests --config pyproject.toml
 ```
 
-### Pre-commit Hooks
+### Pre-commit 钩子
+
+自动格式化检查：
 
-For automatic formatting checks:
 ```bash
 pre-commit install  # One-time setup
 pre-commit run --all-files  # Manual run
 ```
 
-### Linting
+### 代码检查（Linting）
 
-Run linting before submitting PRs:
+提交 PR 前请运行代码检查：
 
 ```bash
 pylint --rcfile=.pylintrc langextract tests
 ```
 
-See [CONTRIBUTING.md](CONTRIBUTING.md) for full development guidelines.
+完整开发指南请参阅 [CONTRIBUTING.md](CONTRIBUTING.md)。
 
-## Disclaimer
+## 免责声明
 
-This is not an officially supported Google product. If you use
-LangExtract in production or publications, please cite accordingly and
-acknowledge usage. Use is subject to the [Apache 2.0 License](https://github.com/google/langextract/blob/main/LICENSE).
-For health-related applications, use of LangExtract is also subject to the
-[Health AI Developer Foundations Terms of Use](https://developers.google.com/health-ai-developer-foundations/terms).
+这不是 Google 官方支持的产品。若你在生产环境或出版物中使用 LangExtract，请按规定引用并注明使用情况。使用须遵守 [Apache 2.0 许可证](https://github.com/google/langextract/blob/main/LICENSE).
+。对于医疗相关应用，LangExtract 的使用还须遵守[Health AI Developer Foundations 使用条款](https://developers.google.com/health-ai-developer-foundations/terms).
 
 ---
 
-**Happy Extracting!**
+**祝你提取愉快！**