docs: make Chinese README the default
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<!-- WEHUB_ZH_README -->
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> [!NOTE]
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> 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。
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> [English](./README.en.md) · [原始项目](https://github.com/microsoft/promptflow) · [上游 README](https://github.com/microsoft/promptflow/blob/HEAD/README.md)
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> 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。
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# Prompt flow
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[](https://pypi.org/project/promptflow/)
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@@ -12,166 +18,156 @@
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[](https://github.com/microsoft/promptflow/blob/main/CONTRIBUTING.md)
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[](https://github.com/microsoft/promptflow/blob/main/LICENSE)
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> Welcome to join us to make prompt flow better by
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> participating [discussions](https://github.com/microsoft/promptflow/discussions),
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> opening [issues](https://github.com/microsoft/promptflow/issues/new/choose),
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> submitting [PRs](https://github.com/microsoft/promptflow/pulls).
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> 欢迎加入我们，通过参与 [讨论](https://github.com/microsoft/promptflow/discussions),
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> 提交 [issue](https://github.com/microsoft/promptflow/issues/new/choose),
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> 贡献 [PR](https://github.com/microsoft/promptflow/pulls).
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> 让 prompt flow 变得更好。
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**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.
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**Prompt flow** 是一套开发工具，旨在简化基于 LLM 的 AI 应用端到端开发周期，涵盖构思、原型设计、测试、评估到生产部署与监控。它让提示词工程（prompt engineering）更加轻松，并帮助你构建具备生产质量的 LLM 应用。
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With prompt flow, you will be able to:
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借助 prompt flow，你可以：
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- **Create and iteratively develop flow**
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- Create executable [flows](https://microsoft.github.io/promptflow/concepts/concept-flows.html) that link LLMs, prompts, Python code and other [tools](https://microsoft.github.io/promptflow/concepts/concept-tools.html) together.
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- Debug and iterate your flows, especially [tracing interaction with LLMs](https://microsoft.github.io/promptflow/how-to-guides/tracing/index.html) with ease.
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- **Evaluate flow quality and performance**
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- Evaluate your flow's quality and performance with larger datasets.
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- Integrate the testing and evaluation into your CI/CD system to ensure quality of your flow.
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- **Streamlined development cycle for production**
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- Deploy your flow to the serving platform you choose or integrate into your app's code base easily.
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- (Optional but highly recommended) Collaborate with your team by leveraging the cloud version of [Prompt flow in Azure AI](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/overview-what-is-prompt-flow?view=azureml-api-2).
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- **创建并迭代开发 flow**
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- 创建可执行的 [flow](https://microsoft.github.io/promptflow/concepts/concept-flows.html)，将 LLM、提示词、Python 代码及其他 [工具](https://microsoft.github.io/promptflow/concepts/concept-tools.html) 串联在一起。
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- 调试并迭代你的 flow，尤其是轻松实现 [与 LLM 交互的追踪（tracing）](https://microsoft.github.io/promptflow/how-to-guides/tracing/index.html)。
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- **评估 flow 质量与性能**
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- 使用更大数据集评估 flow 的质量与性能。
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- 将测试与评估集成到 CI/CD 系统中，确保 flow 质量。
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- **面向生产的精简开发周期**
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- 将 flow 部署到你选择的 serving 平台，或轻松集成到应用代码库中。
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- （可选但强烈推荐）通过 [Azure AI 中的 Prompt flow](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/overview-what-is-prompt-flow?view=azureml-api-2). 云版本与团队协作。
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------
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## Installation
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## 安装
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To get started quickly, you can use a pre-built development environment. **Click the button below** to open the repo in GitHub Codespaces, and then continue the readme!
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若要快速上手，可使用预构建的开发环境。**点击下方按钮**在 GitHub Codespaces 中打开该仓库，然后继续阅读 readme！
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[](https://codespaces.new/microsoft/promptflow?quickstart=1)
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If you want to get started in your local environment, first install the packages:
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若要在本地环境开始，请先安装相关包：
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Ensure you have a python environment, `python>=3.9, <=3.11` is recommended.
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请确保已配置 Python 环境，推荐使用 `python>=3.9, <=3.11`。
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```sh
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pip install promptflow promptflow-tools
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```
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## Quick Start ⚡
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## 快速开始 ⚡
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**Create a chatbot with prompt flow**
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**使用 prompt flow 创建聊天机器人**
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Run the command to initiate a prompt flow from a chat template, it creates folder named `my_chatbot` and generates required files within it:
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运行以下命令，从聊天模板初始化一个 prompt flow；它会创建名为 `my_chatbot` 的文件夹，并在其中生成所需文件：
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```sh
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pf flow init --flow ./my_chatbot --type chat
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```
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**Setup a connection for your API key**
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**为你的 API 密钥配置连接**
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For OpenAI key, establish a connection by running the command, using the `openai.yaml` file in the `my_chatbot` folder, which stores your OpenAI key (override keys and name with --set to avoid yaml file changes):
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对于 OpenAI 密钥，运行以下命令建立连接，使用 `my_chatbot` 文件夹中的 `openai.yaml` 文件存储你的 OpenAI 密钥（可通过 --set 覆盖密钥和名称，避免修改 yaml 文件）：
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```sh
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pf connection create --file ./my_chatbot/openai.yaml --set api_key=<your_api_key> --name open_ai_connection
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```
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For Azure OpenAI key, establish the connection by running the command, using the `azure_openai.yaml` file:
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对于 Azure OpenAI 密钥，运行以下命令建立连接，使用 `azure_openai.yaml` 文件：
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```sh
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pf connection create --file ./my_chatbot/azure_openai.yaml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection
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```
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**Chat with your flow**
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**与你的 flow 对话**
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In the `my_chatbot` folder, there's a `flow.dag.yaml` file that outlines the flow, including inputs/outputs, nodes, connection, and the LLM model, etc
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在 `my_chatbot` 文件夹中，有一个 `flow.dag.yaml` 文件，其中概述了该 flow，包括输入/输出、节点、连接以及 LLM 模型等。
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> Note that in the `chat` node, we're using a connection named `open_ai_connection` (specified in `connection` field) and the `gpt-35-turbo` model (specified in `deployment_name` field). The deployment_name filed is to specify the OpenAI model, or the Azure OpenAI deployment resource.
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> 请注意，在 `chat` 节点中，我们使用名为 `open_ai_connection` 的连接（在 `connection` 字段中指定）以及 `gpt-35-turbo` 模型（在 `deployment_name` 字段中指定）。deployment_name 字段用于指定 OpenAI 模型，或 Azure OpenAI 部署资源。
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Interact with your chatbot by running: (press `Ctrl + C` to end the session)
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运行以下命令与你的聊天机器人交互：（按 `Ctrl + C` 结束会话）
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```sh
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pf flow test --flow ./my_chatbot --interactive
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```
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**Core value: ensuring "High Quality” from prototype to production**
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**核心价值：确保从原型到生产的“高质量”**
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Explore our [**15-minute tutorial**](examples/tutorials/flow-fine-tuning-evaluation/promptflow-quality-improvement.md) that guides you through prompt tuning ➡ batch testing ➡ evaluation, all designed to ensure high quality ready for production.
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探索我们的 [**15 分钟教程**](examples/tutorials/flow-fine-tuning-evaluation/promptflow-quality-improvement.md)，它将引导你完成提示词调优 ➡ 批量测试 ➡ 评估，全程旨在确保达到可上线的生产质量。
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Next Step! Continue with the **Tutorial** 👇 section to delve deeper into prompt flow.
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下一步！继续阅读下方的 **教程** 👇 部分，深入了解 prompt flow。
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## Tutorial 🏃♂️
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## 教程 🏃♂️
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Prompt flow is a tool designed to **build high quality LLM apps**, the development process in prompt flow follows these steps: develop a flow, improve the flow quality, deploy the flow to production.
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Prompt flow 是一款旨在 **构建高质量 LLM 应用** 的工具；在 prompt flow 中的开发流程遵循以下步骤：开发 flow、提升 flow 质量、将 flow 部署到生产环境。
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### Develop your own LLM apps
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### 开发你自己的 LLM 应用
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#### VS Code Extension
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We also offer a VS Code extension (a flow designer) for an interactive flow development experience with UI.
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我们还提供 VS Code 扩展（flow 设计器），通过 UI 带来交互式 flow 开发体验。
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<img src="docs/media/readme/vsc.png" alt="vsc" width="1000"/>
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You can install it from the <a href="https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow">visualstudio marketplace</a>.
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你可以从 <a href="https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow">Visual Studio Marketplace</a> 安装。
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#### Deep delve into flow development
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#### 深入 flow 开发
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[Getting started with prompt flow](./docs/how-to-guides/quick-start.md): A step by step guidance to invoke your first flow run.
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[Getting started with prompt flow](./docs/how-to-guides/quick-start.md)：分步指南，帮助你完成首次 flow 运行。
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### Learn from use cases
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### 从用例中学习
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[Tutorial: Chat with PDF](https://github.com/microsoft/promptflow/blob/main/examples/tutorials/e2e-development/chat-with-pdf.md): An end-to-end tutorial on how to build a high quality chat application with prompt flow, including flow development and evaluation with metrics.
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> More examples can be found [here](https://microsoft.github.io/promptflow/tutorials/index.html#samples). We welcome contributions of new use cases!
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[Tutorial: Chat with PDF](https://github.com/microsoft/promptflow/blob/main/examples/tutorials/e2e-development/chat-with-pdf.md): 一份端到端教程，讲解如何使用 prompt flow 构建高质量聊天应用，包括 flow 开发与基于指标的评估。
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> 更多示例见 [此处](https://microsoft.github.io/promptflow/tutorials/index.html#samples). 欢迎贡献新的用例！
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### Setup for contributors
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### 贡献者环境配置
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If you're interested in contributing, please start with our dev setup guide: [dev_setup.md](./docs/dev/dev_setup.md).
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若你有兴趣参与贡献，请从我们的开发环境配置指南开始：[dev_setup.md](./docs/dev/dev_setup.md)。
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Next Step! Continue with the **Contributing** 👇 section to contribute to prompt flow.
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下一步！继续阅读下方的 **贡献** 👇 部分，为 prompt flow 做出贡献。
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## Contributing
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## 贡献
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This project welcomes contributions and suggestions. Most contributions require you to agree to a
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Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
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the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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本项目欢迎贡献与建议。大多数贡献需要你同意贡献者许可协议（Contributor License Agreement，CLA），声明你有权并确实授予我们使用你贡献内容的权利。详情请访问 https://cla.opensource.microsoft.com.
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When you submit a pull request, a CLA bot will automatically determine whether you need to provide
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a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
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provided by the bot. You will only need to do this once across all repos using our CLA.
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当你提交 pull request 时，CLA 机器人会自动判断你是否需要提供 CLA，并相应地标注 PR（例如状态检查、评论）。只需按照机器人提供的说明操作即可。在我们使用 CLA 的所有仓库中，你只需完成一次该流程。
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
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contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
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本项目已采用 [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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更多信息请参阅 [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)，或通过 [opencode@microsoft.com](mailto:opencode@microsoft.com) 联系我们就其他问题或意见进行咨询。
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## Trademarks
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## 商标
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This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
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trademarks or logos is subject to and must follow
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本项目可能包含项目、产品或服务的商标或标识。经授权的 Microsoft 商标或标识使用须遵守并遵循
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[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
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Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
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Any use of third-party trademarks or logos are subject to those third-party's policies.
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在修改版本中使用 Microsoft 商标或标识不得造成混淆，或暗示 Microsoft 赞助。任何第三方商标或标识的使用均须遵守相应第三方的政策。
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## Code of Conduct
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## 行为准则
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This project has adopted the
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[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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For more information see the
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[Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
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or contact [opencode@microsoft.com](mailto:opencode@microsoft.com)
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with any additional questions or comments.
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本项目已采纳
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[Microsoft 开源行为准则](https://opensource.microsoft.com/codeofconduct/).
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更多信息请参阅
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[行为准则常见问题（FAQ）](https://opensource.microsoft.com/codeofconduct/faq/)
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，或通过 [opencode@microsoft.com](mailto:opencode@microsoft.com)
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就其他问题或意见与我们联系。
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## Data Collection
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## 数据收集
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The software may collect information about you and your use of the software and
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send it to Microsoft if configured to enable telemetry.
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Microsoft may use this information to provide services and improve our products and services.
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You may turn on the telemetry as described in the repository.
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There are also some features in the software that may enable you and Microsoft
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to collect data from users of your applications. If you use these features, you
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must comply with applicable law, including providing appropriate notices to
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users of your applications together with a copy of Microsoft's privacy
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statement. Our privacy statement is located at
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https://go.microsoft.com/fwlink/?LinkID=824704. You can learn more about data
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collection and use in the help documentation and our privacy statement. Your
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use of the software operates as your consent to these practices.
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若配置为启用遥测（telemetry），本软件可能会收集有关您及您使用本软件的信息，
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并将其发送至 Microsoft。
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Microsoft 可能会使用这些信息来提供服务并改进我们的产品和服务。
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您可以按照本仓库中的说明开启遥测。
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本软件中还有一些功能可能使您与 Microsoft 能够收集应用程序用户的数据。若您使用这些功能，您
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必须遵守适用法律，包括向应用程序用户提供适当的告知，并附上 Microsoft 隐私
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声明的副本。我们的隐私声明位于
|
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https://go.microsoft.com/fwlink/?LinkID=824704. 您可以在帮助文档和我们的隐私声明中了解更多关于数据
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收集与使用的信息。您使用本软件即表示您同意这些做法。
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### Telemetry Configuration
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### 遥测配置
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Telemetry collection is on by default.
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默认开启遥测收集。
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To opt out, please run `pf config set telemetry.enabled=false` to turn it off.
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若要选择退出，请运行 `pf config set telemetry.enabled=false` 以将其关闭。
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## License
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||||
## 许可证
|
||||
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||||
Copyright (c) Microsoft Corporation. All rights reserved.
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||||
Licensed under the [MIT](LICENSE) license.
|
||||
根据 [MIT](LICENSE) 许可证授权。
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||||
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||||
Reference in New Issue
Block a user