# 开发者资源

- [开发者概述](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/developer.md): Rememberizer 开发者工具、API 和集成选项概述
- [集成选项](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/integration-options.md): 关于开发工具和集成选项的概述，以便利用 Rememberizer 的语义搜索功能构建应用程序
- [注册和使用 API 密钥](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/integration-options/registering-and-using-api-keys.md): 在本教程中，您将学习如何在 Rememberizer 中创建一个公共知识并获取其 API 密钥，以通过 API 调用连接和检索其文档。
- [注册 Rememberizer 应用](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/integration-options/registering-rememberizer-apps.md): 您可以在您的帐户下创建和注册 Rememberizer 应用。Rememberizer 应用可以代表用户执行操作。
- [授权 Rememberizer 应用](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/integration-options/authorizing-rememberizer-apps.md)
- [创建一个 Rememberizer GPT](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/integration-options/creating-a-rememberizer-gpt.md): 在本教程中，您将学习如何创建一个 Rememberizer 应用程序并连接 OpenAI GPT，使 GPT 能够访问 Rememberizer API 功能。
- [LangChain 集成](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/integration-options/langchain-integration.md): 了解如何将 Rememberizer 作为 LangChain 检索器集成，以便为您的 LangChain 应用程序提供强大的向量数据库搜索访问。
- [向量存储](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/integration-options/vector-stores.md): 本指南将帮助您了解如何作为开发者使用 Rememberizer 向量存储。
- [与 Slack 对话的示例 Web 应用](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/integration-options/talk-to-slack-the-sample-web-app.md): 创建一个简单的网络应用程序，以通过对 Rememberizer 的查询将 LLM 与用户知识集成是非常简单的。
- [企业集成](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/enterprise-integration.md): 记忆者在组织环境中的企业集成功能、架构模式和部署策略概述
- [企业集成模式](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/enterprise-integration/enterprise-integration-patterns.md): 与 Rememberizer 的企业集成的架构模式、安全考虑和最佳实践
- [API 参考](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs.md)
- [身份验证](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/authentication.md)
- [获取所有添加的公共知识](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/get-all-added-public-knowledge.md)
- [列出可用的数据源集成](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/list-available-data-source-integrations.md)
- [备忘录 API](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/mementos.md)
- [将内容记忆到 Rememberizer](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/memorize-content-to-rememberizer.md)
- [检索当前用户的账户详情](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/retrieve-current-user-account-details.md)
- [检索文档内容](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/retrieve-document-contents.md)
- [检索文档](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/retrieve-documents.md)
- [检索 Slack 的内容](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/retrieve-slacks-content.md)
- [按语义相似性搜索文档](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/search-for-documents-by-semantic-similarity.md): 具有批处理能力的语义搜索端点
- [向量存储 API](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/vector-store.md)
- [向向量存储添加新文本文档](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/vector-store/add-new-text-document-to-a-vector-store.md)
- [获取向量存储中的文档列表](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/vector-store/get-a-list-of-documents-in-a-vector-store.md)
- [获取文档的信息](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/vector-store/get-the-information-of-a-document.md)
- [获取向量存储的信息](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/vector-store/get-vector-stores-information.md)
- [在向量存储中删除文档](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/vector-store/remove-a-document-in-vector-store.md)
- [按语义相似性搜索向量存储文档](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/vector-store/search-for-vector-store-documents-by-semantic-similarity.md): 通过语义相似性和批量操作搜索向量存储文档
- [更新向量存储中文件的内容](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/vector-store/update-files-content-in-a-vector-store.md)
- [将文件上传到向量存储](https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan/api-docs/vector-store/upload-files-to-a-vector-store.md): 将文件内容批量上传到向量存储


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Perform an HTTP GET request on the current page URL with the `ask` query parameter:

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GET https://docs.rememberizer.ai/zh-cn/kai-fa-zhe-zi-yuan.md?ask=<question>
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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
