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* docs: update wiki for RAG engine plugin architecture Replace KnowledgeRetriever component docs with RAGEngine across all three languages (zh/en/ja). Key changes: - Add new rag-engine.mdx component docs covering RAGEngine base class, creation_schema/retrieval_schema, capability declarations, lifecycle hooks, host RAG APIs, and complete code examples - Update knowledge base usage docs to reflect unified KB model (remove internal/external distinction, add RAG engine selection flow) - Update component list (add.mdx) and plugin intro to reference RAGEngine - Add RAG API section to plugin common APIs (invoke_embedding, vector_upsert/search/delete, get_rag_file_stream) - Update docs.json navigation to point to rag-engine pages Companion to LangBot PR #1995 and SDK PR #34. Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: sync with sdk * docs: update vector API docs with metadata filters, search_type, and documents support - Replace Warning tags about unimplemented filters with Info tags documenting canonical filter format (Chroma-style where syntax) and supported operators - Document search_type/query_text params for full-text and hybrid search - Document documents param for vector_upsert - Update RetrievalContext.filters description from reserved to active - Add filter usage examples (en/zh/ja) Co-Authored-By: Claude Opus 4.6 <[email protected]> * docs: enhance RAG Engine documentation across multiple languages - Expanded the description of the RAG Engine component to include bridging capabilities with advanced knowledge base services like Dify, RAGFlow, and FastGPT. - Clarified the `DOC_INGESTION` capability description, specifying conditions under which the declaration can be omitted. - Updated documentation in English, Japanese, and Chinese to ensure consistency and clarity. * docs: add instructions for show_if conditional rendering * docs: add Parser component documentation (zh/en/ja) Add Parser as a new component type in plugin docs. Parser extracts structured text from binary files (PDF, Word, etc.) before RAG engine ingestion. Includes manifest format, parse method API, data models (ParseContext/ParseResult/TextSection), RAGEngine integration via parsed_content, and cross-plugin invoke_parser API. Also add DOC_PARSING capability to RAG engine docs and parsed_content field to IngestionContext. Companion to SDK PR langbot-app/langbot-plugin-sdk#37 and LangBot PR langbot-app/LangBot#2000. Co-Authored-By: Claude Opus 4.6 <[email protected]> * docs: rename RAGEngine components to KnowledgeEngine * refactor: change rag engine to knowledge engine --------- Co-authored-by: Claude Opus 4.6 <[email protected]> Co-authored-by: Junyan Qin <[email protected]>
56 lines
2.0 KiB
Plaintext
56 lines
2.0 KiB
Plaintext
---
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title: "LangBot 插件"
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---
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LangBot 内部由多种组件组成，如大模型工具、命令、消息平台适配器、大模型请求器等。为了满足扩展性和灵活性的需求，我们实现了一套生产级的插件系统。
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每个插件被运行在独立的进程中，由 Plugin Runtime 统一管理。
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<img width="600" src="/images/zh/plugin/dev/plugin_system_arch.png" />
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## 插件结构
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插件根据具体功能可提供以下组件：
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- 事件处理器：监听流水线执行期间的事件，对上下文或流水线进行修改。
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- 命令：由用户通过`!`（或其他已设置的前缀）开头的命令消息触发。
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- 工具：供 LangBot 内置的 Local Agent 在执行期间由 LLM 调用。
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- RAG 引擎：管理知识库的文档索引和检索。
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后续还将支持更多组件的插件化。
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## 安装插件
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目前支持上传插件包安装、从插件市场安装以及从 GitHub Release 安装插件。
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在 LangBot 的插件管理页面，点击右上角即可选择安装方式
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<img width="600" src="/images/zh/plugin/install_method.png" />
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点击本地安装，选择他人分享的或从 Marketplace 下载的`.lbpkg`文件，即可安装插件。
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或点击插件市场 Tab，选择插件后点击安装。
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<img width="600" src="/images/zh/plugin/install_from_marketplace.png" />
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安装插件后，您需要到流水线配置中，选择所需要使用的插件。
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<img width="600" src="/images/zh/plugin/pipeline_extensions.png" />
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## 插件管理
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### 插件配置项
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某些插件可能会要求您填入特定的配置项（请参考插件 README 说明），请点击插件卡片进入插件详情页，按照提示获取并输入。
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<img width="600" src="/images/zh/plugin/plugin_config.png" />
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### 更新插件
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仅支持更新从插件市场安装的插件。
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<img width="600" src="/images/zh/plugin/update_plugin.png" />
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## 插件市场
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插件市场可以从 LangBot 内部的`插件管理`页面进入，也可以访问独立站点[LangBot 插件市场](https://space.langbot.app/market)。
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