- Implemented one-click setup instructions for DingTalk, Lark, WeCom, and WeChat, enhancing user experience by automating credential input.
- Added new images for the setup processes, including success screens and QR code scanning steps.
- Created a new webpage bot documentation, detailing the embedding process and configuration options.
- Updated existing documentation for better clarity and structure, including manual setup instructions and configuration details.
Localized Cloud promotion tips for English and Japanese versions.
EN tips emphasize Slack, LINE, WeCom (internationally relevant).
JA tips emphasize LINE and Lark (Japan-relevant platforms).
* 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]>
* 重构文档结构：移除 docs 目录，调整文件组织结构
* Reorder "插件 SDK API" section in docs.json
* Refactor and clarify various documentation sections
* Refine warnings and info boxes across multiple docs
* Refactor and simplify Japanese documentation content
* Update package.json to include mintlify as a dependency and add pnpm-lock.yaml for dependency management
* Update blog link and add description in docs.json
---------
Co-authored-by: Junyan Chin <[email protected]>
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