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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]>
--- title: "Using the Knowledge Base" --- LangBot natively supports RAG (Retrieval-Augmented Generation). You can create a knowledge base and bind it to a pipeline, allowing the pipeline to answer questions based on the contents of the knowledge base. Knowledge bases are powered by Knowledge Engine plugins, with different Knowledge Engines providing different indexing and retrieval strategies. You can find available Knowledge Engine plugins in the [Plugin Marketplace](https://space.langbot.app/market). ## Creating a Knowledge Base On the knowledge base page, click the `Create Knowledge Base` button: 1. Fill in the knowledge base name 2. Select a Knowledge Engine (provided by installed plugins) 3. Fill in the relevant parameters based on the selected engine's configuration form (e.g., embedding model, chunk size, etc.) 4. Click the `Create` button <Info> Different Knowledge Engines have different configuration parameters, depending on the engine's `creation_schema` definition. Some engines may require configuring an embedding model first. Please read [Configure Models](/en/usage/models/readme). </Info> If the selected Knowledge Engine supports document upload (declares `DOC_INGESTION` capability), after creation go to the "Documents" tab of your knowledge base and upload documents. LangBot will automatically parse and index these in the background. ## Using the Knowledge Base Go to your pipeline configuration, under the "AI" tab, choose `Local Agent` as the runner, then select the knowledge base you just created. <img width="400px" src="/images/zh/deploy/knowledge/use_local_agent.png" alt="use_kb" /> <img width="400px" src="/images/zh/deploy/knowledge/use_kb.png" alt="use_kb" /> <Info> LangBot's built-in knowledge base can be used only when the runner is set to `Local Agent`. For other runners, refer to their documentation. </Info> Now you can use the knowledge base in `Chat Debug`, or through the bot linked to this pipeline: <img width="400px" src="/images/zh/deploy/knowledge/use_kb_in_chat.png" alt="use_kb_in_chat" /> ## Developing Custom Knowledge Engines If existing Knowledge engine plugins don't meet your needs, you can develop your own Knowledge engine plugin. See [Component: Knowledge Engine](/en/plugin/dev/components/knowledge-engine) for development details.