Overview

Knowledge compilation converts unstructured documents into structured knowledge content. The system analyzes information in documents with a large language model and generates different types of knowledge artifacts based on the compilation template selected by the user.

Generated knowledge artifacts can be used for knowledge retrieval, intelligent Q&A, and Agent applications, helping users quickly understand and use key information in documents. The following knowledge artifact types are currently supported:

• Knowledge graph: Displays entities in documents and their relationships. It is suitable for content such as personal relationships, organizational structures, and product relationships.
• Knowledge tree: Organizes document content by hierarchy. It is suitable for chapter structures, topic classification, and knowledge system organization.
• Page index: Preserves the original document structure and enhances chapter positioning. It is suitable for manuals, specifications, reports, and similar materials.
• Mindmap: Expands content relationships around core topics and helps users quickly understand the overall document structure.
• Timeline: Organizes event information in chronological order. It is suitable for historical materials, project records, event tracking, and similar content.
• Knowledge page: Generates interconnected knowledge pages. It is suitable for enterprise knowledge, product materials, and domain knowledge management.

Generated knowledge artifacts can be used as auxiliary information for subsequent retrieval and Q&A, improving the efficiency of knowledge queries and content understanding.

Core Concepts

Before using knowledge compilation, you need to understand the following basic concepts:

• Compilation template: Defines how knowledge compilation is generated, including the information types to extract, the organization structure, and generation rules. Users can select different templates based on actual requirements to generate the corresponding knowledge artifacts.
• Knowledge artifact: A structured result produced by knowledge compilation, including knowledge graphs, knowledge trees, page indexes, mind maps, timelines, and knowledge pages. Different knowledge artifact types are suitable for different information organization scenarios.
• Compilation node: A processing node in the knowledge compilation flow that executes a specified compilation task. When using a compilation node, you need to associate it with the corresponding compilation template to determine the format and structure of the generated content.

Template Selection Recommendations

Knowledge compilation provides multiple built-in templates. Different templates are suitable for different knowledge organization methods. When creating a knowledge compilation template, select an appropriate template based on the document content and the expected knowledge artifact.

Template	Applicable Scenario
Graph	Suitable for extracting entities and relationships between entities in documents, such as people, organizations, products, and their relationships.
Tree	Suitable for organizing document content by topic and hierarchy, arranging knowledge into a tree structure.
PageIndex	Suitable for preserving the original chapter and page structure of a document and building a hierarchical index for quick content positioning and retrieval.
Mindmap	Suitable for extracting core topics and branch content from documents and displaying the knowledge structure as a mind map.
Timeline	Suitable for documents that contain clear time information and events, organizing and displaying events in chronological order.
Wiki	Suitable for documents with substantial content and relationships between topics, organizing the content into interconnected Wiki pages.

After selecting a template, you can also adjust global rules and template-specific configurations based on actual business requirements to control the content and generation results of knowledge compilation.

Preparation Before Starting

Before configuration, confirm the following conditions:

• An available LLM has been configured, and the model has strong text understanding, structured output, and reasoning capabilities.
• You have created or plan to create an Ingestion Pipeline that includes Parser, Chunker, Compiler, and Indexer.
• Source documents with clear topics and reliable content are ready.
• The template type to use has been determined based on the target knowledge structure.

Recommendation: When using this feature for the first time, select a small number of representative documents for testing. After confirming the output structure and quality, process data at a larger scale.

Standard Workflow

1. Create a knowledge compilation template: Open Agent and select Compilation operator. Choose Create from blank to open Add template. Enter Name, optionally enter Description, and select Template: Graph, Mindmap, PageIndex, Timeline, Tree, or Wiki. Complete Global rules and the settings displayed for the selected type, then click Save. See Basic Information Configuration.
2. Configure the ingestion pipeline: Create or open a pipeline under Agent > Ingestion pipeline. Add Compiler, select the saved template group under Operator, and choose the model used for compilation. Connect the processing steps to Indexer.
3. Publish the pipeline: Click Save draft, resolve any reported configuration issues, then click Publish and confirm.
4. Apply the pipeline: Open the target Dataset's Configuration, open Parsing mode, expand the custom pipeline, select its published version within that dropdown, and click Save.
5. Execute and check compilation: Upload and parse a representative document. Document parsing and background Dataset-level artifact processing complete separately; Wiki generation and merging may finish later. Check the compilation status and Logs > Dataset for pending tasks or errors, and wait for background processing to complete before assessing Dataset-level artifacts. A missing artifact immediately after parsing does not by itself indicate a pipeline failure. Inspect the completed results before processing more documents. See Apply a Knowledge Compilation Template.

Typical flow: Parser -> Chunker -> Compiler -> Indexer.

Parser is responsible for parsing, Chunker is responsible for splitting, Compiler is responsible for knowledge compilation, and Indexer is responsible for building the indexes required for subsequent retrieval.