Create a knowledge base

Select the knowledge base type and use case, then configure parsing and chunking strategies.

A knowledge base is the core entity of the RAG platform, organizing a set of documents, chunks, embeddings, and index configurations to provide retrieval and question-answering capabilities.

Knowledge base types

You must select a knowledge base type during creation. Each type corresponds to different data formats and retrieval methods:

Use cases

When selecting Document Search as the knowledge base type, you must further specify a use case. Different use cases determine distinct retrieval and response-generation strategies:

Console-based creation

1. Navigate to Knowledge Management: Log on to the console and go to Data Ingestion → Knowledge Management, then click Create Knowledge Base in the top-right corner.

2. Enter basic information:

   Field	Required	Description
   Name	Yes	1–20 characters; unique within the workspace
   Description	Yes	Up to 200 characters; aids filtering
   Knowledge Base Type	Yes	Document Search / Data Query / Image Q&A / Audio/Video Search
   Use Case	Yes	Basic Document Q&A / Rich-Media Responses / Visual Understanding / Ultra-Fast Q&A

3. Import data: Choose a data source.

   Data Source	Description
   Upload Files	Upload files directly via the UI; up to 50 files per batch, each ≤ 150 MB
   Select Category	Import from dataset file categories; supports automatic synchronization
   Select Files	Choose specific files under a dataset category for import

   Supported formats: PDF / DOCX / XLSX / PPTX / TXT / MD / HTML / CSV, etc.

4. Choose a parsing method: By default, Default Settings are used—the system automatically selects an appropriate parser based on file type, requiring no manual adjustment in most scenarios.

   To customize parsing per format, click Custom Settings. Available parsing methods include:

   Parsing Method	Description	Applicable Scenarios
   Electronic Document Parsing	Standard text extraction	Well-formatted electronic documents
   Intelligent Document Parsing	Layout-level OCR + structural recovery	Scanned documents, complex layouts
   LLM-based Document Parsing	Uses large language models to understand document structure	Non-standard or irregular document formats
   Qwen-VL Parsing	Vision-language model	Documents with mixed text and images
   Audio/Video Parsing	Speech-to-text transcription + timestamped segment localization	Audio/video files

5. Configure index settings:

   Parameter	Default Value	Description
   Chunking Method	Smart Chunking	Alternatives: by length / by page / by heading / by regex / by delimiter
   Maximum Chunk Length	600	Range: 10–6000
   Embedding Model	text-embedding-v4	Semantic vector model supporting both Chinese and English
   Vector Storage	Platform Storage	Alternative: self-managed ADB-PG engine

   See Chunking and vectorization for details.

   NoteRetrieval parameters—including ranking model, TopK, and similarity threshold—are configured in the retrieval service. See Knowledge retrieval for details.

6. Complete the creation: Click Create Knowledge Base. The system automatically performs parsing, chunking, vectorization, and index building. Once the knowledge base status changes to Ready, it becomes available for retrieval. Processing progress can be monitored in the document list.

API-based creation

Use the Create a knowledge base and import data API to create a knowledge base and submit an import task in one step:

curl -X POST https://{workspace_id}.cn-beijing.maas.aliyuncs.com/api/v1/indices/rag/index/create_v2 \
  -H "Authorization: Bearer $DASHSCOPE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "my-knowledge-base",
    "structureType": "unstructured",
    "sinkType": "BUILT_IN",
    "sourceType": "DATA_CENTER_CATEGORY",
    "embeddingModelName": "text-embedding-v4",
    "chunkSize": 600,
    "source_ids": ["cate_xxx"]
  }'