Get Knowledge Base - Mindsdb

GET /api/projects/{project_name}/knowledge_bases/{knowledge_base_name}

This API endpoint lists details about a knowledge base using the GET method.

<Tip> Learn more about knowledge bases following [this doc page](/mindsdb_sql/knowledge_bases/overview). </Tip>

Path Parameters

<ParamField body='project_name' type='string' required> Defines the project where the knowledge bases are located. Note that the default project name is `mindsdb`. </ParamField> <ParamField body='knowledge_base_name' type='string' required> Defines the knowledge base name to get its details. </ParamField>

Body

None.

Response

<ResponseField name="id" type="string" required> Unique identifier for the knowledge base. </ResponseField> <ResponseField name="name" type="string" required> The name assigned to the knowledge base. </ResponseField> <ResponseField name="project_id" type="string" required> The ID of the project where the knowledge base resides. </ResponseField> <ResponseField name="project_name" type="string" required> The name of the project where the knowledge base resides. </ResponseField> <ResponseField name="vector_database" type="string" required> The vector store used for storing vector embeddings. </ResponseField> <ResponseField name="vector_database_table" type="string" required> The name of the collection or table within the vector database. </ResponseField> <ResponseField name="updated_at" type="string" required> Timestamp indicating when the knowledge base was last updated. </ResponseField> <ResponseField name="created_at" type="string" required> Timestamp indicating when the knowledge base was created. </ResponseField> <ResponseField name="query_id" type="string" required="false"> Optional field for linking specific queries to this knowledge base. </ResponseField> <ResponseField name="embedding_model" type="string" required="false"> The embedding model used to convert content into vector representations. </ResponseField> <ResponseField name="reranking_model" type="string" required="false"> Optional model used to rerank search results based on relevance. </ResponseField> <ResponseField name="metadata_columns" type="list" required="false"> Optional list of columns used for metadata-based filtering or enrichment. </ResponseField> <ResponseField name="content_columns" type="list" required="false"> Optional list of columns treated as the main content for embedding and retrieval. </ResponseField> <ResponseField name="id_column" type="string" required="false"> The name of the column that uniquely identifies each content row. </ResponseField> <ResponseField name="params" type="object" required="false"> A nested object that contains additional configuration parameters. </ResponseField> <ResponseField name="params.created_embedding_model" type="string" required="false"> The name of the embedding model associated with this knowledge base at creation time. </ResponseField> <RequestExample>

shell

curl -X GET http://127.0.0.1:47334/api/projects/mindsdb/knowledge_bases/my_kb

</RequestExample> <ResponseExample>

json

{
  "id": 2,
  "name": "my_kb",
  "project_id": 1,
  "vector_database": "my_kb_chromadb",
  "vector_database_table": "default_collection",
  "updated_at": "2025-06-26 10:24:06.311655",
  "created_at": "2025-06-26 10:24:06.311654",
  "query_id": null,
  "embedding_model": {
    "provider": "openai",
    "model_name": "text-embedding-3-small",
    "api_key": "******"
  },
  "reranking_model": {
    "provider": "openai",
    "model_name": "gpt-4o",
    "api_key": "******"
  },
  "metadata_columns": [
    "product"
  ],
  "content_columns": [
    "notes"
  ],
  "id_column": "order_id",
  "params": {
    "created_embedding_model": "kb_embedding_my_kb"
  }
}

</ResponseExample>