curl -X POST http://127.0.0.1:47334/api/projects/mindsdb/knowledge_bases \
  -H "Content-Type: application/json" \
  -d '{
    "knowledge_base": {
      "name": "my_kb",
      "storage": {
        "database": "my_kb_chromadb",
        "table": "default_collection"
      },
      "embedding_model": {
        "provider": "openai",
        "model_name": "text-embedding-3-small",
        "api_key": "sk-xxx"
      },
      "reranking_model": {
        "provider": "openai",
        "model_name": "gpt-4o",
        "api_key": "sk-xxx"
      },
      "content_columns": ["notes"],
      "metadata_columns": ["product"],
      "id_column": "order_id"
    }
  }'


{
  "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_kbxxx"
  }
}

POST /api/projects/{project_name}/knowledge_bases

This API endpoint creates a knowledge base using the POST method.

Learn more about knowledge bases following this doc page.

​
Path Parameters

​
project_name
string
required

Defines the project where the knowledge bases are located. Note that the default project name is mindsdb.

​
Body

​
name
string
required

Name of the knowledge base.

​
storage
string

Underlying vector database that stores the embeddings.

​
embedding_model
string

Defines the embedding model used to embed data in vector representation.

​
reranking_model
string

Defines the reranking model used to rerank the search results by relevance.

​
content_columns
string

Defines the columns that store content to be embedded.

​
metadata_columns
string

Defines the columns that are considered metadata.

​
id_column
string

Defines the column that uniquely identifies each row from the data inserted into the knowledge base.

​
Response

​
id
string
required

Unique identifier for the knowledge base.

​
name
string
required

The name assigned to the knowledge base.

​
project_id
string
required

The ID of the project where the knowledge base resides.

​
vector_database
string
required

The vector store used for storing vector embeddings.

​
vector_database_table
string
required

The name of the collection or table within the vector database.

​
updated_at
string
required

Timestamp indicating when the knowledge base was last updated.

​
created_at
string
required

Timestamp indicating when the knowledge base was created.

​
query_id
string
required

Optional field for linking specific queries to this knowledge base.

​
embedding_model
string
required

The embedding model used to convert content into vector representations.

​
reranking_model
string
required

Optional model used to rerank search results based on relevance.

​
metadata_columns
list
required

Optional list of columns used for metadata-based filtering or enrichment.

​
content_columns
list
required

Optional list of columns treated as the main content for embedding and retrieval.

​
id_column
string
required

The name of the column that uniquely identifies each content row.

​
params
object
required

A nested object that contains additional configuration parameters.

​
params.created_embedding_model
string
required

The name of the embedding model associated with this knowledge base at creation time.

curl -X POST http://127.0.0.1:47334/api/projects/mindsdb/knowledge_bases \
  -H "Content-Type: application/json" \
  -d '{
    "knowledge_base": {
      "name": "my_kb",
      "storage": {
        "database": "my_kb_chromadb",
        "table": "default_collection"
      },
      "embedding_model": {
        "provider": "openai",
        "model_name": "text-embedding-3-small",
        "api_key": "sk-xxx"
      },
      "reranking_model": {
        "provider": "openai",
        "model_name": "gpt-4o",
        "api_key": "sk-xxx"
      },
      "content_columns": ["notes"],
      "metadata_columns": ["product"],
      "id_column": "order_id"
    }
  }'


{
  "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_kbxxx"
  }
}