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"
  }
}