Vector Index

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VectorIndex object

namestring: Name of the index

endpoint_namestring: Name of the endpoint associated with the index

primary_keystring: Primary key of the index

index_typestring: Values: DELTA_SYNC, DIRECT_ACCESS

direct_access_index_specobject

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embedding_vector_columnsarray of object

The columns that contain the embedding vectors. The format should be array[double].

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namestring: Name of the column

embedding_dimensionint32: Dimension of the embedding vector

schema_jsonstring: The schema of the index in JSON format. Supported types are integer, long, float, double, boolean, string, date, timestamp. Supported types for vector column: array<float>, array<double>,`.

embedding_source_columnsarray of object

The columns that contain the embedding source. The format should be array[double].

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namestring: Name of the column

embedding_model_endpoint_namestring: Name of the embedding model endpoint, used by default for both ingestion and querying.

model_endpoint_name_for_querystring: Name of the embedding model endpoint which, if specified, is used for querying (not ingestion).

delta_sync_index_specobject

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source_tablestring: The name of the source table.

embedding_source_columnsarray of object

The columns that contain the embedding source.

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namestring: Name of the column

embedding_model_endpoint_namestring: Name of the embedding model endpoint, used by default for both ingestion and querying.

model_endpoint_name_for_querystring: Name of the embedding model endpoint which, if specified, is used for querying (not ingestion).

embedding_vector_columnsarray of object

The columns that contain the embedding vectors.

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namestring: Name of the column

embedding_dimensionint32: Dimension of the embedding vector

pipeline_typestring

Pipeline execution mode.

TRIGGERED: If the pipeline uses the triggered execution mode, the system stops processing after successfully refreshing the source table in the pipeline once, ensuring the table is updated based on the data available when the update started.
CONTINUOUS: If the pipeline uses continuous execution, the pipeline processes new data as it arrives in the source table to keep vector index fresh.

Values: TRIGGERED, CONTINUOUS

pipeline_idstring: The ID of the pipeline that is used to sync the index.

embedding_writeback_tablestring: [Optional] Name of the Delta table to sync the vector index contents and computed embeddings to.

columns_to_syncarray of string: [Optional] Select the columns to sync with the vector index. If you leave this field blank, all columns from the source table are synced with the index. The primary key column and embedding source column or embedding vector column are always synced.

columns_to_indexarray of string: [Optional] Alias for columns_to_sync. Select the columns to include in the vector index. If you leave this field blank, all columns from the source table are included. The primary key column and embedding source column or embedding vector column are always included. Only one of columns_to_sync or columns_to_index may be specified.

statusobject

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messagestring: Message associated with the index status

indexed_row_countint64: Number of rows indexed

readyboolean: Whether the index is ready for search

index_urlstring: Index API Url to be used to perform operations on the index

creatorstring: The user who created the index.

index_subtypestring: The subtype of the index.
Values: VECTOR, FULL_TEXT, HYBRID

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The VectorIndex object

{
  "name": "string",
  "endpoint_name": "string",
  "primary_key": "string",
  "index_type": "string",
  "direct_access_index_spec": {
    "embedding_vector_columns": [
      {}
    ],
    "schema_json": "string",
    "embedding_source_columns": [
      {}
    ]
  },
  "delta_sync_index_spec": {
    "source_table": "string",
    "embedding_source_columns": [
      {}
    ],
    "embedding_vector_columns": [
      {}
    ],
    "pipeline_type": "string",
    "pipeline_id": "string",
    "embedding_writeback_table": "string",
    "columns_to_sync": [
      "string"
    ],
    "columns_to_index": [
      "string"
    ]
  },
  "status": {
    "message": "string",
    "indexed_row_count": 0,
    "ready": true,
    "index_url": "string"
  },
  "creator": "string",
  "index_subtype": "string"
}

Get

GET /api/2.0/vector-search/indexes/{name}

Get an index.

API scopes: vector-search

Parameters

namestringpath: Name of the index

ensure_reranker_compatiblebooleanquery: If true, the URL returned for the index is guaranteed to be compatible with the reranker. Currently this means we return the CP URL regardless of how the index is being accessed. If not set or set to false, the URL may still be compatible with the reranker depending on what URL we return.

Response

Returns the VectorIndex object.

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Response

{
  "creator": "john@example.com",
  "delta_sync_index_spec": {
    "embedding_source_columns": [
      {
        "embedding_model_endpoint_name": "e5-small-v2",
        "name": "text"
      }
    ],
    "pipeline_id": "f4eaf1c8-28f9-4ad7-a2f2-a6c8f9eb0b0e",
    "pipeline_type": "TRIGGERED",
    "source_table": "main_catalog.docs.en_wiki"
  },
  "endpoint_name": "docs-endpoint",
  "index_type": "DELTA_SYNC",
  "name": "main_catalog.docs.en_wiki_index",
  "primary_key": "id",
  "status": {
    "index_url": "demo.cloud.databricks.com/api/2.0/vector-search/endpoints/docs-endpoint/indexes/main_catalog.docs.en_wiki_index",
    "indexed_row_count": 0,
    "message": "Delta sync Index creation is pending. Check latest status in Delta Live Tables: https://demo.cloud.databricks.com#joblist/pipelines/f4eaf1c8-28f9-4ad7-a2f2-a6c8f9eb0b0e",
    "ready": false
  }
}

{
  "creator": "john@example.com",
  "direct_access_index_spec": {
    "embedding_vector_columns": [
      {
        "embedding_dimension": 1024,
        "name": "text_vector"
      }
    ],
    "schema_json": "{\"id\":\"int\",\"text\":\"string\",\"text_vector\":\"array<float>\"}"
  },
  "endpoint_name": "docs-endpoint",
  "index_type": "DIRECT_ACCESS",
  "name": "main_catalog.docs.en_wiki_index",
  "primary_key": "id",
  "status": {
    "index_url": "demo.cloud.databricks.com/api/2.0/vector-search/endpoints/docs-endpoint/indexes/main_catalog.docs.en_wiki_index",
    "indexed_row_count": 0,
    "message": "Direct access index creation succeeded.",
    "ready": true
  }
}

List

GET /api/2.0/vector-search/indexes

List all indexes in the given endpoint.

API scopes: vector-search

Parameters

endpoint_namestringquery: Name of the endpoint

page_tokenstringquery: Token for pagination

Response

vector_indexesarray of object

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namestring: Name of the index

endpoint_namestring: Name of the endpoint associated with the index

primary_keystring: Primary key of the index

index_typestring: Values: DELTA_SYNC, DIRECT_ACCESS

direct_access_index_specobjectRequired

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embedding_vector_columnsarray of object

The columns that contain the embedding vectors. The format should be array[double].

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namestring: Name of the column

embedding_dimensionint32: Dimension of the embedding vector

schema_jsonstring: The schema of the index in JSON format. Supported types are integer, long, float, double, boolean, string, date, timestamp. Supported types for vector column: array<float>, array<double>,`.

embedding_source_columnsarray of object

The columns that contain the embedding source. The format should be array[double].

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namestring: Name of the column

embedding_model_endpoint_namestringRequired: Name of the embedding model endpoint, used by default for both ingestion and querying.

model_endpoint_name_for_querystring: Name of the embedding model endpoint which, if specified, is used for querying (not ingestion).

delta_sync_index_specobjectRequired

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source_tablestring: The name of the source table.

embedding_source_columnsarray of object

The columns that contain the embedding source.

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namestring: Name of the column

embedding_model_endpoint_namestringRequired: Name of the embedding model endpoint, used by default for both ingestion and querying.

model_endpoint_name_for_querystring: Name of the embedding model endpoint which, if specified, is used for querying (not ingestion).

embedding_vector_columnsarray of object

The columns that contain the embedding vectors.

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namestring: Name of the column

embedding_dimensionint32: Dimension of the embedding vector

pipeline_typestring

Pipeline execution mode.

TRIGGERED: If the pipeline uses the triggered execution mode, the system stops processing after successfully refreshing the source table in the pipeline once, ensuring the table is updated based on the data available when the update started.
CONTINUOUS: If the pipeline uses continuous execution, the pipeline processes new data as it arrives in the source table to keep vector index fresh.

Values: TRIGGERED, CONTINUOUS

pipeline_idstring: The ID of the pipeline that is used to sync the index.

embedding_writeback_tablestring: [Optional] Name of the Delta table to sync the vector index contents and computed embeddings to.

columns_to_syncarray of string: [Optional] Select the columns to sync with the vector index. If you leave this field blank, all columns from the source table are synced with the index. The primary key column and embedding source column or embedding vector column are always synced.

columns_to_indexarray of string: [Optional] Alias for columns_to_sync. Select the columns to include in the vector index. If you leave this field blank, all columns from the source table are included. The primary key column and embedding source column or embedding vector column are always included. Only one of columns_to_sync or columns_to_index may be specified.

statusobject

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messagestring: Message associated with the index status

indexed_row_countint64: Number of rows indexed

readyboolean: Whether the index is ready for search

index_urlstring: Index API Url to be used to perform operations on the index

creatorstring: The user who created the index.

index_subtypestring: The subtype of the index.
Values: VECTOR, FULL_TEXT, HYBRID

next_page_tokenstring: A token that can be used to get the next page of results. If not present, there are no more results to show.

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Response

{
  "vector_indexes": [
    {
      "name": "string",
      "endpoint_name": "string",
      "primary_key": "string",
      "index_type": "string",
      "direct_access_index_spec": {},
      "delta_sync_index_spec": {},
      "status": {},
      "creator": "string",
      "index_subtype": "string"
    }
  ],
  "next_page_token": "string"
}

Create

POST /api/2.0/vector-search/indexes

Create a new index.

API scopes: vector-search

Request body

namestring: Name of the index

endpoint_namestring: Name of the endpoint to be used for serving the index

primary_keystring: Primary key of the index

index_typestring: Values: DELTA_SYNC, DIRECT_ACCESS

direct_access_index_specobjectRequired

Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.

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embedding_vector_columnsarray of object

The columns that contain the embedding vectors. The format should be array[double].

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namestring: Name of the column

embedding_dimensionint32: Dimension of the embedding vector

schema_jsonstring: The schema of the index in JSON format. Supported types are integer, long, float, double, boolean, string, date, timestamp. Supported types for vector column: array<float>, array<double>,`.

embedding_source_columnsarray of object

The columns that contain the embedding source. The format should be array[double].

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namestring: Name of the column

embedding_model_endpoint_namestringRequired: Name of the embedding model endpoint, used by default for both ingestion and querying.

model_endpoint_name_for_querystring: Name of the embedding model endpoint which, if specified, is used for querying (not ingestion).

delta_sync_index_specobjectRequired

Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.

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source_tablestring: The name of the source table.

embedding_source_columnsarray of object

The columns that contain the embedding source.

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namestring: Name of the column

embedding_model_endpoint_namestringRequired: Name of the embedding model endpoint, used by default for both ingestion and querying.

model_endpoint_name_for_querystring: Name of the embedding model endpoint which, if specified, is used for querying (not ingestion).

embedding_vector_columnsarray of object

The columns that contain the embedding vectors.

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namestring: Name of the column

embedding_dimensionint32: Dimension of the embedding vector

pipeline_typestring

Pipeline execution mode.

TRIGGERED: If the pipeline uses the triggered execution mode, the system stops processing after successfully refreshing the source table in the pipeline once, ensuring the table is updated based on the data available when the update started.
CONTINUOUS: If the pipeline uses continuous execution, the pipeline processes new data as it arrives in the source table to keep vector index fresh.

Values: TRIGGERED, CONTINUOUS

pipeline_idstring: The ID of the pipeline that is used to sync the index.

embedding_writeback_tablestring: [Optional] Name of the Delta table to sync the vector index contents and computed embeddings to.

columns_to_syncarray of string: [Optional] Select the columns to sync with the vector index. If you leave this field blank, all columns from the source table are synced with the index. The primary key column and embedding source column or embedding vector column are always synced.

columns_to_indexarray of string: [Optional] Alias for columns_to_sync. Select the columns to include in the vector index. If you leave this field blank, all columns from the source table are included. The primary key column and embedding source column or embedding vector column are always included. Only one of columns_to_sync or columns_to_index may be specified.

index_subtypestring: The subtype of the index. Use HYBRID or FULL_TEXT. VECTOR is not supported.
Values: VECTOR, FULL_TEXT, HYBRID

Response

Returns the VectorIndex object.

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Request

{
  "delta_sync_index_spec": {
    "columns_to_sync": [
      "id",
      "text"
    ],
    "embedding_source_columns": [
      {
        "embedding_model_endpoint_name": "e5-small-v2",
        "name": "text"
      }
    ],
    "pipeline_type": "TRIGGERED",
    "source_table": "main_catalog.docs.en_wiki"
  },
  "index_type": "DELTA_SYNC",
  "name": "main_catalog.docs.en_wiki_index",
  "primary_key": "id"
}

{
  "direct_access_index_spec": {
    "embedding_vector_columns": [
      {
        "embedding_dimension": 1024,
        "name": "text_vector"
      }
    ],
    "schema_json": "{\"id\":\"int\",\"text\":\"string\",\"text_vector\":\"array<float>\"}"
  },
  "index_type": "DIRECT_ACCESS",
  "name": "main_catalog.docs.en_wiki_index",
  "primary_key": "id"
}

Response

{
  "creator": "john@example.com",
  "delta_sync_index_spec": {
    "columns_to_sync": [
      "id",
      "text"
    ],
    "embedding_source_columns": [
      {
        "embedding_model_endpoint_name": "e5-small-v2",
        "name": "text"
      }
    ],
    "pipeline_id": "f4eaf1c8-28f9-4ad7-a2f2-a6c8f9eb0b0e",
    "pipeline_type": "TRIGGERED",
    "source_table": "main_catalog.docs.en_wiki"
  },
  "endpoint_name": "docs-endpoint",
  "index_type": "DELTA_SYNC",
  "name": "main_catalog.docs.en_wiki_index",
  "primary_key": "id",
  "status": {
    "index_url": "demo.cloud.databricks.com/api/2.0/vector-search/endpoints/docs-endpoint/indexes/main_catalog.docs.en_wiki_index",
    "indexed_row_count": 0,
    "message": "Delta sync Index creation is pending. Check latest status in Delta Live Tables: https://demo.cloud.databricks.com#joblist/pipelines/f4eaf1c8-28f9-4ad7-a2f2-a6c8f9eb0b0e",
    "ready": false
  }
}

{
  "creator": "john@example.com",
  "direct_access_index_spec": {
    "embedding_vector_columns": [
      {
        "embedding_dimension": 1024,
        "name": "text_vector"
      }
    ],
    "schema_json": "{\"id\":\"int\",\"text\":\"string\",\"text_vector\":\"array<float>\"}"
  },
  "endpoint_name": "docs-endpoint",
  "index_type": "DIRECT_ACCESS",
  "name": "main_catalog.docs.en_wiki_index",
  "primary_key": "id",
  "status": {
    "index_url": "demo.cloud.databricks.com/api/2.0/vector-search/endpoints/docs-endpoint/indexes/main_catalog.docs.en_wiki_index",
    "indexed_row_count": 0,
    "message": "Direct access index creation succeeded.",
    "ready": true
  }
}

Delete

DELETE /api/2.0/vector-search/indexes/{name}

Delete an index.

API scopes: vector-search

Parameters

namestringpath: Name of the index

Delete Data Vector Index

DELETE /api/2.0/vector-search/indexes/{name}/delete-data

Handles the deletion of data from a specified vector index.

API scopes: vector-search

Parameters

namestringpath: Name of the vector index where data is to be deleted. Must be a Direct Vector Access Index.

primary_keysarray of stringquery: List of primary keys for the data to be deleted.

Response

statusstring: Status of the delete operation.
Values: SUCCESS, PARTIAL_SUCCESS, FAILURE

resultobject

Result of the upsert or delete operation.

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success_row_countint64: Count of successfully processed rows.

failed_primary_keysarray of string: List of primary keys for rows that failed to process.

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Request

{
  "primary_keys": [
    "1"
  ]
}

Response

{
  "result": {
    "failed_primary_keys": [
      "1"
    ],
    "success_row_count": 0
  },
  "status": "FAILURE"
}

Query Vector Index

POST /api/2.0/vector-search/indexes/{name}/query

Query the specified vector index.

API scopes: vector-search

Parameters

namestringpath: Name of the vector index to query.

Request body

num_resultsint32: Number of results to return. Defaults to 10.

columnsarray of string: List of column names to include in the response.

filters_jsonstring

JSON string representing query filters.

Example filters:

{"id <": 5}: Filter for id less than 5.
{"id >": 5}: Filter for id greater than 5.
{"id <=": 5}: Filter for id less than equal to 5.
{"id >=": 5}: Filter for id greater than equal to 5.
{"id": 5}: Filter for id equal to 5.

query_vectorarray of float: Query vector. Required for Direct Vector Access Index and Delta Sync Index using self-managed vectors.

query_textstring: Query text. Required for Delta Sync Index using model endpoint.

score_thresholdfloat: Threshold for the approximate nearest neighbor search. Defaults to 0.0.

query_typestring: The query type to use. Choices are ANN and HYBRID and FULL_TEXT. Defaults to ANN.

rerankerobject

If set, the top 50 results are reranked with the Databricks Reranker model before returning the num_results results to the user. The setting columns_to_rerank selects which columns are used for reranking. For each datapoint, the columns selected are concatenated before being sent to the reranking model. See https://docs.databricks.com/aws/en/vector-search/query-vector-search#rerank for more information.

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modelstring

Reranker identifier:

When model_type=BASE/UNSPECIFIED: must be "databricks_reranker".
When model_type=FINETUNED: the Model Serving endpoint name hosting a finetuned reranker.

parametersobject

Parameters that control how the reranker processes the query results.

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columns_to_rerankarray of string

query_columnsarray of string: Text columns to search for query_text. When empty, all text columns are searched.

sort_columnsarray of string: Sort results by column values instead of the default relevance ordering. Each clause has the form "<column> ASC" or "<column> DESC", for example ["rating DESC", "price ASC"].

Response

manifestobject

Metadata about the result set.

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column_countint32: Number of columns in the result set.

columnsarray of object

Information about each column in the result set.

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namestring: Name of the column.

type_textstring: Data type of the column (e.g., "string", "int", "array<float>")

resultobject

Data returned in the query result.

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row_countint32: Number of rows in the result set.

data_arrayarray of array of object: Data rows returned in the query.

next_page_tokenstring: [Optional] Token that can be used in QueryVectorIndexNextPage API to get next page of results. If more than 1000 results satisfy the query, they are returned in groups of 1000. Empty value means no more results. The maximum number of results that can be returned is 10,000.

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Request

{
  "columns": [
    "id",
    "age",
    "text"
  ],
  "filters_json": "{\"id\": 5, \"age >=\": 18}",
  "num_results": 10,
  "query_vector": [
    1,
    2,
    3
  ]
}

Response

Successful response for query operation.

{
  "manifest": {
    "column_count": 3,
    "columns": [
      {
        "name": "id"
      },
      {
        "name": "text"
      },
      {
        "name": "text_vector"
      }
    ]
  },
  "next_page_token": "dummy-next-page-token",
  "result": {
    "data_array": [
      [
        "1",
        "Databricks AI Search",
        [
          1,
          2,
          3
        ]
      ]
    ],
    "row_count": 1
  }
}

Query Vector Index Next Page

POST /api/2.0/vector-search/indexes/{name}/query-next-page

Use next_page_token returned from previous QueryVectorIndex or QueryVectorIndexNextPage request to fetch next page of results.

API scopes: vector-search

Parameters

namestringpath: Name of the vector index to query.

Request body

endpoint_namestring: Name of the endpoint.

page_tokenstring: Page token returned from previous QueryVectorIndex or QueryVectorIndexNextPage API.

Response

manifestobject

Metadata about the result set.

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column_countint32: Number of columns in the result set.

columnsarray of object

Information about each column in the result set.

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namestring: Name of the column.

type_textstring: Data type of the column (e.g., "string", "int", "array<float>")

resultobject

Data returned in the query result.

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row_countint32: Number of rows in the result set.

data_arrayarray of array of object: Data rows returned in the query.

next_page_tokenstring: [Optional] Token that can be used in QueryVectorIndexNextPage API to get next page of results. If more than 1000 results satisfy the query, they are returned in groups of 1000. Empty value means no more results. The maximum number of results that can be returned is 10,000.

View as Markdown

Request

Query next page

{
  "endpoint_name": "demo-endpoint",
  "page_token": "dummy-page-token"
}

Response

Successful response for query operation.

{
  "manifest": {
    "column_count": 3,
    "columns": [
      {
        "name": "id"
      },
      {
        "name": "text"
      },
      {
        "name": "text_vector"
      }
    ]
  },
  "next_page_token": "dummy-next-page-token",
  "result": {
    "data_array": [
      [
        "1",
        "Databricks AI Search",
        [
          1,
          2,
          3
        ]
      ]
    ],
    "row_count": 1
  }
}

Scan Vector Index

POST /api/2.0/vector-search/indexes/{name}/scan

Scan the specified vector index and return the first num_results entries after the exclusive primary_key.

API scopes: vector-search

Parameters

namestringpath: Name of the vector index to scan.

Request body

num_resultsint32: Number of results to return. Defaults to 10.

last_primary_keystring: Primary key of the last entry returned in the previous scan.

Response

dataarray of object

List of data entries

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fieldsarray of object

Data entry, corresponding to a row in a vector index.

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keystring: Column name.

valueobject

Column value, nullable.

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number_valuedoubleRequired

string_valuestringRequired

bool_valuebooleanRequired

struct_valueobjectRequired

list_valueobjectRequired

last_primary_keystring: Primary key of the last entry.

View as Markdown

Request

{
  "num_results": 2
}

Response

Successful response for scan operation.

{
  "data": [
    {
      "fields": [
        {
          "key": "id",
          "value": {
            "number_value": 1
          }
        },
        {
          "key": "text",
          "value": {
            "string_value": "foo"
          }
        },
        {
          "key": "embedding",
          "value": {
            "list_value": {
              "values": [
                {
                  "number_value": 0.1
                },
                {
                  "number_value": 0.2
                },
                {
                  "number_value": 0.3
                }
              ]
            }
          }
        }
      ]
    },
    {
      "fields": [
        {
          "key": "id",
          "value": {
            "number_value": 2
          }
        },
        {
          "key": "text",
          "value": {
            "string_value": "bar"
          }
        },
        {
          "key": "embedding",
          "value": {
            "list_value": {
              "values": [
                {
                  "number_value": 1
                },
                {
                  "number_value": 2
                },
                {
                  "number_value": 3
                }
              ]
            }
          }
        }
      ]
    }
  ],
  "last_primary_key": "2"
}

Sync Vector Index

POST /api/2.0/vector-search/indexes/{name}/sync

Triggers a synchronization process for a specified vector index.

API scopes: vector-search

Parameters

namestringpath: Name of the vector index to synchronize. Must be a Delta Sync Index.

Upsert Data Vector Index

POST /api/2.0/vector-search/indexes/{name}/upsert-data

Handles the upserting of data into a specified vector index.

API scopes: vector-search

Parameters

namestringpath: Name of the vector index where data is to be upserted. Must be a Direct Vector Access Index.

Request body

inputs_jsonstring: JSON string representing the data to be upserted.

Response

statusstring: Status of the upsert operation.
Values: SUCCESS, PARTIAL_SUCCESS, FAILURE

resultobject

Result of the upsert or delete operation.

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success_row_countint64: Count of successfully processed rows.

failed_primary_keysarray of string: List of primary keys for rows that failed to process.

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Request

{
  "inputs_json": "[{\"id\": \"1\", \"text\": \"hello world\", \"text_vector\": [1.0, 2.0, 3.0]}]"
}

Response

{
  "result": {
    "failed_primary_keys": [
      "1"
    ],
    "success_row_count": 0
  },
  "status": "FAILURE"
}

VectorIndex object​

Get​

Parameters​

Response​

List​

Parameters​

Response​

Create​

Request body​

Response​

Delete​

Parameters​

Delete Data Vector Index​

Parameters​

Response​

Query Vector Index​

Parameters​

Request body​

Response​

Query Vector Index Next Page​

Parameters​

Request body​

Response​

Scan Vector Index​

Parameters​

Request body​

Response​

Sync Vector Index​

Parameters​

Upsert Data Vector Index​

Parameters​

Request body​

Response​

VectorIndex object

Get

Parameters

Response

List

Parameters

Response

Create

Request body

Response

Delete

Parameters

Delete Data Vector Index

Parameters

Response

Query Vector Index

Parameters

Request body

Response

Query Vector Index Next Page

Parameters

Request body

Response

Scan Vector Index

Parameters

Request body

Response

Sync Vector Index

Parameters

Upsert Data Vector Index

Parameters

Request body

Response