DBMS_VECTOR_DATABASE

232 DBMS_VECTOR_DATABASE

The DBMS_VECTOR_DATABASE package provides APIs for common Oracle AI Vector Search operations, including vector table management, vector data operations, vector search, index management, and model operations.

These PL/SQL functions accept input parameters in JSON format where noted. The functions return a CLOB that contains the JSON response.

232.1 Summary of DBMS_VECTOR_DATABASE Subprograms

This table lists the DBMS_VECTOR_DATABASE subprograms and briefly describes them.

Table 232-1 DBMS_VECTOR_DATABASE Package Subprograms

Subprogram	Description
Manage Vector Tables: These functions enable you to manage vector tables in Autonomous AI Vector Database and describe vector tables.
LIST_VECTOR_TABLES	Lists all vector tables available in Autonomous AI Vector Database.
CREATE_VECTOR_TABLE	Creates a new vector table.
CREATE_VECTOR_TABLE_FOR_MODEL	Creates a new vector table with integrated embedding using a specified model.
UPDATE_VECTOR_TABLE_ANNOTATION	Updates vector table annotations.
DESCRIBE_VECTOR_TABLE	Retrieves metadata/configuration information about a vector table.
DROP_VECTOR_TABLE	Drops an existing vector table.
Manage Indexes: These functions enable you to manage vector indexes.
REBUILD_INDEX	Rebuilds an existing vector index for a specified table.
INDEX_BUILD_STATUS	Retrieves the status of an index for a given table.
DROP_INDEX	Drops an existing vector index.
Manage Data: These functions enable you to manage vector data in the Autonomous AI Vector Database.
UPSERT_VECTORS	Inserts or updates vectors and metadata into a specified table.
DELETE_VECTORS	Deletes the existing vectors by ID from a table.
LIST_VECTORS	Lists vector IDs from a vector table.
Search Data: These functions enable you to index, rerank search results, and perform similarity search.
CREATE_INDEX	Creates a new vector index.
SEARCH	Performs similarity search using text, vector, or record ID.
RERANK	Reranks results based on their relevance to the query by using a specified rerank model.
Manage Model: These functions enable you to manage vector embedding model operations.
GENERATE_EMBEDDING	Generates vector embeddings using the given embedding model.
LIST_MODELS	Lists all available vector embedding and rerank models that can be loaded.
LOAD_MODEL	Loads an embedding or rerank model from storage into the Autonomous AI Vector Database.
LOAD_VECTORS	Bulk loads vector data from files/URLs into a table; creates table if necessary.
DESCRIBE_MODEL	Retrieves metadata an existing embedding model.
DROP_MODEL	Drops an existing embedding or rerank model (if not in use by vector tables).
Summary : This function enables to generate summary.
SUMMARY	Returns summary reporting total tables, models, and vectors available in the Autonomous AI Vector Database.

232.1.1 CREATE_INDEX

Use the DBMS_VECTOR_DATABASE.CREATE_INDEX PL/SQL function to create or update vector and metadata indexes for a vector table.

Purpose

Use this function to create vector indexes, metadata indexes, or both for an existing vector table. Pass nested index_params to control vector index settings and metadata path indexing.

Syntax

DBMS_VECTOR_DATABASE.CREATE_INDEX (
    table_name                  IN VARCHAR2,
    index_params                IN JSON DEFAULT NULL,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`table_name`	VARCHAR2	Existing vector table	Yes	Name of the table on which to create the index	Must exist in caller schema.
`index_params`	JSON	Object or NULL	No	Vector and metadata index configuration.	Defaults are applied when omitted.
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.create_index(
    table_name => 'product_vectors',
    index_params => JSON('{
      "vector_index_params": {
        "auto_index": true,
        "organization": "INMEMORY GRAPH",
        "distance_metric": "COSINE",
        "accuracy": 95,
        "quantization_type": "SCALAR",
        "compression_ratio": 4,
        "advanced_params": {
          "neighbors": 32,
          "efConstruction": 128,
          "rescore_factor": 4,
          "algorithm": "uniform_quantization"
        }
      },
      "metadata_index_params": {
        "auto_index": true,
        "include_paths": ["category", "price"]
      },
      "parallel_creation": 2
    }')
);

Example response

{
  "message": "Vector index VECIDX_PRODUCT_VECTORS_20260501T100000 and Metadata index(es) for paths [category, price] created successfully for table PRODUCT_VECTORS"
}

232.1.2 CREATE_VECTOR_TABLE

Use the DBMS_VECTOR_DATABASE.CREATE_VECTOR_TABLE PL/SQL function to create a vector table.

Purpose

Use this function to create either a bring-your-own-vector table or a model-backed table. If embed_params is omitted, callers provide vector values directly. If embed_params is supplied, the database uses the configured model and metadata path for text embedding workflows.

Syntax

DBMS_VECTOR_DATABASE.CREATE_VECTOR_TABLE (
    name                        IN VARCHAR2,
    comment                     IN VARCHAR2 DEFAULT NULL,
    annotations                 IN JSON DEFAULT NULL,
    table_params                IN JSON DEFAULT NULL,
    embed_params                IN JSON DEFAULT NULL,
    index_params                IN JSON DEFAULT NULL,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`name`	VARCHAR2	Valid table identifier	Yes	Name of the table to create	The table must not already exist in the caller schema.
`comment`	VARCHAR2	String or NULL	No	Optional table comment	Returned as `comment` in table metadata
`annotations`	JSON	Object or NULL	No	User annotations stored with table metadata	Must be valid JSON
`table_params`	JSON	Object or NULL	No	Table creation options	Currently supports `auto_generate_id`
`embed_params`	JSON	Object or NULL	No	Embedding model configuration for model-backed tables	Supply when the table should generate vectors from metadata text by using a loaded embedding model. Omit for bring-your-own-vector tables.
`index_params`	JSON	Object or NULL	No	Vector and metadata index configuration	Defaults are applied when omitted
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.create_vector_table(
    name => 'product_vectors',
    comment => 'Product catalog embeddings',
    table_params => JSON('{"auto_generate_id": false}'),
    annotations => JSON('{"domain": "retail"}'),
    index_params => JSON('{
      "vector_index_params": {
        "auto_index": true,
        "organization": "PARTITIONS",
        "distance_metric": "COSINE",
        "accuracy": 90
      },
      "metadata_index_params": {
        "auto_index": true,
        "include_paths": ["category", "price"]
      },
      "parallel_creation": 2
    }')
);

Example Response

{
  "table_name": "PRODUCT_VECTORS",
  "comment": "Product catalog embeddings",
  "table_params": {
    "auto_generate_id": false
  },
  "annotations": {
    "domain": "retail"
  },
  "vector_type": "dense",
  "vector_table_type": "BYOV",
  "embed_params": null,
  "index_params": {
    "vector_index_params": {
      "auto_index": true,
      "organization": "PARTITIONS",
      "distance_metric": "COSINE",
      "accuracy": 90
    },
    "metadata_index_params": {
      "auto_index": true,
      "include_paths": ["category", "price"]
    },
    "parallel_creation": 2
  },
  "owner": "APPUSER",
  "indexes": [],
  "status": "Empty",
  "stats": {
    "total_vectors": 0
  },
  "created": "2026-05-01T10:00:00.000000+00:00",
  "updated": "2026-05-01T10:00:00.000000+00:00"
}

232.1.3 CREATE_VECTOR_TABLE_FOR_MODEL

Use the DBMS_VECTOR_DATABASE.CREATE_VECTOR_TABLE_FOR_MODEL PL/SQL function to create a a model-backed vector table.

Purpose

Use this function to create a vector table that automatically generates embeddings from metadata text by using the specified model and embedding metadata path.

Syntax

DBMS_VECTOR_DATABASE.CREATE_VECTOR_TABLE_FOR_MODEL (
    table_name                  IN VARCHAR2,
    description                 IN VARCHAR2 DEFAULT NULL,
    auto_generate_id            IN BOOLEAN DEFAULT FALSE,
    annotations                 IN JSON DEFAULT NULL,
    embed_params                IN JSON,
    index_params                IN JSON DEFAULT NULL,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`table_name`	VARCHAR2	Valid table identifier	Yes	Name of the vector table to create	The table should not already exist in the caller schema
`description`	VARCHAR2	String or NULL	No	Optional table description	Returned as `comment` in table metadata
`auto_generate_id`	Boolean	`TRUE`, `FALSE`	No	Controls whether the database automatically generates IDs for inserted records	Defaults to `FALSE`. Set to `TRUE` when callers do not provide explicit ID
`annotations`	JSON	Object or NULL	No	User annotations stored with table metadata	Must be valid JSON
`embed_params`	JSON	`embed_params` object	Yes	Embedding model configuration	Requires `model` and a metadata text selector in `embed_metadata_jsonpath`
`index_params`	JSON	Object or NULL	No	Vector and metadata index configuration	Defaults are applied when omitted
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.create_vector_table_for_model(
    table_name => 'product_text_vectors',
    description => 'Product text embeddings',
    auto_generate_id => TRUE,
    embed_params => JSON('{
      "model": "DOC_EMBED_MODEL",
      "embed_metadata_jsonpath": "description"
    }'),
    index_params => JSON('{
      "vector_index_params": {
        "auto_index": true,
        "organization": "INMEMORY GRAPH",
        "distance_metric": "COSINE"
      }
    }')
);

Example Response

{
  "table_name": "PRODUCT_TEXT_VECTORS",
  "comment": "Product text embeddings",
  "table_params": {
    "auto_generate_id": true
  },
  "annotations": null,
  "vector_type": "dense",
  "vector_table_type": "MODEL",
  "embed_params": {
    "model": "DOC_EMBED_MODEL",
    "embed_metadata_jsonpath": "description"
  },
  "index_params": {
    "vector_index_params": {
      "auto_index": true,
      "organization": "INMEMORY GRAPH",
      "distance_metric": "COSINE"
    },
    "metadata_index_params": {
      "auto_index": true
    },
    "parallel_creation": 1
  },
  "owner": "APPUSER",
  "indexes": [],
  "status": "Empty",
  "stats": {
    "total_vectors": 0
  },
  "created": "2026-05-01T10:00:00.000000+00:00",
  "updated": "2026-05-01T10:00:00.000000+00:00"
}

232.1.4 DESCRIBE_MODEL

Use the DBMS_VECTOR_DATABASE.DESCRIBE_MODEL PL/SQL function to retrieve model details in the caller schema.

Purpose

Use this function to retrieve model name, algorithm, mining function, creation date, and model attributes for a single model.

Syntax

DBMS_VECTOR_DATABASE.DESCRIBE_MODEL (
    model_name                  IN VARCHAR2,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`model_name`	VARCHAR2	Existing model	Yes	Name of the model to describe	Must exist in caller schema
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example


dbms_vector_database.describe_model('DOC_EMBED_MODEL');

Example Response

{
  "model_name": "DOC_EMBED_MODEL",
  "algorithm": "ONNX",
  "mining_function": "EMBEDDING",
  "creation_date": "2026-05-01T10:00:00.000000+00:00",
  "attributes": [
    {
      "name": "DATA",
      "value": "TEXT",
      "data_type": "VARCHAR2",
      "data_length": 4000,
      "vector_info": null
    }
  ]
}

232.1.5 DESCRIBE_VECTOR_TABLE

Use the DBMS_VECTOR_DATABASE.DESCRIBE_VECTOR_TABLE PL/SQL function to retrieve metadata of a vector table.

Purpose

Use this function to inspect the public metadata of a vector table, including table creation parameters, embedding configuration, index configuration, status, statistics, and timestamps.

Syntax

DBMS_VECTOR_DATABASE.DESCRIBE_VECTOR_TABLE (
    name                        IN VARCHAR2,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`name`	VARCHAR2	Existing vector table	Yes	Name of the table describe	Must exist in caller schema
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.describe_vector_table('product_vectors');

Example Response

{
  "table_name": "PRODUCT_VECTORS",
  "comment": "Product catalog embeddings",
  "table_params": {
    "auto_generate_id": false
  },
  "annotations": {
    "domain": "retail"
  },
  "vector_type": "dense",
  "vector_table_type": "BYOV",
  "embed_params": null,
  "index_params": {
    "vector_index_params": {
      "auto_index": true,
      "organization": "PARTITIONS",
      "distance_metric": "COSINE"
    },
    "metadata_index_params": {
      "auto_index": true
    },
    "parallel_creation": 1
  },
  "owner": "APPUSER",
  "indexes": [],
  "status": "Empty",
  "stats": {
    "total_vectors": 0
  },
  "created": "2026-05-01T10:00:00.000000+00:00",
  "updated": "2026-05-01T10:00:00.000000+00:00"
}

232.1.6 DELETE_VECTORS

Use the DBMS_VECTOR_DATABASE.DELETE_VECTORS PL/SQL function to delete existing vectors by ID from a specified table.

Purpose

Use this function to remove unwanted vectors from a specified table. This function automatically commits deletions after execution, and changes cannot be rolled back as part of the main transaction.

Syntax

DBMS_VECTOR_DATABASE.DELETE_VECTORS (
    table_name                  IN VARCHAR2,
    ids                         IN JSON,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`table_name`	VARCHAR2	Existing vector table	Yes	Name of the target table to delete vectors from	Must exist in caller schema
`ids`	JSON	Array of strings	Yes	JSON array of IDs to delete	Use explicit ID arrays
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.delete_vectors('myvectors', JSON('["vec1", "vec2"]'));

Example Response

{
  "message": "Deleted 2 rows successfully"
}

232.1.7 DROP_INDEX

Use the DBMS_VECTOR_DATABASE.DROP_INDEX PL/SQL function to drop vector and metadata indexes from a vector table.

Purpose

Use this function to drop the vector index, metadata indexes, or both. The optional index_params.index_type field controls the target scope.

Syntax

 DBMS_VECTOR_DATABASE.DROP_INDEX (
    table_name                  IN VARCHAR2,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL,
    index_params                IN JSON DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`table_name`	VARCHAR2	Existing vector table	Yes	Name of the table to remove the index from	Must exist in caller schema
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional
`index_params`	JSON	Object or NULL	No	Drop scope and metadata path selection	Supports `index_type`, `metadata_index_params.include_paths`, and `metadata_index_params.exclude_paths`

Note:

index_params.index_type accepts vector, metadata, or all.
vector_index_params is not supported for DROP_INDEX. Useindex_type to select vector index removal.
metadata_index_params.auto_index is also not supported for DROP_INDEX.

Example

DBMS_VECTOR_DATABASE.DROP_INDEX(
    table_name => 'product_vectors',
    index_params => JSON('{
      "index_type": "metadata",
      "metadata_index_params": {
        "include_paths": ["category", "price"]
      }
    }')
);

Example Response

{
  "message": "MVI index(es) for paths [category, price] dropped successfully from table PRODUCT_VECTORS"
}

232.1.8 DROP_MODEL

Use the DBMS_VECTOR_DATABASE.DROP_MODEL PL/SQL function to drop a model from the caller schema.

Purpose

Use this function to remove a model from the database. Dropping a model can affect model-backed vector tables or work flows that depend on it.

Syntax

DBMS_VECTOR_DATABASE.DROP_MODEL (
    model_name                  IN VARCHAR2,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`model_name`	VARCHAR2	Existing model	Yes	Name of the model to drop	Must exist in caller schema and must not be referenced by a model-backed vector table.
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example


dbms_vector_database.drop_model('DOC_EMBED_MODEL');

Example Response

{
  "message": "Model DOC_EMBED_MODEL dropped successfully"
}

232.1.9 DROP_VECTOR_TABLE

Use the DBMS_VECTOR_DATABASE.DROP_VECTOR_TABLE PL/SQL function to drop an existing vector table.

Purpose

Use this function to delete a vector table and its associated metadata and indexes. This is a destructive operation.

Syntax

 DBMS_VECTOR_DATABASE.DROP_VECTOR_TABLE (
    name                        IN VARCHAR2,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`name`	VARCHAR2	Existing vector table	Yes	Name of the table to drop	Must exist in caller schema
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.drop_vector_table('product_vectors');

Example Response

{
  "message": "Table PRODUCT_VECTORS deleted successfully"
}

232.1.10 GENERATE_EMBEDDING

Use the DBMS_VECTOR_DATABASE.GENERATE_EMBEDDING PL/SQL function to generate vector embeddings from text.

Purpose

Use this function to generate embeddings for a JSON array of input text objects using a database embedding model.

Syntax

DBMS_VECTOR_DATABASE.GENERATE_EMBEDDING (
    model_name                  IN VARCHAR2,
    inputs                      IN SYS.JSON_ARRAY_T,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`model_name`	VARCHAR2	Existing model	Yes	Embedding model name to generate vector embeddings	Must exist in caller schema
`inputs`	SYS.JSON_ARRAY_T	Array of objects	Yes	Input text objects	Each object must include `text`
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

DBMS_VECTOR_DATABASE.GENERATE_EMBEDDING(
    model_name => 'DOC_EMBED_MODEL',
    inputs => SYS.JSON_ARRAY_T('[{"text": "hello world"}]')
);

Example Response

{
  "data": [
    {
      "text": "hello world",
      "embedding": [
        0.0117027815,
        0.00419755466,
        -0.0301191173,
        0.011317092
      ]
    }
  ]
}

232.1.11 INDEX_BUILD_STATUS

Use the DBMS_VECTOR_DATABASE.INDEX_BUILD_STATUS PL/SQL function to retrieve vector index build status for a vector table.

Purpose

Use this function to monitor the build or rebuild status of the vector index associated with a vector table.

Syntax

DBMS_VECTOR_DATABASE.INDEX_BUILD_STATUS (
    table_name                  IN VARCHAR2,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`table_name`	VARCHAR2	Existing vector table	Yes	Name of the table associated with the vector index which is to monitored for build or rebuild status updates	Must exist in caller schema
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.index_build_status('product_vectors');

Response

{
  "Index Status": "Index build in progress...\nCurrent progress: 75%"
}

232.1.12 LIST_MODELS

Use the DBMS_VECTOR_DATABASE.LIST_MODELS PL/SQL function to list mining models available in the caller schema.

Purpose

Use this function to retrieve model metadata, including model name, algorithm, mining function, creation date, and model attributes.

Syntax

DBMS_VECTOR_DATABASE.LIST_MODELS (
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

DBMS_VECTOR_DATABASE.LIST_MODELS();

Response

{
  "models": [
    {
      "model_name": "DOC_EMBED_MODEL",
      "algorithm": "ONNX",
      "mining_function": "EMBEDDING",
      "creation_date": "2026-05-01T10:00:00.000000+00:00",
      "attributes": [
        {
          "name": "DATA",
          "value": "TEXT",
          "data_type": "VARCHAR2",
          "data_length": 4000,
          "vector_info": null
        }
      ]
    }
  ]
}

232.1.13 LIST_VECTORS

Use the DBMS_VECTOR_DATABASE.LIST_VECTORS PL/SQL function to list existing vectors by ID or with pagination.

Purpose

Use this function to list vector IDs from a specified table.

Syntax

DBMS_VECTOR_DATABASE.LIST_VECTORS (
    table_name                  IN VARCHAR2,
    ids                         IN JSON DEFAULT NULL,
    limit                       IN NUMBER DEFAULT 15,
    offset                      IN NUMBER DEFAULT NULL,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`table_name`	VARCHAR2	Existing vector table	Yes	Name of the table to read vectors from	Must exist in caller schema
`ids`	JSON	Array of strings	No	Optional ID filter	Builds an ID IN (…) filter
`limit`	NUMBER	> 0	No	Max rows returned	Defaults to 15
`offset`	NUMBER	>= 0 or NULL	No	Rows to skip	Null is returned as 0
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

DBMS_VECTOR_DATABASE.LIST_VECTORS(
    table_name => 'sample_table',
    ids => JSON('["id1", "id2"]')
);

Example Response

{
  "items": [
    {
      "id": "id1",
      "dense_vector": [0.1, 0.5, 0.4, 0.56],
      "metadata": {
        "product_name": "Noise Cancelling Headphones",
        "category": "electronics",
        "brand": "Sony",
        "price": 299.99
      }
    },
    {
      "id": "id2",
      "dense_vector": [0.14, 0.534, 0.12, 0.58],
      "metadata": {
        "product_name": "Studio Monitor Headphones",
        "category": "electronics",
        "brand": "Audio-Technica",
        "price": 149.99
      }
    }
  ],
  "limit": 2,
  "offset": 0,
  "count": 2
}

232.1.14 LIST_VECTOR_TABLES

Use the DBMS_VECTOR_DATABASE.LIST_VECTOR_TABLES PL/SQL function to list vector tables available in the caller schema.

Purpose

Use this function to retrieve public metadata for vector tables created by DBMS_VECTOR_DATABASE APIs. The function is read-only and returns an empty vector_tables array when no vector tables exist.

Syntax

DBMS_VECTOR_DATABASE.LIST_VECTOR_TABLES (
    debug_flags      IN JSON DEFAULT NULL,
    request_id       IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameter	Type	Value Range	Required	Description	Notes
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	`1-256` effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.list_vector_tables();

Example Response

{
  "vector_tables": [
    {
      "table_name": "MYVECTORS",
      "comment": "Product embeddings",
      "table_params": {
        "auto_generate_id": false
      },
      "annotations": {
        "domain": "retail"
      },
      "vector_type": "dense",
      "vector_table_type": "BYOV",
      "embed_params": null,
      "index_params": {
        "vector_index_params": {
          "auto_index": true,
          "organization": "PARTITIONS",
          "distance_metric": "COSINE"
        },
        "metadata_index_params": {
          "auto_index": true
        },
        "parallel_creation": 1
      },
      "owner": "APPUSER",
      "indexes": [],
      "status": "Empty",
      "stats": {
        "total_vectors": 0
      },
      "created": "2026-05-01T10:00:00.000000+00:00",
      "updated": "2026-05-01T10:00:00.000000+00:00"
    }
  ]
}

232.1.15 LOAD_MODEL

Use the DBMS_VECTOR_DATABASE.LOAD_MODEL PL/SQL function to load an ONNX model from cloud storage into the database.

Purpose

Use this function to load a model from a URI. Optional model_params enables to provide the cloud credential name and model metadata. After loading, the function returns the same model detail shape as DESCRIBE_MODEL.

Syntax

DBMS_VECTOR_DATABASE.LOAD_MODEL (
    model_name                  IN VARCHAR2,
    url                         IN VARCHAR2,
    model_params                IN JSON DEFAULT NULL,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`model_name`	VARCHAR2	Valid model identifier	Yes	Unique name to assign to the loaded model	Must not already exist in the caller schema
`url`	VARCHAR2	Object storage URL	Yes	Object storage URL where the model file is located	Provide `model_params.credential` when the URL requires database cloud credentials
`model_params`	JSON	Object or NULL	No	Optional model loading settings	Supports `credential` and `metadata`
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags for detailed logging flags	Optional unless diagnostics are needed
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

model_params fields

Field	Type	Value Range	Required	Description	Notes
`credential`	String	Valid string	No	Database credential used to access the object storage URL	Required only when the model URL is private. Otherwise, requires authentication
`metadata`	Object	Object or NULL	No	Additional model metadata to store with the loaded model	Returned in model metadata when provided by the service

Example

dbms_vector_database.load_model(
    model_name => 'DOC_EMBED_MODEL',
    url => 'https://objectstorage.example.com/models/doc_embed.onnx',
    model_params => JSON('{
      "credential": "OBJ_STORE_CRED",
      "metadata": {
        "function": "embedding"
      }
    }')
);

Example Response

{
  "model_name": "DOC_EMBED_MODEL",
  "algorithm": "ONNX",
  "mining_function": "EMBEDDING",
  "creation_date": "2026-05-01T10:00:00.000000+00:00",
  "attributes": [
    {
      "name": "DATA",
      "value": "TEXT",
      "data_type": "VARCHAR2",
      "data_length": 4000,
      "vector_info": null
    }
  ]
}

232.1.16 LOAD_VECTORS

Use the DBMS_VECTOR_DATABASE.LOAD_VECTORS PL/SQL function to load vector data from a CSV file in cloud storage into an existing vector table.

Purpose

Use this function to append vector records from cloud storage into an existing vector table. The table should exist in the database. For model-backed tables, the table’s embed_params configuration is used to generate embeddings when needed.

To load vector dataset in CSV format, the CSV format should comply with standard CSV conventions described in Guidelines for Preparing CSV Files for LOAD_VECTORS Function

Syntax

DBMS_VECTOR_DATABASE.LOAD_VECTORS (
    table_name                  IN VARCHAR2,
    url                         IN VARCHAR2,
    params                      IN JSON DEFAULT NULL,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`table_name`	VARCHAR2	Existing vector table	Yes	Target vector table name	Must exist in caller schema
`url`	VARCHAR2	Cloud URI	Yes	CSV file URI	The file is loaded through the database cloud loader
`params`	JSON	Object or NULL	No	Optional load settings	Supports `credential`
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Guidelines for Preparing CSV Files for LOAD_VECTORS Function

General Structure
- The file must be a valid, comma-separated values (CSV) file.
- The first row must define the column names.
- Each field must be separated by a comma (,).
Handling Fields with Commas
- If a dense vector or metadata) contains commas, enclose the entire field in quotation marks ("). This ensures the CSV parser treats the entire value as a single column.
Embedding JSON in CSV

If the following guidelines are not followed, the CSV parser may incorrectly split fields, resulting in ingestion errors.
- JSON fields must use double quotes (") for all property names and string values.
- To include double quotes inside JSON within a CSV field, escape them by doubling: ("").
- Do not use single quotation marks (') in JSON.
- Always enclose JSON content in double quotation marks ("").
- JSON example:
  
  id,metadata 77E0D7F0-1942-494A-ACE2-9004D2BDC59E,"{""PARK_CODE"":""abli"",""NAME"":""Abraham Lincoln Birthplace"",""STATES"":""KY""}"
Header Row Formats
- For model-backed vector tables, use one of the following header row formats:
  - ID, METADATA )
  - METADATA, if auto_generated_id is set to true
- For bring-your-own-vector tables, use one of the following header row formats:
  - ID, DENSE_VECTOR
  - ID,DENSE_VECTOR, METADATA
  - METADATA, if auto_generated_id is set to true
  - DENSE_VECTOR, if auto_generated_id is set to true
- The order of columns is flexible.
- Headers are not case-sensitive (for example: METADATA, Metadata, or "Metadata" are all valid).
ID Field Guidelines
- If table_params.auto_generated_id is set to false, each data row should include a unique ID in the appropriate column.
- If table_params.auto_generated_id is set to true, providing the ID column is optional.
For model-backed tables, metadata must include the text path configured by embed_params.embed_metadata_jsonpath. For example, if embed_metadata_jsonpath is description, then each metadata object should contain a description text value. Use a JSON path, such as $.details.summary, to embed text from nested metadata.

Example


DBMS_VECTOR_DATABASE.LOAD_VECTORS(
    table_name => 'product_vectors',
    url => 'https://objectstorage.example.com/data/product_vectors.csv',
    params => JSON('{"credential": "OBJ_STORE_CRED"}')
);

Example Response

{
  "table_name": "PRODUCT_VECTORS",
  "file_url": "https://objectstorage.example.com/data/product_vectors.csv"
}

232.1.17 REBUILD_INDEX

Use the DBMS_VECTOR_DATABASE.REBUILD_INDEX PL/SQL function to rebuild vector and metadata indexes for a vector table.

Purpose

Use this function to rebuild vector indexes, metadata indexes, or both. Pass nested index_params to select and tune the rebuild.

Syntax

DBMS_VECTOR_DATABASE.REBUILD_INDEX (
    table_name                  IN VARCHAR2,
    index_params                IN JSON DEFAULT NULL,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`table_name`	VARCHAR2	Existing vector table	Yes	Name of the table on which to rebuild the index	Must exist in caller schema
`index_params`	JSON	Object or NULL	No	Rebuild scope and index configuration	`metadata_index_params.auto_index` is not supported for rebuild
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.rebuild_index(
    table_name => 'product_vectors',
    index_params => JSON('{
      "vector_index_params": {
        "auto_index": true,
        "organization": "INMEMORY GRAPH",
        "distance_metric": "COSINE",
        "advanced_params": {
          "neighbors": 32,
          "efConstruction": 128,
          "rescore_factor": 4
        }
      },
      "parallel_creation": 2
    }')
);

Example Response

{
  "message": "Vector index VECIDX_PRODUCT_VECTORS_20260501T100000 Rebuild successfully for table PRODUCT_VECTORS"
}

232.1.18 RERANK

Use the DBMS_VECTOR_DATABASE.RERANK PL/SQL function to rerank documents against a query.

Purpose

Use this function to rerank candidate documents by relevance to a query using a database rerank model. The function returns the response from DBMS_VECTOR.RERANK.

Syntax

DBMS_VECTOR_DATABASE.RERANK (
    model_name                  IN VARCHAR2,
    query                       IN VARCHAR2,
    documents                   IN JSON,
    model_params                IN JSON DEFAULT NULL,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`model_name`	VARCHAR2	Existing model	Yes	Rerank model name	Must exist in caller schema
`query`	VARCHAR2	Non-empty string	Yes	Query used for reranking	Empty values raise validation error
`documents`	JSON	Array	Yes	Candidate documents to rerank	Must be a JSON array
`model_params`	JSON	Object or NULL	No	Rerank options	Supports `top_n`
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.rerank(
    model_name => 'RERANK_MODEL',
    query => 'good headphone',
    documents => JSON('[
      "Studio Monitor Headphones",
      "Wireless Earbuds",
      "Coffee Grinder"
    ]'),
    model_params => JSON('{"top_n": 2}')
);

Example Response

[
  {
    "index": 0,
    "score": 0.92
  },
  {
    "index": 1,
    "score": 0.76
  }
]

232.1.19 SEARCH

Use the DBMS_VECTOR_DATABASE.SEARCH PL/SQL function to perform similarity search with a query text, query vector, or record ID and return the most similar records with distance scores.

Purpose

Use this function to retrieve similar records based on a specified query text, query vector, or record ID. It also supports metadata filters. Text search requires a model-backed table with embed_params.

Use query_by to search with text, a vector, or an ID. Exactly one query mode must be supplied.

Syntax

DBMS_VECTOR_DATABASE.SEARCH (
    table_name                  IN VARCHAR2,
    query_by                    IN JSON,
    filters                     IN JSON DEFAULT NULL,
    top_k                       IN NUMBER,
    include_vectors             IN BOOLEAN DEFAULT FALSE,
    output_selector             IN JSON DEFAULT NULL,
    advanced_options            IN JSON DEFAULT NULL,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`table_name`	VARCHAR2	Existing vector table	Yes	Name of the vector table to search	Must exist in caller schema
`query_by`	JSON	Exactly one query mode	Yes	Query vector source	`text, id`, or `vector`
`filters`	JSON	Object or NULL	No	Metadata filters	Applied to metadata through the QBE helper
`top_k`				Top- k nearest results to return
`include_vectors`	BOOLEAN	TRUE, FALSE	No	Include result vectors in output	Defaults to FALSE
`advanced_options`	JSON	Object or NULL	No	Search tuning options	Supports `distance_metric`, `accuracy`, `idx_parameters`, and `advanced_params`
`output_selector`	JSON	Array of strings or NULL	No	Top-level metadata keys to project in each result	`NULL` returns full metadata; an empty array returns an empty metadata object
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

output_selector values

Value	Behavior	Notes
NULL	Returns full `CONTENT_METADATA`	Default behavior
Array of strings	Projects selected top-level metadata keys into the result `metadata` object	Example: [`'tenant'`, `'title'`]
Empty array	Returns an empty metadata object	Results still include `id` and `distance`
Dotted string	Treats the value as a literal top-level key	`['a.b']` does not navigate nested JSON
Non-string or empty-string entry	Not supported	Use arrays of non-empty strings only

query_by

Exactly one query mode must be supplied

Field	Type	Required	Description	Notes
`text`	String	One of three	Text query embedded with the table model	Only valid for model-backed tables
`id`	Scalar	One of three	Uses the stored vector for the ID	Null or non-scalar values raise validation errors
`vector`	Array of number	One of three	Caller-supplied query vector	Used directly

filters

Search uses QBE translation for filters. The SEARCH signature does not change, but filter validation is handled through the QBE translator

Filter Capability	Notes
Scalar equality	Example: `{"tenant":"acme"}`
Comparison operators	`$eq, $ne, $gt, $gte, $lt, $lte`
Membership operators	`$in, $nin`
Existence	`$exists`
Logical operators	`$and, $or`
QBE/spatial operators	Supported through the QBE translator when valid
Invalid filters	Raise `-57725` or an invalid search filter error

advanced_options

Field	Type	Required	Description	Notes
`distance_metric`	String	No	Query-time distance metric	Validation is case-insensitive. For more information, see Vector Distance Metrics
`accuracy`	Number	No	Query-time trade-off between speed and accuracy	Use a higher value when search quality is more important than speed. For more information, see Understand Approximate Similarity Search Using Vector Indexes
`idx_parameters`	Object	No	Query-time vector index parameters	Can include `efsearch` and `neighbor_part`
`advanced_params`	Object	No	Additional query-time tuning parameters	Supports `rescore_factor`

Example

dbms_vector_database.search(
    table_name => 'product_vectors',
    query_by => JSON('{"text": "good headphone"}'),
    top_k => 5,
    filters => JSON('{
      "$and": [
        {"category": {"$eq": "electronics"}},
        {"price": {"$lt": 200}}
      ]
    }'),
    output_selector => JSON('["product_name", "price", "product_description"]'),
    include_vectors => FALSE
);

Example Response

{
  "results": [
    {
      "id": "id2",
      "distance": 0.212,
      "metadata": {
        "product_name": "Studio Monitor Headphones",
        "price": 149.99,
        "product_description": "Professional wired headphones with accurate sound for studio use."
      }
    },
    {
      "id": "id5",
      "distance": 0.678,
      "metadata": {
        "product_name": "Wireless Earbuds",
        "price": 199.99,
        "product_description": "Noise-cancelling wireless earbuds with rich sound and secure fit."
      }
    }
  ]
}

When include_vectors is TRUE, each result can also include a vector field.

232.1.20 SUMMARY

Use the DBMS_VECTOR_DATABASE.SUMMARY PL/SQL function to retrieve summary counts for vector tables, vectors, and models in the caller schema.

Purpose

Use this function to retrieve a compact summary of vector database objects for the current user.

Syntax

DBMS_VECTOR_DATABASE.SUMMARY (
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameter	Type	Value Range	Required	Description	Notes
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.summary()

Example Response

{
  "total_tables": 3,
  "total_vectors": 12500,
  "total_models": 2
}

232.1.21 UPSERT_VECTORS

Use the DBMS_VECTOR_DATABASE.UPSERT_VECTORS PL/SQL function to insert or update vectors in a specified table.

Purpose

Use this function to ingest new vectors, or add metadata, or update existing vectors.

Syntax

DBMS_VECTOR_DATABASE.UPSERT_VECTORS (
    table_name                  IN VARCHAR2,
    vectors                     IN JSON,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`table_name`	VARCHAR2	Existing vector table	Yes	Name of the target vector table to ingest the new vectors in	Must exist in caller schema
`vectors`	JSON	Array of vector record objects	Yes	Rows to insert or update	Routed to raw or text embedding path based on table metadata
`debug_flags`	JSON	Object or NULL	No	Debug or tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

DBMS_VECTOR_DATABASE.UPSERT_VECTORS(
    'sample_table',
    vectors => JSON('[
      {
        "id": "vec1",
        "dense_vector": [0.1, 0.5, 0.4, 0.56],
        "metadata": {"type": "document", "source": "Wikipedia"}
      },
      {
        "id": "vec2",
        "dense_vector": [0.14, 0.534, 0.12, 0.58],
        "metadata": {"type": "document", "source": "Internal"}
      }
    ]')
);

Example Response

{
  "upserted_count": 2
}

232.1.22 UPDATE_VECTOR_TABLE_ANNOTATION

Use the DBMS_VECTOR_DATABASE.UPDATE_VECTOR_TABLE_ANNOTATION PL/SQL function to update the public comment and annotations of a vector table.

Purpose

Use this function to update user-managed metadata for an existing vector table.

Syntax

DBMS_VECTOR_DATABASE.UPDATE_VECTOR_TABLE_ANNOTATION (
    name                        IN VARCHAR2,
    comment                     IN VARCHAR2 DEFAULT NULL,
    annotations                 IN JSON DEFAULT NULL,
    debug_flags                 IN JSON DEFAULT NULL,
    request_id                  IN VARCHAR2 DEFAULT NULL
)return CLOB;

Parameters

Parameters	Type	Value Range	Required	Description	Notes
`name`	VARCHAR2	Existing vector table	Yes	Name of the table to update	Must exist in caller schema
`comment`	VARCHAR2	String or NULL	No	Updated table comment	Returned as `comment` in table metadata
`annotations`	JSON	Object or NULL	No	Updated annotations	Must be valid JSON
`debug_flags`	JSON	Object or NULL	No	Debug/ tracing flags	Optional
`request_id`	VARCHAR2	1-256 effective chars	No	Correlation ID	Optional

Example

dbms_vector_database.update_vector_table_annotation(
    name => 'product_vectors',
    comment => 'Updated product embeddings',
    annotations => JSON('{"domain": "retail", "owner": "search"}')
);

Example Response

{
  "table_name": "PRODUCT_VECTORS",
  "comment": "Updated product embeddings",
  "table_params": {
    "auto_generate_id": false
  },
  "annotations": {
    "domain": "retail",
    "owner": "search"
  },
  "vector_type": "dense",
  "vector_table_type": "BYOV",
  "embed_params": null,
  "index_params": {
    "vector_index_params": {
      "auto_index": true
    },
    "metadata_index_params": {
      "auto_index": true
    },
    "parallel_creation": 1
  },
  "owner": "APPUSER",
  "indexes": [],
  "status": "Empty",
  "stats": {
    "total_vectors": 0
  },
  "created": "2026-05-01T10:00:00.000000+00:00",
  "updated": "2026-05-02T09:30:00.000000+00:00"
}

232.2 Shared Parameter Objects

Use the following shared object definitions for PL/SQL JSON parameters that accept nested configuration objects.

232.2.1 `advanced_params`

advanced_params controls vector index tuning.

Field	Type	Value Range	Required	Description	Notes
`partitions`	Integer	`1-10000000`	No	IVF or partition count	Applies to `PARTITIONS`
`neighbors`	Integer	`1-2048`	No	HNSW graph neighbor count	Applies to `INMEMORY GRAPH`
`efConstruction`	Integer	`1-65535`	No	HNSW construction beam width	Applies to `INMEMORY GRAPH`
`rescore_factor`	Integer	`1-100`	No	Rescore factor for HNSW or query-time search tuning	Supported for `INMEMORY GRAPH` and `SEARCH.advanced_options.advanced_params`
`algorithm`	String	`uniform_quantization`	No	HNSW quantization algorithm	Used with scalar quantization settings

232.2.2 `embed_params`

embed_params defines how the database generates embeddings automatically from metadata content.

Use embed_params when the table should store vectors generated from text in each row’s metadata JSON. Set model to a loaded embedding model. Set embed_metadata_jsonpath to the metadata field that contains the text to embed, such as description, $.description, or $.details.summary.

To select metadata content for embedding, choose the field that contains the natural-language text. For metadata such as {"description":"Compact wireless headphones","details":{"summary":"Noise-canceling audio"}}, use description or $.description to embed the top-level description, or use $.details.summary to embed the nested summary. Other metadata fields remain available for filtering or indexing, but they can not be used as embedding input by this setting.

Field	Type	Value Range	Required	Description	Notes
`model`	String	`1-128` chars	Yes	Name of the loaded embedding model to use for automatic embedding.	Model must exist in the caller schema. Use a model name returned by `LIST_MODELS`. Load one with `LOAD_MODEL`, if needed.
`embed_metadata_jsonpath`	String	`1-128` chars	Yes	Metadata field or JSON path that selects the text value to embed.	Use a simple field name, such as `description`, or a JSON path, such as `$.details.summary`. Each inserted or loaded row’s metadata must contain this path, and the selected value should be text. Leading `$, $.`, and extra dots are normalized.

232.2.3 `index_params`

index_params defines how vector and metadata indexes are created and managed.

Field	Type	Value Range	Required	Description	Notes
`index_type`	String	`vector`, `metadata`, `all`	No	Index rebuild or drop scope.	Use with `REBUILD_INDEX` or `DROP_INDEX` to target vector indexes, metadata indexes, or all indexes. For `REBUILD_INDEX`, omit to infer the target from supplied `vector_index_params` or `metadata_index_params` use `all` when metadata parameters are supplied and the vector index should also be rebuilt. Omit for create flows.
`vector_index_params`	Object	`vector_index_params` object	No	Controls how the vector index is built and managed.	Contains vector index settings and advanced tuning parameters.
`metadata_index_params`	Object	`metadata_index_params` object	No	Controls how metadata JSON paths are indexed and maintained.	Supports automatic path discovery and explicit include or exclude overrides.
`parallel_creation`	Integer or null	>= 1	No	Controls index creation or rebuild parallelism.	Uses parallel DDL for vector and metadata index creation when supported; defaults to `1` when normalized.

232.2.4 `metadata_index_params`

metadata_index_params controls how metadata JSON paths are indexed and maintained for metadata filtering.

Use it when you want the database to:

Discover metadata paths automatically
Force specific paths to be indexed
Exclude paths that should not be indexed
Disable metadata indexing

Field	Type	Value Range	Required	Description	Notes
`auto_index`	Boolean	`true, false`	No	Controls whether metadata indexes are automatically created and maintained.	Defaults to `true` in create flows. When `true`, the database automatically creates and maintains indexes on qualifying metadata paths, up to the supported limit. When `false`, only user-specified paths are indexed.
`include_paths`	Array of string	JSON paths or `*`	No	Explicit list of metadata JSON paths to include in metadata indexing.	Use explicit paths, such as `year` or `details.genre`, to force those paths into the metadata index selection. Use `*` to request all qualifying paths. Exact paths are normalized.
`exclude_paths`	Array of string	JSON paths or `*`	No	Explicit list of metadata JSON paths to exclude from metadata indexing.	Use explicit paths to override automatic selection or an `include_paths` wildcard. Using`include_paths=[""]` with `exclude_paths=[""]` is invalid.

Metadata indexing supports up to the first 50 qualifying paths when automatic path discovery is used. Qualifying paths are scalar metadata values that can be indexed. Arrays, objects, strings longer than 2 KB, and the path referenced by embed_metadata_jsonpath are not qualifying metadata index paths.

Use these common patterns:

Automatically discover and index qualifying metadata paths, up to the supported limit:

{
  "metadata_index_params": {
    "auto_index": true
  }
}

Index only the selected metadata paths:

{
  "metadata_index_params": {
    "auto_index": false,
    "include_paths": ["year", "genre"]
  }
}

Request all qualifying metadata paths except the excluded paths:

{
  "metadata_index_params": {
    "include_paths": ["*"],
    "exclude_paths": ["title"]
  }
}

232.2.5 `table_params`

table_params defines table creation options.

Field	Type	Value Range	Required	Description	Notes
`auto_generate_id`	Boolean	`true, false`	No	Controls whether the database automatically generates IDs for inserted records	Defaults to `false`. Set to `true` when callers do not provide explicit IDs.

232.2.6 `vector_index_params`

vector_index_params controls vector index configuration. It defines how the vector index is built and managed, including vector index settings and advanced tuning parameters.

Field	Type	Value Range	Required	Description	Notes
`auto_index`	Boolean	`true, false`	No	Controls whether the vector index is created automatically or only when requested explicitly.	Defaults to `true` when vector parameters are normalized. Set to `false` when you want to create or manage the vector index through explicit index lifecycle operations.
`organization`	String	`PARTITIONS, INMEMORY GRAPH`	No	Controls vector index organization.	Defaults to `PARTITIONS`. `PARTITIONS` builds an IVF index, and `INMEMORY GRAPH` builds an HNSW index.
`distance_metric`	String	`MANHATTAN, HAMMING, DOT, COSINE, EUCLIDEAN, EUCLIDEAN_SQUARED, JACCARD, L2_SQUARED`	No	Distance or similarity metric used by the vector index.	Defaults to COSINE when omitted. Validation is case-insensitive. For more information, see .Vector Distance Metrics
`accuracy`	Integer	`0-100`	No	Controls target index accuracy.	Optional tuning parameter. The implementation default is used when omitted. Use this value to adjust the trade-off between speed and accuracy. For more information, see Understand Approximate Similarity Search
`quantization_type`	String or null	`NONE` , `SCALAR`	No	Vector index quantization mode.	Use `SCALAR` with `compression_ratio` to compress vectors. Omit or use `NONE` when scalar quantization is not needed.
`compression_ratio`	Number or null	`2, 4, 8`	No	Controls how much to compress vectors when scalar quantization is enabled.	Populate when `quantization_type` is `SCALAR`. Valid values are `2`, `4`, and `8`.
`distribute_params`	Object or null	Object or NULL	No	Optional JSON object that controls distribution for distributed HNSW vector indexes.	Applies to `INMEMORY GRAPH`. Supports `distribute_method`.
`advanced_params`	Object	`advanced_params` object	No	Advanced vector index tuning parameters.	Supported fields depend on `organization`. Use partitions with `PARTITIONS`; use graph parameters, such as `neighbors` and `efConstruction`, with `INMEMORY GRAPH`.

232 DBMS_VECTOR_DATABASE

232.1 Summary of DBMS_VECTOR_DATABASE Subprograms

232.1.1 CREATE_INDEX

232.1.2 CREATE_VECTOR_TABLE

232.1.3 CREATE_VECTOR_TABLE_FOR_MODEL

232.1.4 DESCRIBE_MODEL

232.1.5 DESCRIBE_VECTOR_TABLE

232.1.6 DELETE_VECTORS

232.1.7 DROP_INDEX

232.1.8 DROP_MODEL

232.1.9 DROP_VECTOR_TABLE

232.1.10 GENERATE_EMBEDDING

232.1.11 INDEX_BUILD_STATUS

232.1.12 LIST_MODELS

232.1.13 LIST_VECTORS

232.1.14 LIST_VECTOR_TABLES

232.1.15 LOAD_MODEL

232.1.16 LOAD_VECTORS

232.1.17 REBUILD_INDEX

232.1.18 RERANK

232.1.19 SEARCH

232.1.20 SUMMARY

232.1.21 UPSERT_VECTORS

232.1.22 UPDATE_VECTOR_TABLE_ANNOTATION

232.2 Shared Parameter Objects

232.2.1 advanced_params

232.2.2 embed_params

232.2.3 index_params

232.2.4 metadata_index_params

232.2.5 table_params

232.2.6 vector_index_params

232.2.1 `advanced_params`

232.2.2 `embed_params`

232.2.3 `index_params`

232.2.4 `metadata_index_params`

232.2.5 `table_params`

232.2.6 `vector_index_params`