# Vector databases
URL: https://docs.datarobot.com/en/docs/api/reference/sdk/gen-vector-databases.html

# Vector Databases

### class datarobot.models.genai.vector_database.CustomModelVectorDatabaseValidation

Validation record checking the ability of the deployment to serve as a vector database.

- Variables:

### class datarobot.models.genai.vector_database.SupportedEmbeddings

All supported embedding models including the recommended default model.

- Variables:

### class datarobot.models.genai.vector_database.SupportedTextChunkings

Supported text chunking configurations which includes a set of
recommended chunking parameters for each supported embedding model.

- Variables: text_chunking_configs – All supported text chunking configurations.

### class datarobot.models.genai.vector_database.VectorDatabase

Metadata for a DataRobot vector database accessible to the user.

- Variables:

#### classmethod get_supported_embeddings(dataset_id=None, use_case=None)

Get all supported and the recommended embedding models.

- Parameters:
- Returns: supported_embeddings – The supported embedding models.
- Return type: SupportedEmbeddings

#### submit_export_dataset_job()

Submit the vector database dataset export job.

- Returns: result – The result of the vector database dataset export job containing the exported dataset id.
- Return type: VectorDatabaseDatasetExportJob

#### classmethod get_supported_retrieval_settings()

Get supported retrieval settings.

- Returns: supported_retrieval_settings – The supported retriever settings.
- Return type: SupportedRetrievalSettings

#### classmethod create(dataset_id, chunking_parameters=None, use_case=None, name=None, parent_vector_database_id=None, update_llm_blueprints=None, update_deployments=None, external_vector_database_connection=None, metadata_dataset_id=None, metadata_combination_strategy=None)

Create a new vector database.

- Parameters:
- Returns: vector database – The created vector database with execution status ‘new’.
- Return type: VectorDatabase

#### classmethod create_from_custom_model(name, use_case=None, validation_id=None, prompt_column_name=None, target_column_name=None, deployment_id=None, model_id=None)

Create a new vector database from validated custom model deployment.

- Parameters:
- Returns: vector database – The created vector database.
- Return type: VectorDatabase

#### classmethod get(vector_database_id)

Retrieve a single vector database.

- Parameters: vector_database_id ( str ) – The ID of the vector database you want to retrieve.
- Returns: vector database – The requested vector database.
- Return type: VectorDatabase

#### classmethod list(use_case=None, playground=None, search=None, sort=None, completed_only=None)

List all vector databases associated with a specific use case available to the user.

- Parameters:
- Returns: vectorbases – A list of vector databases available to the user.
- Return type: list[VectorDatabase]

#### update(name=None, credential_id=None)

Update the vector database.

- Parameters:
- Returns: vector database – The updated vector database.
- Return type: VectorDatabase

#### update_connected(dataset_id, metadata_dataset_id=None, metadata_combination_strategy=None)

Update a connected vector database.

- Parameters:
- Returns: vector database – The updated vector database.
- Return type: VectorDatabase

#### delete()

Delete the vector database.

- Return type: None

#### classmethod get_supported_text_chunkings()

Get all supported text chunking configurations which includes
a set of recommended chunking parameters for each supported embedding model.

- Returns: supported_text_chunkings – The supported text chunking configurations.
- Return type: SupportedTextChunkings

#### download_text_and_embeddings_asset(file_path=None, part=None)

Download a parquet file with text chunks and corresponding embeddings created
by a vector database.

- Parameters:
- Return type: None

#### send_to_custom_model_workshop(maximum_memory=None, resource_bundle_id=None, replicas=None, network_egress_policy=None)

Create a new CustomModelVersion for this vector database.

- Parameters:
- Return type: CustomModelVersion

#### deploy(default_prediction_server_id=None, prediction_environment_id=None, credential_id=None, maximum_memory=None, resource_bundle_id=None, replicas=None, network_egress_policy=None)

Create a new Custom Model for this vector database and deploy it on a new Deployment.

- Parameters:
- Return type: Deployment

### class datarobot.models.genai.vector_database.SupportedRetrievalSetting

A single supported retrieval setting.

- Variables:

### class datarobot.models.genai.vector_database.VectorDatabaseDatasetExportJob

Response for the vector database dataset export job.

- Variables:

### class datarobot.models.genai.chat_prompt.Citation

Citation for documents retrieved from a vector database.

- Variables:

### class datarobot.models.genai.llm_blueprint.VectorDatabaseSettings

Settings for a DataRobot GenAI vector database associated with an LLM blueprint.

- Variables:

### class datarobot.models.genai.vector_database.ChunkingParameters

Parameters defining how documents are split and embedded.

- Variables:

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