> ## Documentation Index > Fetch the complete documentation index at: https://docs.gp.scale.com/llms.txt > Use this file to discover all available pages before exploring further. # Configure Vector Store > Update the indexed metadata fields configuration for a vector store. Only indexed metadata fields can be used for filtering during query, list, and count operations. Non-indexed fields cannot be filtered on. **Field Types:** Only STRING, NUMBER, and BOOLEAN fields can be indexed (maximum 20 fields). OBJECT and LIST types are stored but cannot be indexed for filtering purposes. **Adding Fields:** New indexed fields can be added at any time. Existing documents containing those fields will have their metadata automatically indexed. **Removing Fields:** Indexed fields cannot be removed once added. Each indexed field increases write latency and storage overhead, so only index fields you actively filter on. **Note:** The `name` and `embedding_config` are immutable after creation. ## OpenAPI ````yaml https://api.dev-sgp.scale.com/openapi-versions/v5/openapi.json post /v5/vector-stores/{vector_store_name}/configure openapi: 3.1.0 info: title: EGP API V5 description: >- This is the parent API for all EGP APIs. If you are looking for the EGP API, please go to https://api.egp.scale.com/docs. contact: name: Scale Generative AI Platform url: https://scale.com/genai-platform version: 0.1.0 servers: - url: https://api.egp.scale.com security: [] paths: /v5/vector-stores/{vector_store_name}/configure: post: tags: - Vector Stores summary: Configure Vector Store description: >- Update the indexed metadata fields configuration for a vector store. Only indexed metadata fields can be used for filtering during query, list, and count operations. Non-indexed fields cannot be filtered on. **Field Types:** Only STRING, NUMBER, and BOOLEAN fields can be indexed (maximum 20 fields). OBJECT and LIST types are stored but cannot be indexed for filtering purposes. **Adding Fields:** New indexed fields can be added at any time. Existing documents containing those fields will have their metadata automatically indexed. **Removing Fields:** Indexed fields cannot be removed once added. Each indexed field increases write latency and storage overhead, so only index fields you actively filter on. **Note:** The `name` and `embedding_config` are immutable after creation. operationId: POST-V5-/v5/vector-stores/vector_store_name/configure parameters: - name: vector_store_name in: path required: true schema: type: string description: The name of the vector store title: Vector Store Name description: The name of the vector store - name: x-selected-account-id in: header required: false schema: anyOf: - type: string - type: 'null' title: Account ID Header requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/ConfigureVectorStoreRequest' responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/VectorStoreResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - APIKeyHeader: [] components: schemas: ConfigureVectorStoreRequest: properties: indexed_metadata_fields: additionalProperties: $ref: '#/components/schemas/DocumentFieldType' type: object title: Indexed Metadata Fields description: >- Dictionary mapping metadata field names to their types. Only STRING, NUMBER, and BOOLEAN types can be indexed. type: object required: - indexed_metadata_fields title: ConfigureVectorStoreRequest description: Request to configure a vector store. VectorStoreResponse: properties: name: type: string title: Name description: The name of the vector store embedding_config: $ref: '#/components/schemas/EmbeddingConfig' description: >- Embedding configuration identifying the model and its type. None for raw-embedding-only stores. embedding_dimensions: type: integer title: Embedding Dimensions description: Dimensionality of the embedding vectors indexed_metadata_fields: title: Indexed Metadata Fields description: Dictionary mapping metadata field names to their types additionalProperties: $ref: '#/components/schemas/DocumentFieldType' type: object created_at: type: string format: date-time title: Created At description: Timestamp of creation updated_at: type: string format: date-time title: Updated At description: Timestamp of last update type: object required: - name - embedding_dimensions - created_at - updated_at title: VectorStoreResponse description: Response model for vector store operations. HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError DocumentFieldType: type: string enum: - string - number - boolean title: DocumentFieldType description: |- Supported document field types for indexed metadata fields. Only STRING, NUMBER, and BOOLEAN types can be indexed for filtering. OBJECT and LIST values in document metadata are automatically stored as non-indexed fields and do not need to be declared here. EmbeddingConfig: anyOf: - $ref: '#/components/schemas/EmbeddingConfigModelsAPI' - $ref: '#/components/schemas/EmbeddingConfigBase' title: EmbeddingConfig ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: title: Error Type type: string input: title: Input ctx: type: object title: Context additionalProperties: true type: object required: - loc - msg - type title: ValidationError EmbeddingConfigModelsAPI: properties: type: type: string const: models_api title: Type description: The type of the embedding configuration. model_deployment_id: type: string title: Model Deployment Id description: The ID of the deployment of the created model in the Models API V3. type: object required: - type - model_deployment_id title: EmbeddingConfigModelsAPI EmbeddingConfigBase: properties: type: type: string const: base title: Type description: The type of the embedding configuration. default: base embedding_model: $ref: '#/components/schemas/EmbeddingModelName' description: >- The name of the base embedding model to use. To use custom models, change to type 'models'. type: object required: - embedding_model title: EmbeddingConfigBase EmbeddingModelName: type: string enum: - sentence-transformers/all-MiniLM-L12-v2 - sentence-transformers/all-mpnet-base-v2 - sentence-transformers/multi-qa-distilbert-cos-v1 - sentence-transformers/paraphrase-multilingual-mpnet-base-v2 - openai/text-embedding-ada-002 - openai/text-embedding-3-small - openai/text-embedding-3-large - embed-english-v3.0 - embed-english-light-v3.0 - embed-multilingual-v3.0 - gemini/text-embedding-005 - gemini/text-multilingual-embedding-002 - gemini/gemini-embedding-001 title: EmbeddingModelName securitySchemes: APIKeyHeader: type: apiKey in: header name: x-api-key ````