> ## 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. # Create Vector Store > Create a new vector store for storing and querying document embeddings. The vector store name must be unique within your account and follow naming conventions (3-63 characters, alphanumeric with hyphens/underscores). Once created, the embedding configuration and dimensions are immutable and cannot be changed. To use a different model, you must create a new vector store. **Embedding Configuration:** Provide `embedding_config` (for base or custom model deployments), `embedding_model` (shorthand for a base model), or `dimensions` only (raw embeddings). - With `embedding_config` or `embedding_model`: dimensions are auto-derived, and documents can be upserted with text content (auto-embedded) or with pre-computed embeddings. - With `dimensions` only: the store accepts only pre-computed embeddings. Semantic/hybrid queries are not supported (lexical search only). **Indexed Fields:** Optionally specify metadata fields to index at creation time. Only indexed fields can be used for filtering -- indexing is required, not just a performance optimization. Additional indexed fields can be added later using the configure endpoint, but cannot be removed once added. Keep in mind that each indexed field increases write latency and storage overhead, so only index fields you actively filter on. ## OpenAPI ````yaml https://api.dev-sgp.scale.com/openapi-versions/v5/openapi.json post /v5/vector-stores/create 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/create: post: tags: - Vector Stores summary: Create Vector Store description: >- Create a new vector store for storing and querying document embeddings. The vector store name must be unique within your account and follow naming conventions (3-63 characters, alphanumeric with hyphens/underscores). Once created, the embedding configuration and dimensions are immutable and cannot be changed. To use a different model, you must create a new vector store. **Embedding Configuration:** Provide `embedding_config` (for base or custom model deployments), `embedding_model` (shorthand for a base model), or `dimensions` only (raw embeddings). - With `embedding_config` or `embedding_model`: dimensions are auto-derived, and documents can be upserted with text content (auto-embedded) or with pre-computed embeddings. - With `dimensions` only: the store accepts only pre-computed embeddings. Semantic/hybrid queries are not supported (lexical search only). **Indexed Fields:** Optionally specify metadata fields to index at creation time. Only indexed fields can be used for filtering -- indexing is required, not just a performance optimization. Additional indexed fields can be added later using the configure endpoint, but cannot be removed once added. Keep in mind that each indexed field increases write latency and storage overhead, so only index fields you actively filter on. operationId: POST-V5-/v5/vector-stores/create parameters: - 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/CreateVectorStoreRequest' 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: CreateVectorStoreRequest: properties: name: type: string title: Name description: A unique name for the vector store within the account embedding_config: $ref: '#/components/schemas/EmbeddingConfig' description: >- The embedding configuration. Either 'base' type with an embedding_model, or 'models_api' type with a model_deployment_id for custom models. embedding_model: $ref: '#/components/schemas/EmbeddingModelName' description: >- The base embedding model to use. Shorthand for embedding_config with type 'base'. Provide either embedding_config or embedding_model, not both. dimensions: title: Dimensions description: >- Dimension size of embedding vectors. Required when neither 'embedding_config' nor 'embedding_model' is set. Automatically derived when an embedding model is provided. type: integer exclusiveMinimum: 0 indexed_metadata_fields: title: Indexed Metadata Fields description: >- Dictionary mapping metadata field names to their types for efficient filtering. Only STRING, NUMBER, and BOOLEAN types can be indexed. additionalProperties: $ref: '#/components/schemas/DocumentFieldType' type: object type: object required: - name title: CreateVectorStoreRequest description: Request to create 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 EmbeddingConfig: anyOf: - $ref: '#/components/schemas/EmbeddingConfigModelsAPI' - $ref: '#/components/schemas/EmbeddingConfigBase' title: EmbeddingConfig 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 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. 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 securitySchemes: APIKeyHeader: type: apiKey in: header name: x-api-key ````