Documentation

Vector Stores

vector_stores

Methods

List Vector Stores -> CursorPage<
VectorStore
>
get/v5/vector-stores

List all vector stores for the current account with pagination.

Returns a paginated list of vector stores owned by the authenticated account.

Create Vector Store ->
VectorStore
post/v5/vector-stores/create

Create a new vector store.

The vector store will be scoped to the authenticated account. The name must be unique within the account.

Args: request: Vector store creation parameters including name, dimensions, and optional model vector_store_use_case: Injected vector store use case

Returns: The created vector store details

Get Vector Store ->
VectorStore
get/v5/vector-stores/{vector_store_name}

Get a vector store by name.

Args: vector_store_name: The unique name of the vector store within the account vector_store_use_case: Injected vector store use case

Returns: The vector store details

Configure Vector Store ->
VectorStore
post/v5/vector-stores/{vector_store_name}/configure

Configure the settings of a vector store.

Currently only supports updating indexed_metadata_fields. The name, embedding_dimensions, and embedding_model are immutable after creation.

Args: request: Configuration update with indexed_metadata_fields vector_store_name: The unique name of the vector store within the account vector_store_use_case: Injected vector store use case

Returns: The updated vector store details

Drop Vector Store -> { name }
post/v5/vector-stores/{vector_store_name}/drop

Delete (drop) a vector store by name.

This is a hard delete operation that permanently removes the vector store.

Args: vector_store_name: The unique name of the vector store within the account vector_store_use_case: Injected vector store use case

Returns: Deletion confirmation with the vector store ID

Upsert Vectors -> { failed, successful, total_attempted, 1 more... }
post/v5/vector-stores/{vector_store_name}/upsert

Insert or update documents in a vector store.

Existing documents (by ID) will be updated, new documents will be inserted.

Args: request: Array of documents to upsert vector_store_name: The unique name of the vector store vector_store_use_case: Injected vector store use case

Returns: Batch operation results with success/failure counts

Delete Vectors -> { deleted_count }
post/v5/vector-stores/{vector_store_name}/delete

Delete documents from a vector store by IDs or filter.

Args: request: Either IDs or filter criteria for deletion vector_store_name: The unique name of the vector store vector_store_use_case: Injected vector store use case

Returns: Number of documents deleted

List Vectors -> { vectors, next_cursor }
get/v5/vector-stores/{vector_store_name}/vectors

List documents in a vector store with pagination.

Args: vector_store_name: The unique name of the vector store limit: Maximum number of documents per page cursor: Pagination cursor from previous response filter_param: Optional metadata filter (JSON string) include_vectors: Whether to include embedding vectors vector_store_use_case: Injected vector store use case

Returns: Paginated list of documents

Count Vectors -> { count }
post/v5/vector-stores/{vector_store_name}/count

Count documents in a vector store, optionally filtered by metadata.

Args: vector_store_name: The unique name of the vector store request: Optional filter criteria (empty body counts all documents) vector_store_use_case: Injected vector store use case

Returns: The count of documents matching the criteria

path Parameters
vector_store_name: string

The name of the vector store

Response fields
count: number

Number of documents matching the criteria

Request example
200Example
Query Vectors -> { metadata, vectors }
post/v5/vector-stores/{vector_store_name}/query

Query documents by similarity search.

Supports semantic (vector), lexical (text), or hybrid search modes.

Args: request: Query parameters including text, filters, and reranking options vector_store_name: The unique name of the vector store vector_store_use_case: Injected vector store use case

Returns: Matching documents with similarity scores and query metadata

Domain types

VectorStore = { created_at, embedding_dimensions, embedding_model, 3 more... }

Response model for vector store operations.