Documentation | Vector Stores

Vector Stores

VectorStoresResource

Methods

client.vector_stores.list() -> SyncCursorPage[

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.

client.vector_stores.create() ->

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

client.vector_stores.retrieve() ->

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

client.vector_stores.configure(, ) ->

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

client.vector_stores.drop() ->

VectorStoreDropResponse

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

client.vector_stores.upsert(, ) ->

VectorStoreUpsertResponse

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

client.vector_stores.delete(, ) ->

VectorStoreDeleteResponse

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

client.vector_stores.vectors(, ) ->

VectorStoreVectorsResponse

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 (max 100 with vectors, 1000 without) 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

client.vector_stores.count(, ) ->

VectorStoreCountResponse

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

client.vector_stores.query(, ) ->

VectorStoreQueryResponse

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

Parameters

vector_store_name: str

The name of the vector store

text: str

Text query for automatic embedding (required)

filter: Optional[Dict[str,

object

]]

Metadata filter expression

include_vectors: Optional[

bool

]

Include embedding vectors in response

query_type: Optional[Literal["semantic", "lexical", "hybrid"]]

(default: "semantic")

Query type: semantic, lexical, or hybrid

"semantic"

"lexical"

"hybrid"

rerank: Optional[

bool

]

Enable reranking of search results

rerank_model: Optional[str]

Reranking model to use (uses system default if not specified)

rerank_top_n: Optional[int]

Number of results after reranking (defaults to top_k)

top_k: Optional[int]

(minimum: 1, default: 10)

Number of search results to return

Returns

class VectorStoreQueryResponse:

Response for query operation.

metadata:

Metadata

Query execution metadata

vectors: List[

Vector

]

Array of matching documents

Request example

import os
from scale_gp_beta import SGPClient

client = SGPClient(
    api_key=os.environ.get("SGP_API_KEY"),  # This is the default and can be omitted
)
response = client.vector_stores.query(
    vector_store_name="vector_store_name",
    text="text",
)
print(response.metadata)

200Example

{
  "metadata": {
    "embedding_model": "embedding_model",
    "search_type": "search_type",
    "total_query_time_ms": 0,
    "embedding_time_ms": 0,
    "index_query_time_ms": 0,
    "reranking_model": "reranking_model",
    "reranking_time_ms": 0
  },
  "vectors": [
    {
      "id": "id",
      "metadata": {
        "foo": "bar"
      },
      "score": 0,
      "text": "text",
      "vector": [
        0
      ]
    }
  ]
}

Domain types

class VectorStore: ...

Response model for vector store operations.