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API Reference

Vector Stores

List Vector Stores
vector_stores.list(VectorStoreListParams**kwargs) -> SyncCursorPageByName[VectorStore]
GET/v5/vector-stores
Create Vector Store
vector_stores.create(VectorStoreCreateParams**kwargs) -> VectorStore
POST/v5/vector-stores/create
Get Vector Store
vector_stores.retrieve(strvector_store_name) -> VectorStore
GET/v5/vector-stores/{vector_store_name}
Configure Vector Store
vector_stores.configure(strvector_store_name, VectorStoreConfigureParams**kwargs) -> VectorStore
POST/v5/vector-stores/{vector_store_name}/configure
Drop Vector Store
vector_stores.drop(strvector_store_name) -> VectorStoreDropResponse
POST/v5/vector-stores/{vector_store_name}/drop
Upsert Vectors
vector_stores.upsert(strvector_store_name, VectorStoreUpsertParams**kwargs) -> VectorStoreUpsertResponse
POST/v5/vector-stores/{vector_store_name}/upsert
Delete Vectors
vector_stores.delete(strvector_store_name, VectorStoreDeleteParams**kwargs) -> VectorStoreDeleteResponse
POST/v5/vector-stores/{vector_store_name}/delete
Count Vectors
vector_stores.count(strvector_store_name, VectorStoreCountParams**kwargs) -> VectorStoreCountResponse
POST/v5/vector-stores/{vector_store_name}/count
Query Vectors
vector_stores.query(strvector_store_name, VectorStoreQueryParams**kwargs) -> VectorStoreQueryResponse
POST/v5/vector-stores/{vector_store_name}/query
ModelsExpand Collapse
One of the following:
class EmbeddingConfigModelsAPI: …
model_deployment_id: str

The ID of the deployment of the created model in the Models API V3.

type: Literal["models_api"]

The type of the embedding configuration.

class EmbeddingConfigBase: …
embedding_model: Union[EmbeddingModelName, str]

The name of the base embedding model to use. Either a known base model (EmbeddingModelName) or, in ray-serve deployments with NATIVE_OPENAI_EMBEDDING_GATEWAY enabled, any model id served by the OpenAI-compatible inference proxy (e.g. ‘nomic-embed-text-v1.5’). For fully custom deployments, use type ‘models_api’ with a model_deployment_id.

One of the following:
Literal["sentence-transformers/all-MiniLM-L12-v2", "sentence-transformers/multi-qa-distilbert-cos-v1", "openai/text-embedding-ada-002", 8 more]
One of the following:
"sentence-transformers/all-MiniLM-L12-v2"
"sentence-transformers/multi-qa-distilbert-cos-v1"
"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"
str
type: Optional[Literal["base"]]

The type of the embedding configuration.

class EmbeddingConfigBase: …
embedding_model: Union[EmbeddingModelName, str]

The name of the base embedding model to use. Either a known base model (EmbeddingModelName) or, in ray-serve deployments with NATIVE_OPENAI_EMBEDDING_GATEWAY enabled, any model id served by the OpenAI-compatible inference proxy (e.g. ‘nomic-embed-text-v1.5’). For fully custom deployments, use type ‘models_api’ with a model_deployment_id.

One of the following:
Literal["sentence-transformers/all-MiniLM-L12-v2", "sentence-transformers/multi-qa-distilbert-cos-v1", "openai/text-embedding-ada-002", 8 more]
One of the following:
"sentence-transformers/all-MiniLM-L12-v2"
"sentence-transformers/multi-qa-distilbert-cos-v1"
"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"
str
type: Optional[Literal["base"]]

The type of the embedding configuration.

class EmbeddingConfigModelsAPI: …
model_deployment_id: str

The ID of the deployment of the created model in the Models API V3.

type: Literal["models_api"]

The type of the embedding configuration.

Literal["sentence-transformers/all-MiniLM-L12-v2", "sentence-transformers/multi-qa-distilbert-cos-v1", "openai/text-embedding-ada-002", 8 more]
One of the following:
"sentence-transformers/all-MiniLM-L12-v2"
"sentence-transformers/multi-qa-distilbert-cos-v1"
"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"
class TextContent: …

Text content for documents.

text: str

Text content to be embedded

type: Optional[Literal["text"]]

Content type identifier

class VectorStore: …

Response model for vector store operations.

id: str

The unique identifier of the vector store

created_at: datetime

Timestamp of creation

formatdate-time
embedding_dimensions: int

Dimensionality of the embedding vectors

name: str

The name of the vector store

updated_at: datetime

Timestamp of last update

formatdate-time
embedding_config: Optional[EmbeddingConfig]

Embedding configuration identifying the model and its type. None for raw-embedding-only stores.

One of the following:
class EmbeddingConfigModelsAPI: …
model_deployment_id: str

The ID of the deployment of the created model in the Models API V3.

type: Literal["models_api"]

The type of the embedding configuration.

class EmbeddingConfigBase: …
embedding_model: Union[EmbeddingModelName, str]

The name of the base embedding model to use. Either a known base model (EmbeddingModelName) or, in ray-serve deployments with NATIVE_OPENAI_EMBEDDING_GATEWAY enabled, any model id served by the OpenAI-compatible inference proxy (e.g. ‘nomic-embed-text-v1.5’). For fully custom deployments, use type ‘models_api’ with a model_deployment_id.

One of the following:
Literal["sentence-transformers/all-MiniLM-L12-v2", "sentence-transformers/multi-qa-distilbert-cos-v1", "openai/text-embedding-ada-002", 8 more]
One of the following:
"sentence-transformers/all-MiniLM-L12-v2"
"sentence-transformers/multi-qa-distilbert-cos-v1"
"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"
str
type: Optional[Literal["base"]]

The type of the embedding configuration.

indexed_metadata_fields: Optional[Dict[str, Literal["string", "number", "boolean"]]]

Dictionary mapping metadata field names to their types

One of the following:
"string"
"number"
"boolean"
class VectorStoreDropResponse: …

Response for vector store deletion.

name: str

The name of the deleted vector store

class VectorStoreUpsertResponse: …

Response for batch insert/upsert operations.

failure_count: int

Number of failed documents

success_count: int

Number of successfully processed documents

failed: Optional[List[Failed]]

Failed documents with their error messages

id: str

Document ID

error: str

Error message describing why the document failed

succeeded: Optional[List[str]]

IDs of successfully processed documents

class VectorStoreDeleteResponse: …

Response for delete operation.

deleted_count: int

Number of documents deleted

class VectorStoreCountResponse: …

Response for count operation.

count: int

Number of documents matching the criteria

class VectorStoreQueryResponse: …

Response for query operation.

metadata: Metadata

Query execution metadata

search_type: str

Type of search performed (semantic, lexical, hybrid)

total_query_time_ms: int

Total end-to-end query execution time in milliseconds

embedding_config: Optional[EmbeddingConfig]

Embedding configuration used for query vectorization. None for lexical queries on model-less stores.

One of the following:
class EmbeddingConfigModelsAPI: …
model_deployment_id: str

The ID of the deployment of the created model in the Models API V3.

type: Literal["models_api"]

The type of the embedding configuration.

class EmbeddingConfigBase: …
embedding_model: Union[EmbeddingModelName, str]

The name of the base embedding model to use. Either a known base model (EmbeddingModelName) or, in ray-serve deployments with NATIVE_OPENAI_EMBEDDING_GATEWAY enabled, any model id served by the OpenAI-compatible inference proxy (e.g. ‘nomic-embed-text-v1.5’). For fully custom deployments, use type ‘models_api’ with a model_deployment_id.

One of the following:
Literal["sentence-transformers/all-MiniLM-L12-v2", "sentence-transformers/multi-qa-distilbert-cos-v1", "openai/text-embedding-ada-002", 8 more]
One of the following:
"sentence-transformers/all-MiniLM-L12-v2"
"sentence-transformers/multi-qa-distilbert-cos-v1"
"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"
str
type: Optional[Literal["base"]]

The type of the embedding configuration.

embedding_time_ms: Optional[int]

Time spent generating embeddings in milliseconds (None for lexical queries)

index_query_time_ms: Optional[int]

Time spent querying the vector index (OpenSearch) in milliseconds

reranking_model: Optional[str]

Reranking model used (None if reranking not enabled)

reranking_time_ms: Optional[int]

Time spent reranking results in milliseconds (None if reranking not enabled)

vectors: List[Vector]

Array of matching documents

id: str

Document ID

score: float

Similarity score indicating relevance

content: Optional[TextContent]

Text content for documents.

text: str

Text content to be embedded

type: Optional[Literal["text"]]

Content type identifier

metadata: Optional[Dict[str, object]]

Key-value metadata

vector: Optional[List[float]]

Embedding vector (if requested)

Vector StoresVectors

List Vectors
vector_stores.vectors.list(strvector_store_name, VectorListParams**kwargs) -> SyncCursorPageVectors[VectorDocument]
GET/v5/vector-stores/{vector_store_name}/vectors
Get Vector
vector_stores.vectors.retrieve(strvector_id, VectorRetrieveParams**kwargs) -> VectorDocument
GET/v5/vector-stores/{vector_store_name}/vectors/{vector_id}
ModelsExpand Collapse
class VectorDocument: …

A document returned from direct lookups (get/list operations).

id: str

Document ID

content: Optional[TextContent]

Text content for documents.

text: str

Text content to be embedded

type: Optional[Literal["text"]]

Content type identifier

metadata: Optional[Dict[str, object]]

Key-value metadata

vector: Optional[List[float]]

Embedding vector (if requested)