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API Reference
Models
Embeddings
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List Embedding Models

models.embeddings.list() -> EmbeddingListResponse
get/v1/models/embedding

List available embedding models using the asynchronous implementation for improved performance.

Returns EmbeddingModel format which extends EmbeddingConfig with additional metadata fields. Legacy EmbeddingConfig fields are marked as deprecated but still available for backward compatibility.

ReturnsExpand Collapse
EmbeddingListResponse = List[EmbeddingModel]
display_name: str

Display name for the model shown in UI

embedding_dim: int

The dimension of the embedding

Deprecatedembedding_endpoint_type: Literal["openai", "anthropic", "bedrock", 16 more]

Deprecated: Use 'provider_type' field instead. The endpoint type for the embedding model.

Accepts one of the following:
"openai"
"anthropic"
"bedrock"
"google_ai"
"google_vertex"
"azure"
"groq"
"ollama"
"webui"
"webui-legacy"
"lmstudio"
"lmstudio-legacy"
"llamacpp"
"koboldcpp"
"vllm"
"hugging-face"
"mistral"
"together"
"pinecone"
Deprecatedembedding_model: str

Deprecated: Use 'name' field instead. Embedding model name.

name: str

The actual model name used by the provider

provider_name: str

The name of the provider

provider_type: ProviderType

The type of the provider

Accepts one of the following:
"anthropic"
"azure"
"bedrock"
"cerebras"
"deepseek"
"google_ai"
"google_vertex"
"groq"
"hugging-face"
"letta"
"lmstudio_openai"
"mistral"
"ollama"
"openai"
"together"
"vllm"
"xai"
Deprecatedazure_deployment: Optional[str]

Deprecated: The Azure deployment for the model.

Deprecatedazure_endpoint: Optional[str]

Deprecated: The Azure endpoint for the model.

Deprecatedazure_version: Optional[str]

Deprecated: The Azure version for the model.

Deprecatedbatch_size: Optional[int]

Deprecated: The maximum batch size for processing embeddings.

Deprecatedembedding_chunk_size: Optional[int]

Deprecated: The chunk size of the embedding.

Deprecatedembedding_endpoint: Optional[str]

Deprecated: The endpoint for the model.

handle: Optional[str]

The handle for this config, in the format provider/model-name.

model_type: Optional[Literal["embedding"]]

Type of model (llm or embedding)

Accepts one of the following:
"embedding"
List Embedding Models
  • HTTP
  • TypeScript
  • Python
from letta_client import Letta

client = Letta(
    api_key="My API Key",
)
embedding_models = client.models.embeddings.list()
print(embedding_models)
[
  {
    "display_name": "display_name",
    "embedding_dim": 0,
    "embedding_endpoint_type": "openai",
    "embedding_model": "embedding_model",
    "name": "name",
    "provider_name": "provider_name",
    "provider_type": "anthropic",
    "azure_deployment": "azure_deployment",
    "azure_endpoint": "azure_endpoint",
    "azure_version": "azure_version",
    "batch_size": 0,
    "embedding_chunk_size": 0,
    "embedding_endpoint": "embedding_endpoint",
    "handle": "handle",
    "model_type": "embedding"
  }
]
Returns Examples
[
  {
    "display_name": "display_name",
    "embedding_dim": 0,
    "embedding_endpoint_type": "openai",
    "embedding_model": "embedding_model",
    "name": "name",
    "provider_name": "provider_name",
    "provider_type": "anthropic",
    "azure_deployment": "azure_deployment",
    "azure_endpoint": "azure_endpoint",
    "azure_version": "azure_version",
    "batch_size": 0,
    "embedding_chunk_size": 0,
    "embedding_endpoint": "embedding_endpoint",
    "handle": "handle",
    "model_type": "embedding"
  }
]