Get Archive By Id

archives.retrieve() -> Archive

get/v1/archives/{archive_id}

Get a single archive by its ID.

ParametersExpand Collapse

archive_id: str

The ID of the archive in the format 'archive-'

minLength44

maxLength44

ReturnsExpand Collapse

class Archive: …

Representation of an archive - a collection of archival passages that can be shared between agents.

id: str

The human-friendly ID of the Archive

created_at: datetime

The creation date of the archive

formatdate-time

embedding_config: EmbeddingConfig

Embedding configuration for passages in this archive

embedding_dim: int

The dimension of the embedding.

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

The endpoint type for the 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"

embedding_model: str

The model for the embedding.

azure_deployment: Optional[str]

The Azure deployment for the model.

azure_endpoint: Optional[str]

The Azure endpoint for the model.

azure_version: Optional[str]

The Azure version for the model.

batch_size: Optional[int]

The maximum batch size for processing embeddings.

embedding_chunk_size: Optional[int]

The chunk size of the embedding.

embedding_endpoint: Optional[str]

The endpoint for the model (None if local).

handle: Optional[str]

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

name: str

The name of the archive

created_by_id: Optional[str]

The id of the user that made this object.

description: Optional[str]

A description of the archive

last_updated_by_id: Optional[str]

The id of the user that made this object.

metadata: Optional[Dict[str, object]]

Additional metadata

updated_at: Optional[datetime]

The timestamp when the object was last updated.

formatdate-time

vector_db_provider: Optional[VectorDBProvider]

The vector database provider used for this archive's passages

Accepts one of the following:

"native"

"tpuf"

"pinecone"

Get Archive By Id

from letta_client import Letta

client = Letta(
    api_key="My API Key",
)
archive = client.archives.retrieve(
    "archive-123e4567-e89b-42d3-8456-426614174000",
)
print(archive.id)

{
  "id": "archive-123e4567-e89b-12d3-a456-426614174000",
  "created_at": "2019-12-27T18:11:19.117Z",
  "embedding_config": {
    "embedding_dim": 0,
    "embedding_endpoint_type": "openai",
    "embedding_model": "embedding_model",
    "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"
  },
  "name": "name",
  "created_by_id": "created_by_id",
  "description": "description",
  "last_updated_by_id": "last_updated_by_id",
  "metadata": {
    "foo": "bar"
  },
  "updated_at": "2019-12-27T18:11:19.117Z",
  "vector_db_provider": "native"
}

Returns Examples

{
  "id": "archive-123e4567-e89b-12d3-a456-426614174000",
  "created_at": "2019-12-27T18:11:19.117Z",
  "embedding_config": {
    "embedding_dim": 0,
    "embedding_endpoint_type": "openai",
    "embedding_model": "embedding_model",
    "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"
  },
  "name": "name",
  "created_by_id": "created_by_id",
  "description": "description",
  "last_updated_by_id": "last_updated_by_id",
  "metadata": {
    "foo": "bar"
  },
  "updated_at": "2019-12-27T18:11:19.117Z",
  "vector_db_provider": "native"
}