Create Archive

client.archives.create(, ?): Archive { id, created_at, embedding_config, 7 more }

post/v1/archives/

Create a new archive.

ParametersExpand Collapse

body: ArchiveCreateParams { embedding_config, name, description }

embedding_config: EmbeddingConfig { embedding_dim, embedding_endpoint_type, embedding_model, 7 more }

Embedding configuration for the archive

embedding_dim: number

The dimension of the embedding.

embedding_endpoint_type: "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: string

The model for the embedding.

azure_deployment?: string | null

The Azure deployment for the model.

azure_endpoint?: string | null

The Azure endpoint for the model.

azure_version?: string | null

The Azure version for the model.

batch_size?: number

The maximum batch size for processing embeddings.

embedding_chunk_size?: number | null

The chunk size of the embedding.

embedding_endpoint?: string | null

The endpoint for the model (None if local).

handle?: string | null

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

name: string

description?: string | null

ReturnsExpand Collapse

Archive { id, created_at, embedding_config, 7 more }

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

id: string

The human-friendly ID of the Archive

created_at: string

The creation date of the archive

formatdate-time

embedding_config: EmbeddingConfig { embedding_dim, embedding_endpoint_type, embedding_model, 7 more }

Embedding configuration for passages in this archive

embedding_dim: number

The dimension of the embedding.

embedding_endpoint_type: "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: string

The model for the embedding.

azure_deployment?: string | null

The Azure deployment for the model.

azure_endpoint?: string | null

The Azure endpoint for the model.

azure_version?: string | null

The Azure version for the model.

batch_size?: number

The maximum batch size for processing embeddings.

embedding_chunk_size?: number | null

The chunk size of the embedding.

embedding_endpoint?: string | null

The endpoint for the model (None if local).

handle?: string | null

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

name: string

The name of the archive

created_by_id?: string | null

The id of the user that made this object.

description?: string | null

A description of the archive

last_updated_by_id?: string | null

The id of the user that made this object.

metadata?: Record<string, unknown> | null

Additional metadata

updated_at?: string | null

The timestamp when the object was last updated.

formatdate-time

vector_db_provider?: VectorDBProvider

The vector database provider used for this archive's passages

Accepts one of the following:

"native"

"tpuf"

"pinecone"

Create Archive

import Letta from '@letta-ai/letta-client';

const client = new Letta({
  apiKey: 'My API Key',
});

const archive = await client.archives.create({
  embedding_config: {
    embedding_dim: 0,
    embedding_endpoint_type: 'openai',
    embedding_model: 'embedding_model',
  },
  name: 'name',
});

console.log(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"
}