Create Folder

post/v1/folders/

Create a new data folder.

Body ParametersExpand Collapse

name: string

The name of the source.

description: optional string

The description of the source.

embedding: optional string

The handle for the embedding config used by the source.

embedding_chunk_size: optional number

The chunk size of the embedding.

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

Configuration for embedding model connection and processing parameters.

embedding_dim: number

The dimension of the embedding.

embedding_endpoint_type: "openai" or "anthropic" or "bedrock" or 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: optional string

The Azure deployment for the model.

azure_endpoint: optional string

The Azure endpoint for the model.

azure_version: optional string

The Azure version for the model.

batch_size: optional number

The maximum batch size for processing embeddings.

embedding_chunk_size: optional number

The chunk size of the embedding.

embedding_endpoint: optional string

The endpoint for the model (None if local).

handle: optional string

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

instructions: optional string

Instructions for how to use the source.

metadata: optional map[unknown]

Metadata associated with the source.

ReturnsExpand Collapse

Folder = object { id, embedding_config, name, 7 more }

Representation of a folder, which is a collection of files and passages.

id: string

The human-friendly ID of the Source

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

The embedding configuration used by the folder.

embedding_dim: number

The dimension of the embedding.

embedding_endpoint_type: "openai" or "anthropic" or "bedrock" or 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: optional string

The Azure deployment for the model.

azure_endpoint: optional string

The Azure endpoint for the model.

azure_version: optional string

The Azure version for the model.

batch_size: optional number

The maximum batch size for processing embeddings.

embedding_chunk_size: optional number

The chunk size of the embedding.

embedding_endpoint: optional string

The endpoint for the model (None if local).

handle: optional string

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

name: string

The name of the folder.

created_at: optional string

The timestamp when the folder was created.

formatdate-time

created_by_id: optional string

The id of the user that made this Tool.

description: optional string

The description of the folder.

instructions: optional string

Instructions for how to use the folder.

last_updated_by_id: optional string

The id of the user that made this Tool.

metadata: optional map[unknown]

Metadata associated with the folder.

updated_at: optional string

The timestamp when the folder was last updated.

formatdate-time

Create Folder

curl https://api.letta.com/v1/folders/ \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LETTA_API_KEY" \
    -d '{
          "name": "name"
        }'

{
  "id": "source-123e4567-e89b-12d3-a456-426614174000",
  "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_at": "2019-12-27T18:11:19.117Z",
  "created_by_id": "created_by_id",
  "description": "description",
  "instructions": "instructions",
  "last_updated_by_id": "last_updated_by_id",
  "metadata": {
    "foo": "bar"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}

Returns Examples

{
  "id": "source-123e4567-e89b-12d3-a456-426614174000",
  "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_at": "2019-12-27T18:11:19.117Z",
  "created_by_id": "created_by_id",
  "description": "description",
  "instructions": "instructions",
  "last_updated_by_id": "last_updated_by_id",
  "metadata": {
    "foo": "bar"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}