Create Passage In Archive

client.archives.passages.create(, , ?): PassageCreateResponse { embedding, embedding_config, text, 12 more }

post/v1/archives/{archive_id}/passages

Create a new passage in an archive.

This adds a passage to the archive and creates embeddings for vector storage.

ParametersExpand Collapse

archiveID: string

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

minLength44

maxLength44

body: PassageCreateParams { text, metadata, tags }

text: string

The text content of the passage

metadata?: Record<string, unknown> | null

Optional metadata for the passage

tags?: Array<string> | null

Optional tags for categorizing the passage

ReturnsExpand Collapse

PassageCreateResponse { embedding, embedding_config, text, 12 more }

Representation of a passage, which is stored in archival memory.

embedding: Array<number> | null

The embedding of the passage.

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

Configuration for embedding model connection and processing parameters.

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.

text: string

The text of the passage.

id?: string

The human-friendly ID of the Passage

archive_id?: string | null

The unique identifier of the archive containing this passage.

created_at?: string

The creation date of the passage.

formatdate-time

created_by_id?: string | null

The id of the user that made this object.

file_id?: string | null

The unique identifier of the file associated with the passage.

file_name?: string | null

The name of the file (only for source passages).

is_deleted?: boolean

Whether this passage is deleted or not.

last_updated_by_id?: string | null

The id of the user that made this object.

metadata?: Record<string, unknown> | null

The metadata of the passage.

source_id?: string | null

The data source of the passage.

tags?: Array<string> | null

Tags associated with this passage.

updated_at?: string | null

The timestamp when the object was last updated.

formatdate-time

Create Passage In Archive

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

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

const passage = await client.archives.passages.create('archive-123e4567-e89b-42d3-8456-426614174000', {
  text: 'text',
});

console.log(passage.id);

{
  "embedding": [
    0
  ],
  "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"
  },
  "text": "text",
  "id": "passage-123e4567-e89b-12d3-a456-426614174000",
  "archive_id": "archive_id",
  "created_at": "2019-12-27T18:11:19.117Z",
  "created_by_id": "created_by_id",
  "file_id": "file_id",
  "file_name": "file_name",
  "is_deleted": true,
  "last_updated_by_id": "last_updated_by_id",
  "metadata": {
    "foo": "bar"
  },
  "source_id": "source_id",
  "tags": [
    "string"
  ],
  "updated_at": "2019-12-27T18:11:19.117Z"
}

Returns Examples

{
  "embedding": [
    0
  ],
  "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"
  },
  "text": "text",
  "id": "passage-123e4567-e89b-12d3-a456-426614174000",
  "archive_id": "archive_id",
  "created_at": "2019-12-27T18:11:19.117Z",
  "created_by_id": "created_by_id",
  "file_id": "file_id",
  "file_name": "file_name",
  "is_deleted": true,
  "last_updated_by_id": "last_updated_by_id",
  "metadata": {
    "foo": "bar"
  },
  "source_id": "source_id",
  "tags": [
    "string"
  ],
  "updated_at": "2019-12-27T18:11:19.117Z"
}