Create Passage

Deprecated

agents.passages.create(, ) -> PassageCreateResponse

post/v1/agents/{agent_id}/archival-memory

Insert a memory into an agent's archival memory store.

ParametersExpand Collapse

agent_id: str

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

minLength42

maxLength42

text: str

Text to write to archival memory.

created_at: Optional[Union[str, datetime, null]]

Optional timestamp for the memory (defaults to current UTC time).

formatdate-time

tags: Optional[SequenceNotStr[str]]

Optional list of tags to attach to the memory.

ReturnsExpand Collapse

PassageCreateResponse = List[Passage]

embedding: Optional[List[float]]

The embedding of the passage.

embedding_config: Optional[EmbeddingConfig]

Configuration for embedding model connection and processing parameters.

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.

text: str

The text of the passage.

id: Optional[str]

The human-friendly ID of the Passage

archive_id: Optional[str]

The unique identifier of the archive containing this passage.

created_at: Optional[datetime]

The creation date of the passage.

formatdate-time

created_by_id: Optional[str]

The id of the user that made this object.

file_id: Optional[str]

The unique identifier of the file associated with the passage.

file_name: Optional[str]

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

is_deleted: Optional[bool]

Whether this passage is deleted or not.

last_updated_by_id: Optional[str]

The id of the user that made this object.

metadata: Optional[Dict[str, object]]

The metadata of the passage.

Deprecatedsource_id: Optional[str]

Deprecated: Use folder_id field instead. The data source of the passage.

tags: Optional[List[str]]

Tags associated with this passage.

updated_at: Optional[datetime]

The timestamp when the object was last updated.

formatdate-time

Create Passage

from letta_client import Letta

client = Letta(
    api_key="My API Key",
)
passages = client.agents.passages.create(
    agent_id="agent-123e4567-e89b-42d3-8456-426614174000",
    text="text",
)
print(passages)

[
  {
    "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"
  }
]