Letta · Schema
Passage
Representation of a passage, which is stored in archival memory.
AIAgentsStateful AgentsMemoryMemGPTContinual LearningMCPMulti-AgentRAGOpen Source
Properties
| Name | Type | Description |
|---|---|---|
| created_by_id | object | The id of the user that made this object. |
| last_updated_by_id | object | The id of the user that made this object. |
| created_at | object | The creation date of the passage. |
| updated_at | object | The timestamp when the object was last updated. |
| is_deleted | boolean | Whether this passage is deleted or not. |
| archive_id | object | The unique identifier of the archive containing this passage. |
| source_id | object | Deprecated: Use `folder_id` field instead. The data source of the passage. |
| file_id | object | The unique identifier of the file associated with the passage. |
| file_name | object | The name of the file (only for source passages). |
| metadata | object | The metadata of the passage. |
| tags | object | Tags associated with this passage. |
| id | string | The human-friendly ID of the Passage |
| text | string | The text of the passage. |
| embedding | object | The embedding of the passage. |
| embedding_config | object | The embedding configuration used by the passage. |
JSON Schema
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"$id": "https://raw.githubusercontent.com/api-evangelist/letta/main/json-schema/letta-passage-schema.json",
"title": "Passage",
"description": "Representation of a passage, which is stored in archival memory.",
"properties": {
"created_by_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Created By Id",
"description": "The id of the user that made this object."
},
"last_updated_by_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Last Updated By Id",
"description": "The id of the user that made this object."
},
"created_at": {
"anyOf": [
{
"type": "string",
"format": "date-time"
},
{
"type": "null"
}
],
"title": "Created At",
"description": "The creation date of the passage."
},
"updated_at": {
"anyOf": [
{
"type": "string",
"format": "date-time"
},
{
"type": "null"
}
],
"title": "Updated At",
"description": "The timestamp when the object was last updated."
},
"is_deleted": {
"type": "boolean",
"title": "Is Deleted",
"description": "Whether this passage is deleted or not.",
"default": false
},
"archive_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Archive Id",
"description": "The unique identifier of the archive containing this passage."
},
"source_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Source Id",
"description": "Deprecated: Use `folder_id` field instead. The data source of the passage.",
"deprecated": true
},
"file_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "File Id",
"description": "The unique identifier of the file associated with the passage."
},
"file_name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "File Name",
"description": "The name of the file (only for source passages)."
},
"metadata": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"title": "Metadata",
"description": "The metadata of the passage.",
"default": {}
},
"tags": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"title": "Tags",
"description": "Tags associated with this passage."
},
"id": {
"type": "string",
"pattern": "^passage-[a-fA-F0-9]{8}",
"title": "Id",
"description": "The human-friendly ID of the Passage",
"examples": [
"passage-123e4567-e89b-12d3-a456-426614174000"
]
},
"text": {
"type": "string",
"title": "Text",
"description": "The text of the passage."
},
"embedding": {
"anyOf": [
{
"items": {
"type": "number"
},
"type": "array"
},
{
"type": "null"
}
],
"title": "Embedding",
"description": "The embedding of the passage."
},
"embedding_config": {
"anyOf": [
{
"$ref": "#/$defs/EmbeddingConfig"
},
{
"type": "null"
}
],
"description": "The embedding configuration used by the passage."
}
},
"additionalProperties": false,
"type": "object",
"required": [
"text",
"embedding",
"embedding_config"
],
"$defs": {
"EmbeddingConfig": {
"properties": {
"embedding_endpoint_type": {
"type": "string",
"enum": [
"openai",
"anthropic",
"bedrock",
"google_ai",
"google_vertex",
"azure",
"groq",
"ollama",
"webui",
"webui-legacy",
"lmstudio",
"lmstudio-legacy",
"llamacpp",
"koboldcpp",
"vllm",
"hugging-face",
"mistral",
"together",
"pinecone"
],
"title": "Embedding Endpoint Type",
"description": "The endpoint type for the model."
},
"embedding_endpoint": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Embedding Endpoint",
"description": "The endpoint for the model (`None` if local)."
},
"embedding_model": {
"type": "string",
"title": "Embedding Model",
"description": "The model for the embedding."
},
"embedding_dim": {
"type": "integer",
"title": "Embedding Dim",
"description": "The dimension of the embedding."
},
"embedding_chunk_size": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"title": "Embedding Chunk Size",
"description": "The chunk size of the embedding.",
"default": 300
},
"handle": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Handle",
"description": "The handle for this config, in the format provider/model-name."
},
"batch_size": {
"type": "integer",
"title": "Batch Size",
"description": "The maximum batch size for processing embeddings.",
"default": 32
},
"azure_endpoint": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Azure Endpoint",
"description": "The Azure endpoint for the model."
},
"azure_version": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Azure Version",
"description": "The Azure version for the model."
},
"azure_deployment": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Azure Deployment",
"description": "The Azure deployment for the model."
}
},
"type": "object",
"required": [
"embedding_endpoint_type",
"embedding_model",
"embedding_dim"
],
"title": "EmbeddingConfig",
"description": "Configuration for embedding model connection and processing parameters."
}
}
}