# Archives ## Create Archive `archives.create(ArchiveCreateParams**kwargs) -> Archive` **post** `/v1/archives/` Create a new archive. ### Parameters - `name: str` - `description: Optional[str]` - `embedding: Optional[str]` Embedding model handle for the archive - `embedding_config: Optional[EmbeddingConfigParam]` 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. - `"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. ### Returns - `class Archive: …` Representation of an archive - a collection of archival passages that can be shared between agents. - `id: str` The human-friendly ID of the Archive - `created_at: datetime` The creation date of the archive - `name: str` The name of the archive - `created_by_id: Optional[str]` The id of the user that made this object. - `description: Optional[str]` A description of the archive - `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. - `"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. - `last_updated_by_id: Optional[str]` The id of the user that made this object. - `metadata: Optional[Dict[str, object]]` Additional metadata - `updated_at: Optional[datetime]` The timestamp when the object was last updated. - `vector_db_provider: Optional[VectorDBProvider]` The vector database provider used for this archive's passages - `"native"` - `"tpuf"` - `"pinecone"` ### Example ```python import os from letta_client import Letta client = Letta( api_key=os.environ.get("LETTA_API_KEY"), # This is the default and can be omitted ) archive = client.archives.create( name="name", ) print(archive.id) ``` #### Response ```json { "id": "archive-123e4567-e89b-12d3-a456-426614174000", "created_at": "2019-12-27T18:11:19.117Z", "name": "name", "created_by_id": "created_by_id", "description": "description", "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" }, "last_updated_by_id": "last_updated_by_id", "metadata": { "foo": "bar" }, "updated_at": "2019-12-27T18:11:19.117Z", "vector_db_provider": "native" } ``` ## List Archives `archives.list(ArchiveListParams**kwargs) -> SyncArrayPage[Archive]` **get** `/v1/archives/` Get a list of all archives for the current organization with optional filters and pagination. ### Parameters - `after: Optional[str]` Cursor for pagination (archive ID). Returns results relative to this ID in the specified sort order. Expected format: 'archive-' - `agent_id: Optional[str]` Only archives attached to this agent ID - `before: Optional[str]` Cursor for pagination (archive ID). Returns results relative to this ID in the specified sort order. Expected format: 'archive-' - `limit: Optional[int]` Maximum number of archives to return - `name: Optional[str]` Filter by archive name (exact match) - `order: Optional[Literal["asc", "desc"]]` Sort order for archives by creation time. 'asc' for oldest first, 'desc' for newest first - `"asc"` - `"desc"` - `order_by: Optional[Literal["created_at"]]` Field to sort by - `"created_at"` ### Returns - `class Archive: …` Representation of an archive - a collection of archival passages that can be shared between agents. - `id: str` The human-friendly ID of the Archive - `created_at: datetime` The creation date of the archive - `name: str` The name of the archive - `created_by_id: Optional[str]` The id of the user that made this object. - `description: Optional[str]` A description of the archive - `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. - `"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. - `last_updated_by_id: Optional[str]` The id of the user that made this object. - `metadata: Optional[Dict[str, object]]` Additional metadata - `updated_at: Optional[datetime]` The timestamp when the object was last updated. - `vector_db_provider: Optional[VectorDBProvider]` The vector database provider used for this archive's passages - `"native"` - `"tpuf"` - `"pinecone"` ### Example ```python import os from letta_client import Letta client = Letta( api_key=os.environ.get("LETTA_API_KEY"), # This is the default and can be omitted ) page = client.archives.list() page = page.items[0] print(page.id) ``` #### Response ```json [ { "id": "archive-123e4567-e89b-12d3-a456-426614174000", "created_at": "2019-12-27T18:11:19.117Z", "name": "name", "created_by_id": "created_by_id", "description": "description", "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" }, "last_updated_by_id": "last_updated_by_id", "metadata": { "foo": "bar" }, "updated_at": "2019-12-27T18:11:19.117Z", "vector_db_provider": "native" } ] ``` ## Update Archive `archives.update(strarchive_id, ArchiveUpdateParams**kwargs) -> Archive` **patch** `/v1/archives/{archive_id}` Update an existing archive's name and/or description. ### Parameters - `archive_id: str` The ID of the archive in the format 'archive-' - `description: Optional[str]` - `name: Optional[str]` ### Returns - `class Archive: …` Representation of an archive - a collection of archival passages that can be shared between agents. - `id: str` The human-friendly ID of the Archive - `created_at: datetime` The creation date of the archive - `name: str` The name of the archive - `created_by_id: Optional[str]` The id of the user that made this object. - `description: Optional[str]` A description of the archive - `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. - `"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. - `last_updated_by_id: Optional[str]` The id of the user that made this object. - `metadata: Optional[Dict[str, object]]` Additional metadata - `updated_at: Optional[datetime]` The timestamp when the object was last updated. - `vector_db_provider: Optional[VectorDBProvider]` The vector database provider used for this archive's passages - `"native"` - `"tpuf"` - `"pinecone"` ### Example ```python import os from letta_client import Letta client = Letta( api_key=os.environ.get("LETTA_API_KEY"), # This is the default and can be omitted ) archive = client.archives.update( archive_id="archive-123e4567-e89b-42d3-8456-426614174000", ) print(archive.id) ``` #### Response ```json { "id": "archive-123e4567-e89b-12d3-a456-426614174000", "created_at": "2019-12-27T18:11:19.117Z", "name": "name", "created_by_id": "created_by_id", "description": "description", "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" }, "last_updated_by_id": "last_updated_by_id", "metadata": { "foo": "bar" }, "updated_at": "2019-12-27T18:11:19.117Z", "vector_db_provider": "native" } ``` ## Retrieve Archive `archives.retrieve(strarchive_id) -> Archive` **get** `/v1/archives/{archive_id}` Get a single archive by its ID. ### Parameters - `archive_id: str` The ID of the archive in the format 'archive-' ### Returns - `class Archive: …` Representation of an archive - a collection of archival passages that can be shared between agents. - `id: str` The human-friendly ID of the Archive - `created_at: datetime` The creation date of the archive - `name: str` The name of the archive - `created_by_id: Optional[str]` The id of the user that made this object. - `description: Optional[str]` A description of the archive - `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. - `"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. - `last_updated_by_id: Optional[str]` The id of the user that made this object. - `metadata: Optional[Dict[str, object]]` Additional metadata - `updated_at: Optional[datetime]` The timestamp when the object was last updated. - `vector_db_provider: Optional[VectorDBProvider]` The vector database provider used for this archive's passages - `"native"` - `"tpuf"` - `"pinecone"` ### Example ```python import os from letta_client import Letta client = Letta( api_key=os.environ.get("LETTA_API_KEY"), # This is the default and can be omitted ) archive = client.archives.retrieve( "archive-123e4567-e89b-42d3-8456-426614174000", ) print(archive.id) ``` #### Response ```json { "id": "archive-123e4567-e89b-12d3-a456-426614174000", "created_at": "2019-12-27T18:11:19.117Z", "name": "name", "created_by_id": "created_by_id", "description": "description", "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" }, "last_updated_by_id": "last_updated_by_id", "metadata": { "foo": "bar" }, "updated_at": "2019-12-27T18:11:19.117Z", "vector_db_provider": "native" } ``` ## Delete Archive `archives.delete(strarchive_id)` **delete** `/v1/archives/{archive_id}` Delete an archive by its ID. ### Parameters - `archive_id: str` The ID of the archive in the format 'archive-' ### Example ```python import os from letta_client import Letta client = Letta( api_key=os.environ.get("LETTA_API_KEY"), # This is the default and can be omitted ) client.archives.delete( "archive-123e4567-e89b-42d3-8456-426614174000", ) ``` ## Domain Types ### Archive - `class Archive: …` Representation of an archive - a collection of archival passages that can be shared between agents. - `id: str` The human-friendly ID of the Archive - `created_at: datetime` The creation date of the archive - `name: str` The name of the archive - `created_by_id: Optional[str]` The id of the user that made this object. - `description: Optional[str]` A description of the archive - `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. - `"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. - `last_updated_by_id: Optional[str]` The id of the user that made this object. - `metadata: Optional[Dict[str, object]]` Additional metadata - `updated_at: Optional[datetime]` The timestamp when the object was last updated. - `vector_db_provider: Optional[VectorDBProvider]` The vector database provider used for this archive's passages - `"native"` - `"tpuf"` - `"pinecone"` ### Vector DB Provider - `Literal["native", "tpuf", "pinecone"]` Supported vector database providers for archival memory - `"native"` - `"tpuf"` - `"pinecone"` # Passages ## Create Passage In Archive `archives.passages.create(strarchive_id, PassageCreateParams**kwargs) -> Passage` **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. ### Parameters - `archive_id: str` The ID of the archive in the format 'archive-' - `text: str` The text content of the passage - `created_at: Optional[str]` Optional creation datetime for the passage (ISO 8601 format) - `metadata: Optional[Dict[str, object]]` Optional metadata for the passage - `tags: Optional[Sequence[str]]` Optional tags for categorizing the passage ### Returns - `class Passage: …` Representation of a passage, which is stored in archival memory. - `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. - `"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. - `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. - `source_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. ### Example ```python import os from letta_client import Letta client = Letta( api_key=os.environ.get("LETTA_API_KEY"), # This is the default and can be omitted ) passage = client.archives.passages.create( archive_id="archive-123e4567-e89b-42d3-8456-426614174000", text="text", ) print(passage.id) ``` #### Response ```json { "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" } ``` ## Create Passages In Archive `archives.passages.create_many(strarchive_id, PassageCreateManyParams**kwargs) -> PassageCreateManyResponse` **post** `/v1/archives/{archive_id}/passages/batch` Create multiple passages in an archive. This adds passages to the archive and creates embeddings for vector storage. ### Parameters - `archive_id: str` The ID of the archive in the format 'archive-' - `passages: Iterable[Passage]` Passages to create in the archive - `text: str` The text content of the passage - `created_at: Optional[str]` Optional creation datetime for the passage (ISO 8601 format) - `metadata: Optional[Dict[str, object]]` Optional metadata for the passage - `tags: Optional[Sequence[str]]` Optional tags for categorizing the passage ### Returns - `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. - `"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. - `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. - `source_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. ### Example ```python import os from letta_client import Letta client = Letta( api_key=os.environ.get("LETTA_API_KEY"), # This is the default and can be omitted ) passages = client.archives.passages.create_many( archive_id="archive-123e4567-e89b-42d3-8456-426614174000", passages=[{ "text": "text" }], ) print(passages) ``` #### Response ```json [ { "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" } ] ``` ## Delete Passage From Archive `archives.passages.delete(strpassage_id, PassageDeleteParams**kwargs)` **delete** `/v1/archives/{archive_id}/passages/{passage_id}` Delete a passage from an archive. This permanently removes the passage from both the database and vector storage (if applicable). ### Parameters - `archive_id: str` The ID of the archive in the format 'archive-' - `passage_id: str` The ID of the passage in the format 'passage-' ### Example ```python import os from letta_client import Letta client = Letta( api_key=os.environ.get("LETTA_API_KEY"), # This is the default and can be omitted ) client.archives.passages.delete( passage_id="passage-123e4567-e89b-42d3-8456-426614174000", archive_id="archive-123e4567-e89b-42d3-8456-426614174000", ) ``` ## Domain Types ### Passage Create Many Response - `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. - `"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. - `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. - `source_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.