# Folders ## Retrieve Folder `folders.retrieve(strfolder_id) -> Folder` **get** `/v1/folders/{folder_id}` Get a folder by ID ### Parameters - `folder_id: str` The ID of the source in the format 'source-' ### Returns - `class Folder: …` Representation of a folder, which is a collection of files and passages. - `id: str` The human-friendly ID of the Source - `embedding_config: EmbeddingConfig` The embedding configuration used by the folder. - `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. - `name: str` The name of the folder. - `created_at: Optional[datetime]` The timestamp when the folder was created. - `created_by_id: Optional[str]` The id of the user that made this Tool. - `description: Optional[str]` The description of the folder. - `instructions: Optional[str]` Instructions for how to use the folder. - `last_updated_by_id: Optional[str]` The id of the user that made this Tool. - `metadata: Optional[Dict[str, object]]` Metadata associated with the folder. - `updated_at: Optional[datetime]` The timestamp when the folder 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 ) folder = client.folders.retrieve( "source-123e4567-e89b-42d3-8456-426614174000", ) print(folder.id) ``` #### Response ```json { "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" } ``` ## Update Folder `folders.update(strfolder_id, FolderUpdateParams**kwargs) -> Folder` **patch** `/v1/folders/{folder_id}` Update the name or documentation of an existing data folder. ### Parameters - `folder_id: str` The ID of the source in the format 'source-' - `description: Optional[str]` The description of the source. - `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. - `instructions: Optional[str]` Instructions for how to use the source. - `metadata: Optional[Dict[str, object]]` Metadata associated with the source. - `name: Optional[str]` The name of the source. ### Returns - `class Folder: …` Representation of a folder, which is a collection of files and passages. - `id: str` The human-friendly ID of the Source - `embedding_config: EmbeddingConfig` The embedding configuration used by the folder. - `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. - `name: str` The name of the folder. - `created_at: Optional[datetime]` The timestamp when the folder was created. - `created_by_id: Optional[str]` The id of the user that made this Tool. - `description: Optional[str]` The description of the folder. - `instructions: Optional[str]` Instructions for how to use the folder. - `last_updated_by_id: Optional[str]` The id of the user that made this Tool. - `metadata: Optional[Dict[str, object]]` Metadata associated with the folder. - `updated_at: Optional[datetime]` The timestamp when the folder 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 ) folder = client.folders.update( folder_id="source-123e4567-e89b-42d3-8456-426614174000", ) print(folder.id) ``` #### Response ```json { "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" } ``` ## Delete Folder `folders.delete(strfolder_id) -> object` **delete** `/v1/folders/{folder_id}` Delete a data folder. ### Parameters - `folder_id: str` The ID of the source in the format 'source-' ### Returns - `object` ### 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 ) folder = client.folders.delete( "source-123e4567-e89b-42d3-8456-426614174000", ) print(folder) ``` #### Response ```json {} ``` ## List Folders `folders.list(FolderListParams**kwargs) -> SyncArrayPage[Folder]` **get** `/v1/folders/` List all data folders created by a user. ### Parameters - `after: Optional[str]` Cursor for pagination (source ID). Returns results relative to this ID in the specified sort order. Expected format: 'source-' - `before: Optional[str]` Cursor for pagination (source ID). Returns results relative to this ID in the specified sort order. Expected format: 'source-' - `limit: Optional[int]` Maximum number of folders to return - `name: Optional[str]` Folder name to filter by - `order: Optional[Literal["asc", "desc"]]` Sort order for folders 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 Folder: …` Representation of a folder, which is a collection of files and passages. - `id: str` The human-friendly ID of the Source - `embedding_config: EmbeddingConfig` The embedding configuration used by the folder. - `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. - `name: str` The name of the folder. - `created_at: Optional[datetime]` The timestamp when the folder was created. - `created_by_id: Optional[str]` The id of the user that made this Tool. - `description: Optional[str]` The description of the folder. - `instructions: Optional[str]` Instructions for how to use the folder. - `last_updated_by_id: Optional[str]` The id of the user that made this Tool. - `metadata: Optional[Dict[str, object]]` Metadata associated with the folder. - `updated_at: Optional[datetime]` The timestamp when the folder 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 ) page = client.folders.list() page = page.items[0] print(page.id) ``` #### Response ```json [ { "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" } ] ``` ## Create Folder `folders.create(FolderCreateParams**kwargs) -> Folder` **post** `/v1/folders/` Create a new data folder. ### Parameters - `name: str` The name of the source. - `description: Optional[str]` The description of the source. - `embedding: Optional[str]` The handle for the embedding config used by the source. - `embedding_chunk_size: Optional[int]` The chunk size of the embedding. - `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. - `instructions: Optional[str]` Instructions for how to use the source. - `metadata: Optional[Dict[str, object]]` Metadata associated with the source. ### Returns - `class Folder: …` Representation of a folder, which is a collection of files and passages. - `id: str` The human-friendly ID of the Source - `embedding_config: EmbeddingConfig` The embedding configuration used by the folder. - `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. - `name: str` The name of the folder. - `created_at: Optional[datetime]` The timestamp when the folder was created. - `created_by_id: Optional[str]` The id of the user that made this Tool. - `description: Optional[str]` The description of the folder. - `instructions: Optional[str]` Instructions for how to use the folder. - `last_updated_by_id: Optional[str]` The id of the user that made this Tool. - `metadata: Optional[Dict[str, object]]` Metadata associated with the folder. - `updated_at: Optional[datetime]` The timestamp when the folder 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 ) folder = client.folders.create( name="name", ) print(folder.id) ``` #### Response ```json { "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" } ``` ## Domain Types ### Folder - `class Folder: …` Representation of a folder, which is a collection of files and passages. - `id: str` The human-friendly ID of the Source - `embedding_config: EmbeddingConfig` The embedding configuration used by the folder. - `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. - `name: str` The name of the folder. - `created_at: Optional[datetime]` The timestamp when the folder was created. - `created_by_id: Optional[str]` The id of the user that made this Tool. - `description: Optional[str]` The description of the folder. - `instructions: Optional[str]` Instructions for how to use the folder. - `last_updated_by_id: Optional[str]` The id of the user that made this Tool. - `metadata: Optional[Dict[str, object]]` Metadata associated with the folder. - `updated_at: Optional[datetime]` The timestamp when the folder was last updated. # Files ## Upload File To Folder `folders.files.upload(strfolder_id, FileUploadParams**kwargs) -> FileUploadResponse` **post** `/v1/folders/{folder_id}/upload` Upload a file to a data folder. ### Parameters - `folder_id: str` The ID of the source in the format 'source-' - `file: FileTypes` - `duplicate_handling: Optional[Literal["skip", "error", "suffix", "replace"]]` How to handle duplicate filenames - `"skip"` - `"error"` - `"suffix"` - `"replace"` - `name: Optional[str]` Optional custom name to override the uploaded file's name ### Returns - `class FileUploadResponse: …` Representation of a single FileMetadata - `id: str` The human-friendly ID of the File - `source_id: str` Deprecated: Use `folder_id` field instead. The unique identifier of the source associated with the document. - `chunks_embedded: Optional[int]` Number of chunks that have been embedded. - `content: Optional[str]` Optional full-text content of the file; only populated on demand due to its size. - `created_at: Optional[datetime]` The creation date of the file. - `error_message: Optional[str]` Optional error message if the file failed processing. - `file_creation_date: Optional[str]` The creation date of the file. - `file_last_modified_date: Optional[str]` The last modified date of the file. - `file_name: Optional[str]` The name of the file. - `file_path: Optional[str]` The path to the file. - `file_size: Optional[int]` The size of the file in bytes. - `file_type: Optional[str]` The type of the file (MIME type). - `original_file_name: Optional[str]` The original name of the file as uploaded. - `processing_status: Optional[Literal["pending", "parsing", "embedding", 2 more]]` The current processing status of the file (e.g. pending, parsing, embedding, completed, error). - `"pending"` - `"parsing"` - `"embedding"` - `"completed"` - `"error"` - `total_chunks: Optional[int]` Total number of chunks for the file. - `updated_at: Optional[datetime]` The update date of the file. ### 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 ) response = client.folders.files.upload( folder_id="source-123e4567-e89b-42d3-8456-426614174000", file=b"Example data", ) print(response.id) ``` #### Response ```json { "id": "file-123e4567-e89b-12d3-a456-426614174000", "source_id": "source_id", "chunks_embedded": 0, "content": "content", "created_at": "2019-12-27T18:11:19.117Z", "error_message": "error_message", "file_creation_date": "file_creation_date", "file_last_modified_date": "file_last_modified_date", "file_name": "file_name", "file_path": "file_path", "file_size": 0, "file_type": "file_type", "original_file_name": "original_file_name", "processing_status": "pending", "total_chunks": 0, "updated_at": "2019-12-27T18:11:19.117Z" } ``` ## List Files For Folder `folders.files.list(strfolder_id, FileListParams**kwargs) -> SyncArrayPage[FileListResponse]` **get** `/v1/folders/{folder_id}/files` List paginated files associated with a data folder. ### Parameters - `folder_id: str` The ID of the source in the format 'source-' - `after: Optional[str]` Cursor for pagination (file ID). Returns results relative to this ID in the specified sort order. Expected format: 'file-' - `before: Optional[str]` Cursor for pagination (file ID). Returns results relative to this ID in the specified sort order. Expected format: 'file-' - `include_content: Optional[bool]` Whether to include full file content - `limit: Optional[int]` Maximum number of files to return - `order: Optional[Literal["asc", "desc"]]` Sort order for files 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 FileListResponse: …` Representation of a single FileMetadata - `id: str` The human-friendly ID of the File - `source_id: str` Deprecated: Use `folder_id` field instead. The unique identifier of the source associated with the document. - `chunks_embedded: Optional[int]` Number of chunks that have been embedded. - `content: Optional[str]` Optional full-text content of the file; only populated on demand due to its size. - `created_at: Optional[datetime]` The creation date of the file. - `error_message: Optional[str]` Optional error message if the file failed processing. - `file_creation_date: Optional[str]` The creation date of the file. - `file_last_modified_date: Optional[str]` The last modified date of the file. - `file_name: Optional[str]` The name of the file. - `file_path: Optional[str]` The path to the file. - `file_size: Optional[int]` The size of the file in bytes. - `file_type: Optional[str]` The type of the file (MIME type). - `original_file_name: Optional[str]` The original name of the file as uploaded. - `processing_status: Optional[Literal["pending", "parsing", "embedding", 2 more]]` The current processing status of the file (e.g. pending, parsing, embedding, completed, error). - `"pending"` - `"parsing"` - `"embedding"` - `"completed"` - `"error"` - `total_chunks: Optional[int]` Total number of chunks for the file. - `updated_at: Optional[datetime]` The update date of the file. ### 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.folders.files.list( folder_id="source-123e4567-e89b-42d3-8456-426614174000", ) page = page.items[0] print(page.id) ``` #### Response ```json [ { "id": "file-123e4567-e89b-12d3-a456-426614174000", "source_id": "source_id", "chunks_embedded": 0, "content": "content", "created_at": "2019-12-27T18:11:19.117Z", "error_message": "error_message", "file_creation_date": "file_creation_date", "file_last_modified_date": "file_last_modified_date", "file_name": "file_name", "file_path": "file_path", "file_size": 0, "file_type": "file_type", "original_file_name": "original_file_name", "processing_status": "pending", "total_chunks": 0, "updated_at": "2019-12-27T18:11:19.117Z" } ] ``` ## Retrieve File `folders.files.retrieve(strfile_id, FileRetrieveParams**kwargs) -> FileRetrieveResponse` **get** `/v1/folders/{folder_id}/files/{file_id}` Retrieve a file from a folder by ID. ### Parameters - `folder_id: str` The ID of the source in the format 'source-' - `file_id: str` The ID of the file in the format 'file-' - `include_content: Optional[bool]` Whether to include full file content ### Returns - `class FileRetrieveResponse: …` Representation of a single FileMetadata - `id: str` The human-friendly ID of the File - `source_id: str` Deprecated: Use `folder_id` field instead. The unique identifier of the source associated with the document. - `chunks_embedded: Optional[int]` Number of chunks that have been embedded. - `content: Optional[str]` Optional full-text content of the file; only populated on demand due to its size. - `created_at: Optional[datetime]` The creation date of the file. - `error_message: Optional[str]` Optional error message if the file failed processing. - `file_creation_date: Optional[str]` The creation date of the file. - `file_last_modified_date: Optional[str]` The last modified date of the file. - `file_name: Optional[str]` The name of the file. - `file_path: Optional[str]` The path to the file. - `file_size: Optional[int]` The size of the file in bytes. - `file_type: Optional[str]` The type of the file (MIME type). - `original_file_name: Optional[str]` The original name of the file as uploaded. - `processing_status: Optional[Literal["pending", "parsing", "embedding", 2 more]]` The current processing status of the file (e.g. pending, parsing, embedding, completed, error). - `"pending"` - `"parsing"` - `"embedding"` - `"completed"` - `"error"` - `total_chunks: Optional[int]` Total number of chunks for the file. - `updated_at: Optional[datetime]` The update date of the file. ### 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 ) file = client.folders.files.retrieve( file_id="file-123e4567-e89b-42d3-8456-426614174000", folder_id="source-123e4567-e89b-42d3-8456-426614174000", ) print(file.id) ``` #### Response ```json { "id": "file-123e4567-e89b-12d3-a456-426614174000", "source_id": "source_id", "chunks_embedded": 0, "content": "content", "created_at": "2019-12-27T18:11:19.117Z", "error_message": "error_message", "file_creation_date": "file_creation_date", "file_last_modified_date": "file_last_modified_date", "file_name": "file_name", "file_path": "file_path", "file_size": 0, "file_type": "file_type", "original_file_name": "original_file_name", "processing_status": "pending", "total_chunks": 0, "updated_at": "2019-12-27T18:11:19.117Z" } ``` ## Delete File From Folder `folders.files.delete(strfile_id, FileDeleteParams**kwargs)` **delete** `/v1/folders/{folder_id}/{file_id}` Delete a file from a folder. ### Parameters - `folder_id: str` The ID of the source in the format 'source-' - `file_id: str` The ID of the file in the format 'file-' ### 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.folders.files.delete( file_id="file-123e4567-e89b-42d3-8456-426614174000", folder_id="source-123e4567-e89b-42d3-8456-426614174000", ) ``` ## Domain Types ### File Upload Response - `class FileUploadResponse: …` Representation of a single FileMetadata - `id: str` The human-friendly ID of the File - `source_id: str` Deprecated: Use `folder_id` field instead. The unique identifier of the source associated with the document. - `chunks_embedded: Optional[int]` Number of chunks that have been embedded. - `content: Optional[str]` Optional full-text content of the file; only populated on demand due to its size. - `created_at: Optional[datetime]` The creation date of the file. - `error_message: Optional[str]` Optional error message if the file failed processing. - `file_creation_date: Optional[str]` The creation date of the file. - `file_last_modified_date: Optional[str]` The last modified date of the file. - `file_name: Optional[str]` The name of the file. - `file_path: Optional[str]` The path to the file. - `file_size: Optional[int]` The size of the file in bytes. - `file_type: Optional[str]` The type of the file (MIME type). - `original_file_name: Optional[str]` The original name of the file as uploaded. - `processing_status: Optional[Literal["pending", "parsing", "embedding", 2 more]]` The current processing status of the file (e.g. pending, parsing, embedding, completed, error). - `"pending"` - `"parsing"` - `"embedding"` - `"completed"` - `"error"` - `total_chunks: Optional[int]` Total number of chunks for the file. - `updated_at: Optional[datetime]` The update date of the file. ### File List Response - `class FileListResponse: …` Representation of a single FileMetadata - `id: str` The human-friendly ID of the File - `source_id: str` Deprecated: Use `folder_id` field instead. The unique identifier of the source associated with the document. - `chunks_embedded: Optional[int]` Number of chunks that have been embedded. - `content: Optional[str]` Optional full-text content of the file; only populated on demand due to its size. - `created_at: Optional[datetime]` The creation date of the file. - `error_message: Optional[str]` Optional error message if the file failed processing. - `file_creation_date: Optional[str]` The creation date of the file. - `file_last_modified_date: Optional[str]` The last modified date of the file. - `file_name: Optional[str]` The name of the file. - `file_path: Optional[str]` The path to the file. - `file_size: Optional[int]` The size of the file in bytes. - `file_type: Optional[str]` The type of the file (MIME type). - `original_file_name: Optional[str]` The original name of the file as uploaded. - `processing_status: Optional[Literal["pending", "parsing", "embedding", 2 more]]` The current processing status of the file (e.g. pending, parsing, embedding, completed, error). - `"pending"` - `"parsing"` - `"embedding"` - `"completed"` - `"error"` - `total_chunks: Optional[int]` Total number of chunks for the file. - `updated_at: Optional[datetime]` The update date of the file. ### File Retrieve Response - `class FileRetrieveResponse: …` Representation of a single FileMetadata - `id: str` The human-friendly ID of the File - `source_id: str` Deprecated: Use `folder_id` field instead. The unique identifier of the source associated with the document. - `chunks_embedded: Optional[int]` Number of chunks that have been embedded. - `content: Optional[str]` Optional full-text content of the file; only populated on demand due to its size. - `created_at: Optional[datetime]` The creation date of the file. - `error_message: Optional[str]` Optional error message if the file failed processing. - `file_creation_date: Optional[str]` The creation date of the file. - `file_last_modified_date: Optional[str]` The last modified date of the file. - `file_name: Optional[str]` The name of the file. - `file_path: Optional[str]` The path to the file. - `file_size: Optional[int]` The size of the file in bytes. - `file_type: Optional[str]` The type of the file (MIME type). - `original_file_name: Optional[str]` The original name of the file as uploaded. - `processing_status: Optional[Literal["pending", "parsing", "embedding", 2 more]]` The current processing status of the file (e.g. pending, parsing, embedding, completed, error). - `"pending"` - `"parsing"` - `"embedding"` - `"completed"` - `"error"` - `total_chunks: Optional[int]` Total number of chunks for the file. - `updated_at: Optional[datetime]` The update date of the file. # Agents ## List Agents For Folder `folders.agents.list(strfolder_id, AgentListParams**kwargs) -> AgentListResponse` **get** `/v1/folders/{folder_id}/agents` Get all agent IDs that have the specified folder attached. ### Parameters - `folder_id: str` The ID of the source in the format 'source-' - `after: Optional[str]` Cursor for pagination (agent ID). Returns results relative to this ID in the specified sort order. Expected format: 'agent-' - `before: Optional[str]` Cursor for pagination (agent ID). Returns results relative to this ID in the specified sort order. Expected format: 'agent-' - `limit: Optional[int]` Maximum number of agents to return - `order: Optional[Literal["asc", "desc"]]` Sort order for agents 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 - `List[str]` ### 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 ) agents = client.folders.agents.list( folder_id="source-123e4567-e89b-42d3-8456-426614174000", ) print(agents) ``` #### Response ```json [ "string" ] ``` ## Domain Types ### Agent List Response - `List[str]`