# Archives ## Create Archive `client.archives.create(ArchiveCreateParamsbody, RequestOptionsoptions?): Archive` **post** `/v1/archives/` Create a new archive. ### Parameters - `body: ArchiveCreateParams` - `name: string` - `description?: string | null` - `embedding?: string | null` Embedding model handle for the archive - `embedding_config?: EmbeddingConfig | 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. - `"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. ### Returns - `Archive` Representation of an archive - a collection of archival passages that can be shared between agents. - `id: string` The human-friendly ID of the Archive - `created_at: string` The creation date of the archive - `name: string` The name of the archive - `created_by_id?: string | null` The id of the user that made this object. - `description?: string | null` A description of the archive - `embedding_config?: EmbeddingConfig | 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. - `"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. - `last_updated_by_id?: string | null` The id of the user that made this object. - `metadata?: Record | null` Additional metadata - `updated_at?: string | null` The timestamp when the object was last updated. - `vector_db_provider?: VectorDBProvider` The vector database provider used for this archive's passages - `"native"` - `"tpuf"` - `"pinecone"` ### Example ```typescript import Letta from '@letta-ai/letta-client'; const client = new Letta({ apiKey: process.env['LETTA_API_KEY'], // This is the default and can be omitted }); const archive = await client.archives.create({ name: 'name' }); console.log(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 `client.archives.list(ArchiveListParamsquery?, RequestOptionsoptions?): ArrayPage` **get** `/v1/archives/` Get a list of all archives for the current organization with optional filters and pagination. ### Parameters - `query: ArchiveListParams` - `after?: string | null` Cursor for pagination (archive ID). Returns results relative to this ID in the specified sort order. Expected format: 'archive-' - `agent_id?: string | null` Only archives attached to this agent ID - `before?: string | null` Cursor for pagination (archive ID). Returns results relative to this ID in the specified sort order. Expected format: 'archive-' - `limit?: number | null` Maximum number of archives to return - `name?: string | null` Filter by archive name (exact match) - `order?: "asc" | "desc"` Sort order for archives by creation time. 'asc' for oldest first, 'desc' for newest first - `"asc"` - `"desc"` - `order_by?: "created_at"` Field to sort by - `"created_at"` ### Returns - `Archive` Representation of an archive - a collection of archival passages that can be shared between agents. - `id: string` The human-friendly ID of the Archive - `created_at: string` The creation date of the archive - `name: string` The name of the archive - `created_by_id?: string | null` The id of the user that made this object. - `description?: string | null` A description of the archive - `embedding_config?: EmbeddingConfig | 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. - `"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. - `last_updated_by_id?: string | null` The id of the user that made this object. - `metadata?: Record | null` Additional metadata - `updated_at?: string | null` The timestamp when the object was last updated. - `vector_db_provider?: VectorDBProvider` The vector database provider used for this archive's passages - `"native"` - `"tpuf"` - `"pinecone"` ### Example ```typescript import Letta from '@letta-ai/letta-client'; const client = new Letta({ apiKey: process.env['LETTA_API_KEY'], // This is the default and can be omitted }); // Automatically fetches more pages as needed. for await (const archive of client.archives.list()) { console.log(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" } ] ``` ## Update Archive `client.archives.update(stringarchiveID, ArchiveUpdateParamsbody, RequestOptionsoptions?): Archive` **patch** `/v1/archives/{archive_id}` Update an existing archive's name and/or description. ### Parameters - `archiveID: string` The ID of the archive in the format 'archive-' - `body: ArchiveUpdateParams` - `description?: string | null` - `name?: string | null` ### Returns - `Archive` Representation of an archive - a collection of archival passages that can be shared between agents. - `id: string` The human-friendly ID of the Archive - `created_at: string` The creation date of the archive - `name: string` The name of the archive - `created_by_id?: string | null` The id of the user that made this object. - `description?: string | null` A description of the archive - `embedding_config?: EmbeddingConfig | 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. - `"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. - `last_updated_by_id?: string | null` The id of the user that made this object. - `metadata?: Record | null` Additional metadata - `updated_at?: string | null` The timestamp when the object was last updated. - `vector_db_provider?: VectorDBProvider` The vector database provider used for this archive's passages - `"native"` - `"tpuf"` - `"pinecone"` ### Example ```typescript import Letta from '@letta-ai/letta-client'; const client = new Letta({ apiKey: process.env['LETTA_API_KEY'], // This is the default and can be omitted }); const archive = await client.archives.update('archive-123e4567-e89b-42d3-8456-426614174000'); console.log(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 `client.archives.retrieve(stringarchiveID, RequestOptionsoptions?): Archive` **get** `/v1/archives/{archive_id}` Get a single archive by its ID. ### Parameters - `archiveID: string` The ID of the archive in the format 'archive-' ### Returns - `Archive` Representation of an archive - a collection of archival passages that can be shared between agents. - `id: string` The human-friendly ID of the Archive - `created_at: string` The creation date of the archive - `name: string` The name of the archive - `created_by_id?: string | null` The id of the user that made this object. - `description?: string | null` A description of the archive - `embedding_config?: EmbeddingConfig | 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. - `"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. - `last_updated_by_id?: string | null` The id of the user that made this object. - `metadata?: Record | null` Additional metadata - `updated_at?: string | null` The timestamp when the object was last updated. - `vector_db_provider?: VectorDBProvider` The vector database provider used for this archive's passages - `"native"` - `"tpuf"` - `"pinecone"` ### Example ```typescript import Letta from '@letta-ai/letta-client'; const client = new Letta({ apiKey: process.env['LETTA_API_KEY'], // This is the default and can be omitted }); const archive = await client.archives.retrieve('archive-123e4567-e89b-42d3-8456-426614174000'); console.log(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 `client.archives.delete(stringarchiveID, RequestOptionsoptions?): void` **delete** `/v1/archives/{archive_id}` Delete an archive by its ID. ### Parameters - `archiveID: string` The ID of the archive in the format 'archive-' ### Example ```typescript import Letta from '@letta-ai/letta-client'; const client = new Letta({ apiKey: process.env['LETTA_API_KEY'], // This is the default and can be omitted }); await client.archives.delete('archive-123e4567-e89b-42d3-8456-426614174000'); ``` ## Domain Types ### Archive - `Archive` Representation of an archive - a collection of archival passages that can be shared between agents. - `id: string` The human-friendly ID of the Archive - `created_at: string` The creation date of the archive - `name: string` The name of the archive - `created_by_id?: string | null` The id of the user that made this object. - `description?: string | null` A description of the archive - `embedding_config?: EmbeddingConfig | 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. - `"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. - `last_updated_by_id?: string | null` The id of the user that made this object. - `metadata?: Record | null` Additional metadata - `updated_at?: string | null` The timestamp when the object was last updated. - `vector_db_provider?: VectorDBProvider` The vector database provider used for this archive's passages - `"native"` - `"tpuf"` - `"pinecone"` ### Vector DB Provider - `VectorDBProvider = "native" | "tpuf" | "pinecone"` Supported vector database providers for archival memory - `"native"` - `"tpuf"` - `"pinecone"` # Passages ## Create Passage In Archive `client.archives.passages.create(stringarchiveID, PassageCreateParamsbody, RequestOptionsoptions?): 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 - `archiveID: string` The ID of the archive in the format 'archive-' - `body: PassageCreateParams` - `text: string` The text content of the passage - `created_at?: string | null` Optional creation datetime for the passage (ISO 8601 format) - `metadata?: Record | null` Optional metadata for the passage - `tags?: Array | null` Optional tags for categorizing the passage ### Returns - `Passage` Representation of a passage, which is stored in archival memory. - `embedding: Array | null` The embedding of the passage. - `embedding_config: EmbeddingConfig | 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. - `"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 | null` The creation date of the passage. - `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 | null` The metadata of the passage. - `source_id?: string | null` Deprecated: Use `folder_id` field instead. The data source of the passage. - `tags?: Array | null` Tags associated with this passage. - `updated_at?: string | null` The timestamp when the object was last updated. ### Example ```typescript import Letta from '@letta-ai/letta-client'; const client = new Letta({ apiKey: process.env['LETTA_API_KEY'], // This is the default and can be omitted }); const passage = await client.archives.passages.create( 'archive-123e4567-e89b-42d3-8456-426614174000', { text: 'text' }, ); console.log(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 `client.archives.passages.createMany(stringarchiveID, PassageCreateManyParamsbody, RequestOptionsoptions?): 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 - `archiveID: string` The ID of the archive in the format 'archive-' - `body: PassageCreateManyParams` - `passages: Array` Passages to create in the archive - `text: string` The text content of the passage - `created_at?: string | null` Optional creation datetime for the passage (ISO 8601 format) - `metadata?: Record | null` Optional metadata for the passage - `tags?: Array | null` Optional tags for categorizing the passage ### Returns - `PassageCreateManyResponse = Array` - `embedding: Array | null` The embedding of the passage. - `embedding_config: EmbeddingConfig | 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. - `"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 | null` The creation date of the passage. - `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 | null` The metadata of the passage. - `source_id?: string | null` Deprecated: Use `folder_id` field instead. The data source of the passage. - `tags?: Array | null` Tags associated with this passage. - `updated_at?: string | null` The timestamp when the object was last updated. ### Example ```typescript import Letta from '@letta-ai/letta-client'; const client = new Letta({ apiKey: process.env['LETTA_API_KEY'], // This is the default and can be omitted }); const passages = await client.archives.passages.createMany( 'archive-123e4567-e89b-42d3-8456-426614174000', { passages: [{ text: 'text' }] }, ); console.log(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 `client.archives.passages.delete(stringpassageID, PassageDeleteParamsparams, RequestOptionsoptions?): void` **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 - `passageID: string` The ID of the passage in the format 'passage-' - `params: PassageDeleteParams` - `archive_id: string` The ID of the archive in the format 'archive-' ### Example ```typescript import Letta from '@letta-ai/letta-client'; const client = new Letta({ apiKey: process.env['LETTA_API_KEY'], // This is the default and can be omitted }); await client.archives.passages.delete('passage-123e4567-e89b-42d3-8456-426614174000', { archive_id: 'archive-123e4567-e89b-42d3-8456-426614174000', }); ``` ## Domain Types ### Passage Create Many Response - `PassageCreateManyResponse = Array` - `embedding: Array | null` The embedding of the passage. - `embedding_config: EmbeddingConfig | 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. - `"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 | null` The creation date of the passage. - `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 | null` The metadata of the passage. - `source_id?: string | null` Deprecated: Use `folder_id` field instead. The data source of the passage. - `tags?: Array | null` Tags associated with this passage. - `updated_at?: string | null` The timestamp when the object was last updated.