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ModelsExpand Collapse
Archive = object { id, created_at, embedding_config, 7 more }
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
embedding_config: EmbeddingConfig { embedding_dim, embedding_endpoint_type, embedding_model, 7 more }
Embedding configuration for passages in this archive
embedding_dim: number
The dimension of the embedding.
embedding_endpoint_type: "openai" or "anthropic" or "bedrock" or 16 more
The endpoint type for the model.
embedding_model: string
The model for the embedding.
azure_deployment: optional string
The Azure deployment for the model.
azure_endpoint: optional string
The Azure endpoint for the model.
azure_version: optional string
The Azure version for the model.
batch_size: optional number
The maximum batch size for processing embeddings.
embedding_chunk_size: optional number
The chunk size of the embedding.
embedding_endpoint: optional string
The endpoint for the model (None if local).
handle: optional string
The handle for this config, in the format provider/model-name.
name: string
The name of the archive
created_by_id: optional string
The id of the user that made this object.
description: optional string
A description of the archive
last_updated_by_id: optional string
The id of the user that made this object.
metadata: optional map[unknown]
Additional metadata
updated_at: optional string
The timestamp when the object was last updated.
The vector database provider used for this archive's passages
VectorDBProvider = "native" or "tpuf" or "pinecone"
Supported vector database providers for archival memory