Embedding
Embeddings serve as the A.I.-native approach for representing various data types, including text, images, and soon, audio and video, making them the perfect fit for integration with A.I.-powered tools and algorithms.
Within the Index Network, an Item can have many embeddings which serves as multi-dimensional representations for items in specific contexts.
Storing embeddings in a decentralized way efficiently promotes a cohesive and interconnected knowledge network for A.I.-driven applications.
Schema
The Embedding
schema consists of several key properties:
modelName
Name of the model used for generating the embedding.
String
vector
The embedding vector, represented as an array of floats.
Array of Floats
context
Context information associated with the embedding, detailed in the EmbeddingContext
model.
EmbeddingContext
indexId
Identifier of the Index associated with this embedding.
StreamID
index
The actual Index object associated with this embedding, accessed through indexId
.
Index
itemId
Identifier of the item (node) associated with this embedding.
StreamID
item
The actual Node object associated with this embedding, accessed through itemId
.
Node
createdAt
The timestamp when the Embedding was initially created, for historical record-keeping.
DateTime
updatedAt
The timestamp when the Embedding was last updated, ensuring data accuracy.
DateTime
deletedAt
The timestamp when the Embedding was deleted (if applicable).
DateTime
controllerDID
The Decentralized Identifier (DID) of the controller for access control.
DID
version
The CommitID representing the version or state of the Embedding for version control.
CommitID
Context Schema
The EmbeddingContext
type in a data model plays a crucial role in enhancing the interoperability of embeddings. It provides a standardized and enriched representation of data, such as a node's content augmented with additional context, ensuring that embeddings are universally applicable across different systems. T
context
The transformed representation of a node before obtaining embeddings. This includes the node's content with added context information for embedding tasks like summaries, etc. Raw document is used if it's null.
description
A human-readable description of the related context. It helps in understanding the nature and purpose of the context used for embedding.
category
The category or namespace for the embedding. Example values might include "summaries", "knowledge_graph", "document", etc., indicating the domain or type of embedding being used.
Endpoints
This section provides details about the available API endpoints.
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