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:
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
Endpoints
This section provides details about the available API endpoints.
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