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:

Property	Description	Type
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

Property	Description
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.

List embeddings

GEThttps://dev.index.network/api/embeddings

Query parameters

Response

200

List of embeddings

Request

JavaScriptCurlPython

const response = await fetch('https://dev.index.network/api/embeddings?indexId=text&itemId=text', {
    method: 'GET',
    headers: {},
});
const data = await response.json();

Create embedding

POSThttps://dev.index.network/api/embeddings

Authorization

didSessionAuth

Body

application/json

indexIdstring

itemIdstring

modelNamestring

categorystring

contextstring

vectorarray of number

descriptionstring

Response

201

Embedding created

Request

JavaScriptCurlPython

const response = await fetch('https://dev.index.network/api/embeddings', {
    method: 'POST',
    headers: {
      "Authorization": "Bearer DID_SESSION_SERIALIZED",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({}),
});
const data = await response.json();

Delete embedding

DELETEhttps://dev.index.network/api/embeddings

Authorization

didSessionAuth

Response

200

Embedding deleted

Request

JavaScriptCurlPython

const response = await fetch('https://dev.index.network/api/embeddings', {
    method: 'DELETE',
    headers: {
      "Authorization": "Bearer DID_SESSION_SERIALIZED"
    },
});
const data = await response.json();

Update embedding

PATCHhttps://dev.index.network/api/embeddings

Authorization

didSessionAuth

Body

application/json

indexIdstring

itemIdstring

modelNamestring

categorystring

contextstring

vectorarray of number

descriptionstring

Response

200

Embedding updated

Request

JavaScriptCurlPython

const response = await fetch('https://dev.index.network/api/embeddings', {
    method: 'PATCH',
    headers: {
      "Authorization": "Bearer DID_SESSION_SERIALIZED",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({}),
});
const data = await response.json();