TrustGraph Embeddings API

Request/response

Request

The request contains the following fields:

• text: A string, the text to apply the embedding to

Response

The response contains the following fields:

• vectors: Embeddings response, an array of arrays. An embedding is an array of floating-point numbers. As multiple embeddings may be returned, an array of embeddings is returned, hence an array of arrays.

REST service

The REST service accepts a request object containing the question field. The response is a JSON object containing the answer field.

e.g.

Request:

{
    "text": "What does NASA stand for?"
}

Response:

{
    "vectors": [ 0.231341245, ... ]
}

Websocket

Embeddings requests have a request object containing the text field. Responses have a response object containing vectors field.

e.g.

Request:

{
    "id": "qgzw1287vfjc8wsk-2",
    "service": "embeddings",
    "flow": "default",
    "request": {
        "text": "What is a cat?"
    }
}

Responses:

{
    "id": "qgzw1287vfjc8wsk-2",
    "response": {
        "vectors": [
            [
                0.04013510048389435,
                0.07536131888628006,
                ...
                -0.023531345650553703,
                0.03591292351484299
            ]
        ]
    },
    "complete": true
}

Pulsar

The Pulsar schema for the Embeddings API is defined in Python code here:

https://github.com/trustgraph-ai/trustgraph/blob/master/trustgraph-base/trustgraph/schema/models.py

Default request queue: non-persistent://tg/request/embeddings

Default response queue: non-persistent://tg/response/embeddings

Request schema: trustgraph.schema.EmbeddingsRequest

Response schema: trustgraph.schema.EmbeddingsResponse

Pulsar Python client

The client class is trustgraph.clients.EmbeddingsClient

https://github.com/trustgraph-ai/trustgraph/blob/master/trustgraph-base/trustgraph/clients/embeddings_client.py