TrustGraph Graph Embeddings API

The purpose of this API is to search for knowledge graph entities by embeddings. The request is a list of embeddings, the response is a list of knowledge graph entities. The search is performed using a vector store.

Request/response

Request

The request contains the following fields:

• vectors: An array of embeddings. Each embedding is itself an array of numbers.
• limit: Optional: a limit on the number of graph entities to return.

Response

The response contains the following fields:

• entities: An array of graph entities. The entity type is described here:

TrustGraph uses the same schema for knowledge graph elements:

• value: the entity URI or literal value depending on whether this is graph entity or literal value.
• is_uri: A boolean value which is true if this is a graph entity i.e. value is a URI, not a literal value.

REST service

The REST service accepts a request object containing the vectors field. The response is a JSON object containing the entities field.

To reduce the size of the JSON, the graph entities are encoded as an object with value and is_uri mapped to v and e respectively.

e.g.

Request:

{
    "vectors": [
      [
          0.04013510048389435,
          0.07536131888628006,
          ...
          -0.10790473222732544,
          0.03591292351484299
      ]
    ],
    "limit": 15
}

Response:

{
    "entities": [
        {
            "v": "http://trustgraph.ai/e/space-station-modules",
            "e": true
        },
        {
            "v": "http://trustgraph.ai/e/rocket-propellants",
            "e": true
        },
    ]
}

Websocket

The websocket service accepts a request object containing the vectors field. The response is a JSON object containing the entities field.

To reduce the size of the JSON, the graph entities are encoded as an object with value and is_uri mapped to v and e respectively.

e.g.

Request:

{
    "id": "qgzw1287vfjc8wsk-3",
    "service": "graph-embeddings-query",
    "flow": "default",
    "request": {
        "vectors": [
          [
              0.04013510048389435,
              0.07536131888628006,
              ...
              -0.10790473222732544,
              0.03591292351484299
          ]
        ],
        "limit": 15
    }
}

Response:

{
    "id": "qgzw1287vfjc8wsk-3",
    "response": {
        "entities": [
            {
                "v": "http://trustgraph.ai/e/space-station-modules",
                "e": true
            },
            {
                "v": "http://trustgraph.ai/e/rocket-propellants",
                "e": true
            },
        ]
    },
    "complete": true
}

Pulsar

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

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

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

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

Request schema: trustgraph.schema.GraphEmbeddingsRequest

Response schema: trustgraph.schema.GraphEmbeddingsResponse

Pulsar Python client

The client class is trustgraph.clients.GraphEmbeddingsClient

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