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106 lines
1.9 KiB
Markdown
106 lines
1.9 KiB
Markdown
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# TrustGraph Embeddings API
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## Request/response
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### Request
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The request contains the following fields:
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- `text`: A string, the text to apply the embedding to
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### Response
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The request contains the following fields:
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- `vectors`: Embeddings response, an array of arrays. An embedding is
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an array of floating-point numbers. As multiple embeddings may be
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returned, an array of embeddings is returned, hence an array
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of arrays.
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## REST service
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The REST service accepts a request object containing the question field.
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The response is a JSON object containing the `answer` field.
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e.g.
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Request:
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```
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{
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"text": "What does NASA stand for?"
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}
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```
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Response:
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```
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{
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"vectors": [ 0.231341245, ... ]
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}
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```
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## Websocket
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Embeddings requests have a `request` object containing the `text` field.
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Responses have a `response` object containing `vectors` field.
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e.g.
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Request:
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```
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{
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"id": "qgzw1287vfjc8wsk-2",
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"service": "embeddings",
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"request": {
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"text": "What is a cat?"
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}
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}
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```
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Responses:
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```
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{
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"id": "qgzw1287vfjc8wsk-2",
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"response": {
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"vectors": [
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[
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0.04013510048389435,
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0.07536131888628006,
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...
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-0.023531345650553703,
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0.03591292351484299
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]
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]
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},
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"complete": true
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}
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```
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## Pulsar
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The Pulsar schema for the Embeddings API is defined in Python code here:
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https://github.com/trustgraph-ai/trustgraph/blob/master/trustgraph-base/trustgraph/schema/models.py
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Default request queue:
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`non-persistent://tg/request/embeddings`
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Default response queue:
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`non-persistent://tg/response/embeddings`
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Request schema:
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`trustgraph.schema.EmbeddingsRequest`
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Response schema:
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`trustgraph.schema.EmbeddingsResponse`
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## Pulsar Python client
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The client class is
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`trustgraph.clients.EmbeddingsClient`
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https://github.com/trustgraph-ai/trustgraph/blob/master/trustgraph-base/trustgraph/clients/embeddings_client.py
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