Support MRL (Matryoshka Representation Learning)

[kozistr]

What does this PR do?

Fixes #673

Add dimensions field to the embed request API spec.

• compatible with OpenAI client
• both http & grpc
• add a test case for the MRL feature
• remove the unused test cases in router/tests

$ ./target/release/text-embeddings-router --model-id ../Qwen3-Embedding-0.6B/ --pooling last-token --port 8080 --dtype float32 --auto-truncate --max-batch-tokens 512

from openai import OpenAI

client = OpenAI(api_key='', base_url='http://127.0.0.1:8080')

response = client.embeddings.create(
    model='Qwen3-Embedding-0.6B',
    input='test',
    dimensions=256,
)

len(response.data[0].embedding)
256

tei_results = np.asarray(
    requests.post(
        'http://127.0.0.1:8080/embed',
        data=json.dumps({'inputs': sentences[0], 'normalize': True, 'dimensions': 256}),
        headers={'Content-type': 'application/json'},
    ).json()
)
tei_results.shape
(1, 256)

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@Narsil @alvarobartt

[alvarobartt]

LGTM, thanks for the addition! Also I like the macros for Prometheus metrics, those look cleaner IMO.

P.S. Guessing that the snapshots were removed because those were not used, right?

[kozistr]

LGTM, thanks for the addition! Also I like the macros for Prometheus metrics, those look cleaner IMO.

P.S. Guessing that the snapshots were removed because those were not used, right?

oh, sorry for the missing context. you're right!

looks like these test cases (router/tests) were all commented, so I've removed the snapshots.

updated)
I've just updated the PR description too!

thanks for the pointing this :)