dify/api/tests/integration_tests/model_runtime/replicate/test_text_embedding.py

import os

import pytest

from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.replicate.text_embedding.text_embedding import ReplicateEmbeddingModel


def test_validate_credentials_one():
    model = ReplicateEmbeddingModel()

    with pytest.raises(CredentialsValidateFailedError):
        model.validate_credentials(
            model='replicate/all-mpnet-base-v2',
            credentials={
                'replicate_api_token': 'invalid_key',
                'model_version': 'b6b7585c9640cd7a9572c6e129c9549d79c9c31f0d3fdce7baac7c67ca38f305'
            }
        )

    model.validate_credentials(
        model='replicate/all-mpnet-base-v2',
        credentials={
            'replicate_api_token': os.environ.get('REPLICATE_API_KEY'),
            'model_version': 'b6b7585c9640cd7a9572c6e129c9549d79c9c31f0d3fdce7baac7c67ca38f305'
        }
    )


def test_validate_credentials_two():
    model = ReplicateEmbeddingModel()

    with pytest.raises(CredentialsValidateFailedError):
        model.validate_credentials(
            model='nateraw/bge-large-en-v1.5',
            credentials={
                'replicate_api_token': 'invalid_key',
                'model_version': '9cf9f015a9cb9c61d1a2610659cdac4a4ca222f2d3707a68517b18c198a9add1'
            }
        )

    model.validate_credentials(
        model='nateraw/bge-large-en-v1.5',
        credentials={
            'replicate_api_token': os.environ.get('REPLICATE_API_KEY'),
            'model_version': '9cf9f015a9cb9c61d1a2610659cdac4a4ca222f2d3707a68517b18c198a9add1'
        }
    )


def test_invoke_model_one():
    model = ReplicateEmbeddingModel()

    result = model.invoke(
        model='nateraw/bge-large-en-v1.5',
        credentials={
            'replicate_api_token': os.environ.get('REPLICATE_API_KEY'),
            'model_version': '9cf9f015a9cb9c61d1a2610659cdac4a4ca222f2d3707a68517b18c198a9add1'
        },
        texts=[
            "hello",
            "world"
        ],
        user="abc-123"
    )

    assert isinstance(result, TextEmbeddingResult)
    assert len(result.embeddings) == 2
    assert result.usage.total_tokens == 2


def test_invoke_model_two():
    model = ReplicateEmbeddingModel()

    result = model.invoke(
        model='andreasjansson/clip-features',
        credentials={
            'replicate_api_token': os.environ.get('REPLICATE_API_KEY'),
            'model_version': '75b33f253f7714a281ad3e9b28f63e3232d583716ef6718f2e46641077ea040a'
        },
        texts=[
            "hello",
            "world"
        ],
        user="abc-123"
    )

    assert isinstance(result, TextEmbeddingResult)
    assert len(result.embeddings) == 2
    assert result.usage.total_tokens == 2


def test_invoke_model_three():
    model = ReplicateEmbeddingModel()

    result = model.invoke(
        model='replicate/all-mpnet-base-v2',
        credentials={
            'replicate_api_token': os.environ.get('REPLICATE_API_KEY'),
            'model_version': 'b6b7585c9640cd7a9572c6e129c9549d79c9c31f0d3fdce7baac7c67ca38f305'
        },
        texts=[
            "hello",
            "world"
        ],
        user="abc-123"
    )

    assert isinstance(result, TextEmbeddingResult)
    assert len(result.embeddings) == 2
    assert result.usage.total_tokens == 2


def test_invoke_model_four():
    model = ReplicateEmbeddingModel()

    result = model.invoke(
        model='nateraw/jina-embeddings-v2-base-en',
        credentials={
            'replicate_api_token': os.environ.get('REPLICATE_API_KEY'),
            'model_version': 'f8367a1c072ba2bc28af549d1faeacfe9b88b3f0e475add7a75091dac507f79e'
        },
        texts=[
            "hello",
            "world"
        ],
        user="abc-123"
    )

    assert isinstance(result, TextEmbeddingResult)
    assert len(result.embeddings) == 2
    assert result.usage.total_tokens == 2


def test_get_num_tokens():
    model = ReplicateEmbeddingModel()

    num_tokens = model.get_num_tokens(
        model='nateraw/jina-embeddings-v2-base-en',
        credentials={
            'replicate_api_token': os.environ.get('REPLICATE_API_KEY'),
            'model_version': 'f8367a1c072ba2bc28af549d1faeacfe9b88b3f0e475add7a75091dac507f79e'
        },
        texts=[
            "hello",
            "world"
        ]
    )

    assert num_tokens == 2