test_embedding.py

# -*- coding: utf-8 -*-
# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import math
from typing import Any
import unittest
import unittest.mock as mock

from google.generativeai import protos

from google.generativeai import embedding

from google.generativeai import client
from absl.testing import absltest
from absl.testing import parameterized

DEFAULT_EMB_MODEL = "models/embedding-001"


class UnitTests(parameterized.TestCase):
    def setUp(self):
        self.client = unittest.mock.MagicMock()

        client._client_manager.clients["generative"] = self.client
        client._client_manager.clients["model"] = self.client

        self.observed_requests = []

        def add_client_method(f):
            name = f.__name__
            setattr(self.client, name, f)
            return f

        @add_client_method
        def embed_content(
            request: protos.EmbedContentRequest,
            **kwargs,
        ) -> protos.EmbedContentResponse:
            self.observed_requests.append(request)
            return protos.EmbedContentResponse(embedding=protos.ContentEmbedding(values=[1, 2, 3]))

        @add_client_method
        def batch_embed_contents(
            request: protos.BatchEmbedContentsRequest,
            **kwargs,
        ) -> protos.BatchEmbedContentsResponse:
            self.observed_requests.append(request)
            return protos.BatchEmbedContentsResponse(
                embeddings=[protos.ContentEmbedding(values=[1, 2, 3])] * len(request.requests)
            )

    def test_embed_content(self):
        text = "What are you?"
        emb = embedding.embed_content(model=DEFAULT_EMB_MODEL, content=text)

        self.assertIsInstance(emb, dict)
        self.assertEqual(
            self.observed_requests[-1],
            protos.EmbedContentRequest(
                model=DEFAULT_EMB_MODEL,
                content=protos.Content(parts=[protos.Part(text="What are you?")]),
            ),
        )
        self.assertIsInstance(emb["embedding"][0], float)

    @parameterized.named_parameters(
        [
            dict(
                testcase_name="even-batch",
                batch_size=100,
            ),
            dict(
                testcase_name="even-batch-plus-one",
                batch_size=101,
            ),
            dict(testcase_name="odd-batch", batch_size=237),
        ]
    )
    def test_batch_embed_contents(self, batch_size):
        text = ["What are you?"]
        texts = text * batch_size
        emb = embedding.embed_content(model=DEFAULT_EMB_MODEL, content=texts)

        self.assertIsInstance(emb, dict)

        # Check that the list has the right length.
        self.assertIsInstance(emb["embedding"][0], list)
        self.assertLen(emb["embedding"], len(texts))

        # Check that the right number of requests were sent.
        self.assertLen(
            self.observed_requests,
            math.ceil(len(texts) / embedding.EMBEDDING_MAX_BATCH_SIZE),
        )

    def test_embed_content_title_and_task_1(self):
        text = "What are you?"
        emb = embedding.embed_content(
            model=DEFAULT_EMB_MODEL,
            content=text,
            task_type="retrieval_document",
            title="Exploring AI",
        )

        self.assertEqual(
            embedding.to_task_type("retrieval_document"),
            embedding.EmbeddingTaskType.RETRIEVAL_DOCUMENT,
        )

    def test_embed_content_title_and_task_2(self):
        text = "What are you?"
        with self.assertRaises(ValueError):
            embedding.embed_content(
                model=DEFAULT_EMB_MODEL, content=text, task_type="similarity", title="Exploring AI"
            )

    def test_embed_content_with_negative_output_dimensionality(self):
        text = "What are you?"
        with self.assertRaises(ValueError):
            embedding.embed_content(model=DEFAULT_EMB_MODEL, content=text, output_dimensionality=-1)

    def test_generate_answer_called_with_request_options(self):
        self.client.embed_content = mock.MagicMock()
        request = mock.ANY
        request_options = {"timeout": 120}

        text = "What are you?"
        try:
            embedding.embed_content(
                model=DEFAULT_EMB_MODEL,
                content=text,
                request_options=request_options,
            )
        except AttributeError:
            pass

        self.client.embed_content.assert_called_once_with(request, **request_options)

    def test_batch_embed_contents_called_with_request_options(self):
        self.client.batch_embed_contents = mock.MagicMock()
        request = mock.ANY
        request_options = {"timeout": 120}

        text = "What are you?"
        try:
            embedding.embed_content(
                model=DEFAULT_EMB_MODEL,
                content=[text],
                request_options=request_options,
            )
        except AttributeError:
            pass

        self.client.batch_embed_contents.assert_called_once_with(request, **request_options)

    def test_embed_content_called_with_request_options(self):
        self.client.embed_content = mock.MagicMock()
        request = mock.ANY
        request_options = {"timeout": 120}

        try:
            text = "What are you?"
            embedding.embed_content(
                model=DEFAULT_EMB_MODEL, content=text, request_options=request_options
            )
        except AttributeError:
            pass

        self.client.embed_content.assert_called_once_with(request, **request_options)


if __name__ == "__main__":
    absltest.main()