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참고/neo4j-graphrag-python-main/tests/unit/embeddings/test_bedrock_embedder.py

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+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  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
+#  #
+#      https://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.
+from __future__ import annotations
+
+import io
+import json
+from typing import Any, Generator
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from neo4j_graphrag.embeddings.bedrock import BedrockEmbeddings
+from neo4j_graphrag.exceptions import EmbeddingsGenerationError
+
+
+@pytest.fixture
+def mock_boto3() -> Generator[MagicMock, None, None]:
+    with patch("neo4j_graphrag.embeddings.bedrock.boto3") as mock_boto:
+        mock_client = MagicMock()
+        mock_boto.client.return_value = mock_client
+        yield mock_boto
+
+
+def _make_invoke_response(embedding: list[float]) -> dict[str, Any]:
+    body_bytes = json.dumps({"embedding": embedding}).encode()
+    return {"body": io.BytesIO(body_bytes)}
+
+
+@patch("neo4j_graphrag.embeddings.bedrock.boto3", None)
+def test_bedrock_embedder_missing_dependency() -> None:
+    with pytest.raises(ImportError) as exc:
+        BedrockEmbeddings()
+    assert "Could not import boto3 python client" in str(exc.value)
+
+
+def test_bedrock_embedder_default_model_from_env(mock_boto3: MagicMock) -> None:
+    with patch.dict(
+        "os.environ",
+        {"BEDROCK_EMBED_MODEL_ID": "custom-model", "BEDROCK_EMBED_DIMENSIONS": "256"},
+    ):
+        import importlib
+        import sys
+
+        # Ensure reload picks up the mock instead of real boto3
+        original_boto3 = sys.modules.get("boto3")
+        sys.modules["boto3"] = mock_boto3
+
+        try:
+            import neo4j_graphrag.embeddings.bedrock as bedrock_mod
+
+            importlib.reload(bedrock_mod)
+
+            assert bedrock_mod.DEFAULT_MODEL_ID == "custom-model"
+            assert bedrock_mod.DEFAULT_DIMENSIONS == 256
+
+            embedder = bedrock_mod.BedrockEmbeddings()
+            assert embedder.model_id == "custom-model"
+            assert embedder.dimensions == 256
+        finally:
+            # Restore real boto3 and reload to reset defaults
+            if original_boto3 is not None:
+                sys.modules["boto3"] = original_boto3
+            importlib.reload(bedrock_mod)
+
+
+def test_bedrock_embed_query_happy_path(mock_boto3: MagicMock) -> None:
+    mock_client = mock_boto3.client.return_value
+    mock_client.invoke_model.return_value = _make_invoke_response([0.1, 0.2, 0.3])
+
+    embedder = BedrockEmbeddings()
+    res = embedder.embed_query("hello")
+
+    assert res == [0.1, 0.2, 0.3]
+    mock_client.invoke_model.assert_called_once()
+    call_kwargs = mock_client.invoke_model.call_args[1]
+    body = json.loads(call_kwargs["body"])
+    assert body["inputText"] == "hello"
+    assert body["dimensions"] == 1024
+    assert body["normalize"] is True
+
+
+@pytest.mark.asyncio
+async def test_bedrock_async_embed_query_happy_path(mock_boto3: MagicMock) -> None:
+    mock_client = mock_boto3.client.return_value
+    mock_client.invoke_model.return_value = _make_invoke_response([0.4, 0.5, 0.6])
+
+    embedder = BedrockEmbeddings()
+    res = await embedder.async_embed_query("hello")
+
+    assert res == [0.4, 0.5, 0.6]
+    mock_client.invoke_model.assert_called_once()
+
+
+def test_bedrock_embed_query_error(mock_boto3: MagicMock) -> None:
+    mock_client = mock_boto3.client.return_value
+    mock_client.invoke_model.side_effect = Exception("API error")
+
+    embedder = BedrockEmbeddings()
+    with pytest.raises(
+        EmbeddingsGenerationError, match="Failed to generate embedding with Bedrock"
+    ):
+        embedder.embed_query("hello")
+
+    assert mock_client.invoke_model.call_count == 1
+
+
+def test_bedrock_embed_query_custom_params(mock_boto3: MagicMock) -> None:
+    mock_client = mock_boto3.client.return_value
+    mock_client.invoke_model.return_value = _make_invoke_response([1.0, 2.0])
+
+    embedder = BedrockEmbeddings(
+        model_id="amazon.titan-embed-text-v1",
+        dimensions=512,
+        normalize=False,
+        region_name="eu-west-1",
+    )
+    res = embedder.embed_query("test")
+
+    assert res == [1.0, 2.0]
+    call_kwargs = mock_client.invoke_model.call_args[1]
+    assert call_kwargs["modelId"] == "amazon.titan-embed-text-v1"
+    body = json.loads(call_kwargs["body"])
+    assert body["dimensions"] == 512
+    assert body["normalize"] is False
+
+
+def test_bedrock_embed_query_empty_response(mock_boto3: MagicMock) -> None:
+    mock_client = mock_boto3.client.return_value
+    body_bytes = json.dumps({"embedding": None}).encode()
+    mock_client.invoke_model.return_value = {"body": io.BytesIO(body_bytes)}
+
+    embedder = BedrockEmbeddings()
+    with pytest.raises(
+        EmbeddingsGenerationError, match="Failed to generate embedding with Bedrock"
+    ):
+        embedder.embed_query("hello")