Move test to e2e tests

Author: stellasia

tests/e2e/data/config_files/simple_kg_pipeline_config.json

@@ -1,6 +1,12 @@
 {
     "version_": "1",
     "template_": "SimpleKGPipeline",
+    "extras": {
+        "openai_api_key": {
+            "resolver_": "ENV",
+            "key_": "MY_OPENAI_KEY"
+        }
+    },
     "neo4j_config": {
         "params_": {
             "uri": {
@@ -36,8 +42,8 @@
         "class_": "OpenAIEmbeddings",
         "params_": {
             "api_key": {
-                "resolver_": "ENV",
-                "var_": "OPENAI_API_KEY"
+                "resolver_": "CONFIG_KEY",
+                "key_": "extras.openai_api_key"
             }
         }
     },

tests/e2e/experimental/pipeline/config/test_pipeline_runner_e2e.py

@@ -129,10 +129,18 @@ async def test_simple_kg_pipeline_from_json_config(
     os.environ["NEO4J_USER"] = "neo4j"
     os.environ["NEO4J_PASSWORD"] = "password"
     os.environ["OPENAI_API_KEY"] = "sk-my-secret-key"
+    os.environ["MY_OPENAI_KEY"] = "my-openai-key"
 
     runner = PipelineRunner.from_config_file(
         "tests/e2e/data/config_files/simple_kg_pipeline_config.json"
     )
+    config = runner.config
+    assert config._global_data["extras"] == {"openai_api_key": "my-openai-key"}
+    default_llm = config._global_data["llm_config"]["default"]
+    assert default_llm.client.api_key == "my-openai-key"  # read from extras
+    default_embedder = config._global_data["embedder_config"]["default"]
+    assert default_embedder.client.api_key == "sk-my-secret-key"  # read from env vaf
+
     res = await runner.run({"file_path": "tests/e2e/data/documents/harry_potter.pdf"})
     assert isinstance(res, PipelineResult)
     # print(await runner.pipeline.store.get_result_for_component(res.run_id, "splitter"))

tests/unit/experimental/pipeline/config/test_pipeline_config.py

@@ -12,9 +12,7 @@
 #  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 json
-import tempfile
-from unittest.mock import MagicMock, Mock, patch
+from unittest.mock import Mock, patch
 
 import neo4j
 from neo4j_graphrag.embeddings import Embedder
@@ -32,7 +30,6 @@
 from neo4j_graphrag.experimental.pipeline.config.pipeline_config import (
     AbstractPipelineConfig,
 )
-from neo4j_graphrag.experimental.pipeline.config.runner import PipelineRunner
 from neo4j_graphrag.experimental.pipeline.types import ComponentDefinition
 from neo4j_graphrag.llm import LLMInterface
 
@@ -379,69 +376,3 @@ def test_abstract_pipeline_config_resolve_component_definition_with_run_params(
     assert component_definition.component == component
     assert component_definition.run_params == {"param": "resolver param result"}
     mock_resolve_params.assert_called_once_with({"param1": "value1"})
-
-
-@patch(
-    "neo4j_graphrag.experimental.pipeline.config.object_config.Neo4jDriverConfig.parse"
-)
-@patch("os.environ.get")
-def test_simple_kg_pipeline_config_from_file(
-    mock_getenv: Mock,
-    mock_neo4j_config: Mock,
-) -> None:
-    mock_getenv.return_value = "some_value"
-    neo4j_driver_mock = MagicMock()
-    neo4j_driver_mock.return_value.execute_query.return_value = ("5.23", None, None)
-    mock_neo4j_config.return_value.execute_query.return_value = (
-        [{"versions": ["5.23"]}],
-        None,
-        None,
-    )
-    json_config_dict = {
-        "version_": "1",
-        "template_": "SimpleKGPipeline",
-        "extras": {
-            "openai_api_key": {"resolver_": "ENV", "var_": "MY_ENV_VAR"},
-        },
-        "neo4j_config": {
-            "params_": {
-                "uri": "bolt://myhost",
-                "user": "neo4j",
-                "password": "password",
-            }
-        },
-        "llm_config": {
-            "class_": "OpenAILLM",
-            "params_": {
-                "model_name": "gpt-4o",
-                "api_key": {
-                    "resolver_": "CONFIG_KEY",
-                    "key_": "extras.openai_api_key",
-                },
-            },
-        },
-        "embedder_config": {
-            "class_": "OpenAIEmbeddings",
-            "params_": {
-                "api_key": {
-                    "resolver_": "CONFIG_KEY",
-                    "key_": "extras.openai_api_key",
-                }
-            },
-        },
-    }
-    json_string = json.dumps(json_config_dict)
-    with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as fp:
-        fp.write(json_string)
-        fp.close()
-        # the file is closed, but not removed
-        # open the file again by using its name
-        runner = PipelineRunner.from_config_file(fp.name)
-        config = runner.config
-
-    assert config is not None
-    # check 'extras' have been resolved
-    assert config._global_data["extras"] == {"openai_api_key": "some_value"}
-    # check api key is propagated to the LLM
-    default_llm = config._global_data["llm_config"]["default"]
-    assert default_llm.client.api_key == "some_value"