"extras" must be resolved (#248)

* "extras" must be resolved

* Add a test case

* Ruff

* Move test to e2e tests

* mypy + comments

* Fix test

Author: stellasia

src/neo4j_graphrag/experimental/pipeline/config/pipeline_config.py

@@ -27,6 +27,9 @@
     LLMType,
     Neo4jDriverType,
 )
+from neo4j_graphrag.experimental.pipeline.config.param_resolver import (
+    ParamConfig,
+)
 from neo4j_graphrag.experimental.pipeline.config.types import PipelineType
 from neo4j_graphrag.experimental.pipeline.types import (
     ComponentDefinition,
@@ -48,7 +51,7 @@ class AbstractPipelineConfig(AbstractConfig):
     llm_config: dict[str, LLMType] = {}
     embedder_config: dict[str, EmbedderType] = {}
     # extra parameters values that can be used in different places of the config file
-    extras: dict[str, Any] = {}
+    extras: dict[str, ParamConfig] = {}
 
     DEFAULT_NAME: ClassVar[str] = "default"
     """Name of the default item in dict

tests/e2e/data/config_files/simple_kg_pipeline_config.json

@@ -1,6 +1,12 @@
 {
     "version_": "1",
     "template_": "SimpleKGPipeline",
+    "extras": {
+        "openai_api_key": {
+            "resolver_": "ENV",
+            "var_": "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

@@ -129,19 +129,33 @@ 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"
     )
+
+    # check extras and API keys are handled as expected
+    config = runner.config
+    assert config is not None
+    # extras must be resolved:
+    assert config._global_data["extras"] == {"openai_api_key": "my-openai-key"}
+    # API key for LLM is read from env vars (see config file)
+    default_llm = config._global_data["llm_config"]["default"]
+    assert default_llm.client.api_key == "sk-my-secret-key"
+    # API key for embedder is read from extras (see config file)
+    default_embedder = config._global_data["embedder_config"]["default"]
+    assert default_embedder.client.api_key == "my-openai-key"
+
+    # then run pipeline and check results
     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"))
     assert res.result["resolver"] == {
         "number_of_nodes_to_resolve": 3,
         "number_of_created_nodes": 3,
     }
     nodes = driver.execute_query("MATCH (n) RETURN n")
-    # 1 chunk + 1 document + 3 nodes
+    # 1 chunk + 1 document + 3 __Entity__ nodes
     assert len(nodes.records) == 5