test: add Anthropic LLM plugin integration test (#2424)

* test: add Anthropic LLM integration test

* test: enrich test

* chore: bump version to 0.2.5

Author: Stream29

models/anthropic/manifest.yaml

@@ -25,4 +25,4 @@ resource:
     model:
       enabled: false
 type: plugin
-version: 0.2.4
+version: 0.2.5

models/anthropic/pyproject.toml

@@ -14,4 +14,4 @@ dependencies = [
 
 # uv run black . -C -l 100 && uv run ruff check --fix
 [dependency-groups]
-dev = []
+dev = ["pytest>=8.0.0"]

models/anthropic/tests/test_llm_call.py

@@ -0,0 +1,71 @@
+import os
+from pathlib import Path
+
+import pytest
+
+from dify_plugin.config.integration_config import IntegrationConfig
+from dify_plugin.core.entities.plugin.request import (
+    ModelActions,
+    ModelInvokeLLMRequest,
+    PluginInvokeType,
+)
+from dify_plugin.entities.model import ModelType
+from dify_plugin.entities.model.llm import LLMResultChunk
+from dify_plugin.integration.run import PluginRunner
+
+
+def get_all_models() -> list[str]:
+    """Discover all model names from models/llm/*.yaml files."""
+    models_dir = Path(__file__).parent.parent / "models" / "llm"
+    models: list[str] = []
+    for yaml_file in models_dir.glob("*.yaml"):
+        if yaml_file.name.startswith("_"):
+            continue
+        model_name = yaml_file.stem
+        models.append(model_name)
+    return models
+
+
+@pytest.mark.parametrize("model_name", get_all_models())
+def test_llm_invoke(model_name: str) -> None:
+    api_key = os.getenv("ANTHROPIC_API_KEY")
+    if not api_key:
+        raise ValueError("ANTHROPIC_API_KEY environment variable is required")
+
+    plugin_path = os.getenv("PLUGIN_FILE_PATH")
+    if not plugin_path:
+        raise ValueError("PLUGIN_FILE_PATH environment variable is required")
+
+    payload = ModelInvokeLLMRequest(
+        user_id="test_user",
+        provider="anthropic",
+        model_type=ModelType.LLM,
+        model=model_name,
+        credentials={"anthropic_api_key": api_key},
+        prompt_messages=[{"role": "user", "content": "Say hello in one word."}],
+        model_parameters={"max_tokens": 100},
+        stop=None,
+        tools=None,
+        stream=True,
+    )
+
+    with PluginRunner(
+        config=IntegrationConfig(), plugin_package_path=plugin_path
+    ) as runner:
+        results: list[LLMResultChunk] = []
+        for result in runner.invoke(
+            access_type=PluginInvokeType.Model,
+            access_action=ModelActions.InvokeLLM,
+            payload=payload,
+            response_type=LLMResultChunk,
+        ):
+            results.append(result)
+
+        # Verify we received multiple chunks
+        assert len(results) > 0, f"No results received for model {model_name}"
+
+        # Verify concatenated content is non-empty
+        full_content = "".join(
+            r.delta.message.content for r in results if r.delta.message.content
+        )
+        assert len(full_content) > 0, f"Empty content for model {model_name}"