reuse pydantic example for local model picking

eval_protocol/pytest/default_pydantic_ai_rollout_processor.py

@@ -46,12 +46,20 @@ def __call__(self, rows: list[EvaluationRow], config: RolloutProcessorConfig) ->
         """Create agent rollout tasks and return them for external handling."""
 
         semaphore = config.semaphore
-        agent = self._setup_agent(config)
+        # NOTE: Do not create the agent outside of the semaphore or multiple rows
+        # will initialize clients and start network calls concurrently. This can
+        # overwhelm local providers like Ollama where only one request should be
+        # active at a time. Instead, construct the agent within the semaphore-guarded
+        # section per row.
 
         async def process_row(row: EvaluationRow) -> EvaluationRow:
             """Process a single row with agent rollout."""
             start_time = time.perf_counter()
 
+            # Build the agent lazily inside the semaphore guard to ensure we fully
+            # respect max_concurrent_rollouts across both setup and run phases.
+            agent = self._setup_agent(config)
+
             tools = []
             for toolset in agent.toolsets:
                 if isinstance(toolset, FunctionToolset):

tests/chinook/pydantic/agent.py

@@ -61,6 +61,7 @@ def execute_sql(ctx: RunContext, query: str) -> str:
 
             return "\n".join(table_lines)
         except Exception as e:
+            print("Show exception: ", e)
             connection.rollback()
             raise ModelRetry("Please try again with a different query. Here is the error: " + str(e))
 

tests/chinook/pydantic/test_pydantic_complex_queries.py

@@ -2,6 +2,7 @@
 from pydantic import BaseModel
 from pydantic_ai import Agent
 from pydantic_ai.models.openai import OpenAIChatModel, OpenAIChatModelSettings
+from pydantic_ai.providers.openai import OpenAIProvider
 import pytest
 
 from eval_protocol.models import EvaluateResult, EvaluationRow
@@ -24,10 +25,23 @@
 
 def agent_factory(config: RolloutProcessorConfig) -> Agent:
     model_name = config.completion_params["model"]
-    provider = config.completion_params.get("provider")
+    provider_param = config.completion_params.get("provider")
     reasoning = config.completion_params.get("reasoning")
-    settings = OpenAIChatModelSettings(openai_reasoning_effort=reasoning)
-    model = OpenAIChatModel(model_name, provider=provider or "openai", settings=settings)
+    # gpt-4o-mini does not support reasoning
+    if model_name == "gpt-4o-mini":
+        settings = OpenAIChatModelSettings()
+    else:
+        settings = OpenAIChatModelSettings(openai_reasoning_effort=reasoning)
+    base_url = config.completion_params.get("base_url")
+    api_key = config.completion_params.get("api_key") or os.getenv("OPENAI_API_KEY") or "dummy"
+    if base_url or provider_param == "ollama":
+        provider = OpenAIProvider(
+            api_key=api_key,
+            base_url=base_url or os.getenv("OLLAMA_OPENAI_BASE_URL", "http://localhost:11434/v1"),
+        )
+    else:
+        provider = provider_param or "openai"
+    model = OpenAIChatModel(model_name, provider=provider, settings=settings)
     return setup_agent(model)
 
 
@@ -51,7 +65,11 @@ def agent_factory(config: RolloutProcessorConfig) -> Agent:
         #     "model": "accounts/fireworks/models/kimi-k2-instruct-0905",
         #     "provider": "fireworks",
         # },
-        {"model": "gpt-5"},
+        # {"model": "gpt-4o-mini"},
+        {"model": "gpt-5-nano-2025-08-07"},
+        # {"model": "qwen3:4b", "provider": "ollama", "base_url": os.getenv("OLLAMA_OPENAI_BASE_URL", "http://localhost:11434/v1")},
+        # {"model": "qwen3:8b", "provider": "ollama", "base_url": os.getenv("OLLAMA_OPENAI_BASE_URL", "http://localhost:11434/v1")},
+        # {"model": "granite4:micro", "provider": "ollama", "base_url": os.getenv("OLLAMA_OPENAI_BASE_URL", "http://localhost:11434/v1")},
         # {"model": "gpt-5", "reasoning": "high"},
     ],
     rollout_processor=PydanticAgentRolloutProcessor(agent_factory),