correlate evaluation logs with traces (#282)

* correlate evaluation logs with traces

* Fix rollout logging filter fallbacks (#283)

Author: benjibc

eval_protocol/log_utils/elasticsearch_direct_http_handler.py

@@ -60,6 +60,17 @@ def emit(self, record: logging.LogRecord) -> None:
             if status_info:
                 data.update(status_info)
 
+            # Optional correlation enrichment
+            experiment_id = getattr(record, "experiment_id", None)
+            if experiment_id is not None:
+                data["experiment_id"] = experiment_id
+            run_id = getattr(record, "run_id", None)
+            if run_id is not None:
+                data["run_id"] = run_id
+            rollout_ids = getattr(record, "rollout_ids", None)
+            if rollout_ids is not None:
+                data["rollout_ids"] = rollout_ids
+
             # Schedule the HTTP request to run asynchronously
             self._schedule_async_send(data, record)
         except Exception as e:

eval_protocol/log_utils/fireworks_tracing_http_handler.py

@@ -46,15 +46,36 @@ def _build_payload(self, record: logging.LogRecord, rollout_id: str) -> Dict[str
             tags.append(f"experiment_id:{cast(Any, getattr(record, 'experiment_id'))}")
         if hasattr(record, "run_id") and cast(Any, getattr(record, "run_id")):
             tags.append(f"run_id:{cast(Any, getattr(record, 'run_id'))}")
+        # Groupwise list of rollout_ids
+        if hasattr(record, "rollout_ids") and cast(Any, getattr(record, "rollout_ids")):
+            try:
+                for rid in cast(List[str], getattr(record, "rollout_ids")):
+                    tags.append(f"rollout_id:{rid}")
+            except Exception:
+                pass
         program = cast(Optional[str], getattr(record, "program", None)) or "eval_protocol"
         status_val = cast(Any, getattr(record, "status", None))
         status = status_val if isinstance(status_val, str) else None
+        # Capture optional structured status fields if present
+        metadata: Dict[str, Any] = {}
+        status_code = cast(Any, getattr(record, "status_code", None))
+        if isinstance(status_code, int):
+            metadata["status_code"] = status_code
+        status_message = cast(Any, getattr(record, "status_message", None))
+        if isinstance(status_message, str):
+            metadata["status_message"] = status_message
+        status_details = getattr(record, "status_details", None)
+        if status_details is not None:
+            metadata["status_details"] = status_details
+        extra_metadata = cast(Any, getattr(record, "metadata", None))
+        if isinstance(extra_metadata, dict):
+            metadata.update(extra_metadata)
         return {
             "program": program,
             "status": status,
             "message": message,
             "tags": tags,
-            "metadata": cast(Any, getattr(record, "metadata", None)),
+            "metadata": metadata or None,
             "extras": {
                 "logger_name": record.name,
                 "level": record.levelname,

eval_protocol/log_utils/init.py

@@ -0,0 +1,61 @@
+import logging
+import os
+from typing import Optional
+
+from eval_protocol.log_utils.fireworks_tracing_http_handler import (
+    FireworksTracingHttpHandler,
+)
+from eval_protocol.log_utils.elasticsearch_direct_http_handler import (
+    ElasticsearchDirectHttpHandler,
+)
+from eval_protocol.log_utils.rollout_context import ContextRolloutIdFilter
+from eval_protocol.types.remote_rollout_processor import ElasticsearchConfig
+
+
+_INITIALIZED = False
+
+
+def _get_env(name: str) -> Optional[str]:
+    val = os.getenv(name)
+    return val if val and val.strip() else None
+
+
+def init_external_logging_from_env() -> None:
+    """
+    Initialize external logging sinks (Fireworks tracing, optional Elasticsearch) from env vars.
+
+    Idempotent: safe to call multiple times.
+
+    Environment variables:
+      - FW_TRACING_GATEWAY_BASE_URL: enable Fireworks tracing handler when set
+      - EP_ELASTICSEARCH_URL, EP_ELASTICSEARCH_API_KEY, EP_ELASTICSEARCH_INDEX: enable ES when all set
+    """
+    global _INITIALIZED
+    if _INITIALIZED:
+        return
+
+    root_logger = logging.getLogger()
+
+    # Ensure we do not add duplicate handlers if already present
+    existing_handler_types = {type(h).__name__ for h in root_logger.handlers}
+
+    # Fireworks tracing
+    fw_url = _get_env("FW_TRACING_GATEWAY_BASE_URL")
+    if fw_url and "FireworksTracingHttpHandler" not in existing_handler_types:
+        fw_handler = FireworksTracingHttpHandler(gateway_base_url=fw_url)
+        fw_handler.setLevel(logging.INFO)
+        fw_handler.addFilter(ContextRolloutIdFilter())
+        root_logger.addHandler(fw_handler)
+
+    # Elasticsearch
+    es_url = _get_env("EP_ELASTICSEARCH_URL")
+    es_api_key = _get_env("EP_ELASTICSEARCH_API_KEY")
+    es_index = _get_env("EP_ELASTICSEARCH_INDEX")
+    if es_url and es_api_key and es_index and "ElasticsearchDirectHttpHandler" not in existing_handler_types:
+        es_config = ElasticsearchConfig(url=es_url, api_key=es_api_key, index_name=es_index)
+        es_handler = ElasticsearchDirectHttpHandler(elasticsearch_config=es_config)
+        es_handler.setLevel(logging.INFO)
+        es_handler.addFilter(ContextRolloutIdFilter())
+        root_logger.addHandler(es_handler)
+
+    _INITIALIZED = True

eval_protocol/log_utils/rollout_context.py

@@ -0,0 +1,84 @@
+import logging
+import os
+from contextlib import asynccontextmanager
+from typing import List, Optional
+
+import contextvars
+
+
+# Context variables used to correlate logs with rollouts under concurrency
+current_rollout_id: contextvars.ContextVar[Optional[str]] = contextvars.ContextVar("ep_rollout_id", default=None)
+current_rollout_ids: contextvars.ContextVar[Optional[List[str]]] = contextvars.ContextVar(
+    "ep_rollout_ids", default=None
+)
+current_experiment_id: contextvars.ContextVar[Optional[str]] = contextvars.ContextVar("ep_experiment_id", default=None)
+current_run_id: contextvars.ContextVar[Optional[str]] = contextvars.ContextVar("ep_run_id", default=None)
+
+
+class ContextRolloutIdFilter(logging.Filter):
+    """
+    Logging filter that injects correlation fields into a LogRecord from ContextVars.
+
+    The filter is intended to be attached ONLY to external sink handlers (e.g.,
+    Fireworks or Elasticsearch). If there is no active rollout context, it drops
+    the record for that handler to avoid shipping uncorrelated logs.
+    """
+
+    def filter(self, record: logging.LogRecord) -> bool:  # type: ignore[override]
+        rollout_id = current_rollout_id.get()
+        if not rollout_id:
+            # Allow explicit rollout IDs on the record or via environment fallback.
+            rollout_id = getattr(record, "rollout_id", None) or os.getenv("EP_ROLLOUT_ID")
+        if not rollout_id:
+            # No correlation context → do not emit to external sink
+            return False
+
+        # Inject primary correlation fields
+        setattr(record, "rollout_id", rollout_id)
+
+        rollout_ids = current_rollout_ids.get()
+        if rollout_ids:
+            setattr(record, "rollout_ids", rollout_ids)
+
+        experiment_id = current_experiment_id.get()
+        if experiment_id:
+            setattr(record, "experiment_id", experiment_id)
+
+        run_id = current_run_id.get()
+        if run_id:
+            setattr(record, "run_id", run_id)
+
+        return True
+
+
+@asynccontextmanager
+async def rollout_logging_context(
+    rollout_id: str,
+    *,
+    experiment_id: Optional[str] = None,
+    run_id: Optional[str] = None,
+    rollout_ids: Optional[List[str]] = None,
+):
+    """
+    Async context manager to set correlation ContextVars for the current task.
+
+    Args:
+        rollout_id: Primary rollout identifier for correlation.
+        experiment_id: Optional experiment ID for tagging.
+        run_id: Optional run ID for tagging.
+        rollout_ids: Optional list of related rollout IDs (e.g., groupwise mode).
+    """
+    t_rollout = current_rollout_id.set(rollout_id)
+    t_rollouts = current_rollout_ids.set(rollout_ids) if rollout_ids is not None else None
+    t_experiment = current_experiment_id.set(experiment_id) if experiment_id is not None else None
+    t_run = current_run_id.set(run_id) if run_id is not None else None
+    try:
+        yield
+    finally:
+        current_rollout_id.reset(t_rollout)
+        if t_rollouts is not None:
+            current_rollout_ids.reset(t_rollouts)
+        if t_experiment is not None:
+            current_experiment_id.reset(t_experiment)
+        if t_run is not None:
+            current_run_id.reset(t_run)

eval_protocol/pytest/evaluation_test.py

@@ -63,6 +63,8 @@
     run_tasks_with_run_progress,
 )
 from eval_protocol.utils.show_results_url import store_local_ui_results_url, generate_invocation_filter_url
+from eval_protocol.log_utils.init import init_external_logging_from_env
+from eval_protocol.log_utils.rollout_context import rollout_logging_context
 from eval_protocol.utils.browser_utils import is_logs_server_running, open_browser_tab
 
 from ..common_utils import load_jsonl
@@ -254,6 +256,9 @@ def create_wrapper_with_signature() -> Callable[[], None]:
             async def wrapper_body(**kwargs: Unpack[ParameterizedTestKwargs]) -> None:
                 nonlocal browser_opened_for_invocation
 
+                # Initialize external logging sinks (Fireworks/ES) from env (idempotent)
+                init_external_logging_from_env()
+
                 # Store URL for viewing results (after all postprocessing is complete)
                 store_local_ui_results_url(invocation_id)
 
@@ -419,11 +424,16 @@ async def _execute_pointwise_eval_with_semaphore(
                         ) -> EvaluationRow:
                             async with semaphore:
                                 evaluation_test_kwargs = kwargs.get("evaluation_test_kwargs") or {}
-                                result = await execute_pytest(
-                                    test_func,
-                                    processed_row=row,
-                                    evaluation_test_kwargs=evaluation_test_kwargs,
-                                )
+                                async with rollout_logging_context(
+                                    row.execution_metadata.rollout_id or "",
+                                    experiment_id=experiment_id,
+                                    run_id=run_id,
+                                ):
+                                    result = await execute_pytest(
+                                        test_func,
+                                        processed_row=row,
+                                        evaluation_test_kwargs=evaluation_test_kwargs,
+                                    )
                                 if not isinstance(result, EvaluationRow):
                                     raise ValueError(
                                         f"Test function {test_func.__name__} did not return an EvaluationRow instance. You must return an EvaluationRow instance from your test function decorated with @evaluation_test."
@@ -435,11 +445,21 @@ async def _execute_groupwise_eval_with_semaphore(
                         ) -> list[EvaluationRow]:
                             async with semaphore:
                                 evaluation_test_kwargs = kwargs.get("evaluation_test_kwargs") or {}
-                                results = await execute_pytest(
-                                    test_func,
-                                    processed_dataset=rows,
-                                    evaluation_test_kwargs=evaluation_test_kwargs,
-                                )
+                                primary_rollout_id = rows[0].execution_metadata.rollout_id if rows else None
+                                group_rollout_ids = [
+                                    r.execution_metadata.rollout_id for r in rows if r.execution_metadata.rollout_id
+                                ]
+                                async with rollout_logging_context(
+                                    primary_rollout_id or "",
+                                    experiment_id=experiment_id,
+                                    run_id=run_id,
+                                    rollout_ids=group_rollout_ids or None,
+                                ):
+                                    results = await execute_pytest(
+                                        test_func,
+                                        processed_dataset=rows,
+                                        evaluation_test_kwargs=evaluation_test_kwargs,
+                                    )
                                 if not isinstance(results, list):
                                     raise ValueError(
                                         f"Test function {test_func.__name__} did not return a list of EvaluationRow instances. You must return a list of EvaluationRow instances from your test function decorated with @evaluation_test."
@@ -516,11 +536,25 @@ async def _collect_result(config, lst):
                                 input_dataset.append(row)
                             # NOTE: we will still evaluate errored rows (give users control over this)
                             # i.e., they can choose to give EvaluateResult.score = 0 for errored rows in their test_func
-                            results = await execute_pytest(
-                                test_func,
-                                processed_dataset=input_dataset,
-                                evaluation_test_kwargs=kwargs.get("evaluation_test_kwargs") or {},
+                            primary_rollout_id = (
+                                input_dataset[0].execution_metadata.rollout_id if input_dataset else None
                             )
+                            group_rollout_ids = [
+                                r.execution_metadata.rollout_id
+                                for r in input_dataset
+                                if r.execution_metadata.rollout_id
+                            ]
+                            async with rollout_logging_context(
+                                primary_rollout_id or "",
+                                experiment_id=experiment_id,
+                                run_id=run_id,
+                                rollout_ids=group_rollout_ids or None,
+                            ):
+                                results = await execute_pytest(
+                                    test_func,
+                                    processed_dataset=input_dataset,
+                                    evaluation_test_kwargs=kwargs.get("evaluation_test_kwargs") or {},
+                                )
                             if (
                                 results is None
                                 or not isinstance(results, list)

tests/chinook/pydantic/test_pydantic_complex_queries_responses.py

@@ -1,4 +1,5 @@
 from collections.abc import Awaitable, Callable
+import logging
 import os
 from typing_extensions import cast
 from pydantic_ai import Agent
@@ -36,7 +37,7 @@ def agent_factory(config: RolloutProcessorConfig) -> Agent:
     input_rows=[collect_dataset()],
     completion_params=[
         {
-            "model": "gpt-5",
+            "model": "gpt-5-nano",
         },
     ],
     rollout_processor=PydanticAgentRolloutProcessor(agent_factory),
@@ -45,6 +46,19 @@ async def test_pydantic_complex_queries_responses(row: EvaluationRow) -> Evaluat
     """
     Evaluation of complex queries for the Chinook database using PydanticAI
     """
+
+    logger = logging.getLogger("tests.chinook.pydantic.complex_queries_responses")
+    logger.info(
+        "Starting chinook responses evaluation",
+        extra={"status": {"code": 101, "message": "RUNNING"}},
+    )
+
     casted_evaluation_test = cast(Callable[[EvaluationRow], Awaitable[EvaluationRow]], eval)
     evaluated_row = await casted_evaluation_test(row)
+
+    logger.info(
+        "Finished chinook responses evaluation",
+        extra={"status": {"code": 100, "message": "FINISHED"}},
+    )
+
     return evaluated_row