diff --git a/TELEMETRY-CONTRACT.md b/TELEMETRY-CONTRACT.md index 212610b8..0fba00de 100644 --- a/TELEMETRY-CONTRACT.md +++ b/TELEMETRY-CONTRACT.md @@ -242,6 +242,26 @@ model-generated prose about the user's conversation, so it follows the judge han handler or the process default withholds it. The reasoning is always returned to the caller in `judgeResults` / `judge_results`; only the telemetry copy is gated. +### Judge spans point at the run they graded + +A judge runs after the graded handler has ended its span, so its spans start a trace of their own, +with their own `launchdarkly.run.id`. To tie them back, `run_judges` (inline) and `run_judge` +(deferred) wrap each judge's run in `stamp_span_attributes`, so every SDK span of that run carries +the graded run's identity, write-if-absent: + +| Span attribute | Value | +|---|---| +| `launchdarkly.judge.target.run.id` | graded run's `TrackData.runId` | +| `launchdarkly.judge.target.config.key` | graded run's `TrackData.configKey` | +| `launchdarkly.judge.target.variation.key` | graded run's `TrackData.variationKey` | + +The constants are `LD_JUDGE_TARGET_*` in `launchdarkly_ai_server/judges.py`. Inline judges read +`base_track_data`; deferred judges read `JudgeTask.parent_track_data`. Each key is written only when +its value is a non-empty string. They are identifiers, not content, so capture settings do not gate +them. The judge's metric event already carries the graded `runId` in its `TrackData`; these +attributes put the same link on the trace. Offline evaluations stamp `launchdarkly.evaluation_run.id` +on every span of a row the same way. + --- ## 5. Finish reasons diff --git a/packages/client/src/launchdarkly_ai_server/judges.py b/packages/client/src/launchdarkly_ai_server/judges.py index cf9ec3b8..36bf39a9 100644 --- a/packages/client/src/launchdarkly_ai_server/judges.py +++ b/packages/client/src/launchdarkly_ai_server/judges.py @@ -2,10 +2,11 @@ import logging import random -from collections.abc import Callable, Mapping +from collections.abc import Callable, Iterator, Mapping +from contextlib import contextmanager from typing import Any -from .conversation import with_judge_evaluation +from .conversation import stamp_span_attributes, with_judge_evaluation from .judge_scoring import ( build_message_history, numeric_score, @@ -82,6 +83,35 @@ def _handler_for( logger = logging.getLogger(__name__) +#: The run id of the config a judge graded, on every span of that judge's run. +LD_JUDGE_TARGET_RUN_ID = "launchdarkly.judge.target.run.id" +#: The key of the config a judge graded. +LD_JUDGE_TARGET_CONFIG_KEY = "launchdarkly.judge.target.config.key" +#: The variation key of the config a judge graded. +LD_JUDGE_TARGET_VARIATION_KEY = "launchdarkly.judge.target.variation.key" + + +@contextmanager +def _judge_target(target: TrackData | None) -> Iterator[None]: + """Point a judge's spans at the run it graded. + + A judge runs after the graded handler has ended its span, so its spans start + a trace of their own. Without these, the only tie back to the graded run is + the judge's metric event. Each key is written only when its value is a + non-empty string. + """ + attributes: dict[str, str | int] = {} + for attribute, field in ( + (LD_JUDGE_TARGET_RUN_ID, "runId"), + (LD_JUDGE_TARGET_CONFIG_KEY, "configKey"), + (LD_JUDGE_TARGET_VARIATION_KEY, "variationKey"), + ): + value = (target or {}).get(field) + if isinstance(value, str) and value: + attributes[attribute] = value + with stamp_span_attributes(attributes): + yield + async def run_judges( *, @@ -187,78 +217,79 @@ async def run_judges( output=llm_response, ) - async with with_judge_evaluation(judge_key) as record_evaluation: - result = await execute_and_track( - config_key=judge_key, - config=effective_judge_config, - meta=judge_meta, - user_context=user_context, - handler=judge_handler, - user_input=llm_response, - tool_handlers=None, - graph_key=graph_key, - variables={ - "message_history": message_history, - "response_to_evaluate": llm_response, - **({"input": user_input} if user_input else {}), - }, - evaluated_value=variation.get("value"), - ) + with _judge_target(base_track_data): + async with with_judge_evaluation(judge_key) as record_evaluation: + result = await execute_and_track( + config_key=judge_key, + config=effective_judge_config, + meta=judge_meta, + user_context=user_context, + handler=judge_handler, + user_input=llm_response, + tool_handlers=None, + graph_key=graph_key, + variables={ + "message_history": message_history, + "response_to_evaluate": llm_response, + **({"input": user_input} if user_input else {}), + }, + evaluated_value=variation.get("value"), + ) - usage = to_usage_dict(result["usage"]) - entries = typesafe_judge_entries(result["response"]) - if entries is not None: - # One Jev call. Every label is returned with that call's - # full usage, and each label is tracked under its eventKey. - from .lifecycle import get_client - - client = get_client() - for entry in entries: - result_key = f"{judge_key}.{entry['key']}" - score = entry["score"] - judge_results[result_key] = JudgeResult( - usage=usage, - response=entry["reason"], - score=score, - event_key=entry["eventKey"], + usage = to_usage_dict(result["usage"]) + entries = typesafe_judge_entries(result["response"]) + if entries is not None: + # One Jev call. Every label is returned with that call's + # full usage, and each label is tracked under its eventKey. + from .lifecycle import get_client + + client = get_client() + for entry in entries: + result_key = f"{judge_key}.{entry['key']}" + score = entry["score"] + judge_results[result_key] = JudgeResult( + usage=usage, + response=entry["reason"], + score=score, + event_key=entry["eventKey"], + ) + record_evaluation(score, None, result_key) + client.track( + entry["eventKey"], + to_ld_context(client, user_context), + {**base_track_data, "judgeConfigKey": result_key}, + score, + ) + continue + + score, reasoning = parse_judge_response(result["response"]) + judge_results[judge_key] = JudgeResult( + usage=usage, + response=reasoning, + score=score, + ) + metric_score = numeric_score(score) + if metric_score is not None: + record_evaluation( + metric_score, + reasoning if judge_handler.capture_content else None, ) - record_evaluation(score, None, result_key) + + evaluation_metric_key = ( + judge_ai_config.get("evaluationMetricKey") + if isinstance(judge_ai_config, dict) + else None + ) + if evaluation_metric_key and score is not None: + from .lifecycle import get_client + + client = get_client() client.track( - entry["eventKey"], + evaluation_metric_key, to_ld_context(client, user_context), - {**base_track_data, "judgeConfigKey": result_key}, + {**base_track_data, "judgeConfigKey": judge_key}, score, ) - continue - - score, reasoning = parse_judge_response(result["response"]) - judge_results[judge_key] = JudgeResult( - usage=usage, - response=reasoning, - score=score, - ) - metric_score = numeric_score(score) - if metric_score is not None: - record_evaluation( - metric_score, - reasoning if judge_handler.capture_content else None, - ) - - evaluation_metric_key = ( - judge_ai_config.get("evaluationMetricKey") - if isinstance(judge_ai_config, dict) - else None - ) - if evaluation_metric_key and score is not None: - from .lifecycle import get_client - - client = get_client() - client.track( - evaluation_metric_key, - to_ld_context(client, user_context), - {**base_track_data, "judgeConfigKey": judge_key}, - score, - ) except Exception as exc: logger.error("Judge '%s' failed: %s", judge_key, exc) @@ -446,90 +477,94 @@ async def run_judge( output=task.actual_output, ) - async with with_judge_evaluation(task.config_key) as record_evaluation: - result = await execute_and_track( - config_key=task.config_key, - config=effective_config, - meta=task.judge_meta, - user_context=task.user_context, - handler=judge_handler, - user_input=task.actual_output, - tool_handlers=None, - variables={ - **(task.variables or {}), - "message_history": message_history, - "response_to_evaluate": task.actual_output, - **({"input": task.user_input} if task.user_input else {}), - }, - evaluated_value=task.judge_value, - ) - - usage = to_usage_dict(result["usage"]) - try: - entries = typesafe_judge_entries(result["response"]) - except ValueError: - return None - if entries is not None: - first = entries[0] - result_key = f"{task.config_key}.{first['key']}" - expanded = { - f"{task.config_key}.{entry['key']}": JudgeResult( + with _judge_target(task.parent_track_data): + async with with_judge_evaluation(task.config_key) as record_evaluation: + result = await execute_and_track( + config_key=task.config_key, + config=effective_config, + meta=task.judge_meta, + user_context=task.user_context, + handler=judge_handler, + user_input=task.actual_output, + tool_handlers=None, + variables={ + **(task.variables or {}), + "message_history": message_history, + "response_to_evaluate": task.actual_output, + **({"input": task.user_input} if task.user_input else {}), + }, + evaluated_value=task.judge_value, + ) + + usage = to_usage_dict(result["usage"]) + try: + entries = typesafe_judge_entries(result["response"]) + except ValueError: + return None + if entries is not None: + first = entries[0] + result_key = f"{task.config_key}.{first['key']}" + expanded = { + f"{task.config_key}.{entry['key']}": JudgeResult( + usage=usage, + response=entry["reason"], + score=entry["score"], + event_key=entry["eventKey"], + ) + for entry in entries + } + metrics = [ + { + "eventKey": entry["eventKey"], + "score": entry["score"], + "judgeConfigKey": f"{task.config_key}.{entry['key']}", + } + for entry in entries + ] + for entry in entries: + record_evaluation( + entry["score"], None, f"{task.config_key}.{entry['key']}" + ) + # The score belongs to the evaluated config. Each metric carries its own judgeConfigKey. + typesafe_track_data: TrackData = { + **task.parent_track_data, + "judgeConfigKey": result_key, + } + return JudgeRunResult( + score=first["score"], + response=first["reason"], usage=usage, - response=entry["reason"], - score=entry["score"], - event_key=entry["eventKey"], + track_data=typesafe_track_data, + results=expanded, + metrics=metrics, + event_key=first["eventKey"], ) - for entry in entries - } - metrics = [ - { - "eventKey": entry["eventKey"], - "score": entry["score"], - "judgeConfigKey": f"{task.config_key}.{entry['key']}", - } - for entry in entries - ] - for entry in entries: + + try: + score, reasoning = parse_judge_response(result["response"]) + except ValueError: + return None + + # The score is reported as the judge gave it. A missing or null score + # is not a zero: coercing it would record a gen_ai.evaluation of 0 -- + # indistinguishable from a judge that scored the output a hard fail -- + # where every other non-numeric judge output skips the metric instead. + metric_score = numeric_score(score) + if metric_score is not None: record_evaluation( - entry["score"], None, f"{task.config_key}.{entry['key']}" + metric_score, + reasoning if judge_handler.capture_content else None, ) - # The score belongs to the evaluated config. Each metric carries its own judgeConfigKey. - typesafe_track_data: TrackData = { + # The score belongs to the evaluated (parent) config, exactly as in + # the inline run_judges path. + merged_track_data: TrackData = { **task.parent_track_data, - "judgeConfigKey": result_key, + "judgeConfigKey": task.config_key, } + return JudgeRunResult( - score=first["score"], - response=first["reason"], + score=score, + response=reasoning, usage=usage, - track_data=typesafe_track_data, - results=expanded, - metrics=metrics, - event_key=first["eventKey"], - ) - - try: - score, reasoning = parse_judge_response(result["response"]) - except ValueError: - return None - - # The score is reported as the judge gave it. A missing or null score - # is not a zero: coercing it would record a gen_ai.evaluation of 0 -- - # indistinguishable from a judge that scored the output a hard fail -- - # where every other non-numeric judge output skips the metric instead. - metric_score = numeric_score(score) - if metric_score is not None: - record_evaluation( - metric_score, - reasoning if judge_handler.capture_content else None, + track_data=merged_track_data, ) - # The score belongs to the evaluated (parent) config, exactly as in - # the inline run_judges path. - merged_track_data: TrackData = { - **task.parent_track_data, - "judgeConfigKey": task.config_key, - } - - return JudgeRunResult( - score=score, response=reasoning, usage=usage, track_data=merged_track_data - ) diff --git a/packages/client/tests/test_judge_target_run.py b/packages/client/tests/test_judge_target_run.py new file mode 100644 index 00000000..06d73263 --- /dev/null +++ b/packages/client/tests/test_judge_target_run.py @@ -0,0 +1,382 @@ +"""A judge's spans point at the run it graded. + +A judge runs after the graded handler has ended its span, so its spans start a +trace of their own. Every span of a judge's run carries the run id, config key +and variation key of the run it graded, so the two traces can be joined exactly +instead of by timestamp. +""" + +from __future__ import annotations + +import random +from collections.abc import Iterator +from typing import Any +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest +from opentelemetry import trace +from opentelemetry.sdk.trace import ReadableSpan, TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter + +import launchdarkly_ai_server.lifecycle as lifecycle_module +from launchdarkly_ai_server import ProviderHandler, graph, run_judges +from launchdarkly_ai_server.conversation import ConversationIdSpanProcessor +from launchdarkly_ai_server.judges import ( + LD_JUDGE_TARGET_CONFIG_KEY, + LD_JUDGE_TARGET_RUN_ID, + LD_JUDGE_TARGET_VARIATION_KEY, + run_judge, +) +from launchdarkly_ai_server.types import JudgeTask + +_exporter = InMemorySpanExporter() +_provider = TracerProvider() +_provider.add_span_processor(ConversationIdSpanProcessor()) +_provider.add_span_processor(SimpleSpanProcessor(_exporter)) +_tracer = _provider.get_tracer("@launchdarkly/ai-claude-messages") +_foreign_tracer = _provider.get_tracer("opentelemetry.instrumentation.httpx") + +TARGET_KEYS = ( + LD_JUDGE_TARGET_RUN_ID, + LD_JUDGE_TARGET_CONFIG_KEY, + LD_JUDGE_TARGET_VARIATION_KEY, +) + +CONTEXT = {"kind": "user", "key": "alexis"} + +GRADED_RUN = { + "runId": "graded-run-id", + "configKey": "pr-reviewer", + "variationKey": "opus", + "version": 3, + "modelName": "claude-opus-5-5", + "providerName": "Anthropic", +} + +JUDGE_CONFIG = { + "model": {"name": "claude-sonnet-5"}, + "provider": {"name": "Anthropic"}, + "instructions": "You are a judge.", + "evaluationMetricKey": "$ld:ai:judge:no-nitpicks", +} +JUDGE_META = { + "enabled": True, + "variationKey": "default", + "version": 1, + "mode": "messages", +} + +JUDGE_OUTPUT = { + "output": '{"score": 1, "reasoning": "ok"}', + "usage": {"input_tokens": 1, "output_tokens": 1}, +} + + +def _open_judge_spans(label: str = "judge") -> None: + """Stands in for a judge handler's spans. + + Opens the judge's ``invoke_agent`` and a ``chat`` span under it on an SDK + tracer, after the graded run is over. ``label`` lets a test tell apart the + spans of different judges in one trace. + """ + with _tracer.start_as_current_span( + "invoke_agent", attributes={"test.label": label} + ): + _tracer.start_span("chat", attributes={"test.label": label}).end() + + +async def _judge_fn( + config: Any, + user_input: Any, + tool_handlers: Any, + variables: Any, + history: Any = None, +) -> dict[str, Any]: + _open_judge_spans() + return JUDGE_OUTPUT + + +HANDLER = ProviderHandler(fn=_judge_fn, provides_for=("Anthropic", "messages")) # type: ignore[arg-type] + + +def _finished() -> list[ReadableSpan]: + return list(_exporter.get_finished_spans()) + + +@pytest.fixture(autouse=True) +def _reset_exporter() -> Iterator[None]: + _exporter.clear() + yield + _exporter.clear() + + +@pytest.fixture +def mock_ld_client() -> Iterator[MagicMock]: + client = MagicMock() + client.track = MagicMock() + client.flush = AsyncMock() + client.close = AsyncMock() + client.variation = AsyncMock(return_value={**JUDGE_CONFIG, "_ldMeta": JUDGE_META}) + lifecycle_module._set_client_for_testing(client) + yield client + lifecycle_module._reset_for_testing() + + +async def _run_inline( + handler: ProviderHandler = HANDLER, + base_track_data: dict[str, Any] | None = None, +) -> None: + with patch.object(random, "random", return_value=0.0): + await run_judges( + config={ + **JUDGE_CONFIG, + "judgeConfiguration": { + "judges": [{"key": "no-nitpicks", "samplingRate": 1.0}] + }, + }, + user_context=CONTEXT, + handler=handler, + user_input=None, + llm_response="Verdict: approve", + base_track_data=dict( + GRADED_RUN if base_track_data is None else base_track_data + ), + ) + + +def _expect_every_span_points_at_the_graded_run() -> None: + spans = _finished() + assert sorted(span.name for span in spans) == ["chat", "invoke_agent"] + for span in spans: + attributes = span.attributes or {} + assert attributes[LD_JUDGE_TARGET_RUN_ID] == "graded-run-id" + assert attributes[LD_JUDGE_TARGET_CONFIG_KEY] == "pr-reviewer" + assert attributes[LD_JUDGE_TARGET_VARIATION_KEY] == "opus" + + +class TestJudgeSpansPointAtTheRunTheyGraded: + async def test_stamps_every_span_of_an_inline_judge_with_the_graded_run( + self, mock_ld_client: MagicMock + ) -> None: + await _run_inline() + _expect_every_span_points_at_the_graded_run() + + async def test_stamps_every_span_of_a_deferred_judge_with_the_graded_run_from_the_task( + self, mock_ld_client: MagicMock + ) -> None: + task = JudgeTask( + config_key="no-nitpicks", + judge_config=dict(JUDGE_CONFIG), + judge_meta=dict(JUDGE_META), # type: ignore[arg-type] + actual_output="Verdict: approve", + user_context=CONTEXT, + judge_provider="Anthropic", + judge_mode="messages", + collapse_messages=False, + parent_track_data=dict(GRADED_RUN), + ) + await run_judge(task, [HANDLER]) + _expect_every_span_points_at_the_graded_run() + + def test_leaves_spans_outside_a_judge_run_alone(self) -> None: + _tracer.start_span("invoke_agent").end() + [span] = _finished() + attributes = span.attributes or {} + assert not [key for key in TARGET_KEYS if key in attributes] + + async def test_leaves_spans_from_other_tracers_alone( + self, mock_ld_client: MagicMock + ) -> None: + async def fn(*args: Any, **kwargs: Any) -> dict[str, Any]: + _open_judge_spans() + _foreign_tracer.start_span("POST").end() + return JUDGE_OUTPUT + + await _run_inline( + ProviderHandler(fn=fn, provides_for=("Anthropic", "messages")) + ) # type: ignore[arg-type] + [foreign] = [span for span in _finished() if span.name == "POST"] + attributes = foreign.attributes or {} + assert not [key for key in TARGET_KEYS if key in attributes] + + async def test_writes_the_attributes_with_content_capture_off( + self, mock_ld_client: MagicMock + ) -> None: + handler = ProviderHandler( + fn=_judge_fn, # type: ignore[arg-type] + provides_for=("Anthropic", "messages"), + capture_content=False, + ) + await _run_inline(handler) + _expect_every_span_points_at_the_graded_run() + + async def test_omits_an_empty_value_and_writes_the_others( + self, mock_ld_client: MagicMock + ) -> None: + await _run_inline(base_track_data={**GRADED_RUN, "variationKey": ""}) + spans = _finished() + assert sorted(span.name for span in spans) == ["chat", "invoke_agent"] + for span in spans: + attributes = span.attributes or {} + assert LD_JUDGE_TARGET_VARIATION_KEY not in attributes + assert attributes[LD_JUDGE_TARGET_RUN_ID] == "graded-run-id" + assert attributes[LD_JUDGE_TARGET_CONFIG_KEY] == "pr-reviewer" + + async def test_keeps_a_value_the_span_already_has( + self, mock_ld_client: MagicMock + ) -> None: + async def fn(*args: Any, **kwargs: Any) -> dict[str, Any]: + _open_judge_spans() + _tracer.start_span("own", attributes={LD_JUDGE_TARGET_RUN_ID: "own"}).end() + return JUDGE_OUTPUT + + await _run_inline( + ProviderHandler(fn=fn, provides_for=("Anthropic", "messages")) + ) # type: ignore[arg-type] + [own] = [span for span in _finished() if span.name == "own"] + attributes = own.attributes or {} + assert attributes[LD_JUDGE_TARGET_RUN_ID] == "own" + assert attributes[LD_JUDGE_TARGET_CONFIG_KEY] == "pr-reviewer" + + +# --------------------------------------------------------------------------- +# Graphs +# --------------------------------------------------------------------------- + +GRAPH_VARIATION = { + "root": "root-node", + "edges": {}, + "_ldMeta": {"enabled": True, "variationKey": "graph-v2", "version": 2}, +} +NODE_INSTRUCTIONS = "I am the root node." +NODE_JUDGE_INSTRUCTIONS = "You judge the node." +GRAPH_JUDGE_INSTRUCTIONS = "You judge the graph." + + +def _graph_variation_for(key: str) -> dict[str, Any]: + if key == "graph-key": + return GRAPH_VARIATION + judge_meta = { + "enabled": True, + "variationKey": "j1", + "version": 1, + "mode": "messages", + } + if key == "node-judge": + return { + **JUDGE_CONFIG, + "provider": {"name": "TestProvider"}, + "instructions": NODE_JUDGE_INSTRUCTIONS, + "evaluationMetricKey": "$ld:ai:judge:node", + "_ldMeta": judge_meta, + } + if key == "graph-judge": + return { + **JUDGE_CONFIG, + "provider": {"name": "TestProvider"}, + "instructions": GRAPH_JUDGE_INSTRUCTIONS, + "evaluationMetricKey": "$ld:ai:judge:graph", + "_ldMeta": judge_meta, + } + return { + "model": {"name": "gpt-4"}, + "provider": {"name": "TestProvider"}, + "instructions": NODE_INSTRUCTIONS, + "judgeConfiguration": {"judges": [{"key": "node-judge", "samplingRate": 1.0}]}, + "_ldMeta": { + "enabled": True, + "variationKey": "node-v1", + "version": 1, + "mode": "messages", + }, + } + + +async def _graph_handler_fn( + config: Any, + user_input: Any, + tool_handlers: Any, + variables: Any, + history: Any = None, +) -> dict[str, Any]: + instructions = config.get("instructions", "") + if instructions == NODE_JUDGE_INSTRUCTIONS: + _open_judge_spans("node-judge") + return JUDGE_OUTPUT + if instructions == GRAPH_JUDGE_INSTRUCTIONS: + _open_judge_spans("graph-judge") + return JUDGE_OUTPUT + _open_judge_spans("node") + return {"output": "final", "usage": {"input_tokens": 1, "output_tokens": 1}} + + +@pytest.fixture +def graph_ld_client() -> Iterator[MagicMock]: + client = MagicMock() + client.track = MagicMock() + client.flush = AsyncMock() + client.close = AsyncMock() + + async def variation(key: str, ctx: Any, default: Any) -> Any: + return _graph_variation_for(key) + + client.variation = AsyncMock(side_effect=variation) + lifecycle_module._set_client_for_testing(client) + # graph.py opens the graph span on trace.get_tracer, and the global provider + # is claimed process-wide, so hand it this file's tracer instead. + with patch.object(trace, "get_tracer", return_value=_tracer): + yield client + lifecycle_module._reset_for_testing() + + +def _metric_track_data(client: MagicMock, event_key: str) -> dict[str, Any]: + [call] = [c for c in client.track.call_args_list if c.args[0] == event_key] + return dict(call.args[2]) + + +def _labelled(label: str) -> list[ReadableSpan]: + return [s for s in _finished() if (s.attributes or {}).get("test.label") == label] + + +class TestGraphJudgeTargets: + async def test_node_and_graph_judges_point_at_the_runs_they_graded( + self, graph_ld_client: MagicMock + ) -> None: + handler = ProviderHandler( + fn=_graph_handler_fn, # type: ignore[arg-type] + provides_for=("TestProvider", "messages"), + ) + await graph("graph-key", handlers=[handler], graph_judge="graph-judge").invoke( + "hi", CONTEXT + ) + + # A per-node judge points at the node's own run: the run its metric event names. + node_metric = _metric_track_data(graph_ld_client, "$ld:ai:judge:node") + assert node_metric["configKey"] == "root-node" + node_judge_spans = _labelled("node-judge") + assert sorted(s.name for s in node_judge_spans) == ["chat", "invoke_agent"] + for span in node_judge_spans: + attributes = span.attributes or {} + assert attributes[LD_JUDGE_TARGET_RUN_ID] == node_metric["runId"] + assert attributes[LD_JUDGE_TARGET_CONFIG_KEY] == "root-node" + assert attributes[LD_JUDGE_TARGET_VARIATION_KEY] == "node-v1" + + # The graph-level judge points at the graph run. + [graph_span] = [s for s in _finished() if s.name == "launchdarkly.graph"] + graph_run_id = (graph_span.attributes or {})["launchdarkly.run.id"] + graph_metric = _metric_track_data(graph_ld_client, "$ld:ai:judge:graph") + assert graph_metric["runId"] == graph_run_id + assert graph_run_id != node_metric["runId"] + graph_judge_spans = _labelled("graph-judge") + assert sorted(s.name for s in graph_judge_spans) == ["chat", "invoke_agent"] + for span in graph_judge_spans: + attributes = span.attributes or {} + assert attributes[LD_JUDGE_TARGET_RUN_ID] == graph_run_id + assert attributes[LD_JUDGE_TARGET_CONFIG_KEY] == "graph-key" + assert attributes[LD_JUDGE_TARGET_VARIATION_KEY] == "graph-v2" + + # The graded node's own spans are not a judge run. + for span in _labelled("node"): + attributes = span.attributes or {} + assert not [key for key in TARGET_KEYS if key in attributes]