diff --git a/.github/workflows/integration-testing.yml b/.github/workflows/integration-testing.yml index 44d29e76..090476a2 100644 --- a/.github/workflows/integration-testing.yml +++ b/.github/workflows/integration-testing.yml @@ -120,7 +120,7 @@ jobs: - group: evaluation path: tests_integ/evaluation timeout: 15 - extra-deps: "strands-agents-evals deepeval autoevals" + extra-deps: "strands-agents-evals deepeval autoevals ragas langchain-community<0.4.2" ignore: "" - group: services path: tests_integ/services diff --git a/pyproject.toml b/pyproject.toml index 24e0ef15..78d5b720 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -153,6 +153,9 @@ dev = [ "strands-agents-evals>=1.0.3,<2.0.0", "deepeval>=3.5.0,<5.0.0", "autoevals>=0.3.0,<1.0.0", + "ragas>=0.4.3,<1.0.0", + # ragas<1.0 imports langchain_community.chat_models.vertexai, removed in 0.4.2 + "langchain-community>=0.3.0,<0.4.2", "langchain>=1.0.0", "langgraph>=1.0.0", "langchain-mcp-adapters>=0.1.0", @@ -193,6 +196,12 @@ autoevals = [ "autoevals>=0.3.0,<1.0.0", "strands-agents-evals>=1.0.3,<2.0.0", ] +ragas = [ + "ragas>=0.4.3,<1.0.0", + # ragas<1.0 imports langchain_community.chat_models.vertexai, removed in 0.4.2 + "langchain-community>=0.3.0,<0.4.2", + "strands-agents-evals>=1.0.3,<2.0.0", +] [tool.uv] conflicts = [ diff --git a/src/bedrock_agentcore/evaluation/custom_code_based_evaluators/third_party/ragas/__init__.py b/src/bedrock_agentcore/evaluation/custom_code_based_evaluators/third_party/ragas/__init__.py new file mode 100644 index 00000000..63d77108 --- /dev/null +++ b/src/bedrock_agentcore/evaluation/custom_code_based_evaluators/third_party/ragas/__init__.py @@ -0,0 +1,8 @@ +"""RAGAS adapter for AgentCore code-based evaluators.""" + +from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas.adapter import ( + RAGASAdapter, + RagasAdapter, +) + +__all__ = ["RAGASAdapter", "RagasAdapter"] diff --git a/src/bedrock_agentcore/evaluation/custom_code_based_evaluators/third_party/ragas/adapter.py b/src/bedrock_agentcore/evaluation/custom_code_based_evaluators/third_party/ragas/adapter.py new file mode 100644 index 00000000..3c3dd615 --- /dev/null +++ b/src/bedrock_agentcore/evaluation/custom_code_based_evaluators/third_party/ragas/adapter.py @@ -0,0 +1,538 @@ +"""RAGAS adapter for AgentCore code-based evaluators. + +Scores metrics through RAGAS's per-sample APIs (``metric.single_turn_score()`` +/ ``metric.multi_turn_score()`` for legacy metrics, ``metric.score(**kwargs)`` +for ``ragas.metrics.collections`` metrics) rather than the batch +``ragas.evaluate()`` pipeline. The adapter itself therefore adds no dependency +on the heavyweight ``datasets``/``pyarrow``/``pandas`` stack. Note that ragas +<1.0 still imports ``datasets`` when the ragas package itself is imported, so +size-constrained deployments (e.g. zip-based Lambda) additionally need a ragas +build with that import chain trimmed; this adapter is compatible with such +builds because it never calls the ``datasets``-backed APIs. +""" + +import json +import logging +import math +from collections import defaultdict, deque +from collections.abc import Sequence +from inspect import Parameter, signature +from typing import Any, Callable, Dict, List, Optional, Tuple, Union, get_origin + +from bedrock_agentcore.evaluation.custom_code_based_evaluators.models import EvaluatorInput, EvaluatorOutput +from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.base import BaseAdapter + +logger = logging.getLogger(__name__) + +# A scoring call yields either a (score, reason) pair or an EvaluatorOutput +# carrying an error / categorical label. +_ScoreOutcome = Union[Tuple[float, Optional[str]], EvaluatorOutput] + +# Separators for ground truth and retrieval context embedded in the user +# message. ADOT trace formats have no dedicated fields for these, so dataset +# preparation commonly appends them to the user input with known markers. +_REFERENCE_SEPARATOR = "\n\nReference Answer:\n" +_CONTEXT_SEPARATOR = "\n\nContext:\n" + +# RAGAS field names whose values are conversation-shaped (message lists / tool +# call objects) rather than the flat strings used by single-turn metrics. +_CONVERSATION_FIELDS = frozenset({"user_input", "reference_tool_calls"}) + + +class RAGASAdapter(BaseAdapter): + """Adapter that runs a RAGAS metric against AgentCore evaluation events. + + Supports both RAGAS metric generations: + + - Legacy single-turn metrics (``ragas.metrics``): scored via + ``metric.single_turn_score(SingleTurnSample(...))`` + - Legacy multi-turn metrics (e.g. ToolCallAccuracy): scored via + ``metric.multi_turn_score(MultiTurnSample(...))``, with conversation + messages built from extracted turns and tool calls + - Collections metrics (``ragas.metrics.collections``) and decorator-based + custom metrics (``@discrete_metric`` / ``@numeric_metric``): scored via + ``metric.score(**fields)`` + + Numeric results produce value + Pass/Fail label; discrete (string-valued) + metrics produce a categorical label. When the metric provides reasoning + (``MetricResult.reason``), it is surfaced as the explanation. + + The adapter evaluates one event at a time by design; it does not use the + batch ``ragas.evaluate()`` API. + + Conversation-shaped metrics receive the same converted inputs whichever + generation they belong to: the legacy multi-turn classes via + ``multi_turn_score()``, and collections metrics (e.g. collections + ToolCallAccuracy) via ``score()`` when their signature declares sequence + types. Predicted tool calls are attached to the final AI message. + ``reference_tool_calls`` come from ``expected_trajectory.toolNames``, which + carries names only; the predicted arguments of same-named calls are adopted + so argument-aware metrics score tool selection and sequence. Supply a + custom_mapper with full ``ragas.messages.ToolCall`` objects to compare + arguments against independent ground truth. + + Fields with no span source (e.g. ``reference_topics`` for + TopicAdherenceScore, ``reference_contexts`` for non-LLM context comparison + metrics) require a custom_mapper. + + Metrics supporting both modes (e.g. AspectCritic) are always scored + single-turn; wrap the metric or use a custom evaluator for multi-turn use. + + Example (default span mapping):: + + from ragas.metrics import Faithfulness + from langchain_aws import ChatBedrockConverse + from ragas.llms import LangchainLLMWrapper + from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas import RAGASAdapter + + eval_llm = LangchainLLMWrapper(ChatBedrockConverse( + model_id="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + region_name="us-east-1", + )) + adapter = RAGASAdapter(metric=Faithfulness(), llm=eval_llm) + + Example (custom mapper returning RAGAS field dict):: + + from typing import Dict, Any + + def my_mapper(ev: EvaluatorInput) -> Dict[str, Any]: + return { + "user_input": ev.session_spans[0]["attributes"]["question"], + "response": ev.session_spans[0]["attributes"]["answer"], + "retrieved_contexts": ["some context"], + } + + adapter = RAGASAdapter( + metric=Faithfulness(), + llm=eval_llm, + custom_mapper=my_mapper, + ) + """ + + def __init__( + self, + metric: Any, + custom_mapper: Optional[Callable[[EvaluatorInput], Dict[str, Any]]] = None, + llm: Optional[Any] = None, + embeddings: Optional[Any] = None, + threshold: Optional[float] = None, + ): + """Initialize the adapter. + + Args: + metric: A RAGAS metric instance (e.g., Faithfulness(), ContextRecall()). + Legacy (``ragas.metrics``) and collections + (``ragas.metrics.collections``) metrics are both supported. + custom_mapper: Optional callable that receives the EvaluatorInput and + returns a dict with RAGAS-native field keys + (user_input, response, reference, retrieved_contexts, reference_contexts). + Bypasses default span mapping when provided. + llm: Optional LLM wrapper to set on the metric (e.g., LangchainLLMWrapper). + Required for most RAGAS metrics when not using OpenAI. + embeddings: Optional embeddings wrapper to set on the metric. + Required for embedding-based metrics (AnswerSimilarity, AnswerCorrectness). + threshold: Optional Pass/Fail threshold override. When None, the + metric's own threshold is used, defaulting to 0.5 (useful for + collections metrics, which carry no threshold of their own). + """ + self.metric = metric + self.custom_mapper = custom_mapper + self.threshold = threshold + + if llm is not None: + self.metric.llm = llm + if embeddings is not None and hasattr(self.metric, "embeddings"): + self.metric.embeddings = embeddings + + def _run(self, evaluator_input: EvaluatorInput) -> EvaluatorOutput: + """Run the RAGAS metric pipeline.""" + span_result = None + if self.custom_mapper is not None: + fields = self.custom_mapper(evaluator_input) + else: + span_result = self._default_extract(evaluator_input) + if not span_result.input or not span_result.actual_output: + missing: List[str] = [] + if not span_result.input: + missing.append("input") + if not span_result.actual_output: + missing.append("actual_output") + return EvaluatorOutput( + errorCode="MISSING_REQUIRED_FIELD", + errorMessage=f"Field(s) {missing} required by {self._metric_name()} but not found in " + f"evaluation event. Provide a custom_mapper or ensure spans contain the necessary data.", + ) + fields = self._build_fields(span_result) + + # Dispatch on metric generation. Legacy single-turn metrics expose + # single_turn_score(), legacy multi-turn metrics expose + # multi_turn_score(), and collections metrics expose score(**kwargs). + if hasattr(self.metric, "single_turn_score"): + outcome = self._score_legacy(fields) + elif hasattr(self.metric, "multi_turn_score"): + outcome = self._score_multi_turn(fields, span_result) + elif hasattr(self.metric, "ascore") and hasattr(self.metric, "score"): + outcome = self._score_collections(fields, span_result) + else: + return EvaluatorOutput( + errorCode="UNSUPPORTED_METRIC", + errorMessage=f"{type(self.metric).__name__} does not expose a supported RAGAS scoring " + f"API (single_turn_score, multi_turn_score, or score). Pass a ragas.metrics " + f"or ragas.metrics.collections metric instance.", + ) + + if isinstance(outcome, EvaluatorOutput): + return outcome + score, metric_reason = outcome + + if math.isnan(score): + return EvaluatorOutput( + errorCode="INVALID_SCORE", + errorMessage=f"RAGAS metric '{self._metric_name()}' returned NaN. This usually means " + f"required fields were empty or the metric could not be computed.", + ) + + # Adapter override > metric threshold > 0.5 default. Some metrics + # (e.g. SemanticSimilarity) set threshold=None explicitly; getattr's + # default only applies when the attribute is missing entirely. + threshold = self.threshold + if threshold is None: + threshold = getattr(self.metric, "threshold", None) + threshold = threshold if threshold is not None else 0.5 + label = "Pass" if score >= threshold else "Fail" + explanation = metric_reason or f"RAGAS {self._metric_name()}: {score:.4f} (threshold={threshold})" + + return EvaluatorOutput(value=score, label=label, explanation=explanation) + + def _build_fields(self, result: Any) -> Dict[str, Any]: + r"""Map a SpanMapResult to RAGAS-native field names. + + Also parses reference and context embedded in the user input field. + Dataset recipes may embed these as + ``"{user_input}\n\nContext:\n{context}\n\nReference Answer:\n{reference}"`` + since ADOT trace formats have no dedicated reference/context fields. + """ + user_input, embedded_reference, embedded_context = self._split_embedded(result.input) + + fields: Dict[str, Any] = { + "user_input": user_input, + "response": result.actual_output, + } + + # Reference priority: reference_inputs > embedded > assertions + if result.expected_output: + fields["reference"] = result.expected_output + elif embedded_reference: + fields["reference"] = embedded_reference + elif result.assertions: + fields["reference"] = "\n".join(result.assertions) + + # Retrieval context priority: span tool results > embedded. + # reference_contexts (ground-truth contexts) is deliberately NOT + # defaulted from retrieved contexts: doing so would make reference- + # comparison metrics score retrieval output against itself. Supply it + # via a custom_mapper when a genuine ground-truth source exists. + if result.retrieval_context: + fields["retrieved_contexts"] = result.retrieval_context + elif embedded_context: + fields["retrieved_contexts"] = self._interpret_embedded_context(embedded_context) + + return fields + + @staticmethod + def _split_embedded(text: str) -> Tuple[str, Optional[str], Optional[str]]: + """Split embedded reference/context sections out of a user message. + + Returns ``(clean_text, reference, context)`` where reference/context + are None when the corresponding marker is absent. + """ + reference: Optional[str] = None + context: Optional[str] = None + if _REFERENCE_SEPARATOR in text: + text, reference = text.split(_REFERENCE_SEPARATOR, 1) + if _CONTEXT_SEPARATOR in text: + text, context = text.split(_CONTEXT_SEPARATOR, 1) + return text, reference, context + + @staticmethod + def _interpret_embedded_context(embedded_context: str) -> List[str]: + """Recover a ranked chunk list from embedded context text. + + Multi-chunk retrieval contexts are embedded as a JSON-serialized list + of strings so chunk boundaries and rank order survive the round trip. + This matters for rank-aware metrics (LLMContextPrecision and friends): + joining N ranked chunks into one string collapses precision@k to a + binary judgment on a single blob. + + If the text is not a JSON list of strings, it is treated as a single + chunk (backward compatible with plain-text contexts). + """ + try: + parsed = json.loads(embedded_context) + except (json.JSONDecodeError, ValueError): + return [embedded_context] + if isinstance(parsed, list) and parsed and all(isinstance(item, str) for item in parsed): + return parsed + return [embedded_context] + + def _invoke(self, score_fn: Callable[[], _ScoreOutcome]) -> _ScoreOutcome: + """Run a ragas scoring call, mapping known failure modes to error outputs.""" + try: + return score_fn() + except ImportError as e: + return self._missing_dependency_error(e) + except Exception as e: + hint = self._dependency_hint(e) + if hint: + return hint + raise + + def _score_sample( + self, + fields: Dict[str, Any], + sample_cls: Any, + required_key: str, + score_fn: Callable[[Any], float], + ) -> _ScoreOutcome: + """Validate declared required fields, then score through a legacy Sample class. + + Shared by the single-turn and multi-turn paths, which differ only in the + sample class, the ``required_columns`` key, and the metric's scoring method. + """ + required = getattr(self.metric, "required_columns", {}).get(required_key, set()) + missing = sorted(c for c in required if not fields.get(c)) + if missing: + return self._missing_fields_error(missing) + + sample = sample_cls(**{k: v for k, v in fields.items() if k in sample_cls.model_fields}) + return self._invoke(lambda: (float(score_fn(sample)), None)) + + def _score_legacy(self, fields: Dict[str, Any]) -> _ScoreOutcome: + """Score with a legacy (``ragas.metrics``) single-turn metric.""" + from ragas.dataset_schema import SingleTurnSample + + return self._score_sample(fields, SingleTurnSample, "SINGLE_TURN", self.metric.single_turn_score) + + def _score_collections(self, fields: Dict[str, Any], span_result: Any = None) -> _ScoreOutcome: + """Score with a collections (``ragas.metrics.collections``) metric. + + Collections metrics declare their inputs as typed ``ascore()`` + parameters, so filter the extracted fields to that signature. + Conversation-shaped metrics (e.g. collections ToolCallAccuracy, whose + ``user_input`` is a message list rather than a string) receive the same + converted messages and reference tool calls the legacy multi-turn path + builds, selected by the parameter's declared type. + + Decorator-based metrics (``@discrete_metric`` / ``@numeric_metric``) + expose ``ascore(*args, **kwargs)`` instead; for those, all fields are + passed through and the metric validates its own inputs (pair them with + a custom_mapper whose keys match the wrapped function's parameters). + + Returns a ``(score, reason)`` tuple, an EvaluatorOutput error, or a + categorical EvaluatorOutput for discrete (string-valued) metrics. + """ + params = signature(self.metric.ascore).parameters + if any(p.kind is Parameter.VAR_KEYWORD for p in params.values()): + kwargs = dict(fields) + else: + resolved = dict(fields) + # Parameters naming a conversation field with a sequence type need + # the converted messages / tool calls rather than the flat + # string values used by single-turn metrics. + conversation_params = [ + name + for name in _CONVERSATION_FIELDS + if name in params and self._expects_sequence(params[name].annotation) + ] + if span_result is not None and conversation_params: + conv = self._conversation_fields(span_result) + resolved.update({name: conv[name] for name in conversation_params if name in conv}) + + accepted = {name for name in params if name != "self"} + required = sorted( + name + for name, p in params.items() + if name != "self" and p.default is Parameter.empty and not resolved.get(name) + ) + if required: + return self._missing_fields_error(required) + kwargs = {k: v for k, v in resolved.items() if k in accepted} + + def score() -> _ScoreOutcome: + result = self.metric.score(**kwargs) + value = result.value + reason = getattr(result, "reason", None) + if isinstance(value, str): + # Discrete metrics return categorical labels with user-defined + # allowed_values; surface them as-is rather than guessing a number. + return EvaluatorOutput(label=value, explanation=reason) + return float(value), reason + + return self._invoke(score) + + def _score_multi_turn(self, fields: Dict[str, Any], span_result: Any) -> _ScoreOutcome: + """Score with a legacy multi-turn metric (e.g. ToolCallAccuracy). + + Builds conversation messages and reference tool calls from the span + extraction. With a custom_mapper, the fields dict supplies + MultiTurnSample fields directly (``user_input`` as a list of + ``ragas.messages`` objects, ``reference_tool_calls``, etc.). + """ + from ragas.dataset_schema import MultiTurnSample + + if span_result is not None: + fields = {**fields, **self._conversation_fields(span_result)} + return self._score_sample(fields, MultiTurnSample, "MULTI_TURN", self.metric.multi_turn_score) + + def _conversation_fields(self, span_result: Any) -> Dict[str, Any]: + """Build the conversation-shaped fields shared by multi-turn scoring paths. + + Returns ``user_input`` as a ragas message list, plus + ``reference_tool_calls`` when the reference inputs supplied an + expected tool trajectory. + """ + conv: Dict[str, Any] = {"user_input": self._build_messages(span_result)} + if span_result.expected_tools: + reference = self._build_ragas_tool_calls(span_result.expected_tools) + # expectedTrajectory.toolNames expresses "call these tools, in this + # order" and carries no arguments. Adopt the predicted arguments for + # same-named calls so argument-aware metrics score tool selection and + # sequence instead of counting every argument as a mismatch. + if span_result.tools_called: + reference = self._align_reference_args(reference, span_result.tools_called) + conv["reference_tool_calls"] = reference + return conv + + @staticmethod + def _align_reference_args(reference: List[Any], tools_called: List[Dict[str, Any]]) -> List[Any]: + """Fill argument-less reference tool calls from the predicted calls of the same name. + + Only applies to references with no arguments at all (i.e. built from + ``expectedTrajectory.toolNames``); references supplied with real + arguments are left untouched. Predicted arguments are consumed in call + order per tool name, so a tool expected several times pairs with each + invocation rather than reusing the first one's arguments. + """ + from ragas.messages import ToolCall + + pending: Dict[str, Any] = defaultdict(deque) + for tc in tools_called: + name = tc.get("name") + args = tc.get("input_parameters") + if name and isinstance(args, dict): + pending[name].append(args) + + aligned: List[Any] = [] + for call in reference: + if not call.args and pending.get(call.name): + aligned.append(ToolCall(name=call.name, args=pending[call.name].popleft())) + else: + aligned.append(call) + return aligned + + @staticmethod + def _expects_sequence(annotation: Any) -> bool: + """Whether a parameter annotation declares a list/sequence type.""" + # Bare `list` has no typing origin; parameterized `List[x]` does. + if annotation in (list, tuple, Sequence): + return True + if get_origin(annotation) in (list, tuple, Sequence): + return True + if isinstance(annotation, str): + # Unresolved annotations, e.g. under `from __future__ import annotations`. + return annotation.lstrip().startswith(("List[", "list[", "Sequence[", "typing.List[")) + return False + + def _build_messages(self, span_result: Any) -> List[Any]: + """Build a ragas message list from extracted turns and tool calls. + + Uses SpanMapResult.turns when the session has multiple invocations, + falling back to a single user/assistant pair. Predicted tool calls + are attached to the final AI message. + """ + from ragas.messages import AIMessage, HumanMessage + + messages: List[Any] = [] + if span_result.turns: + for turn in span_result.turns: + role = turn.get("role") + content = turn.get("content", "") + if role == "user": + # Strip embedded reference/context so ground truth does not + # leak into the conversation the metric judges. + clean, _, _ = self._split_embedded(content) + messages.append(HumanMessage(content=clean)) + elif role == "assistant": + messages.append(AIMessage(content=content)) + else: + clean, _, _ = self._split_embedded(span_result.input or "") + messages = [ + HumanMessage(content=clean), + AIMessage(content=span_result.actual_output or ""), + ] + + if span_result.tools_called: + tool_calls = self._build_ragas_tool_calls(span_result.tools_called) + for i in range(len(messages) - 1, -1, -1): + if isinstance(messages[i], AIMessage): + messages[i] = AIMessage(content=messages[i].content, tool_calls=tool_calls) + break + + return messages + + @staticmethod + def _build_ragas_tool_calls(tool_dicts: List[Dict[str, Any]]) -> List[Any]: + """Convert extracted tool call dicts to ragas ToolCall objects. + + Used for both predicted calls (which carry ``input_parameters``) and + reference calls from ``expected_trajectory``, which name tools without + arguments and so default to an empty dict. + """ + from ragas.messages import ToolCall + + result: List[Any] = [] + for tc in tool_dicts: + name = tc.get("name", "") + if not name: + continue + args = tc.get("input_parameters") + result.append(ToolCall(name=name, args=args if isinstance(args, dict) else {})) + return result + + def _missing_fields_error(self, missing: List[str]) -> EvaluatorOutput: + """Build a MISSING_REQUIRED_FIELD error for the metric's declared inputs.""" + return EvaluatorOutput( + errorCode="MISSING_REQUIRED_FIELD", + errorMessage=f"RAGAS metric '{self._metric_name()}' requires field(s) {missing} which were not " + f"found in the evaluation event. Provide ground truth via evaluationReferenceInputs, " + f"embed it in the user message, or supply a custom_mapper.", + ) + + def _missing_dependency_error(self, e: ImportError) -> EvaluatorOutput: + """Build a MISSING_DEPENDENCY error for packages absent from the environment.""" + return EvaluatorOutput( + errorCode="MISSING_DEPENDENCY", + errorMessage=f"RAGAS metric '{self._metric_name()}' requires a package that is not " + f"installed in this environment: {e}. Install the missing dependency or use a " + f"metric with a lighter footprint.", + ) + + def _dependency_hint(self, e: Exception) -> Optional[EvaluatorOutput]: + """Return a targeted error when the metric is missing its LLM/embeddings.""" + msg = str(e).lower() + if "not set" in msg and ("llm" in msg or "embedding" in msg): + return EvaluatorOutput( + errorCode="METRIC_ERROR", + errorMessage=f"RAGAS metric '{self._metric_name()}' failed: {e}. Pass an LLM or embeddings " + f"wrapper to the adapter, e.g. RAGASAdapter(metric=..., " + f"llm=LangchainLLMWrapper(ChatBedrockConverse(...))).", + ) + return None + + def _metric_name(self) -> str: + """The metric's declared name, falling back to the class name.""" + return getattr(self.metric, "name", None) or type(self.metric).__name__ + + +# Alias following the repo's brand-casing convention (DeepEvalAdapter, AutoEvalsAdapter). +RagasAdapter = RAGASAdapter diff --git a/tests/bedrock_agentcore/evaluation/custom_code_based_evaluators/third_party/ragas/__init__.py b/tests/bedrock_agentcore/evaluation/custom_code_based_evaluators/third_party/ragas/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/bedrock_agentcore/evaluation/custom_code_based_evaluators/third_party/ragas/test_adapter.py b/tests/bedrock_agentcore/evaluation/custom_code_based_evaluators/third_party/ragas/test_adapter.py new file mode 100644 index 00000000..8d8d77ac --- /dev/null +++ b/tests/bedrock_agentcore/evaluation/custom_code_based_evaluators/third_party/ragas/test_adapter.py @@ -0,0 +1,869 @@ +"""Tests for RAGASAdapter.""" + +from types import SimpleNamespace +from typing import Any, List +from unittest.mock import MagicMock + +from bedrock_agentcore.evaluation.custom_code_based_evaluators.models import EvaluatorInput, EvaluatorOutput +from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas.adapter import RAGASAdapter + + +def _make_spans(user_content='[{"text": "What is AI?"}]', assistant_message="AI is artificial intelligence."): + """Build CloudWatch split format spans with the given user/assistant content.""" + return [ + { + "traceId": "t1", + "spanId": "s1", + "scope": {"name": "strands.telemetry.tracer"}, + "name": "invoke_agent", + "kind": "INTERNAL", + "startTimeUnixNano": 1000000000, + "endTimeUnixNano": 2000000000, + "attributes": {"gen_ai.operation.name": "invoke_agent", "session.id": "test-session"}, + "status": {"code": "UNSET"}, + }, + { + "traceId": "t1", + "spanId": "s1", + "scope": {"name": "strands.telemetry.tracer"}, + "timeUnixNano": 2000000000, + "observedTimeUnixNano": 2000000001, + "severityNumber": 9, + "body": { + "input": {"messages": [{"role": "user", "content": {"content": user_content}}]}, + "output": {"messages": [{"role": "assistant", "content": {"message": assistant_message}}]}, + }, + }, + ] + + +def _make_evaluator_input(spans=None, reference_inputs=None): + """Build an EvaluatorInput with agent-level spans (CloudWatch split format).""" + if spans is None: + spans = _make_spans() + return EvaluatorInput( + evaluation_level="TRACE", + session_spans=spans, + target_trace_id="t1", + reference_inputs=reference_inputs or [], + ) + + +def _mock_legacy_metric(name="faithfulness", threshold=0.5, score=0.85, required=None): + """Create a mock legacy RAGAS metric scored via single_turn_score().""" + metric = MagicMock() + metric.name = name + metric.threshold = threshold + metric.required_columns = {"SINGLE_TURN": required if required is not None else {"user_input", "response"}} + metric.single_turn_score = MagicMock(return_value=score) + return metric + + +class _FakeCollectionsMetric: + """Fake ragas.metrics.collections-style metric (no single_turn_score).""" + + name = "exact_match" + + def __init__(self, value=1.0, reason=None): + self._value = value + self._reason = reason + self.score_calls = [] + + async def ascore(self, reference: str, response: str): + """Typed signature used by the adapter for field filtering/validation.""" + + def score(self, **kwargs): + self.score_calls.append(kwargs) + return SimpleNamespace(value=self._value, reason=self._reason) + + +class _FakeConversationalCollectionsMetric: + """Fake collections metric whose ascore expects conversation-shaped inputs. + + Mirrors ragas.metrics.collections.ToolCallAccuracy, which declares + ``user_input`` as a message list and requires ``reference_tool_calls``. + """ + + name = "tool_call_accuracy" + + def __init__(self, value=1.0): + self._value = value + self.score_calls = [] + + async def ascore(self, user_input: List[Any], reference_tool_calls: List[Any]): + """Typed signature the adapter introspects to detect conversation inputs.""" + + def score(self, **kwargs): + self.score_calls.append(kwargs) + return SimpleNamespace(value=self._value, reason=None) + + +class _FakeDecoratorMetric: + """Fake @discrete_metric/@numeric_metric-style metric with an untyped signature.""" + + name = "my_check" + + def __init__(self, value="pass", reason="looks good"): + self._value = value + self._reason = reason + self.score_calls = [] + + async def ascore(self, *args, **kwargs): + """Decorator metrics expose an untyped passthrough signature.""" + + def score(self, **kwargs): + self.score_calls.append(kwargs) + return SimpleNamespace(value=self._value, reason=self._reason) + + +class TestRAGASAdapterSuccess: + def test_returns_pass_when_score_above_threshold(self): + metric = _mock_legacy_metric(threshold=0.7, score=0.9) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert isinstance(result, EvaluatorOutput) + assert result.value == 0.9 + assert result.label == "Pass" + + def test_returns_fail_when_score_below_threshold(self): + metric = _mock_legacy_metric(threshold=0.7, score=0.3) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.value == 0.3 + assert result.label == "Fail" + + def test_returns_pass_at_exact_threshold(self): + metric = _mock_legacy_metric(threshold=0.7, score=0.7) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.label == "Pass" + + def test_sample_built_from_extracted_fields(self): + metric = _mock_legacy_metric(score=0.9) + adapter = RAGASAdapter(metric=metric) + + adapter(_make_evaluator_input()) + + metric.single_turn_score.assert_called_once() + sample = metric.single_turn_score.call_args[0][0] + assert sample.user_input == "What is AI?" + assert sample.response == "AI is artificial intelligence." + + def test_custom_mapper(self): + metric = _mock_legacy_metric(score=0.9) + adapter = RAGASAdapter( + metric=metric, + custom_mapper=lambda ev: { + "user_input": "mapped input", + "response": "mapped output", + "retrieved_contexts": ["some context"], + }, + ) + + result = adapter(_make_evaluator_input()) + + assert result.value == 0.9 + sample = metric.single_turn_score.call_args[0][0] + assert sample.user_input == "mapped input" + assert sample.response == "mapped output" + assert sample.retrieved_contexts == ["some context"] + + def test_reference_inputs_populates_reference(self): + metric = _mock_legacy_metric(name="answer_correctness", score=0.8) + adapter = RAGASAdapter(metric=metric) + + evaluator_input = _make_evaluator_input( + reference_inputs=[{"expectedResponse": {"text": "AI stands for artificial intelligence."}}], + ) + + result = adapter(evaluator_input) + + assert result.value == 0.8 + sample = metric.single_turn_score.call_args[0][0] + assert sample.reference == "AI stands for artificial intelligence." + + def test_llm_override_sets_metric_llm(self): + metric = _mock_legacy_metric() + mock_llm = MagicMock() + RAGASAdapter(metric=metric, llm=mock_llm) + + assert metric.llm == mock_llm + + def test_embeddings_override_sets_metric_embeddings(self): + metric = _mock_legacy_metric() + metric.embeddings = None + mock_embeddings = MagicMock() + RAGASAdapter(metric=metric, embeddings=mock_embeddings) + + assert metric.embeddings == mock_embeddings + + +class TestRAGASAdapterCollectionsMetrics: + def test_scores_via_score_kwargs(self): + metric = _FakeCollectionsMetric(value=1.0) + adapter = RAGASAdapter( + metric=metric, + custom_mapper=lambda ev: {"user_input": "q", "response": "4", "reference": "4"}, + ) + + result = adapter(_make_evaluator_input()) + + assert result.value == 1.0 + assert result.label == "Pass" + # Fields not in the ascore signature are filtered out + assert metric.score_calls == [{"response": "4", "reference": "4"}] + + def test_missing_required_kwarg_returns_error(self): + metric = _FakeCollectionsMetric() + adapter = RAGASAdapter(metric=metric) + + # Default span extraction produces user_input/response but no reference + result = adapter(_make_evaluator_input()) + + assert result.errorCode == "MISSING_REQUIRED_FIELD" + assert "reference" in result.errorMessage + assert metric.score_calls == [] + + def test_embedded_reference_satisfies_required_kwarg(self): + metric = _FakeCollectionsMetric(value=0.0) + adapter = RAGASAdapter(metric=metric) + + spans = _make_spans(user_content='[{"text": "What is 2+2?\\n\\nReference Answer:\\n4"}]') + result = adapter(_make_evaluator_input(spans=spans)) + + assert result.value == 0.0 + assert result.label == "Fail" + assert metric.score_calls[0]["reference"] == "4" + + def test_metric_reason_used_as_explanation(self): + metric = _FakeCollectionsMetric(value=0.8, reason="The response covers 4/5 key points") + adapter = RAGASAdapter( + metric=metric, + custom_mapper=lambda ev: {"response": "a", "reference": "b"}, + ) + + result = adapter(_make_evaluator_input()) + + assert result.value == 0.8 + assert result.explanation == "The response covers 4/5 key points" + + def test_no_reason_falls_back_to_synthetic_explanation(self): + metric = _FakeCollectionsMetric(value=0.8, reason=None) + adapter = RAGASAdapter( + metric=metric, + custom_mapper=lambda ev: {"response": "a", "reference": "b"}, + ) + + result = adapter(_make_evaluator_input()) + + assert "exact_match" in result.explanation + assert "0.8" in result.explanation + + def test_discrete_string_value_returns_categorical_label(self): + metric = _FakeDecoratorMetric(value="excellent", reason="Meets all criteria") + adapter = RAGASAdapter( + metric=metric, + custom_mapper=lambda ev: {"user_input": "q", "response": "a"}, + ) + + result = adapter(_make_evaluator_input()) + + assert result.label == "excellent" + assert result.value is None + assert result.explanation == "Meets all criteria" + assert result.errorCode is None + + def test_decorator_metric_receives_all_fields(self): + """Untyped ascore(*args, **kwargs) signatures get all fields passed through.""" + metric = _FakeDecoratorMetric(value=0.9, reason=None) + adapter = RAGASAdapter( + metric=metric, + custom_mapper=lambda ev: {"user_input": "q", "response": "a", "my_custom_field": "x"}, + ) + + result = adapter(_make_evaluator_input()) + + assert result.value == 0.9 + assert metric.score_calls == [{"user_input": "q", "response": "a", "my_custom_field": "x"}] + + +class TestRAGASAdapterConversationalCollections: + """Collections metrics with conversation-shaped signatures get converted fields. + + Without this, a string user_input and absent reference_tool_calls make the + metric fail validation even though the trace carries the needed data. + """ + + def test_conversation_fields_supplied_to_collections_metric(self): + from ragas.messages import AIMessage, HumanMessage + + metric = _FakeConversationalCollectionsMetric(value=1.0) + adapter = RAGASAdapter(metric=metric) + + evaluator_input = _make_evaluator_input( + reference_inputs=[{"expectedTrajectory": {"toolNames": ["search"]}}], + ) + result = adapter(evaluator_input) + + assert result.errorCode is None + assert result.value == 1.0 + kwargs = metric.score_calls[0] + # user_input must be a message list, not the flat string + assert isinstance(kwargs["user_input"], list) + assert isinstance(kwargs["user_input"][0], HumanMessage) + assert isinstance(kwargs["user_input"][1], AIMessage) + assert [tc.name for tc in kwargs["reference_tool_calls"]] == ["search"] + + def test_missing_expected_trajectory_returns_error(self): + metric = _FakeConversationalCollectionsMetric() + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.errorCode == "MISSING_REQUIRED_FIELD" + assert "reference_tool_calls" in result.errorMessage + assert metric.score_calls == [] + + def test_expects_sequence_detects_bare_and_parameterized_annotations(self): + """Bare `list` has no typing origin, so it needs explicit handling.""" + assert RAGASAdapter._expects_sequence(list) is True + assert RAGASAdapter._expects_sequence(List[Any]) is True + assert RAGASAdapter._expects_sequence(List[str]) is True + assert RAGASAdapter._expects_sequence(str) is False + assert RAGASAdapter._expects_sequence(int) is False + + def test_single_turn_collections_metric_keeps_flat_fields(self): + """Signatures declaring str inputs must not receive message lists.""" + metric = _FakeCollectionsMetric(value=1.0) + adapter = RAGASAdapter(metric=metric) + + spans = _make_spans(user_content='[{"text": "What is 2+2?\\n\\nReference Answer:\\n4"}]') + result = adapter(_make_evaluator_input(spans=spans)) + + assert result.value == 1.0 + kwargs = metric.score_calls[0] + assert kwargs["response"] == "AI is artificial intelligence." + assert kwargs["reference"] == "4" + assert "user_input" not in kwargs # not in the ascore signature + + +class TestRAGASAdapterReferenceToolCallArgs: + """Argument alignment for reference tool calls, shared by both conversational paths. + + expectedTrajectory.toolNames names tools without arguments, so the + predicted arguments of same-named calls are adopted. + """ + + def test_reference_args_aligned_from_predicted_call(self): + from ragas.messages import ToolCall + + adapter = RAGASAdapter(metric=_mock_multi_turn_metric()) + reference = [ToolCall(name="search", args={}), ToolCall(name="untouched", args={"a": 1})] + tools_called = [ + {"name": "search", "input_parameters": {"q": "python"}}, + {"name": "untouched", "input_parameters": {"a": 2}}, + ] + + aligned = adapter._align_reference_args(reference, tools_called) + + assert aligned[0].args == {"q": "python"} # filled from prediction + assert aligned[1].args == {"a": 1} # already had args, left alone + + def test_repeated_tool_name_args_aligned_in_call_order(self): + """A tool expected twice pairs with each invocation, not the first one twice.""" + from ragas.messages import ToolCall + + adapter = RAGASAdapter(metric=_mock_multi_turn_metric()) + reference = [ToolCall(name="search", args={}), ToolCall(name="search", args={})] + tools_called = [ + {"name": "search", "input_parameters": {"q": "first"}}, + {"name": "search", "input_parameters": {"q": "second"}}, + ] + + aligned = adapter._align_reference_args(reference, tools_called) + + assert [call.args for call in aligned] == [{"q": "first"}, {"q": "second"}] + + def test_uncalled_reference_tool_keeps_empty_args(self): + """A tool that was never called has no arguments to adopt.""" + from ragas.messages import ToolCall + + adapter = RAGASAdapter(metric=_mock_multi_turn_metric()) + reference = [ToolCall(name="never_called", args={})] + + aligned = adapter._align_reference_args(reference, [{"name": "other", "input_parameters": {"a": 1}}]) + + assert aligned[0].args == {} + + +class TestRAGASAdapterValidation: + def test_missing_required_column_returns_error(self): + metric = _mock_legacy_metric(name="context_precision", required={"user_input", "response", "reference"}) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.errorCode == "MISSING_REQUIRED_FIELD" + assert "reference" in result.errorMessage + assert "evaluationReferenceInputs" in result.errorMessage + metric.single_turn_score.assert_not_called() + + def test_missing_retrieved_contexts_returns_error(self): + metric = _mock_legacy_metric(required={"user_input", "response", "retrieved_contexts"}) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.errorCode == "MISSING_REQUIRED_FIELD" + assert "retrieved_contexts" in result.errorMessage + metric.single_turn_score.assert_not_called() + + def test_unsupported_metric_type_returns_error(self): + class NotAMetric: + name = "mystery" + + adapter = RAGASAdapter(metric=NotAMetric()) + + result = adapter(_make_evaluator_input()) + + assert result.errorCode == "UNSUPPORTED_METRIC" + + +def _mock_multi_turn_metric(name="agent_goal_accuracy", score=0.8, required=None): + """Create a mock legacy multi-turn RAGAS metric (no single_turn_score).""" + metric = MagicMock() + metric.name = name + metric.threshold = 0.5 + metric.required_columns = {"MULTI_TURN": required if required is not None else {"user_input"}} + del metric.single_turn_score + metric.multi_turn_score = MagicMock(return_value=score) + return metric + + +class TestRAGASAdapterMultiTurn: + def test_multi_turn_metric_scores_from_extracted_turns(self): + metric = _mock_multi_turn_metric(score=0.8) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.value == 0.8 + assert result.label == "Pass" + sample = metric.multi_turn_score.call_args[0][0] + # Single invocation falls back to a user/assistant message pair + assert len(sample.user_input) == 2 + assert sample.user_input[0].content == "What is AI?" + assert sample.user_input[1].content == "AI is artificial intelligence." + + def test_expected_trajectory_builds_reference_tool_calls(self): + metric = _mock_multi_turn_metric(required={"user_input", "reference_tool_calls"}) + adapter = RAGASAdapter(metric=metric) + + evaluator_input = _make_evaluator_input( + reference_inputs=[{"expectedTrajectory": {"toolNames": ["search", "calculate"]}}], + ) + + result = adapter(evaluator_input) + + assert result.value == 0.8 + sample = metric.multi_turn_score.call_args[0][0] + assert [tc.name for tc in sample.reference_tool_calls] == ["search", "calculate"] + assert sample.reference_tool_calls[0].args == {} + + def test_missing_multi_turn_required_field_returns_error(self): + metric = _mock_multi_turn_metric(required={"user_input", "reference_tool_calls"}) + adapter = RAGASAdapter(metric=metric) + + # No expectedTrajectory provided → reference_tool_calls unavailable + result = adapter(_make_evaluator_input()) + + assert result.errorCode == "MISSING_REQUIRED_FIELD" + assert "reference_tool_calls" in result.errorMessage + metric.multi_turn_score.assert_not_called() + + def test_embedded_reference_stripped_from_multi_turn_messages(self): + """Ground truth embedded in the user message must not leak into the conversation.""" + metric = _mock_multi_turn_metric(score=0.8) + adapter = RAGASAdapter(metric=metric) + + spans = _make_spans(user_content='[{"text": "What is 2+2?\\n\\nReference Answer:\\n4"}]') + result = adapter(_make_evaluator_input(spans=spans)) + + assert result.value == 0.8 + sample = metric.multi_turn_score.call_args[0][0] + assert sample.user_input[0].content == "What is 2+2?" + assert "Reference Answer" not in sample.user_input[0].content + + def test_build_messages_from_turns(self): + """Multi-invocation sessions build interleaved messages from turns.""" + from ragas.messages import AIMessage, HumanMessage + + adapter = RAGASAdapter(metric=_mock_multi_turn_metric()) + span_result = SimpleNamespace( + turns=[ + {"role": "user", "content": "first question\n\nContext:\nsome ctx"}, + {"role": "assistant", "content": "first answer"}, + {"role": "user", "content": "second question"}, + {"role": "assistant", "content": "second answer"}, + ], + input="first question", + actual_output="second answer", + tools_called=None, + ) + + messages = adapter._build_messages(span_result) + + assert len(messages) == 4 + assert isinstance(messages[0], HumanMessage) + assert messages[0].content == "first question" # embedded context stripped + assert isinstance(messages[3], AIMessage) + assert messages[3].content == "second answer" + + def test_tools_called_attached_to_last_ai_message(self): + adapter = RAGASAdapter(metric=_mock_multi_turn_metric()) + span_result = SimpleNamespace( + turns=[ + {"role": "user", "content": "q1"}, + {"role": "assistant", "content": "a1"}, + {"role": "user", "content": "q2"}, + {"role": "assistant", "content": "a2"}, + ], + input="q1", + actual_output="a2", + tools_called=[{"name": "search", "input_parameters": {"q": "x"}, "output": "r"}], + ) + + messages = adapter._build_messages(span_result) + + assert messages[1].tool_calls is None or messages[1].tool_calls == [] + assert [tc.name for tc in messages[3].tool_calls] == ["search"] + assert messages[3].tool_calls[0].args == {"q": "x"} + + def test_real_tool_call_accuracy_with_custom_mapper(self): + """ToolCallAccuracy is deterministic — validate end-to-end with the real metric.""" + from ragas.messages import AIMessage, HumanMessage, ToolCall + from ragas.metrics import ToolCallAccuracy + + adapter = RAGASAdapter( + metric=ToolCallAccuracy(), + custom_mapper=lambda ev: { + "user_input": [ + HumanMessage(content="Book a flight to NYC"), + AIMessage(content="Booking", tool_calls=[ToolCall(name="book_flight", args={"to": "NYC"})]), + ], + "reference_tool_calls": [ToolCall(name="book_flight", args={"to": "NYC"})], + }, + ) + + result = adapter(_make_evaluator_input()) + + assert result.value == 1.0 + assert result.label == "Pass" + + def test_real_tool_call_accuracy_mismatch_fails(self): + from ragas.messages import AIMessage, HumanMessage, ToolCall + from ragas.metrics import ToolCallAccuracy + + adapter = RAGASAdapter( + metric=ToolCallAccuracy(), + custom_mapper=lambda ev: { + "user_input": [ + HumanMessage(content="Book a flight to NYC"), + AIMessage(content="Booking", tool_calls=[ToolCall(name="wrong_tool", args={})]), + ], + "reference_tool_calls": [ToolCall(name="book_flight", args={"to": "NYC"})], + }, + ) + + result = adapter(_make_evaluator_input()) + + assert result.value == 0.0 + assert result.label == "Fail" + + +class TestRAGASAdapterErrors: + def test_no_agent_spans_returns_error(self): + spans = [ + { + "traceId": "t1", + "spanId": "s1", + "attributes": {"gen_ai.operation.name": "chat"}, + "span_events": [], + } + ] + metric = _mock_legacy_metric() + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input(spans=spans)) + + assert isinstance(result, EvaluatorOutput) + assert result.errorCode == "FIELD_EXTRACTION_ERROR" + + def test_missing_input_returns_error(self): + spans = [ + { + "traceId": "t1", + "spanId": "s1", + "scope": {"name": "strands.telemetry.tracer", "version": ""}, + "attributes": {"gen_ai.operation.name": "invoke_agent"}, + "span_events": [ + { + "body": { + "output": {"messages": [{"role": "assistant", "content": "answer"}]}, + } + } + ], + } + ] + metric = _mock_legacy_metric() + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input(spans=spans)) + + assert result.errorCode in ("MISSING_REQUIRED_FIELD", "FIELD_EXTRACTION_ERROR") + assert result.errorMessage + + def test_metric_execution_exception_returns_error(self): + metric = _mock_legacy_metric() + metric.single_turn_score = MagicMock(side_effect=RuntimeError("RAGAS timeout")) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.errorCode == "METRIC_ERROR" + assert "RAGAS timeout" in result.errorMessage + + def test_llm_not_set_returns_hint(self): + metric = _mock_legacy_metric() + metric.single_turn_score = MagicMock(side_effect=AssertionError("LLM is not set")) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.errorCode == "METRIC_ERROR" + assert "llm=" in result.errorMessage + + def test_import_error_returns_missing_dependency(self): + metric = _mock_legacy_metric() + metric.single_turn_score = MagicMock(side_effect=ImportError("No module named 'pandas'")) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.errorCode == "MISSING_DEPENDENCY" + assert "pandas" in result.errorMessage + + def test_nan_score_returns_error(self): + metric = _mock_legacy_metric(score=float("nan")) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.errorCode == "INVALID_SCORE" + assert "NaN" in result.errorMessage + + def test_never_raises(self): + metric = _mock_legacy_metric() + metric.single_turn_score = MagicMock(side_effect=Exception("unexpected catastrophic failure")) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert isinstance(result, EvaluatorOutput) + assert result.errorCode is not None + + +class TestRAGASAdapterEdgeCases: + def test_metric_score_zero(self): + metric = _mock_legacy_metric(score=0.0) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.value == 0.0 + assert result.label == "Fail" + + def test_metric_score_one(self): + metric = _mock_legacy_metric(score=1.0) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.value == 1.0 + assert result.label == "Pass" + + def test_default_threshold_when_missing(self): + metric = _mock_legacy_metric(score=0.6) + del metric.threshold + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.label == "Pass" + + +class TestRAGASAdapterEmbeddedParsing: + """Tests for parsing reference/context embedded in the user message. + + Trace formats have no dedicated reference/context fields, so dataset + recipes embed them in the user message with known separators. + """ + + def test_parses_embedded_reference_from_user_input(self): + metric = _mock_legacy_metric(name="exact_match", score=1.0) + adapter = RAGASAdapter(metric=metric) + + spans = _make_spans(user_content='[{"text": "What is 2+2?\\n\\nReference Answer:\\n4"}]') + result = adapter(_make_evaluator_input(spans=spans)) + + assert result.value == 1.0 + sample = metric.single_turn_score.call_args[0][0] + assert sample.user_input == "What is 2+2?" + assert sample.reference == "4" + + def test_parses_embedded_context_from_user_input(self): + metric = _mock_legacy_metric(score=0.9) + adapter = RAGASAdapter(metric=metric) + + spans = _make_spans( + user_content='[{"text": "What is AI?\\n\\nContext:\\nAI is a branch of computer science."}]' + ) + result = adapter(_make_evaluator_input(spans=spans)) + + assert result.value == 0.9 + sample = metric.single_turn_score.call_args[0][0] + assert sample.user_input == "What is AI?" + assert sample.retrieved_contexts == ["AI is a branch of computer science."] + # reference_contexts must NOT be defaulted from retrieved contexts: + # reference-comparison metrics would score retrieval against itself + assert sample.reference_contexts is None + + def test_json_list_context_recovers_ranked_chunks(self): + """Contexts embedded as json.dumps([...]) recover chunk boundaries and rank order.""" + import json + + metric = _mock_legacy_metric(name="llm_context_precision", score=0.67) + adapter = RAGASAdapter(metric=metric) + + inner = "What is AI?\n\nContext:\n" + json.dumps(["chunk1", "chunk2", "chunk3"]) + spans = _make_spans(user_content=json.dumps([{"text": inner}])) + result = adapter(_make_evaluator_input(spans=spans)) + + assert result.value == 0.67 + sample = metric.single_turn_score.call_args[0][0] + assert sample.retrieved_contexts == ["chunk1", "chunk2", "chunk3"] + assert sample.reference_contexts is None + + def test_json_non_list_context_treated_as_single_chunk(self): + """JSON that isn't a list of strings stays a single chunk.""" + import json + + metric = _mock_legacy_metric(score=0.9) + adapter = RAGASAdapter(metric=metric) + + inner = 'What is AI?\n\nContext:\n{"not": "a list"}' + spans = _make_spans(user_content=json.dumps([{"text": inner}])) + result = adapter(_make_evaluator_input(spans=spans)) + + assert result.value == 0.9 + sample = metric.single_turn_score.call_args[0][0] + assert sample.retrieved_contexts == ['{"not": "a list"}'] + + def test_parses_combined_context_and_reference(self): + metric = _mock_legacy_metric(name="context_precision", score=0.75) + adapter = RAGASAdapter(metric=metric) + + spans = _make_spans( + user_content=( + '[{"text": "What is AI?\\n\\nContext:\\nAI is computer science.' + '\\n\\nReference Answer:\\nArtificial Intelligence"}]' + ) + ) + result = adapter(_make_evaluator_input(spans=spans)) + + assert result.value == 0.75 + sample = metric.single_turn_score.call_args[0][0] + assert sample.user_input == "What is AI?" + assert sample.retrieved_contexts == ["AI is computer science."] + assert sample.reference == "Artificial Intelligence" + + def test_no_embedded_markers_leaves_input_unchanged(self): + metric = _mock_legacy_metric(score=0.8) + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.value == 0.8 + sample = metric.single_turn_score.call_args[0][0] + assert sample.user_input == "What is AI?" + assert sample.reference is None + + def test_reference_inputs_takes_precedence_over_embedded(self): + metric = _mock_legacy_metric(name="answer_correctness", score=0.9) + adapter = RAGASAdapter(metric=metric) + + spans = _make_spans(user_content='[{"text": "Q\\n\\nReference Answer:\\nembedded ref"}]') + evaluator_input = _make_evaluator_input( + spans=spans, + reference_inputs=[{"expectedResponse": {"text": "service-provided ref"}}], + ) + + result = adapter(evaluator_input) + + assert result.value == 0.9 + sample = metric.single_turn_score.call_args[0][0] + assert sample.reference == "service-provided ref" + + +class TestRAGASAdapterThresholdNone: + """Tests for metrics where threshold is explicitly None (e.g. SemanticSimilarity).""" + + def test_threshold_none_defaults_to_half(self): + """No TypeError comparing score >= None, and 0.5 is used instead.""" + metric = _mock_legacy_metric(name="semantic_similarity", score=0.6) + metric.threshold = None + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.value == 0.6 + assert result.label == "Pass" + assert "threshold=0.5" in result.explanation + + def test_threshold_none_score_below_default(self): + metric = _mock_legacy_metric(name="semantic_similarity", score=0.3) + metric.threshold = None + adapter = RAGASAdapter(metric=metric) + + result = adapter(_make_evaluator_input()) + + assert result.value == 0.3 + assert result.label == "Fail" + + def test_adapter_threshold_overrides_metric_threshold(self): + metric = _mock_legacy_metric(threshold=0.5, score=0.6) + adapter = RAGASAdapter(metric=metric, threshold=0.9) + + result = adapter(_make_evaluator_input()) + + assert result.value == 0.6 + assert result.label == "Fail" # adapter override wins over metric's 0.5 + assert "threshold=0.9" in result.explanation + + def test_adapter_threshold_applies_to_collections_metrics(self): + metric = _FakeCollectionsMetric(value=0.6) + adapter = RAGASAdapter( + metric=metric, + threshold=0.7, + custom_mapper=lambda ev: {"response": "a", "reference": "b"}, + ) + + result = adapter(_make_evaluator_input()) + + assert result.label == "Fail" diff --git a/tests_integ/evaluation/test_third_party_adapters.py b/tests_integ/evaluation/test_third_party_adapters.py index 8d79b395..ad0b96ab 100644 --- a/tests_integ/evaluation/test_third_party_adapters.py +++ b/tests_integ/evaluation/test_third_party_adapters.py @@ -1,6 +1,6 @@ """Integration tests for third-party evaluation adapters. -These tests require `deepeval` and `autoevals` packages to be installed. +These tests require `deepeval`, `autoevals`, and `ragas` packages to be installed. They verify the full adapter flow from EvaluatorInput through span parsing to metric execution, using real library metrics (not mocks). @@ -9,7 +9,7 @@ repo-specific workflow secret. SETUP: - pip install deepeval autoevals + pip install deepeval autoevals ragas export OPENAI_API_KEY=... RUN: @@ -34,16 +34,48 @@ def _text_content(text): return {"content": json.dumps([{"text": text}])} +def _tool_use_content(name, args, tool_use_id): + """Wrap a tool call in the toolUse content block the CloudWatch mapper reads.""" + return {"content": json.dumps([{"toolUse": {"name": name, "input": args, "toolUseId": tool_use_id}}])} + + +def _tool_result_content(tool_use_id, result): + """Wrap a tool result in the toolResult content block paired to a toolUse id.""" + return {"content": json.dumps([{"toolResult": {"toolUseId": tool_use_id, "content": [{"text": result}]}}])} + + def _make_agent_evaluator_input( user_prompt="What is the capital of France?", agent_response="The capital of France is Paris.", tool_messages=None, + tool_calls=None, + reference_tool_names=None, ): - """Build an EvaluatorInput with agent-level spans.""" + """Build an EvaluatorInput with agent-level spans. + + Args: + user_prompt: The user message content. + agent_response: The final assistant message content. + tool_messages: Optional plain-text tool outputs (become retrieval context). + tool_calls: Optional list of ``{"name": ..., "args": {...}}`` dicts, emitted + as paired toolUse/toolResult blocks so span mapping populates + ``tools_called``. + reference_tool_names: Optional expected tool trajectory, supplied as + ``evaluationReferenceInputs`` the way the service would. + """ output_messages = [] if tool_messages: for msg in tool_messages: output_messages.append({"role": "tool", "content": _text_content(msg)}) + if tool_calls: + for index, call in enumerate(tool_calls): + tool_use_id = f"integ-tool-{index}" + output_messages.append( + {"role": "assistant", "content": _tool_use_content(call["name"], call.get("args", {}), tool_use_id)} + ) + output_messages.append( + {"role": "user", "content": _tool_result_content(tool_use_id, call.get("result", "ok"))} + ) output_messages.append({"role": "assistant", "content": _text_content(agent_response)}) spans = [ @@ -63,10 +95,14 @@ def _make_agent_evaluator_input( ], } ] + reference_inputs = [] + if reference_tool_names: + reference_inputs = [{"expectedTrajectory": {"toolNames": list(reference_tool_names)}}] return EvaluatorInput( evaluation_level="TRACE", session_spans=spans, target_trace_id="integ-trace-1", + reference_inputs=reference_inputs, ) @@ -202,3 +238,219 @@ def test_with_custom_mapper(self, openai_client): assert isinstance(result, EvaluatorOutput) assert result.value is not None + + +class TestRAGASAdapterIntegration: + """Integration tests for RAGASAdapter with real RAGAS metrics. + + Uses deterministic (non-LLM) metrics so no model access is required. + """ + + @pytest.fixture(autouse=True) + def check_ragas(self): + """Verify ragas is installed.""" + import ragas # noqa: F401 + + def test_exact_match_with_embedded_reference(self): + from ragas.metrics import ExactMatch + + from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas import RAGASAdapter + + adapter = RAGASAdapter(metric=ExactMatch()) + + result = adapter( + _make_agent_evaluator_input( + user_prompt="What is the capital of France?\n\nReference Answer:\nThe capital of France is Paris.", + agent_response="The capital of France is Paris.", + ) + ) + + assert isinstance(result, EvaluatorOutput) + assert result.value == 1.0 + assert result.label == "Pass" + + def test_exact_match_fails_on_mismatch(self): + from ragas.metrics import ExactMatch + + from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas import RAGASAdapter + + adapter = RAGASAdapter(metric=ExactMatch()) + + result = adapter( + _make_agent_evaluator_input( + user_prompt="What is the capital of France?\n\nReference Answer:\nParis", + agent_response="The capital of France is Paris.", + ) + ) + + assert isinstance(result, EvaluatorOutput) + assert result.value == 0.0 + assert result.label == "Fail" + + def test_missing_reference_returns_error(self): + from ragas.metrics import ExactMatch + + from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas import RAGASAdapter + + adapter = RAGASAdapter(metric=ExactMatch()) + + result = adapter( + _make_agent_evaluator_input( + user_prompt="What is the capital of France?", + agent_response="The capital of France is Paris.", + ) + ) + + assert isinstance(result, EvaluatorOutput) + # ExactMatch declares reference as required; the adapter validates + # before scoring rather than letting ragas return a silent 0.0 + assert result.errorCode == "MISSING_REQUIRED_FIELD" + assert "reference" in result.errorMessage + + def test_with_custom_mapper(self): + from ragas.metrics import ExactMatch + + from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas import RAGASAdapter + + adapter = RAGASAdapter( + metric=ExactMatch(), + custom_mapper=lambda ev: { + "user_input": "What is 2+2?", + "response": "4", + "reference": "4", + }, + ) + + result = adapter(_make_agent_evaluator_input()) + + assert isinstance(result, EvaluatorOutput) + assert result.value == 1.0 + assert result.label == "Pass" + + def test_collections_metric_exact_match(self): + """New-generation ragas.metrics.collections metrics score via metric.score(**kwargs).""" + from ragas.metrics.collections import ExactMatch as CollectionsExactMatch + + from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas import RAGASAdapter + + adapter = RAGASAdapter(metric=CollectionsExactMatch()) + + result = adapter( + _make_agent_evaluator_input( + user_prompt="What is the capital of France?\n\nReference Answer:\nThe capital of France is Paris.", + agent_response="The capital of France is Paris.", + ) + ) + + assert isinstance(result, EvaluatorOutput) + assert result.value == 1.0 + assert result.label == "Pass" + + def test_decorator_discrete_metric(self): + """Decorator-based custom metrics return categorical labels.""" + from ragas.metrics import discrete_metric + + from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas import RAGASAdapter + + @discrete_metric(name="mentions_paris", allowed_values=["yes", "no"]) + def mentions_paris(response: str) -> str: + return "yes" if "Paris" in response else "no" + + adapter = RAGASAdapter( + metric=mentions_paris, + custom_mapper=lambda ev: {"response": "The capital of France is Paris."}, + ) + + result = adapter(_make_agent_evaluator_input()) + + assert isinstance(result, EvaluatorOutput) + assert result.label == "yes" + + def test_collections_tool_call_accuracy_default_mapping(self): + """Real collections ToolCallAccuracy via default span mapping (no custom mapper). + + Collections metrics take a conversation-shaped user_input and + reference_tool_calls, so the adapter must supply the converted messages + and tool calls rather than the flat single-turn fields. + """ + from ragas.metrics.collections import ToolCallAccuracy + + from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas import RAGASAdapter + + adapter = RAGASAdapter(metric=ToolCallAccuracy()) + + result = adapter( + _make_agent_evaluator_input( + user_prompt="Book a flight to NYC", + agent_response="Booked your flight.", + tool_calls=[{"name": "book_flight", "args": {"destination": "NYC"}}], + reference_tool_names=["book_flight"], + ) + ) + + assert isinstance(result, EvaluatorOutput) + assert result.errorCode is None + assert result.value == 1.0 + assert result.label == "Pass" + + def test_collections_tool_call_accuracy_wrong_tool_fails(self): + from ragas.metrics.collections import ToolCallAccuracy + + from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas import RAGASAdapter + + adapter = RAGASAdapter(metric=ToolCallAccuracy()) + + result = adapter( + _make_agent_evaluator_input( + user_prompt="Book a flight to NYC", + agent_response="Looked up the weather instead.", + tool_calls=[{"name": "get_weather", "args": {"city": "NYC"}}], + reference_tool_names=["book_flight"], + ) + ) + + assert isinstance(result, EvaluatorOutput) + assert result.value == 0.0 + assert result.label == "Fail" + + def test_legacy_tool_call_accuracy_default_mapping(self): + """The legacy multi-turn class scores identically through default mapping.""" + from ragas.metrics import ToolCallAccuracy + + from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas import RAGASAdapter + + adapter = RAGASAdapter(metric=ToolCallAccuracy()) + + result = adapter( + _make_agent_evaluator_input( + user_prompt="Book a flight to NYC", + agent_response="Booked your flight.", + tool_calls=[{"name": "book_flight", "args": {"destination": "NYC"}}], + reference_tool_names=["book_flight"], + ) + ) + + assert isinstance(result, EvaluatorOutput) + assert result.errorCode is None + assert result.value == 1.0 + + def test_adapter_imports_without_datasets(self): + """The adapter module itself must not require the HF datasets library at import time. + + Scope: this verifies the adapter adds no datasets dependency of its + own. ragas <1.0 still imports datasets when the ragas package is + imported; slim deployments handle that separately with a trimmed + ragas build. + """ + import subprocess + import sys + + code = ( + "import sys\n" + "sys.modules['datasets'] = None # poison the import\n" + "from bedrock_agentcore.evaluation.custom_code_based_evaluators.third_party.ragas import RAGASAdapter\n" + "print('OK')\n" + ) + proc = subprocess.run([sys.executable, "-c", code], capture_output=True, text=True) + assert proc.returncode == 0, proc.stderr + assert "OK" in proc.stdout diff --git a/uv.lock b/uv.lock index ca504ada..9ae06fbb 100644 --- a/uv.lock +++ b/uv.lock @@ -2,14 +2,19 @@ version = 1 revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.13' and extra != 'extra-17-bedrock-agentcore-a2a' and extra == 'extra-17-bedrock-agentcore-a2a-v1'", - "python_full_version >= '3.13' and extra == 'extra-17-bedrock-agentcore-a2a' and extra != 'extra-17-bedrock-agentcore-a2a-v1' and extra != 'group-17-bedrock-agentcore-dev'", - "python_full_version >= '3.13' and extra != 'extra-17-bedrock-agentcore-a2a' and extra != 'extra-17-bedrock-agentcore-a2a-v1'", - "python_full_version >= '3.11' and python_full_version < '3.13' and extra != 'extra-17-bedrock-agentcore-a2a' and extra == 'extra-17-bedrock-agentcore-a2a-v1'", - "python_full_version >= '3.11' and python_full_version < '3.13' and extra != 'extra-17-bedrock-agentcore-a2a' and extra != 'extra-17-bedrock-agentcore-a2a-v1'", - "python_full_version < '3.11' and extra != 'extra-17-bedrock-agentcore-a2a' and extra == 'extra-17-bedrock-agentcore-a2a-v1'", - "python_full_version < '3.13' and extra == 'extra-17-bedrock-agentcore-a2a' and extra != 'extra-17-bedrock-agentcore-a2a-v1' and extra != 'group-17-bedrock-agentcore-dev'", - "python_full_version < '3.11' and extra != 'extra-17-bedrock-agentcore-a2a' and extra != 'extra-17-bedrock-agentcore-a2a-v1'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", + "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten'", + "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.11'", ] conflicts = [[ { package = "bedrock-agentcore", extra = "a2a" }, @@ -24,8 +29,19 @@ name = "a2a-sdk" version = "0.3.26" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version < '3.13'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", + "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten'", + "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.11'", ] dependencies = [ { name = "google-api-core" }, @@ -51,8 +67,18 @@ name = "a2a-sdk" version = "1.1.2" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version >= '3.11' and python_full_version < '3.13'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", + "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten'", + "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", "python_full_version < '3.11'", ] dependencies = [ @@ -108,8 +134,18 @@ name = "aiofile" version = "3.11.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version >= '3.11' and python_full_version < '3.13'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", + "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten'", + "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] dependencies = [ { name = "caio", marker = "python_full_version >= '3.11'" }, @@ -308,13 +344,22 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = 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