diff --git a/litellm/integrations/otel/emitter.py b/litellm/integrations/otel/emitter.py index 101dbc6538d..e9441ee2a9a 100644 --- a/litellm/integrations/otel/emitter.py +++ b/litellm/integrations/otel/emitter.py @@ -1,15 +1,19 @@ """The span engine: dedup, start, run the mapper chain, set status, end.""" from collections import OrderedDict -from collections.abc import Callable, Sequence +from collections.abc import Callable, Mapping, Sequence +from types import MappingProxyType from typing import Final from opentelemetry.context import Context +from opentelemetry.sdk.trace import ReadableSpan, SpanLimits +from opentelemetry.sdk.trace import Span as SdkSpan from opentelemetry.trace import Link, Span, Tracer from opentelemetry.trace.status import Status, StatusCode from litellm.integrations.otel.mappers import resolve_mappers -from litellm.integrations.otel.mappers.base import AttributeMapper, SpanData +from litellm.integrations.otel.mappers.base import AttributeMapper, AttrValue, SpanData +from litellm.integrations.otel.mappers.openinference import fit_indexed_messages from litellm.integrations.otel.model.config import OpenTelemetryV2Config from litellm.integrations.otel.model.payloads import ( GuardrailSpanData, @@ -52,25 +56,48 @@ _NAME_BUILDERS: Final[dict[SpanRole, Callable[..., str]]] = { _DEDUP_CACHE_MAX: Final = 10_000 -def _stamp_otel_error_attributes(span: Span, error_type: str, resolved_message: str) -> None: - """Stamp the OTel-semconv error attributes (``error.type`` + ``error.message``). - ``error_type`` and ``resolved_message`` are ``finish_span``'s already-computed - fallback chains, so the pair on the status, event, and attributes stays in - lockstep.""" - span.set_attribute(Error.TYPE, error_type) - span.set_attribute(Error.MESSAGE, resolved_message) +def _resolve_error(error: SpanError) -> tuple[str, str] | None: + """The ``(error_type, message)`` fallback chain shared by the status, the event and the attributes, or + ``None`` when ``error`` carries neither a type nor a message.""" + if not (error.error_type or error.message): + return None + return error.error_type or "error", error.message or error.error_type or "error" -def _stamp_litellm_error_attributes(span: Span, error: SpanError) -> None: - """Stamp litellm-specific error detail attributes. Emitted only when the - corresponding field is populated so guardrail-shape errors carrying only a - message aren't polluted with empty detail keys.""" - if error.code: - span.set_attribute(LiteLLMError.CODE, error.code) - if error.stack_trace: - span.set_attribute(LiteLLMError.STACK_TRACE, error.stack_trace) - if error.llm_provider: - span.set_attribute(LiteLLMError.LLM_PROVIDER, error.llm_provider) +_NO_ATTRIBUTES: Final[Mapping[str, AttrValue]] = MappingProxyType({}) + + +def error_attributes(error: SpanError) -> Mapping[str, AttrValue]: + """The v2 error attribute set: the OTel-semconv ``error.*`` pair plus the litellm detail keys that are + populated, so guardrail-shape errors carrying only a message aren't polluted with empty detail keys.""" + resolved: Final = _resolve_error(error) + if resolved is None: + return _NO_ATTRIBUTES + error_type, message = resolved + pairs: Final = ( + (Error.TYPE, error_type), + (Error.MESSAGE, message), + (LiteLLMError.CODE, error.code), + (LiteLLMError.STACK_TRACE, error.stack_trace), + (LiteLLMError.LLM_PROVIDER, error.llm_provider), + ) + return MappingProxyType({key: value for key, value in pairs if value}) + + +def span_attribute_limit(span: Span) -> int | None: + """The attribute count limit ``span`` was built with, ``None`` when unbounded.""" + if not isinstance(span, SdkSpan): + return SpanLimits().max_span_attributes + return span._limits.max_span_attributes # pyright: ignore[reportPrivateUsage] # SDK has no public getter + + +def attribute_budget(span: Span, reserved: int) -> int | None: + """How many mapped attributes fit on ``span`` next to what it already carries and ``reserved`` more.""" + limit: Final = span_attribute_limit(span) + if limit is None: + return None + on_span: Final = len(span.attributes or ()) if isinstance(span, ReadableSpan) else 0 + return limit - on_span - reserved def stamp_error( @@ -93,12 +120,12 @@ def stamp_error( ``set_status`` are opt-outs for callers whose span lifecycle (``use_span``) or owner (the FastAPI instrumentor) already records the event or the status. """ - if not (error.error_type or error.message): + resolved: Final = _resolve_error(error) + if resolved is None: return None - error_type: Final = error.error_type or "error" - message: Final = error.message or error.error_type or "error" - _stamp_otel_error_attributes(span, error_type, message) - _stamp_litellm_error_attributes(span, error) + error_type, message = resolved + for key, value in error_attributes(error).items(): + span.set_attribute(key, value) if set_status: span.set_status(Status(StatusCode.ERROR, message)) if record_event: @@ -238,9 +265,6 @@ class SpanEmitter: data, since the boundary opener only has a provisional name. """ span.update_name(_NAME_BUILDERS[role](data)) - for mapper in self._mappers: - for key, value in mapper.map(data).items(): - span.set_attribute(key, value) error: Final = ( data.error if isinstance( @@ -255,6 +279,13 @@ class SpanEmitter: ) else None ) + mapped: Final = MappingProxyType( + {key: value for mapper in self._mappers for key, value in mapper.map(data).items()} + ) + stamped_later: Final = error_attributes(error) if error else _NO_ATTRIBUTES + reserved: Final = len(stamped_later.keys() - mapped.keys()) + for key, value in fit_indexed_messages(mapped, attribute_budget(span, reserved)).items(): + span.set_attribute(key, value) if error: stamped: Final = stamp_error(span, error) if stamped is not None and self._event_recorder is not None and role is SpanRole.LLM_CALL: diff --git a/litellm/integrations/otel/mappers/openinference.py b/litellm/integrations/otel/mappers/openinference.py index a7e0f1af3ac..a064c2c7e61 100644 --- a/litellm/integrations/otel/mappers/openinference.py +++ b/litellm/integrations/otel/mappers/openinference.py @@ -7,12 +7,13 @@ Phoenix + any other OpenInference-aware backend simultaneously. """ import json -from collections.abc import Callable, Sequence +from collections.abc import Callable, Mapping, Sequence +from itertools import accumulate, chain, groupby +from types import MappingProxyType from typing import Final from litellm.integrations.otel.mappers.base import AttributeMap, AttrValue, SpanData from litellm.integrations.otel.mappers.utils import ( - MAX_MESSAGE_ATTRS_PER_SPAN, MAX_TOOL_DEFINITION_ATTRS_PER_SPAN, collect, drop_none, @@ -27,7 +28,53 @@ from litellm.integrations.otel.model.payloads import ( ToolDefinition, ) -_MAX_INDEXED_MESSAGES: Final = MAX_MESSAGE_ATTRS_PER_SPAN // 2 +_INPUT_MESSAGES: Final = "llm.input_messages" +_OUTPUT_MESSAGES: Final = "llm.output_messages" +_MESSAGE_FAMILIES: Final = (_INPUT_MESSAGES, _OUTPUT_MESSAGES) + + +def _message_key_groups(attrs: Mapping[str, AttrValue]) -> Mapping[tuple[str, int], tuple[str, ...]]: + """Per-index message keys in ``attrs`` grouped by ``(family, index)``.""" + tagged: Final = sorted( + (family, int(key.split(".")[2]), key) + for key in attrs + for family in _MESSAGE_FAMILIES + if key.startswith(f"{family}.") + ) + return MappingProxyType( + {group: tuple(key for _, _, key in keys) for group, keys in groupby(tagged, key=lambda tag: tag[:2])} + ) + + +def _shed_order(groups: Mapping[tuple[str, int], tuple[str, ...]]) -> tuple[tuple[str, int], ...]: + """Message groups least valuable first: middle prompt turns, extra choices, then the opener, the newest turn + and the first choice.""" + inputs: Final = sorted(idx for family, idx in groups if family == _INPUT_MESSAGES) + outputs: Final = sorted(idx for family, idx in groups if family == _OUTPUT_MESSAGES) + pinned_inputs: Final = tuple(dict.fromkeys((*inputs[:1], *inputs[-1:]))) + return ( + *((_INPUT_MESSAGES, idx) for idx in inputs[1:-1]), + *((_OUTPUT_MESSAGES, idx) for idx in reversed(outputs[1:])), + *((_INPUT_MESSAGES, idx) for idx in pinned_inputs), + *((_OUTPUT_MESSAGES, idx) for idx in outputs[:1]), + ) + + +def fit_indexed_messages(attrs: Mapping[str, AttrValue], budget: int | None) -> Mapping[str, AttrValue]: + """``attrs`` with whole per-index messages shed, least valuable first, until at most ``budget`` keys remain. + + ``None`` means the span has no attribute count limit. Every message still rides the ``input.value`` and + ``output.value`` blobs, so shedding a per-index pair loses no content. + """ + if budget is None or len(attrs) <= budget: + return attrs + groups: Final = _message_key_groups(attrs) + order: Final = _shed_order(groups) + running: Final = tuple(accumulate(len(groups[group]) for group in order)) + excess: Final = len(attrs) - budget + shed_count: Final = next((n + 1 for n, total in enumerate(running) if total >= excess), len(order)) + shed: Final = frozenset(chain.from_iterable(groups[group] for group in order[:shed_count])) + return MappingProxyType({key: value for key, value in attrs.items() if key not in shed}) class OpenInferenceMapper: @@ -87,42 +134,22 @@ class OpenInferenceMapper: return {} def _llm_call(self, data: LLMCallSpanData) -> AttributeMap: - outputs: Final = output_messages(data) - indexed_in, indexed_out = self._indexed_split(len(data.messages_in), len(outputs)) return { **collect(self._LLM_CALL_ATTRS, data), **collect(self._BLOB_ATTRS, data), - **self._messages( - "llm.input_messages", - "input.value", - data.messages_in, - self._prompt_positions(len(data.messages_in), indexed_in), - ), - **self._messages("llm.output_messages", "output.value", outputs, range(indexed_out)), + **self._messages(_INPUT_MESSAGES, "input.value", data.messages_in), + **self._messages(_OUTPUT_MESSAGES, "output.value", output_messages(data)), **self._tools(data), } @staticmethod - def _indexed_split(inputs: int, outputs: int) -> tuple[int, int]: - """Prompt and response share one allowance; the response is reserved at least half of it.""" - indexed_out: Final = min(outputs, max(_MAX_INDEXED_MESSAGES // 2, _MAX_INDEXED_MESSAGES - inputs)) - return _MAX_INDEXED_MESSAGES - indexed_out, indexed_out - - @staticmethod - def _prompt_positions(total: int, indexed: int) -> tuple[int, ...]: - """Prompt messages that get per-index attributes: message 0 and the most recent turns.""" - if total <= indexed: - return tuple(range(total)) - return (0, *range(total - indexed + 1, total)) - - @staticmethod - def _messages(prefix: str, value_key: str, messages: Sequence[object], positions: Sequence[int]) -> AttributeMap: - """``{prefix}.{idx}.message.*`` keys for the messages at ``positions`` + the ``value_key`` blob of all.""" + def _messages(prefix: str, value_key: str, messages: Sequence[object]) -> AttributeMap: + """``{prefix}.{idx}.message.*`` keys for every message + the ``value_key`` blob of all of them.""" parsed: Final = [(m.get("role") if isinstance(m, dict) else None, message_content(m)) for m in messages] attrs: Final = drop_none( { key: value - for idx, (role, content) in ((idx, parsed[idx]) for idx in positions) + for idx, (role, content) in enumerate(parsed) for key, value in ( (f"{prefix}.{idx}.message.role", role if isinstance(role, str) else None), (f"{prefix}.{idx}.message.content", content), diff --git a/litellm/integrations/otel/mappers/utils.py b/litellm/integrations/otel/mappers/utils.py index c023621d2ef..d45dca782b2 100644 --- a/litellm/integrations/otel/mappers/utils.py +++ b/litellm/integrations/otel/mappers/utils.py @@ -32,14 +32,6 @@ core telemetry no matter how many vocabularies are configured. """ -MAX_MESSAGE_ATTRS_PER_SPAN: Final = DEFAULT_SPAN_ATTRIBUTE_LIMIT // 8 -"""Span-wide ceiling on per-index chat message attributes, prompt and response together. - -An eighth is the largest share that still fits beside the tool ceiling and the core -of every vocabulary at once. The complete conversation still rides the JSON blobs. -""" - - def tool_attr_budget(vocabularies: int) -> int: """Split the span-wide tool-definition ceiling across active vocabularies.""" return MAX_TOOL_DEFINITION_ATTRS_PER_SPAN // max(vocabularies, 1) diff --git a/tests/test_litellm/integrations/otel/test_otel_v2_emitter.py b/tests/test_litellm/integrations/otel/test_otel_v2_emitter.py index 6e2e467b856..11b2aa5fd67 100644 --- a/tests/test_litellm/integrations/otel/test_otel_v2_emitter.py +++ b/tests/test_litellm/integrations/otel/test_otel_v2_emitter.py @@ -7,7 +7,10 @@ import pytest pytest.importorskip("opentelemetry") -from opentelemetry.trace import SpanKind # noqa: E402 +from opentelemetry.sdk.trace import SpanLimits, TracerProvider # noqa: E402 +from opentelemetry.sdk.trace.export import SimpleSpanProcessor # noqa: E402 +from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter # noqa: E402 +from opentelemetry.trace import INVALID_SPAN, SpanKind # noqa: E402 from opentelemetry.trace.status import StatusCode # noqa: E402 from litellm.integrations.otel import ( # noqa: E402 @@ -17,12 +20,9 @@ from litellm.integrations.otel import ( # noqa: E402 ) from litellm.integrations.otel.plumbing import context as ctx_mod # noqa: E402 from litellm.integrations.otel.plumbing import providers # noqa: E402 -from litellm.integrations.otel.emitter import SpanEmitter # noqa: E402 +from litellm.integrations.otel.emitter import SpanEmitter, span_attribute_limit # noqa: E402 from litellm.integrations.otel.emitter import stamp_error # noqa: E402 -from litellm.integrations.otel.mappers.utils import ( # noqa: E402 - MAX_MESSAGE_ATTRS_PER_SPAN, - MAX_TOOL_DEFINITION_ATTRS_PER_SPAN, -) +from litellm.integrations.otel.mappers.utils import MAX_TOOL_DEFINITION_ATTRS_PER_SPAN # noqa: E402 from litellm.integrations.otel.model.payloads import ( # noqa: E402 GuardrailSpanData, LLMCallSpanData, @@ -127,9 +127,7 @@ def test_llm_call_span_golden(): def test_legacy_dual_emit_on(): engine, exporter = _engine(legacy_compat=True) - engine.emit( - SpanRole.LLM_CALL, LLMCallSpanData.from_standard_logging_payload(_payload()) - ) + engine.emit(SpanRole.LLM_CALL, LLMCallSpanData.from_standard_logging_payload(_payload())) (span,) = exporter.get_finished_spans() # canonical AND legacy keys are both present assert span.attributes[GenAI.USAGE_OUTPUT_TOKENS] == 5 @@ -139,9 +137,7 @@ def test_legacy_dual_emit_on(): def test_legacy_dual_emit_off(): engine, exporter = _engine(legacy_compat=False) - engine.emit( - SpanRole.LLM_CALL, LLMCallSpanData.from_standard_logging_payload(_payload()) - ) + engine.emit(SpanRole.LLM_CALL, LLMCallSpanData.from_standard_logging_payload(_payload())) (span,) = exporter.get_finished_spans() # canonical present, legacy absent assert span.attributes[GenAI.USAGE_OUTPUT_TOKENS] == 5 @@ -155,9 +151,7 @@ def test_error_span_sets_status_and_error_type(): status="failure", error_information={"error_class": "RateLimitError", "error_message": "429"}, ) - engine.emit( - SpanRole.LLM_CALL, LLMCallSpanData.from_standard_logging_payload(payload) - ) + engine.emit(SpanRole.LLM_CALL, LLMCallSpanData.from_standard_logging_payload(payload)) (span,) = exporter.get_finished_spans() assert span.status.status_code is StatusCode.ERROR assert span.attributes["error.type"] == "RateLimitError" @@ -209,15 +203,11 @@ def test_hierarchy_and_kinds_match_registry(): root = engine.start_span(SpanRole.PROXY_REQUEST, "POST /chat/completions") root_ctx = ctx_mod.context_from_span(root) engine.emit(SpanRole.LLM_CALL, data, parent_context=root_ctx) - engine.emit( - SpanRole.GUARDRAIL, GuardrailSpanData("presidio", status="success"), root_ctx - ) + engine.emit(SpanRole.GUARDRAIL, GuardrailSpanData("presidio", status="success"), root_ctx) # An outbound datastore call (DB_CALL) and an internal service call differ in # span kind; both are named "{service} {call_type}". engine.emit(SpanRole.DB_CALL, ServiceSpanData("redis", call_type="set"), root_ctx) - engine.emit( - SpanRole.SERVICE, ServiceSpanData("router", call_type="acompletion"), root_ctx - ) + engine.emit(SpanRole.SERVICE, ServiceSpanData("router", call_type="acompletion"), root_ctx) root.end() by_name = {s.name: s for s in exporter.get_finished_spans()} @@ -255,9 +245,7 @@ def test_dedup_cache_is_bounded(monkeypatch): for i in range(10): engine.emit( SpanRole.LLM_CALL, - LLMCallSpanData.from_standard_logging_payload( - _payload(litellm_call_id=f"call_{i}") - ), + LLMCallSpanData.from_standard_logging_payload(_payload(litellm_call_id=f"call_{i}")), ) assert len(engine._emitted) <= 3 @@ -268,9 +256,7 @@ def test_service_error_span(): engine, exporter = _engine() engine.emit( SpanRole.SERVICE, - ServiceSpanData( - "postgres", call_type="query", error=SpanError("DBError", "boom") - ), + ServiceSpanData("postgres", call_type="query", error=SpanError("DBError", "boom")), ) (span,) = exporter.get_finished_spans() assert span.status.status_code is StatusCode.ERROR @@ -305,9 +291,7 @@ def test_guardrail_success_span_is_unset(): engine, exporter = _engine() engine.emit( SpanRole.GUARDRAIL, - GuardrailSpanData.from_logging_entry( - {"guardrail_name": "g", "guardrail_status": "success"} - ), + GuardrailSpanData.from_logging_entry({"guardrail_name": "g", "guardrail_status": "success"}), ) (span,) = exporter.get_finished_spans() assert span.status.status_code is StatusCode.UNSET @@ -396,11 +380,7 @@ def _tool_span(mapper_names, tool_count): def _tool_definition_keys(attributes): - return [ - key - for key in attributes - if key.startswith(("gen_ai.tool.", "llm.request.functions.", "llm.tools.")) - ] + return [key for key in attributes if key.startswith(("gen_ai.tool.", "llm.request.functions.", "llm.tools."))] @pytest.mark.parametrize( @@ -461,15 +441,19 @@ def _conversation_payload(turns, choices=1, **overrides): ) -def _conversation_span(mapper_names, payload, legacy_compat=False): - """The exported LLM-call span for ``payload`` with content capture on.""" +def _conversation_span(mapper_names, payload, legacy_compat=False, span_limits=None): + """The exported LLM-call span for ``payload`` with content capture on. + + ``span_limits`` builds the provider with programmatic limits instead of the environment's.""" cfg = OpenTelemetryV2Config( exporter="in_memory", legacy_compat=legacy_compat, mapper_names=list(mapper_names), capture_message_content="span_only", ) - provider, exporter = providers.in_memory_provider(cfg) + provider, exporter = ( + providers.in_memory_provider(cfg) if span_limits is None else _provider_with_limits(span_limits) + ) engine = SpanEmitter(providers.get_tracer(provider, "litellm-test"), cfg) engine.emit( SpanRole.LLM_CALL, @@ -479,37 +463,56 @@ def _conversation_span(mapper_names, payload, legacy_compat=False): return span -def _indexed_message_count(attributes, prefix): - return len({key.split(".")[2] for key in attributes if key.startswith(f"{prefix}.")}) +def _provider_with_limits(span_limits): + provider = TracerProvider(span_limits=span_limits) + exporter = InMemorySpanExporter() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + return provider, exporter -@pytest.mark.parametrize("turns", [60, 200]) -def test_long_conversation_does_not_evict_core_attributes(turns): - """Per-message OpenInference attributes must never crowd core telemetry off the span.""" - span = _conversation_span(["genai", "openinference"], _conversation_payload(turns)) +def _indexed_messages(attributes, prefix): + return sorted({int(key.split(".")[2]) for key in attributes if key.startswith(f"{prefix}.")}) + + +def _assert_core_intact(span): a = span.attributes - assert span.dropped_attributes == 0 assert a[GenAI.REQUEST_MODEL] == "gpt-4o" assert a[GenAI.PROVIDER_NAME] == "openai" assert a[GenAI.USAGE_INPUT_TOKENS] == 10 assert a[GenAI.USAGE_OUTPUT_TOKENS] == 5 - assert a[GenAI.RESPONSE_FINISH_REASONS] == ("stop",) + assert set(a[GenAI.RESPONSE_FINISH_REASONS]) == {"stop"} assert a[f"{LiteLLM.COST_PREFIX}total"] == 0.002 - assert a["llm.input_messages.0.message.content"] == "turn 0" - assert a["llm.output_messages.0.message.content"] == "reply 0" + +@pytest.mark.parametrize("turns", [60, 200]) +def test_long_conversation_does_not_evict_core_attributes(turns): + """Per-message OpenInference attributes fill the span's headroom and never crowd core telemetry off it.""" + span = _conversation_span(["genai", "openinference"], _conversation_payload(turns)) + _assert_core_intact(span) + a = span.attributes + limit = SpanLimits().max_span_attributes + + assert limit - 1 <= len(a) <= limit + indexed = _indexed_messages(a, "llm.input_messages") + assert 1 < len(indexed) < turns + assert indexed[0] == 0 + assert indexed[1:] == list(range(indexed[1], turns)) assert a[f"llm.input_messages.{turns - 1}.message.content"] == f"turn {turns - 1}" - assert f"llm.input_messages.{turns // 2}.message.role" not in a + assert a["llm.output_messages.0.message.content"] == "reply 0" assert len(json.loads(a["input.value"])) == turns assert len(json.loads(a["output.value"])) == 1 assert len(json.loads(a[GenAI.INPUT_MESSAGES])) == turns -def test_short_conversation_keeps_every_message_indexed(): - """Below the cap nothing is truncated in either direction.""" - a = _conversation_span(["genai", "openinference"], _conversation_payload(4, choices=2)).attributes - for idx in range(4): +@pytest.mark.parametrize("turns", [4, 8, 40]) +def test_conversation_that_fits_the_span_keeps_every_message_indexed(turns): + """No per-index message is shed while the span has room for all of them.""" + span = _conversation_span(["genai", "openinference"], _conversation_payload(turns, choices=2)) + _assert_core_intact(span) + a = span.attributes + for idx in range(turns): + assert a[f"llm.input_messages.{idx}.message.role"] == ("user", "assistant")[idx % 2] assert a[f"llm.input_messages.{idx}.message.content"] == f"turn {idx}" for idx in range(2): assert a[f"llm.output_messages.{idx}.message.content"] == f"reply {idx}" @@ -535,28 +538,159 @@ def test_indexed_prompt_keeps_opener_and_latest_turns_under_a_value_length_limit assert a["llm.input_messages.59.message.role"] == "user" assert a["llm.input_messages.59.message.content"] == "LATEST-TURN" assert a["llm.output_messages.0.message.content"] == "reply 0" - assert [int(key.split(".")[2]) for key in a if key.endswith("message.content") and key.startswith("llm.input_")] == [ - 0, - *range(54, 60), - ] + indexed = _indexed_messages(a, "llm.input_messages") + assert indexed[0] == 0 and indexed[-1] == 59 and len(indexed) < 60 + assert indexed[1:] == list(range(indexed[1], 60)) -def test_message_cap_is_shared_across_input_and_output(): - """One span-wide allowance covers both directions, and the response always keeps a share.""" +def test_prompt_turns_are_shed_before_response_choices(): + """Under pressure the middle of the prompt goes first; every response choice keeps its keys.""" long_prompt = _conversation_span(["genai", "openinference"], _conversation_payload(60, choices=1)).attributes - many_choices = _conversation_span(["genai", "openinference"], _conversation_payload(60, choices=20)).attributes + many_choices = _conversation_span(["genai", "openinference"], _conversation_payload(60, choices=20)) + _assert_core_intact(many_choices) - single_reply_indexed = _indexed_message_count(long_prompt, "llm.output_messages") - assert single_reply_indexed == 1 - assert _indexed_message_count(long_prompt, "llm.input_messages") + single_reply_indexed == ( - MAX_MESSAGE_ATTRS_PER_SPAN // 2 + assert _indexed_messages(long_prompt, "llm.output_messages") == [0] + assert _indexed_messages(many_choices.attributes, "llm.output_messages") == list(range(20)) + assert ( + 1 + < len(_indexed_messages(many_choices.attributes, "llm.input_messages")) + < len(_indexed_messages(long_prompt, "llm.input_messages")) ) - assert _indexed_message_count(many_choices, "llm.input_messages") > 0 - assert _indexed_message_count(many_choices, "llm.output_messages") > single_reply_indexed - assert _indexed_message_count(many_choices, "llm.input_messages") + _indexed_message_count( - many_choices, "llm.output_messages" - ) == (MAX_MESSAGE_ATTRS_PER_SPAN // 2) + +def test_indexed_messages_respect_a_lower_span_attribute_count_limit(monkeypatch): + """The budget follows the SDK's configured limit, not a hardcoded default.""" + monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", "48") + span = _conversation_span(["genai", "openinference"], _conversation_payload(60)) + _assert_core_intact(span) + a = span.attributes + assert 47 <= len(a) <= 48 + assert a["llm.input_messages.0.message.content"] == "turn 0" + assert a["llm.input_messages.59.message.content"] == "turn 59" + assert a["llm.output_messages.0.message.content"] == "reply 0" + + +def test_a_tight_span_keeps_the_reply_and_newest_turn_before_the_opener(monkeypatch): + monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", "1000") + full = _conversation_span(["genai", "openinference"], _conversation_payload(6)).attributes + unindexed = [key for key in full if not key.startswith(("llm.input_messages.", "llm.output_messages."))] + + monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", str(len(unindexed) + 4)) + a = _conversation_span(["genai", "openinference"], _conversation_payload(6)).attributes + assert _indexed_messages(a, "llm.output_messages") == [0] + assert _indexed_messages(a, "llm.input_messages") == [5] + + monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", str(len(unindexed) + 2)) + a = _conversation_span(["genai", "openinference"], _conversation_payload(6)).attributes + assert _indexed_messages(a, "llm.output_messages") == [0] + assert _indexed_messages(a, "llm.input_messages") == [] + + +def test_shedding_stops_exactly_at_the_limit(monkeypatch): + """A span that fits exactly sheds nothing, and shedding never takes one pair more than the excess needs.""" + monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", "1000") + full = dict(_conversation_span(["genai", "openinference"], _conversation_payload(30)).attributes) + assert _indexed_messages(full, "llm.input_messages") == list(range(30)) + + monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", str(len(full))) + exact = _conversation_span(["genai", "openinference"], _conversation_payload(30)) + assert exact.dropped_attributes == 0 + assert dict(exact.attributes) == full + + monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", str(len(full) - 2)) + tight = _conversation_span(["genai", "openinference"], _conversation_payload(30)) + assert tight.dropped_attributes == 0 + assert len(tight.attributes) == len(full) - 2 + assert _indexed_messages(tight.attributes, "llm.input_messages") == [0, *range(2, 30)] + + +def test_error_and_pre_stamped_attributes_keep_their_room_on_a_long_conversation(): + """Attributes already on the span and the error set stamped after mapping both count against the budget.""" + cfg = OpenTelemetryV2Config(exporter="in_memory", mapper_names=["genai", "openinference"]) + provider, exporter = providers.in_memory_provider(cfg) + engine = SpanEmitter(providers.get_tracer(provider, "litellm-test"), cfg) + span = engine.start_span(SpanRole.LLM_CALL, "chat") + for idx in range(10): + span.set_attribute(f"litellm.metadata.baggage_{idx}", f"value {idx}") + payload = _conversation_payload( + 60, + status="failure", + error_information={ + "error_class": "RateLimitError", + "error_message": "429", + "error_code": "429", + "llm_provider": "openai", + "traceback": "tb", + }, + ) + engine.finish_span( + SpanRole.LLM_CALL, span, LLMCallSpanData.from_standard_logging_payload(payload, capture_content=True) + ) + (s,) = exporter.get_finished_spans() + a = s.attributes + + assert s.dropped_attributes == 0 + assert SpanLimits().max_span_attributes - 1 <= len(a) <= SpanLimits().max_span_attributes + assert a[GenAI.REQUEST_MODEL] == "gpt-4o" + assert a["litellm.metadata.baggage_0"] == "value 0" + assert a["error.type"] == "RateLimitError" + assert a["litellm.provider.error.stack_trace"] == "tb" + assert a["llm.input_messages.0.message.content"] == "turn 0" + assert a["llm.input_messages.59.message.content"] == "turn 59" + + +def test_indexed_messages_follow_the_providers_own_span_limits(monkeypatch): + """A provider built with programmatic ``SpanLimits`` sets the budget, whatever the environment says.""" + monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", "1000") + span = _conversation_span( + ["genai", "openinference"], _conversation_payload(60), span_limits=SpanLimits(max_span_attributes=40) + ) + _assert_core_intact(span) + a = span.attributes + assert 39 <= len(a) <= 40 + assert a["llm.input_messages.0.message.content"] == "turn 0" + assert a["llm.input_messages.59.message.content"] == "turn 59" + assert a["llm.output_messages.0.message.content"] == "reply 0" + + monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", "48") + unbounded = _conversation_span( + ["genai", "openinference"], + _conversation_payload(60), + span_limits=SpanLimits(max_span_attributes=SpanLimits.UNSET), + ) + _assert_core_intact(unbounded) + assert _indexed_messages(unbounded.attributes, "llm.input_messages") == list(range(60)) + + +@pytest.mark.parametrize("opened_at_boundary", [False, True], ids=["emit", "start_span+finish_span"]) +def test_indexed_messages_follow_the_span_limits_of_a_per_request_tracer_override(monkeypatch, opened_at_boundary): + """A routed ``tracer`` builds the span, so its provider's limits set the budget, not the bound tracer's. + + Holds whether the span is emitted in one shot or opened at the pre_call boundary and finished later. + """ + monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", "1000") + cfg = OpenTelemetryV2Config( + exporter="in_memory", mapper_names=["genai", "openinference"], capture_message_content="span_only" + ) + bound_provider, _ = _provider_with_limits(SpanLimits(max_span_attributes=1000)) + routed_provider, routed_exporter = _provider_with_limits(SpanLimits(max_span_attributes=40)) + engine = SpanEmitter(providers.get_tracer(bound_provider, "litellm-test"), cfg) + routed_tracer = providers.get_tracer(routed_provider, "litellm-routed") + data = LLMCallSpanData.from_standard_logging_payload(_conversation_payload(60), capture_content=True) + if opened_at_boundary: + opened = engine.start_span(SpanRole.LLM_CALL, "chat", tracer=routed_tracer) + engine.finish_span(SpanRole.LLM_CALL, opened, data) + else: + engine.emit(SpanRole.LLM_CALL, data, tracer=routed_tracer) + (span,) = routed_exporter.get_finished_spans() + _assert_core_intact(span) + assert 39 <= len(span.attributes) <= 40 + assert span.attributes["llm.output_messages.0.message.content"] == "reply 0" + + +def test_span_attribute_limit_falls_back_to_the_environment_for_spans_outside_the_sdk(monkeypatch): + monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", "48") + assert span_attribute_limit(INVALID_SPAN) == 48 def test_fully_populated_span_with_every_vocabulary_stays_within_the_attribute_limit():