fix failing checks and clean up imports

Author: lmolkova

instrumentation-genai/AGENTS.md

@@ -39,4 +39,3 @@ except Exception as exc:
   tests and callers, not in the instrumentation layer.
 - When catching exceptions from the underlying library to record telemetry, always re-raise
   the original exception unmodified.
-- Do not wrap, replace, or suppress exceptions — telemetry must be transparent to callers.

instrumentation-genai/opentelemetry-instrumentation-anthropic/src/opentelemetry/instrumentation/anthropic/patch.py

@@ -24,10 +24,10 @@
     gen_ai_attributes as GenAIAttributes,
 )
 from opentelemetry.util.genai.handler import TelemetryHandler
-from opentelemetry.util.genai.inference_invocation import (
+from opentelemetry.util.genai.types import (
+    Error,
     LLMInvocation,  # pyright: ignore[reportDeprecated]  # TODO: migrate to InferenceInvocation
 )
-from opentelemetry.util.genai.types import Error
 from opentelemetry.util.genai.utils import (
     should_capture_content_on_spans_in_experimental_mode,
 )

instrumentation-genai/opentelemetry-instrumentation-anthropic/src/opentelemetry/instrumentation/anthropic/wrappers.py

@@ -19,11 +19,9 @@
 from typing import TYPE_CHECKING, Callable, Iterator, Optional
 
 from opentelemetry.util.genai.handler import TelemetryHandler
-from opentelemetry.util.genai.inference_invocation import (
-    LLMInvocation,  # pyright: ignore[reportDeprecated]  # TODO: migrate to InferenceInvocation
-)
 from opentelemetry.util.genai.types import (
     Error,
+    LLMInvocation,  # pyright: ignore[reportDeprecated]  # TODO: migrate to InferenceInvocation
     MessagePart,
     OutputMessage,
 )

instrumentation-genai/opentelemetry-instrumentation-langchain/src/opentelemetry/instrumentation/langchain/callback_handler.py

@@ -25,12 +25,10 @@
     _InvocationManager,
 )
 from opentelemetry.util.genai.handler import TelemetryHandler
-from opentelemetry.util.genai.inference_invocation import (
-    LLMInvocation,  # pyright: ignore[reportDeprecated]  # TODO: migrate to InferenceInvocation
-)
 from opentelemetry.util.genai.types import (
     Error,
     InputMessage,
+    LLMInvocation,  # pyright: ignore[reportDeprecated]  # TODO: migrate to InferenceInvocation
     MessagePart,
     OutputMessage,
     Text,
@@ -160,7 +158,7 @@ def on_chat_model_start(
         self._invocation_manager.add_invocation_state(
             run_id=run_id,
             parent_run_id=parent_run_id,
-            invocation=llm_invocation,
+            invocation=llm_invocation,  # pyright: ignore[reportArgumentType]
         )
 
     def on_llm_end(

instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/patch.py

@@ -33,12 +33,10 @@
 from opentelemetry.trace import Span, SpanKind, Tracer
 from opentelemetry.trace.propagation import set_span_in_context
 from opentelemetry.util.genai.handler import TelemetryHandler
-from opentelemetry.util.genai.inference_invocation import (
-    LLMInvocation,  # pylint: disable=no-name-in-module  # TODO: migrate to InferenceInvocation
-)
 from opentelemetry.util.genai.types import (
     ContentCapturingMode,
     Error,
+    LLMInvocation,  # pylint: disable=no-name-in-module  # TODO: migrate to InferenceInvocation
     OutputMessage,
     Text,
     ToolCallRequest,

instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/response_wrappers.py

@@ -8,10 +8,10 @@
 from typing import TYPE_CHECKING, Callable, Generator, Generic, TypeVar
 
 from opentelemetry.util.genai.handler import TelemetryHandler
-from opentelemetry.util.genai.inference_invocation import (
+from opentelemetry.util.genai.types import (
+    Error,
     LLMInvocation,  # pylint: disable=no-name-in-module  # TODO: migrate to InferenceInvocation
 )
-from opentelemetry.util.genai.types import Error
 
 # OpenAI Responses internals are version-gated (added in openai>=1.66.0), so
 # pylint may not resolve them in all lint environments even though we guard

instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/utils.py

@@ -36,11 +36,9 @@
     error_attributes as ErrorAttributes,
 )
 from opentelemetry.trace.status import Status, StatusCode
-from opentelemetry.util.genai.inference_invocation import (
-    LLMInvocation,  # pylint: disable=no-name-in-module  # TODO: migrate to InferenceInvocation
-)
 from opentelemetry.util.genai.types import (
     InputMessage,
+    LLMInvocation,  # pylint: disable=no-name-in-module  # TODO: migrate to InferenceInvocation
     OutputMessage,
     Text,
     ToolCallRequest,

util/opentelemetry-util-genai/AGENTS.md

@@ -54,7 +54,6 @@ propagation, so all telemetry calls become no-ops. Always use `handler.start_*()
   tests and callers, not telemetry internals.
 - When catching exceptions from the underlying library to record telemetry, always re-raise
   the original exception unmodified.
-- Do not wrap, replace, or suppress exceptions — telemetry must be transparent to callers.
 
 ## 4. Documentation
 

…metry/util/genai/embedding_invocation.py …etry/util/genai/_embedding_invocation.pyutil/opentelemetry-util-genai/src/opentelemetry/util/genai/embedding_invocation.py renamed to util/opentelemetry-util-genai/src/opentelemetry/util/genai/_embedding_invocation.py util/opentelemetry-util-genai/src/opentelemetry/util/genai/embedding_invocation.py renamed to util/opentelemetry-util-genai/src/opentelemetry/util/genai/_embedding_invocation.py

@@ -14,7 +14,7 @@
 
 from __future__ import annotations
 
-from dataclasses import asdict
+from dataclasses import asdict, dataclass, field
 from typing import Any
 
 from typing_extensions import deprecated
@@ -24,12 +24,7 @@
     gen_ai_attributes as GenAI,
 )
 from opentelemetry.semconv.attributes import server_attributes
-from opentelemetry.trace import (
-    INVALID_SPAN,
-    SpanKind,
-    Tracer,
-    set_span_in_context,
-)
+from opentelemetry.trace import INVALID_SPAN, Span, SpanKind, Tracer
 from opentelemetry.util.genai._invocation import Error, GenAIInvocation
 from opentelemetry.util.genai.metrics import InvocationMetricsRecorder
 from opentelemetry.util.genai.types import (
@@ -260,122 +255,107 @@ def _emit_event(self) -> None:
 
 
 @deprecated("LLMInvocation is deprecated. Use InferenceInvocation instead.")
-class LLMInvocation(InferenceInvocation):
-    """Deprecated. Use InferenceInvocation instead."""
+@dataclass
+class LLMInvocation:
+    """Deprecated. Use InferenceInvocation instead.
 
-    def __init__(  # pylint: disable=too-many-locals
-        self,
-        tracer: Tracer | None = None,
-        metrics_recorder: InvocationMetricsRecorder | None = None,
-        logger: Logger | None = None,
-        provider: str = "",
-        *,
-        request_model: str | None = None,
-        input_messages: list[InputMessage] | None = None,
-        output_messages: list[OutputMessage] | None = None,
-        system_instruction: list[MessagePart] | None = None,
-        response_model_name: str | None = None,
-        response_id: str | None = None,
-        finish_reasons: list[str] | None = None,
-        input_tokens: int | None = None,
-        output_tokens: int | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        frequency_penalty: float | None = None,
-        presence_penalty: float | None = None,
-        max_tokens: int | None = None,
-        stop_sequences: list[str] | None = None,
-        seed: int | None = None,
-        server_address: str | None = None,
-        server_port: int | None = None,
-        attributes: dict[str, Any] | None = None,
-        metric_attributes: dict[str, Any] | None = None,
-    ) -> None:
-        if tracer is not None:
-            super().__init__(
-                tracer,
-                metrics_recorder,
-                logger,
-                provider,
-                request_model=request_model,
-                input_messages=input_messages,
-                output_messages=output_messages,
-                system_instruction=system_instruction,
-                response_model_name=response_model_name,
-                response_id=response_id,
-                finish_reasons=finish_reasons,
-                input_tokens=input_tokens,
-                output_tokens=output_tokens,
-                temperature=temperature,
-                top_p=top_p,
-                frequency_penalty=frequency_penalty,
-                presence_penalty=presence_penalty,
-                max_tokens=max_tokens,
-                stop_sequences=stop_sequences,
-                seed=seed,
-                server_address=server_address,
-                server_port=server_port,
-                attributes=attributes,
-                metric_attributes=metric_attributes,
-            )
-            return
-        # Old-style: data container, started later via handler.start_llm()
-        # _tracer/_metrics_recorder/_logger are set by _start_with_handler() in that case
-        self._operation_name = GenAI.GenAiOperationNameValues.CHAT.value
-        self._tracer = None
-        self._metrics_recorder = None
-        self._logger = None
-        self.attributes = {} if attributes is None else attributes
-        self.metric_attributes = (
-            {} if metric_attributes is None else metric_attributes
-        )
-        self.span = INVALID_SPAN
-        self._span_context = set_span_in_context(INVALID_SPAN)
-        self._span_kind = SpanKind.CLIENT
-        self._context_token = None
-        self._monotonic_start_s = None
-        self.provider = provider
-        self.request_model = request_model
-        self.input_messages = [] if input_messages is None else input_messages
-        self.output_messages = (
-            [] if output_messages is None else output_messages
-        )
-        self.system_instruction = (
-            [] if system_instruction is None else system_instruction
-        )
-        self.response_model_name = response_model_name
-        self.response_id = response_id
-        self.finish_reasons = finish_reasons
-        self.input_tokens = input_tokens
-        self.output_tokens = output_tokens
-        self.temperature = temperature
-        self.top_p = top_p
-        self.frequency_penalty = frequency_penalty
-        self.presence_penalty = presence_penalty
-        self.max_tokens = max_tokens
-        self.stop_sequences = stop_sequences
-        self.seed = seed
-        self.server_address = server_address
-        self.server_port = server_port
-        self._span_name = (
-            f"{self._operation_name} {request_model}"
-            if request_model
-            else self._operation_name
-        )
+    Data container for an LLM invocation. Pass to handler.start_llm() to start
+    the span, then update fields and call handler.stop_llm() or handler.fail_llm().
+    """
 
-    @property
-    def invocation(self) -> LLMInvocation | None:  # pyright: ignore[reportDeprecated]
-        """Returns self once started, None before handler.start_llm() is called."""
-        return self if self._context_token is not None else None
+    request_model: str | None = None
+    input_messages: list[InputMessage] = field(default_factory=list)
+    output_messages: list[OutputMessage] = field(default_factory=list)
+    system_instruction: list[MessagePart] = field(default_factory=list)
+    provider: str | None = None
+    response_model_name: str | None = None
+    response_id: str | None = None
+    finish_reasons: list[str] | None = None
+    input_tokens: int | None = None
+    output_tokens: int | None = None
+    attributes: dict[str, Any] = field(default_factory=dict)
+    """Additional attributes to set on spans and/or events. Not set on metrics."""
+    metric_attributes: dict[str, Any] = field(default_factory=dict)
+    """Additional attributes to set on metrics. Must be low cardinality. Not set on spans or events."""
+    temperature: float | None = None
+    top_p: float | None = None
+    frequency_penalty: float | None = None
+    presence_penalty: float | None = None
+    max_tokens: int | None = None
+    stop_sequences: list[str] | None = None
+    seed: int | None = None
+    server_address: str | None = None
+    server_port: int | None = None
+
+    _inference_invocation: InferenceInvocation | None = field(
+        default=None, init=False, repr=False
+    )
 
     def _start_with_handler(
         self,
         tracer: Tracer,
         metrics_recorder: InvocationMetricsRecorder,
         logger: Logger,
     ) -> None:
-        """Attach telemetry components and start the span. Called by handler.start_llm()."""
-        self._tracer = tracer
-        self._metrics_recorder = metrics_recorder
-        self._logger = logger
-        self._start()
+        """Create and start an InferenceInvocation from this data container. Called by handler.start_llm()."""
+        self._inference_invocation = InferenceInvocation(
+            tracer,
+            metrics_recorder,
+            logger,
+            self.provider or "",
+            request_model=self.request_model,
+            input_messages=self.input_messages,
+            output_messages=self.output_messages,
+            system_instruction=self.system_instruction,
+            response_model_name=self.response_model_name,
+            response_id=self.response_id,
+            finish_reasons=self.finish_reasons,
+            input_tokens=self.input_tokens,
+            output_tokens=self.output_tokens,
+            temperature=self.temperature,
+            top_p=self.top_p,
+            frequency_penalty=self.frequency_penalty,
+            presence_penalty=self.presence_penalty,
+            max_tokens=self.max_tokens,
+            stop_sequences=self.stop_sequences,
+            seed=self.seed,
+            server_address=self.server_address,
+            server_port=self.server_port,
+            attributes=self.attributes,
+            metric_attributes=self.metric_attributes,
+        )
+
+    def _sync_to_invocation(self) -> None:
+        inv = self._inference_invocation
+        if inv is None:
+            return
+        inv.provider = self.provider or ""
+        inv.request_model = self.request_model
+        inv.input_messages = self.input_messages
+        inv.output_messages = self.output_messages
+        inv.system_instruction = self.system_instruction
+        inv.response_model_name = self.response_model_name
+        inv.response_id = self.response_id
+        inv.finish_reasons = self.finish_reasons
+        inv.input_tokens = self.input_tokens
+        inv.output_tokens = self.output_tokens
+        inv.temperature = self.temperature
+        inv.top_p = self.top_p
+        inv.frequency_penalty = self.frequency_penalty
+        inv.presence_penalty = self.presence_penalty
+        inv.max_tokens = self.max_tokens
+        inv.stop_sequences = self.stop_sequences
+        inv.seed = self.seed
+        inv.server_address = self.server_address
+        inv.server_port = self.server_port
+        inv.attributes = self.attributes
+        inv.metric_attributes = self.metric_attributes
+
+    @property
+    def span(self) -> Span:
+        """The underlying span, for back-compat with code that checks span.is_recording()."""
+        return (
+            self._inference_invocation.span
+            if self._inference_invocation is not None
+            else INVALID_SPAN
+        )