Genai utils | Add AgentInvocation type

util/opentelemetry-util-genai/CHANGELOG.md

@@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Unreleased
 
+- Add `AgentInvocation` type with `invoke_agent` span lifecycle via `start_invoke_local_agent` / `start_invoke_remote_agent` factory methods and `invoke_local_agent` / `invoke_remote_agent` context managers; add shared `_content.py` helper to deduplicate message serialization between `AgentInvocation` and `InferenceInvocation`; add `tool_definitions`, `cache_creation_input_tokens`, and `cache_read_input_tokens` to `InferenceInvocation`
+  ([#4274](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/4274))
 - Add metrics support for EmbeddingInvocation
   ([#4377](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/4377))
 - Add support for workflow in genAI utils handler.

util/opentelemetry-util-genai/pyproject.toml

@@ -27,7 +27,7 @@ classifiers = [
 dependencies = [
   "opentelemetry-instrumentation ~= 0.60b0",
   "opentelemetry-semantic-conventions ~= 0.60b0",
-  "opentelemetry-api>=1.39",
+  "opentelemetry-api ~= 1.39",
 ]
 
 [project.entry-points.opentelemetry_genai_completion_hook]

util/opentelemetry-util-genai/src/opentelemetry/util/genai/_agent_invocation.py

@@ -0,0 +1,218 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing import Any
+
+from opentelemetry._logs import Logger
+from opentelemetry.semconv._incubating.attributes import (
+    gen_ai_attributes as GenAI,
+)
+from opentelemetry.semconv.attributes import server_attributes
+from opentelemetry.trace import SpanKind, Tracer
+from opentelemetry.util.genai._invocation import (
+    Error,
+    GenAIInvocation,
+    get_content_attributes,
+)
+from opentelemetry.util.genai.metrics import InvocationMetricsRecorder
+from opentelemetry.util.genai.types import (
+    InputMessage,
+    MessagePart,
+    OutputMessage,
+    ToolDefinition,
+)
+
+_GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS: str = getattr(
+    GenAI,
+    "GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS",
+    "gen_ai.usage.cache_creation.input_tokens",
+)
+_GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS: str = getattr(
+    GenAI,
+    "GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS",
+    "gen_ai.usage.cache_read.input_tokens",
+)
+
+
+class AgentInvocation(GenAIInvocation):
+    """Represents a single agent invocation (invoke_agent span).
+
+    Use handler.start_invoke_local_agent() / handler.start_invoke_remote_agent()
+    or the handler.invoke_local_agent() / handler.invoke_remote_agent() context
+    managers rather than constructing this directly.
+
+    Reference:
+        Client span: https://github.com/open-telemetry/semantic-conventions/blob/main/docs/gen-ai/gen-ai-agent-spans.md#invoke-agent-client-span
+        Internal span: https://github.com/open-telemetry/semantic-conventions/blob/main/docs/gen-ai/gen-ai-agent-spans.md#invoke-agent-internal-span
+    """
+
+    def __init__(
+        self,
+        tracer: Tracer,
+        metrics_recorder: InvocationMetricsRecorder,
+        logger: Logger,
+        provider: str,
+        *,
+        span_kind: SpanKind = SpanKind.INTERNAL,
+        request_model: str | 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:
+        """Use handler.start_invoke_local_agent() or handler.start_invoke_remote_agent() instead of calling this directly."""
+        _operation_name = GenAI.GenAiOperationNameValues.INVOKE_AGENT.value
+        super().__init__(
+            tracer,
+            metrics_recorder,
+            logger,
+            operation_name=_operation_name,
+            span_name=_operation_name,
+            span_kind=span_kind,
+            attributes=attributes,
+            metric_attributes=metric_attributes,
+        )
+        self.provider = provider
+        self.request_model = request_model
+        self.server_address = server_address
+        self.server_port = server_port
+
+        self.agent_name: str | None = None
+        self.agent_id: str | None = None
+        self.agent_description: str | None = None
+        self.agent_version: str | None = None
+
+        self.conversation_id: str | None = None
+        self.data_source_id: str | None = None
+        self.output_type: str | None = None
+
+        self.temperature: float | None = None
+        self.top_p: float | None = None
+        self.frequency_penalty: float | None = None
+        self.presence_penalty: float | None = None
+        self.max_tokens: int | None = None
+        self.stop_sequences: list[str] | None = None
+        self.seed: int | None = None
+        self.choice_count: int | None = None
+
+        self.input_tokens: int | None = None
+        self.output_tokens: int | None = None
+        self.cache_creation_input_tokens: int | None = None
+        self.cache_read_input_tokens: int | None = None
+
+        self.input_messages: list[InputMessage] = []
+        self.output_messages: list[OutputMessage] = []
+        self.system_instruction: list[MessagePart] = []
+        self.tool_definitions: list[ToolDefinition] | None = None
+
+        self._start()
+
+    def _get_common_attributes(self) -> dict[str, Any]:
+        optional_attrs = (
+            (GenAI.GEN_AI_REQUEST_MODEL, self.request_model),
+            (server_attributes.SERVER_ADDRESS, self.server_address),
+            (server_attributes.SERVER_PORT, self.server_port),
+            (GenAI.GEN_AI_AGENT_NAME, self.agent_name),
+            (GenAI.GEN_AI_AGENT_ID, self.agent_id),
+            (GenAI.GEN_AI_AGENT_DESCRIPTION, self.agent_description),
+            (GenAI.GEN_AI_AGENT_VERSION, self.agent_version),
+        )
+        return {
+            GenAI.GEN_AI_OPERATION_NAME: self._operation_name,
+            GenAI.GEN_AI_PROVIDER_NAME: self.provider,
+            **{k: v for k, v in optional_attrs if v is not None},
+        }
+
+    def _get_request_attributes(self) -> dict[str, Any]:
+        optional_attrs = (
+            (GenAI.GEN_AI_CONVERSATION_ID, self.conversation_id),
+            (GenAI.GEN_AI_DATA_SOURCE_ID, self.data_source_id),
+            (GenAI.GEN_AI_OUTPUT_TYPE, self.output_type),
+            (GenAI.GEN_AI_REQUEST_TEMPERATURE, self.temperature),
+            (GenAI.GEN_AI_REQUEST_TOP_P, self.top_p),
+            (GenAI.GEN_AI_REQUEST_FREQUENCY_PENALTY, self.frequency_penalty),
+            (GenAI.GEN_AI_REQUEST_PRESENCE_PENALTY, self.presence_penalty),
+            (GenAI.GEN_AI_REQUEST_MAX_TOKENS, self.max_tokens),
+            (GenAI.GEN_AI_REQUEST_STOP_SEQUENCES, self.stop_sequences),
+            (GenAI.GEN_AI_REQUEST_SEED, self.seed),
+            (GenAI.GEN_AI_REQUEST_CHOICE_COUNT, self.choice_count),
+        )
+        return {k: v for k, v in optional_attrs if v is not None}
+
+    def _get_usage_attributes(self) -> dict[str, Any]:
+        optional_attrs = (
+            (GenAI.GEN_AI_USAGE_INPUT_TOKENS, self.input_tokens),
+            (GenAI.GEN_AI_USAGE_OUTPUT_TOKENS, self.output_tokens),
+            (
+                _GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS,
+                self.cache_creation_input_tokens,
+            ),
+            (
+                _GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS,
+                self.cache_read_input_tokens,
+            ),
+        )
+        return {k: v for k, v in optional_attrs if v is not None}
+
+    def _get_content_attributes_for_span(self) -> dict[str, Any]:
+        return get_content_attributes(
+            input_messages=self.input_messages,
+            output_messages=self.output_messages,
+            system_instruction=self.system_instruction,
+            tool_definitions=self.tool_definitions,
+            for_span=True,
+        )
+
+    def _get_metric_attributes(self) -> dict[str, Any]:
+        optional_attrs = (
+            (GenAI.GEN_AI_PROVIDER_NAME, self.provider),
+            (GenAI.GEN_AI_REQUEST_MODEL, self.request_model),
+            (server_attributes.SERVER_ADDRESS, self.server_address),
+            (server_attributes.SERVER_PORT, self.server_port),
+        )
+        attrs: dict[str, Any] = {
+            GenAI.GEN_AI_OPERATION_NAME: self._operation_name,
+            **{k: v for k, v in optional_attrs if v is not None},
+        }
+        attrs.update(self.metric_attributes)
+        return attrs
+
+    def _get_metric_token_counts(self) -> dict[str, int]:
+        counts: dict[str, int] = {}
+        if self.input_tokens is not None:
+            counts[GenAI.GenAiTokenTypeValues.INPUT.value] = self.input_tokens
+        if self.output_tokens is not None:
+            counts[GenAI.GenAiTokenTypeValues.OUTPUT.value] = (
+                self.output_tokens
+            )
+        return counts
+
+    def _apply_finish(self, error: Error | None = None) -> None:
+        if error is not None:
+            self._apply_error_attributes(error)
+
+        # Update span name if agent_name was set after construction
+        if self.agent_name:
+            self.span.update_name(f"{self._operation_name} {self.agent_name}")
+
+        attributes: dict[str, Any] = {}
+        attributes.update(self._get_common_attributes())
+        attributes.update(self._get_request_attributes())
+        attributes.update(self._get_usage_attributes())
+        attributes.update(self._get_content_attributes_for_span())
+        attributes.update(self.attributes)
+        self.span.set_attributes(attributes)
+        self._metrics_recorder.record(self)

util/opentelemetry-util-genai/src/opentelemetry/util/genai/_inference_invocation.py

@@ -14,7 +14,7 @@
 
 from __future__ import annotations
 
-from dataclasses import asdict, dataclass, field
+from dataclasses import dataclass, field
 from typing import Any
 
 from opentelemetry._logs import Logger, LogRecord
@@ -23,21 +23,34 @@
 )
 from opentelemetry.semconv.attributes import server_attributes
 from opentelemetry.trace import INVALID_SPAN, Span, SpanKind, Tracer
-from opentelemetry.util.genai._invocation import Error, GenAIInvocation
+from opentelemetry.util.genai._invocation import (
+    Error,
+    GenAIInvocation,
+    get_content_attributes,
+)
 from opentelemetry.util.genai.metrics import InvocationMetricsRecorder
 from opentelemetry.util.genai.types import (
     InputMessage,
     MessagePart,
     OutputMessage,
+    ToolDefinition,
 )
 from opentelemetry.util.genai.utils import (
-    ContentCapturingMode,
-    gen_ai_json_dumps,
-    get_content_capturing_mode,
     is_experimental_mode,
     should_emit_event,
 )
 
+_GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS: str = getattr(
+    GenAI,
+    "GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS",
+    "gen_ai.usage.cache_creation.input_tokens",
+)
+_GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS: str = getattr(
+    GenAI,
+    "GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS",
+    "gen_ai.usage.cache_read.input_tokens",
+)
+
 
 class InferenceInvocation(GenAIInvocation):
     """Represents a single LLM chat/completion call.
@@ -113,53 +126,19 @@ def __init__(  # pylint: disable=too-many-locals
         self.seed = seed
         self.server_address = server_address
         self.server_port = server_port
+        self.cache_creation_input_tokens: int | None = None
+        self.cache_read_input_tokens: int | None = None
+        self.tool_definitions: list[ToolDefinition] | None = None
         self._start()
 
     def _get_message_attributes(self, *, for_span: bool) -> dict[str, Any]:
-        if not is_experimental_mode():
-            return {}
-        mode = get_content_capturing_mode()
-        allowed_modes = (
-            (
-                ContentCapturingMode.SPAN_ONLY,
-                ContentCapturingMode.SPAN_AND_EVENT,
-            )
-            if for_span
-            else (
-                ContentCapturingMode.EVENT_ONLY,
-                ContentCapturingMode.SPAN_AND_EVENT,
-            )
-        )
-        if mode not in allowed_modes:
-            return {}
-
-        def serialize(items: list[Any]) -> Any:
-            dicts = [asdict(item) for item in items]
-            return gen_ai_json_dumps(dicts) if for_span else dicts
-
-        optional_attrs = (
-            (
-                GenAI.GEN_AI_INPUT_MESSAGES,
-                serialize(self.input_messages)
-                if self.input_messages
-                else None,
-            ),
-            (
-                GenAI.GEN_AI_OUTPUT_MESSAGES,
-                serialize(self.output_messages)
-                if self.output_messages
-                else None,
-            ),
-            (
-                GenAI.GEN_AI_SYSTEM_INSTRUCTIONS,
-                serialize(self.system_instruction)
-                if self.system_instruction
-                else None,
-            ),
+        return get_content_attributes(
+            input_messages=self.input_messages,
+            output_messages=self.output_messages,
+            system_instruction=self.system_instruction,
+            tool_definitions=self.tool_definitions,
+            for_span=for_span,
         )
-        return {
-            key: value for key, value in optional_attrs if value is not None
-        }
 
     def _get_finish_reasons(self) -> list[str] | None:
         if self.finish_reasons is not None:
@@ -200,6 +179,14 @@ def _get_attributes(self) -> dict[str, Any]:
             (GenAI.GEN_AI_RESPONSE_ID, self.response_id),
             (GenAI.GEN_AI_USAGE_INPUT_TOKENS, self.input_tokens),
             (GenAI.GEN_AI_USAGE_OUTPUT_TOKENS, self.output_tokens),
+            (
+                _GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS,
+                self.cache_creation_input_tokens,
+            ),
+            (
+                _GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS,
+                self.cache_read_input_tokens,
+            ),
         )
         attrs.update({k: v for k, v in optional_attrs if v is not None})
         return attrs