From f839c2913ea1d5b2852a917215107ac4fab9ea26 Mon Sep 17 00:00:00 2001 From: Aviv Halfon Date: Sun, 22 Mar 2026 17:37:45 +0200 Subject: [PATCH 01/21] feat(gemini): migrate vertexai and google-generativeai to OTel GenAI semantic conventions --- .../google_generativeai/__init__.py | 13 +- .../google_generativeai/event_emitter.py | 2 +- .../google_generativeai/span_utils.py | 196 ++-- .../pyproject.toml | 2 +- .../tests/test_generate_content.py | 23 +- .../uv.lock | 1032 +++++++++-------- .../instrumentation/vertexai/__init__.py | 10 +- .../instrumentation/vertexai/event_emitter.py | 4 +- .../instrumentation/vertexai/span_utils.py | 68 +- .../pyproject.toml | 2 +- .../tests/disabled_test_bison.py | 36 +- .../tests/disabled_test_gemini.py | 23 +- .../tests/test_role_attributes.py | 78 +- .../uv.lock | 14 +- 14 files changed, 725 insertions(+), 778 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py index 9982d66982..023165c194 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py @@ -32,7 +32,6 @@ ) from opentelemetry.semconv_ai import ( SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY, - LLMRequestTypeValues, SpanAttributes, Meters ) @@ -205,8 +204,8 @@ async def _awrap( name, kind=SpanKind.CLIENT, attributes={ - GenAIAttributes.GEN_AI_SYSTEM: "Google", - SpanAttributes.LLM_REQUEST_TYPE: LLMRequestTypeValues.COMPLETION.value, + GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", + GenAIAttributes.GEN_AI_OPERATION_NAME: "chat", }, ) start_time = time.perf_counter() @@ -224,7 +223,7 @@ async def _awrap( duration_histogram.record( duration, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "Google", + GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, }, ) @@ -281,8 +280,8 @@ def _wrap( name, kind=SpanKind.CLIENT, attributes={ - GenAIAttributes.GEN_AI_SYSTEM: "Google", - SpanAttributes.LLM_REQUEST_TYPE: LLMRequestTypeValues.COMPLETION.value, + GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", + GenAIAttributes.GEN_AI_OPERATION_NAME: "chat", }, ) @@ -301,7 +300,7 @@ def _wrap( duration_histogram.record( duration, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "Google", + GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, }, ) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py index 554c672929..01159960bf 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py @@ -28,7 +28,7 @@ class Roles(Enum): VALID_MESSAGE_ROLES = {role.value for role in Roles} """The valid roles for naming the message event.""" -EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_SYSTEM: "gemini"} +EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai"} """The attributes to be used for the event.""" diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index 0c92bc6840..a2a94035ac 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -19,6 +19,9 @@ logger = logging.getLogger(__name__) +GEN_AI_INPUT_MESSAGES = "gen_ai.input.messages" +GEN_AI_OUTPUT_MESSAGES = "gen_ai.output.messages" + def _set_span_attribute(span, name, value): if value is not None: @@ -160,20 +163,6 @@ async def _process_content_part(part, span, part_index): return {"type": "text", "text": str(part)} -def _set_prompt_attributes(span, prompt_index, processed_content, content_item): - """Set span attributes for a processed prompt""" - _set_span_attribute( - span, - f"{SpanAttributes.LLM_PROMPTS}.{prompt_index}.content", - json.dumps(processed_content), - ) - _set_span_attribute( - span, - f"{SpanAttributes.LLM_PROMPTS}.{prompt_index}.role", - getattr(content_item, "role", "user"), - ) - - async def _process_argument(argument, span): """Process a single argument from args list""" processed_content = [] @@ -213,49 +202,36 @@ async def set_input_attributes(span, args, kwargs, llm_model): if not should_send_prompts(): return + messages = [] + if "contents" in kwargs: contents = kwargs["contents"] if isinstance(contents, str): - # Simple string content in OpenAI format - _set_span_attribute( - span, - f"{GenAIAttributes.GEN_AI_PROMPT}.0.content", - contents, - ) - _set_span_attribute( - span, - f"{GenAIAttributes.GEN_AI_PROMPT}.0.role", - "user", - ) + messages.append({"role": "user", "content": contents}) elif isinstance(contents, list): - for prompt_index, content_item in enumerate(contents): + for content_item in contents: processed_content = await _process_content_item(content_item, span) - if processed_content: - _set_prompt_attributes(span, prompt_index, processed_content, content_item) - + messages.append({ + "role": getattr(content_item, "role", "user"), + "content": json.dumps(processed_content), + }) elif args and len(args) > 0: - # Handle args - process each argument - for arg_index, argument in enumerate(args): + for argument in args: processed_content = await _process_argument(argument, span) - if processed_content: - _set_span_attribute( - span, - f"{SpanAttributes.LLM_PROMPTS}.{arg_index}.content", - json.dumps(processed_content), - ) - _set_span_attribute( - span, - f"{SpanAttributes.LLM_PROMPTS}.{arg_index}.role", - "user", - ) + messages.append({ + "role": "user", + "content": json.dumps(processed_content), + }) elif "prompt" in kwargs: - _set_span_attribute( - span, f"{SpanAttributes.LLM_PROMPTS}.0.content", - json.dumps([{"type": "text", "text": kwargs["prompt"]}]) - ) - _set_span_attribute(span, f"{SpanAttributes.LLM_PROMPTS}.0.role", "user") + messages.append({ + "role": "user", + "content": json.dumps([{"type": "text", "text": kwargs["prompt"]}]), + }) + + if messages: + _set_span_attribute(span, GEN_AI_INPUT_MESSAGES, json.dumps(messages)) # Keep sync version for backward compatibility @@ -268,27 +244,20 @@ def set_input_attributes_sync(span, args, kwargs, llm_model): if not should_send_prompts(): return + messages = [] + if "contents" in kwargs: contents = kwargs["contents"] if isinstance(contents, str): - # Simple string content in OpenAI format - _set_span_attribute( - span, - f"{GenAIAttributes.GEN_AI_PROMPT}.0.content", - json.dumps([{"type": "text", "text": contents}]), - ) - _set_span_attribute( - span, - f"{GenAIAttributes.GEN_AI_PROMPT}.0.role", - "user", - ) + messages.append({ + "role": "user", + "content": json.dumps([{"type": "text", "text": contents}]), + }) elif isinstance(contents, list): - # Process content list - could be mixed text and Part objects - for i, content in enumerate(contents): + for content in contents: processed_content = [] if hasattr(content, "parts"): - # Content with parts (Google GenAI Content object) for j, part in enumerate(content.parts): if hasattr(part, "text") and part.text: processed_content.append({"type": "text", "text": part.text}) @@ -299,36 +268,25 @@ def set_input_attributes_sync(span, args, kwargs, llm_model): if processed_image is not None: processed_content.append(processed_image) else: - # Other part types processed_content.append({"type": "text", "text": str(part)}) elif isinstance(content, str): - # Direct string in the list processed_content.append({"type": "text", "text": content}) elif _is_image_part(content): - # Direct Part object that's an image processed_image = _process_image_part_sync( content, span.context.trace_id, span.context.span_id, 0 ) if processed_image is not None: processed_content.append(processed_image) else: - # Other content types processed_content.append({"type": "text", "text": str(content)}) if processed_content: - _set_span_attribute( - span, - f"{SpanAttributes.LLM_PROMPTS}.{i}.content", - json.dumps(processed_content), - ) - _set_span_attribute( - span, - f"{SpanAttributes.LLM_PROMPTS}.{i}.role", - getattr(content, "role", "user"), - ) + messages.append({ + "role": getattr(content, "role", "user"), + "content": json.dumps(processed_content), + }) elif args and len(args) > 0: - # Handle args - process each argument - for i, arg in enumerate(args): + for arg in args: processed_content = [] if isinstance(arg, str): @@ -355,22 +313,18 @@ def set_input_attributes_sync(span, args, kwargs, llm_model): processed_content.append({"type": "text", "text": str(arg)}) if processed_content: - _set_span_attribute( - span, - f"{SpanAttributes.LLM_PROMPTS}.{i}.content", - json.dumps(processed_content), - ) - _set_span_attribute( - span, - f"{SpanAttributes.LLM_PROMPTS}.{i}.role", - "user", - ) + messages.append({ + "role": "user", + "content": json.dumps(processed_content), + }) elif "prompt" in kwargs: - _set_span_attribute( - span, f"{GenAIAttributes.GEN_AI_PROMPT}.0.content", - json.dumps([{"type": "text", "text": kwargs["prompt"]}]) - ) - _set_span_attribute(span, f"{GenAIAttributes.GEN_AI_PROMPT}.0.role", "user") + messages.append({ + "role": "user", + "content": json.dumps([{"type": "text", "text": kwargs["prompt"]}]), + }) + + if messages: + _set_span_attribute(span, GEN_AI_INPUT_MESSAGES, json.dumps(messages)) def set_model_request_attributes(span, kwargs, llm_model): @@ -386,10 +340,10 @@ def set_model_request_attributes(span, kwargs, llm_model): _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TOP_P, kwargs.get("top_p")) _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TOP_K, kwargs.get("top_k")) _set_span_attribute( - span, SpanAttributes.LLM_PRESENCE_PENALTY, kwargs.get("presence_penalty") + span, GenAIAttributes.GEN_AI_REQUEST_PRESENCE_PENALTY, kwargs.get("presence_penalty") ) _set_span_attribute( - span, SpanAttributes.LLM_FREQUENCY_PENALTY, kwargs.get("frequency_penalty") + span, GenAIAttributes.GEN_AI_REQUEST_FREQUENCY_PENALTY, kwargs.get("frequency_penalty") ) generation_config = kwargs.get("generation_config") @@ -397,7 +351,7 @@ def set_model_request_attributes(span, kwargs, llm_model): try: _set_span_attribute( span, - SpanAttributes.LLM_REQUEST_STRUCTURED_OUTPUT_SCHEMA, + SpanAttributes.GEN_AI_REQUEST_STRUCTURED_OUTPUT_SCHEMA, json.dumps(generation_config.response_schema), ) except Exception: @@ -407,7 +361,7 @@ def set_model_request_attributes(span, kwargs, llm_model): try: _set_span_attribute( span, - SpanAttributes.LLM_REQUEST_STRUCTURED_OUTPUT_SCHEMA, + SpanAttributes.GEN_AI_REQUEST_STRUCTURED_OUTPUT_SCHEMA, json.dumps(kwargs.get("response_schema")), ) except Exception: @@ -418,32 +372,24 @@ def set_model_request_attributes(span, kwargs, llm_model): def set_response_attributes(span, response, llm_model): if not should_send_prompts(): return + + messages = [] + if hasattr(response, "usage_metadata"): if isinstance(response.text, list): - for index, item in enumerate(response): - prefix = f"{GenAIAttributes.GEN_AI_COMPLETION}.{index}" - _set_span_attribute(span, f"{prefix}.content", item.text) - _set_span_attribute(span, f"{prefix}.role", "assistant") + for item in response: + messages.append({"role": "assistant", "content": item.text}) elif isinstance(response.text, str): - _set_span_attribute( - span, f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content", response.text - ) - _set_span_attribute( - span, f"{GenAIAttributes.GEN_AI_COMPLETION}.0.role", "assistant" - ) + messages.append({"role": "assistant", "content": response.text}) else: if isinstance(response, list): - for index, item in enumerate(response): - prefix = f"{GenAIAttributes.GEN_AI_COMPLETION}.{index}" - _set_span_attribute(span, f"{prefix}.content", item) - _set_span_attribute(span, f"{prefix}.role", "assistant") + for item in response: + messages.append({"role": "assistant", "content": item}) elif isinstance(response, str): - _set_span_attribute( - span, f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content", response - ) - _set_span_attribute( - span, f"{GenAIAttributes.GEN_AI_COMPLETION}.0.role", "assistant" - ) + messages.append({"role": "assistant", "content": response}) + + if messages: + _set_span_attribute(span, GEN_AI_OUTPUT_MESSAGES, json.dumps(messages)) def set_model_response_attributes(span, response, llm_model, token_histogram): @@ -455,7 +401,7 @@ def set_model_response_attributes(span, response, llm_model, token_histogram): if hasattr(response, "usage_metadata"): _set_span_attribute( span, - SpanAttributes.LLM_USAGE_TOTAL_TOKENS, + SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS, response.usage_metadata.total_token_count, ) _set_span_attribute( @@ -473,18 +419,18 @@ def set_model_response_attributes(span, response, llm_model, token_histogram): token_histogram.record( response.usage_metadata.prompt_token_count, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "Google", + GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", GenAIAttributes.GEN_AI_TOKEN_TYPE: "input", GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, } ) token_histogram.record( - response.usage_metadata.candidates_token_count, - attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "Google", - GenAIAttributes.GEN_AI_TOKEN_TYPE: "output", - GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, - }, - ) + response.usage_metadata.candidates_token_count, + attributes={ + GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", + GenAIAttributes.GEN_AI_TOKEN_TYPE: "output", + GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, + }, + ) span.set_status(Status(StatusCode.OK)) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/pyproject.toml b/packages/opentelemetry-instrumentation-google-generativeai/pyproject.toml index 09149257d1..91030b77fb 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/pyproject.toml +++ b/packages/opentelemetry-instrumentation-google-generativeai/pyproject.toml @@ -12,7 +12,7 @@ requires-python = ">=3.10,<4" dependencies = [ "opentelemetry-api>=1.38.0,<2", "opentelemetry-instrumentation>=0.59b0", - "opentelemetry-semantic-conventions-ai>=0.4.13,<0.5.0", + "opentelemetry-semantic-conventions-ai>=0.5.0,<0.6.0", "opentelemetry-semantic-conventions>=0.59b0", ] diff --git a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py index f78e7fd2d4..0023ff7991 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py @@ -1,4 +1,5 @@ +import json import pytest from opentelemetry.trace import StatusCode, SpanKind from opentelemetry.semconv_ai import ( @@ -13,7 +14,7 @@ def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict): assert log.log_record.event_name == event_name - assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) == "gemini" + assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) == "gcp.gen_ai" if not expected_content: assert not log.log_record.body @@ -40,18 +41,24 @@ def test_client_spans(exporter, genai_client): attrs = span.attributes - assert attrs[GenAIAttributes.GEN_AI_SYSTEM] == "Google" - assert attrs[SpanAttributes.LLM_REQUEST_TYPE] == "completion" + assert attrs[GenAIAttributes.GEN_AI_PROVIDER_NAME] == "gcp.gen_ai" + assert attrs[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat" assert attrs[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gemini-2.5-flash" assert attrs[GenAIAttributes.GEN_AI_RESPONSE_MODEL] == "gemini-2.5-flash" - assert "gen_ai.prompt.0.content" in attrs - assert attrs["gen_ai.prompt.0.role"] == "user" + # Input messages are now a single JSON array + assert "gen_ai.input.messages" in attrs + input_messages = json.loads(attrs["gen_ai.input.messages"]) + assert len(input_messages) > 0 + assert input_messages[0]["role"] == "user" - assert "gen_ai.completion.0.content" in attrs - assert attrs["gen_ai.completion.0.role"] == "assistant" + # Output messages are now a single JSON array + assert "gen_ai.output.messages" in attrs + output_messages = json.loads(attrs["gen_ai.output.messages"]) + assert len(output_messages) > 0 + assert output_messages[0]["role"] == "assistant" - assert attrs[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] > 0 + assert attrs[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] > 0 assert attrs[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] > 0 assert attrs[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] > 0 diff --git a/packages/opentelemetry-instrumentation-google-generativeai/uv.lock b/packages/opentelemetry-instrumentation-google-generativeai/uv.lock index 8519298e21..4b976d0885 100644 --- 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b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py index 5d96fad8b3..7bcd15e44c 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py @@ -27,8 +27,6 @@ ) from opentelemetry.semconv_ai import ( SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY, - LLMRequestTypeValues, - SpanAttributes, ) from opentelemetry.trace import SpanKind, get_tracer from opentelemetry.trace.status import Status, StatusCode @@ -238,8 +236,8 @@ async def _awrap(tracer, event_logger, to_wrap, wrapped, instance, args, kwargs) name, kind=SpanKind.CLIENT, attributes={ - GenAIAttributes.GEN_AI_SYSTEM: "Google", - SpanAttributes.LLM_REQUEST_TYPE: LLMRequestTypeValues.COMPLETION.value, + GenAIAttributes.GEN_AI_PROVIDER_NAME: "vertex_ai", + GenAIAttributes.GEN_AI_OPERATION_NAME: "chat", }, ) @@ -287,8 +285,8 @@ def _wrap(tracer, event_logger, to_wrap, wrapped, instance, args, kwargs): name, kind=SpanKind.CLIENT, attributes={ - GenAIAttributes.GEN_AI_SYSTEM: "Google", - SpanAttributes.LLM_REQUEST_TYPE: LLMRequestTypeValues.COMPLETION.value, + GenAIAttributes.GEN_AI_PROVIDER_NAME: "vertex_ai", + GenAIAttributes.GEN_AI_OPERATION_NAME: "chat", }, ) diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py index 609820f3df..929955a58f 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py @@ -29,9 +29,7 @@ class Roles(Enum): VALID_MESSAGE_ROLES = {role.value for role in Roles} """The valid roles for naming the message event.""" -EVENT_ATTRIBUTES = { - GenAIAttributes.GEN_AI_SYSTEM: GenAIAttributes.GenAiSystemValues.VERTEX_AI.value -} +EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_PROVIDER_NAME: "vertex_ai"} """The attributes to be used for the event.""" diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py index 27a8afba84..39b8950163 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py @@ -14,6 +14,9 @@ logger = logging.getLogger(__name__) +GEN_AI_INPUT_MESSAGES = "gen_ai.input.messages" +GEN_AI_OUTPUT_MESSAGES = "gen_ai.output.messages" + def _set_span_attribute(span, name, value): if value is not None: @@ -212,21 +215,16 @@ async def set_input_attributes(span, args): if not span.is_recording(): return if should_send_prompts() and args is not None and len(args) > 0: - # Process each argument using extracted helper methods - for arg_index, argument in enumerate(args): + messages = [] + for argument in args: processed_content = await _process_vertexai_argument(argument, span) - if processed_content: - _set_span_attribute( - span, - f"{GenAIAttributes.GEN_AI_PROMPT}.{arg_index}.role", - "user" - ) - _set_span_attribute( - span, - f"{GenAIAttributes.GEN_AI_PROMPT}.{arg_index}.content", - json.dumps(processed_content) - ) + messages.append({ + "role": "user", + "content": json.dumps(processed_content), + }) + if messages: + _set_span_attribute(span, GEN_AI_INPUT_MESSAGES, json.dumps(messages)) # Sync version with image processing support @@ -236,21 +234,16 @@ def set_input_attributes_sync(span, args): if not span.is_recording(): return if should_send_prompts() and args is not None and len(args) > 0: - # Process each argument using extracted helper methods - for arg_index, argument in enumerate(args): + messages = [] + for argument in args: processed_content = _process_vertexai_argument_sync(argument, span) - if processed_content: - _set_span_attribute( - span, - f"{GenAIAttributes.GEN_AI_PROMPT}.{arg_index}.role", - "user" - ) - _set_span_attribute( - span, - f"{GenAIAttributes.GEN_AI_PROMPT}.{arg_index}.content", - json.dumps(processed_content) - ) + messages.append({ + "role": "user", + "content": json.dumps(processed_content), + }) + if messages: + _set_span_attribute(span, GEN_AI_INPUT_MESSAGES, json.dumps(messages)) @dont_throw @@ -258,9 +251,12 @@ def set_model_input_attributes(span, kwargs, llm_model): if not span.is_recording(): return _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_MODEL, llm_model) - _set_span_attribute( - span, f"{GenAIAttributes.GEN_AI_PROMPT}.0.user", kwargs.get("prompt") - ) + + prompt = kwargs.get("prompt") + if prompt: + messages = [{"role": "user", "content": prompt}] + _set_span_attribute(span, GEN_AI_INPUT_MESSAGES, json.dumps(messages)) + _set_span_attribute( span, GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE, kwargs.get("temperature") ) @@ -270,10 +266,10 @@ def set_model_input_attributes(span, kwargs, llm_model): _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TOP_P, kwargs.get("top_p")) _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TOP_K, kwargs.get("top_k")) _set_span_attribute( - span, SpanAttributes.LLM_PRESENCE_PENALTY, kwargs.get("presence_penalty") + span, GenAIAttributes.GEN_AI_REQUEST_PRESENCE_PENALTY, kwargs.get("presence_penalty") ) _set_span_attribute( - span, SpanAttributes.LLM_FREQUENCY_PENALTY, kwargs.get("frequency_penalty") + span, GenAIAttributes.GEN_AI_REQUEST_FREQUENCY_PENALTY, kwargs.get("frequency_penalty") ) @@ -281,12 +277,8 @@ def set_model_input_attributes(span, kwargs, llm_model): def set_response_attributes(span, llm_model, generation_text): if not span.is_recording() or not should_send_prompts(): return - _set_span_attribute(span, f"{GenAIAttributes.GEN_AI_COMPLETION}.0.role", "assistant") - _set_span_attribute( - span, - f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content", - generation_text, - ) + messages = [{"role": "assistant", "content": generation_text}] + _set_span_attribute(span, GEN_AI_OUTPUT_MESSAGES, json.dumps(messages)) @dont_throw @@ -298,7 +290,7 @@ def set_model_response_attributes(span, llm_model, token_usage): if token_usage: _set_span_attribute( span, - SpanAttributes.LLM_USAGE_TOTAL_TOKENS, + SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS, token_usage.total_token_count, ) _set_span_attribute( diff --git a/packages/opentelemetry-instrumentation-vertexai/pyproject.toml b/packages/opentelemetry-instrumentation-vertexai/pyproject.toml index 432933f534..a58266b545 100644 --- a/packages/opentelemetry-instrumentation-vertexai/pyproject.toml +++ b/packages/opentelemetry-instrumentation-vertexai/pyproject.toml @@ -14,7 +14,7 @@ requires-python = ">=3.10,<4" dependencies = [ "opentelemetry-api>=1.38.0,<2", "opentelemetry-instrumentation>=0.59b0", - "opentelemetry-semantic-conventions-ai>=0.4.13,<0.5.0", + "opentelemetry-semantic-conventions-ai>=0.5.0,<0.6.0", "opentelemetry-semantic-conventions>=0.59b0", ] diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py index 0eb54adc05..2342b40bea 100644 --- a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py +++ b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py @@ -10,6 +10,7 @@ gen_ai_attributes as GenAIAttributes, ) from vertexai.language_models import ChatModel, InputOutputTextPair, TextGenerationModel +import json vertexai.init() @@ -41,13 +42,13 @@ def test_vertexai_predict(instrument_legacy, span_exporter, log_exporter): ) assert ( "Give me ten interview questions for the role of program manager." - in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] + in vertexai_span.attributes["gen_ai.input.messages"] ) assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 assert ( - vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content"] + json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["content"] == response ) @@ -84,13 +85,13 @@ async def async_predict_text() -> str: ) assert ( "Give me ten interview questions for the role of program manager." - in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] + in vertexai_span.attributes["gen_ai.input.messages"] ) assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 assert ( - vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content"] + json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["content"] == response ) @@ -120,14 +121,13 @@ def test_vertexai_stream(instrument_legacy, span_exporter, log_exporter): assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "text-bison" assert ( "Give me ten interview questions for the role of program manager." - in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] + in vertexai_span.attributes["gen_ai.input.messages"] ) assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - assert vertexai_span.attributes[ - f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content" - ] == "".join(response) + output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) + assert output_messages[0]["content"] == "".join(response) @pytest.mark.vcr @@ -160,14 +160,13 @@ async def async_streaming_prediction() -> list: assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "text-bison" assert ( "Give me ten interview questions for the role of program manager." - in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] + in vertexai_span.attributes["gen_ai.input.messages"] ) assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - assert vertexai_span.attributes[ - f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content" - ] == "".join(response) + output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) + assert output_messages[0]["content"] == "".join(response) @pytest.mark.vcr @@ -207,13 +206,13 @@ def test_vertexai_chat(instrument_legacy, span_exporter, log_exporter): ) assert ( "How many planets are there in the solar system?" - in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] + in vertexai_span.attributes["gen_ai.input.messages"] ) assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.95 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 assert ( - vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content"] + json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["content"] == response ) @@ -259,16 +258,15 @@ def test_vertexai_chat_stream(instrument_legacy, span_exporter, log_exporter): assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - assert vertexai_span.attributes[ - f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content" - ] == "".join(response) + output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) + assert output_messages[0]["content"] == "".join(response) def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict): assert log.log_record.attributes.get(EventAttributes.EVENT_NAME) == event_name assert ( - log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) - == GenAIAttributes.GenAiSystemValues.VERTEX_AI.value + log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) + == "vertex_ai" ) if not expected_content: diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py index b081303dc8..bcfa7aeb15 100644 --- a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py +++ b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py @@ -7,6 +7,7 @@ gen_ai_attributes as GenAIAttributes, ) from opentelemetry.semconv_ai import SpanAttributes +import json from vertexai.preview.generative_models import GenerativeModel, Part vertexai.init() @@ -30,16 +31,14 @@ def test_vertexai_generate_content(instrument_legacy, span_exporter, log_exporte ] vertexai_span = spans[0] - assert ( - "what is shown in this image?" - in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] - ) + input_messages = json.loads(vertexai_span.attributes["gen_ai.input.messages"]) + assert any("what is shown in this image?" in msg.get("content", "") for msg in input_messages) assert ( vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gemini-2.0-flash-lite" ) assert ( - vertexai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] + vertexai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == response._raw_response.usage_metadata.total_token_count ) assert ( @@ -50,10 +49,8 @@ def test_vertexai_generate_content(instrument_legacy, span_exporter, log_exporte vertexai_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == response._raw_response.usage_metadata.candidates_token_count ) - assert ( - vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content"] - == response.text - ) + output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) + assert output_messages[0]["content"] == response.text logs = log_exporter.get_finished_logs() assert ( @@ -88,7 +85,7 @@ def test_vertexai_generate_content_with_events_with_content( == "gemini-2.0-flash-lite" ) assert ( - vertexai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] + vertexai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == response._raw_response.usage_metadata.total_token_count ) assert ( @@ -148,7 +145,7 @@ def test_vertexai_generate_content_with_events_with_no_content( == "gemini-2.0-flash-lite" ) assert ( - vertexai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] + vertexai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == response._raw_response.usage_metadata.total_token_count ) assert ( @@ -174,8 +171,8 @@ def test_vertexai_generate_content_with_events_with_no_content( def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict): assert log.log_record.attributes.get(EventAttributes.EVENT_NAME) == event_name assert ( - log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) - == GenAIAttributes.GenAiSystemValues.VERTEX_AI.value + log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) + == "vertex_ai" ) if not expected_content: diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py b/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py index 5f2189ddaf..e5abc725ef 100644 --- a/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py +++ b/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py @@ -5,7 +5,8 @@ set_input_attributes, set_input_attributes_sync, ) -from opentelemetry.semconv_ai import SpanAttributes + +GEN_AI_INPUT_MESSAGES = "gen_ai.input.messages" class TestRoleAttributes: @@ -25,6 +26,11 @@ def capture_attribute(key, value): self.mock_span.set_attribute = capture_attribute + def _parse_input_messages(self): + """Helper to parse gen_ai.input.messages JSON from span attributes""" + assert GEN_AI_INPUT_MESSAGES in self.span_attributes + return json.loads(self.span_attributes[GEN_AI_INPUT_MESSAGES]) + @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") @pytest.mark.asyncio async def test_async_role_attribute_for_string_args(self, mock_should_send_prompts): @@ -35,14 +41,13 @@ async def test_async_role_attribute_for_string_args(self, mock_should_send_promp args = ["Hello, world!"] await set_input_attributes(self.mock_span, args) - # Verify role attribute is set - assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" + messages = self._parse_input_messages() + assert len(messages) == 1 + assert messages[0]["role"] == "user" # Verify content is also set - assert f"{SpanAttributes.LLM_PROMPTS}.0.content" in self.span_attributes expected_content = json.dumps([{"type": "text", "text": "Hello, world!"}]) - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.content"] == expected_content + assert messages[0]["content"] == expected_content @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") @pytest.mark.asyncio @@ -54,12 +59,10 @@ async def test_async_role_attribute_for_multiple_args(self, mock_should_send_pro args = ["First message", "Second message"] await set_input_attributes(self.mock_span, args) - # Verify role attributes are set for both arguments - assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" - - assert f"{SpanAttributes.LLM_PROMPTS}.1.role" in self.span_attributes - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.1.role"] == "user" + messages = self._parse_input_messages() + assert len(messages) == 2 + assert messages[0]["role"] == "user" + assert messages[1]["role"] == "user" @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") @pytest.mark.asyncio @@ -71,9 +74,9 @@ async def test_async_role_attribute_with_mixed_content(self, mock_should_send_pr args = [["Text content", "More text"]] await set_input_attributes(self.mock_span, args) - # Verify role attribute is set - assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" + messages = self._parse_input_messages() + assert len(messages) == 1 + assert messages[0]["role"] == "user" @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") def test_sync_role_attribute_for_string_args(self, mock_should_send_prompts): @@ -84,14 +87,13 @@ def test_sync_role_attribute_for_string_args(self, mock_should_send_prompts): args = ["Hello, world!"] set_input_attributes_sync(self.mock_span, args) - # Verify role attribute is set - assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" + messages = self._parse_input_messages() + assert len(messages) == 1 + assert messages[0]["role"] == "user" # Verify content is also set - assert f"{SpanAttributes.LLM_PROMPTS}.0.content" in self.span_attributes expected_content = json.dumps([{"type": "text", "text": "Hello, world!"}]) - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.content"] == expected_content + assert messages[0]["content"] == expected_content @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") def test_sync_role_attribute_for_multiple_args(self, mock_should_send_prompts): @@ -102,12 +104,10 @@ def test_sync_role_attribute_for_multiple_args(self, mock_should_send_prompts): args = ["First message", "Second message"] set_input_attributes_sync(self.mock_span, args) - # Verify role attributes are set for both arguments - assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" - - assert f"{SpanAttributes.LLM_PROMPTS}.1.role" in self.span_attributes - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.1.role"] == "user" + messages = self._parse_input_messages() + assert len(messages) == 2 + assert messages[0]["role"] == "user" + assert messages[1]["role"] == "user" @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") def test_sync_role_attribute_with_mixed_content(self, mock_should_send_prompts): @@ -118,9 +118,9 @@ def test_sync_role_attribute_with_mixed_content(self, mock_should_send_prompts): args = [["Text content", "More text"]] set_input_attributes_sync(self.mock_span, args) - # Verify role attribute is set - assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" + messages = self._parse_input_messages() + assert len(messages) == 1 + assert messages[0]["role"] == "user" @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") @pytest.mark.asyncio @@ -184,12 +184,10 @@ async def test_async_role_attribute_for_image_content(self, mock_should_send_pro args = [["Hello with image", mock_image_part]] await set_input_attributes(self.mock_span, args) - # Verify role attribute is set even when content includes images - assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" - - # Verify content is also set - assert f"{SpanAttributes.LLM_PROMPTS}.0.content" in self.span_attributes + messages = self._parse_input_messages() + assert len(messages) == 1 + assert messages[0]["role"] == "user" + assert messages[0]["content"] # content should be set @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") def test_sync_role_attribute_for_image_content(self, mock_should_send_prompts): @@ -204,9 +202,7 @@ def test_sync_role_attribute_for_image_content(self, mock_should_send_prompts): args = [["Hello with image", mock_image_part]] set_input_attributes_sync(self.mock_span, args) - # Verify role attribute is set even when content includes images - assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes - assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" - - # Verify content is also set - assert f"{SpanAttributes.LLM_PROMPTS}.0.content" in self.span_attributes + messages = self._parse_input_messages() + assert len(messages) == 1 + assert messages[0]["role"] == "user" + assert messages[0]["content"] # content should be set diff --git a/packages/opentelemetry-instrumentation-vertexai/uv.lock b/packages/opentelemetry-instrumentation-vertexai/uv.lock index a7d388db38..0f749c03eb 100644 --- a/packages/opentelemetry-instrumentation-vertexai/uv.lock +++ b/packages/opentelemetry-instrumentation-vertexai/uv.lock @@ -586,7 +586,7 @@ wheels = [ [[package]] name = "opentelemetry-instrumentation-vertexai" -version = "0.51.1" +version = "0.53.3" source = { editable = "." } dependencies = [ { name = "opentelemetry-api" }, @@ -623,7 +623,7 @@ requires-dist = [ { name = "opentelemetry-api", specifier = ">=1.38.0,<2" }, { name = "opentelemetry-instrumentation", specifier = ">=0.59b0" }, { name = "opentelemetry-semantic-conventions", specifier = ">=0.59b0" }, - { name = "opentelemetry-semantic-conventions-ai", specifier = ">=0.4.13,<0.5.0" }, + { name = "opentelemetry-semantic-conventions-ai", specifier = ">=0.5.0,<0.6.0" }, ] provides-extras = ["instruments"] @@ -673,11 +673,15 @@ wheels = [ [[package]] name = "opentelemetry-semantic-conventions-ai" -version = "0.4.13" +version = "0.5.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/ba/e6/40b59eda51ac47009fb47afcdf37c6938594a0bd7f3b9fadcbc6058248e3/opentelemetry_semantic_conventions_ai-0.4.13.tar.gz", hash = "sha256:94efa9fb4ffac18c45f54a3a338ffeb7eedb7e1bb4d147786e77202e159f0036", size = 5368, upload-time = "2025-08-22T10:14:17.387Z" } +dependencies = [ + { name = "opentelemetry-sdk" }, + { name = "opentelemetry-semantic-conventions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c6/0b/0ff2326417a9eed74ff6717629075246098dcbda067a62fd73095139babb/opentelemetry_semantic_conventions_ai-0.5.0.tar.gz", hash = "sha256:64c21c5ae0c971ee2ecab986d66e93bb50e616b52e18a1284e118a323a9e6869", size = 25202, upload-time = "2026-03-20T08:47:05.751Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/35/b5/cf25da2218910f0d6cdf7f876a06bed118c4969eacaf60a887cbaef44f44/opentelemetry_semantic_conventions_ai-0.4.13-py3-none-any.whl", hash = "sha256:883a30a6bb5deaec0d646912b5f9f6dcbb9f6f72557b73d0f2560bf25d13e2d5", size = 6080, upload-time = "2025-08-22T10:14:16.477Z" }, + { url = "https://files.pythonhosted.org/packages/a7/18/35fec29ed6e49bcbbe629b790cc0deb5bb58da9caceee29b39b54d3d7f47/opentelemetry_semantic_conventions_ai-0.5.0-py3-none-any.whl", hash = "sha256:8727f474f590138f5e4937945378878a5b2f4ea82bc24ffd93265ca9fbdc48a4", size = 9983, upload-time = "2026-03-20T08:47:06.843Z" }, ] [[package]] From c13d0e2dee6e6fd3bdd1779610e9ee7c19142468 Mon Sep 17 00:00:00 2001 From: Aviv Halfon Date: Mon, 23 Mar 2026 11:19:03 +0200 Subject: [PATCH 02/21] fix(google-generativeai): remove unused SpanAttributes import --- .../instrumentation/google_generativeai/__init__.py | 1 - 1 file changed, 1 deletion(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py index 023165c194..00c4bc9abc 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py @@ -32,7 +32,6 @@ ) from opentelemetry.semconv_ai import ( SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY, - SpanAttributes, Meters ) from opentelemetry.metrics import Meter, get_meter From a61054ff49436a5d1f498a31c5ec2aa324fc5b25 Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Sun, 29 Mar 2026 13:07:00 +0300 Subject: [PATCH 03/21] fix --- .../google_generativeai/__init__.py | 25 +- .../google_generativeai/event_emitter.py | 4 +- .../google_generativeai/span_utils.py | 647 ++++++++++++------ .../tests/test_generate_content.py | 27 +- .../instrumentation/vertexai/__init__.py | 76 +- .../instrumentation/vertexai/event_emitter.py | 4 +- .../instrumentation/vertexai/span_utils.py | 171 +++-- .../tests/disabled_test_bison.py | 35 +- .../tests/disabled_test_gemini.py | 10 +- .../tests/test_role_attributes.py | 23 +- 10 files changed, 697 insertions(+), 325 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py index 00c4bc9abc..670e665e89 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py @@ -32,12 +32,15 @@ ) from opentelemetry.semconv_ai import ( SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY, - Meters + Meters, ) from opentelemetry.metrics import Meter, get_meter from opentelemetry.trace import SpanKind, get_tracer, StatusCode from wrapt import wrap_function_wrapper +_GCP_GEN_AI = GenAIAttributes.GenAiProviderNameValues.GCP_GEN_AI.value +_GEN_CONTENT = GenAIAttributes.GenAiOperationNameValues.GENERATE_CONTENT.value + logger = logging.getLogger(__name__) WRAPPED_METHODS = [ @@ -95,7 +98,9 @@ def _build_from_streaming_response( if should_emit_events() and event_logger: emit_choice_events(response, event_logger) else: - set_response_attributes(span, complete_response, llm_model) + set_response_attributes( + span, complete_response, llm_model, stream_last_chunk=last_chunk + ) set_model_response_attributes( span, last_chunk or response, llm_model, token_histogram ) @@ -117,7 +122,9 @@ async def _abuild_from_streaming_response( if should_emit_events() and event_logger: emit_choice_events(response, event_logger) else: - set_response_attributes(span, complete_response, llm_model) + set_response_attributes( + span, complete_response, llm_model, stream_last_chunk=last_chunk + ) set_model_response_attributes( span, last_chunk if last_chunk else response, llm_model, token_histogram ) @@ -203,8 +210,8 @@ async def _awrap( name, kind=SpanKind.CLIENT, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", - GenAIAttributes.GEN_AI_OPERATION_NAME: "chat", + GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, + GenAIAttributes.GEN_AI_OPERATION_NAME: _GEN_CONTENT, }, ) start_time = time.perf_counter() @@ -222,7 +229,7 @@ async def _awrap( duration_histogram.record( duration, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", + GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, }, ) @@ -279,8 +286,8 @@ def _wrap( name, kind=SpanKind.CLIENT, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", - GenAIAttributes.GEN_AI_OPERATION_NAME: "chat", + GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, + GenAIAttributes.GEN_AI_OPERATION_NAME: _GEN_CONTENT, }, ) @@ -299,7 +306,7 @@ def _wrap( duration_histogram.record( duration, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", + GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, }, ) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py index 01159960bf..7296550603 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py @@ -17,6 +17,8 @@ gen_ai_attributes as GenAIAttributes, ) +_GCP_GEN_AI = GenAIAttributes.GenAiProviderNameValues.GCP_GEN_AI.value + class Roles(Enum): USER = "user" @@ -28,7 +30,7 @@ class Roles(Enum): VALID_MESSAGE_ROLES = {role.value for role in Roles} """The valid roles for naming the message event.""" -EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai"} +EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI} """The attributes to be used for the event.""" diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index a2a94035ac..66071c925c 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -3,6 +3,7 @@ import logging import asyncio import threading +from opentelemetry.instrumentation.google_generativeai.config import Config from opentelemetry.instrumentation.google_generativeai.utils import ( dont_throw, should_send_prompts, @@ -10,18 +11,15 @@ from opentelemetry.semconv._incubating.attributes import ( gen_ai_attributes as GenAIAttributes, ) -from opentelemetry.instrumentation.google_generativeai.config import Config from opentelemetry.semconv_ai import ( SpanAttributes, ) from opentelemetry.trace.status import Status, StatusCode +_GCP_GEN_AI = GenAIAttributes.GenAiProviderNameValues.GCP_GEN_AI.value logger = logging.getLogger(__name__) -GEN_AI_INPUT_MESSAGES = "gen_ai.input.messages" -GEN_AI_OUTPUT_MESSAGES = "gen_ai.output.messages" - def _set_span_attribute(span, name, value): if value is not None: @@ -30,53 +28,157 @@ def _set_span_attribute(span, name, value): return +def _map_gemini_finish_reason(finish_reason): + """Map Gemini FinishReason to OTel; None / UNSPECIFIED -> None (never fabricate stop).""" + if finish_reason is None: + return None + name = getattr(finish_reason, "name", None) or str(finish_reason) + mapping = { + "STOP": "stop", + "MAX_TOKENS": "length", + "SAFETY": "content_filter", + "RECITATION": "content_filter", + "BLOCKLIST": "content_filter", + "PROHIBITED_CONTENT": "content_filter", + "SPII": "content_filter", + "IMAGE_SAFETY": "content_filter", + "IMAGE_PROHIBITED_CONTENT": "content_filter", + "IMAGE_RECITATION": "content_filter", + "LANGUAGE": "content_filter", + "FINISH_REASON_UNSPECIFIED": None, + "MALFORMED_FUNCTION_CALL": "error", + "OTHER": "error", + "UNEXPECTED_TOOL_CALL": "error", + "NO_IMAGE": "error", + "IMAGE_OTHER": "error", + } + return mapping.get(name) + + +def _normalize_message_role(role): + if role is None: + return "user" + r = str(role).lower() + if r == "model": + return "assistant" + return r + + +def _parse_function_call_arguments(args): + if args is None: + return {} + if isinstance(args, dict): + return args + if isinstance(args, str): + try: + return json.loads(args) + except (json.JSONDecodeError, TypeError): + return {"_raw": args} + return {"_value": args} + + +def _function_response_to_str(response): + if response is None: + return "" + if isinstance(response, str): + return response + try: + return json.dumps(response, default=str) + except TypeError: + return str(response) + + def _is_image_part(item): """Check if item is a Google GenAI Part object containing image data""" try: - # Check if it has the Part attributes we expect for new Google GenAI SDK - if hasattr(item, 'inline_data') and item.inline_data is not None: - # Check if it's an image mime type and has data - if (hasattr(item.inline_data, 'mime_type') and - item.inline_data.mime_type and - 'image/' in item.inline_data.mime_type and - hasattr(item.inline_data, 'data') and - item.inline_data.data): + if hasattr(item, "inline_data") and item.inline_data is not None: + if ( + hasattr(item.inline_data, "mime_type") + and item.inline_data.mime_type + and "image/" in item.inline_data.mime_type + and hasattr(item.inline_data, "data") + and item.inline_data.data + ): return True return False except Exception: return False +def _otel_image_part_from_genai_part(part, span, part_index, sync): + """BlobPart after upload attempt, or UriPart when upload is configured and succeeds.""" + if not _is_image_part(part): + return None + mime = ( + part.inline_data.mime_type + if hasattr(part, "inline_data") and part.inline_data + else None + ) or "application/octet-stream" + if Config.upload_base64_image: + if sync: + uploaded = _process_image_part_sync( + part, span.context.trace_id, span.context.span_id, part_index + ) + else: + return None # async path must use _otel_image_part_from_genai_part_async + if uploaded and uploaded.get("image_url") and uploaded["image_url"].get("url"): + return { + "type": "uri", + "modality": "image", + "uri": uploaded["image_url"]["url"], + } + binary_data = part.inline_data.data + b64 = base64.b64encode(binary_data).decode("utf-8") + return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} + + +async def _otel_image_part_from_genai_part_async(part, span, part_index): + if not _is_image_part(part): + return None + mime = ( + part.inline_data.mime_type + if hasattr(part, "inline_data") and part.inline_data + else None + ) or "application/octet-stream" + if Config.upload_base64_image: + uploaded = await _process_image_part( + part, span.context.trace_id, span.context.span_id, part_index + ) + if uploaded and uploaded.get("image_url") and uploaded["image_url"].get("url"): + return { + "type": "uri", + "modality": "image", + "uri": uploaded["image_url"]["url"], + } + binary_data = part.inline_data.data + b64 = base64.b64encode(binary_data).decode("utf-8") + return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} + + async def _process_image_part(item, trace_id, span_id, content_index): - """Process a Google GenAI Part object containing image data""" + """Upload image when configured; used only from async content processing.""" if not Config.upload_base64_image: return None try: - # Extract format from mime type (e.g., 'image/jpeg' -> 'jpeg') - image_format = item.inline_data.mime_type.split('/')[1] if item.inline_data.mime_type else 'unknown' + image_format = ( + item.inline_data.mime_type.split("/")[1] + if item.inline_data.mime_type + else "unknown" + ) image_name = f"content_{content_index}.{image_format}" - - # Convert binary data to base64 string for upload binary_data = item.inline_data.data - base64_string = base64.b64encode(binary_data).decode('utf-8') - - # Upload the base64 data - convert IDs to strings - url = await Config.upload_base64_image(str(trace_id), str(span_id), image_name, base64_string) - - # Return OpenAI-compatible format for consistency across LLM providers - return { - "type": "image_url", - "image_url": {"url": url} - } + base64_string = base64.b64encode(binary_data).decode("utf-8") + url = await Config.upload_base64_image( + str(trace_id), str(span_id), image_name, base64_string + ) + return {"type": "image_url", "image_url": {"url": url}} except Exception as e: logger.warning(f"Failed to process image part: {e}") - # Return None to skip adding this image to the span return None def run_async(method): - """Handle async method in sync context, following OpenAI's battle-tested approach""" try: loop = asyncio.get_running_loop() except RuntimeError: @@ -91,240 +193,322 @@ def run_async(method): def _process_image_part_sync(item, trace_id, span_id, content_index): - """Synchronous version of image part processing using OpenAI's pattern""" if not Config.upload_base64_image: return None try: - # Extract format from mime type (e.g., 'image/jpeg' -> 'jpeg') - image_format = item.inline_data.mime_type.split('/')[1] if item.inline_data.mime_type else 'unknown' + image_format = ( + item.inline_data.mime_type.split("/")[1] + if item.inline_data.mime_type + else "unknown" + ) image_name = f"content_{content_index}.{image_format}" - - # Convert binary data to base64 string for upload binary_data = item.inline_data.data - base64_string = base64.b64encode(binary_data).decode('utf-8') - - # Use OpenAI's run_async pattern to handle the async upload function + base64_string = base64.b64encode(binary_data).decode("utf-8") url = None async def upload_task(): nonlocal url - url = await Config.upload_base64_image(str(trace_id), str(span_id), image_name, base64_string) + url = await Config.upload_base64_image( + str(trace_id), str(span_id), image_name, base64_string + ) run_async(upload_task()) - return { - "type": "image_url", - "image_url": {"url": url} - } + return {"type": "image_url", "image_url": {"url": url}} except Exception as e: logger.warning(f"Failed to process image part sync: {e}") - # Return None to skip adding this image to the span return None -async def _process_content_item(content_item, span): - """Process a single content item, handling different types (Content objects, strings, Parts)""" - processed_content = [] +def _parts_from_genai_part_sync(part, span, part_index): + out = [] + if getattr(part, "text", None): + out.append({"type": "text", "content": part.text}) + img = _otel_image_part_from_genai_part(part, span, part_index, sync=True) + if img: + out.append(img) + fc = getattr(part, "function_call", None) + if fc is not None: + fid = getattr(fc, "id", None) or "" + fname = getattr(fc, "name", None) or "" + out.append( + { + "type": "tool_call", + "id": fid, + "name": fname, + "arguments": _parse_function_call_arguments(getattr(fc, "args", None)), + } + ) + fr = getattr(part, "function_response", None) + if fr is not None: + fid = getattr(fr, "id", None) or "" + out.append( + { + "type": "tool_call_response", + "id": fid, + "response": _function_response_to_str(getattr(fr, "response", None)), + } + ) + if getattr(part, "thought", None): + out.append({"type": "reasoning", "content": str(part.thought)}) + if not out: + if getattr(part, "executable_code", None): + out.append({"type": "text", "content": str(part.executable_code)}) + elif getattr(part, "code_execution_result", None): + out.append({"type": "text", "content": str(part.code_execution_result)}) + else: + out.append({"type": "text", "content": str(part)}) + return out + + +async def _parts_from_genai_part_async(part, span, part_index): + out = [] + if getattr(part, "text", None): + out.append({"type": "text", "content": part.text}) + if _is_image_part(part): + img = await _otel_image_part_from_genai_part_async(part, span, part_index) + if img: + out.append(img) + fc = getattr(part, "function_call", None) + if fc is not None: + fid = getattr(fc, "id", None) or "" + fname = getattr(fc, "name", None) or "" + out.append( + { + "type": "tool_call", + "id": fid, + "name": fname, + "arguments": _parse_function_call_arguments(getattr(fc, "args", None)), + } + ) + fr = getattr(part, "function_response", None) + if fr is not None: + fid = getattr(fr, "id", None) or "" + out.append( + { + "type": "tool_call_response", + "id": fid, + "response": _function_response_to_str(getattr(fr, "response", None)), + } + ) + if getattr(part, "thought", None): + out.append({"type": "reasoning", "content": str(part.thought)}) + if not out: + if getattr(part, "executable_code", None): + out.append({"type": "text", "content": str(part.executable_code)}) + elif getattr(part, "code_execution_result", None): + out.append({"type": "text", "content": str(part.code_execution_result)}) + else: + out.append({"type": "text", "content": str(part)}) + return out + +async def _process_content_item(content_item, span): + parts_acc = [] if hasattr(content_item, "parts"): - # Content with parts (Google GenAI Content object) for part_index, part in enumerate(content_item.parts): - processed_part = await _process_content_part(part, span, part_index) - if processed_part is not None: - processed_content.append(processed_part) + parts_acc.extend(await _parts_from_genai_part_async(part, span, part_index)) elif isinstance(content_item, str): - # Direct string in the list - processed_content.append({"type": "text", "text": content_item}) + parts_acc.append({"type": "text", "content": content_item}) elif _is_image_part(content_item): - # Direct Part object that's an image - processed_image = await _process_image_part( - content_item, span.context.trace_id, span.context.span_id, 0 - ) - if processed_image is not None: - processed_content.append(processed_image) + img = await _otel_image_part_from_genai_part_async(content_item, span, 0) + if img: + parts_acc.append(img) else: - # Other content types - processed_content.append({"type": "text", "text": str(content_item)}) + parts_acc.append({"type": "text", "content": str(content_item)}) + return parts_acc - return processed_content +async def _process_argument(argument, span): + if isinstance(argument, str): + return [{"type": "text", "content": argument}] + if isinstance(argument, list): + parts_acc = [] + for sub_index, sub_item in enumerate(argument): + if isinstance(sub_item, str): + parts_acc.append({"type": "text", "content": sub_item}) + elif _is_image_part(sub_item): + img = await _otel_image_part_from_genai_part_async(sub_item, span, sub_index) + if img: + parts_acc.append(img) + else: + parts_acc.append({"type": "text", "content": str(sub_item)}) + return parts_acc + if _is_image_part(argument): + img = await _otel_image_part_from_genai_part_async(argument, span, 0) + return [img] if img else [] + return [{"type": "text", "content": str(argument)}] -async def _process_content_part(part, span, part_index): - """Process a single part within a Content object""" - if hasattr(part, "text") and part.text: - return {"type": "text", "text": part.text} - elif _is_image_part(part): - return await _process_image_part( - part, span.context.trace_id, span.context.span_id, part_index - ) - else: - # Other part types - return {"type": "text", "text": str(part)} +def _process_content_item_sync(content_item, span): + parts_acc = [] + if hasattr(content_item, "parts"): + for part_index, part in enumerate(content_item.parts): + parts_acc.extend(_parts_from_genai_part_sync(part, span, part_index)) + elif isinstance(content_item, str): + parts_acc.append({"type": "text", "content": content_item}) + elif _is_image_part(content_item): + img = _otel_image_part_from_genai_part(content_item, span, 0, sync=True) + if img: + parts_acc.append(img) + else: + parts_acc.append({"type": "text", "content": str(content_item)}) + return parts_acc -async def _process_argument(argument, span): - """Process a single argument from args list""" - processed_content = [] +def _process_argument_sync(argument, span): if isinstance(argument, str): - processed_content.append({"type": "text", "text": argument}) - elif isinstance(argument, list): + return [{"type": "text", "content": argument}] + if isinstance(argument, list): + parts_acc = [] for sub_index, sub_item in enumerate(argument): if isinstance(sub_item, str): - processed_content.append({"type": "text", "text": sub_item}) + parts_acc.append({"type": "text", "content": sub_item}) elif _is_image_part(sub_item): - processed_image = await _process_image_part( - sub_item, span.context.trace_id, span.context.span_id, sub_index - ) - if processed_image is not None: - processed_content.append(processed_image) + img = _otel_image_part_from_genai_part(sub_item, span, sub_index, sync=True) + if img: + parts_acc.append(img) else: - processed_content.append({"type": "text", "text": str(sub_item)}) - elif _is_image_part(argument): - processed_image = await _process_image_part( - argument, span.context.trace_id, span.context.span_id, 0 - ) - if processed_image is not None: - processed_content.append(processed_image) - else: - processed_content.append({"type": "text", "text": str(argument)}) + parts_acc.append({"type": "text", "content": str(sub_item)}) + return parts_acc + if _is_image_part(argument): + img = _otel_image_part_from_genai_part(argument, span, 0, sync=True) + return [img] if img else [] + return [{"type": "text", "content": str(argument)}] + + +def _collect_finish_reasons_from_response(response): + reasons = [] + candidates = getattr(response, "candidates", None) or [] + for cand in candidates: + mapped = _map_gemini_finish_reason(getattr(cand, "finish_reason", None)) + if mapped is not None: + reasons.append(mapped) + return reasons - return processed_content + +def _output_messages_from_generate_response(span, response): + messages = [] + candidates = getattr(response, "candidates", None) or [] + for cand in candidates: + parts = [] + content = getattr(cand, "content", None) + if content and getattr(content, "parts", None): + for idx, p in enumerate(content.parts): + parts.extend(_parts_from_genai_part_sync(p, span, idx)) + fr = _map_gemini_finish_reason(getattr(cand, "finish_reason", None)) + role = "assistant" + if content and getattr(content, "role", None): + role = _normalize_message_role(content.role) + msg = {"role": role, "parts": parts} + if fr is not None: + msg["finish_reason"] = fr + messages.append(msg) + if not messages and hasattr(response, "text") and response.text: + text = response.text + if isinstance(text, str) and text: + msg = {"role": "assistant", "parts": [{"type": "text", "content": text}]} + messages.append(msg) + return messages @dont_throw async def set_input_attributes(span, args, kwargs, llm_model): - """Process input arguments, handling both text and image content""" if not span.is_recording(): return - if not should_send_prompts(): return messages = [] - if "contents" in kwargs: contents = kwargs["contents"] if isinstance(contents, str): - messages.append({"role": "user", "content": contents}) + messages.append( + { + "role": "user", + "parts": [{"type": "text", "content": contents}], + } + ) elif isinstance(contents, list): for content_item in contents: - processed_content = await _process_content_item(content_item, span) - if processed_content: - messages.append({ - "role": getattr(content_item, "role", "user"), - "content": json.dumps(processed_content), - }) + parts = await _process_content_item(content_item, span) + if parts: + messages.append( + { + "role": _normalize_message_role( + getattr(content_item, "role", "user") + ), + "parts": parts, + } + ) elif args and len(args) > 0: for argument in args: - processed_content = await _process_argument(argument, span) - if processed_content: - messages.append({ - "role": "user", - "content": json.dumps(processed_content), - }) + parts = await _process_argument(argument, span) + if parts: + messages.append({"role": "user", "parts": parts}) elif "prompt" in kwargs: - messages.append({ - "role": "user", - "content": json.dumps([{"type": "text", "text": kwargs["prompt"]}]), - }) + messages.append( + { + "role": "user", + "parts": [{"type": "text", "content": kwargs["prompt"]}], + } + ) if messages: - _set_span_attribute(span, GEN_AI_INPUT_MESSAGES, json.dumps(messages)) + _set_span_attribute( + span, + GenAIAttributes.GEN_AI_INPUT_MESSAGES, + json.dumps(messages), + ) -# Keep sync version for backward compatibility @dont_throw def set_input_attributes_sync(span, args, kwargs, llm_model): - """Synchronous version with image processing support""" if not span.is_recording(): return - if not should_send_prompts(): return messages = [] - if "contents" in kwargs: contents = kwargs["contents"] if isinstance(contents, str): - messages.append({ - "role": "user", - "content": json.dumps([{"type": "text", "text": contents}]), - }) + messages.append( + { + "role": "user", + "parts": [{"type": "text", "content": contents}], + } + ) elif isinstance(contents, list): for content in contents: - processed_content = [] - - if hasattr(content, "parts"): - for j, part in enumerate(content.parts): - if hasattr(part, "text") and part.text: - processed_content.append({"type": "text", "text": part.text}) - elif _is_image_part(part): - processed_image = _process_image_part_sync( - part, span.context.trace_id, span.context.span_id, j - ) - if processed_image is not None: - processed_content.append(processed_image) - else: - processed_content.append({"type": "text", "text": str(part)}) - elif isinstance(content, str): - processed_content.append({"type": "text", "text": content}) - elif _is_image_part(content): - processed_image = _process_image_part_sync( - content, span.context.trace_id, span.context.span_id, 0 + parts = _process_content_item_sync(content, span) + if parts: + messages.append( + { + "role": _normalize_message_role(getattr(content, "role", "user")), + "parts": parts, + } ) - if processed_image is not None: - processed_content.append(processed_image) - else: - processed_content.append({"type": "text", "text": str(content)}) - - if processed_content: - messages.append({ - "role": getattr(content, "role", "user"), - "content": json.dumps(processed_content), - }) elif args and len(args) > 0: for arg in args: - processed_content = [] - - if isinstance(arg, str): - processed_content.append({"type": "text", "text": arg}) - elif isinstance(arg, list): - for j, subarg in enumerate(arg): - if isinstance(subarg, str): - processed_content.append({"type": "text", "text": subarg}) - elif _is_image_part(subarg): - processed_image = _process_image_part_sync( - subarg, span.context.trace_id, span.context.span_id, j - ) - if processed_image is not None: - processed_content.append(processed_image) - else: - processed_content.append({"type": "text", "text": str(subarg)}) - elif _is_image_part(arg): - processed_image = _process_image_part_sync( - arg, span.context.trace_id, span.context.span_id, 0 - ) - if processed_image is not None: - processed_content.append(processed_image) - else: - processed_content.append({"type": "text", "text": str(arg)}) - - if processed_content: - messages.append({ - "role": "user", - "content": json.dumps(processed_content), - }) + parts = _process_argument_sync(arg, span) + if parts: + messages.append({"role": "user", "parts": parts}) elif "prompt" in kwargs: - messages.append({ - "role": "user", - "content": json.dumps([{"type": "text", "text": kwargs["prompt"]}]), - }) + messages.append( + { + "role": "user", + "parts": [{"type": "text", "content": kwargs["prompt"]}], + } + ) if messages: - _set_span_attribute(span, GEN_AI_INPUT_MESSAGES, json.dumps(messages)) + _set_span_attribute( + span, + GenAIAttributes.GEN_AI_INPUT_MESSAGES, + json.dumps(messages), + ) def set_model_request_attributes(span, kwargs, llm_model): @@ -340,10 +524,14 @@ def set_model_request_attributes(span, kwargs, llm_model): _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TOP_P, kwargs.get("top_p")) _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TOP_K, kwargs.get("top_k")) _set_span_attribute( - span, GenAIAttributes.GEN_AI_REQUEST_PRESENCE_PENALTY, kwargs.get("presence_penalty") + span, + GenAIAttributes.GEN_AI_REQUEST_PRESENCE_PENALTY, + kwargs.get("presence_penalty"), ) _set_span_attribute( - span, GenAIAttributes.GEN_AI_REQUEST_FREQUENCY_PENALTY, kwargs.get("frequency_penalty") + span, + GenAIAttributes.GEN_AI_REQUEST_FREQUENCY_PENALTY, + kwargs.get("frequency_penalty"), ) generation_config = kwargs.get("generation_config") @@ -369,27 +557,51 @@ def set_model_request_attributes(span, kwargs, llm_model): @dont_throw -def set_response_attributes(span, response, llm_model): +def set_response_attributes(span, response, llm_model, stream_last_chunk=None): + if not span.is_recording(): + return if not should_send_prompts(): return - messages = [] + if isinstance(response, str): + fr = None + if stream_last_chunk is not None: + reasons = _collect_finish_reasons_from_response(stream_last_chunk) + fr = reasons[0] if reasons else None + msg = {"role": "assistant", "parts": []} + if response: + msg["parts"].append({"type": "text", "content": response}) + if fr is not None: + msg["finish_reason"] = fr + if msg["parts"] or fr is not None: + _set_span_attribute( + span, + GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, + json.dumps([msg]), + ) + return - if hasattr(response, "usage_metadata"): - if isinstance(response.text, list): - for item in response: - messages.append({"role": "assistant", "content": item.text}) - elif isinstance(response.text, str): - messages.append({"role": "assistant", "content": response.text}) + if isinstance(response, list): + messages = [] + for item in response: + if isinstance(item, str): + messages.append( + { + "role": "assistant", + "parts": [{"type": "text", "content": item}], + } + ) + else: + messages.extend(_output_messages_from_generate_response(span, item)) else: - if isinstance(response, list): - for item in response: - messages.append({"role": "assistant", "content": item}) - elif isinstance(response, str): - messages.append({"role": "assistant", "content": response}) + messages = _output_messages_from_generate_response(span, response) if messages: - _set_span_attribute(span, GEN_AI_OUTPUT_MESSAGES, json.dumps(messages)) + _set_span_attribute( + span, + GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, + json.dumps(messages), + ) def set_model_response_attributes(span, response, llm_model, token_histogram): @@ -398,36 +610,47 @@ def set_model_response_attributes(span, response, llm_model, token_histogram): _set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_MODEL, llm_model) - if hasattr(response, "usage_metadata"): + rid = getattr(response, "response_id", None) + if rid: + _set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_ID, rid) + + reasons = _collect_finish_reasons_from_response(response) + if reasons: + _set_span_attribute( + span, GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS, reasons + ) + + um = getattr(response, "usage_metadata", None) + if um is not None: _set_span_attribute( span, SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS, - response.usage_metadata.total_token_count, + um.total_token_count, ) _set_span_attribute( span, GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS, - response.usage_metadata.candidates_token_count, + um.candidates_token_count, ) _set_span_attribute( span, GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS, - response.usage_metadata.prompt_token_count, + um.prompt_token_count, ) - if token_histogram and hasattr(response, "usage_metadata"): + if token_histogram and um is not None: token_histogram.record( - response.usage_metadata.prompt_token_count, + um.prompt_token_count, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", + GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, GenAIAttributes.GEN_AI_TOKEN_TYPE: "input", GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, - } + }, ) token_histogram.record( - response.usage_metadata.candidates_token_count, + um.candidates_token_count, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "gcp.gen_ai", + GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, GenAIAttributes.GEN_AI_TOKEN_TYPE: "output", GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, }, diff --git a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py index 0023ff7991..79c78d1865 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py @@ -14,7 +14,9 @@ def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict): assert log.log_record.event_name == event_name - assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) == "gcp.gen_ai" + assert log.log_record.attributes.get( + GenAIAttributes.GEN_AI_PROVIDER_NAME + ) == GenAIAttributes.GenAiProviderNameValues.GCP_GEN_AI.value if not expected_content: assert not log.log_record.body @@ -41,8 +43,14 @@ def test_client_spans(exporter, genai_client): attrs = span.attributes - assert attrs[GenAIAttributes.GEN_AI_PROVIDER_NAME] == "gcp.gen_ai" - assert attrs[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat" + assert ( + attrs[GenAIAttributes.GEN_AI_PROVIDER_NAME] + == GenAIAttributes.GenAiProviderNameValues.GCP_GEN_AI.value + ) + assert ( + attrs[GenAIAttributes.GEN_AI_OPERATION_NAME] + == GenAIAttributes.GenAiOperationNameValues.GENERATE_CONTENT.value + ) assert attrs[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gemini-2.5-flash" assert attrs[GenAIAttributes.GEN_AI_RESPONSE_MODEL] == "gemini-2.5-flash" @@ -51,12 +59,23 @@ def test_client_spans(exporter, genai_client): input_messages = json.loads(attrs["gen_ai.input.messages"]) assert len(input_messages) > 0 assert input_messages[0]["role"] == "user" + assert "parts" in input_messages[0] + assert input_messages[0]["parts"][0]["type"] == "text" + assert "content" in input_messages[0]["parts"][0] - # Output messages are now a single JSON array + # Output messages are now a single JSON array (parts-based OTel schema) assert "gen_ai.output.messages" in attrs output_messages = json.loads(attrs["gen_ai.output.messages"]) assert len(output_messages) > 0 assert output_messages[0]["role"] == "assistant" + assert "parts" in output_messages[0] + assert output_messages[0]["parts"][0]["type"] == "text" + assert "content" in output_messages[0]["parts"][0] + + assert GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS in attrs + finish_reasons = attrs[GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS] + assert isinstance(finish_reasons, (list, tuple)) + assert len(finish_reasons) > 0 assert attrs[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] > 0 assert attrs[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] > 0 diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py index 7bcd15e44c..058d2e33f3 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py @@ -14,6 +14,7 @@ emit_response_events, ) from opentelemetry.instrumentation.vertexai.span_utils import ( + _map_vertex_finish_reason, set_input_attributes, set_input_attributes_sync, set_model_input_attributes, @@ -32,6 +33,11 @@ from opentelemetry.trace.status import Status, StatusCode from wrapt import wrap_function_wrapper +_GCP_VERTEX_AI = GenAIAttributes.GenAiProviderNameValues.GCP_VERTEX_AI.value +_OP_CHAT = GenAIAttributes.GenAiOperationNameValues.CHAT.value +_OP_GENERATE_CONTENT = GenAIAttributes.GenAiOperationNameValues.GENERATE_CONTENT.value +_OP_TEXT_COMPLETION = GenAIAttributes.GenAiOperationNameValues.TEXT_COMPLETION.value + logger = logging.getLogger(__name__) _instruments = ("google-cloud-aiplatform >= 1.38.1",) @@ -124,6 +130,14 @@ ] +def _gen_ai_operation_name(span_name: str) -> str: + if "predict" in span_name: + return _OP_TEXT_COMPLETION + if "send_message" in span_name: + return _OP_CHAT + return _OP_GENERATE_CONTENT + + def is_streaming_response(response): return isinstance(response, types.GeneratorType) @@ -133,12 +147,23 @@ def is_async_streaming_response(response): @dont_throw -def handle_streaming_response(span, event_logger, llm_model, response, token_usage): - set_model_response_attributes(span, llm_model, token_usage) +def handle_streaming_response( + span, event_logger, llm_model, complete_response, token_usage, stream_last_chunk=None +): + set_model_response_attributes( + span, llm_model, token_usage, response_meta=stream_last_chunk + ) + finish_reason_otel = None + if stream_last_chunk and getattr(stream_last_chunk, "candidates", None): + finish_reason_otel = _map_vertex_finish_reason( + stream_last_chunk.candidates[0].finish_reason + ) if should_emit_events(): - emit_response_events(response, event_logger) + emit_response_events(complete_response, event_logger) else: - set_response_attributes(span, llm_model, response) + set_response_attributes( + span, llm_model, complete_response, finish_reason_otel=finish_reason_otel + ) if span.is_recording(): span.set_status(Status(StatusCode.OK)) @@ -146,8 +171,10 @@ def handle_streaming_response(span, event_logger, llm_model, response, token_usa def _build_from_streaming_response(span, event_logger, response, llm_model): complete_response = "" token_usage = None + last_item = None for item in response: item_to_yield = item + last_item = item complete_response += str(item.text) if item.usage_metadata: token_usage = item.usage_metadata @@ -155,7 +182,12 @@ def _build_from_streaming_response(span, event_logger, response, llm_model): yield item_to_yield handle_streaming_response( - span, event_logger, llm_model, complete_response, token_usage + span, + event_logger, + llm_model, + complete_response, + token_usage, + stream_last_chunk=last_item, ) span.set_status(Status(StatusCode.OK)) @@ -165,15 +197,24 @@ def _build_from_streaming_response(span, event_logger, response, llm_model): async def _abuild_from_streaming_response(span, event_logger, response, llm_model): complete_response = "" token_usage = None + last_item = None async for item in response: item_to_yield = item + last_item = item complete_response += str(item.text) if item.usage_metadata: token_usage = item.usage_metadata yield item_to_yield - handle_streaming_response(span, event_logger, llm_model, response, token_usage) + handle_streaming_response( + span, + event_logger, + llm_model, + complete_response, + token_usage, + stream_last_chunk=last_item, + ) span.set_status(Status(StatusCode.OK)) span.end() @@ -189,12 +230,23 @@ async def _handle_request(span, event_logger, args, kwargs, llm_model): def _handle_response(span, event_logger, response, llm_model): - set_model_response_attributes(span, llm_model, response.usage_metadata) + set_model_response_attributes( + span, llm_model, response.usage_metadata, response_meta=response + ) + finish_reason_otel = None + if response.candidates: + finish_reason_otel = _map_vertex_finish_reason( + response.candidates[0].finish_reason + ) + generation_text = response.candidates[0].text if response.candidates else "" if should_emit_events(): emit_response_events(response, event_logger) else: set_response_attributes( - span, llm_model, response.candidates[0].text if response.candidates else "" + span, + llm_model, + generation_text, + finish_reason_otel=finish_reason_otel, ) if span.is_recording(): span.set_status(Status(StatusCode.OK)) @@ -236,8 +288,8 @@ async def _awrap(tracer, event_logger, to_wrap, wrapped, instance, args, kwargs) name, kind=SpanKind.CLIENT, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "vertex_ai", - GenAIAttributes.GEN_AI_OPERATION_NAME: "chat", + GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_VERTEX_AI, + GenAIAttributes.GEN_AI_OPERATION_NAME: _gen_ai_operation_name(name), }, ) @@ -285,8 +337,8 @@ def _wrap(tracer, event_logger, to_wrap, wrapped, instance, args, kwargs): name, kind=SpanKind.CLIENT, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "vertex_ai", - GenAIAttributes.GEN_AI_OPERATION_NAME: "chat", + GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_VERTEX_AI, + GenAIAttributes.GEN_AI_OPERATION_NAME: _gen_ai_operation_name(name), }, ) diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py index 929955a58f..c255fb484f 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py @@ -18,6 +18,8 @@ from vertexai.generative_models import GenerationResponse +_GCP_VERTEX_AI = GenAIAttributes.GenAiProviderNameValues.GCP_VERTEX_AI.value + class Roles(Enum): USER = "user" @@ -29,7 +31,7 @@ class Roles(Enum): VALID_MESSAGE_ROLES = {role.value for role in Roles} """The valid roles for naming the message event.""" -EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_PROVIDER_NAME: "vertex_ai"} +EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_VERTEX_AI} """The attributes to be used for the event.""" diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py index 39b8950163..b9d0ecf105 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py @@ -14,9 +14,6 @@ logger = logging.getLogger(__name__) -GEN_AI_INPUT_MESSAGES = "gen_ai.input.messages" -GEN_AI_OUTPUT_MESSAGES = "gen_ai.output.messages" - def _set_span_attribute(span, name, value): if value is not None: @@ -33,9 +30,9 @@ def _is_base64_image_part(item): return False # Check if it's an image mime type and has inline data - if item.mime_type and 'image/' in item.mime_type and item.inline_data: - # Check if the inline_data has actual data - if hasattr(item.inline_data, 'data') and item.inline_data.data: + if item.mime_type and "image/" in item.mime_type and item.inline_data: + data = getattr(item.inline_data, "data", None) + if isinstance(data, (bytes, bytearray)) and len(data) > 0: return True return False @@ -43,6 +40,63 @@ def _is_base64_image_part(item): return False +def _map_vertex_finish_reason(finish_reason): + if finish_reason is None: + return None + name = getattr(finish_reason, "name", None) or str(finish_reason) + mapping = { + "STOP": "stop", + "MAX_TOKENS": "length", + "SAFETY": "content_filter", + "RECITATION": "content_filter", + "BLOCKLIST": "content_filter", + "PROHIBITED_CONTENT": "content_filter", + "SPII": "content_filter", + "LANGUAGE": "content_filter", + "FINISH_REASON_UNSPECIFIED": None, + "OTHER": "error", + } + return mapping.get(name) + + +async def _otel_image_part_vertex_async(item, span, item_index): + if not _is_base64_image_part(item): + return None + mime = item.mime_type or "application/octet-stream" + if Config.upload_base64_image: + uploaded = await _process_image_part( + item, span.context.trace_id, span.context.span_id, item_index + ) + if uploaded and uploaded.get("image_url", {}).get("url"): + return { + "type": "uri", + "modality": "image", + "uri": uploaded["image_url"]["url"], + } + binary_data = item.inline_data.data + b64 = base64.b64encode(binary_data).decode("utf-8") + return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} + + +def _otel_image_part_vertex_sync(item, span, item_index): + if not _is_base64_image_part(item): + return None + mime = item.mime_type or "application/octet-stream" + if Config.upload_base64_image: + uploaded = _process_image_part_sync( + item, span.context.trace_id, span.context.span_id, item_index + ) + if uploaded and uploaded.get("image_url", {}).get("url"): + return { + "type": "uri", + "modality": "image", + "uri": uploaded["image_url"]["url"], + } + binary_data = item.inline_data.data + b64 = base64.b64encode(binary_data).decode("utf-8") + return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} + + async def _process_image_part(item, trace_id, span_id, content_index): """Process a VertexAI Part object containing image data""" if not Config.upload_base64_image: @@ -122,11 +176,9 @@ async def upload_task(): async def _process_vertexai_argument(argument, span): """Process a single argument for VertexAI, handling different types""" if isinstance(argument, str): - # Simple text argument in OpenAI format - return [{"type": "text", "text": argument}] + return [{"type": "text", "content": argument}] elif isinstance(argument, list): - # List of mixed content (text strings and Part objects) - deep copy and process content_list = copy.deepcopy(argument) processed_items = [] @@ -138,7 +190,6 @@ async def _process_vertexai_argument(argument, span): return processed_items else: - # Single Part object - convert to OpenAI format processed_item = await _process_content_item_vertexai(argument, span, 0) return [processed_item] if processed_item is not None else [] @@ -146,32 +197,25 @@ async def _process_vertexai_argument(argument, span): async def _process_content_item_vertexai(content_item, span, item_index): """Process a single content item for VertexAI""" if isinstance(content_item, str): - # Convert text to OpenAI format - return {"type": "text", "text": content_item} + return {"type": "text", "content": content_item} elif _is_base64_image_part(content_item): - # Process image part - return await _process_image_part( - content_item, span.context.trace_id, span.context.span_id, item_index - ) + return await _otel_image_part_vertex_async(content_item, span, item_index) - elif hasattr(content_item, 'text'): - # Text part to OpenAI format - return {"type": "text", "text": content_item.text} + elif hasattr(content_item, "text"): + t = getattr(content_item, "text", None) + if isinstance(t, str) and t: + return {"type": "text", "content": t} - else: - # Other types as text - return {"type": "text", "text": str(content_item)} + return {"type": "text", "content": str(content_item)} def _process_vertexai_argument_sync(argument, span): """Synchronous version of argument processing for VertexAI""" if isinstance(argument, str): - # Simple text argument in OpenAI format - return [{"type": "text", "text": argument}] + return [{"type": "text", "content": argument}] elif isinstance(argument, list): - # List of mixed content (text strings and Part objects) - deep copy and process content_list = copy.deepcopy(argument) processed_items = [] @@ -183,7 +227,6 @@ def _process_vertexai_argument_sync(argument, span): return processed_items else: - # Single Part object - convert to OpenAI format processed_item = _process_content_item_vertexai_sync(argument, span, 0) return [processed_item] if processed_item is not None else [] @@ -191,22 +234,17 @@ def _process_vertexai_argument_sync(argument, span): def _process_content_item_vertexai_sync(content_item, span, item_index): """Synchronous version of content item processing for VertexAI""" if isinstance(content_item, str): - # Convert text to OpenAI format - return {"type": "text", "text": content_item} + return {"type": "text", "content": content_item} elif _is_base64_image_part(content_item): - # Process image part - return _process_image_part_sync( - content_item, span.context.trace_id, span.context.span_id, item_index - ) + return _otel_image_part_vertex_sync(content_item, span, item_index) - elif hasattr(content_item, 'text'): - # Text part to OpenAI format - return {"type": "text", "text": content_item.text} + elif hasattr(content_item, "text"): + t = getattr(content_item, "text", None) + if isinstance(t, str) and t: + return {"type": "text", "content": t} - else: - # Other types as text - return {"type": "text", "text": str(content_item)} + return {"type": "text", "content": str(content_item)} @dont_throw @@ -219,12 +257,11 @@ async def set_input_attributes(span, args): for argument in args: processed_content = await _process_vertexai_argument(argument, span) if processed_content: - messages.append({ - "role": "user", - "content": json.dumps(processed_content), - }) + messages.append({"role": "user", "parts": processed_content}) if messages: - _set_span_attribute(span, GEN_AI_INPUT_MESSAGES, json.dumps(messages)) + _set_span_attribute( + span, GenAIAttributes.GEN_AI_INPUT_MESSAGES, json.dumps(messages) + ) # Sync version with image processing support @@ -238,12 +275,11 @@ def set_input_attributes_sync(span, args): for argument in args: processed_content = _process_vertexai_argument_sync(argument, span) if processed_content: - messages.append({ - "role": "user", - "content": json.dumps(processed_content), - }) + messages.append({"role": "user", "parts": processed_content}) if messages: - _set_span_attribute(span, GEN_AI_INPUT_MESSAGES, json.dumps(messages)) + _set_span_attribute( + span, GenAIAttributes.GEN_AI_INPUT_MESSAGES, json.dumps(messages) + ) @dont_throw @@ -254,8 +290,12 @@ def set_model_input_attributes(span, kwargs, llm_model): prompt = kwargs.get("prompt") if prompt: - messages = [{"role": "user", "content": prompt}] - _set_span_attribute(span, GEN_AI_INPUT_MESSAGES, json.dumps(messages)) + messages = [ + {"role": "user", "parts": [{"type": "text", "content": prompt}]}, + ] + _set_span_attribute( + span, GenAIAttributes.GEN_AI_INPUT_MESSAGES, json.dumps(messages) + ) _set_span_attribute( span, GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE, kwargs.get("temperature") @@ -274,19 +314,42 @@ def set_model_input_attributes(span, kwargs, llm_model): @dont_throw -def set_response_attributes(span, llm_model, generation_text): +def set_response_attributes(span, llm_model, generation_text, finish_reason_otel=None): if not span.is_recording() or not should_send_prompts(): return - messages = [{"role": "assistant", "content": generation_text}] - _set_span_attribute(span, GEN_AI_OUTPUT_MESSAGES, json.dumps(messages)) + parts = [] + if generation_text: + parts.append({"type": "text", "content": generation_text}) + msg = {"role": "assistant", "parts": parts} + if finish_reason_otel is not None: + msg["finish_reason"] = finish_reason_otel + if parts or finish_reason_otel is not None: + _set_span_attribute( + span, GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, json.dumps([msg]) + ) @dont_throw -def set_model_response_attributes(span, llm_model, token_usage): +def set_model_response_attributes(span, llm_model, token_usage, response_meta=None): if not span.is_recording(): return _set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_MODEL, llm_model) + if response_meta is not None: + candidates = getattr(response_meta, "candidates", None) or [] + reasons = [] + for cand in candidates: + mapped = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) + if mapped is not None: + reasons.append(mapped) + if reasons: + _set_span_attribute( + span, GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS, reasons + ) + rid = getattr(response_meta, "response_id", None) + if rid: + _set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_ID, rid) + if token_usage: _set_span_attribute( span, diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py index 2342b40bea..e2d0fdf6fa 100644 --- a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py +++ b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py @@ -47,10 +47,8 @@ def test_vertexai_predict(instrument_legacy, span_exporter, log_exporter): assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - assert ( - json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["content"] - == response - ) + out = json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["parts"] + assert "".join(p["content"] for p in out if p.get("type") == "text") == response @pytest.mark.vcr @@ -90,10 +88,8 @@ async def async_predict_text() -> str: assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - assert ( - json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["content"] - == response - ) + out = json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["parts"] + assert "".join(p["content"] for p in out if p.get("type") == "text") == response @pytest.mark.vcr @@ -127,7 +123,10 @@ def test_vertexai_stream(instrument_legacy, span_exporter, log_exporter): assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) - assert output_messages[0]["content"] == "".join(response) + parts = output_messages[0]["parts"] + assert "".join(p["content"] for p in parts if p.get("type") == "text") == "".join( + response + ) @pytest.mark.vcr @@ -166,7 +165,10 @@ async def async_streaming_prediction() -> list: assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) - assert output_messages[0]["content"] == "".join(response) + parts = output_messages[0]["parts"] + assert "".join(p["content"] for p in parts if p.get("type") == "text") == "".join( + response + ) @pytest.mark.vcr @@ -211,10 +213,8 @@ def test_vertexai_chat(instrument_legacy, span_exporter, log_exporter): assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.95 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - assert ( - json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["content"] - == response - ) + out = json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["parts"] + assert "".join(p["content"] for p in out if p.get("type") == "text") == response @pytest.mark.vcr @@ -259,14 +259,17 @@ def test_vertexai_chat_stream(instrument_legacy, span_exporter, log_exporter): assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) - assert output_messages[0]["content"] == "".join(response) + parts = output_messages[0]["parts"] + assert "".join(p["content"] for p in parts if p.get("type") == "text") == "".join( + response + ) def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict): assert log.log_record.attributes.get(EventAttributes.EVENT_NAME) == event_name assert ( log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) - == "vertex_ai" + == GenAIAttributes.GenAiProviderNameValues.GCP_VERTEX_AI.value ) if not expected_content: diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py index bcfa7aeb15..c9a1675005 100644 --- a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py +++ b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py @@ -32,7 +32,11 @@ def test_vertexai_generate_content(instrument_legacy, span_exporter, log_exporte vertexai_span = spans[0] input_messages = json.loads(vertexai_span.attributes["gen_ai.input.messages"]) - assert any("what is shown in this image?" in msg.get("content", "") for msg in input_messages) + assert any( + "what is shown in this image?" in part.get("content", "") + for msg in input_messages + for part in msg.get("parts", []) + ) assert ( vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gemini-2.0-flash-lite" @@ -50,7 +54,7 @@ def test_vertexai_generate_content(instrument_legacy, span_exporter, log_exporte == response._raw_response.usage_metadata.candidates_token_count ) output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) - assert output_messages[0]["content"] == response.text + assert output_messages[0]["parts"][0]["content"] == response.text logs = log_exporter.get_finished_logs() assert ( @@ -172,7 +176,7 @@ def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_con assert log.log_record.attributes.get(EventAttributes.EVENT_NAME) == event_name assert ( log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) - == "vertex_ai" + == GenAIAttributes.GenAiProviderNameValues.GCP_VERTEX_AI.value ) if not expected_content: diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py b/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py index e5abc725ef..46c7d86678 100644 --- a/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py +++ b/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py @@ -1,13 +1,14 @@ import json import pytest from unittest.mock import Mock, patch +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) from opentelemetry.instrumentation.vertexai.span_utils import ( set_input_attributes, set_input_attributes_sync, ) -GEN_AI_INPUT_MESSAGES = "gen_ai.input.messages" - class TestRoleAttributes: """Test cases for role attribute handling in VertexAI instrumentation""" @@ -28,8 +29,9 @@ def capture_attribute(key, value): def _parse_input_messages(self): """Helper to parse gen_ai.input.messages JSON from span attributes""" - assert GEN_AI_INPUT_MESSAGES in self.span_attributes - return json.loads(self.span_attributes[GEN_AI_INPUT_MESSAGES]) + key = GenAIAttributes.GEN_AI_INPUT_MESSAGES + assert key in self.span_attributes + return json.loads(self.span_attributes[key]) @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") @pytest.mark.asyncio @@ -44,10 +46,7 @@ async def test_async_role_attribute_for_string_args(self, mock_should_send_promp messages = self._parse_input_messages() assert len(messages) == 1 assert messages[0]["role"] == "user" - - # Verify content is also set - expected_content = json.dumps([{"type": "text", "text": "Hello, world!"}]) - assert messages[0]["content"] == expected_content + assert messages[0]["parts"][0] == {"type": "text", "content": "Hello, world!"} @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") @pytest.mark.asyncio @@ -91,9 +90,7 @@ def test_sync_role_attribute_for_string_args(self, mock_should_send_prompts): assert len(messages) == 1 assert messages[0]["role"] == "user" - # Verify content is also set - expected_content = json.dumps([{"type": "text", "text": "Hello, world!"}]) - assert messages[0]["content"] == expected_content + assert messages[0]["parts"][0] == {"type": "text", "content": "Hello, world!"} @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") def test_sync_role_attribute_for_multiple_args(self, mock_should_send_prompts): @@ -187,7 +184,7 @@ async def test_async_role_attribute_for_image_content(self, mock_should_send_pro messages = self._parse_input_messages() assert len(messages) == 1 assert messages[0]["role"] == "user" - assert messages[0]["content"] # content should be set + assert messages[0]["parts"] @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") def test_sync_role_attribute_for_image_content(self, mock_should_send_prompts): @@ -205,4 +202,4 @@ def test_sync_role_attribute_for_image_content(self, mock_should_send_prompts): messages = self._parse_input_messages() assert len(messages) == 1 assert messages[0]["role"] == "user" - assert messages[0]["content"] # content should be set + assert messages[0]["parts"] From 8683440926759319086729e1778ef6426f351fac Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Sun, 29 Mar 2026 13:47:03 +0300 Subject: [PATCH 04/21] last-fix --- .../google_generativeai/__init__.py | 71 ++++- .../google_generativeai/span_utils.py | 64 +++- .../tests/test_generate_content.py | 27 +- .../instrumentation/vertexai/__init__.py | 73 +++-- .../instrumentation/vertexai/span_utils.py | 297 +++++++++++++++++- 5 files changed, 477 insertions(+), 55 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py index 670e665e89..e30b97d02b 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py @@ -15,6 +15,7 @@ emit_message_events, ) from opentelemetry.instrumentation.google_generativeai.span_utils import ( + accumulate_stream_finish_reasons, set_input_attributes_sync, set_model_request_attributes, set_model_response_attributes, @@ -86,23 +87,44 @@ def _build_from_streaming_response( event_logger, token_histogram, ): - complete_response = "" + emit_events = should_emit_events() and event_logger + text_parts = [] last_chunk = None + stream_finish_ordered = [] + stream_finish_seen = set() for item in response: item_to_yield = item last_chunk = item - complete_response += str(item.text) + if not emit_events: + t = getattr(item, "text", None) + if isinstance(t, str): + text_parts.append(t) + accumulate_stream_finish_reasons( + stream_finish_ordered, stream_finish_seen, item + ) yield item_to_yield - if should_emit_events() and event_logger: + complete_response = "".join(text_parts) + + if emit_events: emit_choice_events(response, event_logger) else: - set_response_attributes( - span, complete_response, llm_model, stream_last_chunk=last_chunk - ) + # Assumes the SDK aggregates candidate parts on each chunk; the last chunk with + # candidates should reflect the full model turn (incl. tool_call). Pure text deltas + # on the final chunk fall back to ``complete_response`` below. + if last_chunk is not None and getattr(last_chunk, "candidates", None): + set_response_attributes(span, last_chunk, llm_model) + else: + set_response_attributes( + span, complete_response, llm_model, stream_last_chunk=last_chunk + ) set_model_response_attributes( - span, last_chunk or response, llm_model, token_histogram + span, + last_chunk or response, + llm_model, + token_histogram, + stream_finish_reasons=stream_finish_ordered or None, ) span.end() @@ -110,23 +132,42 @@ def _build_from_streaming_response( async def _abuild_from_streaming_response( span, response: GenerateContentResponse, llm_model, event_logger, token_histogram ): - complete_response = "" + emit_events = should_emit_events() and event_logger + text_parts = [] last_chunk = None + stream_finish_ordered = [] + stream_finish_seen = set() async for item in response: item_to_yield = item last_chunk = item - complete_response += str(item.text) + if not emit_events: + t = getattr(item, "text", None) + if isinstance(t, str): + text_parts.append(t) + accumulate_stream_finish_reasons( + stream_finish_ordered, stream_finish_seen, item + ) yield item_to_yield - if should_emit_events() and event_logger: + complete_response = "".join(text_parts) + + if emit_events: emit_choice_events(response, event_logger) else: - set_response_attributes( - span, complete_response, llm_model, stream_last_chunk=last_chunk - ) + # Same aggregation assumption as sync streaming (see _build_from_streaming_response). + if last_chunk is not None and getattr(last_chunk, "candidates", None): + set_response_attributes(span, last_chunk, llm_model) + else: + set_response_attributes( + span, complete_response, llm_model, stream_last_chunk=last_chunk + ) set_model_response_attributes( - span, last_chunk if last_chunk else response, llm_model, token_histogram + span, + last_chunk if last_chunk else response, + llm_model, + token_histogram, + stream_finish_reasons=stream_finish_ordered or None, ) span.end() @@ -212,6 +253,7 @@ async def _awrap( attributes={ GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, GenAIAttributes.GEN_AI_OPERATION_NAME: _GEN_CONTENT, + GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, }, ) start_time = time.perf_counter() @@ -288,6 +330,7 @@ def _wrap( attributes={ GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, GenAIAttributes.GEN_AI_OPERATION_NAME: _GEN_CONTENT, + GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, }, ) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index 66071c925c..a58113fd94 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -193,6 +193,13 @@ def run_async(method): def _process_image_part_sync(item, trace_id, span_id, content_index): + """Sync path for optional image upload. + + Uses ``run_async`` in a new thread when the current thread already has a + running event loop (e.g. FastAPI/Jupyter). That pattern can deadlock if the + async upload waits on the same loop — prefer async instrumentation paths + where possible. + """ if not Config.upload_base64_image: return None @@ -388,6 +395,15 @@ def _collect_finish_reasons_from_response(response): return reasons +def accumulate_stream_finish_reasons(ordered, seen, chunk): + """Merge finish reasons from a streaming chunk; preserves first-seen order (metadata, not gated by prompts).""" + for cand in getattr(chunk, "candidates", None) or []: + mapped = _map_gemini_finish_reason(getattr(cand, "finish_reason", None)) + if mapped is not None and mapped not in seen: + seen.add(mapped) + ordered.append(mapped) + + def _output_messages_from_generate_response(span, response): messages = [] candidates = getattr(response, "candidates", None) or [] @@ -555,6 +571,45 @@ def set_model_request_attributes(span, kwargs, llm_model): except Exception: pass + if should_send_prompts(): + si = _system_instruction_from_kwargs(kwargs) + if si is not None: + try: + parts = _system_instruction_to_parts(si, span) + if parts: + _set_span_attribute( + span, + GenAIAttributes.GEN_AI_SYSTEM_INSTRUCTIONS, + json.dumps(parts), + ) + except Exception: + pass + + +def _system_instruction_from_kwargs(kwargs): + """Top-level kwarg or unified-client ``config.system_instruction``.""" + si = kwargs.get("system_instruction") + if si is not None: + return si + config = kwargs.get("config") + if config is not None: + si = getattr(config, "system_instruction", None) + if si is not None: + return si + return None + + +def _system_instruction_to_parts(si, span): + """OTel: flat array of parts for gen_ai.system_instructions.""" + if isinstance(si, str): + return [{"type": "text", "content": si}] + if hasattr(si, "parts") and si.parts: + out = [] + for idx, p in enumerate(si.parts): + out.extend(_parts_from_genai_part_sync(p, span, idx)) + return out + return [{"type": "text", "content": str(si)}] + @dont_throw def set_response_attributes(span, response, llm_model, stream_last_chunk=None): @@ -604,7 +659,9 @@ def set_response_attributes(span, response, llm_model, stream_last_chunk=None): ) -def set_model_response_attributes(span, response, llm_model, token_histogram): +def set_model_response_attributes( + span, response, llm_model, token_histogram, stream_finish_reasons=None +): if not span.is_recording(): return @@ -614,7 +671,10 @@ def set_model_response_attributes(span, response, llm_model, token_histogram): if rid: _set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_ID, rid) - reasons = _collect_finish_reasons_from_response(response) + if stream_finish_reasons is not None: + reasons = stream_finish_reasons + else: + reasons = _collect_finish_reasons_from_response(response) if reasons: _set_span_attribute( span, GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS, reasons diff --git a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py index 041aef74dc..8def4ec0a2 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py @@ -1,5 +1,6 @@ - import json +from unittest.mock import MagicMock + import pytest from opentelemetry.trace import StatusCode, SpanKind from opentelemetry.semconv_ai import ( @@ -157,3 +158,27 @@ def test_generate_metrics(metrics_test_context, genai_client, exporter): # Required semantic attributes assert GenAIAttributes.GEN_AI_PROVIDER_NAME in dp.attributes assert GenAIAttributes.GEN_AI_RESPONSE_MODEL in dp.attributes + + +def test_set_model_request_attributes_reads_system_instruction_from_config(monkeypatch): + from opentelemetry.instrumentation.google_generativeai import span_utils as su + + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + + span = MagicMock() + span.is_recording.return_value = True + + class GenerateContentConfig: + system_instruction = "Reply in one sentence." + + su.set_model_request_attributes(span, {"config": GenerateContentConfig()}, "gemini-pro") + + sys_attr_calls = [ + c[0] + for c in span.set_attribute.call_args_list + if c[0][0] == GenAIAttributes.GEN_AI_SYSTEM_INSTRUCTIONS + ] + assert len(sys_attr_calls) == 1 + parts = json.loads(sys_attr_calls[0][1]) + assert parts[0]["type"] == "text" + assert parts[0]["content"] == "Reply in one sentence." diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py index 058d2e33f3..cdf4ad4823 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py @@ -15,6 +15,7 @@ ) from opentelemetry.instrumentation.vertexai.span_utils import ( _map_vertex_finish_reason, + accumulate_vertex_stream_finish_reasons, set_input_attributes, set_input_attributes_sync, set_model_input_attributes, @@ -148,10 +149,20 @@ def is_async_streaming_response(response): @dont_throw def handle_streaming_response( - span, event_logger, llm_model, complete_response, token_usage, stream_last_chunk=None + span, + event_logger, + llm_model, + complete_response, + token_usage, + stream_last_chunk=None, + stream_finish_reasons=None, ): set_model_response_attributes( - span, llm_model, token_usage, response_meta=stream_last_chunk + span, + llm_model, + token_usage, + response_meta=stream_last_chunk, + stream_finish_reasons=stream_finish_reasons, ) finish_reason_otel = None if stream_last_chunk and getattr(stream_last_chunk, "candidates", None): @@ -161,26 +172,44 @@ def handle_streaming_response( if should_emit_events(): emit_response_events(complete_response, event_logger) else: - set_response_attributes( - span, llm_model, complete_response, finish_reason_otel=finish_reason_otel - ) + # Prefer full candidate on the last chunk when present (see google-generativeai streaming). + if stream_last_chunk is not None and getattr( + stream_last_chunk, "candidates", None + ): + set_response_attributes(span, llm_model, stream_last_chunk) + else: + set_response_attributes( + span, + llm_model, + complete_response, + finish_reason_otel=finish_reason_otel, + ) if span.is_recording(): span.set_status(Status(StatusCode.OK)) def _build_from_streaming_response(span, event_logger, response, llm_model): - complete_response = "" + text_parts = [] token_usage = None last_item = None + stream_finish_ordered = [] + stream_finish_seen = set() for item in response: item_to_yield = item last_item = item - complete_response += str(item.text) + t = getattr(item, "text", None) + if isinstance(t, str): + text_parts.append(t) if item.usage_metadata: token_usage = item.usage_metadata + accumulate_vertex_stream_finish_reasons( + stream_finish_ordered, stream_finish_seen, item + ) yield item_to_yield + complete_response = "".join(text_parts) + handle_streaming_response( span, event_logger, @@ -188,6 +217,7 @@ def _build_from_streaming_response(span, event_logger, response, llm_model): complete_response, token_usage, stream_last_chunk=last_item, + stream_finish_reasons=stream_finish_ordered or None, ) span.set_status(Status(StatusCode.OK)) @@ -195,18 +225,27 @@ def _build_from_streaming_response(span, event_logger, response, llm_model): async def _abuild_from_streaming_response(span, event_logger, response, llm_model): - complete_response = "" + text_parts = [] token_usage = None last_item = None + stream_finish_ordered = [] + stream_finish_seen = set() async for item in response: item_to_yield = item last_item = item - complete_response += str(item.text) + t = getattr(item, "text", None) + if isinstance(t, str): + text_parts.append(t) if item.usage_metadata: token_usage = item.usage_metadata + accumulate_vertex_stream_finish_reasons( + stream_finish_ordered, stream_finish_seen, item + ) yield item_to_yield + complete_response = "".join(text_parts) + handle_streaming_response( span, event_logger, @@ -214,6 +253,7 @@ async def _abuild_from_streaming_response(span, event_logger, response, llm_mode complete_response, token_usage, stream_last_chunk=last_item, + stream_finish_reasons=stream_finish_ordered or None, ) span.set_status(Status(StatusCode.OK)) @@ -233,21 +273,10 @@ def _handle_response(span, event_logger, response, llm_model): set_model_response_attributes( span, llm_model, response.usage_metadata, response_meta=response ) - finish_reason_otel = None - if response.candidates: - finish_reason_otel = _map_vertex_finish_reason( - response.candidates[0].finish_reason - ) - generation_text = response.candidates[0].text if response.candidates else "" if should_emit_events(): emit_response_events(response, event_logger) else: - set_response_attributes( - span, - llm_model, - generation_text, - finish_reason_otel=finish_reason_otel, - ) + set_response_attributes(span, llm_model, response) if span.is_recording(): span.set_status(Status(StatusCode.OK)) @@ -290,6 +319,7 @@ async def _awrap(tracer, event_logger, to_wrap, wrapped, instance, args, kwargs) attributes={ GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_VERTEX_AI, GenAIAttributes.GEN_AI_OPERATION_NAME: _gen_ai_operation_name(name), + GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, }, ) @@ -339,6 +369,7 @@ def _wrap(tracer, event_logger, to_wrap, wrapped, instance, args, kwargs): attributes={ GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_VERTEX_AI, GenAIAttributes.GEN_AI_OPERATION_NAME: _gen_ai_operation_name(name), + GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, }, ) diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py index b9d0ecf105..842509bf57 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py @@ -52,13 +52,239 @@ def _map_vertex_finish_reason(finish_reason): "BLOCKLIST": "content_filter", "PROHIBITED_CONTENT": "content_filter", "SPII": "content_filter", + "IMAGE_SAFETY": "content_filter", + "IMAGE_PROHIBITED_CONTENT": "content_filter", + "IMAGE_RECITATION": "content_filter", "LANGUAGE": "content_filter", "FINISH_REASON_UNSPECIFIED": None, + "MALFORMED_FUNCTION_CALL": "error", "OTHER": "error", + "UNEXPECTED_TOOL_CALL": "error", + "NO_IMAGE": "error", + "IMAGE_OTHER": "error", } return mapping.get(name) +def _normalize_message_role(role): + if role is None: + return "user" + r = str(role).lower() + if r == "model": + return "assistant" + return r + + +def _parse_vertex_function_args(args): + if args is None: + return {} + if isinstance(args, dict): + return args + if isinstance(args, str): + try: + return json.loads(args) + except (json.JSONDecodeError, TypeError): + return {"_raw": args} + return {"_value": args} + + +def _vertex_function_response_to_str(response): + if response is None: + return "" + if isinstance(response, str): + return response + try: + return json.dumps(response, default=str) + except TypeError: + return str(response) + + +def accumulate_vertex_stream_finish_reasons(ordered, seen, chunk): + for cand in getattr(chunk, "candidates", None) or []: + mapped = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) + if mapped is not None and mapped not in seen: + seen.add(mapped) + ordered.append(mapped) + + +def _parts_from_vertex_part_sync(part, span, part_index): + out = [] + t = getattr(part, "text", None) + if isinstance(t, str) and t: + out.append({"type": "text", "content": t}) + img = _otel_image_part_vertex_sync(part, span, part_index) + if img: + out.append(img) + fc = getattr(part, "function_call", None) + if fc is not None: + fid = getattr(fc, "id", None) or "" + fname = getattr(fc, "name", None) or "" + out.append( + { + "type": "tool_call", + "id": fid, + "name": fname, + "arguments": _parse_vertex_function_args(getattr(fc, "args", None)), + } + ) + fr = getattr(part, "function_response", None) + if fr is not None: + fid = getattr(fr, "id", None) or "" + out.append( + { + "type": "tool_call_response", + "id": fid, + "response": _vertex_function_response_to_str( + getattr(fr, "response", None) + ), + } + ) + if not out: + out.append({"type": "text", "content": str(part)}) + return out + + +async def _parts_from_vertex_part_async(part, span, part_index): + out = [] + t = getattr(part, "text", None) + if isinstance(t, str) and t: + out.append({"type": "text", "content": t}) + if _is_base64_image_part(part): + img = await _otel_image_part_vertex_async(part, span, part_index) + if img: + out.append(img) + fc = getattr(part, "function_call", None) + if fc is not None: + fid = getattr(fc, "id", None) or "" + fname = getattr(fc, "name", None) or "" + out.append( + { + "type": "tool_call", + "id": fid, + "name": fname, + "arguments": _parse_vertex_function_args(getattr(fc, "args", None)), + } + ) + fr = getattr(part, "function_response", None) + if fr is not None: + fid = getattr(fr, "id", None) or "" + out.append( + { + "type": "tool_call_response", + "id": fid, + "response": _vertex_function_response_to_str( + getattr(fr, "response", None) + ), + } + ) + if not out: + out.append({"type": "text", "content": str(part)}) + return out + + +def _vertex_parts_list_from_content_sync(content, span): + parts_acc = [] + for idx, p in enumerate(content.parts): + parts_acc.extend(_parts_from_vertex_part_sync(p, span, idx)) + return parts_acc + + +async def _vertex_parts_list_from_content_async(content, span): + parts_acc = [] + for idx, p in enumerate(content.parts): + parts_acc.extend(await _parts_from_vertex_part_async(p, span, idx)) + return parts_acc + + +def _vertex_expand_arg_to_messages_sync(argument, span): + messages = [] + if hasattr(argument, "parts") and hasattr(argument, "role"): + parts = _vertex_parts_list_from_content_sync(argument, span) + if parts: + messages.append( + {"role": _normalize_message_role(argument.role), "parts": parts} + ) + return messages + if isinstance(argument, list): + if argument and all( + hasattr(x, "parts") and hasattr(x, "role") for x in argument + ): + for c in argument: + parts = _vertex_parts_list_from_content_sync(c, span) + if parts: + messages.append( + {"role": _normalize_message_role(c.role), "parts": parts} + ) + return messages + processed = _process_vertexai_argument_sync(argument, span) + if processed: + messages.append({"role": "user", "parts": processed}) + return messages + + +async def _vertex_expand_arg_to_messages_async(argument, span): + messages = [] + if hasattr(argument, "parts") and hasattr(argument, "role"): + parts = await _vertex_parts_list_from_content_async(argument, span) + if parts: + messages.append( + {"role": _normalize_message_role(argument.role), "parts": parts} + ) + return messages + if isinstance(argument, list): + if argument and all( + hasattr(x, "parts") and hasattr(x, "role") for x in argument + ): + for c in argument: + parts = await _vertex_parts_list_from_content_async(c, span) + if parts: + messages.append( + {"role": _normalize_message_role(c.role), "parts": parts} + ) + return messages + processed = await _process_vertexai_argument(argument, span) + if processed: + messages.append({"role": "user", "parts": processed}) + return messages + + +def _output_messages_from_vertex_response(span, response): + messages = [] + for cand in getattr(response, "candidates", None) or []: + parts = [] + content = getattr(cand, "content", None) + if content and getattr(content, "parts", None): + for idx, p in enumerate(content.parts): + parts.extend(_parts_from_vertex_part_sync(p, span, idx)) + fr = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) + role = "assistant" + if content and getattr(content, "role", None): + role = _normalize_message_role(content.role) + msg = {"role": role, "parts": parts} + if fr is not None: + msg["finish_reason"] = fr + messages.append(msg) + if not messages and getattr(response, "text", None): + messages.append( + { + "role": "assistant", + "parts": [{"type": "text", "content": response.text}], + } + ) + return messages + + +def _vertex_system_instruction_to_parts(si, span): + if isinstance(si, str): + return [{"type": "text", "content": si}] + if hasattr(si, "parts") and si.parts: + out = [] + for idx, p in enumerate(si.parts): + out.extend(_parts_from_vertex_part_sync(p, span, idx)) + return out + return [{"type": "text", "content": str(si)}] + + async def _otel_image_part_vertex_async(item, span, item_index): if not _is_base64_image_part(item): return None @@ -141,7 +367,11 @@ def run_async(method): def _process_image_part_sync(item, trace_id, span_id, content_index): - """Synchronous version of image part processing using OpenAI's pattern""" + """Sync upload path; uses ``run_async`` + thread when a loop is already running. + + Can deadlock if the upload depends on the same event loop (e.g. some ASGI/Jupyter + setups). Prefer async request paths where possible. + """ if not Config.upload_base64_image: return None @@ -255,9 +485,7 @@ async def set_input_attributes(span, args): if should_send_prompts() and args is not None and len(args) > 0: messages = [] for argument in args: - processed_content = await _process_vertexai_argument(argument, span) - if processed_content: - messages.append({"role": "user", "parts": processed_content}) + messages.extend(await _vertex_expand_arg_to_messages_async(argument, span)) if messages: _set_span_attribute( span, GenAIAttributes.GEN_AI_INPUT_MESSAGES, json.dumps(messages) @@ -273,9 +501,7 @@ def set_input_attributes_sync(span, args): if should_send_prompts() and args is not None and len(args) > 0: messages = [] for argument in args: - processed_content = _process_vertexai_argument_sync(argument, span) - if processed_content: - messages.append({"role": "user", "parts": processed_content}) + messages.extend(_vertex_expand_arg_to_messages_sync(argument, span)) if messages: _set_span_attribute( span, GenAIAttributes.GEN_AI_INPUT_MESSAGES, json.dumps(messages) @@ -312,14 +538,47 @@ def set_model_input_attributes(span, kwargs, llm_model): span, GenAIAttributes.GEN_AI_REQUEST_FREQUENCY_PENALTY, kwargs.get("frequency_penalty") ) + if should_send_prompts(): + si = _vertex_system_instruction_from_kwargs(kwargs) + if si is not None: + try: + parts = _vertex_system_instruction_to_parts(si, span) + if parts: + _set_span_attribute( + span, + GenAIAttributes.GEN_AI_SYSTEM_INSTRUCTIONS, + json.dumps(parts), + ) + except Exception: + pass + + +def _vertex_system_instruction_from_kwargs(kwargs): + si = kwargs.get("system_instruction") + if si is not None: + return si + config = kwargs.get("config") + if config is not None: + si = getattr(config, "system_instruction", None) + if si is not None: + return si + return None + @dont_throw -def set_response_attributes(span, llm_model, generation_text, finish_reason_otel=None): +def set_response_attributes(span, llm_model, generation_or_text, finish_reason_otel=None): if not span.is_recording() or not should_send_prompts(): return + if hasattr(generation_or_text, "candidates"): + messages = _output_messages_from_vertex_response(span, generation_or_text) + if messages: + _set_span_attribute( + span, GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, json.dumps(messages) + ) + return parts = [] - if generation_text: - parts.append({"type": "text", "content": generation_text}) + if generation_or_text: + parts.append({"type": "text", "content": generation_or_text}) msg = {"role": "assistant", "parts": parts} if finish_reason_otel is not None: msg["finish_reason"] = finish_reason_otel @@ -330,18 +589,22 @@ def set_response_attributes(span, llm_model, generation_text, finish_reason_otel @dont_throw -def set_model_response_attributes(span, llm_model, token_usage, response_meta=None): +def set_model_response_attributes( + span, llm_model, token_usage, response_meta=None, stream_finish_reasons=None +): if not span.is_recording(): return _set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_MODEL, llm_model) if response_meta is not None: - candidates = getattr(response_meta, "candidates", None) or [] - reasons = [] - for cand in candidates: - mapped = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) - if mapped is not None: - reasons.append(mapped) + if stream_finish_reasons is not None: + reasons = stream_finish_reasons + else: + reasons = [] + for cand in getattr(response_meta, "candidates", None) or []: + mapped = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) + if mapped is not None: + reasons.append(mapped) if reasons: _set_span_attribute( span, GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS, reasons From 3c24e3c530541196c990a124560518019f5d325c Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Sun, 29 Mar 2026 13:51:33 +0300 Subject: [PATCH 05/21] coderabbit --- .../instrumentation/google_generativeai/__init__.py | 4 ++++ .../instrumentation/google_generativeai/span_utils.py | 5 +++++ .../tests/test_generate_content.py | 4 ++++ 3 files changed, 13 insertions(+) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py index e30b97d02b..9335be10a4 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py @@ -272,6 +272,8 @@ async def _awrap( duration, attributes={ GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, + GenAIAttributes.GEN_AI_OPERATION_NAME: _GEN_CONTENT, + GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, }, ) @@ -350,6 +352,8 @@ def _wrap( duration, attributes={ GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, + GenAIAttributes.GEN_AI_OPERATION_NAME: _GEN_CONTENT, + GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, }, ) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index a58113fd94..bfbea8b66e 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -17,6 +17,7 @@ from opentelemetry.trace.status import Status, StatusCode _GCP_GEN_AI = GenAIAttributes.GenAiProviderNameValues.GCP_GEN_AI.value +_GEN_CONTENT = GenAIAttributes.GenAiOperationNameValues.GENERATE_CONTENT.value logger = logging.getLogger(__name__) @@ -703,6 +704,8 @@ def set_model_response_attributes( um.prompt_token_count, attributes={ GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, + GenAIAttributes.GEN_AI_OPERATION_NAME: _GEN_CONTENT, + GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, GenAIAttributes.GEN_AI_TOKEN_TYPE: "input", GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, }, @@ -711,6 +714,8 @@ def set_model_response_attributes( um.candidates_token_count, attributes={ GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, + GenAIAttributes.GEN_AI_OPERATION_NAME: _GEN_CONTENT, + GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, GenAIAttributes.GEN_AI_TOKEN_TYPE: "output", GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, }, diff --git a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py index 8def4ec0a2..dfa2078e33 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py @@ -136,6 +136,8 @@ def test_generate_metrics(metrics_test_context, genai_client, exporter): # Required attributes (values are intentionally not hard-coded) assert GenAIAttributes.GEN_AI_PROVIDER_NAME in duration_dp.attributes + assert GenAIAttributes.GEN_AI_OPERATION_NAME in duration_dp.attributes + assert GenAIAttributes.GEN_AI_REQUEST_MODEL in duration_dp.attributes assert GenAIAttributes.GEN_AI_RESPONSE_MODEL in duration_dp.attributes token_metric = metrics[Meters.LLM_TOKEN_USAGE] @@ -157,6 +159,8 @@ def test_generate_metrics(metrics_test_context, genai_client, exporter): # Required semantic attributes assert GenAIAttributes.GEN_AI_PROVIDER_NAME in dp.attributes + assert GenAIAttributes.GEN_AI_OPERATION_NAME in dp.attributes + assert GenAIAttributes.GEN_AI_REQUEST_MODEL in dp.attributes assert GenAIAttributes.GEN_AI_RESPONSE_MODEL in dp.attributes From 3d67642d679b63fe23db0f49572a0480623b0324 Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Mon, 30 Mar 2026 13:33:44 +0300 Subject: [PATCH 06/21] fix(google-generativeai, vertexai): OTel semconv compliance fixes - BlobPart: use "data" key instead of "content" for base64 payloads (OTel schema) - OutputMessage: always set finish_reason (required by schema), use None when unavailable - Event emitters: map finish reasons to OTel canonical values (stop, length, content_filter, error) Co-Authored-By: Claude Opus 4.6 --- .../google_generativeai/event_emitter.py | 5 +++- .../google_generativeai/span_utils.py | 13 ++++------ .../instrumentation/vertexai/event_emitter.py | 24 +++++++++---------- .../instrumentation/vertexai/span_utils.py | 13 ++++------ 4 files changed, 26 insertions(+), 29 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py index 7296550603..3c529da22f 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py @@ -8,6 +8,9 @@ ChoiceEvent, MessageEvent, ) +from opentelemetry.instrumentation.google_generativeai.span_utils import ( + _map_gemini_finish_reason, +) from opentelemetry.instrumentation.google_generativeai.utils import ( part_to_dict, should_emit_events, @@ -67,7 +70,7 @@ def emit_choice_events( "content": [part_to_dict(i) for i in candidate.content.parts], "role": candidate.content.role, }, - finish_reason=candidate.finish_reason.name, + finish_reason=_map_gemini_finish_reason(candidate.finish_reason), ), event_logger, ) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index bfbea8b66e..307af3a78b 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -130,7 +130,7 @@ def _otel_image_part_from_genai_part(part, span, part_index, sync): } binary_data = part.inline_data.data b64 = base64.b64encode(binary_data).decode("utf-8") - return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} + return {"type": "blob", "modality": "image", "mime_type": mime, "data": b64} async def _otel_image_part_from_genai_part_async(part, span, part_index): @@ -153,7 +153,7 @@ async def _otel_image_part_from_genai_part_async(part, span, part_index): } binary_data = part.inline_data.data b64 = base64.b64encode(binary_data).decode("utf-8") - return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} + return {"type": "blob", "modality": "image", "mime_type": mime, "data": b64} async def _process_image_part(item, trace_id, span_id, content_index): @@ -418,14 +418,12 @@ def _output_messages_from_generate_response(span, response): role = "assistant" if content and getattr(content, "role", None): role = _normalize_message_role(content.role) - msg = {"role": role, "parts": parts} - if fr is not None: - msg["finish_reason"] = fr + msg = {"role": role, "parts": parts, "finish_reason": fr} messages.append(msg) if not messages and hasattr(response, "text") and response.text: text = response.text if isinstance(text, str) and text: - msg = {"role": "assistant", "parts": [{"type": "text", "content": text}]} + msg = {"role": "assistant", "parts": [{"type": "text", "content": text}], "finish_reason": None} messages.append(msg) return messages @@ -627,8 +625,7 @@ def set_response_attributes(span, response, llm_model, stream_last_chunk=None): msg = {"role": "assistant", "parts": []} if response: msg["parts"].append({"type": "text", "content": response}) - if fr is not None: - msg["finish_reason"] = fr + msg["finish_reason"] = fr if msg["parts"] or fr is not None: _set_span_attribute( span, diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py index c255fb484f..773f92e1c8 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py @@ -37,19 +37,19 @@ class Roles(Enum): def _parse_vertex_finish_reason(reason): if reason is None: - return "unknown" + return None finish_reason_map = { - 0: "unspecified", - 1: "stop", - 2: "max_tokens", - 3: "safety", - 4: "recitation", - 5: "other", - 6: "blocklist", - 7: "prohibited_content", - 8: "spii", - 9: "malformed_function_call", + 0: None, # FINISH_REASON_UNSPECIFIED + 1: "stop", # STOP + 2: "length", # MAX_TOKENS + 3: "content_filter", # SAFETY + 4: "content_filter", # RECITATION + 5: "error", # OTHER + 6: "content_filter", # BLOCKLIST + 7: "content_filter", # PROHIBITED_CONTENT + 8: "content_filter", # SPII + 9: "error", # MALFORMED_FUNCTION_CALL } if hasattr(reason, "value"): @@ -57,7 +57,7 @@ def _parse_vertex_finish_reason(reason): else: reason_value = reason - return finish_reason_map.get(reason_value, "unknown") + return finish_reason_map.get(reason_value) @dont_throw diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py index 842509bf57..b133f05b08 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py @@ -260,15 +260,14 @@ def _output_messages_from_vertex_response(span, response): role = "assistant" if content and getattr(content, "role", None): role = _normalize_message_role(content.role) - msg = {"role": role, "parts": parts} - if fr is not None: - msg["finish_reason"] = fr + msg = {"role": role, "parts": parts, "finish_reason": fr} messages.append(msg) if not messages and getattr(response, "text", None): messages.append( { "role": "assistant", "parts": [{"type": "text", "content": response.text}], + "finish_reason": None, } ) return messages @@ -301,7 +300,7 @@ async def _otel_image_part_vertex_async(item, span, item_index): } binary_data = item.inline_data.data b64 = base64.b64encode(binary_data).decode("utf-8") - return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} + return {"type": "blob", "modality": "image", "mime_type": mime, "data": b64} def _otel_image_part_vertex_sync(item, span, item_index): @@ -320,7 +319,7 @@ def _otel_image_part_vertex_sync(item, span, item_index): } binary_data = item.inline_data.data b64 = base64.b64encode(binary_data).decode("utf-8") - return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} + return {"type": "blob", "modality": "image", "mime_type": mime, "data": b64} async def _process_image_part(item, trace_id, span_id, content_index): @@ -579,9 +578,7 @@ def set_response_attributes(span, llm_model, generation_or_text, finish_reason_o parts = [] if generation_or_text: parts.append({"type": "text", "content": generation_or_text}) - msg = {"role": "assistant", "parts": parts} - if finish_reason_otel is not None: - msg["finish_reason"] = finish_reason_otel + msg = {"role": "assistant", "parts": parts, "finish_reason": finish_reason_otel} if parts or finish_reason_otel is not None: _set_span_attribute( span, GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, json.dumps([msg]) From 0f352b3b0a49634ded056118328f8de2b438a1e4 Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Mon, 30 Mar 2026 13:46:01 +0300 Subject: [PATCH 07/21] revert: remove event emitter changes (pre-existing, not in branch scope) Co-Authored-By: Claude Opus 4.6 --- .../google_generativeai/event_emitter.py | 5 +--- .../instrumentation/vertexai/event_emitter.py | 24 +++++++++---------- 2 files changed, 13 insertions(+), 16 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py index 3c529da22f..7296550603 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py @@ -8,9 +8,6 @@ ChoiceEvent, MessageEvent, ) -from opentelemetry.instrumentation.google_generativeai.span_utils import ( - _map_gemini_finish_reason, -) from opentelemetry.instrumentation.google_generativeai.utils import ( part_to_dict, should_emit_events, @@ -70,7 +67,7 @@ def emit_choice_events( "content": [part_to_dict(i) for i in candidate.content.parts], "role": candidate.content.role, }, - finish_reason=_map_gemini_finish_reason(candidate.finish_reason), + finish_reason=candidate.finish_reason.name, ), event_logger, ) diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py index 773f92e1c8..c255fb484f 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py @@ -37,19 +37,19 @@ class Roles(Enum): def _parse_vertex_finish_reason(reason): if reason is None: - return None + return "unknown" finish_reason_map = { - 0: None, # FINISH_REASON_UNSPECIFIED - 1: "stop", # STOP - 2: "length", # MAX_TOKENS - 3: "content_filter", # SAFETY - 4: "content_filter", # RECITATION - 5: "error", # OTHER - 6: "content_filter", # BLOCKLIST - 7: "content_filter", # PROHIBITED_CONTENT - 8: "content_filter", # SPII - 9: "error", # MALFORMED_FUNCTION_CALL + 0: "unspecified", + 1: "stop", + 2: "max_tokens", + 3: "safety", + 4: "recitation", + 5: "other", + 6: "blocklist", + 7: "prohibited_content", + 8: "spii", + 9: "malformed_function_call", } if hasattr(reason, "value"): @@ -57,7 +57,7 @@ def _parse_vertex_finish_reason(reason): else: reason_value = reason - return finish_reason_map.get(reason_value) + return finish_reason_map.get(reason_value, "unknown") @dont_throw From f58b77d7680f56d53088d9d4288d23377e1da586 Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Mon, 30 Mar 2026 13:56:10 +0300 Subject: [PATCH 08/21] =?UTF-8?q?fix:=20revert=20BlobPart=20field=20name?= =?UTF-8?q?=20=E2=80=94=20OTel=20spec=20uses=20"content",=20not=20"data"?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Verified against canonical JSON schema (gen-ai-input-messages.json): BlobPart.content is the correct field name for base64 data. The original code was already correct. Co-Authored-By: Claude Opus 4.6 --- .../instrumentation/google_generativeai/span_utils.py | 4 ++-- .../opentelemetry/instrumentation/vertexai/span_utils.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index 307af3a78b..f4e78c5f69 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -130,7 +130,7 @@ def _otel_image_part_from_genai_part(part, span, part_index, sync): } binary_data = part.inline_data.data b64 = base64.b64encode(binary_data).decode("utf-8") - return {"type": "blob", "modality": "image", "mime_type": mime, "data": b64} + return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} async def _otel_image_part_from_genai_part_async(part, span, part_index): @@ -153,7 +153,7 @@ async def _otel_image_part_from_genai_part_async(part, span, part_index): } binary_data = part.inline_data.data b64 = base64.b64encode(binary_data).decode("utf-8") - return {"type": "blob", "modality": "image", "mime_type": mime, "data": b64} + return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} async def _process_image_part(item, trace_id, span_id, content_index): diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py index b133f05b08..2a34258e08 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py @@ -300,7 +300,7 @@ async def _otel_image_part_vertex_async(item, span, item_index): } binary_data = item.inline_data.data b64 = base64.b64encode(binary_data).decode("utf-8") - return {"type": "blob", "modality": "image", "mime_type": mime, "data": b64} + return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} def _otel_image_part_vertex_sync(item, span, item_index): @@ -319,7 +319,7 @@ def _otel_image_part_vertex_sync(item, span, item_index): } binary_data = item.inline_data.data b64 = base64.b64encode(binary_data).decode("utf-8") - return {"type": "blob", "modality": "image", "mime_type": mime, "data": b64} + return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} async def _process_image_part(item, trace_id, span_id, content_index): From 4a213961f70fb7280ad952dd327c1d428784f121 Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Tue, 31 Mar 2026 11:58:12 +0300 Subject: [PATCH 09/21] wip --- .../google_generativeai/span_utils.py | 35 +++++++----- .../instrumentation/vertexai/__init__.py | 2 +- .../instrumentation/vertexai/span_utils.py | 56 +++++++++++-------- 3 files changed, 55 insertions(+), 38 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index f4e78c5f69..0818201135 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -30,9 +30,12 @@ def _set_span_attribute(span, name, value): def _map_gemini_finish_reason(finish_reason): - """Map Gemini FinishReason to OTel; None / UNSPECIFIED -> None (never fabricate stop).""" + """Map Gemini FinishReason to OTel finish reason string. + + Returns ``"unknown"`` for ``None``, unspecified, and unmapped values. + """ if finish_reason is None: - return None + return "unknown" name = getattr(finish_reason, "name", None) or str(finish_reason) mapping = { "STOP": "stop", @@ -46,14 +49,14 @@ def _map_gemini_finish_reason(finish_reason): "IMAGE_PROHIBITED_CONTENT": "content_filter", "IMAGE_RECITATION": "content_filter", "LANGUAGE": "content_filter", - "FINISH_REASON_UNSPECIFIED": None, + "FINISH_REASON_UNSPECIFIED": "unknown", "MALFORMED_FUNCTION_CALL": "error", "OTHER": "error", "UNEXPECTED_TOOL_CALL": "error", "NO_IMAGE": "error", "IMAGE_OTHER": "error", } - return mapping.get(name) + return mapping.get(name, "unknown") def _normalize_message_role(role): @@ -391,7 +394,7 @@ def _collect_finish_reasons_from_response(response): candidates = getattr(response, "candidates", None) or [] for cand in candidates: mapped = _map_gemini_finish_reason(getattr(cand, "finish_reason", None)) - if mapped is not None: + if mapped != "unknown": reasons.append(mapped) return reasons @@ -400,7 +403,7 @@ def accumulate_stream_finish_reasons(ordered, seen, chunk): """Merge finish reasons from a streaming chunk; preserves first-seen order (metadata, not gated by prompts).""" for cand in getattr(chunk, "candidates", None) or []: mapped = _map_gemini_finish_reason(getattr(cand, "finish_reason", None)) - if mapped is not None and mapped not in seen: + if mapped != "unknown" and mapped not in seen: seen.add(mapped) ordered.append(mapped) @@ -423,7 +426,7 @@ def _output_messages_from_generate_response(span, response): if not messages and hasattr(response, "text") and response.text: text = response.text if isinstance(text, str) and text: - msg = {"role": "assistant", "parts": [{"type": "text", "content": text}], "finish_reason": None} + msg = {"role": "assistant", "parts": [{"type": "text", "content": text}], "finish_reason": "unknown"} messages.append(msg) return messages @@ -618,20 +621,21 @@ def set_response_attributes(span, response, llm_model, stream_last_chunk=None): return if isinstance(response, str): - fr = None + fr = "unknown" if stream_last_chunk is not None: reasons = _collect_finish_reasons_from_response(stream_last_chunk) - fr = reasons[0] if reasons else None + fr = reasons[0] if reasons else "unknown" msg = {"role": "assistant", "parts": []} if response: msg["parts"].append({"type": "text", "content": response}) msg["finish_reason"] = fr - if msg["parts"] or fr is not None: - _set_span_attribute( - span, - GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, - json.dumps([msg]), - ) + if not msg["parts"] and fr == "unknown": + return + _set_span_attribute( + span, + GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, + json.dumps([msg]), + ) return if isinstance(response, list): @@ -642,6 +646,7 @@ def set_response_attributes(span, response, llm_model, stream_last_chunk=None): { "role": "assistant", "parts": [{"type": "text", "content": item}], + "finish_reason": "unknown", } ) else: diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py index cdf4ad4823..09ae2febb8 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py @@ -271,7 +271,7 @@ async def _handle_request(span, event_logger, args, kwargs, llm_model): def _handle_response(span, event_logger, response, llm_model): set_model_response_attributes( - span, llm_model, response.usage_metadata, response_meta=response + span, llm_model, getattr(response, "usage_metadata", None), response_meta=response ) if should_emit_events(): emit_response_events(response, event_logger) diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py index 2a34258e08..d4a24a25ac 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py @@ -41,8 +41,12 @@ def _is_base64_image_part(item): def _map_vertex_finish_reason(finish_reason): + """Map VertexAI FinishReason to OTel finish reason string. + + Returns ``"unknown"`` for ``None``, unspecified, and unmapped values. + """ if finish_reason is None: - return None + return "unknown" name = getattr(finish_reason, "name", None) or str(finish_reason) mapping = { "STOP": "stop", @@ -56,14 +60,14 @@ def _map_vertex_finish_reason(finish_reason): "IMAGE_PROHIBITED_CONTENT": "content_filter", "IMAGE_RECITATION": "content_filter", "LANGUAGE": "content_filter", - "FINISH_REASON_UNSPECIFIED": None, + "FINISH_REASON_UNSPECIFIED": "unknown", "MALFORMED_FUNCTION_CALL": "error", "OTHER": "error", "UNEXPECTED_TOOL_CALL": "error", "NO_IMAGE": "error", "IMAGE_OTHER": "error", } - return mapping.get(name) + return mapping.get(name, "unknown") def _normalize_message_role(role): @@ -102,7 +106,7 @@ def _vertex_function_response_to_str(response): def accumulate_vertex_stream_finish_reasons(ordered, seen, chunk): for cand in getattr(chunk, "candidates", None) or []: mapped = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) - if mapped is not None and mapped not in seen: + if mapped != "unknown" and mapped not in seen: seen.add(mapped) ordered.append(mapped) @@ -139,6 +143,8 @@ def _parts_from_vertex_part_sync(part, span, part_index): ), } ) + if getattr(part, "thought", None): + out.append({"type": "reasoning", "content": str(part.thought)}) if not out: out.append({"type": "text", "content": str(part)}) return out @@ -177,6 +183,8 @@ async def _parts_from_vertex_part_async(part, span, part_index): ), } ) + if getattr(part, "thought", None): + out.append({"type": "reasoning", "content": str(part.thought)}) if not out: out.append({"type": "text", "content": str(part)}) return out @@ -267,7 +275,7 @@ def _output_messages_from_vertex_response(span, response): { "role": "assistant", "parts": [{"type": "text", "content": response.text}], - "finish_reason": None, + "finish_reason": "unknown", } ) return messages @@ -578,11 +586,13 @@ def set_response_attributes(span, llm_model, generation_or_text, finish_reason_o parts = [] if generation_or_text: parts.append({"type": "text", "content": generation_or_text}) - msg = {"role": "assistant", "parts": parts, "finish_reason": finish_reason_otel} - if parts or finish_reason_otel is not None: - _set_span_attribute( - span, GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, json.dumps([msg]) - ) + fr = finish_reason_otel or "unknown" + if not parts and fr == "unknown": + return + msg = {"role": "assistant", "parts": parts, "finish_reason": fr} + _set_span_attribute( + span, GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, json.dumps([msg]) + ) @dont_throw @@ -593,19 +603,21 @@ def set_model_response_attributes( return _set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_MODEL, llm_model) + if stream_finish_reasons is not None: + reasons = stream_finish_reasons + elif response_meta is not None: + reasons = [] + for cand in getattr(response_meta, "candidates", None) or []: + mapped = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) + if mapped != "unknown": + reasons.append(mapped) + else: + reasons = [] + if reasons: + _set_span_attribute( + span, GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS, reasons + ) if response_meta is not None: - if stream_finish_reasons is not None: - reasons = stream_finish_reasons - else: - reasons = [] - for cand in getattr(response_meta, "candidates", None) or []: - mapped = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) - if mapped is not None: - reasons.append(mapped) - if reasons: - _set_span_attribute( - span, GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS, reasons - ) rid = getattr(response_meta, "response_id", None) if rid: _set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_ID, rid) From ce00bb573f22431076717fd537c7b3a4839f3b7d Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Tue, 31 Mar 2026 12:06:09 +0300 Subject: [PATCH 10/21] cleanup --- .../google_generativeai/event_emitter.py | 5 +++- .../instrumentation/vertexai/event_emitter.py | 29 +++---------------- 2 files changed, 8 insertions(+), 26 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py index 7296550603..f9ca89fc0f 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py @@ -16,6 +16,9 @@ from opentelemetry.semconv._incubating.attributes import ( gen_ai_attributes as GenAIAttributes, ) +from opentelemetry.instrumentation.google_generativeai.span_utils import ( + _map_gemini_finish_reason, +) _GCP_GEN_AI = GenAIAttributes.GenAiProviderNameValues.GCP_GEN_AI.value @@ -67,7 +70,7 @@ def emit_choice_events( "content": [part_to_dict(i) for i in candidate.content.parts], "role": candidate.content.role, }, - finish_reason=candidate.finish_reason.name, + finish_reason=_map_gemini_finish_reason(candidate.finish_reason), ), event_logger, ) diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py index c255fb484f..f019dd4f72 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py @@ -12,6 +12,9 @@ should_emit_events, should_send_prompts, ) +from opentelemetry.instrumentation.vertexai.span_utils import ( + _map_vertex_finish_reason, +) from opentelemetry.semconv._incubating.attributes import ( gen_ai_attributes as GenAIAttributes, ) @@ -35,30 +38,6 @@ class Roles(Enum): """The attributes to be used for the event.""" -def _parse_vertex_finish_reason(reason): - if reason is None: - return "unknown" - - finish_reason_map = { - 0: "unspecified", - 1: "stop", - 2: "max_tokens", - 3: "safety", - 4: "recitation", - 5: "other", - 6: "blocklist", - 7: "prohibited_content", - 8: "spii", - 9: "malformed_function_call", - } - - if hasattr(reason, "value"): - reason_value = reason.value - else: - reason_value = reason - - return finish_reason_map.get(reason_value, "unknown") - @dont_throw def emit_prompt_events(args, event_logger): @@ -92,7 +71,7 @@ def emit_response_events(response, event_logger): "content": candidate.text, "role": Roles.ASSISTANT.value, }, - finish_reason=_parse_vertex_finish_reason(candidate.finish_reason), + finish_reason=_map_vertex_finish_reason(candidate.finish_reason), ), event_logger, ) From 2a6da1af3fed44135b8a8eeb47b35a4e1f8053f5 Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Tue, 31 Mar 2026 12:15:41 +0300 Subject: [PATCH 11/21] more-test --- .../tests/test_finish_reasons.py | 296 +++++++++++ .../tests/test_finish_reasons.py | 469 ++++++++++++++++++ 2 files changed, 765 insertions(+) create mode 100644 packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py create mode 100644 packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py diff --git a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py new file mode 100644 index 0000000000..7944573136 --- /dev/null +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py @@ -0,0 +1,296 @@ +"""Unit tests for finish_reason mapping and propagation in span_utils.""" + +import json +from unittest.mock import MagicMock + +import pytest +from opentelemetry.instrumentation.google_generativeai import span_utils as su +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _make_enum(name): + """Return a simple object whose ``.name`` attribute equals *name*.""" + obj = MagicMock() + obj.name = name + return obj + + +def _make_candidate(finish_reason=None, text=None): + """Build a lightweight candidate mock. + + *finish_reason* can be ``None``, a string name (turned into a mock enum), + or an already-constructed mock enum. + """ + cand = MagicMock() + if finish_reason is None: + cand.finish_reason = None + elif isinstance(finish_reason, str): + cand.finish_reason = _make_enum(finish_reason) + else: + cand.finish_reason = finish_reason + + # Minimal content with a single text part + if text is not None: + part = MagicMock() + part.text = text + part.function_call = None + part.function_response = None + part.thought = None + part.inline_data = None + part.executable_code = None + part.code_execution_result = None + cand.content = MagicMock() + cand.content.parts = [part] + cand.content.role = "model" + else: + cand.content = None + return cand + + +def _make_response(candidates=None, text=None): + """Build a response mock with optional candidates list and fallback text.""" + resp = MagicMock() + resp.candidates = candidates + if text is not None: + resp.text = text + else: + # Avoid hasattr returning True for an unwanted .text + del resp.text + return resp + + +def _recorded_output_messages(span_mock): + """Extract the parsed output-messages JSON from a mock span's set_attribute calls.""" + for call in span_mock.set_attribute.call_args_list: + args = call[0] + if args[0] == GenAIAttributes.GEN_AI_OUTPUT_MESSAGES: + return json.loads(args[1]) + return None + + +# =========================================================================== +# 1. _map_gemini_finish_reason +# =========================================================================== + +class TestMapGeminiFinishReason: + def test_none_returns_unknown(self): + assert su._map_gemini_finish_reason(None) == "unknown" + + def test_stop(self): + assert su._map_gemini_finish_reason(_make_enum("STOP")) == "stop" + + def test_max_tokens(self): + assert su._map_gemini_finish_reason(_make_enum("MAX_TOKENS")) == "length" + + @pytest.mark.parametrize("name", [ + "SAFETY", + "RECITATION", + "BLOCKLIST", + "PROHIBITED_CONTENT", + "SPII", + "IMAGE_SAFETY", + "IMAGE_PROHIBITED_CONTENT", + "IMAGE_RECITATION", + "LANGUAGE", + ]) + def test_content_filter_variants(self, name): + assert su._map_gemini_finish_reason(_make_enum(name)) == "content_filter" + + def test_finish_reason_unspecified(self): + assert su._map_gemini_finish_reason(_make_enum("FINISH_REASON_UNSPECIFIED")) == "unknown" + + def test_malformed_function_call(self): + assert su._map_gemini_finish_reason(_make_enum("MALFORMED_FUNCTION_CALL")) == "error" + + def test_other(self): + assert su._map_gemini_finish_reason(_make_enum("OTHER")) == "error" + + def test_unmapped_enum_returns_unknown(self): + assert su._map_gemini_finish_reason(_make_enum("TOTALLY_NEW_VALUE")) == "unknown" + + +# =========================================================================== +# 2. _collect_finish_reasons_from_response +# =========================================================================== + +class TestCollectFinishReasonsFromResponse: + def test_all_none_returns_empty(self): + """When every candidate has finish_reason=None the result is empty (unknown filtered out).""" + resp = _make_response(candidates=[_make_candidate(None), _make_candidate(None)]) + assert su._collect_finish_reasons_from_response(resp) == [] + + def test_meaningful_values_included(self): + resp = _make_response(candidates=[ + _make_candidate("STOP"), + _make_candidate("MAX_TOKENS"), + ]) + assert su._collect_finish_reasons_from_response(resp) == ["stop", "length"] + + def test_mixed_candidates(self): + """Only real (non-unknown) reasons are returned.""" + resp = _make_response(candidates=[ + _make_candidate(None), + _make_candidate("STOP"), + _make_candidate(None), + ]) + assert su._collect_finish_reasons_from_response(resp) == ["stop"] + + def test_no_candidates_attribute(self): + resp = MagicMock(spec=[]) # no attributes at all + assert su._collect_finish_reasons_from_response(resp) == [] + + +# =========================================================================== +# 3. accumulate_stream_finish_reasons +# =========================================================================== + +class TestAccumulateStreamFinishReasons: + def test_unknown_not_added(self): + ordered, seen = [], set() + chunk = _make_response(candidates=[_make_candidate(None)]) + su.accumulate_stream_finish_reasons(ordered, seen, chunk) + assert ordered == [] + assert seen == set() + + def test_real_values_added(self): + ordered, seen = [], set() + chunk = _make_response(candidates=[_make_candidate("STOP")]) + su.accumulate_stream_finish_reasons(ordered, seen, chunk) + assert ordered == ["stop"] + assert "stop" in seen + + def test_deduplication(self): + ordered, seen = [], set() + chunk1 = _make_response(candidates=[_make_candidate("STOP")]) + chunk2 = _make_response(candidates=[_make_candidate("STOP")]) + su.accumulate_stream_finish_reasons(ordered, seen, chunk1) + su.accumulate_stream_finish_reasons(ordered, seen, chunk2) + assert ordered == ["stop"] + + def test_order_preserved(self): + ordered, seen = [], set() + su.accumulate_stream_finish_reasons( + ordered, seen, + _make_response(candidates=[_make_candidate("MAX_TOKENS")]), + ) + su.accumulate_stream_finish_reasons( + ordered, seen, + _make_response(candidates=[_make_candidate("STOP")]), + ) + su.accumulate_stream_finish_reasons( + ordered, seen, + _make_response(candidates=[_make_candidate("MAX_TOKENS")]), + ) + assert ordered == ["length", "stop"] + + +# =========================================================================== +# 4. _output_messages_from_generate_response +# =========================================================================== + +class TestOutputMessagesFromGenerateResponse: + def test_every_message_has_finish_reason_key(self): + span = MagicMock() + resp = _make_response(candidates=[ + _make_candidate("STOP", text="hello"), + _make_candidate(None, text="world"), + ]) + messages = su._output_messages_from_generate_response(span, resp) + for msg in messages: + assert "finish_reason" in msg + + def test_finish_reason_is_always_string(self): + span = MagicMock() + resp = _make_response(candidates=[ + _make_candidate(None, text="a"), + _make_candidate("STOP", text="b"), + ]) + messages = su._output_messages_from_generate_response(span, resp) + for msg in messages: + assert isinstance(msg["finish_reason"], str) + assert msg["finish_reason"] is not None + + def test_none_finish_reason_becomes_unknown(self): + span = MagicMock() + resp = _make_response(candidates=[_make_candidate(None, text="hi")]) + messages = su._output_messages_from_generate_response(span, resp) + assert messages[0]["finish_reason"] == "unknown" + + def test_stop_finish_reason(self): + span = MagicMock() + resp = _make_response(candidates=[_make_candidate("STOP", text="done")]) + messages = su._output_messages_from_generate_response(span, resp) + assert messages[0]["finish_reason"] == "stop" + + def test_text_fallback_has_unknown_finish_reason(self): + """When there are no candidates but response.text exists, the fallback message gets 'unknown'.""" + span = MagicMock() + resp = _make_response(candidates=None, text="fallback text") + messages = su._output_messages_from_generate_response(span, resp) + assert len(messages) == 1 + assert messages[0]["finish_reason"] == "unknown" + assert messages[0]["parts"][0]["content"] == "fallback text" + + +# =========================================================================== +# 5. set_response_attributes +# =========================================================================== + +class TestSetResponseAttributes: + def test_string_response_has_finish_reason(self, monkeypatch): + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + span = MagicMock() + span.is_recording.return_value = True + + su.set_response_attributes(span, "Hello world", "gemini-pro") + + messages = _recorded_output_messages(span) + assert messages is not None + assert len(messages) == 1 + assert "finish_reason" in messages[0] + assert messages[0]["finish_reason"] == "unknown" + + def test_string_empty_no_stream_chunk_no_output(self, monkeypatch): + """Empty string with no stream_last_chunk and unknown finish_reason -> nothing emitted.""" + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + span = MagicMock() + span.is_recording.return_value = True + + su.set_response_attributes(span, "", "gemini-pro") + + messages = _recorded_output_messages(span) + assert messages is None # no set_attribute call for output messages + + def test_string_empty_with_stream_last_chunk_stop(self, monkeypatch): + """Empty text but stream_last_chunk carries STOP -> message emitted with 'stop'.""" + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + span = MagicMock() + span.is_recording.return_value = True + + last_chunk = _make_response(candidates=[_make_candidate("STOP")]) + su.set_response_attributes(span, "", "gemini-pro", stream_last_chunk=last_chunk) + + messages = _recorded_output_messages(span) + assert messages is not None + assert len(messages) == 1 + assert messages[0]["finish_reason"] == "stop" + + def test_list_of_strings_each_has_finish_reason(self, monkeypatch): + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + span = MagicMock() + span.is_recording.return_value = True + + su.set_response_attributes(span, ["one", "two", "three"], "gemini-pro") + + messages = _recorded_output_messages(span) + assert messages is not None + assert len(messages) == 3 + for msg in messages: + assert msg["finish_reason"] == "unknown" + assert msg["role"] == "assistant" diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py b/packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py new file mode 100644 index 0000000000..7c6b4f6cbf --- /dev/null +++ b/packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py @@ -0,0 +1,469 @@ +import json +import pytest +from unittest.mock import Mock, patch +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.instrumentation.vertexai.span_utils import ( + _map_vertex_finish_reason, + accumulate_vertex_stream_finish_reasons, + _output_messages_from_vertex_response, + set_response_attributes, + _parts_from_vertex_part_sync, + _parts_from_vertex_part_async, +) + + +def _make_enum(name): + """Create a simple mock object that mimics a VertexAI enum with a .name attribute.""" + obj = Mock() + obj.name = name + return obj + + +def _make_span(): + """Create a mock span that captures set_attribute calls.""" + span = Mock() + span.is_recording.return_value = True + span.context.trace_id = "test_trace_id" + span.context.span_id = "test_span_id" + attrs = {} + + def capture(key, value): + attrs[key] = value + + span.set_attribute = capture + span._attrs = attrs + return span + + +# --------------------------------------------------------------------------- +# 1. _map_vertex_finish_reason +# --------------------------------------------------------------------------- +class TestMapVertexFinishReason: + def test_none_returns_unknown(self): + assert _map_vertex_finish_reason(None) == "unknown" + + def test_stop_enum(self): + assert _map_vertex_finish_reason(_make_enum("STOP")) == "stop" + + def test_max_tokens_enum(self): + assert _map_vertex_finish_reason(_make_enum("MAX_TOKENS")) == "length" + + def test_safety_enum(self): + assert _map_vertex_finish_reason(_make_enum("SAFETY")) == "content_filter" + + def test_recitation_enum(self): + assert _map_vertex_finish_reason(_make_enum("RECITATION")) == "content_filter" + + def test_blocklist_enum(self): + assert _map_vertex_finish_reason(_make_enum("BLOCKLIST")) == "content_filter" + + def test_prohibited_content_enum(self): + assert _map_vertex_finish_reason(_make_enum("PROHIBITED_CONTENT")) == "content_filter" + + def test_spii_enum(self): + assert _map_vertex_finish_reason(_make_enum("SPII")) == "content_filter" + + def test_image_safety_enum(self): + assert _map_vertex_finish_reason(_make_enum("IMAGE_SAFETY")) == "content_filter" + + def test_image_prohibited_content_enum(self): + assert _map_vertex_finish_reason(_make_enum("IMAGE_PROHIBITED_CONTENT")) == "content_filter" + + def test_image_recitation_enum(self): + assert _map_vertex_finish_reason(_make_enum("IMAGE_RECITATION")) == "content_filter" + + def test_language_enum(self): + assert _map_vertex_finish_reason(_make_enum("LANGUAGE")) == "content_filter" + + def test_finish_reason_unspecified_enum(self): + assert _map_vertex_finish_reason(_make_enum("FINISH_REASON_UNSPECIFIED")) == "unknown" + + def test_malformed_function_call_enum(self): + assert _map_vertex_finish_reason(_make_enum("MALFORMED_FUNCTION_CALL")) == "error" + + def test_other_enum(self): + assert _map_vertex_finish_reason(_make_enum("OTHER")) == "error" + + def test_unexpected_tool_call_enum(self): + assert _map_vertex_finish_reason(_make_enum("UNEXPECTED_TOOL_CALL")) == "error" + + def test_no_image_enum(self): + assert _map_vertex_finish_reason(_make_enum("NO_IMAGE")) == "error" + + def test_image_other_enum(self): + assert _map_vertex_finish_reason(_make_enum("IMAGE_OTHER")) == "error" + + def test_unmapped_enum_returns_unknown(self): + assert _map_vertex_finish_reason(_make_enum("TOTALLY_NEW_REASON")) == "unknown" + + +# --------------------------------------------------------------------------- +# 2. accumulate_vertex_stream_finish_reasons +# --------------------------------------------------------------------------- +class TestAccumulateVertexStreamFinishReasons: + def test_unknown_is_not_added(self): + """Candidates with None finish_reason map to 'unknown' and are skipped.""" + ordered = [] + seen = set() + chunk = Mock() + cand = Mock() + cand.finish_reason = None # maps to "unknown" + chunk.candidates = [cand] + + accumulate_vertex_stream_finish_reasons(ordered, seen, chunk) + + assert ordered == [] + assert seen == set() + + def test_real_values_are_added(self): + ordered = [] + seen = set() + + chunk1 = Mock() + cand1 = Mock() + cand1.finish_reason = _make_enum("STOP") + chunk1.candidates = [cand1] + + accumulate_vertex_stream_finish_reasons(ordered, seen, chunk1) + assert ordered == ["stop"] + assert "stop" in seen + + def test_duplicates_are_skipped(self): + ordered = [] + seen = set() + + for _ in range(3): + chunk = Mock() + cand = Mock() + cand.finish_reason = _make_enum("STOP") + chunk.candidates = [cand] + accumulate_vertex_stream_finish_reasons(ordered, seen, chunk) + + assert ordered == ["stop"] + + def test_order_is_preserved(self): + ordered = [] + seen = set() + + for name in ["STOP", "MAX_TOKENS", "SAFETY"]: + chunk = Mock() + cand = Mock() + cand.finish_reason = _make_enum(name) + chunk.candidates = [cand] + accumulate_vertex_stream_finish_reasons(ordered, seen, chunk) + + assert ordered == ["stop", "length", "content_filter"] + + def test_chunk_with_no_candidates(self): + ordered = [] + seen = set() + chunk = Mock() + chunk.candidates = None + + accumulate_vertex_stream_finish_reasons(ordered, seen, chunk) + assert ordered == [] + + +# --------------------------------------------------------------------------- +# 3. _output_messages_from_vertex_response +# --------------------------------------------------------------------------- +class TestOutputMessagesFromVertexResponse: + def _make_candidate(self, finish_reason=None, text="Hello"): + """Build a mock candidate with optional finish_reason and text.""" + part = Mock() + part.text = text + # No image, function_call, function_response, or thought attributes + part.inline_data = None + part.mime_type = None + part.function_call = None + part.function_response = None + part.thought = None + content = Mock() + content.parts = [part] + content.role = "model" + cand = Mock() + cand.content = content + cand.finish_reason = finish_reason + return cand + + def test_every_message_has_finish_reason_key(self): + span = _make_span() + response = Mock() + response.candidates = [ + self._make_candidate(finish_reason=_make_enum("STOP")), + self._make_candidate(finish_reason=_make_enum("MAX_TOKENS")), + ] + + messages = _output_messages_from_vertex_response(span, response) + + for msg in messages: + assert "finish_reason" in msg + + def test_finish_reason_is_always_string(self): + span = _make_span() + response = Mock() + response.candidates = [ + self._make_candidate(finish_reason=_make_enum("STOP")), + self._make_candidate(finish_reason=None), + ] + + messages = _output_messages_from_vertex_response(span, response) + + for msg in messages: + assert isinstance(msg["finish_reason"], str) + assert msg["finish_reason"] is not None + + def test_no_finish_reason_returns_unknown(self): + span = _make_span() + response = Mock() + response.candidates = [self._make_candidate(finish_reason=None)] + + messages = _output_messages_from_vertex_response(span, response) + + assert len(messages) == 1 + assert messages[0]["finish_reason"] == "unknown" + + def test_stop_finish_reason(self): + span = _make_span() + response = Mock() + response.candidates = [self._make_candidate(finish_reason=_make_enum("STOP"))] + + messages = _output_messages_from_vertex_response(span, response) + + assert len(messages) == 1 + assert messages[0]["finish_reason"] == "stop" + + def test_text_fallback_has_unknown_finish_reason(self): + """When there are no candidates but response.text exists, finish_reason is 'unknown'.""" + span = _make_span() + response = Mock() + response.candidates = [] + response.text = "Fallback text" + + messages = _output_messages_from_vertex_response(span, response) + + assert len(messages) == 1 + assert messages[0]["finish_reason"] == "unknown" + assert messages[0]["parts"][0]["content"] == "Fallback text" + + +# --------------------------------------------------------------------------- +# 4. set_response_attributes +# --------------------------------------------------------------------------- +class TestSetResponseAttributes: + @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") + def test_full_response_object_with_candidates(self, mock_should_send): + mock_should_send.return_value = True + span = _make_span() + + part = Mock() + part.text = "Generated text" + part.inline_data = None + part.mime_type = None + part.function_call = None + part.function_response = None + part.thought = None + content = Mock() + content.parts = [part] + content.role = "model" + cand = Mock() + cand.content = content + cand.finish_reason = _make_enum("STOP") + + response = Mock() + response.candidates = [cand] + + set_response_attributes(span, "gemini-pro", response) + + key = GenAIAttributes.GEN_AI_OUTPUT_MESSAGES + assert key in span._attrs + messages = json.loads(span._attrs[key]) + assert len(messages) == 1 + assert messages[0]["finish_reason"] == "stop" + assert messages[0]["role"] == "assistant" + + @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") + def test_plain_text_with_finish_reason_otel(self, mock_should_send): + mock_should_send.return_value = True + span = _make_span() + + set_response_attributes(span, "gemini-pro", "Hello world", finish_reason_otel="stop") + + key = GenAIAttributes.GEN_AI_OUTPUT_MESSAGES + assert key in span._attrs + messages = json.loads(span._attrs[key]) + assert len(messages) == 1 + assert messages[0]["finish_reason"] == "stop" + assert messages[0]["parts"][0]["content"] == "Hello world" + + @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") + def test_plain_text_without_finish_reason_otel(self, mock_should_send): + mock_should_send.return_value = True + span = _make_span() + + set_response_attributes(span, "gemini-pro", "Hello world") + + key = GenAIAttributes.GEN_AI_OUTPUT_MESSAGES + assert key in span._attrs + messages = json.loads(span._attrs[key]) + assert len(messages) == 1 + assert messages[0]["finish_reason"] == "unknown" + + @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") + def test_empty_text_no_finish_reason_no_output(self, mock_should_send): + """Empty text + no finish_reason_otel => no output message emitted.""" + mock_should_send.return_value = True + span = _make_span() + + set_response_attributes(span, "gemini-pro", "") + + key = GenAIAttributes.GEN_AI_OUTPUT_MESSAGES + assert key not in span._attrs + + @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") + def test_empty_text_with_finish_reason_emits_message(self, mock_should_send): + """Empty text but with a real finish_reason_otel still emits a message.""" + mock_should_send.return_value = True + span = _make_span() + + set_response_attributes(span, "gemini-pro", "", finish_reason_otel="stop") + + key = GenAIAttributes.GEN_AI_OUTPUT_MESSAGES + assert key in span._attrs + messages = json.loads(span._attrs[key]) + assert len(messages) == 1 + assert messages[0]["finish_reason"] == "stop" + assert messages[0]["parts"] == [] + + @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") + def test_no_output_when_prompts_disabled(self, mock_should_send): + mock_should_send.return_value = False + span = _make_span() + + set_response_attributes(span, "gemini-pro", "Hello world", finish_reason_otel="stop") + + assert GenAIAttributes.GEN_AI_OUTPUT_MESSAGES not in span._attrs + + +# --------------------------------------------------------------------------- +# 5. _parts_from_vertex_part_sync — reasoning / thinking blocks +# --------------------------------------------------------------------------- +class TestPartsFromVertexPartSyncReasoning: + def test_thought_only_produces_reasoning(self): + """Part with thought attribute produces a reasoning-type output.""" + part = Mock() + part.text = None + part.inline_data = None + part.mime_type = None + part.function_call = None + part.function_response = None + part.thought = "Let me think about this..." + + span = _make_span() + result = _parts_from_vertex_part_sync(part, span, 0) + + assert any(p["type"] == "reasoning" for p in result) + reasoning = [p for p in result if p["type"] == "reasoning"][0] + assert reasoning["content"] == "Let me think about this..." + + def test_text_and_thought_produces_both(self): + """Part with both text and thought produces text and reasoning parts.""" + part = Mock() + part.text = "The answer is 42." + part.inline_data = None + part.mime_type = None + part.function_call = None + part.function_response = None + part.thought = "I need to calculate..." + + span = _make_span() + result = _parts_from_vertex_part_sync(part, span, 0) + + types = [p["type"] for p in result] + assert "text" in types + assert "reasoning" in types + text_part = [p for p in result if p["type"] == "text"][0] + reasoning_part = [p for p in result if p["type"] == "reasoning"][0] + assert text_part["content"] == "The answer is 42." + assert reasoning_part["content"] == "I need to calculate..." + + def test_no_recognized_attributes_falls_through_to_str(self): + """Part with no recognized attributes produces str(part) as text fallback.""" + part = Mock() + part.text = None + part.inline_data = None + part.mime_type = None + part.function_call = None + part.function_response = None + part.thought = None + part.__str__ = lambda self: "fallback-string-repr" + + span = _make_span() + result = _parts_from_vertex_part_sync(part, span, 0) + + assert len(result) == 1 + assert result[0]["type"] == "text" + assert result[0]["content"] == "fallback-string-repr" + + +# --------------------------------------------------------------------------- +# 6. _parts_from_vertex_part_async — same reasoning tests but async +# --------------------------------------------------------------------------- +class TestPartsFromVertexPartAsyncReasoning: + @pytest.mark.asyncio + async def test_thought_only_produces_reasoning(self): + part = Mock() + part.text = None + part.inline_data = None + part.mime_type = None + part.function_call = None + part.function_response = None + part.thought = "Let me think about this..." + + span = _make_span() + result = await _parts_from_vertex_part_async(part, span, 0) + + assert any(p["type"] == "reasoning" for p in result) + reasoning = [p for p in result if p["type"] == "reasoning"][0] + assert reasoning["content"] == "Let me think about this..." + + @pytest.mark.asyncio + async def test_text_and_thought_produces_both(self): + part = Mock() + part.text = "The answer is 42." + part.inline_data = None + part.mime_type = None + part.function_call = None + part.function_response = None + part.thought = "I need to calculate..." + + span = _make_span() + result = await _parts_from_vertex_part_async(part, span, 0) + + types = [p["type"] for p in result] + assert "text" in types + assert "reasoning" in types + text_part = [p for p in result if p["type"] == "text"][0] + reasoning_part = [p for p in result if p["type"] == "reasoning"][0] + assert text_part["content"] == "The answer is 42." + assert reasoning_part["content"] == "I need to calculate..." + + @pytest.mark.asyncio + async def test_no_recognized_attributes_falls_through_to_str(self): + part = Mock() + part.text = None + part.inline_data = None + part.mime_type = None + part.function_call = None + part.function_response = None + part.thought = None + part.__str__ = lambda self: "fallback-string-repr" + + span = _make_span() + result = await _parts_from_vertex_part_async(part, span, 0) + + assert len(result) == 1 + assert result[0]["type"] == "text" + assert result[0]["content"] == "fallback-string-repr" From 7d013afee0257ff0f5ca2436b2f8fdb7e651c1de Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Tue, 31 Mar 2026 14:27:36 +0300 Subject: [PATCH 12/21] empty --- .../google_generativeai/span_utils.py | 26 +++++++++---------- .../tests/test_finish_reasons.py | 14 +++++----- .../instrumentation/vertexai/event_emitter.py | 2 +- .../instrumentation/vertexai/span_utils.py | 18 ++++++------- .../tests/test_finish_reasons.py | 14 +++++----- 5 files changed, 37 insertions(+), 37 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index 0818201135..8a0e546a79 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -32,10 +32,10 @@ def _set_span_attribute(span, name, value): def _map_gemini_finish_reason(finish_reason): """Map Gemini FinishReason to OTel finish reason string. - Returns ``"unknown"`` for ``None``, unspecified, and unmapped values. + Returns ``""`` for ``None``, unspecified, and unmapped values. """ if finish_reason is None: - return "unknown" + return "" name = getattr(finish_reason, "name", None) or str(finish_reason) mapping = { "STOP": "stop", @@ -49,14 +49,14 @@ def _map_gemini_finish_reason(finish_reason): "IMAGE_PROHIBITED_CONTENT": "content_filter", "IMAGE_RECITATION": "content_filter", "LANGUAGE": "content_filter", - "FINISH_REASON_UNSPECIFIED": "unknown", + "FINISH_REASON_UNSPECIFIED": "", "MALFORMED_FUNCTION_CALL": "error", "OTHER": "error", "UNEXPECTED_TOOL_CALL": "error", "NO_IMAGE": "error", "IMAGE_OTHER": "error", } - return mapping.get(name, "unknown") + return mapping.get(name, "") def _normalize_message_role(role): @@ -168,7 +168,7 @@ async def _process_image_part(item, trace_id, span_id, content_index): image_format = ( item.inline_data.mime_type.split("/")[1] if item.inline_data.mime_type - else "unknown" + else "" ) image_name = f"content_{content_index}.{image_format}" binary_data = item.inline_data.data @@ -211,7 +211,7 @@ def _process_image_part_sync(item, trace_id, span_id, content_index): image_format = ( item.inline_data.mime_type.split("/")[1] if item.inline_data.mime_type - else "unknown" + else "" ) image_name = f"content_{content_index}.{image_format}" binary_data = item.inline_data.data @@ -394,7 +394,7 @@ def _collect_finish_reasons_from_response(response): candidates = getattr(response, "candidates", None) or [] for cand in candidates: mapped = _map_gemini_finish_reason(getattr(cand, "finish_reason", None)) - if mapped != "unknown": + if mapped: reasons.append(mapped) return reasons @@ -403,7 +403,7 @@ def accumulate_stream_finish_reasons(ordered, seen, chunk): """Merge finish reasons from a streaming chunk; preserves first-seen order (metadata, not gated by prompts).""" for cand in getattr(chunk, "candidates", None) or []: mapped = _map_gemini_finish_reason(getattr(cand, "finish_reason", None)) - if mapped != "unknown" and mapped not in seen: + if mapped and mapped not in seen: seen.add(mapped) ordered.append(mapped) @@ -426,7 +426,7 @@ def _output_messages_from_generate_response(span, response): if not messages and hasattr(response, "text") and response.text: text = response.text if isinstance(text, str) and text: - msg = {"role": "assistant", "parts": [{"type": "text", "content": text}], "finish_reason": "unknown"} + msg = {"role": "assistant", "parts": [{"type": "text", "content": text}], "finish_reason": ""} messages.append(msg) return messages @@ -621,15 +621,15 @@ def set_response_attributes(span, response, llm_model, stream_last_chunk=None): return if isinstance(response, str): - fr = "unknown" + fr = "" if stream_last_chunk is not None: reasons = _collect_finish_reasons_from_response(stream_last_chunk) - fr = reasons[0] if reasons else "unknown" + fr = reasons[0] if reasons else "" msg = {"role": "assistant", "parts": []} if response: msg["parts"].append({"type": "text", "content": response}) msg["finish_reason"] = fr - if not msg["parts"] and fr == "unknown": + if not msg["parts"] and not fr: return _set_span_attribute( span, @@ -646,7 +646,7 @@ def set_response_attributes(span, response, llm_model, stream_last_chunk=None): { "role": "assistant", "parts": [{"type": "text", "content": item}], - "finish_reason": "unknown", + "finish_reason": "", } ) else: diff --git a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py index 7944573136..1b5cf3340d 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py @@ -80,7 +80,7 @@ def _recorded_output_messages(span_mock): class TestMapGeminiFinishReason: def test_none_returns_unknown(self): - assert su._map_gemini_finish_reason(None) == "unknown" + assert su._map_gemini_finish_reason(None) == "" def test_stop(self): assert su._map_gemini_finish_reason(_make_enum("STOP")) == "stop" @@ -103,7 +103,7 @@ def test_content_filter_variants(self, name): assert su._map_gemini_finish_reason(_make_enum(name)) == "content_filter" def test_finish_reason_unspecified(self): - assert su._map_gemini_finish_reason(_make_enum("FINISH_REASON_UNSPECIFIED")) == "unknown" + assert su._map_gemini_finish_reason(_make_enum("FINISH_REASON_UNSPECIFIED")) == "" def test_malformed_function_call(self): assert su._map_gemini_finish_reason(_make_enum("MALFORMED_FUNCTION_CALL")) == "error" @@ -112,7 +112,7 @@ def test_other(self): assert su._map_gemini_finish_reason(_make_enum("OTHER")) == "error" def test_unmapped_enum_returns_unknown(self): - assert su._map_gemini_finish_reason(_make_enum("TOTALLY_NEW_VALUE")) == "unknown" + assert su._map_gemini_finish_reason(_make_enum("TOTALLY_NEW_VALUE")) == "" # =========================================================================== @@ -220,7 +220,7 @@ def test_none_finish_reason_becomes_unknown(self): span = MagicMock() resp = _make_response(candidates=[_make_candidate(None, text="hi")]) messages = su._output_messages_from_generate_response(span, resp) - assert messages[0]["finish_reason"] == "unknown" + assert messages[0]["finish_reason"] == "" def test_stop_finish_reason(self): span = MagicMock() @@ -234,7 +234,7 @@ def test_text_fallback_has_unknown_finish_reason(self): resp = _make_response(candidates=None, text="fallback text") messages = su._output_messages_from_generate_response(span, resp) assert len(messages) == 1 - assert messages[0]["finish_reason"] == "unknown" + assert messages[0]["finish_reason"] == "" assert messages[0]["parts"][0]["content"] == "fallback text" @@ -254,7 +254,7 @@ def test_string_response_has_finish_reason(self, monkeypatch): assert messages is not None assert len(messages) == 1 assert "finish_reason" in messages[0] - assert messages[0]["finish_reason"] == "unknown" + assert messages[0]["finish_reason"] == "" def test_string_empty_no_stream_chunk_no_output(self, monkeypatch): """Empty string with no stream_last_chunk and unknown finish_reason -> nothing emitted.""" @@ -292,5 +292,5 @@ def test_list_of_strings_each_has_finish_reason(self, monkeypatch): assert messages is not None assert len(messages) == 3 for msg in messages: - assert msg["finish_reason"] == "unknown" + assert msg["finish_reason"] == "" assert msg["role"] == "assistant" diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py index f019dd4f72..2405d90bbb 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py @@ -58,7 +58,7 @@ def emit_response_events(response, event_logger): ChoiceEvent( index=0, message={"content": response, "role": Roles.ASSISTANT.value}, - finish_reason="unknown", + finish_reason="", ), event_logger, ) diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py index d4a24a25ac..8468682a52 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py @@ -43,10 +43,10 @@ def _is_base64_image_part(item): def _map_vertex_finish_reason(finish_reason): """Map VertexAI FinishReason to OTel finish reason string. - Returns ``"unknown"`` for ``None``, unspecified, and unmapped values. + Returns ``""`` for ``None``, unspecified, and unmapped values. """ if finish_reason is None: - return "unknown" + return "" name = getattr(finish_reason, "name", None) or str(finish_reason) mapping = { "STOP": "stop", @@ -60,14 +60,14 @@ def _map_vertex_finish_reason(finish_reason): "IMAGE_PROHIBITED_CONTENT": "content_filter", "IMAGE_RECITATION": "content_filter", "LANGUAGE": "content_filter", - "FINISH_REASON_UNSPECIFIED": "unknown", + "FINISH_REASON_UNSPECIFIED": "", "MALFORMED_FUNCTION_CALL": "error", "OTHER": "error", "UNEXPECTED_TOOL_CALL": "error", "NO_IMAGE": "error", "IMAGE_OTHER": "error", } - return mapping.get(name, "unknown") + return mapping.get(name, "") def _normalize_message_role(role): @@ -106,7 +106,7 @@ def _vertex_function_response_to_str(response): def accumulate_vertex_stream_finish_reasons(ordered, seen, chunk): for cand in getattr(chunk, "candidates", None) or []: mapped = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) - if mapped != "unknown" and mapped not in seen: + if mapped and mapped not in seen: seen.add(mapped) ordered.append(mapped) @@ -275,7 +275,7 @@ def _output_messages_from_vertex_response(span, response): { "role": "assistant", "parts": [{"type": "text", "content": response.text}], - "finish_reason": "unknown", + "finish_reason": "", } ) return messages @@ -586,8 +586,8 @@ def set_response_attributes(span, llm_model, generation_or_text, finish_reason_o parts = [] if generation_or_text: parts.append({"type": "text", "content": generation_or_text}) - fr = finish_reason_otel or "unknown" - if not parts and fr == "unknown": + fr = finish_reason_otel or "" + if not parts and not fr: return msg = {"role": "assistant", "parts": parts, "finish_reason": fr} _set_span_attribute( @@ -609,7 +609,7 @@ def set_model_response_attributes( reasons = [] for cand in getattr(response_meta, "candidates", None) or []: mapped = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) - if mapped != "unknown": + if mapped: reasons.append(mapped) else: reasons = [] diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py b/packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py index 7c6b4f6cbf..c7c5706fc2 100644 --- a/packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py +++ b/packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py @@ -42,7 +42,7 @@ def capture(key, value): # --------------------------------------------------------------------------- class TestMapVertexFinishReason: def test_none_returns_unknown(self): - assert _map_vertex_finish_reason(None) == "unknown" + assert _map_vertex_finish_reason(None) == "" def test_stop_enum(self): assert _map_vertex_finish_reason(_make_enum("STOP")) == "stop" @@ -78,7 +78,7 @@ def test_language_enum(self): assert _map_vertex_finish_reason(_make_enum("LANGUAGE")) == "content_filter" def test_finish_reason_unspecified_enum(self): - assert _map_vertex_finish_reason(_make_enum("FINISH_REASON_UNSPECIFIED")) == "unknown" + assert _map_vertex_finish_reason(_make_enum("FINISH_REASON_UNSPECIFIED")) == "" def test_malformed_function_call_enum(self): assert _map_vertex_finish_reason(_make_enum("MALFORMED_FUNCTION_CALL")) == "error" @@ -96,7 +96,7 @@ def test_image_other_enum(self): assert _map_vertex_finish_reason(_make_enum("IMAGE_OTHER")) == "error" def test_unmapped_enum_returns_unknown(self): - assert _map_vertex_finish_reason(_make_enum("TOTALLY_NEW_REASON")) == "unknown" + assert _map_vertex_finish_reason(_make_enum("TOTALLY_NEW_REASON")) == "" # --------------------------------------------------------------------------- @@ -109,7 +109,7 @@ def test_unknown_is_not_added(self): seen = set() chunk = Mock() cand = Mock() - cand.finish_reason = None # maps to "unknown" + cand.finish_reason = None # maps to "" chunk.candidates = [cand] accumulate_vertex_stream_finish_reasons(ordered, seen, chunk) @@ -223,7 +223,7 @@ def test_no_finish_reason_returns_unknown(self): messages = _output_messages_from_vertex_response(span, response) assert len(messages) == 1 - assert messages[0]["finish_reason"] == "unknown" + assert messages[0]["finish_reason"] == "" def test_stop_finish_reason(self): span = _make_span() @@ -245,7 +245,7 @@ def test_text_fallback_has_unknown_finish_reason(self): messages = _output_messages_from_vertex_response(span, response) assert len(messages) == 1 - assert messages[0]["finish_reason"] == "unknown" + assert messages[0]["finish_reason"] == "" assert messages[0]["parts"][0]["content"] == "Fallback text" @@ -309,7 +309,7 @@ def test_plain_text_without_finish_reason_otel(self, mock_should_send): assert key in span._attrs messages = json.loads(span._attrs[key]) assert len(messages) == 1 - assert messages[0]["finish_reason"] == "unknown" + assert messages[0]["finish_reason"] == "" @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") def test_empty_text_no_finish_reason_no_output(self, mock_should_send): From 63aa0a2b7d6d544ca18f01d6d5c81807352b7a7e Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Tue, 31 Mar 2026 15:45:49 +0300 Subject: [PATCH 13/21] fix --- .../google_generativeai/span_utils.py | 32 ++++++++++++++++--- 1 file changed, 28 insertions(+), 4 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index 8a0e546a79..4dbb6fc321 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -317,7 +317,17 @@ async def _parts_from_genai_part_async(part, span, part_index): async def _process_content_item(content_item, span): parts_acc = [] - if hasattr(content_item, "parts"): + if isinstance(content_item, dict): + for p in content_item.get("parts", []): + if isinstance(p, dict): + text = p.get("text") + if text is not None: + parts_acc.append({"type": "text", "content": text}) + else: + parts_acc.append({"type": "text", "content": str(p)}) + else: + parts_acc.extend(await _parts_from_genai_part_async(p, span, 0)) + elif hasattr(content_item, "parts"): for part_index, part in enumerate(content_item.parts): parts_acc.extend(await _parts_from_genai_part_async(part, span, part_index)) elif isinstance(content_item, str): @@ -354,7 +364,17 @@ async def _process_argument(argument, span): def _process_content_item_sync(content_item, span): parts_acc = [] - if hasattr(content_item, "parts"): + if isinstance(content_item, dict): + for p in content_item.get("parts", []): + if isinstance(p, dict): + text = p.get("text") + if text is not None: + parts_acc.append({"type": "text", "content": text}) + else: + parts_acc.append({"type": "text", "content": str(p)}) + else: + parts_acc.extend(_parts_from_genai_part_sync(p, span, 0)) + elif hasattr(content_item, "parts"): for part_index, part in enumerate(content_item.parts): parts_acc.extend(_parts_from_genai_part_sync(part, span, part_index)) elif isinstance(content_item, str): @@ -455,7 +475,8 @@ async def set_input_attributes(span, args, kwargs, llm_model): messages.append( { "role": _normalize_message_role( - getattr(content_item, "role", "user") + content_item.get("role", "user") if isinstance(content_item, dict) + else getattr(content_item, "role", "user") ), "parts": parts, } @@ -504,7 +525,10 @@ def set_input_attributes_sync(span, args, kwargs, llm_model): if parts: messages.append( { - "role": _normalize_message_role(getattr(content, "role", "user")), + "role": _normalize_message_role( + content.get("role", "user") if isinstance(content, dict) + else getattr(content, "role", "user") + ), "parts": parts, } ) From 07d03cce7e1d9b0b7e717bd81f803a300d2fac55 Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Sun, 5 Apr 2026 11:00:57 +0300 Subject: [PATCH 14/21] revert: remove vertex ai semconv changes from branch Vertex AI SDK (google-cloud-aiplatform) generative AI modules are deprecated (June 2025) and being removed June 2026. The unified google-genai SDK covers both Gemini and Vertex AI, so the google-generativeai instrumentation is sufficient going forward. Co-Authored-By: Claude Opus 4.6 --- .../instrumentation/vertexai/__init__.py | 121 +-- .../instrumentation/vertexai/event_emitter.py | 37 +- .../instrumentation/vertexai/span_utils.py | 483 ++-------- .../tests/disabled_test_bison.py | 57 +- .../tests/disabled_test_gemini.py | 25 +- .../tests/test_finish_reasons.py | 469 ---------- .../tests/test_role_attributes.py | 85 +- .../uv.lock | 861 +++++++----------- 8 files changed, 562 insertions(+), 1576 deletions(-) delete mode 100644 packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py index 09ae2febb8..5d96fad8b3 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/__init__.py @@ -14,8 +14,6 @@ emit_response_events, ) from opentelemetry.instrumentation.vertexai.span_utils import ( - _map_vertex_finish_reason, - accumulate_vertex_stream_finish_reasons, set_input_attributes, set_input_attributes_sync, set_model_input_attributes, @@ -29,16 +27,13 @@ ) from opentelemetry.semconv_ai import ( SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY, + LLMRequestTypeValues, + SpanAttributes, ) from opentelemetry.trace import SpanKind, get_tracer from opentelemetry.trace.status import Status, StatusCode from wrapt import wrap_function_wrapper -_GCP_VERTEX_AI = GenAIAttributes.GenAiProviderNameValues.GCP_VERTEX_AI.value -_OP_CHAT = GenAIAttributes.GenAiOperationNameValues.CHAT.value -_OP_GENERATE_CONTENT = GenAIAttributes.GenAiOperationNameValues.GENERATE_CONTENT.value -_OP_TEXT_COMPLETION = GenAIAttributes.GenAiOperationNameValues.TEXT_COMPLETION.value - logger = logging.getLogger(__name__) _instruments = ("google-cloud-aiplatform >= 1.38.1",) @@ -131,14 +126,6 @@ ] -def _gen_ai_operation_name(span_name: str) -> str: - if "predict" in span_name: - return _OP_TEXT_COMPLETION - if "send_message" in span_name: - return _OP_CHAT - return _OP_GENERATE_CONTENT - - def is_streaming_response(response): return isinstance(response, types.GeneratorType) @@ -148,76 +135,29 @@ def is_async_streaming_response(response): @dont_throw -def handle_streaming_response( - span, - event_logger, - llm_model, - complete_response, - token_usage, - stream_last_chunk=None, - stream_finish_reasons=None, -): - set_model_response_attributes( - span, - llm_model, - token_usage, - response_meta=stream_last_chunk, - stream_finish_reasons=stream_finish_reasons, - ) - finish_reason_otel = None - if stream_last_chunk and getattr(stream_last_chunk, "candidates", None): - finish_reason_otel = _map_vertex_finish_reason( - stream_last_chunk.candidates[0].finish_reason - ) +def handle_streaming_response(span, event_logger, llm_model, response, token_usage): + set_model_response_attributes(span, llm_model, token_usage) if should_emit_events(): - emit_response_events(complete_response, event_logger) + emit_response_events(response, event_logger) else: - # Prefer full candidate on the last chunk when present (see google-generativeai streaming). - if stream_last_chunk is not None and getattr( - stream_last_chunk, "candidates", None - ): - set_response_attributes(span, llm_model, stream_last_chunk) - else: - set_response_attributes( - span, - llm_model, - complete_response, - finish_reason_otel=finish_reason_otel, - ) + set_response_attributes(span, llm_model, response) if span.is_recording(): span.set_status(Status(StatusCode.OK)) def _build_from_streaming_response(span, event_logger, response, llm_model): - text_parts = [] + complete_response = "" token_usage = None - last_item = None - stream_finish_ordered = [] - stream_finish_seen = set() for item in response: item_to_yield = item - last_item = item - t = getattr(item, "text", None) - if isinstance(t, str): - text_parts.append(t) + complete_response += str(item.text) if item.usage_metadata: token_usage = item.usage_metadata - accumulate_vertex_stream_finish_reasons( - stream_finish_ordered, stream_finish_seen, item - ) yield item_to_yield - complete_response = "".join(text_parts) - handle_streaming_response( - span, - event_logger, - llm_model, - complete_response, - token_usage, - stream_last_chunk=last_item, - stream_finish_reasons=stream_finish_ordered or None, + span, event_logger, llm_model, complete_response, token_usage ) span.set_status(Status(StatusCode.OK)) @@ -225,36 +165,17 @@ def _build_from_streaming_response(span, event_logger, response, llm_model): async def _abuild_from_streaming_response(span, event_logger, response, llm_model): - text_parts = [] + complete_response = "" token_usage = None - last_item = None - stream_finish_ordered = [] - stream_finish_seen = set() async for item in response: item_to_yield = item - last_item = item - t = getattr(item, "text", None) - if isinstance(t, str): - text_parts.append(t) + complete_response += str(item.text) if item.usage_metadata: token_usage = item.usage_metadata - accumulate_vertex_stream_finish_reasons( - stream_finish_ordered, stream_finish_seen, item - ) yield item_to_yield - complete_response = "".join(text_parts) - - handle_streaming_response( - span, - event_logger, - llm_model, - complete_response, - token_usage, - stream_last_chunk=last_item, - stream_finish_reasons=stream_finish_ordered or None, - ) + handle_streaming_response(span, event_logger, llm_model, response, token_usage) span.set_status(Status(StatusCode.OK)) span.end() @@ -270,13 +191,13 @@ async def _handle_request(span, event_logger, args, kwargs, llm_model): def _handle_response(span, event_logger, response, llm_model): - set_model_response_attributes( - span, llm_model, getattr(response, "usage_metadata", None), response_meta=response - ) + set_model_response_attributes(span, llm_model, response.usage_metadata) if should_emit_events(): emit_response_events(response, event_logger) else: - set_response_attributes(span, llm_model, response) + set_response_attributes( + span, llm_model, response.candidates[0].text if response.candidates else "" + ) if span.is_recording(): span.set_status(Status(StatusCode.OK)) @@ -317,9 +238,8 @@ async def _awrap(tracer, event_logger, to_wrap, wrapped, instance, args, kwargs) name, kind=SpanKind.CLIENT, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_VERTEX_AI, - GenAIAttributes.GEN_AI_OPERATION_NAME: _gen_ai_operation_name(name), - GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, + GenAIAttributes.GEN_AI_SYSTEM: "Google", + SpanAttributes.LLM_REQUEST_TYPE: LLMRequestTypeValues.COMPLETION.value, }, ) @@ -367,9 +287,8 @@ def _wrap(tracer, event_logger, to_wrap, wrapped, instance, args, kwargs): name, kind=SpanKind.CLIENT, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_VERTEX_AI, - GenAIAttributes.GEN_AI_OPERATION_NAME: _gen_ai_operation_name(name), - GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, + GenAIAttributes.GEN_AI_SYSTEM: "Google", + SpanAttributes.LLM_REQUEST_TYPE: LLMRequestTypeValues.COMPLETION.value, }, ) diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py index 2405d90bbb..609820f3df 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/event_emitter.py @@ -12,17 +12,12 @@ should_emit_events, should_send_prompts, ) -from opentelemetry.instrumentation.vertexai.span_utils import ( - _map_vertex_finish_reason, -) from opentelemetry.semconv._incubating.attributes import ( gen_ai_attributes as GenAIAttributes, ) from vertexai.generative_models import GenerationResponse -_GCP_VERTEX_AI = GenAIAttributes.GenAiProviderNameValues.GCP_VERTEX_AI.value - class Roles(Enum): USER = "user" @@ -34,10 +29,36 @@ class Roles(Enum): VALID_MESSAGE_ROLES = {role.value for role in Roles} """The valid roles for naming the message event.""" -EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_VERTEX_AI} +EVENT_ATTRIBUTES = { + GenAIAttributes.GEN_AI_SYSTEM: GenAIAttributes.GenAiSystemValues.VERTEX_AI.value +} """The attributes to be used for the event.""" +def _parse_vertex_finish_reason(reason): + if reason is None: + return "unknown" + + finish_reason_map = { + 0: "unspecified", + 1: "stop", + 2: "max_tokens", + 3: "safety", + 4: "recitation", + 5: "other", + 6: "blocklist", + 7: "prohibited_content", + 8: "spii", + 9: "malformed_function_call", + } + + if hasattr(reason, "value"): + reason_value = reason.value + else: + reason_value = reason + + return finish_reason_map.get(reason_value, "unknown") + @dont_throw def emit_prompt_events(args, event_logger): @@ -58,7 +79,7 @@ def emit_response_events(response, event_logger): ChoiceEvent( index=0, message={"content": response, "role": Roles.ASSISTANT.value}, - finish_reason="", + finish_reason="unknown", ), event_logger, ) @@ -71,7 +92,7 @@ def emit_response_events(response, event_logger): "content": candidate.text, "role": Roles.ASSISTANT.value, }, - finish_reason=_map_vertex_finish_reason(candidate.finish_reason), + finish_reason=_parse_vertex_finish_reason(candidate.finish_reason), ), event_logger, ) diff --git a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py index 8468682a52..27a8afba84 100644 --- a/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py +++ b/packages/opentelemetry-instrumentation-vertexai/opentelemetry/instrumentation/vertexai/span_utils.py @@ -30,9 +30,9 @@ def _is_base64_image_part(item): return False # Check if it's an image mime type and has inline data - if item.mime_type and "image/" in item.mime_type and item.inline_data: - data = getattr(item.inline_data, "data", None) - if isinstance(data, (bytes, bytearray)) and len(data) > 0: + if item.mime_type and 'image/' in item.mime_type and item.inline_data: + # Check if the inline_data has actual data + if hasattr(item.inline_data, 'data') and item.inline_data.data: return True return False @@ -40,296 +40,6 @@ def _is_base64_image_part(item): return False -def _map_vertex_finish_reason(finish_reason): - """Map VertexAI FinishReason to OTel finish reason string. - - Returns ``""`` for ``None``, unspecified, and unmapped values. - """ - if finish_reason is None: - return "" - name = getattr(finish_reason, "name", None) or str(finish_reason) - mapping = { - "STOP": "stop", - "MAX_TOKENS": "length", - "SAFETY": "content_filter", - "RECITATION": "content_filter", - "BLOCKLIST": "content_filter", - "PROHIBITED_CONTENT": "content_filter", - "SPII": "content_filter", - "IMAGE_SAFETY": "content_filter", - "IMAGE_PROHIBITED_CONTENT": "content_filter", - "IMAGE_RECITATION": "content_filter", - "LANGUAGE": "content_filter", - "FINISH_REASON_UNSPECIFIED": "", - "MALFORMED_FUNCTION_CALL": "error", - "OTHER": "error", - "UNEXPECTED_TOOL_CALL": "error", - "NO_IMAGE": "error", - "IMAGE_OTHER": "error", - } - return mapping.get(name, "") - - -def _normalize_message_role(role): - if role is None: - return "user" - r = str(role).lower() - if r == "model": - return "assistant" - return r - - -def _parse_vertex_function_args(args): - if args is None: - return {} - if isinstance(args, dict): - return args - if isinstance(args, str): - try: - return json.loads(args) - except (json.JSONDecodeError, TypeError): - return {"_raw": args} - return {"_value": args} - - -def _vertex_function_response_to_str(response): - if response is None: - return "" - if isinstance(response, str): - return response - try: - return json.dumps(response, default=str) - except TypeError: - return str(response) - - -def accumulate_vertex_stream_finish_reasons(ordered, seen, chunk): - for cand in getattr(chunk, "candidates", None) or []: - mapped = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) - if mapped and mapped not in seen: - seen.add(mapped) - ordered.append(mapped) - - -def _parts_from_vertex_part_sync(part, span, part_index): - out = [] - t = getattr(part, "text", None) - if isinstance(t, str) and t: - out.append({"type": "text", "content": t}) - img = _otel_image_part_vertex_sync(part, span, part_index) - if img: - out.append(img) - fc = getattr(part, "function_call", None) - if fc is not None: - fid = getattr(fc, "id", None) or "" - fname = getattr(fc, "name", None) or "" - out.append( - { - "type": "tool_call", - "id": fid, - "name": fname, - "arguments": _parse_vertex_function_args(getattr(fc, "args", None)), - } - ) - fr = getattr(part, "function_response", None) - if fr is not None: - fid = getattr(fr, "id", None) or "" - out.append( - { - "type": "tool_call_response", - "id": fid, - "response": _vertex_function_response_to_str( - getattr(fr, "response", None) - ), - } - ) - if getattr(part, "thought", None): - out.append({"type": "reasoning", "content": str(part.thought)}) - if not out: - out.append({"type": "text", "content": str(part)}) - return out - - -async def _parts_from_vertex_part_async(part, span, part_index): - out = [] - t = getattr(part, "text", None) - if isinstance(t, str) and t: - out.append({"type": "text", "content": t}) - if _is_base64_image_part(part): - img = await _otel_image_part_vertex_async(part, span, part_index) - if img: - out.append(img) - fc = getattr(part, "function_call", None) - if fc is not None: - fid = getattr(fc, "id", None) or "" - fname = getattr(fc, "name", None) or "" - out.append( - { - "type": "tool_call", - "id": fid, - "name": fname, - "arguments": _parse_vertex_function_args(getattr(fc, "args", None)), - } - ) - fr = getattr(part, "function_response", None) - if fr is not None: - fid = getattr(fr, "id", None) or "" - out.append( - { - "type": "tool_call_response", - "id": fid, - "response": _vertex_function_response_to_str( - getattr(fr, "response", None) - ), - } - ) - if getattr(part, "thought", None): - out.append({"type": "reasoning", "content": str(part.thought)}) - if not out: - out.append({"type": "text", "content": str(part)}) - return out - - -def _vertex_parts_list_from_content_sync(content, span): - parts_acc = [] - for idx, p in enumerate(content.parts): - parts_acc.extend(_parts_from_vertex_part_sync(p, span, idx)) - return parts_acc - - -async def _vertex_parts_list_from_content_async(content, span): - parts_acc = [] - for idx, p in enumerate(content.parts): - parts_acc.extend(await _parts_from_vertex_part_async(p, span, idx)) - return parts_acc - - -def _vertex_expand_arg_to_messages_sync(argument, span): - messages = [] - if hasattr(argument, "parts") and hasattr(argument, "role"): - parts = _vertex_parts_list_from_content_sync(argument, span) - if parts: - messages.append( - {"role": _normalize_message_role(argument.role), "parts": parts} - ) - return messages - if isinstance(argument, list): - if argument and all( - hasattr(x, "parts") and hasattr(x, "role") for x in argument - ): - for c in argument: - parts = _vertex_parts_list_from_content_sync(c, span) - if parts: - messages.append( - {"role": _normalize_message_role(c.role), "parts": parts} - ) - return messages - processed = _process_vertexai_argument_sync(argument, span) - if processed: - messages.append({"role": "user", "parts": processed}) - return messages - - -async def _vertex_expand_arg_to_messages_async(argument, span): - messages = [] - if hasattr(argument, "parts") and hasattr(argument, "role"): - parts = await _vertex_parts_list_from_content_async(argument, span) - if parts: - messages.append( - {"role": _normalize_message_role(argument.role), "parts": parts} - ) - return messages - if isinstance(argument, list): - if argument and all( - hasattr(x, "parts") and hasattr(x, "role") for x in argument - ): - for c in argument: - parts = await _vertex_parts_list_from_content_async(c, span) - if parts: - messages.append( - {"role": _normalize_message_role(c.role), "parts": parts} - ) - return messages - processed = await _process_vertexai_argument(argument, span) - if processed: - messages.append({"role": "user", "parts": processed}) - return messages - - -def _output_messages_from_vertex_response(span, response): - messages = [] - for cand in getattr(response, "candidates", None) or []: - parts = [] - content = getattr(cand, "content", None) - if content and getattr(content, "parts", None): - for idx, p in enumerate(content.parts): - parts.extend(_parts_from_vertex_part_sync(p, span, idx)) - fr = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) - role = "assistant" - if content and getattr(content, "role", None): - role = _normalize_message_role(content.role) - msg = {"role": role, "parts": parts, "finish_reason": fr} - messages.append(msg) - if not messages and getattr(response, "text", None): - messages.append( - { - "role": "assistant", - "parts": [{"type": "text", "content": response.text}], - "finish_reason": "", - } - ) - return messages - - -def _vertex_system_instruction_to_parts(si, span): - if isinstance(si, str): - return [{"type": "text", "content": si}] - if hasattr(si, "parts") and si.parts: - out = [] - for idx, p in enumerate(si.parts): - out.extend(_parts_from_vertex_part_sync(p, span, idx)) - return out - return [{"type": "text", "content": str(si)}] - - -async def _otel_image_part_vertex_async(item, span, item_index): - if not _is_base64_image_part(item): - return None - mime = item.mime_type or "application/octet-stream" - if Config.upload_base64_image: - uploaded = await _process_image_part( - item, span.context.trace_id, span.context.span_id, item_index - ) - if uploaded and uploaded.get("image_url", {}).get("url"): - return { - "type": "uri", - "modality": "image", - "uri": uploaded["image_url"]["url"], - } - binary_data = item.inline_data.data - b64 = base64.b64encode(binary_data).decode("utf-8") - return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} - - -def _otel_image_part_vertex_sync(item, span, item_index): - if not _is_base64_image_part(item): - return None - mime = item.mime_type or "application/octet-stream" - if Config.upload_base64_image: - uploaded = _process_image_part_sync( - item, span.context.trace_id, span.context.span_id, item_index - ) - if uploaded and uploaded.get("image_url", {}).get("url"): - return { - "type": "uri", - "modality": "image", - "uri": uploaded["image_url"]["url"], - } - binary_data = item.inline_data.data - b64 = base64.b64encode(binary_data).decode("utf-8") - return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} - - async def _process_image_part(item, trace_id, span_id, content_index): """Process a VertexAI Part object containing image data""" if not Config.upload_base64_image: @@ -374,11 +84,7 @@ def run_async(method): def _process_image_part_sync(item, trace_id, span_id, content_index): - """Sync upload path; uses ``run_async`` + thread when a loop is already running. - - Can deadlock if the upload depends on the same event loop (e.g. some ASGI/Jupyter - setups). Prefer async request paths where possible. - """ + """Synchronous version of image part processing using OpenAI's pattern""" if not Config.upload_base64_image: return None @@ -413,9 +119,11 @@ async def upload_task(): async def _process_vertexai_argument(argument, span): """Process a single argument for VertexAI, handling different types""" if isinstance(argument, str): - return [{"type": "text", "content": argument}] + # Simple text argument in OpenAI format + return [{"type": "text", "text": argument}] elif isinstance(argument, list): + # List of mixed content (text strings and Part objects) - deep copy and process content_list = copy.deepcopy(argument) processed_items = [] @@ -427,6 +135,7 @@ async def _process_vertexai_argument(argument, span): return processed_items else: + # Single Part object - convert to OpenAI format processed_item = await _process_content_item_vertexai(argument, span, 0) return [processed_item] if processed_item is not None else [] @@ -434,25 +143,32 @@ async def _process_vertexai_argument(argument, span): async def _process_content_item_vertexai(content_item, span, item_index): """Process a single content item for VertexAI""" if isinstance(content_item, str): - return {"type": "text", "content": content_item} + # Convert text to OpenAI format + return {"type": "text", "text": content_item} elif _is_base64_image_part(content_item): - return await _otel_image_part_vertex_async(content_item, span, item_index) + # Process image part + return await _process_image_part( + content_item, span.context.trace_id, span.context.span_id, item_index + ) - elif hasattr(content_item, "text"): - t = getattr(content_item, "text", None) - if isinstance(t, str) and t: - return {"type": "text", "content": t} + elif hasattr(content_item, 'text'): + # Text part to OpenAI format + return {"type": "text", "text": content_item.text} - return {"type": "text", "content": str(content_item)} + else: + # Other types as text + return {"type": "text", "text": str(content_item)} def _process_vertexai_argument_sync(argument, span): """Synchronous version of argument processing for VertexAI""" if isinstance(argument, str): - return [{"type": "text", "content": argument}] + # Simple text argument in OpenAI format + return [{"type": "text", "text": argument}] elif isinstance(argument, list): + # List of mixed content (text strings and Part objects) - deep copy and process content_list = copy.deepcopy(argument) processed_items = [] @@ -464,6 +180,7 @@ def _process_vertexai_argument_sync(argument, span): return processed_items else: + # Single Part object - convert to OpenAI format processed_item = _process_content_item_vertexai_sync(argument, span, 0) return [processed_item] if processed_item is not None else [] @@ -471,17 +188,22 @@ def _process_vertexai_argument_sync(argument, span): def _process_content_item_vertexai_sync(content_item, span, item_index): """Synchronous version of content item processing for VertexAI""" if isinstance(content_item, str): - return {"type": "text", "content": content_item} + # Convert text to OpenAI format + return {"type": "text", "text": content_item} elif _is_base64_image_part(content_item): - return _otel_image_part_vertex_sync(content_item, span, item_index) + # Process image part + return _process_image_part_sync( + content_item, span.context.trace_id, span.context.span_id, item_index + ) - elif hasattr(content_item, "text"): - t = getattr(content_item, "text", None) - if isinstance(t, str) and t: - return {"type": "text", "content": t} + elif hasattr(content_item, 'text'): + # Text part to OpenAI format + return {"type": "text", "text": content_item.text} - return {"type": "text", "content": str(content_item)} + else: + # Other types as text + return {"type": "text", "text": str(content_item)} @dont_throw @@ -490,13 +212,21 @@ async def set_input_attributes(span, args): if not span.is_recording(): return if should_send_prompts() and args is not None and len(args) > 0: - messages = [] - for argument in args: - messages.extend(await _vertex_expand_arg_to_messages_async(argument, span)) - if messages: - _set_span_attribute( - span, GenAIAttributes.GEN_AI_INPUT_MESSAGES, json.dumps(messages) - ) + # Process each argument using extracted helper methods + for arg_index, argument in enumerate(args): + processed_content = await _process_vertexai_argument(argument, span) + + if processed_content: + _set_span_attribute( + span, + f"{GenAIAttributes.GEN_AI_PROMPT}.{arg_index}.role", + "user" + ) + _set_span_attribute( + span, + f"{GenAIAttributes.GEN_AI_PROMPT}.{arg_index}.content", + json.dumps(processed_content) + ) # Sync version with image processing support @@ -506,13 +236,21 @@ def set_input_attributes_sync(span, args): if not span.is_recording(): return if should_send_prompts() and args is not None and len(args) > 0: - messages = [] - for argument in args: - messages.extend(_vertex_expand_arg_to_messages_sync(argument, span)) - if messages: - _set_span_attribute( - span, GenAIAttributes.GEN_AI_INPUT_MESSAGES, json.dumps(messages) - ) + # Process each argument using extracted helper methods + for arg_index, argument in enumerate(args): + processed_content = _process_vertexai_argument_sync(argument, span) + + if processed_content: + _set_span_attribute( + span, + f"{GenAIAttributes.GEN_AI_PROMPT}.{arg_index}.role", + "user" + ) + _set_span_attribute( + span, + f"{GenAIAttributes.GEN_AI_PROMPT}.{arg_index}.content", + json.dumps(processed_content) + ) @dont_throw @@ -520,16 +258,9 @@ def set_model_input_attributes(span, kwargs, llm_model): if not span.is_recording(): return _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_MODEL, llm_model) - - prompt = kwargs.get("prompt") - if prompt: - messages = [ - {"role": "user", "parts": [{"type": "text", "content": prompt}]}, - ] - _set_span_attribute( - span, GenAIAttributes.GEN_AI_INPUT_MESSAGES, json.dumps(messages) - ) - + _set_span_attribute( + span, f"{GenAIAttributes.GEN_AI_PROMPT}.0.user", kwargs.get("prompt") + ) _set_span_attribute( span, GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE, kwargs.get("temperature") ) @@ -539,93 +270,35 @@ def set_model_input_attributes(span, kwargs, llm_model): _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TOP_P, kwargs.get("top_p")) _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TOP_K, kwargs.get("top_k")) _set_span_attribute( - span, GenAIAttributes.GEN_AI_REQUEST_PRESENCE_PENALTY, kwargs.get("presence_penalty") + span, SpanAttributes.LLM_PRESENCE_PENALTY, kwargs.get("presence_penalty") ) _set_span_attribute( - span, GenAIAttributes.GEN_AI_REQUEST_FREQUENCY_PENALTY, kwargs.get("frequency_penalty") + span, SpanAttributes.LLM_FREQUENCY_PENALTY, kwargs.get("frequency_penalty") ) - if should_send_prompts(): - si = _vertex_system_instruction_from_kwargs(kwargs) - if si is not None: - try: - parts = _vertex_system_instruction_to_parts(si, span) - if parts: - _set_span_attribute( - span, - GenAIAttributes.GEN_AI_SYSTEM_INSTRUCTIONS, - json.dumps(parts), - ) - except Exception: - pass - - -def _vertex_system_instruction_from_kwargs(kwargs): - si = kwargs.get("system_instruction") - if si is not None: - return si - config = kwargs.get("config") - if config is not None: - si = getattr(config, "system_instruction", None) - if si is not None: - return si - return None - @dont_throw -def set_response_attributes(span, llm_model, generation_or_text, finish_reason_otel=None): +def set_response_attributes(span, llm_model, generation_text): if not span.is_recording() or not should_send_prompts(): return - if hasattr(generation_or_text, "candidates"): - messages = _output_messages_from_vertex_response(span, generation_or_text) - if messages: - _set_span_attribute( - span, GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, json.dumps(messages) - ) - return - parts = [] - if generation_or_text: - parts.append({"type": "text", "content": generation_or_text}) - fr = finish_reason_otel or "" - if not parts and not fr: - return - msg = {"role": "assistant", "parts": parts, "finish_reason": fr} + _set_span_attribute(span, f"{GenAIAttributes.GEN_AI_COMPLETION}.0.role", "assistant") _set_span_attribute( - span, GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, json.dumps([msg]) + span, + f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content", + generation_text, ) @dont_throw -def set_model_response_attributes( - span, llm_model, token_usage, response_meta=None, stream_finish_reasons=None -): +def set_model_response_attributes(span, llm_model, token_usage): if not span.is_recording(): return _set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_MODEL, llm_model) - if stream_finish_reasons is not None: - reasons = stream_finish_reasons - elif response_meta is not None: - reasons = [] - for cand in getattr(response_meta, "candidates", None) or []: - mapped = _map_vertex_finish_reason(getattr(cand, "finish_reason", None)) - if mapped: - reasons.append(mapped) - else: - reasons = [] - if reasons: - _set_span_attribute( - span, GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS, reasons - ) - if response_meta is not None: - rid = getattr(response_meta, "response_id", None) - if rid: - _set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_ID, rid) - if token_usage: _set_span_attribute( span, - SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS, + SpanAttributes.LLM_USAGE_TOTAL_TOKENS, token_usage.total_token_count, ) _set_span_attribute( diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py index e2d0fdf6fa..0eb54adc05 100644 --- a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py +++ b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_bison.py @@ -10,7 +10,6 @@ gen_ai_attributes as GenAIAttributes, ) from vertexai.language_models import ChatModel, InputOutputTextPair, TextGenerationModel -import json vertexai.init() @@ -42,13 +41,15 @@ def test_vertexai_predict(instrument_legacy, span_exporter, log_exporter): ) assert ( "Give me ten interview questions for the role of program manager." - in vertexai_span.attributes["gen_ai.input.messages"] + in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] ) assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - out = json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["parts"] - assert "".join(p["content"] for p in out if p.get("type") == "text") == response + assert ( + vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content"] + == response + ) @pytest.mark.vcr @@ -83,13 +84,15 @@ async def async_predict_text() -> str: ) assert ( "Give me ten interview questions for the role of program manager." - in vertexai_span.attributes["gen_ai.input.messages"] + in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] ) assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - out = json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["parts"] - assert "".join(p["content"] for p in out if p.get("type") == "text") == response + assert ( + vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content"] + == response + ) @pytest.mark.vcr @@ -117,16 +120,14 @@ def test_vertexai_stream(instrument_legacy, span_exporter, log_exporter): assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "text-bison" assert ( "Give me ten interview questions for the role of program manager." - in vertexai_span.attributes["gen_ai.input.messages"] + in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] ) assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) - parts = output_messages[0]["parts"] - assert "".join(p["content"] for p in parts if p.get("type") == "text") == "".join( - response - ) + assert vertexai_span.attributes[ + f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content" + ] == "".join(response) @pytest.mark.vcr @@ -159,16 +160,14 @@ async def async_streaming_prediction() -> list: assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "text-bison" assert ( "Give me ten interview questions for the role of program manager." - in vertexai_span.attributes["gen_ai.input.messages"] + in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] ) assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) - parts = output_messages[0]["parts"] - assert "".join(p["content"] for p in parts if p.get("type") == "text") == "".join( - response - ) + assert vertexai_span.attributes[ + f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content" + ] == "".join(response) @pytest.mark.vcr @@ -208,13 +207,15 @@ def test_vertexai_chat(instrument_legacy, span_exporter, log_exporter): ) assert ( "How many planets are there in the solar system?" - in vertexai_span.attributes["gen_ai.input.messages"] + in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] ) assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.95 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - out = json.loads(vertexai_span.attributes["gen_ai.output.messages"])[0]["parts"] - assert "".join(p["content"] for p in out if p.get("type") == "text") == response + assert ( + vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content"] + == response + ) @pytest.mark.vcr @@ -258,18 +259,16 @@ def test_vertexai_chat_stream(instrument_legacy, span_exporter, log_exporter): assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE] == 0.8 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 256 assert vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 - output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) - parts = output_messages[0]["parts"] - assert "".join(p["content"] for p in parts if p.get("type") == "text") == "".join( - response - ) + assert vertexai_span.attributes[ + f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content" + ] == "".join(response) def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict): assert log.log_record.attributes.get(EventAttributes.EVENT_NAME) == event_name assert ( - log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) - == GenAIAttributes.GenAiProviderNameValues.GCP_VERTEX_AI.value + log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) + == GenAIAttributes.GenAiSystemValues.VERTEX_AI.value ) if not expected_content: diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py index c9a1675005..b081303dc8 100644 --- a/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py +++ b/packages/opentelemetry-instrumentation-vertexai/tests/disabled_test_gemini.py @@ -7,7 +7,6 @@ gen_ai_attributes as GenAIAttributes, ) from opentelemetry.semconv_ai import SpanAttributes -import json from vertexai.preview.generative_models import GenerativeModel, Part vertexai.init() @@ -31,18 +30,16 @@ def test_vertexai_generate_content(instrument_legacy, span_exporter, log_exporte ] vertexai_span = spans[0] - input_messages = json.loads(vertexai_span.attributes["gen_ai.input.messages"]) - assert any( - "what is shown in this image?" in part.get("content", "") - for msg in input_messages - for part in msg.get("parts", []) + assert ( + "what is shown in this image?" + in vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.user"] ) assert ( vertexai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gemini-2.0-flash-lite" ) assert ( - vertexai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] + vertexai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == response._raw_response.usage_metadata.total_token_count ) assert ( @@ -53,8 +50,10 @@ def test_vertexai_generate_content(instrument_legacy, span_exporter, log_exporte vertexai_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == response._raw_response.usage_metadata.candidates_token_count ) - output_messages = json.loads(vertexai_span.attributes["gen_ai.output.messages"]) - assert output_messages[0]["parts"][0]["content"] == response.text + assert ( + vertexai_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content"] + == response.text + ) logs = log_exporter.get_finished_logs() assert ( @@ -89,7 +88,7 @@ def test_vertexai_generate_content_with_events_with_content( == "gemini-2.0-flash-lite" ) assert ( - vertexai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] + vertexai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == response._raw_response.usage_metadata.total_token_count ) assert ( @@ -149,7 +148,7 @@ def test_vertexai_generate_content_with_events_with_no_content( == "gemini-2.0-flash-lite" ) assert ( - vertexai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] + vertexai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == response._raw_response.usage_metadata.total_token_count ) assert ( @@ -175,8 +174,8 @@ def test_vertexai_generate_content_with_events_with_no_content( def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict): assert log.log_record.attributes.get(EventAttributes.EVENT_NAME) == event_name assert ( - log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) - == GenAIAttributes.GenAiProviderNameValues.GCP_VERTEX_AI.value + log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) + == GenAIAttributes.GenAiSystemValues.VERTEX_AI.value ) if not expected_content: diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py b/packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py deleted file mode 100644 index c7c5706fc2..0000000000 --- a/packages/opentelemetry-instrumentation-vertexai/tests/test_finish_reasons.py +++ /dev/null @@ -1,469 +0,0 @@ -import json -import pytest -from unittest.mock import Mock, patch -from opentelemetry.semconv._incubating.attributes import ( - gen_ai_attributes as GenAIAttributes, -) -from opentelemetry.instrumentation.vertexai.span_utils import ( - _map_vertex_finish_reason, - accumulate_vertex_stream_finish_reasons, - _output_messages_from_vertex_response, - set_response_attributes, - _parts_from_vertex_part_sync, - _parts_from_vertex_part_async, -) - - -def _make_enum(name): - """Create a simple mock object that mimics a VertexAI enum with a .name attribute.""" - obj = Mock() - obj.name = name - return obj - - -def _make_span(): - """Create a mock span that captures set_attribute calls.""" - span = Mock() - span.is_recording.return_value = True - span.context.trace_id = "test_trace_id" - span.context.span_id = "test_span_id" - attrs = {} - - def capture(key, value): - attrs[key] = value - - span.set_attribute = capture - span._attrs = attrs - return span - - -# --------------------------------------------------------------------------- -# 1. _map_vertex_finish_reason -# --------------------------------------------------------------------------- -class TestMapVertexFinishReason: - def test_none_returns_unknown(self): - assert _map_vertex_finish_reason(None) == "" - - def test_stop_enum(self): - assert _map_vertex_finish_reason(_make_enum("STOP")) == "stop" - - def test_max_tokens_enum(self): - assert _map_vertex_finish_reason(_make_enum("MAX_TOKENS")) == "length" - - def test_safety_enum(self): - assert _map_vertex_finish_reason(_make_enum("SAFETY")) == "content_filter" - - def test_recitation_enum(self): - assert _map_vertex_finish_reason(_make_enum("RECITATION")) == "content_filter" - - def test_blocklist_enum(self): - assert _map_vertex_finish_reason(_make_enum("BLOCKLIST")) == "content_filter" - - def test_prohibited_content_enum(self): - assert _map_vertex_finish_reason(_make_enum("PROHIBITED_CONTENT")) == "content_filter" - - def test_spii_enum(self): - assert _map_vertex_finish_reason(_make_enum("SPII")) == "content_filter" - - def test_image_safety_enum(self): - assert _map_vertex_finish_reason(_make_enum("IMAGE_SAFETY")) == "content_filter" - - def test_image_prohibited_content_enum(self): - assert _map_vertex_finish_reason(_make_enum("IMAGE_PROHIBITED_CONTENT")) == "content_filter" - - def test_image_recitation_enum(self): - assert _map_vertex_finish_reason(_make_enum("IMAGE_RECITATION")) == "content_filter" - - def test_language_enum(self): - assert _map_vertex_finish_reason(_make_enum("LANGUAGE")) == "content_filter" - - def test_finish_reason_unspecified_enum(self): - assert _map_vertex_finish_reason(_make_enum("FINISH_REASON_UNSPECIFIED")) == "" - - def test_malformed_function_call_enum(self): - assert _map_vertex_finish_reason(_make_enum("MALFORMED_FUNCTION_CALL")) == "error" - - def test_other_enum(self): - assert _map_vertex_finish_reason(_make_enum("OTHER")) == "error" - - def test_unexpected_tool_call_enum(self): - assert _map_vertex_finish_reason(_make_enum("UNEXPECTED_TOOL_CALL")) == "error" - - def test_no_image_enum(self): - assert _map_vertex_finish_reason(_make_enum("NO_IMAGE")) == "error" - - def test_image_other_enum(self): - assert _map_vertex_finish_reason(_make_enum("IMAGE_OTHER")) == "error" - - def test_unmapped_enum_returns_unknown(self): - assert _map_vertex_finish_reason(_make_enum("TOTALLY_NEW_REASON")) == "" - - -# --------------------------------------------------------------------------- -# 2. accumulate_vertex_stream_finish_reasons -# --------------------------------------------------------------------------- -class TestAccumulateVertexStreamFinishReasons: - def test_unknown_is_not_added(self): - """Candidates with None finish_reason map to 'unknown' and are skipped.""" - ordered = [] - seen = set() - chunk = Mock() - cand = Mock() - cand.finish_reason = None # maps to "" - chunk.candidates = [cand] - - accumulate_vertex_stream_finish_reasons(ordered, seen, chunk) - - assert ordered == [] - assert seen == set() - - def test_real_values_are_added(self): - ordered = [] - seen = set() - - chunk1 = Mock() - cand1 = Mock() - cand1.finish_reason = _make_enum("STOP") - chunk1.candidates = [cand1] - - accumulate_vertex_stream_finish_reasons(ordered, seen, chunk1) - assert ordered == ["stop"] - assert "stop" in seen - - def test_duplicates_are_skipped(self): - ordered = [] - seen = set() - - for _ in range(3): - chunk = Mock() - cand = Mock() - cand.finish_reason = _make_enum("STOP") - chunk.candidates = [cand] - accumulate_vertex_stream_finish_reasons(ordered, seen, chunk) - - assert ordered == ["stop"] - - def test_order_is_preserved(self): - ordered = [] - seen = set() - - for name in ["STOP", "MAX_TOKENS", "SAFETY"]: - chunk = Mock() - cand = Mock() - cand.finish_reason = _make_enum(name) - chunk.candidates = [cand] - accumulate_vertex_stream_finish_reasons(ordered, seen, chunk) - - assert ordered == ["stop", "length", "content_filter"] - - def test_chunk_with_no_candidates(self): - ordered = [] - seen = set() - chunk = Mock() - chunk.candidates = None - - accumulate_vertex_stream_finish_reasons(ordered, seen, chunk) - assert ordered == [] - - -# --------------------------------------------------------------------------- -# 3. _output_messages_from_vertex_response -# --------------------------------------------------------------------------- -class TestOutputMessagesFromVertexResponse: - def _make_candidate(self, finish_reason=None, text="Hello"): - """Build a mock candidate with optional finish_reason and text.""" - part = Mock() - part.text = text - # No image, function_call, function_response, or thought attributes - part.inline_data = None - part.mime_type = None - part.function_call = None - part.function_response = None - part.thought = None - content = Mock() - content.parts = [part] - content.role = "model" - cand = Mock() - cand.content = content - cand.finish_reason = finish_reason - return cand - - def test_every_message_has_finish_reason_key(self): - span = _make_span() - response = Mock() - response.candidates = [ - self._make_candidate(finish_reason=_make_enum("STOP")), - self._make_candidate(finish_reason=_make_enum("MAX_TOKENS")), - ] - - messages = _output_messages_from_vertex_response(span, response) - - for msg in messages: - assert "finish_reason" in msg - - def test_finish_reason_is_always_string(self): - span = _make_span() - response = Mock() - response.candidates = [ - self._make_candidate(finish_reason=_make_enum("STOP")), - self._make_candidate(finish_reason=None), - ] - - messages = _output_messages_from_vertex_response(span, response) - - for msg in messages: - assert isinstance(msg["finish_reason"], str) - assert msg["finish_reason"] is not None - - def test_no_finish_reason_returns_unknown(self): - span = _make_span() - response = Mock() - response.candidates = [self._make_candidate(finish_reason=None)] - - messages = _output_messages_from_vertex_response(span, response) - - assert len(messages) == 1 - assert messages[0]["finish_reason"] == "" - - def test_stop_finish_reason(self): - span = _make_span() - response = Mock() - response.candidates = [self._make_candidate(finish_reason=_make_enum("STOP"))] - - messages = _output_messages_from_vertex_response(span, response) - - assert len(messages) == 1 - assert messages[0]["finish_reason"] == "stop" - - def test_text_fallback_has_unknown_finish_reason(self): - """When there are no candidates but response.text exists, finish_reason is 'unknown'.""" - span = _make_span() - response = Mock() - response.candidates = [] - response.text = "Fallback text" - - messages = _output_messages_from_vertex_response(span, response) - - assert len(messages) == 1 - assert messages[0]["finish_reason"] == "" - assert messages[0]["parts"][0]["content"] == "Fallback text" - - -# --------------------------------------------------------------------------- -# 4. set_response_attributes -# --------------------------------------------------------------------------- -class TestSetResponseAttributes: - @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") - def test_full_response_object_with_candidates(self, mock_should_send): - mock_should_send.return_value = True - span = _make_span() - - part = Mock() - part.text = "Generated text" - part.inline_data = None - part.mime_type = None - part.function_call = None - part.function_response = None - part.thought = None - content = Mock() - content.parts = [part] - content.role = "model" - cand = Mock() - cand.content = content - cand.finish_reason = _make_enum("STOP") - - response = Mock() - response.candidates = [cand] - - set_response_attributes(span, "gemini-pro", response) - - key = GenAIAttributes.GEN_AI_OUTPUT_MESSAGES - assert key in span._attrs - messages = json.loads(span._attrs[key]) - assert len(messages) == 1 - assert messages[0]["finish_reason"] == "stop" - assert messages[0]["role"] == "assistant" - - @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") - def test_plain_text_with_finish_reason_otel(self, mock_should_send): - mock_should_send.return_value = True - span = _make_span() - - set_response_attributes(span, "gemini-pro", "Hello world", finish_reason_otel="stop") - - key = GenAIAttributes.GEN_AI_OUTPUT_MESSAGES - assert key in span._attrs - messages = json.loads(span._attrs[key]) - assert len(messages) == 1 - assert messages[0]["finish_reason"] == "stop" - assert messages[0]["parts"][0]["content"] == "Hello world" - - @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") - def test_plain_text_without_finish_reason_otel(self, mock_should_send): - mock_should_send.return_value = True - span = _make_span() - - set_response_attributes(span, "gemini-pro", "Hello world") - - key = GenAIAttributes.GEN_AI_OUTPUT_MESSAGES - assert key in span._attrs - messages = json.loads(span._attrs[key]) - assert len(messages) == 1 - assert messages[0]["finish_reason"] == "" - - @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") - def test_empty_text_no_finish_reason_no_output(self, mock_should_send): - """Empty text + no finish_reason_otel => no output message emitted.""" - mock_should_send.return_value = True - span = _make_span() - - set_response_attributes(span, "gemini-pro", "") - - key = GenAIAttributes.GEN_AI_OUTPUT_MESSAGES - assert key not in span._attrs - - @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") - def test_empty_text_with_finish_reason_emits_message(self, mock_should_send): - """Empty text but with a real finish_reason_otel still emits a message.""" - mock_should_send.return_value = True - span = _make_span() - - set_response_attributes(span, "gemini-pro", "", finish_reason_otel="stop") - - key = GenAIAttributes.GEN_AI_OUTPUT_MESSAGES - assert key in span._attrs - messages = json.loads(span._attrs[key]) - assert len(messages) == 1 - assert messages[0]["finish_reason"] == "stop" - assert messages[0]["parts"] == [] - - @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") - def test_no_output_when_prompts_disabled(self, mock_should_send): - mock_should_send.return_value = False - span = _make_span() - - set_response_attributes(span, "gemini-pro", "Hello world", finish_reason_otel="stop") - - assert GenAIAttributes.GEN_AI_OUTPUT_MESSAGES not in span._attrs - - -# --------------------------------------------------------------------------- -# 5. _parts_from_vertex_part_sync — reasoning / thinking blocks -# --------------------------------------------------------------------------- -class TestPartsFromVertexPartSyncReasoning: - def test_thought_only_produces_reasoning(self): - """Part with thought attribute produces a reasoning-type output.""" - part = Mock() - part.text = None - part.inline_data = None - part.mime_type = None - part.function_call = None - part.function_response = None - part.thought = "Let me think about this..." - - span = _make_span() - result = _parts_from_vertex_part_sync(part, span, 0) - - assert any(p["type"] == "reasoning" for p in result) - reasoning = [p for p in result if p["type"] == "reasoning"][0] - assert reasoning["content"] == "Let me think about this..." - - def test_text_and_thought_produces_both(self): - """Part with both text and thought produces text and reasoning parts.""" - part = Mock() - part.text = "The answer is 42." - part.inline_data = None - part.mime_type = None - part.function_call = None - part.function_response = None - part.thought = "I need to calculate..." - - span = _make_span() - result = _parts_from_vertex_part_sync(part, span, 0) - - types = [p["type"] for p in result] - assert "text" in types - assert "reasoning" in types - text_part = [p for p in result if p["type"] == "text"][0] - reasoning_part = [p for p in result if p["type"] == "reasoning"][0] - assert text_part["content"] == "The answer is 42." - assert reasoning_part["content"] == "I need to calculate..." - - def test_no_recognized_attributes_falls_through_to_str(self): - """Part with no recognized attributes produces str(part) as text fallback.""" - part = Mock() - part.text = None - part.inline_data = None - part.mime_type = None - part.function_call = None - part.function_response = None - part.thought = None - part.__str__ = lambda self: "fallback-string-repr" - - span = _make_span() - result = _parts_from_vertex_part_sync(part, span, 0) - - assert len(result) == 1 - assert result[0]["type"] == "text" - assert result[0]["content"] == "fallback-string-repr" - - -# --------------------------------------------------------------------------- -# 6. _parts_from_vertex_part_async — same reasoning tests but async -# --------------------------------------------------------------------------- -class TestPartsFromVertexPartAsyncReasoning: - @pytest.mark.asyncio - async def test_thought_only_produces_reasoning(self): - part = Mock() - part.text = None - part.inline_data = None - part.mime_type = None - part.function_call = None - part.function_response = None - part.thought = "Let me think about this..." - - span = _make_span() - result = await _parts_from_vertex_part_async(part, span, 0) - - assert any(p["type"] == "reasoning" for p in result) - reasoning = [p for p in result if p["type"] == "reasoning"][0] - assert reasoning["content"] == "Let me think about this..." - - @pytest.mark.asyncio - async def test_text_and_thought_produces_both(self): - part = Mock() - part.text = "The answer is 42." - part.inline_data = None - part.mime_type = None - part.function_call = None - part.function_response = None - part.thought = "I need to calculate..." - - span = _make_span() - result = await _parts_from_vertex_part_async(part, span, 0) - - types = [p["type"] for p in result] - assert "text" in types - assert "reasoning" in types - text_part = [p for p in result if p["type"] == "text"][0] - reasoning_part = [p for p in result if p["type"] == "reasoning"][0] - assert text_part["content"] == "The answer is 42." - assert reasoning_part["content"] == "I need to calculate..." - - @pytest.mark.asyncio - async def test_no_recognized_attributes_falls_through_to_str(self): - part = Mock() - part.text = None - part.inline_data = None - part.mime_type = None - part.function_call = None - part.function_response = None - part.thought = None - part.__str__ = lambda self: "fallback-string-repr" - - span = _make_span() - result = await _parts_from_vertex_part_async(part, span, 0) - - assert len(result) == 1 - assert result[0]["type"] == "text" - assert result[0]["content"] == "fallback-string-repr" diff --git a/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py b/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py index 46c7d86678..5f2189ddaf 100644 --- a/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py +++ b/packages/opentelemetry-instrumentation-vertexai/tests/test_role_attributes.py @@ -1,13 +1,11 @@ import json import pytest from unittest.mock import Mock, patch -from opentelemetry.semconv._incubating.attributes import ( - gen_ai_attributes as GenAIAttributes, -) from opentelemetry.instrumentation.vertexai.span_utils import ( set_input_attributes, set_input_attributes_sync, ) +from opentelemetry.semconv_ai import SpanAttributes class TestRoleAttributes: @@ -27,12 +25,6 @@ def capture_attribute(key, value): self.mock_span.set_attribute = capture_attribute - def _parse_input_messages(self): - """Helper to parse gen_ai.input.messages JSON from span attributes""" - key = GenAIAttributes.GEN_AI_INPUT_MESSAGES - assert key in self.span_attributes - return json.loads(self.span_attributes[key]) - @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") @pytest.mark.asyncio async def test_async_role_attribute_for_string_args(self, mock_should_send_prompts): @@ -43,10 +35,14 @@ async def test_async_role_attribute_for_string_args(self, mock_should_send_promp args = ["Hello, world!"] await set_input_attributes(self.mock_span, args) - messages = self._parse_input_messages() - assert len(messages) == 1 - assert messages[0]["role"] == "user" - assert messages[0]["parts"][0] == {"type": "text", "content": "Hello, world!"} + # Verify role attribute is set + assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes + assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" + + # Verify content is also set + assert f"{SpanAttributes.LLM_PROMPTS}.0.content" in self.span_attributes + expected_content = json.dumps([{"type": "text", "text": "Hello, world!"}]) + assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.content"] == expected_content @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") @pytest.mark.asyncio @@ -58,10 +54,12 @@ async def test_async_role_attribute_for_multiple_args(self, mock_should_send_pro args = ["First message", "Second message"] await set_input_attributes(self.mock_span, args) - messages = self._parse_input_messages() - assert len(messages) == 2 - assert messages[0]["role"] == "user" - assert messages[1]["role"] == "user" + # Verify role attributes are set for both arguments + assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes + assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" + + assert f"{SpanAttributes.LLM_PROMPTS}.1.role" in self.span_attributes + assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.1.role"] == "user" @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") @pytest.mark.asyncio @@ -73,9 +71,9 @@ async def test_async_role_attribute_with_mixed_content(self, mock_should_send_pr args = [["Text content", "More text"]] await set_input_attributes(self.mock_span, args) - messages = self._parse_input_messages() - assert len(messages) == 1 - assert messages[0]["role"] == "user" + # Verify role attribute is set + assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes + assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") def test_sync_role_attribute_for_string_args(self, mock_should_send_prompts): @@ -86,11 +84,14 @@ def test_sync_role_attribute_for_string_args(self, mock_should_send_prompts): args = ["Hello, world!"] set_input_attributes_sync(self.mock_span, args) - messages = self._parse_input_messages() - assert len(messages) == 1 - assert messages[0]["role"] == "user" + # Verify role attribute is set + assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes + assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" - assert messages[0]["parts"][0] == {"type": "text", "content": "Hello, world!"} + # Verify content is also set + assert f"{SpanAttributes.LLM_PROMPTS}.0.content" in self.span_attributes + expected_content = json.dumps([{"type": "text", "text": "Hello, world!"}]) + assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.content"] == expected_content @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") def test_sync_role_attribute_for_multiple_args(self, mock_should_send_prompts): @@ -101,10 +102,12 @@ def test_sync_role_attribute_for_multiple_args(self, mock_should_send_prompts): args = ["First message", "Second message"] set_input_attributes_sync(self.mock_span, args) - messages = self._parse_input_messages() - assert len(messages) == 2 - assert messages[0]["role"] == "user" - assert messages[1]["role"] == "user" + # Verify role attributes are set for both arguments + assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes + assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" + + assert f"{SpanAttributes.LLM_PROMPTS}.1.role" in self.span_attributes + assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.1.role"] == "user" @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") def test_sync_role_attribute_with_mixed_content(self, mock_should_send_prompts): @@ -115,9 +118,9 @@ def test_sync_role_attribute_with_mixed_content(self, mock_should_send_prompts): args = [["Text content", "More text"]] set_input_attributes_sync(self.mock_span, args) - messages = self._parse_input_messages() - assert len(messages) == 1 - assert messages[0]["role"] == "user" + # Verify role attribute is set + assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes + assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") @pytest.mark.asyncio @@ -181,10 +184,12 @@ async def test_async_role_attribute_for_image_content(self, mock_should_send_pro args = [["Hello with image", mock_image_part]] await set_input_attributes(self.mock_span, args) - messages = self._parse_input_messages() - assert len(messages) == 1 - assert messages[0]["role"] == "user" - assert messages[0]["parts"] + # Verify role attribute is set even when content includes images + assert f"{SpanAttributes.LLM_PROMPTS}.0.role" in self.span_attributes + assert self.span_attributes[f"{SpanAttributes.LLM_PROMPTS}.0.role"] == "user" + + # Verify content is also set + assert f"{SpanAttributes.LLM_PROMPTS}.0.content" in self.span_attributes @patch("opentelemetry.instrumentation.vertexai.span_utils.should_send_prompts") def test_sync_role_attribute_for_image_content(self, mock_should_send_prompts): @@ -199,7 +204,9 @@ def test_sync_role_attribute_for_image_content(self, 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501e3b617f7192bc32d51c2b8ee3c79249e6bd62 Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Sun, 5 Apr 2026 12:18:23 +0300 Subject: [PATCH 15/21] fix --- .../google_generativeai/__init__.py | 36 ++--- .../google_generativeai/span_utils.py | 150 ++++++++++-------- .../tests/test_finish_reasons.py | 112 ++++++++----- .../tests/test_generate_content.py | 4 +- .../uv.lock | 2 +- 5 files changed, 171 insertions(+), 133 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py index 9335be10a4..a6876099f5 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py @@ -15,7 +15,7 @@ emit_message_events, ) from opentelemetry.instrumentation.google_generativeai.span_utils import ( - accumulate_stream_finish_reasons, + _collect_finish_reasons_from_response, set_input_attributes_sync, set_model_request_attributes, set_model_response_attributes, @@ -49,25 +49,21 @@ "package": "google.genai.models", "object": "Models", "method": "generate_content", - "span_name": "gemini.generate_content", }, { "package": "google.genai.models", "object": "AsyncModels", "method": "generate_content", - "span_name": "gemini.generate_content", }, { "package": "google.genai.models", "object": "Models", "method": "generate_content_stream", - "span_name": "gemini.generate_content", }, { "package": "google.genai.models", "object": "AsyncModels", "method": "generate_content_stream", - "span_name": "gemini.generate_content", }, ] @@ -90,8 +86,6 @@ def _build_from_streaming_response( emit_events = should_emit_events() and event_logger text_parts = [] last_chunk = None - stream_finish_ordered = [] - stream_finish_seen = set() for item in response: item_to_yield = item last_chunk = item @@ -99,9 +93,6 @@ def _build_from_streaming_response( t = getattr(item, "text", None) if isinstance(t, str): text_parts.append(t) - accumulate_stream_finish_reasons( - stream_finish_ordered, stream_finish_seen, item - ) yield item_to_yield @@ -110,21 +101,22 @@ def _build_from_streaming_response( if emit_events: emit_choice_events(response, event_logger) else: - # Assumes the SDK aggregates candidate parts on each chunk; the last chunk with - # candidates should reflect the full model turn (incl. tool_call). Pure text deltas - # on the final chunk fall back to ``complete_response`` below. if last_chunk is not None and getattr(last_chunk, "candidates", None): set_response_attributes(span, last_chunk, llm_model) else: set_response_attributes( span, complete_response, llm_model, stream_last_chunk=last_chunk ) + + # Finish reasons from the final chunk — Gemini SDK aggregates candidates per chunk, + # so the last chunk reflects all candidates without deduplication artifacts. + stream_reasons = _collect_finish_reasons_from_response(last_chunk) if last_chunk else None set_model_response_attributes( span, last_chunk or response, llm_model, token_histogram, - stream_finish_reasons=stream_finish_ordered or None, + stream_finish_reasons=stream_reasons or None, ) span.end() @@ -135,8 +127,6 @@ async def _abuild_from_streaming_response( emit_events = should_emit_events() and event_logger text_parts = [] last_chunk = None - stream_finish_ordered = [] - stream_finish_seen = set() async for item in response: item_to_yield = item last_chunk = item @@ -144,9 +134,6 @@ async def _abuild_from_streaming_response( t = getattr(item, "text", None) if isinstance(t, str): text_parts.append(t) - accumulate_stream_finish_reasons( - stream_finish_ordered, stream_finish_seen, item - ) yield item_to_yield @@ -155,19 +142,20 @@ async def _abuild_from_streaming_response( if emit_events: emit_choice_events(response, event_logger) else: - # Same aggregation assumption as sync streaming (see _build_from_streaming_response). if last_chunk is not None and getattr(last_chunk, "candidates", None): set_response_attributes(span, last_chunk, llm_model) else: set_response_attributes( span, complete_response, llm_model, stream_last_chunk=last_chunk ) + + stream_reasons = _collect_finish_reasons_from_response(last_chunk) if last_chunk else None set_model_response_attributes( span, last_chunk if last_chunk else response, llm_model, token_histogram, - stream_finish_reasons=stream_finish_ordered or None, + stream_finish_reasons=stream_reasons or None, ) span.end() @@ -246,9 +234,8 @@ async def _awrap( if "model" in kwargs: llm_model = kwargs["model"].replace("models/", "") - name = to_wrap.get("span_name") span = tracer.start_span( - name, + f"{_GEN_CONTENT} {llm_model}", kind=SpanKind.CLIENT, attributes={ GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, @@ -325,9 +312,8 @@ def _wrap( if "model" in kwargs: llm_model = kwargs["model"].replace("models/", "") - name = to_wrap.get("span_name") span = tracer.start_span( - name, + f"{_GEN_CONTENT} {llm_model}", kind=SpanKind.CLIENT, attributes={ GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index 4dbb6fc321..f6e4558cab 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -232,13 +232,13 @@ async def upload_task(): return None -def _parts_from_genai_part_sync(part, span, part_index): +def _extract_non_image_parts(part): + """Extract all non-image OTel parts from a GenAI Part object (shared by sync/async).""" out = [] - if getattr(part, "text", None): + if getattr(part, "thought", False) and getattr(part, "text", None): + out.append({"type": "reasoning", "content": part.text}) + elif getattr(part, "text", None): out.append({"type": "text", "content": part.text}) - img = _otel_image_part_from_genai_part(part, span, part_index, sync=True) - if img: - out.append(img) fc = getattr(part, "function_call", None) if fc is not None: fid = getattr(fc, "id", None) or "" @@ -261,58 +261,62 @@ def _parts_from_genai_part_sync(part, span, part_index): "response": _function_response_to_str(getattr(fr, "response", None)), } ) - if getattr(part, "thought", None): - out.append({"type": "reasoning", "content": str(part.thought)}) - if not out: - if getattr(part, "executable_code", None): - out.append({"type": "text", "content": str(part.executable_code)}) - elif getattr(part, "code_execution_result", None): - out.append({"type": "text", "content": str(part.code_execution_result)}) - else: - out.append({"type": "text", "content": str(part)}) return out +def _fallback_part(part): + """Fallback when no structured parts were extracted.""" + if getattr(part, "executable_code", None): + return {"type": "text", "content": str(part.executable_code)} + if getattr(part, "code_execution_result", None): + return {"type": "text", "content": str(part.code_execution_result)} + return {"type": "text", "content": str(part)} + + +def _parts_from_genai_part_sync(part, span, part_index): + out = _extract_non_image_parts(part) + img = _otel_image_part_from_genai_part(part, span, part_index, sync=True) + if img: + out.append(img) + return out or [_fallback_part(part)] + + async def _parts_from_genai_part_async(part, span, part_index): - out = [] - if getattr(part, "text", None): - out.append({"type": "text", "content": part.text}) + out = _extract_non_image_parts(part) if _is_image_part(part): img = await _otel_image_part_from_genai_part_async(part, span, part_index) if img: out.append(img) - fc = getattr(part, "function_call", None) + return out or [_fallback_part(part)] + + +def _map_dict_part(p): + """Map a dict-based part to OTel part schema, preserving semantics.""" + text = p.get("text") + if text is not None: + return {"type": "text", "content": text} + fc = p.get("functionCall") if fc is not None: - fid = getattr(fc, "id", None) or "" - fname = getattr(fc, "name", None) or "" - out.append( - { - "type": "tool_call", - "id": fid, - "name": fname, - "arguments": _parse_function_call_arguments(getattr(fc, "args", None)), - } - ) - fr = getattr(part, "function_response", None) + return { + "type": "tool_call", + "id": fc.get("id", ""), + "name": fc.get("name", ""), + "arguments": _parse_function_call_arguments(fc.get("args")), + } + fr = p.get("functionResponse") if fr is not None: - fid = getattr(fr, "id", None) or "" - out.append( - { - "type": "tool_call_response", - "id": fid, - "response": _function_response_to_str(getattr(fr, "response", None)), - } - ) - if getattr(part, "thought", None): - out.append({"type": "reasoning", "content": str(part.thought)}) - if not out: - if getattr(part, "executable_code", None): - out.append({"type": "text", "content": str(part.executable_code)}) - elif getattr(part, "code_execution_result", None): - out.append({"type": "text", "content": str(part.code_execution_result)}) - else: - out.append({"type": "text", "content": str(part)}) - return out + return { + "type": "tool_call_response", + "id": fr.get("id", ""), + "response": _function_response_to_str(fr.get("response")), + } + inline = p.get("inlineData") + if inline is not None: + mime = inline.get("mimeType", "application/octet-stream") + data = inline.get("data", "") + modality = next((m for m in ("image", "video", "audio") if f"{m}/" in mime), "data") + return {"type": "blob", "modality": modality, "mime_type": mime, "content": data} + return {"type": "text", "content": str(p)} async def _process_content_item(content_item, span): @@ -320,11 +324,7 @@ async def _process_content_item(content_item, span): if isinstance(content_item, dict): for p in content_item.get("parts", []): if isinstance(p, dict): - text = p.get("text") - if text is not None: - parts_acc.append({"type": "text", "content": text}) - else: - parts_acc.append({"type": "text", "content": str(p)}) + parts_acc.append(_map_dict_part(p)) else: parts_acc.extend(await _parts_from_genai_part_async(p, span, 0)) elif hasattr(content_item, "parts"): @@ -367,11 +367,7 @@ def _process_content_item_sync(content_item, span): if isinstance(content_item, dict): for p in content_item.get("parts", []): if isinstance(p, dict): - text = p.get("text") - if text is not None: - parts_acc.append({"type": "text", "content": text}) - else: - parts_acc.append({"type": "text", "content": str(p)}) + parts_acc.append(_map_dict_part(p)) else: parts_acc.extend(_parts_from_genai_part_sync(p, span, 0)) elif hasattr(content_item, "parts"): @@ -419,14 +415,6 @@ def _collect_finish_reasons_from_response(response): return reasons -def accumulate_stream_finish_reasons(ordered, seen, chunk): - """Merge finish reasons from a streaming chunk; preserves first-seen order (metadata, not gated by prompts).""" - for cand in getattr(chunk, "candidates", None) or []: - mapped = _map_gemini_finish_reason(getattr(cand, "finish_reason", None)) - if mapped and mapped not in seen: - seen.add(mapped) - ordered.append(mapped) - def _output_messages_from_generate_response(span, response): messages = [] @@ -556,7 +544,6 @@ def set_input_attributes_sync(span, args, kwargs, llm_model): def set_model_request_attributes(span, kwargs, llm_model): if not span.is_recording(): return - _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_MODEL, llm_model) _set_span_attribute( span, GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE, kwargs.get("temperature") ) @@ -611,6 +598,17 @@ def set_model_request_attributes(span, kwargs, llm_model): except Exception: pass + tools = _tool_definitions_from_kwargs(kwargs) + if tools: + try: + _set_span_attribute( + span, + GenAIAttributes.GEN_AI_TOOL_DEFINITIONS, + json.dumps(tools), + ) + except Exception: + pass + def _system_instruction_from_kwargs(kwargs): """Top-level kwarg or unified-client ``config.system_instruction``.""" @@ -637,6 +635,26 @@ def _system_instruction_to_parts(si, span): return [{"type": "text", "content": str(si)}] +def _tool_definitions_from_kwargs(kwargs): + """Extract tool definitions from kwargs — source system representation per OTel spec.""" + tools = kwargs.get("tools") + if tools is None: + config = kwargs.get("config") + if config is not None: + tools = getattr(config, "tools", None) + if not tools: + return None + defs = [] + for tool in tools: + if hasattr(tool, "model_dump"): + defs.append(tool.model_dump(exclude_none=True, mode="json")) + elif isinstance(tool, dict): + defs.append(tool) + elif callable(tool): + defs.append({"name": getattr(tool, "__name__", str(tool))}) + return defs or None + + @dont_throw def set_response_attributes(span, response, llm_model, stream_last_chunk=None): if not span.is_recording(): diff --git a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py index 1b5cf3340d..01e751dac8 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py @@ -147,47 +147,31 @@ def test_no_candidates_attribute(self): # =========================================================================== -# 3. accumulate_stream_finish_reasons +# 3. Streaming finish reasons (via _collect_finish_reasons_from_response on last chunk) # =========================================================================== -class TestAccumulateStreamFinishReasons: - def test_unknown_not_added(self): - ordered, seen = [], set() - chunk = _make_response(candidates=[_make_candidate(None)]) - su.accumulate_stream_finish_reasons(ordered, seen, chunk) - assert ordered == [] - assert seen == set() - - def test_real_values_added(self): - ordered, seen = [], set() - chunk = _make_response(candidates=[_make_candidate("STOP")]) - su.accumulate_stream_finish_reasons(ordered, seen, chunk) - assert ordered == ["stop"] - assert "stop" in seen - - def test_deduplication(self): - ordered, seen = [], set() - chunk1 = _make_response(candidates=[_make_candidate("STOP")]) - chunk2 = _make_response(candidates=[_make_candidate("STOP")]) - su.accumulate_stream_finish_reasons(ordered, seen, chunk1) - su.accumulate_stream_finish_reasons(ordered, seen, chunk2) - assert ordered == ["stop"] - - def test_order_preserved(self): - ordered, seen = [], set() - su.accumulate_stream_finish_reasons( - ordered, seen, - _make_response(candidates=[_make_candidate("MAX_TOKENS")]), - ) - su.accumulate_stream_finish_reasons( - ordered, seen, - _make_response(candidates=[_make_candidate("STOP")]), - ) - su.accumulate_stream_finish_reasons( - ordered, seen, - _make_response(candidates=[_make_candidate("MAX_TOKENS")]), - ) - assert ordered == ["length", "stop"] +class TestStreamingFinishReasonsFromLastChunk: + def test_multiple_candidates_same_reason_preserved(self): + """Two candidates both with STOP should produce ["stop", "stop"] — no dedup.""" + last_chunk = _make_response(candidates=[ + _make_candidate("STOP", text="a"), + _make_candidate("STOP", text="b"), + ]) + reasons = su._collect_finish_reasons_from_response(last_chunk) + assert reasons == ["stop", "stop"] + + def test_mixed_reasons_preserve_order(self): + last_chunk = _make_response(candidates=[ + _make_candidate("MAX_TOKENS", text="a"), + _make_candidate("STOP", text="b"), + ]) + reasons = su._collect_finish_reasons_from_response(last_chunk) + assert reasons == ["length", "stop"] + + def test_none_candidates_returns_empty(self): + last_chunk = _make_response(candidates=[_make_candidate(None)]) + reasons = su._collect_finish_reasons_from_response(last_chunk) + assert reasons == [] # =========================================================================== @@ -294,3 +278,53 @@ def test_list_of_strings_each_has_finish_reason(self, monkeypatch): for msg in messages: assert msg["finish_reason"] == "" assert msg["role"] == "assistant" + + +# =========================================================================== +# 6. _parts_from_genai_part_sync — thinking/reasoning +# =========================================================================== + +class TestPartsFromGenaiPartThinking: + def _make_thinking_part(self, text="Let me think...", thought=True): + part = MagicMock() + part.text = text + part.thought = thought + part.function_call = None + part.function_response = None + part.inline_data = None + part.executable_code = None + part.code_execution_result = None + return part + + def test_thought_true_emits_reasoning_with_text_content(self): + """When thought=True, emit a single reasoning part with the text content.""" + part = self._make_thinking_part("Let me analyze the problem...") + span = MagicMock() + result = su._parts_from_genai_part_sync(part, span, 0) + assert len(result) == 1 + assert result[0]["type"] == "reasoning" + assert result[0]["content"] == "Let me analyze the problem..." + + def test_thought_false_emits_text(self): + """When thought=False, emit a normal text part.""" + part = self._make_thinking_part("Hello world", thought=False) + result = su._parts_from_genai_part_sync(part, MagicMock(), 0) + assert len(result) == 1 + assert result[0]["type"] == "text" + assert result[0]["content"] == "Hello world" + + def test_thought_none_emits_text(self): + """When thought=None, emit a normal text part.""" + part = self._make_thinking_part("Hello world", thought=None) + result = su._parts_from_genai_part_sync(part, MagicMock(), 0) + assert len(result) == 1 + assert result[0]["type"] == "text" + assert result[0]["content"] == "Hello world" + + def test_no_duplicate_parts_for_thinking(self): + """Must NOT produce both a text and reasoning part for the same thinking block.""" + part = self._make_thinking_part("Deep thoughts here") + result = su._parts_from_genai_part_sync(part, MagicMock(), 0) + types_emitted = [r["type"] for r in result] + assert types_emitted.count("text") == 0 + assert types_emitted.count("reasoning") == 1 diff --git a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py index dfa2078e33..03943cfde2 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py @@ -34,10 +34,10 @@ def test_client_spans(exporter, genai_client): assert len(spans) > 0, "No spans were recorded" span = next( - (s for s in spans if s.name == "gemini.generate_content"), + (s for s in spans if s.name.startswith("generate_content ")), None, ) - assert span is not None, "gemini.generate_content span not found" + assert span is not None, "generate_content span not found" assert span.kind == SpanKind.CLIENT assert span.status.status_code == StatusCode.OK diff --git a/packages/opentelemetry-instrumentation-google-generativeai/uv.lock b/packages/opentelemetry-instrumentation-google-generativeai/uv.lock index 35636e1d49..0629117d68 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/uv.lock +++ b/packages/opentelemetry-instrumentation-google-generativeai/uv.lock @@ -466,7 +466,7 @@ wheels = [ [[package]] name = "opentelemetry-instrumentation-google-generativeai" -version = "0.53.4" +version = "0.57.0" source = { editable = "." } dependencies = [ { name = "opentelemetry-api" }, From 13ee5e970b75258d53525e01e5fe88af7af17dec Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Mon, 6 Apr 2026 12:51:16 +0300 Subject: [PATCH 16/21] gemini-sample-app-model --- packages/sample-app/sample_app/gemini.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/packages/sample-app/sample_app/gemini.py b/packages/sample-app/sample_app/gemini.py index 2dfb2b2aac..c0a45d02a7 100644 --- a/packages/sample-app/sample_app/gemini.py +++ b/packages/sample-app/sample_app/gemini.py @@ -14,7 +14,7 @@ def predict_text() -> str: """Ideation example with a Large Language Model""" response = client.models.generate_content( - model="gemini-1.5-pro-002", + model="gemini-2.5-flash", contents="Give me ten interview questions for the role of program manager.", ) @@ -26,7 +26,7 @@ async def async_predict_text() -> str: """Async Ideation example with a Large Language Model""" response = client.models.generate_content( - model="gemini-1.5-pro-002", + model="gemini-2.5-flash", contents="Give me ten interview questions for the role of program manager.", ) @@ -39,7 +39,7 @@ def chat() -> str: # First message response1 = client.models.generate_content( - model="gemini-1.5-pro-002", + model="gemini-2.5-flash", contents="Hello, how are you?", ) @@ -51,7 +51,7 @@ def chat() -> str: ] response2 = client.models.generate_content( - model="gemini-1.5-pro-002", + model="gemini-2.5-flash", contents=conversation, ) From 2d7cd0be617f42baccf659665342552d791f41bc Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Mon, 6 Apr 2026 12:55:08 +0300 Subject: [PATCH 17/21] dead-code --- .../google_generativeai/span_utils.py | 149 ------------------ 1 file changed, 149 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index f6e4558cab..f2eb25401e 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -136,52 +136,6 @@ def _otel_image_part_from_genai_part(part, span, part_index, sync): return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} -async def _otel_image_part_from_genai_part_async(part, span, part_index): - if not _is_image_part(part): - return None - mime = ( - part.inline_data.mime_type - if hasattr(part, "inline_data") and part.inline_data - else None - ) or "application/octet-stream" - if Config.upload_base64_image: - uploaded = await _process_image_part( - part, span.context.trace_id, span.context.span_id, part_index - ) - if uploaded and uploaded.get("image_url") and uploaded["image_url"].get("url"): - return { - "type": "uri", - "modality": "image", - "uri": uploaded["image_url"]["url"], - } - binary_data = part.inline_data.data - b64 = base64.b64encode(binary_data).decode("utf-8") - return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} - - -async def _process_image_part(item, trace_id, span_id, content_index): - """Upload image when configured; used only from async content processing.""" - if not Config.upload_base64_image: - return None - - try: - image_format = ( - item.inline_data.mime_type.split("/")[1] - if item.inline_data.mime_type - else "" - ) - image_name = f"content_{content_index}.{image_format}" - binary_data = item.inline_data.data - base64_string = base64.b64encode(binary_data).decode("utf-8") - url = await Config.upload_base64_image( - str(trace_id), str(span_id), image_name, base64_string - ) - return {"type": "image_url", "image_url": {"url": url}} - except Exception as e: - logger.warning(f"Failed to process image part: {e}") - return None - - def run_async(method): try: loop = asyncio.get_running_loop() @@ -281,15 +235,6 @@ def _parts_from_genai_part_sync(part, span, part_index): return out or [_fallback_part(part)] -async def _parts_from_genai_part_async(part, span, part_index): - out = _extract_non_image_parts(part) - if _is_image_part(part): - img = await _otel_image_part_from_genai_part_async(part, span, part_index) - if img: - out.append(img) - return out or [_fallback_part(part)] - - def _map_dict_part(p): """Map a dict-based part to OTel part schema, preserving semantics.""" text = p.get("text") @@ -319,49 +264,6 @@ def _map_dict_part(p): return {"type": "text", "content": str(p)} -async def _process_content_item(content_item, span): - parts_acc = [] - if isinstance(content_item, dict): - for p in content_item.get("parts", []): - if isinstance(p, dict): - parts_acc.append(_map_dict_part(p)) - else: - parts_acc.extend(await _parts_from_genai_part_async(p, span, 0)) - elif hasattr(content_item, "parts"): - for part_index, part in enumerate(content_item.parts): - parts_acc.extend(await _parts_from_genai_part_async(part, span, part_index)) - elif isinstance(content_item, str): - parts_acc.append({"type": "text", "content": content_item}) - elif _is_image_part(content_item): - img = await _otel_image_part_from_genai_part_async(content_item, span, 0) - if img: - parts_acc.append(img) - else: - parts_acc.append({"type": "text", "content": str(content_item)}) - return parts_acc - - -async def _process_argument(argument, span): - if isinstance(argument, str): - return [{"type": "text", "content": argument}] - if isinstance(argument, list): - parts_acc = [] - for sub_index, sub_item in enumerate(argument): - if isinstance(sub_item, str): - parts_acc.append({"type": "text", "content": sub_item}) - elif _is_image_part(sub_item): - img = await _otel_image_part_from_genai_part_async(sub_item, span, sub_index) - if img: - parts_acc.append(img) - else: - parts_acc.append({"type": "text", "content": str(sub_item)}) - return parts_acc - if _is_image_part(argument): - img = await _otel_image_part_from_genai_part_async(argument, span, 0) - return [img] if img else [] - return [{"type": "text", "content": str(argument)}] - - def _process_content_item_sync(content_item, span): parts_acc = [] if isinstance(content_item, dict): @@ -439,57 +341,6 @@ def _output_messages_from_generate_response(span, response): return messages -@dont_throw -async def set_input_attributes(span, args, kwargs, llm_model): - if not span.is_recording(): - return - if not should_send_prompts(): - return - - messages = [] - if "contents" in kwargs: - contents = kwargs["contents"] - if isinstance(contents, str): - messages.append( - { - "role": "user", - "parts": [{"type": "text", "content": contents}], - } - ) - elif isinstance(contents, list): - for content_item in contents: - parts = await _process_content_item(content_item, span) - if parts: - messages.append( - { - "role": _normalize_message_role( - content_item.get("role", "user") if isinstance(content_item, dict) - else getattr(content_item, "role", "user") - ), - "parts": parts, - } - ) - elif args and len(args) > 0: - for argument in args: - parts = await _process_argument(argument, span) - if parts: - messages.append({"role": "user", "parts": parts}) - elif "prompt" in kwargs: - messages.append( - { - "role": "user", - "parts": [{"type": "text", "content": kwargs["prompt"]}], - } - ) - - if messages: - _set_span_attribute( - span, - GenAIAttributes.GEN_AI_INPUT_MESSAGES, - json.dumps(messages), - ) - - @dont_throw def set_input_attributes_sync(span, args, kwargs, llm_model): if not span.is_recording(): From 723b8e6ef893028be7d5c624df3d17661e085b6a Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Mon, 6 Apr 2026 12:57:36 +0300 Subject: [PATCH 18/21] Revert "dead-code" This reverts commit 2d7cd0be617f42baccf659665342552d791f41bc. --- .../google_generativeai/span_utils.py | 149 ++++++++++++++++++ 1 file changed, 149 insertions(+) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index f2eb25401e..f6e4558cab 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -136,6 +136,52 @@ def _otel_image_part_from_genai_part(part, span, part_index, sync): return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} +async def _otel_image_part_from_genai_part_async(part, span, part_index): + if not _is_image_part(part): + return None + mime = ( + part.inline_data.mime_type + if hasattr(part, "inline_data") and part.inline_data + else None + ) or "application/octet-stream" + if Config.upload_base64_image: + uploaded = await _process_image_part( + part, span.context.trace_id, span.context.span_id, part_index + ) + if uploaded and uploaded.get("image_url") and uploaded["image_url"].get("url"): + return { + "type": "uri", + "modality": "image", + "uri": uploaded["image_url"]["url"], + } + binary_data = part.inline_data.data + b64 = base64.b64encode(binary_data).decode("utf-8") + return {"type": "blob", "modality": "image", "mime_type": mime, "content": b64} + + +async def _process_image_part(item, trace_id, span_id, content_index): + """Upload image when configured; used only from async content processing.""" + if not Config.upload_base64_image: + return None + + try: + image_format = ( + item.inline_data.mime_type.split("/")[1] + if item.inline_data.mime_type + else "" + ) + image_name = f"content_{content_index}.{image_format}" + binary_data = item.inline_data.data + base64_string = base64.b64encode(binary_data).decode("utf-8") + url = await Config.upload_base64_image( + str(trace_id), str(span_id), image_name, base64_string + ) + return {"type": "image_url", "image_url": {"url": url}} + except Exception as e: + logger.warning(f"Failed to process image part: {e}") + return None + + def run_async(method): try: loop = asyncio.get_running_loop() @@ -235,6 +281,15 @@ def _parts_from_genai_part_sync(part, span, part_index): return out or [_fallback_part(part)] +async def _parts_from_genai_part_async(part, span, part_index): + out = _extract_non_image_parts(part) + if _is_image_part(part): + img = await _otel_image_part_from_genai_part_async(part, span, part_index) + if img: + out.append(img) + return out or [_fallback_part(part)] + + def _map_dict_part(p): """Map a dict-based part to OTel part schema, preserving semantics.""" text = p.get("text") @@ -264,6 +319,49 @@ def _map_dict_part(p): return {"type": "text", "content": str(p)} +async def _process_content_item(content_item, span): + parts_acc = [] + if isinstance(content_item, dict): + for p in content_item.get("parts", []): + if isinstance(p, dict): + parts_acc.append(_map_dict_part(p)) + else: + parts_acc.extend(await _parts_from_genai_part_async(p, span, 0)) + elif hasattr(content_item, "parts"): + for part_index, part in enumerate(content_item.parts): + parts_acc.extend(await _parts_from_genai_part_async(part, span, part_index)) + elif isinstance(content_item, str): + parts_acc.append({"type": "text", "content": content_item}) + elif _is_image_part(content_item): + img = await _otel_image_part_from_genai_part_async(content_item, span, 0) + if img: + parts_acc.append(img) + else: + parts_acc.append({"type": "text", "content": str(content_item)}) + return parts_acc + + +async def _process_argument(argument, span): + if isinstance(argument, str): + return [{"type": "text", "content": argument}] + if isinstance(argument, list): + parts_acc = [] + for sub_index, sub_item in enumerate(argument): + if isinstance(sub_item, str): + parts_acc.append({"type": "text", "content": sub_item}) + elif _is_image_part(sub_item): + img = await _otel_image_part_from_genai_part_async(sub_item, span, sub_index) + if img: + parts_acc.append(img) + else: + parts_acc.append({"type": "text", "content": str(sub_item)}) + return parts_acc + if _is_image_part(argument): + img = await _otel_image_part_from_genai_part_async(argument, span, 0) + return [img] if img else [] + return [{"type": "text", "content": str(argument)}] + + def _process_content_item_sync(content_item, span): parts_acc = [] if isinstance(content_item, dict): @@ -341,6 +439,57 @@ def _output_messages_from_generate_response(span, response): return messages +@dont_throw +async def set_input_attributes(span, args, kwargs, llm_model): + if not span.is_recording(): + return + if not should_send_prompts(): + return + + messages = [] + if "contents" in kwargs: + contents = kwargs["contents"] + if isinstance(contents, str): + messages.append( + { + "role": "user", + "parts": [{"type": "text", "content": contents}], + } + ) + elif isinstance(contents, list): + for content_item in contents: + parts = await _process_content_item(content_item, span) + if parts: + messages.append( + { + "role": _normalize_message_role( + content_item.get("role", "user") if isinstance(content_item, dict) + else getattr(content_item, "role", "user") + ), + "parts": parts, + } + ) + elif args and len(args) > 0: + for argument in args: + parts = await _process_argument(argument, span) + if parts: + messages.append({"role": "user", "parts": parts}) + elif "prompt" in kwargs: + messages.append( + { + "role": "user", + "parts": [{"type": "text", "content": kwargs["prompt"]}], + } + ) + + if messages: + _set_span_attribute( + span, + GenAIAttributes.GEN_AI_INPUT_MESSAGES, + json.dumps(messages), + ) + + @dont_throw def set_input_attributes_sync(span, args, kwargs, llm_model): if not span.is_recording(): From e8095938c96331aa8b92424ee0ad1f60bcd56cfe Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Mon, 6 Apr 2026 13:08:44 +0300 Subject: [PATCH 19/21] fix-async --- .../google_generativeai/__init__.py | 13 +- .../google_generativeai/utils.py | 21 +- .../tests/test_async_input.py | 277 ++++++++++++++++++ 3 files changed, 309 insertions(+), 2 deletions(-) create mode 100644 packages/opentelemetry-instrumentation-google-generativeai/tests/test_async_input.py diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py index a6876099f5..edd29b1d4b 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/__init__.py @@ -16,6 +16,7 @@ ) from opentelemetry.instrumentation.google_generativeai.span_utils import ( _collect_finish_reasons_from_response, + set_input_attributes, set_input_attributes_sync, set_model_request_attributes, set_model_response_attributes, @@ -170,6 +171,16 @@ def _handle_request(span, args, kwargs, llm_model, event_logger): set_model_request_attributes(span, kwargs, llm_model) +@dont_throw +async def _handle_request_async(span, args, kwargs, llm_model, event_logger): + if should_emit_events() and event_logger: + emit_message_events(args, kwargs, event_logger) + else: + await set_input_attributes(span, args, kwargs, llm_model) + + set_model_request_attributes(span, kwargs, llm_model) + + @dont_throw def _handle_response(span, response, llm_model, event_logger, token_histogram): if should_emit_events() and event_logger: @@ -244,7 +255,7 @@ async def _awrap( }, ) start_time = time.perf_counter() - _handle_request(span, args, kwargs, llm_model, event_logger) + await _handle_request_async(span, args, kwargs, llm_model, event_logger) try: response = await wrapped(*args, **kwargs) except Exception as e: diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/utils.py index 86d0e20e43..d76c620102 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/utils.py @@ -1,3 +1,5 @@ +import asyncio +import functools import importlib import importlib.util import logging @@ -13,13 +15,30 @@ def dont_throw(func): """ A decorator that wraps the passed in function and logs exceptions instead of throwing them. + Supports both sync and async functions. @param func: The function to wrap @return: The wrapper function """ - # Obtain a logger specific to the function's module logger = logging.getLogger(func.__module__) + if asyncio.iscoroutinefunction(func): + @functools.wraps(func) + async def async_wrapper(*args, **kwargs): + try: + return await func(*args, **kwargs) + except Exception as e: + logger.debug( + "OpenLLMetry failed to trace in %s, error: %s", + func.__name__, + traceback.format_exc(), + ) + if Config.exception_logger: + Config.exception_logger(e) + + return async_wrapper + + @functools.wraps(func) def wrapper(*args, **kwargs): try: return func(*args, **kwargs) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_async_input.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_async_input.py new file mode 100644 index 0000000000..9d83f72a57 --- /dev/null +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_async_input.py @@ -0,0 +1,277 @@ +"""Tests for the async input-processing path (set_input_attributes, _handle_request_async).""" + +import asyncio +import json +from unittest.mock import MagicMock, AsyncMock + +import pytest +from opentelemetry.instrumentation.google_generativeai import span_utils as su +from opentelemetry.instrumentation.google_generativeai.utils import dont_throw +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) + + +def _recorded_input_messages(span_mock): + for call in span_mock.set_attribute.call_args_list: + args = call[0] + if args[0] == GenAIAttributes.GEN_AI_INPUT_MESSAGES: + return json.loads(args[1]) + return None + + +# =========================================================================== +# 1. dont_throw — async support +# =========================================================================== + + +class TestDontThrowAsync: + def test_async_function_is_wrapped_as_coroutine(self): + @dont_throw + async def my_async(): + return 42 + + assert asyncio.iscoroutinefunction(my_async) + + def test_async_wrapper_returns_value(self): + @dont_throw + async def my_async(): + return 42 + + result = asyncio.get_event_loop().run_until_complete(my_async()) + assert result == 42 + + def test_async_wrapper_swallows_exception(self): + @dont_throw + async def boom(): + raise ValueError("oops") + + result = asyncio.get_event_loop().run_until_complete(boom()) + assert result is None + + def test_sync_function_stays_sync(self): + @dont_throw + def my_sync(): + return 7 + + assert not asyncio.iscoroutinefunction(my_sync) + assert my_sync() == 7 + + +# =========================================================================== +# 2. set_input_attributes (async) — basic text content +# =========================================================================== + + +class TestSetInputAttributesAsync: + def _make_span(self): + span = MagicMock() + span.is_recording.return_value = True + return span + + @pytest.mark.asyncio + async def test_string_contents(self, monkeypatch): + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + span = self._make_span() + + await su.set_input_attributes(span, (), {"contents": "Hello"}, "gemini-pro") + + messages = _recorded_input_messages(span) + assert messages is not None + assert len(messages) == 1 + assert messages[0]["role"] == "user" + assert messages[0]["parts"][0] == {"type": "text", "content": "Hello"} + + @pytest.mark.asyncio + async def test_list_of_strings(self, monkeypatch): + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + span = self._make_span() + + await su.set_input_attributes( + span, (), {"contents": ["first", "second"]}, "gemini-pro" + ) + + messages = _recorded_input_messages(span) + assert messages is not None + assert len(messages) == 2 + assert messages[0]["parts"][0]["content"] == "first" + assert messages[1]["parts"][0]["content"] == "second" + + @pytest.mark.asyncio + async def test_prompt_kwarg(self, monkeypatch): + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + span = self._make_span() + + await su.set_input_attributes(span, (), {"prompt": "Summarize"}, "gemini-pro") + + messages = _recorded_input_messages(span) + assert messages is not None + assert len(messages) == 1 + assert messages[0]["parts"][0]["content"] == "Summarize" + + @pytest.mark.asyncio + async def test_positional_args(self, monkeypatch): + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + span = self._make_span() + + await su.set_input_attributes(span, ("Tell me a joke",), {}, "gemini-pro") + + messages = _recorded_input_messages(span) + assert messages is not None + assert len(messages) == 1 + assert messages[0]["role"] == "user" + + @pytest.mark.asyncio + async def test_skipped_when_prompts_disabled(self, monkeypatch): + monkeypatch.setattr(su, "should_send_prompts", lambda: False) + span = self._make_span() + + await su.set_input_attributes(span, (), {"contents": "Hello"}, "gemini-pro") + + messages = _recorded_input_messages(span) + assert messages is None + + @pytest.mark.asyncio + async def test_content_object_with_role(self, monkeypatch): + """Content objects with .role and .parts should be processed.""" + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + span = self._make_span() + + part = MagicMock() + part.text = "Hello from model" + part.thought = None + part.function_call = None + part.function_response = None + part.inline_data = None + part.executable_code = None + part.code_execution_result = None + + content = MagicMock() + content.role = "model" + content.parts = [part] + + await su.set_input_attributes( + span, (), {"contents": [content]}, "gemini-pro" + ) + + messages = _recorded_input_messages(span) + assert messages is not None + assert len(messages) == 1 + assert messages[0]["role"] == "assistant" + assert messages[0]["parts"][0]["content"] == "Hello from model" + + +# =========================================================================== +# 3. Async produces identical format to sync +# =========================================================================== + + +class TestAsyncSyncParity: + @pytest.mark.asyncio + async def test_string_contents_match(self, monkeypatch): + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + + span_sync = MagicMock() + span_sync.is_recording.return_value = True + su.set_input_attributes_sync( + span_sync, (), {"contents": "Hello world"}, "gemini-pro" + ) + + span_async = MagicMock() + span_async.is_recording.return_value = True + await su.set_input_attributes( + span_async, (), {"contents": "Hello world"}, "gemini-pro" + ) + + sync_messages = _recorded_input_messages(span_sync) + async_messages = _recorded_input_messages(span_async) + assert sync_messages == async_messages + + @pytest.mark.asyncio + async def test_list_contents_match(self, monkeypatch): + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + + span_sync = MagicMock() + span_sync.is_recording.return_value = True + su.set_input_attributes_sync( + span_sync, (), {"contents": ["a", "b"]}, "gemini-pro" + ) + + span_async = MagicMock() + span_async.is_recording.return_value = True + await su.set_input_attributes( + span_async, (), {"contents": ["a", "b"]}, "gemini-pro" + ) + + sync_messages = _recorded_input_messages(span_sync) + async_messages = _recorded_input_messages(span_async) + assert sync_messages == async_messages + + @pytest.mark.asyncio + async def test_prompt_kwarg_match(self, monkeypatch): + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + + span_sync = MagicMock() + span_sync.is_recording.return_value = True + su.set_input_attributes_sync( + span_sync, (), {"prompt": "Explain quantum physics"}, "gemini-pro" + ) + + span_async = MagicMock() + span_async.is_recording.return_value = True + await su.set_input_attributes( + span_async, (), {"prompt": "Explain quantum physics"}, "gemini-pro" + ) + + sync_messages = _recorded_input_messages(span_sync) + async_messages = _recorded_input_messages(span_async) + assert sync_messages == async_messages + + +# =========================================================================== +# 4. _handle_request_async wiring +# =========================================================================== + + +class TestHandleRequestAsync: + @pytest.mark.asyncio + async def test_calls_set_input_attributes_async(self, monkeypatch): + from opentelemetry.instrumentation.google_generativeai import ( + _handle_request_async, + ) + + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + + span = MagicMock() + span.is_recording.return_value = True + + await _handle_request_async( + span, (), {"contents": "test"}, "gemini-pro", None + ) + + messages = _recorded_input_messages(span) + assert messages is not None + assert messages[0]["parts"][0]["content"] == "test" + + @pytest.mark.asyncio + async def test_sets_model_request_attributes(self, monkeypatch): + from opentelemetry.instrumentation.google_generativeai import ( + _handle_request_async, + ) + + monkeypatch.setattr(su, "should_send_prompts", lambda: True) + + span = MagicMock() + span.is_recording.return_value = True + + await _handle_request_async( + span, (), {"contents": "x", "temperature": 0.5}, "gemini-pro", None + ) + + temp_calls = [ + c[0] + for c in span.set_attribute.call_args_list + if c[0][0] == GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE + ] + assert len(temp_calls) == 1 + assert temp_calls[0][1] == 0.5 From 4b04b86ba6b196158f2f13beeec8b75936d4cf8b Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Mon, 6 Apr 2026 14:24:57 +0300 Subject: [PATCH 20/21] wip --- .../google_generativeai/span_utils.py | 5 +- .../pyproject.toml | 1 + .../tests/test_finish_reasons.py | 49 ++++++++++++++++--- .../uv.lock | 25 ++++++++++ 4 files changed, 70 insertions(+), 10 deletions(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py index f6e4558cab..f23991ded2 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/span_utils.py @@ -410,8 +410,7 @@ def _collect_finish_reasons_from_response(response): candidates = getattr(response, "candidates", None) or [] for cand in candidates: mapped = _map_gemini_finish_reason(getattr(cand, "finish_reason", None)) - if mapped: - reasons.append(mapped) + reasons.append(mapped) return reasons @@ -720,7 +719,7 @@ def set_model_response_attributes( reasons = stream_finish_reasons else: reasons = _collect_finish_reasons_from_response(response) - if reasons: + if reasons and any(reason != "" for reason in reasons): _set_span_attribute( span, GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS, reasons ) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/pyproject.toml b/packages/opentelemetry-instrumentation-google-generativeai/pyproject.toml index aa9ecc249b..e3bf38759f 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/pyproject.toml +++ b/packages/opentelemetry-instrumentation-google-generativeai/pyproject.toml @@ -35,6 +35,7 @@ dev = [ test = [ "google-genai>=1.0.0,<2", "opentelemetry-sdk>=1.38.0,<2", + "pytest-asyncio>=1.2.0,<2", "pytest-recording>=0.13.1,<0.14.0", "pytest-sugar==1.0.0", "pytest>=8.2.2,<9", diff --git a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py index 01e751dac8..cbacc37a28 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py @@ -120,10 +120,10 @@ def test_unmapped_enum_returns_unknown(self): # =========================================================================== class TestCollectFinishReasonsFromResponse: - def test_all_none_returns_empty(self): - """When every candidate has finish_reason=None the result is empty (unknown filtered out).""" + def test_all_none_returns_unknown_entries(self): + """Keep 1:1 candidate alignment even when finish reasons are unavailable.""" resp = _make_response(candidates=[_make_candidate(None), _make_candidate(None)]) - assert su._collect_finish_reasons_from_response(resp) == [] + assert su._collect_finish_reasons_from_response(resp) == ["", ""] def test_meaningful_values_included(self): resp = _make_response(candidates=[ @@ -133,13 +133,13 @@ def test_meaningful_values_included(self): assert su._collect_finish_reasons_from_response(resp) == ["stop", "length"] def test_mixed_candidates(self): - """Only real (non-unknown) reasons are returned.""" + """Preserve candidate order and include unknown placeholders.""" resp = _make_response(candidates=[ _make_candidate(None), _make_candidate("STOP"), _make_candidate(None), ]) - assert su._collect_finish_reasons_from_response(resp) == ["stop"] + assert su._collect_finish_reasons_from_response(resp) == ["", "stop", ""] def test_no_candidates_attribute(self): resp = MagicMock(spec=[]) # no attributes at all @@ -168,10 +168,10 @@ def test_mixed_reasons_preserve_order(self): reasons = su._collect_finish_reasons_from_response(last_chunk) assert reasons == ["length", "stop"] - def test_none_candidates_returns_empty(self): + def test_none_candidates_returns_unknown(self): last_chunk = _make_response(candidates=[_make_candidate(None)]) reasons = su._collect_finish_reasons_from_response(last_chunk) - assert reasons == [] + assert reasons == [""] # =========================================================================== @@ -280,6 +280,41 @@ def test_list_of_strings_each_has_finish_reason(self, monkeypatch): assert msg["role"] == "assistant" +# =========================================================================== +# 5b. set_model_response_attributes finish reason emission +# =========================================================================== + +class TestSetModelResponseAttributes: + def test_omit_top_level_finish_reasons_when_all_unknown(self): + span = MagicMock() + span.is_recording.return_value = True + + response = _make_response(candidates=[_make_candidate(None), _make_candidate(None)]) + su.set_model_response_attributes(span, response, "gemini-pro", token_histogram=None) + + finish_reason_calls = [ + c[0] + for c in span.set_attribute.call_args_list + if c[0][0] == GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS + ] + assert finish_reason_calls == [] + + def test_set_top_level_finish_reasons_with_alignment(self): + span = MagicMock() + span.is_recording.return_value = True + + response = _make_response(candidates=[_make_candidate(None), _make_candidate("STOP")]) + su.set_model_response_attributes(span, response, "gemini-pro", token_histogram=None) + + finish_reason_calls = [ + c[0] + for c in span.set_attribute.call_args_list + if c[0][0] == GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS + ] + assert len(finish_reason_calls) == 1 + assert finish_reason_calls[0][1] == ["", "stop"] + + # =========================================================================== # 6. _parts_from_genai_part_sync — thinking/reasoning # =========================================================================== diff --git a/packages/opentelemetry-instrumentation-google-generativeai/uv.lock b/packages/opentelemetry-instrumentation-google-generativeai/uv.lock index 0629117d68..5f4c38f396 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/uv.lock +++ b/packages/opentelemetry-instrumentation-google-generativeai/uv.lock @@ -44,6 +44,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/9e/43/53afb8ba17218f19b77c7834128566c5bbb100a0ad9ba2e8e89d089d7079/autopep8-2.3.2-py2.py3-none-any.whl", hash = "sha256:ce8ad498672c845a0c3de2629c15b635ec2b05ef8177a6e7c91c74f3e9b51128", size = 45807, upload-time = "2025-01-14T14:46:15.466Z" }, ] +[[package]] +name = "backports-asyncio-runner" +version = "1.2.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/8e/ff/70dca7d7cb1cbc0edb2c6cc0c38b65cba36cccc491eca64cabd5fe7f8670/backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162", size = 69893, upload-time = "2025-07-02T02:27:15.685Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a0/59/76ab57e3fe74484f48a53f8e337171b4a2349e506eabe136d7e01d059086/backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5", size = 12313, upload-time = "2025-07-02T02:27:14.263Z" }, +] + [[package]] name = "certifi" version = "2026.2.25" @@ -491,6 +500,7 @@ test = [ { name = "google-genai" }, { name = "opentelemetry-sdk" }, { name = "pytest" }, + { name = "pytest-asyncio" }, { name = "pytest-recording" }, { name = "pytest-sugar" }, { name = "vcrpy" }, @@ -517,6 +527,7 @@ test = [ { name = "google-genai", specifier = ">=1.0.0,<2" }, { name = "opentelemetry-sdk", specifier = ">=1.38.0,<2" }, { name = "pytest", specifier = ">=8.2.2,<9" }, + { name = "pytest-asyncio", specifier = ">=1.2.0,<2" }, { name = "pytest-recording", specifier = ">=0.13.1,<0.14.0" }, { name = "pytest-sugar", specifier = "==1.0.0" }, { name = "vcrpy", specifier = ">=8.0.0,<9" }, @@ -779,6 +790,20 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/a8/a4/20da314d277121d6534b3a980b29035dcd51e6744bd79075a6ce8fa4eb8d/pytest-8.4.2-py3-none-any.whl", hash = "sha256:872f880de3fc3a5bdc88a11b39c9710c3497a547cfa9320bc3c5e62fbf272e79", size = 365750, upload-time = "2025-09-04T14:34:20.226Z" }, ] +[[package]] +name = "pytest-asyncio" +version = "1.3.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "backports-asyncio-runner", marker = "python_full_version < '3.11'" }, + { name = "pytest" }, + { name = "typing-extensions", marker = "python_full_version < '3.13'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/90/2c/8af215c0f776415f3590cac4f9086ccefd6fd463befeae41cd4d3f193e5a/pytest_asyncio-1.3.0.tar.gz", hash = "sha256:d7f52f36d231b80ee124cd216ffb19369aa168fc10095013c6b014a34d3ee9e5", size = 50087, upload-time = "2025-11-10T16:07:47.256Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e5/35/f8b19922b6a25bc0880171a2f1a003eaeb93657475193ab516fd87cac9da/pytest_asyncio-1.3.0-py3-none-any.whl", hash = "sha256:611e26147c7f77640e6d0a92a38ed17c3e9848063698d5c93d5aa7aa11cebff5", size = 15075, upload-time = "2025-11-10T16:07:45.537Z" }, +] + [[package]] name = "pytest-recording" version = "0.13.4" From 5106246f2a1e7c2c688e6b842878ca93b29db4a9 Mon Sep 17 00:00:00 2001 From: Oz Ben Simhon Date: Thu, 9 Apr 2026 10:35:36 +0300 Subject: [PATCH 21/21] lint --- .../tests/test_async_input.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_async_input.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_async_input.py index 9d83f72a57..3833be9581 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_async_input.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_async_input.py @@ -2,7 +2,7 @@ import asyncio import json -from unittest.mock import MagicMock, AsyncMock +from unittest.mock import MagicMock import pytest from opentelemetry.instrumentation.google_generativeai import span_utils as su