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..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 @@ -15,6 +15,8 @@ emit_message_events, ) 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, @@ -32,14 +34,15 @@ ) from opentelemetry.semconv_ai import ( SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY, - LLMRequestTypeValues, - SpanAttributes, - 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 = [ @@ -47,25 +50,21 @@ "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", }, ] @@ -85,21 +84,40 @@ def _build_from_streaming_response( event_logger, token_histogram, ): - complete_response = "" + emit_events = should_emit_events() and event_logger + text_parts = [] last_chunk = None 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) 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) + 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 + span, + last_chunk or response, + llm_model, + token_histogram, + stream_finish_reasons=stream_reasons or None, ) span.end() @@ -107,21 +125,38 @@ 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 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) 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) + 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 + span, + last_chunk if last_chunk else response, + llm_model, + token_histogram, + stream_finish_reasons=stream_reasons or None, ) span.end() @@ -136,6 +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: @@ -200,17 +245,17 @@ 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_SYSTEM: "Google", - SpanAttributes.LLM_REQUEST_TYPE: LLMRequestTypeValues.COMPLETION.value, + 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() - _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: @@ -224,7 +269,9 @@ 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_OPERATION_NAME: _GEN_CONTENT, + GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, GenAIAttributes.GEN_AI_RESPONSE_MODEL: llm_model, }, ) @@ -276,13 +323,13 @@ 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_SYSTEM: "Google", - SpanAttributes.LLM_REQUEST_TYPE: LLMRequestTypeValues.COMPLETION.value, + GenAIAttributes.GEN_AI_PROVIDER_NAME: _GCP_GEN_AI, + GenAIAttributes.GEN_AI_OPERATION_NAME: _GEN_CONTENT, + GenAIAttributes.GEN_AI_REQUEST_MODEL: llm_model, }, ) @@ -301,7 +348,9 @@ def _wrap( duration_histogram.record( duration, attributes={ - GenAIAttributes.GEN_AI_PROVIDER_NAME: "Google", + 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/event_emitter.py b/packages/opentelemetry-instrumentation-google-generativeai/opentelemetry/instrumentation/google_generativeai/event_emitter.py index 554c672929..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 +16,11 @@ 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 class Roles(Enum): @@ -28,7 +33,7 @@ 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.""" @@ -65,7 +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 0c92bc6840..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 @@ -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, @@ -10,12 +11,13 @@ 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 +_GEN_CONTENT = GenAIAttributes.GenAiOperationNameValues.GENERATE_CONTENT.value logger = logging.getLogger(__name__) @@ -27,53 +29,160 @@ def _set_span_attribute(span, name, value): return +def _map_gemini_finish_reason(finish_reason): + """Map Gemini 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_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 "" + ) 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: @@ -88,295 +197,352 @@ 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 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 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 "" + ) 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 _extract_non_image_parts(part): + """Extract all non-image OTel parts from a GenAI Part object (shared by sync/async).""" + out = [] + 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}) + 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)), + } + ) + 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 = _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") + if text is not None: + return {"type": "text", "content": text} + fc = p.get("functionCall") + if fc is not 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: + 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)} + - if hasattr(content_item, "parts"): - # Content with parts (Google GenAI Content object) +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): - 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) - else: - # Other content types - processed_content.append({"type": "text", "text": str(content_item)}) - - return processed_content - - -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 - ) + img = await _otel_image_part_from_genai_part_async(content_item, span, 0) + if img: + parts_acc.append(img) else: - # Other part types - 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"), - ) + 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 = [] - 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 = await _otel_image_part_from_genai_part_async(sub_item, span, sub_index) + 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) + 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): + for p in content_item.get("parts", []): + if isinstance(p, dict): + parts_acc.append(_map_dict_part(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): + 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: - processed_content.append({"type": "text", "text": str(argument)}) + parts_acc.append({"type": "text", "content": str(content_item)}) + return parts_acc + - return processed_content +def _process_argument_sync(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 = _otel_image_part_from_genai_part(sub_item, span, sub_index, sync=True) + 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 = _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)) + reasons.append(mapped) + return reasons + + + +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, "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}], "finish_reason": ""} + 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): - # 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", + "parts": [{"type": "text", "content": contents}], + } ) elif isinstance(contents, list): - for prompt_index, content_item in enumerate(contents): - processed_content = await _process_content_item(content_item, span) - - if processed_content: - _set_prompt_attributes(span, prompt_index, processed_content, content_item) - + 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: - # Handle args - process each argument - for arg_index, argument in enumerate(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", - ) + 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, f"{SpanAttributes.LLM_PROMPTS}.0.content", - json.dumps([{"type": "text", "text": kwargs["prompt"]}]) + span, + GenAIAttributes.GEN_AI_INPUT_MESSAGES, + json.dumps(messages), ) - _set_span_attribute(span, f"{SpanAttributes.LLM_PROMPTS}.0.role", "user") -# 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): - # 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", + "parts": [{"type": "text", "content": contents}], + } ) elif isinstance(contents, list): - # Process content list - could be mixed text and Part objects - for i, content in enumerate(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}) - 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: - # 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"), + for content in contents: + parts = _process_content_item_sync(content, span) + if parts: + messages.append( + { + "role": _normalize_message_role( + content.get("role", "user") if isinstance(content, dict) + else getattr(content, "role", "user") + ), + "parts": parts, + } ) elif args and len(args) > 0: - # Handle args - process each argument - for i, arg in enumerate(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: - _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", - ) + for arg in args: + parts = _process_argument_sync(arg, 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, f"{GenAIAttributes.GEN_AI_PROMPT}.0.content", - json.dumps([{"type": "text", "text": kwargs["prompt"]}]) + span, + GenAIAttributes.GEN_AI_INPUT_MESSAGES, + json.dumps(messages), ) - _set_span_attribute(span, f"{GenAIAttributes.GEN_AI_PROMPT}.0.role", "user") 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") ) @@ -386,10 +552,14 @@ 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, 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 +567,7 @@ 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: @@ -407,84 +577,191 @@ 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: 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 + + 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``.""" + 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)}] + + +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): +def set_response_attributes(span, response, llm_model, stream_last_chunk=None): + if not span.is_recording(): + return if not should_send_prompts(): return - 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") - 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" - ) + + if isinstance(response, str): + fr = "" + if stream_last_chunk is not None: + reasons = _collect_finish_reasons_from_response(stream_last_chunk) + 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 not fr: + return + _set_span_attribute( + span, + GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, + json.dumps([msg]), + ) + return + + if isinstance(response, list): + messages = [] + for item in response: + if isinstance(item, str): + messages.append( + { + "role": "assistant", + "parts": [{"type": "text", "content": item}], + "finish_reason": "", + } + ) + else: + messages.extend(_output_messages_from_generate_response(span, item)) 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") - 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 = _output_messages_from_generate_response(span, response) + + if messages: + _set_span_attribute( + span, + GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, + json.dumps(messages), + ) -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 _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) + + if stream_finish_reasons is not None: + reasons = stream_finish_reasons + else: + reasons = _collect_finish_reasons_from_response(response) + if reasons and any(reason != "" for reason in 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.LLM_USAGE_TOTAL_TOKENS, - response.usage_metadata.total_token_count, + SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS, + 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: "Google", + 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, - } + }, ) 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, - }, - ) + 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, + }, + ) span.set_status(Status(StatusCode.OK)) 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/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_async_input.py b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_async_input.py new file mode 100644 index 0000000000..3833be9581 --- /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 + +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 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..cbacc37a28 --- /dev/null +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_finish_reasons.py @@ -0,0 +1,365 @@ +"""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) == "" + + 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")) == "" + + 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")) == "" + + +# =========================================================================== +# 2. _collect_finish_reasons_from_response +# =========================================================================== + +class TestCollectFinishReasonsFromResponse: + 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) == ["", ""] + + 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): + """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", ""] + + def test_no_candidates_attribute(self): + resp = MagicMock(spec=[]) # no attributes at all + assert su._collect_finish_reasons_from_response(resp) == [] + + +# =========================================================================== +# 3. Streaming finish reasons (via _collect_finish_reasons_from_response on last chunk) +# =========================================================================== + +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_unknown(self): + last_chunk = _make_response(candidates=[_make_candidate(None)]) + reasons = su._collect_finish_reasons_from_response(last_chunk) + assert reasons == [""] + + +# =========================================================================== +# 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"] == "" + + 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"] == "" + 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"] == "" + + 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"] == "" + 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 +# =========================================================================== + +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 f78e7fd2d4..03943cfde2 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py +++ b/packages/opentelemetry-instrumentation-google-generativeai/tests/test_generate_content.py @@ -1,3 +1,5 @@ +import json +from unittest.mock import MagicMock import pytest from opentelemetry.trace import StatusCode, SpanKind @@ -13,7 +15,9 @@ 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_SYSTEM) == "gemini" + 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 @@ -30,34 +34,58 @@ 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 attrs = span.attributes - assert attrs[GenAIAttributes.GEN_AI_SYSTEM] == "Google" - assert attrs[SpanAttributes.LLM_REQUEST_TYPE] == "completion" + 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" - assert "gen_ai.prompt.0.content" in attrs - assert attrs["gen_ai.prompt.0.role"] == "user" - - assert "gen_ai.completion.0.content" in attrs - assert attrs["gen_ai.completion.0.role"] == "assistant" - - assert attrs[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] > 0 + # 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 "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 (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 assert attrs[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] > 0 @pytest.mark.vcr -def test_generate_metrics(metrics_test_context, genai_client): +def test_generate_metrics(metrics_test_context, genai_client, exporter): + """`exporter` fixture instruments with the session MeterProvider so metrics are recorded.""" _, reader = metrics_test_context # ---- Trigger a generic GenAI request ---- @@ -72,13 +100,19 @@ def test_generate_metrics(metrics_test_context, genai_client): rm = resource_metrics[0] assert rm.scope_metrics, "No ScopeMetrics found" - scope_metrics = rm.scope_metrics[0] + # Aggregate across scopes: SDK internal metrics share the reader with app instrumentation. + metrics = {} + instrument_scope = None + for sm in rm.scope_metrics: + for m in sm.metrics: + metrics[m.name] = m + if any( + name in (Meters.LLM_OPERATION_DURATION, Meters.LLM_TOKEN_USAGE) + for name in (x.name for x in sm.metrics) + ): + instrument_scope = sm.scope - # ---- Instrumentation scope (generic check) ---- - scope = scope_metrics.scope - assert scope.name, "Instrumentation scope name is missing" - - metrics = {m.name: m for m in scope_metrics.metrics} + assert instrument_scope and instrument_scope.name, "GenAI metrics scope not found" # ---- Required metrics (semantic conventions) ---- required_metrics = { @@ -92,7 +126,9 @@ def test_generate_metrics(metrics_test_context, genai_client): assert duration_metric.unit is not None assert duration_metric.data.data_points - duration_dp = duration_metric.data.data_points[0] + duration_dp = next( + dp for dp in duration_metric.data.data_points if dp.count >= 1 + ) # Minimal semantic validation assert duration_dp.count >= 1 @@ -100,6 +136,8 @@ def test_generate_metrics(metrics_test_context, genai_client): # 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] @@ -121,4 +159,30 @@ def test_generate_metrics(metrics_test_context, genai_client): # 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 + + +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-google-generativeai/uv.lock b/packages/opentelemetry-instrumentation-google-generativeai/uv.lock index 32fd2fa36c..5f4c38f396 100644 --- a/packages/opentelemetry-instrumentation-google-generativeai/uv.lock +++ b/packages/opentelemetry-instrumentation-google-generativeai/uv.lock @@ -19,16 +19,16 @@ wheels = [ [[package]] name = "anyio" -version = "4.12.1" +version = "4.13.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "exceptiongroup", marker = "python_full_version 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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, )