diff --git a/.cz.toml b/.cz.toml index e3ec4f15a8..2de0139704 100644 --- a/.cz.toml +++ b/.cz.toml @@ -40,6 +40,8 @@ version_files = [ "packages/opentelemetry-instrumentation-mistralai/opentelemetry/instrumentation/mistralai/version.py", "packages/opentelemetry-instrumentation-ollama/pyproject.toml:^version", "packages/opentelemetry-instrumentation-ollama/opentelemetry/instrumentation/ollama/version.py", + "packages/opentelemetry-instrumentation-oci-genai/pyproject.toml:^version", + "packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/version.py", "packages/opentelemetry-instrumentation-openai/pyproject.toml:^version", "packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/version.py", "packages/opentelemetry-instrumentation-openai-agents/pyproject.toml:^version", diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 07b0b9bb49..8ef639da01 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -122,6 +122,16 @@ updates: - "*" labels: - "dependencies" + - package-ecosystem: "uv" + directory: "/packages/opentelemetry-instrumentation-oci-genai" + schedule: + interval: "weekly" + groups: + gha: + patterns: + - "*" + labels: + - "dependencies" - package-ecosystem: "uv" directory: "/packages/opentelemetry-instrumentation-ollama" schedule: diff --git a/README.md b/README.md index 65ac1c4c42..525f236388 100644 --- a/README.md +++ b/README.md @@ -126,6 +126,7 @@ OpenLLMetry can instrument everything that [OpenTelemetry already instruments](h - ✅ [HuggingFace](https://huggingface.co/) - ✅ [IBM Watsonx AI](https://www.ibm.com/watsonx) - ✅ [Mistral AI](https://mistral.ai/) +- ✅ [OCI Generative AI (Oracle Cloud)](https://www.oracle.com/artificial-intelligence/generative-ai/) - ✅ [Ollama](https://ollama.com/) - ✅ [OpenAI / Azure OpenAI](https://openai.com/) - ✅ [Replicate](https://replicate.com/) diff --git a/packages/opentelemetry-instrumentation-oci-genai/.python-version b/packages/opentelemetry-instrumentation-oci-genai/.python-version new file mode 100644 index 0000000000..c8cfe39591 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/.python-version @@ -0,0 +1 @@ +3.10 diff --git a/packages/opentelemetry-instrumentation-oci-genai/README.md b/packages/opentelemetry-instrumentation-oci-genai/README.md new file mode 100644 index 0000000000..0f9c2afdb0 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/README.md @@ -0,0 +1,52 @@ +# OpenTelemetry OCI Generative AI Instrumentation + + + PyPI package version + + +This library allows tracing calls to the [OCI Generative AI](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm) inference service made with the official [OCI Python SDK](https://github.com/oracle/oci-python-sdk) (`oci.generative_ai_inference.GenerativeAiInferenceClient`). + +Instrumented operations: + +| SDK method | `gen_ai.operation.name` | Notes | +| --------------- | ----------------------- | ------------------------------------------------------------------------ | +| `chat` | `chat` | `GENERIC`, `COHERE` and `COHEREV2` request formats, streaming and non-streaming | +| `generate_text` | `text_completion` | Legacy Cohere / Llama runtimes | +| `embed_text` | `embeddings` | Emits `gen_ai.embeddings.dimension.count` | +| `rerank_text` | `rerank` | Emits `gen_ai.oci.rerank.top_n` and document count | + +Spans follow the OpenTelemetry GenAI semantic conventions (`{operation} {model}` span names, `gen_ai.provider.name`, +`gen_ai.request.*`, `gen_ai.response.*`, `gen_ai.usage.*`, `gen_ai.input.messages` / `gen_ai.output.messages`). +`gen_ai.provider.name` is set to `oracle_cloud.generative_ai`. Both on-demand (`OnDemandServingMode.model_id`) and +dedicated (`DedicatedServingMode.endpoint_id`) serving modes are supported; the serving mode is reported on +`gen_ai.oci.serving_mode`. + +For streaming chat requests (`is_stream=True`) the span is completed once the SSE stream returned in +`response.data.events()` has been consumed. Pass `stream_options=StreamOptions(is_include_usage=True)` to receive +token usage in the final stream events. + +## Installation + +```bash +pip install opentelemetry-instrumentation-oci-genai +``` + +## Example usage + +```python +from opentelemetry.instrumentation.oci_genai import OCIGenAIInstrumentor + +OCIGenAIInstrumentor().instrument() +``` + +## Privacy + +**By default, this instrumentation logs prompts, completions, and embeddings to span attributes**. This gives you a clear visibility into how your LLM application is working, and can make it easy to debug and evaluate the quality of the outputs. + +However, you may want to disable this logging for privacy reasons, as they may contain highly sensitive data from your users. You may also simply want to reduce the size of your traces. + +To disable logging, set the `TRACELOOP_TRACE_CONTENT` environment variable to `false`. + +```bash +TRACELOOP_TRACE_CONTENT=false +``` diff --git a/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/__init__.py b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/__init__.py new file mode 100644 index 0000000000..5dd5fba67f --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/__init__.py @@ -0,0 +1,362 @@ +"""OpenTelemetry OCI Generative AI instrumentation""" + +import logging +import os +import time +import warnings +from functools import partial +from typing import Callable, Collection, Optional + +from opentelemetry import context as context_api +from opentelemetry._logs import get_logger +from opentelemetry.instrumentation.instrumentor import BaseInstrumentor +from opentelemetry.instrumentation.oci_genai.config import Config +from opentelemetry.instrumentation.oci_genai.event_emitter import ( + emit_input_events, + emit_response_events, + emit_streaming_response_events, +) +from opentelemetry.instrumentation.oci_genai.span_utils import ( + CHAT, + EMBEDDINGS, + OCI_GENAI_PROVIDER, + RERANK, + TEXT_COMPLETION, + get_request_model, + is_stream_request, + record_usage_metrics, + set_input_attributes, + set_output_attributes, + set_request_attributes, + set_response_attributes, + set_streaming_output_attributes, + set_streaming_response_attributes, +) +from opentelemetry.instrumentation.oci_genai.streaming import ( + OCIGenAIStreamWrapper, + StreamAccumulator, +) +from opentelemetry.instrumentation.oci_genai.utils import dont_throw, should_emit_events +from opentelemetry.instrumentation.oci_genai.version import __version__ +from opentelemetry.instrumentation.utils import _SUPPRESS_INSTRUMENTATION_KEY, unwrap +from opentelemetry.metrics import Meter, get_meter +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv.attributes.error_attributes import ERROR_TYPE +from opentelemetry.semconv_ai import ( + SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY, + Meters, +) +from opentelemetry.trace import SpanKind, get_tracer, use_span +from opentelemetry.trace.status import Status, StatusCode +from wrapt import wrap_function_wrapper + +logger = logging.getLogger(__name__) + +_instruments = ("oci >= 2.121.1",) + +_CLIENT_MODULE = "oci.generative_ai_inference.generative_ai_inference_client" +_CLIENT_CLASS = "GenerativeAiInferenceClient" + +WRAPPED_METHODS = [ + {"method": "chat", "details_kwarg": "chat_details", "operation": CHAT}, + {"method": "generate_text", "details_kwarg": "generate_text_details", "operation": TEXT_COMPLETION}, + {"method": "embed_text", "details_kwarg": "embed_text_details", "operation": EMBEDDINGS}, + {"method": "rerank_text", "details_kwarg": "rerank_text_details", "operation": RERANK}, +] + + +def is_metrics_enabled() -> bool: + return (os.getenv("TRACELOOP_METRICS_ENABLED") or "true").lower() == "true" + + +def _create_metrics(meter: Meter): + token_histogram = meter.create_histogram( + name=Meters.LLM_TOKEN_USAGE, + unit="token", + description="Measures number of input and output tokens used", + ) + + duration_histogram = meter.create_histogram( + name=Meters.LLM_OPERATION_DURATION, + unit="s", + description="GenAI operation duration", + ) + + return token_histogram, duration_histogram + + +def _span_name(operation, model): + """Build span name per OTel semconv: '{operation_name} {model}'.""" + return f"{operation} {model}" if model else operation + + +def _resolve_details(args, kwargs, details_kwarg): + """The ``*Details`` request object is the first positional argument of every client method.""" + if details_kwarg in kwargs: + return kwargs[details_kwarg] + return args[0] if args else None + + +def _metric_attributes(operation, request_model, response_model=None): + attributes = { + **Config.get_common_metrics_attributes(), + GenAIAttributes.GEN_AI_PROVIDER_NAME: OCI_GENAI_PROVIDER, + GenAIAttributes.GEN_AI_OPERATION_NAME: operation, + GenAIAttributes.GEN_AI_REQUEST_MODEL: request_model, + } + if response_model: + attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL] = response_model + return attributes + + +def _record_exception(span, error): + span.set_attribute(ERROR_TYPE, error.__class__.__name__) + span.record_exception(error) + span.set_status(Status(StatusCode.ERROR, str(error))) + + +def is_streaming_response(operation, details, response): + return is_stream_request(operation, details) and hasattr(getattr(response, "data", None), "events") + + +@dont_throw +def _handle_request(span, operation, details, instance, event_logger): + set_request_attributes(span, operation, details, instance) + if should_emit_events() and event_logger: + emit_input_events(operation, details, event_logger) + else: + set_input_attributes(span, operation, details) + + +@dont_throw +def _handle_response( + span, operation, response, request_model, token_histogram, duration_histogram, start_time, event_logger +): + usage = set_response_attributes(span, operation, response) + if should_emit_events() and event_logger: + emit_response_events(operation, response, event_logger) + else: + set_output_attributes(span, operation, response) + + response_model = getattr(getattr(response, "data", None), "model_id", None) + attributes = _metric_attributes(operation, request_model, response_model) + if duration_histogram: + duration_histogram.record(time.time() - start_time, attributes=attributes) + record_usage_metrics(token_histogram, usage, attributes) + + +def _finish_streaming_span( + span, + operation, + request_model, + token_histogram, + duration_histogram, + event_logger, + start_time, + accumulator, + error, + complete, +): + """Callback invoked once by ``OCIGenAIStreamWrapper`` when the SSE stream is exhausted, fails or is closed early.""" + try: + # The stream is consumed in the caller's context long after ``_wrap`` returned: reactivate the client span so + # the response events and metrics recorded here are correlated with it. + with use_span(span, end_on_exit=False): + if error is not None: + _record_exception(span, error) + else: + usage = set_streaming_response_attributes(span, accumulator) + if should_emit_events() and event_logger: + emit_streaming_response_events(accumulator, event_logger) + else: + set_streaming_output_attributes(span, accumulator) + + attributes = _metric_attributes(operation, request_model) + if duration_histogram: + duration_histogram.record(time.time() - start_time, attributes=attributes) + record_usage_metrics(token_histogram, usage, attributes) + + # A stream the caller closed or abandoned before the end is neither an error nor a success. + if complete and span.is_recording(): + span.set_status(Status(StatusCode.OK)) + except Exception as e: + logger.debug("OpenLLMetry failed to finish OCI GenAI streaming span, error: %s", e) + if Config.exception_logger: + Config.exception_logger(e) + finally: + span.end() + + +def _with_tracer_wrapper(func): + """Helper for providing tracer and metric instruments to wrapper functions.""" + + def _with_tracer(tracer, token_histogram, duration_histogram, event_logger, to_wrap): + def wrapper(wrapped, instance, args, kwargs): + return func( + tracer, + token_histogram, + duration_histogram, + event_logger, + to_wrap, + wrapped, + instance, + args, + kwargs, + ) + + return wrapper + + return _with_tracer + + +@_with_tracer_wrapper +def _wrap( + tracer, + token_histogram, + duration_histogram, + event_logger, + to_wrap, + wrapped, + instance, + args, + kwargs, +): + """Instruments and calls every function defined in WRAPPED_METHODS.""" + if context_api.get_value(_SUPPRESS_INSTRUMENTATION_KEY) or context_api.get_value( + SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY + ): + return wrapped(*args, **kwargs) + + operation = to_wrap.get("operation") + details = _resolve_details(args, kwargs, to_wrap.get("details_kwarg")) + request_model = get_request_model(details) + + span = tracer.start_span( + _span_name(operation, request_model), + kind=SpanKind.CLIENT, + attributes={ + GenAIAttributes.GEN_AI_PROVIDER_NAME: OCI_GENAI_PROVIDER, + GenAIAttributes.GEN_AI_OPERATION_NAME: operation, + GenAIAttributes.GEN_AI_REQUEST_MODEL: request_model, + }, + ) + + # ``start_span`` does not make the span current: activate it so the events emitted here carry its trace + # context and spans created inside the SDK call become children rather than siblings. + with use_span(span, end_on_exit=False): + _handle_request(span, operation, details, instance, event_logger) + + start_time = time.time() + try: + response = wrapped(*args, **kwargs) + except Exception as e: + _record_exception(span, e) + if duration_histogram: + duration_histogram.record( + time.time() - start_time, + attributes={**_metric_attributes(operation, request_model), ERROR_TYPE: e.__class__.__name__}, + ) + span.end() + raise + + if is_streaming_response(operation, details, response): + # The span is completed once the caller has consumed, or closed, ``response.data.events()``. + response.data = OCIGenAIStreamWrapper( + response.data, + StreamAccumulator(), + partial( + _finish_streaming_span, + span, + operation, + request_model, + token_histogram, + duration_histogram, + event_logger, + start_time, + ), + span=span, + ) + return response + + _handle_response( + span, operation, response, request_model, token_histogram, duration_histogram, start_time, event_logger + ) + if span.is_recording(): + span.set_status(Status(StatusCode.OK)) + span.end() + return response + + +class OCIGenAIInstrumentor(BaseInstrumentor): + """An instrumentor for the OCI Python SDK's Generative AI inference client.""" + + def __init__( + self, + exception_logger=None, + use_attributes: Optional[bool] = None, + get_common_metrics_attributes: Callable[[], dict] = lambda: {}, + use_legacy_attributes: Optional[bool] = None, + ): + super().__init__() + if use_attributes is not None and use_legacy_attributes is not None: + raise TypeError( + "Cannot pass both `use_attributes` and `use_legacy_attributes`; " + "`use_legacy_attributes` is deprecated, use `use_attributes` instead." + ) + if use_legacy_attributes is not None: + warnings.warn( + "`use_legacy_attributes` is deprecated and will be removed in a " + "future release; use `use_attributes` instead. The current OTel " + "GenAI spec emits prompts/completions as span attributes " + "(`gen_ai.input.messages` / `gen_ai.output.messages`), which is " + "what `use_attributes=True` (the default) does. " + "`use_attributes=False` opts into the events path instead.", + DeprecationWarning, + stacklevel=2, + ) + use_attributes = use_legacy_attributes + if use_attributes is None: + use_attributes = True + Config.exception_logger = exception_logger + Config.get_common_metrics_attributes = get_common_metrics_attributes + Config.use_legacy_attributes = use_attributes + + def instrumentation_dependencies(self) -> Collection[str]: + return _instruments + + def _instrument(self, **kwargs): + tracer_provider = kwargs.get("tracer_provider") + tracer = get_tracer(__name__, __version__, tracer_provider) + + # meter and histograms are inited here + meter_provider = kwargs.get("meter_provider") + meter = get_meter(__name__, __version__, meter_provider) + + if is_metrics_enabled(): + token_histogram, duration_histogram = _create_metrics(meter) + else: + token_histogram, duration_histogram = None, None + + event_logger = None + if not Config.use_legacy_attributes: + logger_provider = kwargs.get("logger_provider") + event_logger = get_logger(__name__, __version__, logger_provider=logger_provider) + + for wrapped_method in WRAPPED_METHODS: + try: + wrap_function_wrapper( + _CLIENT_MODULE, + f"{_CLIENT_CLASS}.{wrapped_method.get('method')}", + _wrap(tracer, token_histogram, duration_histogram, event_logger, wrapped_method), + ) + except (AttributeError, ModuleNotFoundError): + pass # older SDK releases don't expose every operation (e.g. rerank_text) + + def _uninstrument(self, **kwargs): + for wrapped_method in WRAPPED_METHODS: + try: + unwrap(f"{_CLIENT_MODULE}.{_CLIENT_CLASS}", wrapped_method.get("method")) + except (AttributeError, ModuleNotFoundError): + pass diff --git a/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/config.py b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/config.py new file mode 100644 index 0000000000..f78d2f0d60 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/config.py @@ -0,0 +1,7 @@ +from typing import Callable + + +class Config: + exception_logger = None + get_common_metrics_attributes: Callable[[], dict] = lambda: {} + use_legacy_attributes = True diff --git a/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/event_emitter.py b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/event_emitter.py new file mode 100644 index 0000000000..00c13da19d --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/event_emitter.py @@ -0,0 +1,188 @@ +from dataclasses import asdict +from enum import Enum +from typing import Union + +from opentelemetry._logs import Logger, LogRecord +from opentelemetry.instrumentation.oci_genai.event_models import ChoiceEvent, MessageEvent +from opentelemetry.instrumentation.oci_genai.span_utils import ( + CHAT, + EMBEDDINGS, + OCI_GENAI_PROVIDER, + RERANK, + TEXT_COMPLETION, + chat_request_to_messages, + embedding_input_parts, + response_to_output_messages, + stream_choices_to_messages, +) +from opentelemetry.instrumentation.oci_genai.utils import ( + dont_throw, + should_emit_events, + should_send_prompts, +) +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) + + +class Roles(Enum): + USER = "user" + ASSISTANT = "assistant" + SYSTEM = "system" + TOOL = "tool" + + +VALID_MESSAGE_ROLES = {role.value for role in Roles} +"""The valid roles for naming the message event.""" + +EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_PROVIDER_NAME: OCI_GENAI_PROVIDER} +"""The attributes to be used for the event.""" + + +def _parts_to_content(parts): + """Collapse OTel parts into event body content: a string when text-only, otherwise the parts list.""" + parts = [part for part in parts if part.get("type") != "tool_call"] + if not parts: + return None + if all(part.get("type") == "text" for part in parts): + return "\n".join(part.get("content", "") for part in parts) + if len(parts) == 1 and parts[0].get("type") == "tool_call_response": + return parts[0].get("response") + return parts + + +def _parts_to_tool_calls(parts): + tool_calls = [ + { + "id": part.get("id") or "", + "type": "function", + "function": {"function_name": part.get("name"), "arguments": part.get("arguments")}, + } + for part in parts + if part.get("type") == "tool_call" + ] + return tool_calls or None + + +def _emit_message(message, event_logger): + emit_event( + MessageEvent( + content=_parts_to_content(message["parts"]), + role=message.get("role") or "user", + tool_calls=_parts_to_tool_calls(message["parts"]), + ), + event_logger, + ) + + +def _emit_choices(messages, event_logger): + for index, message in enumerate(messages): + emit_event( + ChoiceEvent( + index=index, + message={"content": _parts_to_content(message["parts"]), "role": message.get("role") or "assistant"}, + finish_reason=message.get("finish_reason") or None, + tool_calls=_parts_to_tool_calls(message["parts"]), + ), + event_logger, + ) + + +@dont_throw +def emit_input_events(operation, details, event_logger): + if operation == CHAT: + messages, system_parts = chat_request_to_messages(getattr(details, "chat_request", None)) + if system_parts: + _emit_message({"role": Roles.SYSTEM.value, "parts": system_parts}, event_logger) + for message in messages: + _emit_message(message, event_logger) + elif operation == TEXT_COMPLETION: + prompt = getattr(getattr(details, "inference_request", None), "prompt", None) + if prompt: + emit_event(MessageEvent(content=prompt, role=Roles.USER.value), event_logger) + elif operation == EMBEDDINGS: + for parts in embedding_input_parts(details): + _emit_message({"role": Roles.USER.value, "parts": parts}, event_logger) + elif operation == RERANK: + for document in getattr(details, "documents", None) or []: + emit_event(MessageEvent(content=document, role=Roles.SYSTEM.value), event_logger) + query = getattr(details, "input", None) + if query: + emit_event(MessageEvent(content=query, role=Roles.USER.value), event_logger) + + +@dont_throw +def emit_response_events(operation, response, event_logger): + _emit_choices(response_to_output_messages(operation, getattr(response, "data", None)), event_logger) + + +@dont_throw +def emit_streaming_response_events(accumulator, event_logger): + _emit_choices(stream_choices_to_messages(accumulator.choices_list()), event_logger) + + +def emit_event(event: Union[MessageEvent, ChoiceEvent], event_logger: Union[Logger, None]) -> None: + """ + Emit an event to the OpenTelemetry SDK. + + Args: + event: The event to emit. + """ + if not should_emit_events() or event_logger is None: + return + + if isinstance(event, MessageEvent): + _emit_message_event(event, event_logger) + elif isinstance(event, ChoiceEvent): + _emit_choice_event(event, event_logger) + else: + raise TypeError("Unsupported event type") + + +def _emit_message_event(event: MessageEvent, event_logger: Logger) -> None: + body = asdict(event) + + if event.role in VALID_MESSAGE_ROLES: + name = "gen_ai.{}.message".format(event.role) + # According to the semantic conventions, the role is conditionally required if available + # and not equal to the "role" in the message name. So, remove the role from the body if + # it is the same as the in the event name. + body.pop("role", None) + else: + name = "gen_ai.user.message" + + # According to the semantic conventions, only the assistant role has tool call + if event.role != Roles.ASSISTANT.value and event.tool_calls is not None: + del body["tool_calls"] + elif event.tool_calls is None: + del body["tool_calls"] + + if not should_send_prompts(): + del body["content"] + if body.get("tool_calls") is not None: + for tool_call in body["tool_calls"]: + tool_call["function"].pop("arguments", None) + + log_record = LogRecord(body=body, attributes=EVENT_ATTRIBUTES, event_name=name) + event_logger.emit(log_record) + + +def _emit_choice_event(event: ChoiceEvent, event_logger: Logger) -> None: + body = asdict(event) + if event.message["role"] == Roles.ASSISTANT.value: + # According to the semantic conventions, the role is conditionally required if available + # and not equal to "assistant", so remove the role from the body if it is "assistant". + body["message"].pop("role", None) + + if event.tool_calls is None: + del body["tool_calls"] + + if not should_send_prompts(): + body["message"].pop("content", None) + body["message"].pop("role", None) + if body.get("tool_calls") is not None: + for tool_call in body["tool_calls"]: + tool_call["function"].pop("arguments", None) + + log_record = LogRecord(body=body, attributes=EVENT_ATTRIBUTES, event_name="gen_ai.choice") + event_logger.emit(log_record) diff --git a/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/event_models.py b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/event_models.py new file mode 100644 index 0000000000..f5f3881494 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/event_models.py @@ -0,0 +1,41 @@ +from dataclasses import dataclass +from typing import Any, List, Literal, Optional, TypedDict + + +class _FunctionToolCall(TypedDict): + function_name: str + arguments: Optional[dict[str, Any]] + + +class ToolCall(TypedDict): + """Represents a tool call in the AI model.""" + + id: str + function: _FunctionToolCall + type: Literal["function"] + + +class CompletionMessage(TypedDict): + """Represents a message in the AI model.""" + + content: Any + role: str = "assistant" + + +@dataclass +class MessageEvent: + """Represents an input event for the AI model.""" + + content: Any + role: str = "user" + tool_calls: Optional[List[ToolCall]] = None + + +@dataclass +class ChoiceEvent: + """Represents a completion event for the AI model.""" + + index: int + message: CompletionMessage + finish_reason: Optional[str] = None + tool_calls: Optional[List[ToolCall]] = None diff --git a/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/span_utils.py b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/span_utils.py new file mode 100644 index 0000000000..5f9dd3cecb --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/span_utils.py @@ -0,0 +1,639 @@ +"""Span attribute helpers for the OCI Generative AI instrumentation. + +Request/response shapes handled here (all from ``oci.generative_ai_inference.models``): + +* ``chat``: ``ChatDetails.chat_request`` is a ``GenericChatRequest`` (``messages`` list, OpenAI-like, used by + Meta, OpenAI, Google and xAI models), a ``CohereChatRequest`` (``message`` + ``chat_history``) or a + ``CohereChatRequestV2`` (``messages`` list). The result is a ``ChatResult`` whose ``chat_response`` is the + matching ``GenericChatResponse`` / ``CohereChatResponse`` / ``CohereChatResponseV2``. +* ``generate_text``: legacy completion API with ``CohereLlmInferenceRequest`` / ``LlamaLlmInferenceRequest``. +* ``embed_text``: ``EmbedTextDetails`` -> ``EmbedTextResult``. +* ``rerank_text``: ``RerankTextDetails`` -> ``RerankTextResult``. +""" + +import json +import logging + +from opentelemetry.instrumentation.oci_genai.utils import ( + dont_throw, + get_server_address, + model_as_dict, + set_span_attribute, + should_send_prompts, +) +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv.attributes.server_attributes import SERVER_ADDRESS +from opentelemetry.semconv_ai import SpanAttributes + +logger = logging.getLogger(__name__) + +OCI_GENAI_PROVIDER = "oracle_cloud.generative_ai" +"""``gen_ai.provider.name`` value for OCI Generative AI (open-telemetry/semantic-conventions-genai#526).""" + +CHAT = GenAIAttributes.GenAiOperationNameValues.CHAT.value +TEXT_COMPLETION = GenAIAttributes.GenAiOperationNameValues.TEXT_COMPLETION.value +EMBEDDINGS = GenAIAttributes.GenAiOperationNameValues.EMBEDDINGS.value +RERANK = "rerank" + +# OCI specific attributes (vendor namespace, same pattern as gen_ai.openai.* / gen_ai.bedrock.*) +GEN_AI_OCI_SERVING_MODE = "gen_ai.oci.serving_mode" +GEN_AI_OCI_ENDPOINT_ID = "gen_ai.oci.endpoint_id" +GEN_AI_OCI_API_FORMAT = "gen_ai.oci.api_format" +GEN_AI_OCI_EMBEDDINGS_INPUT_COUNT = "gen_ai.oci.embeddings.input_count" +GEN_AI_OCI_EMBEDDINGS_INPUT_TYPE = "gen_ai.oci.embeddings.input_type" +GEN_AI_OCI_RERANK_TOP_N = "gen_ai.oci.rerank.top_n" +GEN_AI_OCI_RERANK_DOCUMENT_COUNT = "gen_ai.oci.rerank.document_count" + +# OCI finish reason -> OTel GenAI finish reason (stop, length, content_filter, tool_call, error). +# Unknown values are passed through lower-cased. +FINISH_REASON_MAP = { + # GENERIC api format (OpenAI compatible) + "stop": "stop", + "length": "length", + "tool_calls": "tool_call", + "content_filter": "content_filter", + # COHERE / COHEREV2 api formats and legacy Cohere text generation + "COMPLETE": "stop", + "STOP_SEQUENCE": "stop", + "MAX_TOKENS": "length", + "TOOL_CALL": "tool_call", + "ERROR": "error", + "ERROR_LIMIT": "error", + "ERROR_TOXIC": "content_filter", +} + +_DETAILS_REQUEST_ATTRIBUTE = {CHAT: "chat_request", TEXT_COMPLETION: "inference_request"} + + +def map_finish_reason(reason): + if not reason: + return "" + reason = str(reason) + return FINISH_REASON_MAP.get(reason, reason.lower()) + + +def _normalize_role(role): + if not role: + return "user" + role = str(role).lower() + return "assistant" if role == "chatbot" else role + + +def _first_not_none(*values): + for value in values: + if value is not None: + return value + return None + + +# --------------------------------------------------------------------------- +# OTel message parts +# --------------------------------------------------------------------------- + + +def _text_part(content): + return {"type": "text", "content": content} + + +def _reasoning_part(content): + return {"type": "reasoning", "content": content} + + +def _image_part(url): + if isinstance(url, str) and url.startswith("data:"): + try: + header, data = url.split(",", 1) + mime_type = header.split(":", 1)[1].split(";", 1)[0] + return {"type": "blob", "modality": "image", "mime_type": mime_type, "content": data} + except (ValueError, IndexError): + pass + return {"type": "uri", "modality": "image", "uri": url or ""} + + +def _parse_arguments(raw): + if isinstance(raw, str): + try: + return json.loads(raw) + except (TypeError, ValueError): + return raw + if raw is None: + return {} + return model_as_dict(raw) + + +def _tool_call_part(name, arguments=None, tool_id=None): + part = {"type": "tool_call", "name": name or "", "arguments": _parse_arguments(arguments)} + if tool_id: + part["id"] = tool_id + return part + + +def _tool_call_response_part(tool_id, response): + return { + "type": "tool_call_response", + "id": tool_id or "", + "response": response if response is not None else "", + } + + +def _output_message(role, parts, finish_reason): + """OutputMessage per the OTel GenAI JSON schema: role, parts and finish_reason are required.""" + return {"role": role, "parts": parts, "finish_reason": finish_reason or ""} + + +def _parts_text(parts): + return "".join(part.get("content", "") for part in parts if part.get("type") == "text") + + +def _content_to_parts(content): + """Convert OCI ``ChatContent`` / ``CohereContentV2`` lists (or a plain string) to OTel parts.""" + if content is None: + return [] + if isinstance(content, str): + return [_text_part(content)] if content else [] + parts = [] + for item in content: + if isinstance(item, str): + if item: + parts.append(_text_part(item)) + continue + item_type = str(getattr(item, "type", "") or "").upper() + if item_type == "TEXT" or (not item_type and hasattr(item, "text")): + text = getattr(item, "text", None) + if text: + parts.append(_text_part(text)) + elif item_type == "THINKING": + thinking = getattr(item, "thinking", None) + if thinking: + parts.append(_reasoning_part(thinking)) + elif item_type == "IMAGE": + parts.append(_image_part(getattr(getattr(item, "image_url", None), "url", None))) + else: + # Stopgap for audio/video/document content: encode as text to keep the parts array valid. + parts.append(_text_part(json.dumps(model_as_dict(item), default=str))) + return parts + + +def _field(obj, name): + """Read ``name`` from an SDK model or from a plain dict (some SDK fields are untyped ``object``).""" + if isinstance(obj, dict): + return obj.get(name) + return getattr(obj, name, None) + + +def _tool_calls_to_parts(tool_calls): + """Convert ``FunctionCall`` (GENERIC), ``CohereToolCallV2`` or ``CohereToolCall`` lists to OTel parts.""" + parts = [] + for tool_call in tool_calls or []: + function = _field(tool_call, "function") + if function is not None: + # CohereToolCallV2: {"id", "type", "function": {"name", "arguments"}} + parts.append( + _tool_call_part(_field(function, "name"), _field(function, "arguments"), _field(tool_call, "id")) + ) + elif _field(tool_call, "parameters") is not None: + # CohereToolCall (v1): {"name", "parameters"} + parts.append(_tool_call_part(_field(tool_call, "name"), _field(tool_call, "parameters"))) + else: + # FunctionCall (GENERIC): {"id", "type", "name", "arguments"} + parts.append( + _tool_call_part(_field(tool_call, "name"), _field(tool_call, "arguments"), _field(tool_call, "id")) + ) + return parts + + +def _cohere_tool_results_to_parts(tool_results): + parts = [] + for result in tool_results or []: + call = getattr(result, "call", None) + parts.append( + _tool_call_response_part( + getattr(call, "name", None), + model_as_dict(getattr(result, "outputs", None)) or "", + ) + ) + return parts + + +def _message_to_otel(message): + """Convert a GENERIC ``Message`` or a ``CohereMessageV2`` into an OTel input/output message dict.""" + role = _normalize_role(getattr(message, "role", None)) + content_parts = _content_to_parts(getattr(message, "content", None)) + if role == "tool": + parts = [_tool_call_response_part(getattr(message, "tool_call_id", None), _parts_text(content_parts))] + else: + parts = content_parts + reasoning = getattr(message, "reasoning_content", None) + if reasoning: + parts.append(_reasoning_part(reasoning)) + parts.extend(_tool_calls_to_parts(getattr(message, "tool_calls", None))) + return {"role": role, "parts": parts} + + +def _cohere_history_to_messages(chat_history): + messages = [] + for item in chat_history or []: + role = _normalize_role(getattr(item, "role", None)) + if role == "tool": + parts = _cohere_tool_results_to_parts(getattr(item, "tool_results", None)) + else: + parts = [] + text = getattr(item, "message", None) + if text: + parts.append(_text_part(text)) + parts.extend(_tool_calls_to_parts(getattr(item, "tool_calls", None))) + messages.append({"role": role, "parts": parts}) + return messages + + +def chat_request_to_messages(request): + """Return ``(input_messages, system_instruction_parts)`` for any OCI chat request flavour.""" + if request is None: + return [], [] + api_format = str(getattr(request, "api_format", "") or "").upper() + if api_format == "COHERE": + system_parts = [] + preamble = getattr(request, "preamble_override", None) + if preamble: + system_parts.append(_text_part(preamble)) + messages = _cohere_history_to_messages(getattr(request, "chat_history", None)) + message = getattr(request, "message", None) + if message: + messages.append({"role": "user", "parts": [_text_part(message)]}) + tool_results = getattr(request, "tool_results", None) + if tool_results: + messages.append({"role": "tool", "parts": _cohere_tool_results_to_parts(tool_results)}) + return messages, system_parts + + # GENERIC and COHEREV2 both carry a ``messages`` list + return [_message_to_otel(message) for message in getattr(request, "messages", None) or []], [] + + +def chat_response_to_messages(chat_response): + """Return OTel output messages for a ``GenericChatResponse`` / ``CohereChatResponse`` / ``CohereChatResponseV2``.""" + if chat_response is None: + return [] + api_format = str(getattr(chat_response, "api_format", "") or "").upper() + finish_reason = map_finish_reason(getattr(chat_response, "finish_reason", None)) + if api_format == "COHERE": + parts = [] + text = getattr(chat_response, "text", None) + if text: + parts.append(_text_part(text)) + parts.extend(_tool_calls_to_parts(getattr(chat_response, "tool_calls", None))) + return [_output_message("assistant", parts, finish_reason)] + if api_format == "COHEREV2": + message = getattr(chat_response, "message", None) + otel = _message_to_otel(message) if message is not None else {"role": "assistant", "parts": []} + return [_output_message(otel["role"], otel["parts"], finish_reason)] + + messages = [] + for choice in getattr(chat_response, "choices", None) or []: + message = getattr(choice, "message", None) + otel = _message_to_otel(message) if message is not None else {"role": "assistant", "parts": []} + messages.append( + _output_message(otel["role"], otel["parts"], map_finish_reason(getattr(choice, "finish_reason", None))) + ) + return messages + + +def text_completion_response_to_messages(inference_response): + """Return OTel output messages for a legacy ``CohereLlmInferenceResponse`` / ``LlamaLlmInferenceResponse``.""" + messages = [] + if inference_response is None: + return messages + for generated in getattr(inference_response, "generated_texts", None) or []: + messages.append( + _output_message( + "assistant", + [_text_part(getattr(generated, "text", None) or "")], + map_finish_reason(getattr(generated, "finish_reason", None)), + ) + ) + for choice in getattr(inference_response, "choices", None) or []: + messages.append( + _output_message( + "assistant", + [_text_part(getattr(choice, "text", None) or "")], + map_finish_reason(getattr(choice, "finish_reason", None)), + ) + ) + return messages + + +def rerank_response_to_messages(result): + messages = [] + for rank in getattr(result, "document_ranks", None) or []: + content = f"Doc {getattr(rank, 'index', None)}, Score: {getattr(rank, 'relevance_score', None)}" + document = getattr(rank, "document", None) + if document: + if not isinstance(document, str): + document = json.dumps(model_as_dict(document), default=str) + content += "\n" + document + messages.append(_output_message("assistant", [_text_part(content)], "stop")) + return messages + + +def stream_choices_to_messages(choices): + """Return OTel output messages for the choices aggregated by ``StreamAccumulator``.""" + messages = [] + for choice in choices: + parts = [] + if choice.get("text"): + parts.append(_text_part(choice["text"])) + if choice.get("reasoning"): + parts.append(_reasoning_part(choice["reasoning"])) + for tool_call in choice.get("tool_calls") or []: + parts.append(_tool_call_part(tool_call.get("name"), tool_call.get("arguments"), tool_call.get("id"))) + messages.append(_output_message("assistant", parts, map_finish_reason(choice.get("finish_reason")))) + return messages + + +def response_to_output_messages(operation, data): + if data is None: + return [] + if operation == CHAT: + return chat_response_to_messages(getattr(data, "chat_response", None)) + if operation == TEXT_COMPLETION: + return text_completion_response_to_messages(getattr(data, "inference_response", None)) + if operation == RERANK: + return rerank_response_to_messages(data) + return [] + + +def embedding_input_parts(details): + """Return one OTel parts list per embedding input (``inputs`` strings or ``embed_contents`` items).""" + inputs = getattr(details, "inputs", None) + if inputs: + return [[_text_part(text)] for text in inputs] + parts = [] + for content in getattr(details, "embed_contents", None) or []: + content_type = str(getattr(content, "type", "") or "").upper() + if content_type == "IMAGE": + parts.append([_image_part(getattr(getattr(content, "image_url", None), "url", None))]) + else: + parts.append([_text_part(getattr(content, "text", None) or "")]) + return parts + + +# --------------------------------------------------------------------------- +# Request helpers +# --------------------------------------------------------------------------- + + +def get_request_model(details): + """Model id for on-demand serving, endpoint id for dedicated AI cluster endpoints.""" + serving_mode = getattr(details, "serving_mode", None) + return ( + getattr(serving_mode, "model_id", None) + or getattr(serving_mode, "endpoint_id", None) + or "unknown" + ) + + +def get_inner_request(operation, details): + attribute = _DETAILS_REQUEST_ATTRIBUTE.get(operation) + return getattr(details, attribute, None) if attribute else details + + +def is_stream_request(operation, details): + return bool(getattr(get_inner_request(operation, details), "is_stream", False)) + + +def usage_to_dict(usage, include_output=True): + """Normalise an OCI ``Usage`` model or a camelCase streaming ``usage`` payload.""" + if usage is None: + return None + if isinstance(usage, dict): + prompt_details = usage.get("promptTokensDetails") or usage.get("prompt_tokens_details") or {} + completion_details = usage.get("completionTokensDetails") or usage.get("completion_tokens_details") or {} + data = { + "input_tokens": _first_not_none(usage.get("promptTokens"), usage.get("prompt_tokens")), + "output_tokens": _first_not_none(usage.get("completionTokens"), usage.get("completion_tokens")), + "total_tokens": _first_not_none(usage.get("totalTokens"), usage.get("total_tokens")), + "cached_tokens": _first_not_none(prompt_details.get("cachedTokens"), prompt_details.get("cached_tokens")) + if isinstance(prompt_details, dict) + else None, + "reasoning_tokens": _first_not_none( + completion_details.get("reasoningTokens"), completion_details.get("reasoning_tokens") + ) + if isinstance(completion_details, dict) + else None, + } + else: + data = { + "input_tokens": getattr(usage, "prompt_tokens", None), + "output_tokens": getattr(usage, "completion_tokens", None), + "total_tokens": getattr(usage, "total_tokens", None), + "cached_tokens": getattr(getattr(usage, "prompt_tokens_details", None), "cached_tokens", None), + "reasoning_tokens": getattr(getattr(usage, "completion_tokens_details", None), "reasoning_tokens", None), + } + if not include_output: + data["output_tokens"] = None + data["reasoning_tokens"] = None + if data["total_tokens"] is None and data["input_tokens"] is not None and data["output_tokens"] is not None: + data["total_tokens"] = data["input_tokens"] + data["output_tokens"] + return data + + +def set_usage_attributes(span, usage): + if not usage: + return + set_span_attribute(span, GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS, usage.get("input_tokens")) + set_span_attribute(span, GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS, usage.get("output_tokens")) + set_span_attribute(span, SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS, usage.get("total_tokens")) + set_span_attribute(span, GenAIAttributes.GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS, usage.get("cached_tokens")) + set_span_attribute(span, SpanAttributes.GEN_AI_USAGE_REASONING_TOKENS, usage.get("reasoning_tokens")) + + +def record_usage_metrics(token_histogram, usage, attributes): + if token_histogram is None or not usage: + return + for token_type, key in (("input", "input_tokens"), ("output", "output_tokens")): + value = usage.get(key) + if isinstance(value, int) and value >= 0: + token_histogram.record( + value, + attributes={**attributes, GenAIAttributes.GEN_AI_TOKEN_TYPE: token_type}, + ) + + +def _set_finish_reasons(span, messages): + finish_reasons = [message["finish_reason"] for message in messages if message.get("finish_reason")] + if finish_reasons: + set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS, finish_reasons) + + +# --------------------------------------------------------------------------- +# Span attribute setters +# --------------------------------------------------------------------------- + + +@dont_throw +def set_request_attributes(span, operation, details, instance): + """Request metadata: serving mode, endpoint, sampling parameters and tool definitions.""" + if not span.is_recording(): + return + + serving_mode = getattr(details, "serving_mode", None) + set_span_attribute(span, GEN_AI_OCI_SERVING_MODE, getattr(serving_mode, "serving_type", None)) + set_span_attribute(span, GEN_AI_OCI_ENDPOINT_ID, getattr(serving_mode, "endpoint_id", None)) + set_span_attribute(span, SERVER_ADDRESS, get_server_address(instance)) + + request = get_inner_request(operation, details) + if operation in (CHAT, TEXT_COMPLETION): + set_span_attribute( + span, + GEN_AI_OCI_API_FORMAT, + _first_not_none(getattr(request, "api_format", None), getattr(request, "runtime_type", None)), + ) + set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE, getattr(request, "temperature", None)) + set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TOP_P, getattr(request, "top_p", None)) + set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TOP_K, getattr(request, "top_k", None)) + set_span_attribute( + span, + GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS, + _first_not_none(getattr(request, "max_tokens", None), getattr(request, "max_completion_tokens", None)), + ) + set_span_attribute( + span, GenAIAttributes.GEN_AI_REQUEST_FREQUENCY_PENALTY, getattr(request, "frequency_penalty", None) + ) + set_span_attribute( + span, GenAIAttributes.GEN_AI_REQUEST_PRESENCE_PENALTY, getattr(request, "presence_penalty", None) + ) + set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_SEED, getattr(request, "seed", None)) + stop = _first_not_none(getattr(request, "stop", None), getattr(request, "stop_sequences", None)) + if stop: + set_span_attribute( + span, + GenAIAttributes.GEN_AI_REQUEST_STOP_SEQUENCES, + list(stop) if isinstance(stop, (list, tuple)) else [stop], + ) + set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_CHOICE_COUNT, getattr(request, "num_generations", None)) + set_span_attribute(span, SpanAttributes.GEN_AI_IS_STREAMING, bool(getattr(request, "is_stream", False))) + tools = getattr(request, "tools", None) + if tools and should_send_prompts(): + set_span_attribute( + span, GenAIAttributes.GEN_AI_TOOL_DEFINITIONS, json.dumps(model_as_dict(tools), default=str) + ) + elif operation == EMBEDDINGS: + set_span_attribute(span, GEN_AI_OCI_EMBEDDINGS_INPUT_COUNT, len(embedding_input_parts(details))) + set_span_attribute(span, GEN_AI_OCI_EMBEDDINGS_INPUT_TYPE, getattr(details, "input_type", None)) + embedding_types = getattr(details, "embedding_types", None) + if embedding_types: + set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_ENCODING_FORMATS, list(embedding_types)) + set_span_attribute( + span, GenAIAttributes.GEN_AI_EMBEDDINGS_DIMENSION_COUNT, getattr(details, "output_dimensions", None) + ) + elif operation == RERANK: + set_span_attribute(span, GEN_AI_OCI_RERANK_TOP_N, getattr(details, "top_n", None)) + set_span_attribute(span, GEN_AI_OCI_RERANK_DOCUMENT_COUNT, len(getattr(details, "documents", None) or [])) + + +@dont_throw +def set_input_attributes(span, operation, details): + """Prompt content (``gen_ai.input.messages`` / ``gen_ai.system_instructions``); gated by TRACELOOP_TRACE_CONTENT.""" + if not span.is_recording() or not should_send_prompts(): + return + + messages = [] + if operation == CHAT: + messages, system_parts = chat_request_to_messages(getattr(details, "chat_request", None)) + if system_parts: + set_span_attribute(span, GenAIAttributes.GEN_AI_SYSTEM_INSTRUCTIONS, json.dumps(system_parts)) + elif operation == TEXT_COMPLETION: + prompt = getattr(getattr(details, "inference_request", None), "prompt", None) + if prompt: + messages = [{"role": "user", "parts": [_text_part(prompt)]}] + elif operation == EMBEDDINGS: + messages = [{"role": "user", "parts": parts} for parts in embedding_input_parts(details)] + elif operation == RERANK: + messages = [ + {"role": "system", "parts": [_text_part(document if isinstance(document, str) else str(document))]} + for document in getattr(details, "documents", None) or [] + ] + query = getattr(details, "input", None) + if query: + messages.append({"role": "user", "parts": [_text_part(query)]}) + + if messages: + set_span_attribute(span, GenAIAttributes.GEN_AI_INPUT_MESSAGES, json.dumps(messages)) + + +@dont_throw +def set_response_attributes(span, operation, response): + """Response metadata (model, id, finish reasons, usage). Returns the normalised usage for metrics.""" + if not span.is_recording(): + return None + data = getattr(response, "data", None) + if data is None: + return None + + set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_MODEL, getattr(data, "model_id", None)) + + if operation == CHAT: + chat_response = getattr(data, "chat_response", None) + set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_ID, getattr(chat_response, "id", None)) + _set_finish_reasons(span, chat_response_to_messages(chat_response)) + usage = usage_to_dict(getattr(chat_response, "usage", None)) + set_usage_attributes(span, usage) + return usage + + if operation == TEXT_COMPLETION: + _set_finish_reasons(span, text_completion_response_to_messages(getattr(data, "inference_response", None))) + return None + + if operation == EMBEDDINGS: + set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_ID, getattr(data, "id", None)) + set_span_attribute(span, GenAIAttributes.GEN_AI_EMBEDDINGS_DIMENSION_COUNT, _embedding_dimensions(data)) + usage = usage_to_dict(getattr(data, "usage", None), include_output=False) + set_usage_attributes(span, usage) + return usage + + if operation == RERANK: + set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_ID, getattr(data, "id", None)) + return None + + +def _embedding_dimensions(result): + embeddings = getattr(result, "embeddings", None) + if not embeddings: + by_type = getattr(result, "embeddings_by_type", None) + if isinstance(by_type, dict): + embeddings = next((vectors for vectors in by_type.values() if vectors), None) + if embeddings and hasattr(embeddings[0], "__len__"): + return len(embeddings[0]) + return None + + +@dont_throw +def set_output_attributes(span, operation, response): + """Completion content (``gen_ai.output.messages``); gated by TRACELOOP_TRACE_CONTENT.""" + if not span.is_recording() or not should_send_prompts(): + return + messages = response_to_output_messages(operation, getattr(response, "data", None)) + if messages: + set_span_attribute(span, GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, json.dumps(messages)) + + +@dont_throw +def set_streaming_response_attributes(span, accumulator): + """Response metadata aggregated from a consumed SSE stream. Returns the normalised usage for metrics.""" + if not span.is_recording(): + return None + set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_ID, accumulator.response_id) + _set_finish_reasons(span, stream_choices_to_messages(accumulator.choices_list())) + usage = usage_to_dict(accumulator.usage) + set_usage_attributes(span, usage) + return usage + + +@dont_throw +def set_streaming_output_attributes(span, accumulator): + if not span.is_recording() or not should_send_prompts(): + return + messages = stream_choices_to_messages(accumulator.choices_list()) + if messages: + set_span_attribute(span, GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, json.dumps(messages)) diff --git a/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/streaming.py b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/streaming.py new file mode 100644 index 0000000000..9b27d80315 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/streaming.py @@ -0,0 +1,212 @@ +"""Streaming (SSE) support for OCI Generative AI ``chat`` / ``generate_text`` calls. + +With ``is_stream=True`` the OCI SDK returns an ``oci.response.Response`` whose ``data`` is an +``SSEClient``; callers iterate ``response.data.events()`` and read ``event.data`` (a JSON document or the +``[DONE]`` sentinel). Observed event payloads: + +GENERIC api format:: + + {"index": 0, "message": {"role": "ASSISTANT", "content": [{"type": "TEXT", "text": "Hello"}]}, "pad": "aaa"} + {"message": {...}, "finishReason": "stop", "pad": "a"} + {"usage": {"completionTokens": 8, "promptTokens": 41, "totalTokens": 49}, "pad": "aa"} # is_include_usage + [DONE] + +COHERE api format:: + + {"apiFormat": "COHERE", "text": "Hi", "pad": "aaa"} + {"apiFormat": "COHERE", "text": "", "chatHistory": [...], "finishReason": "COMPLETE", + "usage": {...}} +""" + +import json +import logging +from contextlib import nullcontext + +from opentelemetry.trace import use_span +from wrapt import ObjectProxy + +logger = logging.getLogger(__name__) + +DONE_SENTINEL = "[DONE]" + + +class StreamAccumulator: + """Aggregates SSE event payloads into per-choice text, tool calls, finish reasons and usage.""" + + def __init__(self): + self.api_format = None + self.usage = None + self.response_id = None + self._choices = {} + + def _choice(self, index): + return self._choices.setdefault(index, {"text": "", "reasoning": "", "tool_calls": {}, "finish_reason": None}) + + def process(self, raw): + if raw is None: + return + raw = raw.strip() if isinstance(raw, str) else raw + if not raw or raw == DONE_SENTINEL: + return + try: + payload = json.loads(raw) + except (TypeError, ValueError): + logger.debug("Skipping non-JSON OCI GenAI stream event: %r", raw) + return + if not isinstance(payload, dict): + return + + if payload.get("apiFormat"): + self.api_format = payload["apiFormat"] + if isinstance(payload.get("usage"), dict): + self.usage = payload["usage"] + if payload.get("id"): + self.response_id = payload["id"] + + message = payload.get("message") + if isinstance(message, dict): + self._process_message(payload, message) + elif "text" in payload: + self._process_text(payload) + elif payload.get("finishReason"): + self._choice(payload.get("index", 0))["finish_reason"] = payload["finishReason"] + + def _process_message(self, payload, message): + """GENERIC / COHEREV2 shaped events: partial ``message`` objects, one per choice index.""" + choice = self._choice(payload.get("index", 0)) + content = message.get("content") + if isinstance(content, str): + choice["text"] += content + elif isinstance(content, list): + for part in content: + if not isinstance(part, dict): + continue + part_type = str(part.get("type", "TEXT") or "TEXT").upper() + if part_type == "TEXT" and part.get("text"): + choice["text"] += part["text"] + elif part_type == "THINKING" and part.get("thinking"): + choice["reasoning"] += part["thinking"] + if message.get("reasoningContent"): + choice["reasoning"] += message["reasoningContent"] + self._accumulate_tool_calls(choice, message.get("toolCalls")) + if payload.get("finishReason"): + choice["finish_reason"] = payload["finishReason"] + + def _process_text(self, payload): + """COHERE shaped events: ``text`` deltas, with the terminal event carrying the full text.""" + choice = self._choice(0) + text = payload.get("text") or "" + if payload.get("finishReason"): + if text and len(text) >= len(choice["text"]): + choice["text"] = text + else: + choice["text"] += text + choice["finish_reason"] = payload["finishReason"] + self._accumulate_tool_calls(choice, payload.get("toolCalls")) + else: + choice["text"] += text + + @staticmethod + def _accumulate_tool_calls(choice, tool_calls): + if not isinstance(tool_calls, list): + return + for position, tool_call in enumerate(tool_calls): + if not isinstance(tool_call, dict): + continue + function = tool_call.get("function") if isinstance(tool_call.get("function"), dict) else {} + key = tool_call.get("id") or tool_call.get("index") or f"position-{position}" + entry = choice["tool_calls"].setdefault(key, {"id": tool_call.get("id"), "name": "", "arguments": ""}) + if tool_call.get("id"): + entry["id"] = tool_call["id"] + name = tool_call.get("name") or function.get("name") + if name: + entry["name"] = name + arguments = tool_call.get("arguments", function.get("arguments")) + if arguments is None: + arguments = tool_call.get("parameters") + if isinstance(arguments, str): + entry["arguments"] += arguments + elif arguments is not None: + entry["arguments"] = arguments + + def choices_list(self): + """Choices ordered by index, with tool calls flattened to a list.""" + choices = [] + for index in sorted(self._choices): + choice = self._choices[index] + choices.append( + { + "index": index, + "text": choice["text"], + "reasoning": choice["reasoning"], + "tool_calls": list(choice["tool_calls"].values()), + "finish_reason": choice["finish_reason"], + } + ) + return choices + + +class OCIGenAIStreamWrapper(ObjectProxy): + """Proxy around the SDK's ``SSEClient`` that feeds events to the accumulator and finishes the span. + + ``on_done(accumulator, error, complete)`` is invoked exactly once: with ``complete=True`` when the stream was + consumed to the end, with the exception when iteration failed, and with ``complete=False`` when the caller + stopped early (``close()`` or leaving the ``events()`` loop before exhaustion). + """ + + def __init__(self, wrapped, accumulator, on_done, span=None): + super().__init__(wrapped) + self._self_accumulator = accumulator + self._self_on_done = on_done + self._self_span = span + self._self_done = False + + def _span_scope(self): + """Make the client span current while the SDK stream is read; never held across a ``yield``.""" + if self._self_span is None: + return nullcontext() + # Stream errors are recorded once by ``on_done``; keep ``use_span`` from recording them a second time. + return use_span(self._self_span, end_on_exit=False, record_exception=False, set_status_on_exception=False) + + def events(self): + try: + iterator = self.__wrapped__.events() + while True: + with self._span_scope(): + try: + event = next(iterator) + except StopIteration: + break + self._self_accumulator.process(getattr(event, "data", None)) + yield event + except Exception as error: + self._finish(error, complete=False) + raise + except BaseException: + # ``GeneratorExit`` (the caller left the loop), ``KeyboardInterrupt``...: partial, not successful. The + # caller is done with the stream, so release the SDK's event source instead of waiting for GC. + self._finish(None, complete=False) + self._close_wrapped() + raise + else: + self._finish(None, complete=True) + + def close(self): + """Close the underlying SSE stream; finishes the span as incomplete unless it was already consumed.""" + try: + return self.__wrapped__.close() + finally: + self._finish(None, complete=False) + + def _close_wrapped(self): + """Best-effort close of the SDK's ``SSEClient``; never masks the exception being propagated.""" + try: + self.__wrapped__.close() + except Exception as error: + logger.debug("Failed to close the OCI GenAI SSE stream after cancellation: %s", error) + + def _finish(self, error, complete=True): + if self._self_done: + return + self._self_done = True + self._self_on_done(self._self_accumulator, error, complete) diff --git a/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/utils.py b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/utils.py new file mode 100644 index 0000000000..8415f4c8f9 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/utils.py @@ -0,0 +1,85 @@ +import logging +import os +import re +import traceback +from urllib.parse import urlparse + +from opentelemetry import context as context_api +from opentelemetry.instrumentation.oci_genai.config import Config + +TRACELOOP_TRACE_CONTENT = "TRACELOOP_TRACE_CONTENT" + +# The OCI SDK keeps unresolved realm/dual-stack placeholders such as ``{dualStack?ds.:}`` in +# ``base_client.endpoint`` until the request is actually built; strip them for ``server.address``. +_ENDPOINT_TEMPLATE_RE = re.compile(r"\{[^}]*\}") + + +def set_span_attribute(span, name, value): + if value is not None and value != "": + span.set_attribute(name, value) + + +def should_send_prompts(): + return (os.getenv(TRACELOOP_TRACE_CONTENT) or "true").lower() == "true" or context_api.get_value( + "override_enable_content_tracing" + ) + + +def dont_throw(func): + """ + A decorator that wraps the passed in function and logs exceptions instead of throwing them. + + @param func: The function to wrap + @return: The wrapper function + """ + # Obtain a logger specific to the function's module + logger = logging.getLogger(func.__module__) + + def wrapper(*args, **kwargs): + try: + return 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 wrapper + + +def should_emit_events() -> bool: + """ + Checks if the instrumentation isn't using the legacy attributes + and if the event logger is not None. + """ + + return not Config.use_legacy_attributes + + +def model_as_dict(model): + """Convert an OCI SDK model (or a container of models) into plain Python data.""" + if model is None or isinstance(model, (str, int, float, bool)): + return model + if isinstance(model, dict): + return {key: model_as_dict(value) for key, value in model.items()} + if isinstance(model, (list, tuple)): + return [model_as_dict(item) for item in model] + try: + from oci.util import to_dict + + return to_dict(model) + except Exception: + return getattr(model, "__dict__", str(model)) + + +def get_server_address(instance): + """Return the hostname of the OCI GenAI inference endpoint used by ``instance``.""" + endpoint = getattr(getattr(instance, "base_client", None), "endpoint", None) + if not isinstance(endpoint, str) or not endpoint: + return None + endpoint = _ENDPOINT_TEMPLATE_RE.sub("", endpoint) + parsed = urlparse(endpoint if "://" in endpoint else f"https://{endpoint}") + return parsed.hostname diff --git a/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/version.py b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/version.py new file mode 100644 index 0000000000..6475ab9c9f --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai/version.py @@ -0,0 +1 @@ +__version__ = "0.62.3" diff --git a/packages/opentelemetry-instrumentation-oci-genai/poetry.toml b/packages/opentelemetry-instrumentation-oci-genai/poetry.toml new file mode 100644 index 0000000000..ab1033bd37 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/poetry.toml @@ -0,0 +1,2 @@ +[virtualenvs] +in-project = true diff --git a/packages/opentelemetry-instrumentation-oci-genai/project.json b/packages/opentelemetry-instrumentation-oci-genai/project.json new file mode 100644 index 0000000000..d54bbbda24 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/project.json @@ -0,0 +1,77 @@ +{ + "name": "opentelemetry-instrumentation-oci-genai", + "$schema": "../../node_modules/nx/schemas/project-schema.json", + "projectType": "library", + "sourceRoot": "packages/opentelemetry-instrumentation-oci-genai/opentelemetry/instrumentation/oci_genai", + "targets": { + "lock": { + "executor": "nx:run-commands", + "options": { + "command": "uv lock", + "cwd": "packages/opentelemetry-instrumentation-oci-genai" + } + }, + "add": { + "executor": "@nxlv/python:add", + "options": {} + }, + "update": { + "executor": "@nxlv/python:update", + "options": {} + }, + "remove": { + "executor": "@nxlv/python:remove", + "options": {} + }, + "build": { + "executor": "@nxlv/python:build", + "outputs": [ + "{projectRoot}/dist" + ], + "options": { + "outputPath": "packages/opentelemetry-instrumentation-oci-genai/dist", + "publish": false, + "lockedVersions": true, + "bundleLocalDependencies": true + } + }, + "install": { + "executor": "nx:run-commands", + "options": { + "command": "uv sync --all-groups", + "cwd": "packages/opentelemetry-instrumentation-oci-genai" + } + }, + "lint": { + "executor": "nx:run-commands", + "options": { + "command": "uv run ruff check .", + "cwd": "packages/opentelemetry-instrumentation-oci-genai" + } + }, + "test": { + "executor": "nx:run-commands", + "outputs": [ + "{workspaceRoot}/reports/packages/opentelemetry-instrumentation-oci-genai/unittests", + "{workspaceRoot}/coverage/packages/opentelemetry-instrumentation-oci-genai" + ], + "options": { + "command": "uv run pytest tests/", + "cwd": "packages/opentelemetry-instrumentation-oci-genai" + } + }, + "build-release": { + "executor": "nx:run-commands", + "options": { + "commands": [ + "chmod +x ../../scripts/build-release.sh", + "../../scripts/build-release.sh" + ], + "cwd": "packages/opentelemetry-instrumentation-oci-genai" + } + } + }, + "tags": [ + "instrumentation" + ] +} diff --git a/packages/opentelemetry-instrumentation-oci-genai/pyproject.toml b/packages/opentelemetry-instrumentation-oci-genai/pyproject.toml new file mode 100644 index 0000000000..6af931b90d --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/pyproject.toml @@ -0,0 +1,73 @@ +[project] +name = "opentelemetry-instrumentation-oci-genai" +version = "0.62.3" +description = "OpenTelemetry OCI Generative AI instrumentation" +authors = [ + { name = "Federico Kamelhar", email = "federico.kamelhar@oracle.com" }, +] +license = "Apache-2.0" +readme = "README.md" +requires-python = ">=3.10,<4" +dependencies = [ + "opentelemetry-api>=1.38.0,<2", + "opentelemetry-instrumentation>=0.59b0", + "opentelemetry-semantic-conventions-ai>=0.5.1,<0.6.0", + "opentelemetry-semantic-conventions>=0.63b1", +] + +[project.urls] +Repository = "https://github.com/traceloop/openllmetry/tree/main/packages/opentelemetry-instrumentation-oci-genai" + +[project.optional-dependencies] +instruments = ["oci"] + +[project.entry-points."opentelemetry_instrumentor"] +oci = "opentelemetry.instrumentation.oci_genai:OCIGenAIInstrumentor" + +[dependency-groups] +dev = [ + "autopep8>=2.2.0,<3", + "pytest-sugar==1.0.0", + "pytest>=8.2.2,<9", + "ruff>=0.4.0", +] +test = [ + "oci>=2.121.1,<3", + "opentelemetry-sdk>=1.38.0,<2", + "pytest-recording>=0.13.1,<0.14.0", + "pytest-sugar==1.0.0", + "pytest>=8.2.2,<9", + "vcrpy>=8.0.0,<9", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["opentelemetry"] + +[tool.coverage.run] +branch = true +source = ["opentelemetry/instrumentation/oci_genai"] + +[tool.coverage.report] +exclude_lines = ["if TYPE_CHECKING:"] +show_missing = true + +[tool.ruff] +line-length = 120 +exclude = [ + ".git", + "__pycache__", + "build", + "dist", + ".venv", + ".pytest_cache", +] + +[tool.ruff.lint] +select = ["E", "F", "W"] + +[tool.uv] +constraint-dependencies = ["urllib3>=2.6.3", "pip>=25.3"] diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/__init__.py b/packages/opentelemetry-instrumentation-oci-genai/tests/__init__.py new file mode 100644 index 0000000000..33248208eb --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/__init__.py @@ -0,0 +1,62 @@ +"""unit tests.""" + +from opentelemetry.sdk._logs import ReadableLogRecord +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) + +from opentelemetry.instrumentation.oci_genai.span_utils import OCI_GENAI_PROVIDER + +VALID_OTEL_FINISH_REASONS = {"stop", "tool_call", "length", "content_filter", "error"} +VALID_OTEL_PART_TYPES = {"text", "tool_call", "tool_call_response", "blob", "uri", "reasoning"} + + +def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict): + """Shared helper: validate an emitted OTel GenAI event log record.""" + assert log.log_record.event_name == event_name + assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) == OCI_GENAI_PROVIDER + + if not expected_content: + assert not log.log_record.body + else: + assert log.log_record.body + assert dict(log.log_record.body) == expected_content + + +def assert_valid_parts(parts, context=""): + """Assert every part has a valid OTel type and required fields.""" + assert isinstance(parts, list), f"{context}: parts must be a list, got {type(parts)}" + for i, part in enumerate(parts): + assert isinstance(part, dict), f"{context}: part[{i}] must be a dict" + assert "type" in part, f"{context}: part[{i}] missing 'type' key: {part}" + ptype = part["type"] + assert ptype in VALID_OTEL_PART_TYPES, ( + f"{context}: part[{i}] has invalid type '{ptype}'. Must be one of {VALID_OTEL_PART_TYPES}. Got: {part}" + ) + if ptype == "text": + assert "content" in part, f"{context}: text part[{i}] missing 'content'" + elif ptype == "tool_call": + assert "name" in part, f"{context}: tool_call part[{i}] missing 'name'" + assert "arguments" in part, f"{context}: tool_call part[{i}] missing 'arguments'" + elif ptype == "tool_call_response": + assert "id" in part, f"{context}: tool_call_response part[{i}] missing 'id'" + elif ptype == "blob": + assert "mime_type" in part, f"{context}: blob part[{i}] missing 'mime_type'" + assert "modality" in part, f"{context}: blob part[{i}] missing 'modality'" + elif ptype == "uri": + assert "uri" in part, f"{context}: uri part[{i}] missing 'uri'" + elif ptype == "reasoning": + assert "content" in part, f"{context}: reasoning part[{i}] missing 'content'" + + +def assert_valid_output_message(msg, context=""): + """Assert an output message has role, valid parts, and valid finish_reason.""" + assert isinstance(msg, dict), f"{context}: message must be a dict" + assert "role" in msg, f"{context}: message missing 'role'" + assert "parts" in msg, f"{context}: message missing 'parts'" + assert "finish_reason" in msg, f"{context}: message missing 'finish_reason'" + assert_valid_parts(msg["parts"], context) + if msg["finish_reason"]: + assert msg["finish_reason"] in VALID_OTEL_FINISH_REASONS, ( + f"{context}: invalid finish_reason '{msg['finish_reason']}'. Must be one of {VALID_OTEL_FINISH_REASONS}" + ) diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_cohere_chat_legacy.yaml b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_cohere_chat_legacy.yaml new file mode 100644 index 0000000000..d8bd2ac668 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_cohere_chat_legacy.yaml @@ -0,0 +1,42 @@ +interactions: +- request: + body: '{"compartmentId": "ocid1.compartment.oc1..redacted", "servingMode": {"servingType": + "ON_DEMAND", "modelId": "cohere.command-a-03-2025"}, "chatRequest": {"apiFormat": + "COHERE", "message": "Tell me a joke about opentelemetry", "chatHistory": [{"role": + "USER", "message": "Hello"}, {"role": "CHATBOT", "message": "Hi! How can I help?"}], + "preambleOverride": "You are a terse assistant.", "maxTokens": 100, "temperature": + 0.3, "topP": 0.8, "seed": 42, "stopSequences": ["END"]}}' + headers: + Content-Length: + - '526' + accept: + - application/json, text/event-stream + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-type: + - application/json + host: + - inference.generativeai.us-chicago-1.oci.oraclecloud.com + method: POST + uri: https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/chat + response: + body: + string: '{"modelId":"cohere.command-a-03-2025","modelVersion":"1.0","chatResponse":{"apiFormat":"COHERE","text":"Why + did the OpenTelemetry span cross the road?\n\nTo trace the other side.<|","chatHistory":[{"role":"USER","message":"Hello"},{"role":"CHATBOT","message":"Hi! + How can I help?"},{"role":"USER","message":"Tell me a joke about opentelemetry"},{"role":"CHATBOT","message":"Why + did the OpenTelemetry span cross the road?\n\nTo trace the other side.<|"}],"finishReason":null,"usage":{"completionTokens":19,"promptTokens":22,"totalTokens":41}}}' + headers: + Connection: + - keep-alive + Content-Type: + - application/json + Date: + - Fri, 18 Sep 2026 19:47:08 GMT + content-length: + - '542' + status: + code: 200 + message: OK +version: 1 diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_cohere_chat_streaming.yaml b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_cohere_chat_streaming.yaml new file mode 100644 index 0000000000..916509cabb --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_cohere_chat_streaming.yaml @@ -0,0 +1,97 @@ +interactions: +- request: + body: '{"compartmentId": "ocid1.compartment.oc1..redacted", "servingMode": {"servingType": + "ON_DEMAND", "modelId": "cohere.command-a-03-2025"}, "chatRequest": {"apiFormat": + "COHERE", "message": "Tell me a joke about opentelemetry", "chatHistory": [{"role": + "USER", "message": "Hello"}, {"role": "CHATBOT", "message": "Hi! How can I help?"}], + "preambleOverride": "You are a terse assistant.", "isStream": true, "streamOptions": + {"isIncludeUsage": true}, "maxTokens": 100, "temperature": 0.3}}' + headers: + Content-Length: + - '536' + accept: + - application/json, text/event-stream + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-type: + - application/json + host: + - inference.generativeai.us-chicago-1.oci.oraclecloud.com + method: POST + uri: https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/chat + response: + body: + string: 'data: {"apiFormat":"COHERE","text":"Why","pad":"aaa"} + + + data: {"apiFormat":"COHERE","text":" did","pad":"aaaaaa"} + + + data: {"apiFormat":"COHERE","text":" the","pad":"aaaaaaaa"} + + + data: {"apiFormat":"COHERE","text":" Open","pad":"aaaaa"} + + + data: {"apiFormat":"COHERE","text":"Telemetry","pad":"aaaaaaa"} + + + data: {"apiFormat":"COHERE","text":" span","pad":"aaaaaaaaa"} + + + data: {"apiFormat":"COHERE","text":" cross","pad":"aaaaaaaaaaa"} + + + data: {"apiFormat":"COHERE","text":" the","pad":"aaa"} + + + data: {"apiFormat":"COHERE","text":" road","pad":"aaaaaaaaaaaa"} + + + data: {"apiFormat":"COHERE","text":"?","pad":"aaaaaaaa"} + + + data: {"apiFormat":"COHERE","text":"\n\nTo","pad":"aaaaaaaaaaaa"} + + + data: {"apiFormat":"COHERE","text":" trace","pad":"aaaaaaaaaaa"} + + + data: {"apiFormat":"COHERE","text":" the","pad":"aaaaaa"} + + + data: {"apiFormat":"COHERE","text":" other","pad":"aaaaa"} + + + data: {"apiFormat":"COHERE","text":" side","pad":"aaaaaa"} + + + data: {"apiFormat":"COHERE","text":".","pad":"aaaa"} + + + data: {"apiFormat":"COHERE","text":"Why did the OpenTelemetry span cross the + road?\n\nTo trace the other side.","chatHistory":[{"role":"USER","message":"Hello"},{"role":"CHATBOT","message":"Hi! + How can I help?"},{"role":"USER","message":"Tell me a joke about opentelemetry"},{"role":"CHATBOT","message":"Why + did the OpenTelemetry span cross the road?\n\nTo trace the other side."}],"finishReason":"COMPLETE","pad":"aaaaaaaaaaa","usage":{"completionTokens":18,"promptTokens":22,"totalTokens":40}} + + + ' + headers: + Connection: + - keep-alive + Content-Type: + - text/event-stream + Date: + - Fri, 18 Sep 2026 19:47:09 GMT + Transfer-Encoding: + - chunked + cache-control: + - no-cache + content-length: + - '1471' + status: + code: 200 + message: OK +version: 1 diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_cohere_chat_with_events_with_content.yaml b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_cohere_chat_with_events_with_content.yaml new file mode 100644 index 0000000000..8311d2f7f4 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_cohere_chat_with_events_with_content.yaml @@ -0,0 +1,41 @@ +interactions: +- request: + body: '{"compartmentId": "ocid1.compartment.oc1..redacted", "servingMode": {"servingType": + "ON_DEMAND", "modelId": "cohere.command-a-03-2025"}, "chatRequest": {"apiFormat": + "COHERE", "message": "Tell me a joke about opentelemetry", "chatHistory": [{"role": + "USER", "message": "Hello"}, {"role": "CHATBOT", "message": "Hi! 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(get it?)"}],"toolCalls":[]},"finishReason":"stop","logprobs":{}}],"usage":{"completionTokens":35,"promptTokens":43,"totalTokens":78}}}' + headers: + Connection: + - keep-alive + Content-Type: + - application/json + Date: + - Fri, 18 Sep 2026 19:46:59 GMT + content-length: + - '504' + status: + code: 200 + message: OK +version: 1 diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_generic_chat_streaming.yaml b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_generic_chat_streaming.yaml new file mode 100644 index 0000000000..72cfb8a74d --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_chat/test_generic_chat_streaming.yaml @@ -0,0 +1,172 @@ +interactions: +- request: + body: '{"compartmentId": "ocid1.compartment.oc1..redacted", "servingMode": {"servingType": + "ON_DEMAND", "modelId": "meta.llama-3.3-70b-instruct"}, "chatRequest": {"apiFormat": + "GENERIC", "messages": [{"role": "USER", "content": [{"type": "TEXT", "text": + "Tell me a joke about opentelemetry"}]}], "isStream": true, "streamOptions": + {"isIncludeUsage": true}, "temperature": 0.2, "maxTokens": 100}}' + headers: + Content-Length: + - 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+ headers: + Connection: + - keep-alive + Content-Type: + - application/json + Date: + - Fri, 18 Sep 2026 19:47:13 GMT + content-length: + - '38610' + status: + code: 200 + message: OK +version: 1 diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_rerank/test_rerank_text_legacy.yaml b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_rerank/test_rerank_text_legacy.yaml new file mode 100644 index 0000000000..c6e130295e --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_rerank/test_rerank_text_legacy.yaml @@ -0,0 +1,37 @@ +interactions: +- request: + body: '{"input": "What is the capital of France?", "compartmentId": "ocid1.compartment.oc1..redacted", + "servingMode": {"servingType": "ON_DEMAND", "modelId": "cohere.rerank-v4.0-fast"}, + "documents": ["Paris is the capital of France.", "Berlin is the capital of Germany.", + "The Eiffel Tower is located in Paris."], "topN": 2}' + headers: + Content-Length: + - '369' + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-type: + - application/json + host: + - inference.generativeai.us-chicago-1.oci.oraclecloud.com + method: POST + uri: https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/rerankText + response: + body: + string: '{"id":"REDACTED/REDACTED/REDACTED","modelId":"cohere.rerank-v4.0-fast","documentRanks":[{"index":0,"relevanceScore":0.8570099},{"index":2,"relevanceScore":0.42403036}]}' + headers: + Connection: + - keep-alive + Content-Type: + - application/json + Date: + - Fri, 18 Sep 2026 19:51:44 GMT + content-length: + - '240' + status: + code: 200 + message: OK +version: 1 diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_rerank/test_rerank_text_with_events_with_content.yaml b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_rerank/test_rerank_text_with_events_with_content.yaml new file mode 100644 index 0000000000..389c9435b9 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/cassettes/test_rerank/test_rerank_text_with_events_with_content.yaml @@ -0,0 +1,37 @@ +interactions: +- request: + body: '{"input": "What is the capital of France?", "compartmentId": "ocid1.compartment.oc1..redacted", + "servingMode": {"servingType": "ON_DEMAND", "modelId": "cohere.rerank-v4.0-fast"}, + "documents": ["Paris is the capital of France.", "Berlin is the capital of Germany.", + "The Eiffel Tower is located in Paris."], "topN": 2}' + headers: + Content-Length: + - '369' + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-type: + - application/json + host: + - inference.generativeai.us-chicago-1.oci.oraclecloud.com + method: POST + uri: https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/rerankText + response: + body: + string: '{"id":"REDACTED/REDACTED/REDACTED","modelId":"cohere.rerank-v4.0-fast","documentRanks":[{"index":0,"relevanceScore":0.8570099},{"index":2,"relevanceScore":0.42403036}]}' + headers: + Connection: + - keep-alive + Content-Type: + - application/json + Date: + - Fri, 18 Sep 2026 19:51:14 GMT + content-length: + - '240' + status: + code: 200 + message: OK +version: 1 diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/conftest.py b/packages/opentelemetry-instrumentation-oci-genai/tests/conftest.py new file mode 100644 index 0000000000..ebdfa559c1 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/conftest.py @@ -0,0 +1,219 @@ +"""Unit tests configuration module. + +Recording cassettes requires OCI credentials:: + + OCI_CONFIG_PROFILE= OCI_COMPARTMENT_ID= uv run pytest tests/ --record-mode=once + +Both API key and session token (``security_token_file``) profiles are supported. Cassettes are scrubbed: the +``authorization`` signature (which embeds the session token), ``opc-request-id`` headers, request ids echoed in +response bodies and every OCID (including ``compartmentId``) are replaced before anything is written to disk. +""" + +import os +import re + +import oci +import oci.base_client +import pytest +from cryptography.hazmat.primitives.asymmetric import rsa +from oci.generative_ai_inference import GenerativeAiInferenceClient +from opentelemetry.instrumentation.oci_genai import OCIGenAIInstrumentor +from opentelemetry.instrumentation.oci_genai.utils import TRACELOOP_TRACE_CONTENT +from opentelemetry.sdk._logs import LoggerProvider +from opentelemetry.sdk._logs.export import ( + InMemoryLogExporter, + SimpleLogRecordProcessor, +) +from opentelemetry.sdk.metrics import Counter, Histogram, MeterProvider +from opentelemetry.sdk.metrics.export import ( + AggregationTemporality, + InMemoryMetricReader, +) +from opentelemetry.sdk.resources import Resource +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter +from vcr.stubs.urllib3_stubs import VCRRequestsHTTPSConnection + +REDACTED_COMPARTMENT_ID = "ocid1.compartment.oc1..redacted" +REDACTED_REQUEST_ID = "REDACTED/REDACTED/REDACTED" + +_OCID_RE = re.compile(r"ocid1\.(?P[a-z0-9_-]+)\.oc[0-9]+\.[a-z0-9-]*\.[a-zA-Z0-9]+") +_REQUEST_ID_RE = re.compile(r"[0-9A-F]{32}/[0-9A-F]{32}/[0-9A-F]{32}") +_SCRUBBED_RESPONSE_HEADERS = ("opc-request-id",) + + +def _scrub_text(text: str) -> str: + text = _OCID_RE.sub(lambda match: f"ocid1.{match.group('type')}.oc1..redacted", text) + return _REQUEST_ID_RE.sub(REDACTED_REQUEST_ID, text) + + +def _scrub_body(body): + if body is None: + return body + if isinstance(body, bytes): + return _scrub_text(body.decode("utf-8", errors="replace")).encode("utf-8") + if isinstance(body, str): + return _scrub_text(body) + return body + + +def _scrub_request(request): + request.body = _scrub_body(request.body) + request.uri = _scrub_text(request.uri) + return request + + +def _scrub_response(response): + headers = response.get("headers") or {} + for header in list(headers): + if header.lower() in _SCRUBBED_RESPONSE_HEADERS: + del headers[header] + body = response.get("body") or {} + if "string" in body: + body["string"] = _scrub_body(body["string"]) + return response + + +def _playback_signer(): + """Signer used for cassette playback: a throwaway key and a dummy session token. + + The resulting ``authorization`` header is filtered out of the cassettes and ignored when matching requests. + """ + private_key = rsa.generate_private_key(public_exponent=65537, key_size=2048) + return oci.auth.signers.SecurityTokenSigner("playback-token", private_key) + + +def _build_client(region): + # No retries (deterministic cassettes) and a generous read timeout for cold model endpoints. + client_kwargs = {"retry_strategy": oci.retry.NoneRetryStrategy(), "timeout": (10, 240)} + profile = os.environ.get("OCI_CONFIG_PROFILE") + if not profile: + return GenerativeAiInferenceClient(config={"region": region}, signer=_playback_signer(), **client_kwargs) + + config = oci.config.from_file(profile_name=profile) + if config.get("security_token_file"): + with open(os.path.expanduser(config["security_token_file"])) as token_file: + token = token_file.read() + private_key = oci.signer.load_private_key_from_file( + os.path.expanduser(config["key_file"]), config.get("pass_phrase") + ) + signer = oci.auth.signers.SecurityTokenSigner(token, private_key) + return GenerativeAiInferenceClient(config={"region": region}, signer=signer, **client_kwargs) + config["region"] = region + return GenerativeAiInferenceClient(config, **client_kwargs) + + +@pytest.fixture(scope="function", name="span_exporter") +def fixture_span_exporter(): + exporter = InMemorySpanExporter() + yield exporter + + +@pytest.fixture(scope="function", name="tracer_provider") +def fixture_tracer_provider(span_exporter): + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + return provider + + +@pytest.fixture(scope="function", name="log_exporter") +def fixture_log_exporter(): + exporter = InMemoryLogExporter() + yield exporter + + +@pytest.fixture(scope="function", name="logger_provider") +def fixture_logger_provider(log_exporter): + provider = LoggerProvider() + provider.add_log_record_processor(SimpleLogRecordProcessor(log_exporter)) + return provider + + +@pytest.fixture(scope="function", name="reader") +def fixture_reader(): + reader = InMemoryMetricReader({Counter: AggregationTemporality.DELTA, Histogram: AggregationTemporality.DELTA}) + return reader + + +@pytest.fixture(scope="function", name="meter_provider") +def fixture_meter_provider(reader): + resource = Resource.create() + meter_provider = MeterProvider(metric_readers=[reader], resource=resource) + + return meter_provider + + +@pytest.fixture +def compartment_id(): + return os.environ.get("OCI_COMPARTMENT_ID", REDACTED_COMPARTMENT_ID) + + +@pytest.fixture +def oci_client(): + return _build_client(os.environ.get("OCI_TEST_REGION", "us-chicago-1")) + + +@pytest.fixture(scope="function") +def instrument_legacy(reader, tracer_provider, meter_provider): + instrumentor = OCIGenAIInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + meter_provider=meter_provider, + ) + + yield instrumentor + + instrumentor.uninstrument() + + +@pytest.fixture(scope="function") +def instrument_with_content(reader, tracer_provider, logger_provider, meter_provider, monkeypatch): + monkeypatch.setenv(TRACELOOP_TRACE_CONTENT, "True") + + instrumentor = OCIGenAIInstrumentor(use_attributes=False) + instrumentor.instrument( + tracer_provider=tracer_provider, + logger_provider=logger_provider, + meter_provider=meter_provider, + ) + + yield instrumentor + + instrumentor.uninstrument() + + +@pytest.fixture(scope="function") +def instrument_with_no_content(reader, tracer_provider, logger_provider, meter_provider, monkeypatch): + monkeypatch.setenv(TRACELOOP_TRACE_CONTENT, "False") + + instrumentor = OCIGenAIInstrumentor(use_attributes=False) + instrumentor.instrument( + tracer_provider=tracer_provider, + logger_provider=logger_provider, + meter_provider=meter_provider, + ) + + yield instrumentor + + instrumentor.uninstrument() + + +@pytest.fixture(scope="module") +def vcr_config(): + return { + # The OCI SDK routes HTTPS through its own ``OCIConnectionPool`` (``ConnectionCls = OCIConnection``), + # which bypasses VCR's stock urllib3 patch; patch that pool's connection class explicitly. + "custom_patches": ((oci.base_client.OCIConnectionPool, "ConnectionCls", VCRRequestsHTTPSConnection),), + "filter_headers": [ + "authorization", + "opc-request-id", + "opc-client-info", + "x-content-sha256", + "date", + "user-agent", + ], + "before_record_request": _scrub_request, + "before_record_response": _scrub_response, + "decode_compressed_response": True, + } diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/test_chat.py b/packages/opentelemetry-instrumentation-oci-genai/tests/test_chat.py new file mode 100644 index 0000000000..883cb2a3cd --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/test_chat.py @@ -0,0 +1,413 @@ +import json + +import pytest +from oci.generative_ai_inference import models +from opentelemetry.instrumentation.oci_genai.span_utils import ( + GEN_AI_OCI_API_FORMAT, + GEN_AI_OCI_SERVING_MODE, + OCI_GENAI_PROVIDER, +) +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv_ai import SpanAttributes + +from tests import assert_message_in_logs, assert_valid_output_message, assert_valid_parts + +LLAMA_MODEL = "meta.llama-3.3-70b-instruct" +COHERE_MODEL = "cohere.command-a-03-2025" +OPENAI_MODEL = "openai.gpt-5.4" +PROMPT = "Tell me a joke about opentelemetry" +SERVER_ADDRESS = "inference.generativeai.us-chicago-1.oci.oraclecloud.com" + + +def _generic_request(text, **kwargs): + return models.GenericChatRequest( + api_format=models.BaseChatRequest.API_FORMAT_GENERIC, + messages=[models.UserMessage(content=[models.TextContent(text=text)])], + **kwargs, + ) + + +def _chat(client, compartment_id, model, chat_request): + return client.chat( + models.ChatDetails( + compartment_id=compartment_id, + serving_mode=models.OnDemandServingMode(model_id=model), + chat_request=chat_request, + ) + ) + + +def _assert_common_attributes(span, model, api_format): + assert span.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME] == OCI_GENAI_PROVIDER + assert span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat" + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == model + assert span.attributes[GEN_AI_OCI_SERVING_MODE] == "ON_DEMAND" + assert span.attributes[GEN_AI_OCI_API_FORMAT] == api_format + assert span.attributes["server.address"] == SERVER_ADDRESS + + +def _assert_usage(span): + input_tokens = span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] + output_tokens = span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] + assert input_tokens > 0 + assert output_tokens > 0 + assert span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == input_tokens + output_tokens + + +# --------------------------------------------------------------------------- +# GENERIC api format (Meta Llama) +# --------------------------------------------------------------------------- + + +@pytest.mark.vcr +def test_generic_chat_legacy(instrument_legacy, oci_client, compartment_id, span_exporter, log_exporter): + response = _chat( + oci_client, + compartment_id, + LLAMA_MODEL, + _generic_request(PROMPT, max_tokens=100, temperature=0.2, top_p=0.9, top_k=40, frequency_penalty=0.1), + ) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"chat {LLAMA_MODEL}"] + span = spans[0] + + _assert_common_attributes(span, LLAMA_MODEL, "GENERIC") + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL] == LLAMA_MODEL + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 100 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE] == 0.2 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.9 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 40 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_FREQUENCY_PENALTY] == 0.1 + assert span.attributes[SpanAttributes.GEN_AI_IS_STREAMING] is False + + input_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES]) + assert input_messages == [{"role": "user", "parts": [{"type": "text", "content": PROMPT}]}] + + generated_text = response.data.chat_response.choices[0].message.content[0].text + output_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES]) + assert len(output_messages) == 1 + assert_valid_output_message(output_messages[0]) + assert output_messages[0]["role"] == "assistant" + assert output_messages[0]["parts"] == [{"type": "text", "content": generated_text}] + assert output_messages[0]["finish_reason"] == "stop" + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS] == ("stop",) + + _assert_usage(span) + usage = response.data.chat_response.usage + assert span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == usage.prompt_tokens + assert span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == usage.completion_tokens + + logs = log_exporter.get_finished_logs() + assert len(logs) == 0, "Assert that it doesn't emit logs when use_attributes is True" + + +@pytest.mark.vcr +def test_generic_chat_streaming(instrument_legacy, oci_client, compartment_id, span_exporter, log_exporter): + response = _chat( + oci_client, + compartment_id, + LLAMA_MODEL, + _generic_request( + PROMPT, + max_tokens=100, + temperature=0.2, + is_stream=True, + stream_options=models.StreamOptions(is_include_usage=True), + ), + ) + + # The span is only completed once the SSE stream has been consumed + assert span_exporter.get_finished_spans() == () + + chunks = [] + for event in response.data.events(): + if event.data == "[DONE]": + continue + chunks.append(json.loads(event.data)) + + streamed_text = "".join( + part["text"] for chunk in chunks for part in (chunk.get("message") or {}).get("content", []) + ) + assert streamed_text + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"chat {LLAMA_MODEL}"] + span = spans[0] + + _assert_common_attributes(span, LLAMA_MODEL, "GENERIC") + assert span.attributes[SpanAttributes.GEN_AI_IS_STREAMING] is True + + input_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES]) + assert input_messages[0]["parts"][0]["content"] == PROMPT + + output_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES]) + assert_valid_output_message(output_messages[0]) + assert output_messages[0]["parts"] == [{"type": "text", "content": streamed_text}] + assert output_messages[0]["finish_reason"] == "stop" + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS] == ("stop",) + + usage_chunk = next(chunk["usage"] for chunk in chunks if "usage" in chunk) + assert span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == usage_chunk["promptTokens"] + assert span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == usage_chunk["completionTokens"] + assert span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == usage_chunk["totalTokens"] + + assert len(log_exporter.get_finished_logs()) == 0 + + +@pytest.mark.vcr +def test_generic_chat_with_events_with_content( + instrument_with_content, oci_client, compartment_id, span_exporter, log_exporter +): + response = _chat(oci_client, compartment_id, LLAMA_MODEL, _generic_request(PROMPT, max_tokens=100)) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"chat {LLAMA_MODEL}"] + span = spans[0] + _assert_common_attributes(span, LLAMA_MODEL, "GENERIC") + _assert_usage(span) + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS] == ("stop",) + assert GenAIAttributes.GEN_AI_INPUT_MESSAGES not in span.attributes + assert GenAIAttributes.GEN_AI_OUTPUT_MESSAGES not in span.attributes + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + + assert_message_in_logs(logs[0], "gen_ai.user.message", {"content": PROMPT}) + + generated_text = response.data.chat_response.choices[0].message.content[0].text + assert_message_in_logs( + logs[1], + "gen_ai.choice", + {"index": 0, "finish_reason": "stop", "message": {"content": generated_text}}, + ) + + +@pytest.mark.vcr +def test_generic_chat_with_events_with_no_content( + instrument_with_no_content, oci_client, compartment_id, span_exporter, log_exporter +): + _chat(oci_client, compartment_id, LLAMA_MODEL, _generic_request(PROMPT, max_tokens=100)) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"chat {LLAMA_MODEL}"] + span = spans[0] + _assert_common_attributes(span, LLAMA_MODEL, "GENERIC") + _assert_usage(span) + assert GenAIAttributes.GEN_AI_INPUT_MESSAGES not in span.attributes + assert GenAIAttributes.GEN_AI_OUTPUT_MESSAGES not in span.attributes + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + + assert_message_in_logs(logs[0], "gen_ai.user.message", {}) + assert_message_in_logs(logs[1], "gen_ai.choice", {"index": 0, "finish_reason": "stop", "message": {}}) + + +@pytest.mark.vcr +def test_generic_chat_streaming_with_events_with_content( + instrument_with_content, oci_client, compartment_id, span_exporter, log_exporter +): + response = _chat( + oci_client, + compartment_id, + LLAMA_MODEL, + _generic_request(PROMPT, max_tokens=100, is_stream=True), + ) + streamed_text = "".join( + part["text"] + for event in response.data.events() + if event.data != "[DONE]" + for part in (json.loads(event.data).get("message") or {}).get("content", []) + ) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"chat {LLAMA_MODEL}"] + assert spans[0].attributes[SpanAttributes.GEN_AI_IS_STREAMING] is True + assert GenAIAttributes.GEN_AI_OUTPUT_MESSAGES not in spans[0].attributes + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + assert_message_in_logs(logs[0], "gen_ai.user.message", {"content": PROMPT}) + assert_message_in_logs( + logs[1], + "gen_ai.choice", + {"index": 0, "finish_reason": "stop", "message": {"content": streamed_text}}, + ) + + +# --------------------------------------------------------------------------- +# GENERIC api format (OpenAI) +# --------------------------------------------------------------------------- + + +@pytest.mark.vcr +def test_openai_generic_chat_legacy(instrument_legacy, oci_client, compartment_id, span_exporter, log_exporter): + chat_request = models.GenericChatRequest( + api_format=models.BaseChatRequest.API_FORMAT_GENERIC, + messages=[ + models.SystemMessage(content=[models.TextContent(text="You are a terse assistant.")]), + models.UserMessage(content=[models.TextContent(text=PROMPT)]), + ], + max_completion_tokens=120, + ) + response = _chat(oci_client, compartment_id, OPENAI_MODEL, chat_request) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"chat {OPENAI_MODEL}"] + span = spans[0] + + _assert_common_attributes(span, OPENAI_MODEL, "GENERIC") + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL] == OPENAI_MODEL + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 120 + + input_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES]) + assert [message["role"] for message in input_messages] == ["system", "user"] + assert input_messages[0]["parts"] == [{"type": "text", "content": "You are a terse assistant."}] + assert input_messages[1]["parts"] == [{"type": "text", "content": PROMPT}] + + generated_text = response.data.chat_response.choices[0].message.content[0].text + output_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES]) + assert_valid_output_message(output_messages[0]) + assert output_messages[0]["parts"] == [{"type": "text", "content": generated_text}] + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS] == ("stop",) + + _assert_usage(span) + usage = response.data.chat_response.usage + assert span.attributes[GenAIAttributes.GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS] == ( + usage.prompt_tokens_details.cached_tokens + ) + assert span.attributes[SpanAttributes.GEN_AI_USAGE_REASONING_TOKENS] == ( + usage.completion_tokens_details.reasoning_tokens + ) + + assert len(log_exporter.get_finished_logs()) == 0 + + +# --------------------------------------------------------------------------- +# COHERE api format +# --------------------------------------------------------------------------- + + +def _cohere_request(**kwargs): + return models.CohereChatRequest( + api_format=models.BaseChatRequest.API_FORMAT_COHERE, + preamble_override="You are a terse assistant.", + chat_history=[ + models.CohereUserMessage(message="Hello"), + models.CohereChatBotMessage(message="Hi! How can I help?"), + ], + message=PROMPT, + **kwargs, + ) + + +@pytest.mark.vcr +def test_cohere_chat_legacy(instrument_legacy, oci_client, compartment_id, span_exporter, log_exporter): + response = _chat( + oci_client, + compartment_id, + COHERE_MODEL, + _cohere_request(max_tokens=100, temperature=0.3, top_p=0.8, seed=42, stop_sequences=["END"]), + ) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"chat {COHERE_MODEL}"] + span = spans[0] + + _assert_common_attributes(span, COHERE_MODEL, "COHERE") + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL] == COHERE_MODEL + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 100 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE] == 0.3 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.8 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_SEED] == 42 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_STOP_SEQUENCES] == ("END",) + assert span.attributes[SpanAttributes.GEN_AI_IS_STREAMING] is False + + system_instructions = json.loads(span.attributes[GenAIAttributes.GEN_AI_SYSTEM_INSTRUCTIONS]) + assert system_instructions == [{"type": "text", "content": "You are a terse assistant."}] + + input_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES]) + assert [message["role"] for message in input_messages] == ["user", "assistant", "user"] + for message in input_messages: + assert_valid_parts(message["parts"]) + assert input_messages[0]["parts"] == [{"type": "text", "content": "Hello"}] + assert input_messages[1]["parts"] == [{"type": "text", "content": "Hi! How can I help?"}] + assert input_messages[2]["parts"] == [{"type": "text", "content": PROMPT}] + + output_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES]) + assert len(output_messages) == 1 + assert_valid_output_message(output_messages[0]) + assert output_messages[0]["parts"] == [{"type": "text", "content": response.data.chat_response.text}] + # The service answered this request (which sets stop_sequences) with ``finishReason: null``; the + # OutputMessage schema still requires the key, so it is emitted as an empty string and the + # span-level finish reasons attribute is omitted. + assert response.data.chat_response.finish_reason is None + assert output_messages[0]["finish_reason"] == "" + assert GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS not in span.attributes + + _assert_usage(span) + assert len(log_exporter.get_finished_logs()) == 0 + + +@pytest.mark.vcr +def test_cohere_chat_streaming(instrument_legacy, oci_client, compartment_id, span_exporter, log_exporter): + response = _chat( + oci_client, + compartment_id, + COHERE_MODEL, + _cohere_request( + max_tokens=100, temperature=0.3, is_stream=True, stream_options=models.StreamOptions(is_include_usage=True) + ), + ) + + assert span_exporter.get_finished_spans() == () + + chunks = [json.loads(event.data) for event in response.data.events() if event.data != "[DONE]"] + final_chunk = next(chunk for chunk in chunks if "finishReason" in chunk) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"chat {COHERE_MODEL}"] + span = spans[0] + + _assert_common_attributes(span, COHERE_MODEL, "COHERE") + assert span.attributes[SpanAttributes.GEN_AI_IS_STREAMING] is True + + output_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES]) + assert_valid_output_message(output_messages[0]) + assert output_messages[0]["parts"] == [{"type": "text", "content": final_chunk["text"]}] + assert output_messages[0]["finish_reason"] == "stop" + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS] == ("stop",) + + assert span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == final_chunk["usage"]["promptTokens"] + assert span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == final_chunk["usage"]["completionTokens"] + assert span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == final_chunk["usage"]["totalTokens"] + + assert len(log_exporter.get_finished_logs()) == 0 + + +@pytest.mark.vcr +def test_cohere_chat_with_events_with_content( + instrument_with_content, oci_client, compartment_id, span_exporter, log_exporter +): + response = _chat(oci_client, compartment_id, COHERE_MODEL, _cohere_request(max_tokens=100)) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"chat {COHERE_MODEL}"] + _assert_common_attributes(spans[0], COHERE_MODEL, "COHERE") + assert GenAIAttributes.GEN_AI_INPUT_MESSAGES not in spans[0].attributes + assert GenAIAttributes.GEN_AI_SYSTEM_INSTRUCTIONS not in spans[0].attributes + + logs = log_exporter.get_finished_logs() + assert len(logs) == 5 + assert_message_in_logs(logs[0], "gen_ai.system.message", {"content": "You are a terse assistant."}) + assert_message_in_logs(logs[1], "gen_ai.user.message", {"content": "Hello"}) + assert_message_in_logs(logs[2], "gen_ai.assistant.message", {"content": "Hi! How can I help?"}) + assert_message_in_logs(logs[3], "gen_ai.user.message", {"content": PROMPT}) + assert_message_in_logs( + logs[4], + "gen_ai.choice", + {"index": 0, "finish_reason": "stop", "message": {"content": response.data.chat_response.text}}, + ) diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/test_embeddings.py b/packages/opentelemetry-instrumentation-oci-genai/tests/test_embeddings.py new file mode 100644 index 0000000000..69a679809e --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/test_embeddings.py @@ -0,0 +1,76 @@ +import json + +import pytest +from oci.generative_ai_inference import models +from opentelemetry.instrumentation.oci_genai.span_utils import ( + GEN_AI_OCI_EMBEDDINGS_INPUT_COUNT, + GEN_AI_OCI_EMBEDDINGS_INPUT_TYPE, + GEN_AI_OCI_SERVING_MODE, + OCI_GENAI_PROVIDER, +) +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv_ai import SpanAttributes + +from tests import assert_message_in_logs + +EMBED_MODEL = "cohere.embed-v4.0" +INPUTS = ["OpenTelemetry is an observability framework", "OCI Generative AI serves Cohere embeddings"] + + +def _embed(client, compartment_id): + return client.embed_text( + models.EmbedTextDetails( + compartment_id=compartment_id, + serving_mode=models.OnDemandServingMode(model_id=EMBED_MODEL), + inputs=INPUTS, + input_type=models.EmbedTextDetails.INPUT_TYPE_SEARCH_DOCUMENT, + truncate=models.EmbedTextDetails.TRUNCATE_END, + ) + ) + + +@pytest.mark.vcr +def test_embed_text_legacy(instrument_legacy, oci_client, compartment_id, span_exporter, log_exporter): + response = _embed(oci_client, compartment_id) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"embeddings {EMBED_MODEL}"] + span = spans[0] + + assert span.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME] == OCI_GENAI_PROVIDER + assert span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "embeddings" + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == EMBED_MODEL + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL] == EMBED_MODEL + assert span.attributes[GEN_AI_OCI_SERVING_MODE] == "ON_DEMAND" + assert span.attributes[GEN_AI_OCI_EMBEDDINGS_INPUT_COUNT] == 2 + assert span.attributes[GEN_AI_OCI_EMBEDDINGS_INPUT_TYPE] == "SEARCH_DOCUMENT" + assert span.attributes[GenAIAttributes.GEN_AI_EMBEDDINGS_DIMENSION_COUNT] == len(response.data.embeddings[0]) + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_ID] + + assert span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == response.data.usage.prompt_tokens + assert span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == response.data.usage.total_tokens + assert GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS not in span.attributes + + input_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES]) + assert input_messages == [{"role": "user", "parts": [{"type": "text", "content": text}]} for text in INPUTS] + assert GenAIAttributes.GEN_AI_OUTPUT_MESSAGES not in span.attributes + + assert len(log_exporter.get_finished_logs()) == 0 + + +@pytest.mark.vcr +def test_embed_text_with_events_with_content( + instrument_with_content, oci_client, compartment_id, span_exporter, log_exporter +): + _embed(oci_client, compartment_id) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"embeddings {EMBED_MODEL}"] + assert GenAIAttributes.GEN_AI_INPUT_MESSAGES not in spans[0].attributes + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + assert_message_in_logs(logs[0], "gen_ai.user.message", {"content": INPUTS[0]}) + assert_message_in_logs(logs[1], "gen_ai.user.message", {"content": INPUTS[1]}) diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/test_generate_text.py b/packages/opentelemetry-instrumentation-oci-genai/tests/test_generate_text.py new file mode 100644 index 0000000000..4ce5674ee4 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/test_generate_text.py @@ -0,0 +1,143 @@ +"""``generate_text`` (legacy completion API) tests. + +The legacy text generation endpoint is no longer served in the commercial realm (the service answers 404 "The +requested API is not available"), so these tests stub ``BaseClient.call_api`` with the documented response shapes +instead of recording cassettes. +""" + +import json +from unittest.mock import patch + +import oci +from oci.generative_ai_inference import models +from opentelemetry.instrumentation.oci_genai.span_utils import GEN_AI_OCI_API_FORMAT, OCI_GENAI_PROVIDER +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv_ai import SpanAttributes + +from tests import assert_message_in_logs, assert_valid_output_message + +PROMPT = "Write a haiku about opentelemetry" + + +def _response(result): + return oci.response.Response(200, {"opc-request-id": "REDACTED"}, result, None) + + +def _cohere_result(): + return models.GenerateTextResult( + model_id="cohere.command", + model_version="15.6", + inference_response=models.CohereLlmInferenceResponse( + generated_texts=[ + models.GeneratedText(id="gen-1", text="Traces flow like streams", finish_reason="COMPLETE"), + models.GeneratedText(id="gen-2", text="Spans light the darkest path", finish_reason="MAX_TOKENS"), + ] + ), + ) + + +def _llama_result(): + return models.GenerateTextResult( + model_id="meta.llama-2-70b-chat", + model_version="1.0", + inference_response=models.LlamaLlmInferenceResponse( + created="2024-01-01T00:00:00Z", + choices=[models.Choice(index=0, text="Signals converge", finish_reason="stop")], + ), + ) + + +def _generate(client, compartment_id, model, inference_request): + return client.generate_text( + models.GenerateTextDetails( + compartment_id=compartment_id, + serving_mode=models.OnDemandServingMode(model_id=model), + inference_request=inference_request, + ) + ) + + +def test_generate_text_cohere_legacy(instrument_legacy, oci_client, compartment_id, span_exporter, log_exporter): + request = models.CohereLlmInferenceRequest( + prompt=PROMPT, max_tokens=50, temperature=0.7, top_p=0.9, num_generations=2, stop_sequences=["\n\n"] + ) + with patch.object(oci_client.base_client, "call_api", return_value=_response(_cohere_result())): + response = _generate(oci_client, compartment_id, "cohere.command", request) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == ["text_completion cohere.command"] + span = spans[0] + + assert span.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME] == OCI_GENAI_PROVIDER + assert span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "text_completion" + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "cohere.command" + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL] == "cohere.command" + assert span.attributes[GEN_AI_OCI_API_FORMAT] == "COHERE" + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 50 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE] == 0.7 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_P] == 0.9 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_CHOICE_COUNT] == 2 + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_STOP_SEQUENCES] == ("\n\n",) + assert span.attributes[SpanAttributes.GEN_AI_IS_STREAMING] is False + + input_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES]) + assert input_messages == [{"role": "user", "parts": [{"type": "text", "content": PROMPT}]}] + + output_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES]) + assert len(output_messages) == 2 + for message in output_messages: + assert_valid_output_message(message) + assert output_messages[0]["parts"] == [{"type": "text", "content": "Traces flow like streams"}] + assert output_messages[0]["finish_reason"] == "stop" + assert output_messages[1]["finish_reason"] == "length" + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS] == ("stop", "length") + + assert response.data.inference_response.generated_texts[0].text == "Traces flow like streams" + assert len(log_exporter.get_finished_logs()) == 0 + + +def test_generate_text_llama_legacy(instrument_legacy, oci_client, compartment_id, span_exporter): + request = models.LlamaLlmInferenceRequest(prompt=PROMPT, max_tokens=50, top_k=10) + with patch.object(oci_client.base_client, "call_api", return_value=_response(_llama_result())): + _generate(oci_client, compartment_id, "meta.llama-2-70b-chat", request) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == ["text_completion meta.llama-2-70b-chat"] + span = spans[0] + + assert span.attributes[GEN_AI_OCI_API_FORMAT] == "LLAMA" + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_TOP_K] == 10 + output_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES]) + assert output_messages == [ + {"role": "assistant", "parts": [{"type": "text", "content": "Signals converge"}], "finish_reason": "stop"} + ] + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS] == ("stop",) + + +def test_generate_text_with_events_with_content( + instrument_with_content, oci_client, compartment_id, span_exporter, log_exporter +): + request = models.CohereLlmInferenceRequest(prompt=PROMPT, max_tokens=50) + with patch.object(oci_client.base_client, "call_api", return_value=_response(_cohere_result())): + _generate(oci_client, compartment_id, "cohere.command", request) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == ["text_completion cohere.command"] + assert GenAIAttributes.GEN_AI_INPUT_MESSAGES not in spans[0].attributes + assert GenAIAttributes.GEN_AI_OUTPUT_MESSAGES not in spans[0].attributes + + logs = log_exporter.get_finished_logs() + assert len(logs) == 3 + assert_message_in_logs(logs[0], "gen_ai.user.message", {"content": PROMPT}) + assert_message_in_logs( + logs[1], + "gen_ai.choice", + {"index": 0, "finish_reason": "stop", "message": {"content": "Traces flow like streams"}}, + ) + assert_message_in_logs( + logs[2], + "gen_ai.choice", + {"index": 1, "finish_reason": "length", "message": {"content": "Spans light the darkest path"}}, + ) diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/test_init.py b/packages/opentelemetry-instrumentation-oci-genai/tests/test_init.py new file mode 100644 index 0000000000..e2ff722b2a --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/test_init.py @@ -0,0 +1,292 @@ +"""Instrumentor lifecycle, suppression and error handling tests (no network, no cassettes).""" + +import json +import os +from unittest.mock import MagicMock, patch + +import oci +import pytest +from oci.generative_ai_inference import GenerativeAiInferenceClient, models +from opentelemetry import context as context_api +from opentelemetry import trace +from opentelemetry.instrumentation.oci_genai import OCIGenAIInstrumentor, _wrap +from opentelemetry.instrumentation.utils import _SUPPRESS_INSTRUMENTATION_KEY +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv_ai import SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY, Meters +from opentelemetry.trace.status import StatusCode +from wrapt import FunctionWrapper + +CHAT_SPEC = {"method": "chat", "details_kwarg": "chat_details", "operation": "chat"} + +STREAM_PAYLOADS = [ + '{"index":0,"message":{"role":"ASSISTANT","content":[{"type":"TEXT","text":"Hello"}]}}', + '{"index":0,"message":{"role":"ASSISTANT","content":[{"type":"TEXT","text":", world"}]}}', + '{"message":{"role":"ASSISTANT","content":[]},"finishReason":"stop"}', + '{"usage":{"completionTokens":8,"promptTokens":41,"totalTokens":49}}', + "[DONE]", +] + + +def _chat_details(compartment_id, model="meta.llama-3.3-70b-instruct"): + return models.ChatDetails( + compartment_id=compartment_id, + serving_mode=models.OnDemandServingMode(model_id=model), + chat_request=models.GenericChatRequest( + api_format="GENERIC", + messages=[models.UserMessage(content=[models.TextContent(text="Tell me a joke")])], + ), + ) + + +def _chat_result(model="meta.llama-3.3-70b-instruct"): + return models.ChatResult( + model_id=model, + chat_response=models.GenericChatResponse( + choices=[ + models.ChatChoice( + index=0, + message=models.AssistantMessage(content=[models.TextContent(text="Hi there")]), + finish_reason="stop", + ) + ], + usage=models.Usage(prompt_tokens=4, completion_tokens=2, total_tokens=6), + ), + ) + + +def _current_span_id(): + return trace.get_current_span().get_span_context().span_id + + +class RecordingSSEClient: + """Stand-in for the SDK's ``SSEClient``: records the span current at each pull and whether it was closed.""" + + def __init__(self, payloads): + self._payloads = payloads + self.pull_span_ids = [] + self.closed = False + + def events(self): + for payload in self._payloads: + self.pull_span_ids.append(_current_span_id()) + yield MagicMock(data=payload) + + def close(self): + self.closed = True + + +def _streaming_chat(oci_client, compartment_id, sse_client): + details = _chat_details(compartment_id) + details.chat_request.is_stream = True + response = oci.response.Response(200, {}, sse_client, None) + with patch.object(oci_client.base_client, "call_api", return_value=response): + return oci_client.chat(details) + + +def _assert_logs_correlated_with(logs, span): + assert logs, "expected emitted events" + for log in logs: + assert log.log_record.trace_id == span.context.trace_id + assert log.log_record.span_id == span.context.span_id + + +class TestInstrumentor: + def test_instrument_and_uninstrument_wrap_client_methods(self, tracer_provider, meter_provider): + instrumentor = OCIGenAIInstrumentor() + instrumentor.instrument(tracer_provider=tracer_provider, meter_provider=meter_provider) + try: + for method in ("chat", "generate_text", "embed_text", "rerank_text"): + assert isinstance(vars(GenerativeAiInferenceClient)[method], FunctionWrapper) + finally: + instrumentor.uninstrument() + for method in ("chat", "generate_text", "embed_text", "rerank_text"): + assert not isinstance(vars(GenerativeAiInferenceClient)[method], FunctionWrapper) + + def test_metrics_disabled(self, tracer_provider, meter_provider): + with patch.dict(os.environ, {"TRACELOOP_METRICS_ENABLED": "false"}): + instrumentor = OCIGenAIInstrumentor() + instrumentor.instrument(tracer_provider=tracer_provider, meter_provider=meter_provider) + instrumentor.uninstrument() + + def test_missing_methods_are_skipped(self, tracer_provider, meter_provider): + instrumentor = OCIGenAIInstrumentor() + with patch( + "opentelemetry.instrumentation.oci_genai.wrap_function_wrapper", + side_effect=AttributeError("no such method"), + ): + instrumentor.instrument(tracer_provider=tracer_provider, meter_provider=meter_provider) + instrumentor.uninstrument() + + def test_conflicting_attribute_flags_raise(self): + with pytest.raises(TypeError): + OCIGenAIInstrumentor(use_attributes=True, use_legacy_attributes=True) + + def test_use_legacy_attributes_is_deprecated(self): + with pytest.warns(DeprecationWarning): + OCIGenAIInstrumentor(use_legacy_attributes=True) + + +class TestWrap: + def test_suppression_keys_skip_span(self): + tracer = MagicMock() + wrapped = MagicMock(return_value="result") + wrapper = _wrap(tracer, None, None, None, CHAT_SPEC) + + for key in (_SUPPRESS_INSTRUMENTATION_KEY, SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY): + token = context_api.attach(context_api.set_value(key, True)) + try: + assert wrapper(wrapped, None, [_chat_details("c")], {}) == "result" + finally: + context_api.detach(token) + tracer.start_span.assert_not_called() + + def test_details_passed_as_keyword(self): + tracer = MagicMock() + span = MagicMock() + span.is_recording.return_value = True + tracer.start_span.return_value = span + response = MagicMock() + response.data = None + wrapped = MagicMock(return_value=response) + + wrapper = _wrap(tracer, None, None, None, CHAT_SPEC) + assert wrapper(wrapped, None, [], {"chat_details": _chat_details("c", model="m")}) is response + assert tracer.start_span.call_args.args[0] == "chat m" + span.end.assert_called_once() + + +def test_service_error_sets_span_status(instrument_legacy, oci_client, compartment_id, span_exporter, reader): + error = oci.exceptions.ServiceError( + status=404, code="NotFound", headers={}, message="The requested API is not available." + ) + with patch.object(oci_client.base_client, "call_api", side_effect=error): + with pytest.raises(oci.exceptions.ServiceError): + oci_client.chat(_chat_details(compartment_id)) + + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + span = spans[0] + assert span.name == "chat meta.llama-3.3-70b-instruct" + assert span.status.status_code == StatusCode.ERROR + assert span.attributes["error.type"] == "ServiceError" + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "meta.llama-3.3-70b-instruct" + assert len([event for event in span.events if event.name == "exception"]) == 1 + + metrics = reader.get_metrics_data().resource_metrics[0].scope_metrics[0].metrics + duration = next(metric for metric in metrics if metric.name == Meters.LLM_OPERATION_DURATION) + assert duration.data.data_points[0].attributes["error.type"] == "ServiceError" + + +def test_client_span_is_current_during_call_and_event_emission( + instrument_with_content, oci_client, compartment_id, span_exporter, log_exporter +): + seen = {} + + def fake_call_api(*args, **kwargs): + seen["span_id"] = _current_span_id() + return oci.response.Response(200, {}, _chat_result(), None) + + with patch.object(oci_client.base_client, "call_api", side_effect=fake_call_api): + oci_client.chat(_chat_details(compartment_id)) + + # The client span was current while the SDK call ran, and is no longer current once ``chat`` returned. + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + span = spans[0] + assert seen["span_id"] == span.context.span_id + assert _current_span_id() == trace.INVALID_SPAN_ID + + logs = log_exporter.get_finished_logs() + assert [log.log_record.event_name for log in logs] == ["gen_ai.user.message", "gen_ai.choice"] + _assert_logs_correlated_with(logs, span) + + +def test_streaming_span_is_current_while_pulling_events_and_finishing( + instrument_with_content, oci_client, compartment_id, span_exporter, log_exporter +): + sse_client = RecordingSSEClient(STREAM_PAYLOADS) + result = _streaming_chat(oci_client, compartment_id, sse_client) + assert span_exporter.get_finished_spans() == () + + caller_span_ids = {_current_span_id() for _ in result.data.events()} + + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + span = spans[0] + assert span.status.status_code == StatusCode.OK + # The client span is current while the SDK stream is read, but never leaks into the caller's loop body. + assert sse_client.pull_span_ids == [span.context.span_id] * len(STREAM_PAYLOADS) + assert caller_span_ids == {trace.INVALID_SPAN_ID} + + logs = log_exporter.get_finished_logs() + assert [log.log_record.event_name for log in logs] == ["gen_ai.user.message", "gen_ai.choice"] + _assert_logs_correlated_with(logs, span) + + +def test_streaming_early_exit_finishes_span_as_incomplete( + instrument_legacy, oci_client, compartment_id, span_exporter, reader +): + sse_client = RecordingSSEClient(STREAM_PAYLOADS) + result = _streaming_chat(oci_client, compartment_id, sse_client) + + events = result.data.events() + next(events) + events.close() # what ``for event in events: ... break`` triggers once the generator is collected + + assert sse_client.closed is True + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + span = spans[0] + # Stopped by the caller: the partial output is kept, but the span is neither OK nor an error. + assert span.status.status_code == StatusCode.UNSET + assert "error.type" not in span.attributes + assert GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS not in span.attributes + output_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES]) + assert output_messages[0]["parts"] == [{"type": "text", "content": "Hello"}] + + metrics = reader.get_metrics_data().resource_metrics[0].scope_metrics[0].metrics + assert Meters.LLM_OPERATION_DURATION in [metric.name for metric in metrics] + + +def test_streaming_close_without_iteration_finishes_span(instrument_legacy, oci_client, compartment_id, span_exporter): + sse_client = RecordingSSEClient(STREAM_PAYLOADS) + result = _streaming_chat(oci_client, compartment_id, sse_client) + + result.data.close() + + assert sse_client.closed is True + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert spans[0].status.status_code == StatusCode.UNSET + assert GenAIAttributes.GEN_AI_OUTPUT_MESSAGES not in spans[0].attributes + + # Neither closing again nor iterating afterwards may finish the span a second time. + result.data.close() + list(result.data.events()) + assert len(span_exporter.get_finished_spans()) == 1 + + +def test_streaming_iteration_error_finishes_span(instrument_legacy, oci_client, compartment_id, span_exporter): + class FailingSSEClient: + def events(self): + yield MagicMock(data='{"index":0,"message":{"role":"ASSISTANT","content":[{"type":"TEXT","text":"Hi"}]}}') + raise RuntimeError("Mid-stream failure") + + details = _chat_details(compartment_id) + details.chat_request.is_stream = True + response = oci.response.Response(200, {}, FailingSSEClient(), None) + with patch.object(oci_client.base_client, "call_api", return_value=response): + result = oci_client.chat(details) + + assert span_exporter.get_finished_spans() == () + with pytest.raises(RuntimeError, match="Mid-stream failure"): + for _ in result.data.events(): + pass + + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert spans[0].status.status_code == StatusCode.ERROR + assert spans[0].attributes["error.type"] == "RuntimeError" + assert len([event for event in spans[0].events if event.name == "exception"]) == 1 diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/test_rerank.py b/packages/opentelemetry-instrumentation-oci-genai/tests/test_rerank.py new file mode 100644 index 0000000000..5459e05c74 --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/test_rerank.py @@ -0,0 +1,95 @@ +import json + +import pytest +from oci.generative_ai_inference import models +from opentelemetry.instrumentation.oci_genai.span_utils import ( + GEN_AI_OCI_RERANK_DOCUMENT_COUNT, + GEN_AI_OCI_RERANK_TOP_N, + GEN_AI_OCI_SERVING_MODE, + OCI_GENAI_PROVIDER, +) +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) + +from tests import assert_message_in_logs, assert_valid_output_message + +RERANK_MODEL = "cohere.rerank-v4.0-fast" +QUERY = "What is the capital of France?" +DOCUMENTS = [ + "Paris is the capital of France.", + "Berlin is the capital of Germany.", + "The Eiffel Tower is located in Paris.", +] + + +def _rerank(client, compartment_id): + return client.rerank_text( + models.RerankTextDetails( + compartment_id=compartment_id, + serving_mode=models.OnDemandServingMode(model_id=RERANK_MODEL), + input=QUERY, + documents=DOCUMENTS, + top_n=2, + ) + ) + + +@pytest.mark.vcr +def test_rerank_text_legacy(instrument_legacy, oci_client, compartment_id, span_exporter, log_exporter): + response = _rerank(oci_client, compartment_id) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"rerank {RERANK_MODEL}"] + span = spans[0] + + assert span.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME] == OCI_GENAI_PROVIDER + assert span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "rerank" + assert span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == RERANK_MODEL + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL] == RERANK_MODEL + assert span.attributes[GEN_AI_OCI_SERVING_MODE] == "ON_DEMAND" + assert span.attributes[GEN_AI_OCI_RERANK_TOP_N] == 2 + assert span.attributes[GEN_AI_OCI_RERANK_DOCUMENT_COUNT] == 3 + assert span.attributes[GenAIAttributes.GEN_AI_RESPONSE_ID] + + input_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES]) + assert [message["role"] for message in input_messages] == ["system", "system", "system", "user"] + assert [message["parts"][0]["content"] for message in input_messages] == DOCUMENTS + [QUERY] + + output_messages = json.loads(span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES]) + assert len(output_messages) == 2 + for message, rank in zip(output_messages, response.data.document_ranks): + assert_valid_output_message(message) + assert message["role"] == "assistant" + assert message["parts"][0]["content"] == f"Doc {rank.index}, Score: {rank.relevance_score}" + assert response.data.document_ranks[0].index == 0 + + assert len(log_exporter.get_finished_logs()) == 0 + + +@pytest.mark.vcr +def test_rerank_text_with_events_with_content( + instrument_with_content, oci_client, compartment_id, span_exporter, log_exporter +): + response = _rerank(oci_client, compartment_id) + + spans = span_exporter.get_finished_spans() + assert [span.name for span in spans] == [f"rerank {RERANK_MODEL}"] + assert GenAIAttributes.GEN_AI_INPUT_MESSAGES not in spans[0].attributes + assert GenAIAttributes.GEN_AI_OUTPUT_MESSAGES not in spans[0].attributes + + logs = log_exporter.get_finished_logs() + assert len(logs) == 6 + for log, document in zip(logs[:3], DOCUMENTS): + assert_message_in_logs(log, "gen_ai.system.message", {"content": document}) + assert_message_in_logs(logs[3], "gen_ai.user.message", {"content": QUERY}) + for index, (log, rank) in enumerate(zip(logs[4:], response.data.document_ranks)): + assert_message_in_logs( + log, + "gen_ai.choice", + { + "index": index, + "finish_reason": "stop", + "message": {"content": f"Doc {rank.index}, Score: {rank.relevance_score}"}, + }, + ) diff --git a/packages/opentelemetry-instrumentation-oci-genai/tests/test_span_utils.py b/packages/opentelemetry-instrumentation-oci-genai/tests/test_span_utils.py new file mode 100644 index 0000000000..801359f33f --- /dev/null +++ b/packages/opentelemetry-instrumentation-oci-genai/tests/test_span_utils.py @@ -0,0 +1,479 @@ +"""Unit tests for span_utils / streaming helpers (no network, no cassettes).""" + +import json +from unittest.mock import MagicMock + +import pytest +from oci.generative_ai_inference import models +from opentelemetry.instrumentation.oci_genai.span_utils import ( + CHAT, + EMBEDDINGS, + GEN_AI_OCI_ENDPOINT_ID, + GEN_AI_OCI_SERVING_MODE, + RERANK, + chat_request_to_messages, + chat_response_to_messages, + get_request_model, + map_finish_reason, + set_input_attributes, + set_request_attributes, + set_response_attributes, + stream_choices_to_messages, + usage_to_dict, +) +from opentelemetry.instrumentation.oci_genai.streaming import ( + OCIGenAIStreamWrapper, + StreamAccumulator, +) +from opentelemetry.instrumentation.oci_genai.utils import get_server_address +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) + +from tests import assert_valid_output_message, assert_valid_parts + + +def _span(recording=True): + span = MagicMock() + span.is_recording.return_value = recording + attributes = {} + span.set_attribute = lambda name, value: attributes.__setitem__(name, value) + span._attrs = attributes + return span + + +# --------------------------------------------------------------------------- +# finish reasons / usage +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "raw,expected", + [ + ("stop", "stop"), + ("length", "length"), + ("tool_calls", "tool_call"), + ("content_filter", "content_filter"), + ("COMPLETE", "stop"), + ("STOP_SEQUENCE", "stop"), + ("MAX_TOKENS", "length"), + ("TOOL_CALL", "tool_call"), + ("ERROR_TOXIC", "content_filter"), + ("ERROR", "error"), + ("USER_CANCEL", "user_cancel"), + (None, ""), + ], +) +def test_map_finish_reason(raw, expected): + assert map_finish_reason(raw) == expected + + +def test_usage_to_dict_from_model_and_stream_payload(): + usage = models.Usage( + prompt_tokens=19, + completion_tokens=11, + total_tokens=30, + prompt_tokens_details=models.PromptTokensDetails(cached_tokens=5), + completion_tokens_details=models.CompletionTokensDetails(reasoning_tokens=3), + ) + assert usage_to_dict(usage) == { + "input_tokens": 19, + "output_tokens": 11, + "total_tokens": 30, + "cached_tokens": 5, + "reasoning_tokens": 3, + } + + stream_usage = usage_to_dict({"promptTokens": 41, "completionTokens": 8}) + assert stream_usage["input_tokens"] == 41 + assert stream_usage["output_tokens"] == 8 + assert stream_usage["total_tokens"] == 49 + + embeddings_usage = usage_to_dict(models.Usage(prompt_tokens=4, completion_tokens=0, total_tokens=4), False) + assert embeddings_usage["output_tokens"] is None + assert embeddings_usage["total_tokens"] == 4 + + +# --------------------------------------------------------------------------- +# request -> input messages +# --------------------------------------------------------------------------- + + +def test_generic_request_with_tools_and_images(): + request = models.GenericChatRequest( + api_format="GENERIC", + messages=[ + models.SystemMessage(content=[models.TextContent(text="Be helpful")]), + models.UserMessage( + content=[ + models.TextContent(text="What is in this picture?"), + models.ImageContent(image_url=models.ImageUrl(url="https://example.com/cat.png")), + models.ImageContent(image_url=models.ImageUrl(url="data:image/png;base64,AAAA")), + ] + ), + models.AssistantMessage( + content=[models.TextContent(text="Let me check")], + tool_calls=[ + models.FunctionCall(id="call_1", name="lookup", arguments='{"animal": "cat"}'), + ], + ), + models.ToolMessage(tool_call_id="call_1", content=[models.TextContent(text="a tabby cat")]), + ], + ) + + messages, system_parts = chat_request_to_messages(request) + assert system_parts == [] + assert [message["role"] for message in messages] == ["system", "user", "assistant", "tool"] + for message in messages: + assert_valid_parts(message["parts"]) + + assert messages[1]["parts"] == [ + {"type": "text", "content": "What is in this picture?"}, + {"type": "uri", "modality": "image", "uri": "https://example.com/cat.png"}, + {"type": "blob", "modality": "image", "mime_type": "image/png", "content": "AAAA"}, + ] + assert messages[2]["parts"] == [ + {"type": "text", "content": "Let me check"}, + {"type": "tool_call", "name": "lookup", "arguments": {"animal": "cat"}, "id": "call_1"}, + ] + assert messages[3]["parts"] == [{"type": "tool_call_response", "id": "call_1", "response": "a tabby cat"}] + + +def test_cohere_request_with_history_and_tool_results(): + request = models.CohereChatRequest( + api_format="COHERE", + preamble_override="You are terse.", + message="Book it", + chat_history=[ + models.CohereUserMessage(message="Find flights"), + models.CohereChatBotMessage( + message="Searching", + tool_calls=[models.CohereToolCall(name="search_flights", parameters={"to": "CDG"})], + ), + models.CohereToolMessage( + tool_results=[ + models.CohereToolResult( + call=models.CohereToolCall(name="search_flights", parameters={"to": "CDG"}), + outputs=[{"flight": "AF123"}], + ) + ] + ), + ], + ) + + messages, system_parts = chat_request_to_messages(request) + assert system_parts == [{"type": "text", "content": "You are terse."}] + assert [message["role"] for message in messages] == ["user", "assistant", "tool", "user"] + for message in messages: + assert_valid_parts(message["parts"]) + assert messages[1]["parts"][1] == {"type": "tool_call", "name": "search_flights", "arguments": {"to": "CDG"}} + assert messages[2]["parts"] == [ + {"type": "tool_call_response", "id": "search_flights", "response": [{"flight": "AF123"}]} + ] + assert messages[3]["parts"] == [{"type": "text", "content": "Book it"}] + + +def test_cohere_v2_request_and_response(): + request = models.CohereChatRequestV2( + api_format="COHEREV2", + messages=[ + models.CohereSystemMessageV2(content=[models.CohereTextContentV2(text="Be terse")]), + models.CohereUserMessageV2(content=[models.CohereTextContentV2(text="Hi")]), + ], + ) + messages, system_parts = chat_request_to_messages(request) + assert system_parts == [] + assert messages == [ + {"role": "system", "parts": [{"type": "text", "content": "Be terse"}]}, + {"role": "user", "parts": [{"type": "text", "content": "Hi"}]}, + ] + + response = models.CohereChatResponseV2( + api_format="COHEREV2", + id="resp-1", + finish_reason="TOOL_CALL", + message=models.CohereAssistantMessageV2( + content=[ + models.CohereTextContentV2(text="Calling a tool"), + models.CohereThinkingContentV2(thinking="need weather"), + ], + tool_calls=[ + # ``CohereToolCallV2.function`` is an untyped object (plain dict) in the SDK + models.CohereToolCallV2(id="tc-1", function={"name": "weather", "arguments": '{"city": "Paris"}'}) + ], + ), + ) + output = chat_response_to_messages(response) + assert len(output) == 1 + assert_valid_output_message(output[0]) + assert output[0]["finish_reason"] == "tool_call" + assert output[0]["parts"] == [ + {"type": "text", "content": "Calling a tool"}, + {"type": "reasoning", "content": "need weather"}, + {"type": "tool_call", "name": "weather", "arguments": {"city": "Paris"}, "id": "tc-1"}, + ] + + +def test_generic_response_with_tool_calls_and_multiple_choices(): + response = models.GenericChatResponse( + api_format="GENERIC", + choices=[ + models.ChatChoice( + index=0, + finish_reason="tool_calls", + message=models.AssistantMessage( + content=[], + tool_calls=[models.FunctionCall(id="c1", name="get_time", arguments="{}")], + ), + ), + models.ChatChoice( + index=1, + finish_reason="length", + message=models.AssistantMessage(content=[models.TextContent(text="Partial")]), + ), + ], + ) + output = chat_response_to_messages(response) + assert [message["finish_reason"] for message in output] == ["tool_call", "length"] + assert output[0]["parts"] == [{"type": "tool_call", "name": "get_time", "arguments": {}, "id": "c1"}] + assert output[1]["parts"] == [{"type": "text", "content": "Partial"}] + + +# --------------------------------------------------------------------------- +# dedicated endpoints / attribute setters +# --------------------------------------------------------------------------- + + +def test_dedicated_serving_mode_attributes(): + endpoint_id = "ocid1.generativeaiendpoint.oc1.us-chicago-1.example" + details = models.ChatDetails( + compartment_id="ocid1.compartment.oc1..example", + serving_mode=models.DedicatedServingMode(endpoint_id=endpoint_id), + chat_request=models.GenericChatRequest(api_format="GENERIC", messages=[], is_stream=True), + ) + assert get_request_model(details) == endpoint_id + + span = _span() + set_request_attributes(span, CHAT, details, instance=None) + assert span._attrs[GEN_AI_OCI_SERVING_MODE] == "DEDICATED" + assert span._attrs[GEN_AI_OCI_ENDPOINT_ID] == endpoint_id + assert span._attrs["gen_ai.is_streaming"] is True + assert "server.address" not in span._attrs + + +def test_get_server_address_strips_endpoint_templates(): + instance = MagicMock() + instance.base_client.endpoint = ( + "https://inference.generativeai.us-chicago-1.{dualStack?ds.:}oci.oraclecloud.com/20231130" + ) + assert get_server_address(instance) == "inference.generativeai.us-chicago-1.oci.oraclecloud.com" + assert get_server_address(object()) is None + + +def test_input_attributes_respect_content_tracing(monkeypatch): + details = models.ChatDetails( + serving_mode=models.OnDemandServingMode(model_id="m"), + chat_request=models.GenericChatRequest( + api_format="GENERIC", messages=[models.UserMessage(content=[models.TextContent(text="secret")])] + ), + ) + monkeypatch.setenv("TRACELOOP_TRACE_CONTENT", "false") + span = _span() + set_input_attributes(span, CHAT, details) + assert GenAIAttributes.GEN_AI_INPUT_MESSAGES not in span._attrs + + monkeypatch.setenv("TRACELOOP_TRACE_CONTENT", "true") + set_input_attributes(span, CHAT, details) + assert json.loads(span._attrs[GenAIAttributes.GEN_AI_INPUT_MESSAGES])[0]["parts"][0]["content"] == "secret" + + +def test_embeddings_and_rerank_request_attributes(): + span = _span() + embed_details = models.EmbedTextDetails( + serving_mode=models.OnDemandServingMode(model_id="cohere.embed-v4.0"), + inputs=["a", "b", "c"], + embedding_types=["float"], + output_dimensions=256, + ) + set_request_attributes(span, EMBEDDINGS, embed_details, instance=None) + assert span._attrs["gen_ai.oci.embeddings.input_count"] == 3 + assert span._attrs[GenAIAttributes.GEN_AI_REQUEST_ENCODING_FORMATS] == ["float"] + assert span._attrs[GenAIAttributes.GEN_AI_EMBEDDINGS_DIMENSION_COUNT] == 256 + + result = models.EmbedTextResult(id="req", model_id="cohere.embed-v4.0", embeddings=[[0.1] * 256] * 3) + response = MagicMock() + response.data = result + assert set_response_attributes(span, EMBEDDINGS, response) is None + assert span._attrs[GenAIAttributes.GEN_AI_EMBEDDINGS_DIMENSION_COUNT] == 256 + assert span._attrs[GenAIAttributes.GEN_AI_RESPONSE_ID] == "req" + + span = _span() + rerank_details = models.RerankTextDetails( + serving_mode=models.OnDemandServingMode(model_id="cohere.rerank-v4.0-fast"), + input="q", + documents=["d1", "d2"], + top_n=1, + ) + set_request_attributes(span, RERANK, rerank_details, instance=None) + assert span._attrs["gen_ai.oci.rerank.top_n"] == 1 + assert span._attrs["gen_ai.oci.rerank.document_count"] == 2 + + +# --------------------------------------------------------------------------- +# streaming accumulator +# --------------------------------------------------------------------------- + +GENERIC_EVENTS = [ + '{"index":0,"message":{"role":"ASSISTANT","content":[{"type":"TEXT","text":""}]},"pad":"aaa"}', + '{"index":0,"message":{"role":"ASSISTANT","content":[{"type":"TEXT","text":"Hello"}]},"pad":"a"}', + '{"index":0,"message":{"role":"ASSISTANT","content":[{"type":"TEXT","text":", world"}]},"pad":"aa"}', + '{"message":{"role":"ASSISTANT","content":[{"type":"TEXT","text":""}]},"finishReason":"stop","pad":"aaaa"}', + '{"usage":{"completionTokens":8,"promptTokens":41,"totalTokens":49},"pad":"aaaa"}', + "[DONE]", +] + +COHERE_EVENTS = [ + '{"apiFormat":"COHERE","text":"Hi","pad":"aaaa"}', + '{"apiFormat":"COHERE","text":" there","pad":"aa"}', + '{"apiFormat":"COHERE","text":"Hi there","chatHistory":[{"role":"USER","message":"Say hi."},' + '{"role":"CHATBOT","message":"Hi there"}],"finishReason":"COMPLETE","pad":"a",' + '"usage":{"completionTokens":2,"promptTokens":6,"totalTokens":8}}', +] + + +def test_stream_accumulator_generic(): + accumulator = StreamAccumulator() + for event in GENERIC_EVENTS: + accumulator.process(event) + choices = accumulator.choices_list() + assert len(choices) == 1 + assert choices[0]["text"] == "Hello, world" + assert choices[0]["finish_reason"] == "stop" + assert accumulator.usage == {"completionTokens": 8, "promptTokens": 41, "totalTokens": 49} + + messages = stream_choices_to_messages(choices) + assert_valid_output_message(messages[0]) + assert messages[0] == { + "role": "assistant", + "parts": [{"type": "text", "content": "Hello, world"}], + "finish_reason": "stop", + } + + +def test_stream_accumulator_cohere_terminal_event_carries_full_text(): + accumulator = StreamAccumulator() + for event in COHERE_EVENTS: + accumulator.process(event) + choices = accumulator.choices_list() + assert accumulator.api_format == "COHERE" + assert choices[0]["text"] == "Hi there" + assert choices[0]["finish_reason"] == "COMPLETE" + assert usage_to_dict(accumulator.usage)["total_tokens"] == 8 + assert stream_choices_to_messages(choices)[0]["finish_reason"] == "stop" + + +def test_stream_accumulator_tool_call_deltas_and_garbage(): + accumulator = StreamAccumulator() + accumulator.process("not json") + accumulator.process( + '{"message":{"role":"ASSISTANT","toolCalls":[{"id":"c1","name":"get_weather","arguments":"{\\"ci"}]}}' + ) + accumulator.process('{"message":{"role":"ASSISTANT","toolCalls":[{"id":"c1","arguments":"ty\\": \\"Paris\\"}"}]}}') + accumulator.process('{"message":{"role":"ASSISTANT","content":[]},"finishReason":"tool_calls"}') + messages = stream_choices_to_messages(accumulator.choices_list()) + assert messages[0]["finish_reason"] == "tool_call" + assert messages[0]["parts"] == [ + {"type": "tool_call", "name": "get_weather", "arguments": {"city": "Paris"}, "id": "c1"} + ] + + +class FakeEvent: + def __init__(self, data): + self.data = data + + +class FakeSSEClient: + def __init__(self, events, fail=False): + self._events = events + self._fail = fail + self.closed = False + + def events(self): + for event in self._events: + yield FakeEvent(event) + if self._fail: + raise RuntimeError("boom") + + def close(self): + self.closed = True + + +def _wrap_stream(sse_client, calls): + return OCIGenAIStreamWrapper( + sse_client, StreamAccumulator(), lambda acc, err, complete: calls.append((acc, err, complete)) + ) + + +def test_stream_wrapper_invokes_callback_once_and_on_error(): + calls = [] + wrapper = _wrap_stream(FakeSSEClient(GENERIC_EVENTS), calls) + events = list(wrapper.events()) + assert len(events) == len(GENERIC_EVENTS) + assert len(calls) == 1 + assert calls[0][1] is None + assert calls[0][2] is True + assert calls[0][0].choices_list()[0]["text"] == "Hello, world" + + calls.clear() + wrapper = _wrap_stream(FakeSSEClient(GENERIC_EVENTS[:2], fail=True), calls) + with pytest.raises(RuntimeError, match="boom"): + list(wrapper.events()) + assert len(calls) == 1 + assert isinstance(calls[0][1], RuntimeError) + assert calls[0][2] is False + + +def test_stream_wrapper_early_exit_is_incomplete(): + calls = [] + sse_client = FakeSSEClient(GENERIC_EVENTS) + wrapper = _wrap_stream(sse_client, calls) + events = wrapper.events() + next(events) + next(events) + events.close() # what leaving a ``for`` loop early triggers once the generator is collected + assert len(calls) == 1 + assert calls[0][1] is None + assert calls[0][2] is False + assert calls[0][0].choices_list()[0]["text"] == 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"../opentelemetry-instrumentation-mcp", editable = true } opentelemetry-instrumentation-milvus = { path = "../opentelemetry-instrumentation-milvus", editable = true } opentelemetry-instrumentation-mistralai = { path = "../opentelemetry-instrumentation-mistralai", editable = true } +opentelemetry-instrumentation-oci-genai = { path = "../opentelemetry-instrumentation-oci-genai", editable = true } opentelemetry-instrumentation-ollama = { path = "../opentelemetry-instrumentation-ollama", editable = true } opentelemetry-instrumentation-openai = { path = "../opentelemetry-instrumentation-openai", editable = true } opentelemetry-instrumentation-openai-agents = { path = "../opentelemetry-instrumentation-openai-agents", editable = true } diff --git a/packages/traceloop-sdk/traceloop/sdk/instruments.py b/packages/traceloop-sdk/traceloop/sdk/instruments.py index d4232a3899..9b5cc0955c 100644 --- a/packages/traceloop-sdk/traceloop/sdk/instruments.py +++ b/packages/traceloop-sdk/traceloop/sdk/instruments.py @@ -21,6 +21,7 @@ class Instruments(Enum): MCP = "mcp" MILVUS = "milvus" MISTRAL = "mistral" + OCI_GENAI = "oci_genai" OLLAMA = "ollama" OPENAI = "openai" OPENAI_AGENTS = "openai_agents" diff --git a/packages/traceloop-sdk/traceloop/sdk/tracing/tracing.py b/packages/traceloop-sdk/traceloop/sdk/tracing/tracing.py index 8808f85eab..8e092479e6 100644 --- a/packages/traceloop-sdk/traceloop/sdk/tracing/tracing.py +++ b/packages/traceloop-sdk/traceloop/sdk/tracing/tracing.py @@ -57,6 +57,7 @@ posthog.com, sentry.io, bedrock-runtime, + inference.generativeai, sagemaker-runtime, googleapis.com, githubusercontent.com, @@ -584,6 +585,9 @@ def init_instrumentations( elif instrument == Instruments.MISTRAL: if init_mistralai_instrumentor(): instrument_set = True + elif instrument == Instruments.OCI_GENAI: + if init_oci_genai_instrumentor(use_attributes): + instrument_set = True elif instrument == Instruments.OLLAMA: if init_ollama_instrumentor(): instrument_set = True @@ -846,6 +850,20 @@ def init_litellm_instrumentor(use_attributes: bool = True): return False +def init_oci_genai_instrumentor(use_attributes: bool = True): + try: + if is_package_installed("oci"): + from opentelemetry.instrumentation.oci_genai import OCIGenAIInstrumentor + + instrumentor = OCIGenAIInstrumentor(use_attributes=use_attributes) + if not instrumentor.is_instrumented_by_opentelemetry: + instrumentor.instrument() + return True + except Exception as e: + logging.error(f"Error initializing OCI Generative AI instrumentor: {e}") + return False + + def init_ollama_instrumentor(): try: if is_package_installed("ollama"): diff --git a/packages/traceloop-sdk/uv.lock b/packages/traceloop-sdk/uv.lock index 17b06beee2..74d45a263e 100644 --- a/packages/traceloop-sdk/uv.lock +++ b/packages/traceloop-sdk/uv.lock @@ -1504,7 +1504,7 @@ wheels = [ [[package]] name = "opentelemetry-instrumentation-agno" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-agno" } dependencies = [ { name = "opentelemetry-api" }, @@ -1544,7 +1544,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-alephalpha" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-alephalpha" } dependencies = [ { name = "opentelemetry-api" }, @@ -1582,7 +1582,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-anthropic" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-anthropic" } dependencies = [ { name = "opentelemetry-api" }, @@ -1620,7 +1620,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-bedrock" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-bedrock" } dependencies = [ { name = "opentelemetry-api" }, @@ -1656,7 +1656,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-chromadb" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-chromadb" } dependencies = [ { name = "opentelemetry-api" }, @@ -1691,7 +1691,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-cohere" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-cohere" } dependencies = [ { name = "opentelemetry-api" }, @@ -1729,7 +1729,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-crewai" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-crewai" } dependencies = [ { name = "opentelemetry-api" }, @@ -1765,7 +1765,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-google-generativeai" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-google-generativeai" } dependencies = [ { name = "opentelemetry-api" }, @@ -1802,7 +1802,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-groq" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-groq" } dependencies = [ { name = "opentelemetry-api" }, @@ -1840,7 +1840,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-haystack" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-haystack" } dependencies = [ { name = "opentelemetry-api" }, @@ -1877,7 +1877,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-lancedb" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-lancedb" } dependencies = [ { name = "opentelemetry-api" }, @@ -1917,7 +1917,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-langchain" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-langchain" } dependencies = [ { name = "opentelemetry-api" }, @@ -1971,7 +1971,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-litellm" -version = "0.1.0" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-litellm" } dependencies = [ { name = "opentelemetry-api" }, @@ -2009,7 +2009,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-llamaindex" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-llamaindex" } dependencies = [ { name = "inflection" }, @@ -2073,7 +2073,7 @@ wheels = [ [[package]] name = "opentelemetry-instrumentation-marqo" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-marqo" } dependencies = [ { name = "opentelemetry-api" }, @@ -2111,7 +2111,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-mcp" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-mcp" } dependencies = [ { name = "opentelemetry-api" }, @@ -2151,7 +2151,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-milvus" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-milvus" } dependencies = [ { name = "opentelemetry-api" }, @@ -2187,7 +2187,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-mistralai" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-mistralai" } dependencies = [ { name = "opentelemetry-api" }, @@ -2222,9 +2222,46 @@ test = [ { name = "vcrpy", specifier = ">=8.0.0,<9" }, ] +[[package]] +name = "opentelemetry-instrumentation-oci-genai" +version = "0.62.3" +source = { editable = "../opentelemetry-instrumentation-oci-genai" } +dependencies = [ + { name = "opentelemetry-api" }, + { name = "opentelemetry-instrumentation" }, + { name = "opentelemetry-semantic-conventions" }, + { name = "opentelemetry-semantic-conventions-ai" }, +] + +[package.metadata] +requires-dist = [ + { name = "oci", marker = "extra == 'instruments'" }, + { name = "opentelemetry-api", specifier = ">=1.38.0,<2" }, + { name = "opentelemetry-instrumentation", specifier = ">=0.59b0" }, + { name = "opentelemetry-semantic-conventions", specifier = ">=0.63b1" }, + { name = "opentelemetry-semantic-conventions-ai", specifier = ">=0.5.1,<0.6.0" }, +] +provides-extras = ["instruments"] + +[package.metadata.requires-dev] +dev = [ + { name = "autopep8", specifier = ">=2.2.0,<3" }, + { name = "pytest", specifier = ">=8.2.2,<9" }, + { name = "pytest-sugar", specifier = "==1.0.0" }, + { name = "ruff", specifier = ">=0.4.0" }, +] +test = [ + { name = "oci", specifier = ">=2.121.1,<3" }, + { name = "opentelemetry-sdk", specifier = ">=1.38.0,<2" }, + { name = "pytest", specifier = ">=8.2.2,<9" }, + { name = "pytest-recording", specifier = ">=0.13.1,<0.14.0" }, + { name = "pytest-sugar", specifier = "==1.0.0" }, + { name = "vcrpy", specifier = ">=8.0.0,<9" }, +] + [[package]] name = "opentelemetry-instrumentation-ollama" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-ollama" } dependencies = [ { name = "opentelemetry-api" }, @@ -2262,7 +2299,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-openai" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-openai" } dependencies = [ { name = "opentelemetry-api" }, @@ -2299,7 +2336,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-openai-agents" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-openai-agents" } dependencies = [ { name = "opentelemetry-api" }, @@ -2338,7 +2375,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-pinecone" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-pinecone" } dependencies = [ { name = "opentelemetry-api" }, @@ -2377,7 +2414,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-qdrant" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-qdrant" } dependencies = [ { name = "opentelemetry-api" }, @@ -2425,7 +2462,7 @@ wheels = [ [[package]] name = "opentelemetry-instrumentation-replicate" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-replicate" } dependencies = [ { name = "opentelemetry-api" }, @@ -2477,7 +2514,7 @@ wheels = [ [[package]] name = "opentelemetry-instrumentation-sagemaker" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-sagemaker" } dependencies = [ { name = "opentelemetry-api" }, @@ -2542,7 +2579,7 @@ wheels = [ [[package]] name = "opentelemetry-instrumentation-together" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-together" } dependencies = [ { name = "opentelemetry-api" }, @@ -2580,7 +2617,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-transformers" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-transformers" } dependencies = [ { name = "opentelemetry-api" }, @@ -2627,7 +2664,7 @@ wheels = [ [[package]] name = "opentelemetry-instrumentation-vertexai" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-vertexai" } dependencies = [ { name = "opentelemetry-api" }, @@ -2665,7 +2702,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-voyageai" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-voyageai" } dependencies = [ { name = "opentelemetry-api" }, @@ -2703,7 +2740,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-watsonx" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-watsonx" } dependencies = [ { name = "opentelemetry-api" }, @@ -2741,7 +2778,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-weaviate" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-weaviate" } dependencies = [ { name = "opentelemetry-api" }, @@ -2780,7 +2817,7 @@ test = [ [[package]] name = "opentelemetry-instrumentation-writer" -version = "0.62.1" +version = "0.62.3" source = { editable = "../opentelemetry-instrumentation-writer" } dependencies = [ { name = "opentelemetry-api" }, @@ -3904,7 +3941,7 @@ wheels = [ [[package]] name = "traceloop-sdk" -version = "0.62.1" +version = "0.62.3" source = { editable = "." } dependencies = [ { name = "aiohttp" }, @@ -3934,6 +3971,7 @@ dependencies = [ { name = "opentelemetry-instrumentation-mcp" }, { name = "opentelemetry-instrumentation-milvus" }, { name = "opentelemetry-instrumentation-mistralai" }, + { name = "opentelemetry-instrumentation-oci-genai" }, { name = "opentelemetry-instrumentation-ollama" }, { name = "opentelemetry-instrumentation-openai" }, { name = "opentelemetry-instrumentation-openai-agents" }, @@ -4017,6 +4055,7 @@ requires-dist = [ { name = "opentelemetry-instrumentation-mcp", editable = "../opentelemetry-instrumentation-mcp" }, { name = "opentelemetry-instrumentation-milvus", editable = "../opentelemetry-instrumentation-milvus" }, { name = "opentelemetry-instrumentation-mistralai", editable = "../opentelemetry-instrumentation-mistralai" }, + { name = "opentelemetry-instrumentation-oci-genai", editable = "../opentelemetry-instrumentation-oci-genai" }, { name = "opentelemetry-instrumentation-ollama", editable = "../opentelemetry-instrumentation-ollama" }, { name = "opentelemetry-instrumentation-openai", editable = "../opentelemetry-instrumentation-openai" }, { name = "opentelemetry-instrumentation-openai-agents", editable = "../opentelemetry-instrumentation-openai-agents" },