diff --git a/sentry_sdk/ai/utils.py b/sentry_sdk/ai/utils.py index a3c62600c0..cf52cba6e8 100644 --- a/sentry_sdk/ai/utils.py +++ b/sentry_sdk/ai/utils.py @@ -7,8 +7,8 @@ from sentry_sdk.utils import logger -def _normalize_data(data): - # type: (Any) -> Any +def _normalize_data(data, unpack=True): + # type: (Any, bool) -> Any # convert pydantic data (e.g. OpenAI v1+) to json compatible format if hasattr(data, "model_dump"): @@ -18,18 +18,18 @@ def _normalize_data(data): logger.warning("Could not convert pydantic data to JSON: %s", e) return data if isinstance(data, list): - if len(data) == 1: - return _normalize_data(data[0]) # remove empty dimensions - return list(_normalize_data(x) for x in data) + if unpack and len(data) == 1: + return _normalize_data(data[0], unpack=unpack) # remove empty dimensions + return list(_normalize_data(x, unpack=unpack) for x in data) if isinstance(data, dict): - return {k: _normalize_data(v) for (k, v) in data.items()} + return {k: _normalize_data(v, unpack=unpack) for (k, v) in data.items()} return data -def set_data_normalized(span, key, value): - # type: (Span, str, Any) -> None - normalized = _normalize_data(value) +def set_data_normalized(span, key, value, unpack=True): + # type: (Span, str, Any, bool) -> None + normalized = _normalize_data(value, unpack=unpack) if isinstance(normalized, (int, float, bool, str)): span.set_data(key, normalized) else: diff --git a/sentry_sdk/integrations/anthropic.py b/sentry_sdk/integrations/anthropic.py index 1e1f9112a1..05d45ef62f 100644 --- a/sentry_sdk/integrations/anthropic.py +++ b/sentry_sdk/integrations/anthropic.py @@ -1,8 +1,10 @@ from functools import wraps +import json from typing import TYPE_CHECKING import sentry_sdk from sentry_sdk.ai.monitoring import record_token_usage +from sentry_sdk.ai.utils import set_data_normalized from sentry_sdk.consts import OP, SPANDATA from sentry_sdk.integrations import _check_minimum_version, DidNotEnable, Integration from sentry_sdk.scope import should_send_default_pii @@ -10,9 +12,15 @@ capture_internal_exceptions, event_from_exception, package_version, + safe_serialize, ) try: + try: + from anthropic import NOT_GIVEN + except ImportError: + NOT_GIVEN = None + from anthropic.resources import AsyncMessages, Messages if TYPE_CHECKING: @@ -53,8 +61,11 @@ def _capture_exception(exc): sentry_sdk.capture_event(event, hint=hint) -def _calculate_token_usage(result, span): - # type: (Messages, Span) -> None +def _get_token_usage(result): + # type: (Messages) -> tuple[int, int] + """ + Get token usage from the Anthropic response. + """ input_tokens = 0 output_tokens = 0 if hasattr(result, "usage"): @@ -64,37 +75,13 @@ def _calculate_token_usage(result, span): if hasattr(usage, "output_tokens") and isinstance(usage.output_tokens, int): output_tokens = usage.output_tokens - total_tokens = input_tokens + output_tokens + return input_tokens, output_tokens - record_token_usage( - span, - input_tokens=input_tokens, - output_tokens=output_tokens, - total_tokens=total_tokens, - ) - -def _get_responses(content): - # type: (list[Any]) -> list[dict[str, Any]] +def _collect_ai_data(event, model, input_tokens, output_tokens, content_blocks): + # type: (MessageStreamEvent, str | None, int, int, list[str]) -> tuple[str | None, int, int, list[str]] """ - Get JSON of a Anthropic responses. - """ - responses = [] - for item in content: - if hasattr(item, "text"): - responses.append( - { - "type": item.type, - "text": item.text, - } - ) - return responses - - -def _collect_ai_data(event, input_tokens, output_tokens, content_blocks): - # type: (MessageStreamEvent, int, int, list[str]) -> tuple[int, int, list[str]] - """ - Count token usage and collect content blocks from the AI streaming response. + Collect model information, token usage, and collect content blocks from the AI streaming response. """ with capture_internal_exceptions(): if hasattr(event, "type"): @@ -102,6 +89,7 @@ def _collect_ai_data(event, input_tokens, output_tokens, content_blocks): usage = event.message.usage input_tokens += usage.input_tokens output_tokens += usage.output_tokens + model = event.message.model or model elif event.type == "content_block_start": pass elif event.type == "content_block_delta": @@ -114,31 +102,80 @@ def _collect_ai_data(event, input_tokens, output_tokens, content_blocks): elif event.type == "message_delta": output_tokens += event.usage.output_tokens - return input_tokens, output_tokens, content_blocks + return model, input_tokens, output_tokens, content_blocks -def _add_ai_data_to_span( - span, integration, input_tokens, output_tokens, content_blocks -): - # type: (Span, AnthropicIntegration, int, int, list[str]) -> None +def _set_input_data(span, kwargs, integration): + # type: (Span, dict[str, Any], AnthropicIntegration) -> None """ - Add token usage and content blocks from the AI streaming response to the span. + Set input data for the span based on the provided keyword arguments for the anthropic message creation. """ - with capture_internal_exceptions(): - if should_send_default_pii() and integration.include_prompts: - complete_message = "".join(content_blocks) - span.set_data( - SPANDATA.AI_RESPONSES, - [{"type": "text", "text": complete_message}], - ) - total_tokens = input_tokens + output_tokens - record_token_usage( + messages = kwargs.get("messages") + if ( + messages is not None + and len(messages) > 0 + and should_send_default_pii() + and integration.include_prompts + ): + set_data_normalized( + span, SPANDATA.GEN_AI_REQUEST_MESSAGES, safe_serialize(messages) + ) + + set_data_normalized( + span, SPANDATA.GEN_AI_RESPONSE_STREAMING, kwargs.get("stream", False) + ) + + kwargs_keys_to_attributes = { + "max_tokens": SPANDATA.GEN_AI_REQUEST_MAX_TOKENS, + "model": SPANDATA.GEN_AI_REQUEST_MODEL, + "temperature": SPANDATA.GEN_AI_REQUEST_TEMPERATURE, + "top_k": SPANDATA.GEN_AI_REQUEST_TOP_K, + "top_p": SPANDATA.GEN_AI_REQUEST_TOP_P, + } + for key, attribute in kwargs_keys_to_attributes.items(): + value = kwargs.get(key) + if value is not NOT_GIVEN and value is not None: + set_data_normalized(span, attribute, value) + + # Input attributes: Tools + tools = kwargs.get("tools") + if tools is not NOT_GIVEN and tools is not None and len(tools) > 0: + set_data_normalized( + span, SPANDATA.GEN_AI_REQUEST_AVAILABLE_TOOLS, safe_serialize(tools) + ) + + +def _set_output_data( + span, + integration, + model, + input_tokens, + output_tokens, + content_blocks, + finish_span=False, +): + # type: (Span, AnthropicIntegration, str | None, int | None, int | None, list[Any], bool) -> None + """ + Set output data for the span based on the AI response.""" + span.set_data(SPANDATA.GEN_AI_RESPONSE_MODEL, model) + if should_send_default_pii() and integration.include_prompts: + set_data_normalized( span, - input_tokens=input_tokens, - output_tokens=output_tokens, - total_tokens=total_tokens, + SPANDATA.GEN_AI_RESPONSE_TEXT, + json.dumps(content_blocks), + unpack=False, ) - span.set_data(SPANDATA.AI_STREAMING, True) + + record_token_usage( + span, + input_tokens=input_tokens, + output_tokens=output_tokens, + ) + + # TODO: GEN_AI_RESPONSE_TOOL_CALLS ? + + if finish_span: + span.__exit__(None, None, None) def _sentry_patched_create_common(f, *args, **kwargs): @@ -155,31 +192,41 @@ def _sentry_patched_create_common(f, *args, **kwargs): except TypeError: return f(*args, **kwargs) + model = kwargs.get("model", "") + span = sentry_sdk.start_span( - op=OP.ANTHROPIC_MESSAGES_CREATE, - description="Anthropic messages create", + op=OP.GEN_AI_CHAT, + name=f"chat {model}".strip(), origin=AnthropicIntegration.origin, ) span.__enter__() - result = yield f, args, kwargs + _set_input_data(span, kwargs, integration) - # add data to span and finish it - messages = list(kwargs["messages"]) - model = kwargs.get("model") + result = yield f, args, kwargs with capture_internal_exceptions(): - span.set_data(SPANDATA.AI_MODEL_ID, model) - span.set_data(SPANDATA.AI_STREAMING, False) - - if should_send_default_pii() and integration.include_prompts: - span.set_data(SPANDATA.AI_INPUT_MESSAGES, messages) - if hasattr(result, "content"): - if should_send_default_pii() and integration.include_prompts: - span.set_data(SPANDATA.AI_RESPONSES, _get_responses(result.content)) - _calculate_token_usage(result, span) - span.__exit__(None, None, None) + input_tokens, output_tokens = _get_token_usage(result) + + content_blocks = [] + for content_block in result.content: + if hasattr(content_block, "to_dict"): + content_blocks.append(content_block.to_dict()) + elif hasattr(content_block, "model_dump"): + content_blocks.append(content_block.model_dump()) + elif hasattr(content_block, "text"): + content_blocks.append({"type": "text", "text": content_block.text}) + + _set_output_data( + span=span, + integration=integration, + model=getattr(result, "model", None), + input_tokens=input_tokens, + output_tokens=output_tokens, + content_blocks=content_blocks, + finish_span=True, + ) # Streaming response elif hasattr(result, "_iterator"): @@ -187,37 +234,53 @@ def _sentry_patched_create_common(f, *args, **kwargs): def new_iterator(): # type: () -> Iterator[MessageStreamEvent] + model = None input_tokens = 0 output_tokens = 0 content_blocks = [] # type: list[str] for event in old_iterator: - input_tokens, output_tokens, content_blocks = _collect_ai_data( - event, input_tokens, output_tokens, content_blocks + model, input_tokens, output_tokens, content_blocks = ( + _collect_ai_data( + event, model, input_tokens, output_tokens, content_blocks + ) ) yield event - _add_ai_data_to_span( - span, integration, input_tokens, output_tokens, content_blocks + _set_output_data( + span=span, + integration=integration, + model=model, + input_tokens=input_tokens, + output_tokens=output_tokens, + content_blocks=[{"text": "".join(content_blocks), "type": "text"}], + finish_span=True, ) - span.__exit__(None, None, None) async def new_iterator_async(): # type: () -> AsyncIterator[MessageStreamEvent] + model = None input_tokens = 0 output_tokens = 0 content_blocks = [] # type: list[str] async for event in old_iterator: - input_tokens, output_tokens, content_blocks = _collect_ai_data( - event, input_tokens, output_tokens, content_blocks + model, input_tokens, output_tokens, content_blocks = ( + _collect_ai_data( + event, model, input_tokens, output_tokens, content_blocks + ) ) yield event - _add_ai_data_to_span( - span, integration, input_tokens, output_tokens, content_blocks + _set_output_data( + span=span, + integration=integration, + model=model, + input_tokens=input_tokens, + output_tokens=output_tokens, + content_blocks=[{"text": "".join(content_blocks), "type": "text"}], + finish_span=True, ) - span.__exit__(None, None, None) if str(type(result._iterator)) == "": result._iterator = new_iterator_async() diff --git a/sentry_sdk/integrations/starlite.py b/sentry_sdk/integrations/starlite.py index 6ab80712e5..24707a18b1 100644 --- a/sentry_sdk/integrations/starlite.py +++ b/sentry_sdk/integrations/starlite.py @@ -17,7 +17,7 @@ from starlite.plugins.base import get_plugin_for_value # type: ignore from starlite.routes.http import HTTPRoute # type: ignore from starlite.utils import ConnectionDataExtractor, is_async_callable, Ref # type: ignore - from pydantic import BaseModel + from pydantic import BaseModel # type: ignore except ImportError: raise DidNotEnable("Starlite is not installed") diff --git a/tests/integrations/anthropic/test_anthropic.py b/tests/integrations/anthropic/test_anthropic.py index e6e1a40aa9..eba07a1df6 100644 --- a/tests/integrations/anthropic/test_anthropic.py +++ b/tests/integrations/anthropic/test_anthropic.py @@ -20,7 +20,7 @@ async def __call__(self, *args, **kwargs): from anthropic.types.message_delta_event import MessageDeltaEvent from anthropic.types.message_start_event import MessageStartEvent -from sentry_sdk.integrations.anthropic import _add_ai_data_to_span, _collect_ai_data +from sentry_sdk.integrations.anthropic import _set_output_data, _collect_ai_data from sentry_sdk.utils import package_version try: @@ -112,23 +112,27 @@ def test_nonstreaming_create_message( assert len(event["spans"]) == 1 (span,) = event["spans"] - assert span["op"] == OP.ANTHROPIC_MESSAGES_CREATE - assert span["description"] == "Anthropic messages create" - assert span["data"][SPANDATA.AI_MODEL_ID] == "model" + assert span["op"] == OP.GEN_AI_CHAT + assert span["description"] == "chat model" + assert span["data"][SPANDATA.GEN_AI_REQUEST_MODEL] == "model" if send_default_pii and include_prompts: - assert span["data"][SPANDATA.AI_INPUT_MESSAGES] == messages - assert span["data"][SPANDATA.AI_RESPONSES] == [ - {"type": "text", "text": "Hi, I'm Claude."} - ] + assert ( + span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES] + == '[{"role": "user", "content": "Hello, Claude"}]' + ) + assert ( + span["data"][SPANDATA.GEN_AI_RESPONSE_TEXT] + == '[{"text": "Hi, I\'m Claude.", "type": "text"}]' + ) else: - assert SPANDATA.AI_INPUT_MESSAGES not in span["data"] - assert SPANDATA.AI_RESPONSES not in span["data"] + assert SPANDATA.GEN_AI_REQUEST_MESSAGES not in span["data"] + assert SPANDATA.GEN_AI_RESPONSE_TEXT not in span["data"] - assert span["data"]["gen_ai.usage.input_tokens"] == 10 - assert span["data"]["gen_ai.usage.output_tokens"] == 20 - assert span["data"]["gen_ai.usage.total_tokens"] == 30 - assert span["data"][SPANDATA.AI_STREAMING] is False + assert span["data"][SPANDATA.GEN_AI_USAGE_INPUT_TOKENS] == 10 + assert span["data"][SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 20 + assert span["data"][SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 30 + assert span["data"][SPANDATA.GEN_AI_RESPONSE_STREAMING] is False @pytest.mark.asyncio @@ -180,23 +184,27 @@ async def test_nonstreaming_create_message_async( assert len(event["spans"]) == 1 (span,) = event["spans"] - assert span["op"] == OP.ANTHROPIC_MESSAGES_CREATE - assert span["description"] == "Anthropic messages create" - assert span["data"][SPANDATA.AI_MODEL_ID] == "model" + assert span["op"] == OP.GEN_AI_CHAT + assert span["description"] == "chat model" + assert span["data"][SPANDATA.GEN_AI_REQUEST_MODEL] == "model" if send_default_pii and include_prompts: - assert span["data"][SPANDATA.AI_INPUT_MESSAGES] == messages - assert span["data"][SPANDATA.AI_RESPONSES] == [ - {"type": "text", "text": "Hi, I'm Claude."} - ] + assert ( + span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES] + == '[{"role": "user", "content": "Hello, Claude"}]' + ) + assert ( + span["data"][SPANDATA.GEN_AI_RESPONSE_TEXT] + == '[{"text": "Hi, I\'m Claude.", "type": "text"}]' + ) else: - assert SPANDATA.AI_INPUT_MESSAGES not in span["data"] - assert SPANDATA.AI_RESPONSES not in span["data"] + assert SPANDATA.GEN_AI_REQUEST_MESSAGES not in span["data"] + assert SPANDATA.GEN_AI_RESPONSE_TEXT not in span["data"] - assert span["data"]["gen_ai.usage.input_tokens"] == 10 - assert span["data"]["gen_ai.usage.output_tokens"] == 20 - assert span["data"]["gen_ai.usage.total_tokens"] == 30 - assert span["data"][SPANDATA.AI_STREAMING] is False + assert span["data"][SPANDATA.GEN_AI_USAGE_INPUT_TOKENS] == 10 + assert span["data"][SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 20 + assert span["data"][SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 30 + assert span["data"][SPANDATA.GEN_AI_RESPONSE_STREAMING] is False @pytest.mark.parametrize( @@ -279,24 +287,28 @@ def test_streaming_create_message( assert len(event["spans"]) == 1 (span,) = event["spans"] - assert span["op"] == OP.ANTHROPIC_MESSAGES_CREATE - assert span["description"] == "Anthropic messages create" - assert span["data"][SPANDATA.AI_MODEL_ID] == "model" + assert span["op"] == OP.GEN_AI_CHAT + assert span["description"] == "chat model" + assert span["data"][SPANDATA.GEN_AI_REQUEST_MODEL] == "model" if send_default_pii and include_prompts: - assert span["data"][SPANDATA.AI_INPUT_MESSAGES] == messages - assert span["data"][SPANDATA.AI_RESPONSES] == [ - {"type": "text", "text": "Hi! I'm Claude!"} - ] + assert ( + span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES] + == '[{"role": "user", "content": "Hello, Claude"}]' + ) + assert ( + span["data"][SPANDATA.GEN_AI_RESPONSE_TEXT] + == '[{"text": "Hi! I\'m Claude!", "type": "text"}]' + ) else: - assert SPANDATA.AI_INPUT_MESSAGES not in span["data"] - assert SPANDATA.AI_RESPONSES not in span["data"] + assert SPANDATA.GEN_AI_REQUEST_MESSAGES not in span["data"] + assert SPANDATA.GEN_AI_RESPONSE_TEXT not in span["data"] - assert span["data"]["gen_ai.usage.input_tokens"] == 10 - assert span["data"]["gen_ai.usage.output_tokens"] == 30 - assert span["data"]["gen_ai.usage.total_tokens"] == 40 - assert span["data"][SPANDATA.AI_STREAMING] is True + assert span["data"][SPANDATA.GEN_AI_USAGE_INPUT_TOKENS] == 10 + assert span["data"][SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 30 + assert span["data"][SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 40 + assert span["data"][SPANDATA.GEN_AI_RESPONSE_STREAMING] is True @pytest.mark.asyncio @@ -382,24 +394,28 @@ async def test_streaming_create_message_async( assert len(event["spans"]) == 1 (span,) = event["spans"] - assert span["op"] == OP.ANTHROPIC_MESSAGES_CREATE - assert span["description"] == "Anthropic messages create" - assert span["data"][SPANDATA.AI_MODEL_ID] == "model" + assert span["op"] == OP.GEN_AI_CHAT + assert span["description"] == "chat model" + assert span["data"][SPANDATA.GEN_AI_REQUEST_MODEL] == "model" if send_default_pii and include_prompts: - assert span["data"][SPANDATA.AI_INPUT_MESSAGES] == messages - assert span["data"][SPANDATA.AI_RESPONSES] == [ - {"type": "text", "text": "Hi! I'm Claude!"} - ] + assert ( + span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES] + == '[{"role": "user", "content": "Hello, Claude"}]' + ) + assert ( + span["data"][SPANDATA.GEN_AI_RESPONSE_TEXT] + == '[{"text": "Hi! I\'m Claude!", "type": "text"}]' + ) else: - assert SPANDATA.AI_INPUT_MESSAGES not in span["data"] - assert SPANDATA.AI_RESPONSES not in span["data"] + assert SPANDATA.GEN_AI_REQUEST_MESSAGES not in span["data"] + assert SPANDATA.GEN_AI_RESPONSE_TEXT not in span["data"] - assert span["data"]["gen_ai.usage.input_tokens"] == 10 - assert span["data"]["gen_ai.usage.output_tokens"] == 30 - assert span["data"]["gen_ai.usage.total_tokens"] == 40 - assert span["data"][SPANDATA.AI_STREAMING] is True + assert span["data"][SPANDATA.GEN_AI_USAGE_INPUT_TOKENS] == 10 + assert span["data"][SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 30 + assert span["data"][SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 40 + assert span["data"][SPANDATA.GEN_AI_RESPONSE_STREAMING] is True @pytest.mark.skipif( @@ -512,23 +528,27 @@ def test_streaming_create_message_with_input_json_delta( assert len(event["spans"]) == 1 (span,) = event["spans"] - assert span["op"] == OP.ANTHROPIC_MESSAGES_CREATE - assert span["description"] == "Anthropic messages create" - assert span["data"][SPANDATA.AI_MODEL_ID] == "model" + assert span["op"] == OP.GEN_AI_CHAT + assert span["description"] == "chat model" + assert span["data"][SPANDATA.GEN_AI_REQUEST_MODEL] == "model" if send_default_pii and include_prompts: - assert span["data"][SPANDATA.AI_INPUT_MESSAGES] == messages - assert span["data"][SPANDATA.AI_RESPONSES] == [ - {"text": "{'location': 'San Francisco, CA'}", "type": "text"} - ] + assert ( + span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES] + == '[{"role": "user", "content": "What is the weather like in San Francisco?"}]' + ) + assert ( + span["data"][SPANDATA.GEN_AI_RESPONSE_TEXT] + == '[{"text": "{\'location\': \'San Francisco, CA\'}", "type": "text"}]' + ) else: - assert SPANDATA.AI_INPUT_MESSAGES not in span["data"] - assert SPANDATA.AI_RESPONSES not in span["data"] + assert SPANDATA.GEN_AI_REQUEST_MESSAGES not in span["data"] + assert SPANDATA.GEN_AI_RESPONSE_TEXT not in span["data"] - assert span["data"]["gen_ai.usage.input_tokens"] == 366 - assert span["data"]["gen_ai.usage.output_tokens"] == 51 - assert span["data"]["gen_ai.usage.total_tokens"] == 417 - assert span["data"][SPANDATA.AI_STREAMING] is True + assert span["data"][SPANDATA.GEN_AI_USAGE_INPUT_TOKENS] == 366 + assert span["data"][SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 51 + assert span["data"][SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 417 + assert span["data"][SPANDATA.GEN_AI_RESPONSE_STREAMING] is True @pytest.mark.asyncio @@ -648,24 +668,28 @@ async def test_streaming_create_message_with_input_json_delta_async( assert len(event["spans"]) == 1 (span,) = event["spans"] - assert span["op"] == OP.ANTHROPIC_MESSAGES_CREATE - assert span["description"] == "Anthropic messages create" - assert span["data"][SPANDATA.AI_MODEL_ID] == "model" + assert span["op"] == OP.GEN_AI_CHAT + assert span["description"] == "chat model" + assert span["data"][SPANDATA.GEN_AI_REQUEST_MODEL] == "model" if send_default_pii and include_prompts: - assert span["data"][SPANDATA.AI_INPUT_MESSAGES] == messages - assert span["data"][SPANDATA.AI_RESPONSES] == [ - {"text": "{'location': 'San Francisco, CA'}", "type": "text"} - ] + assert ( + span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES] + == '[{"role": "user", "content": "What is the weather like in San Francisco?"}]' + ) + assert ( + span["data"][SPANDATA.GEN_AI_RESPONSE_TEXT] + == '[{"text": "{\'location\': \'San Francisco, CA\'}", "type": "text"}]' + ) else: - assert SPANDATA.AI_INPUT_MESSAGES not in span["data"] - assert SPANDATA.AI_RESPONSES not in span["data"] + assert SPANDATA.GEN_AI_REQUEST_MESSAGES not in span["data"] + assert SPANDATA.GEN_AI_RESPONSE_TEXT not in span["data"] - assert span["data"]["gen_ai.usage.input_tokens"] == 366 - assert span["data"]["gen_ai.usage.output_tokens"] == 51 - assert span["data"]["gen_ai.usage.total_tokens"] == 417 - assert span["data"][SPANDATA.AI_STREAMING] is True + assert span["data"][SPANDATA.GEN_AI_USAGE_INPUT_TOKENS] == 366 + assert span["data"][SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 51 + assert span["data"][SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 417 + assert span["data"][SPANDATA.GEN_AI_RESPONSE_STREAMING] is True def test_exception_message_create(sentry_init, capture_events): @@ -770,15 +794,16 @@ def test_collect_ai_data_with_input_json_delta(): index=0, type="content_block_delta", ) - + model = None input_tokens = 10 output_tokens = 20 content_blocks = [] - new_input_tokens, new_output_tokens, new_content_blocks = _collect_ai_data( - event, input_tokens, output_tokens, content_blocks + model, new_input_tokens, new_output_tokens, new_content_blocks = _collect_ai_data( + event, model, input_tokens, output_tokens, content_blocks ) + assert model is None assert new_input_tokens == input_tokens assert new_output_tokens == output_tokens assert new_content_blocks == ["test"] @@ -788,7 +813,7 @@ def test_collect_ai_data_with_input_json_delta(): ANTHROPIC_VERSION < (0, 27), reason="Versions <0.27.0 do not include InputJSONDelta.", ) -def test_add_ai_data_to_span_with_input_json_delta(sentry_init): +def test_set_output_data_with_input_json_delta(sentry_init): sentry_init( integrations=[AnthropicIntegration(include_prompts=True)], traces_sample_rate=1.0, @@ -798,19 +823,20 @@ def test_add_ai_data_to_span_with_input_json_delta(sentry_init): with start_transaction(name="test"): span = start_span() integration = AnthropicIntegration() - - _add_ai_data_to_span( + json_deltas = ["{'test': 'data',", "'more': 'json'}"] + _set_output_data( span, integration, + model="", input_tokens=10, output_tokens=20, - content_blocks=["{'test': 'data',", "'more': 'json'}"], + content_blocks=[{"text": "".join(json_deltas), "type": "text"}], ) - assert span._data.get("ai.responses") == [ - {"type": "text", "text": "{'test': 'data','more': 'json'}"} - ] - assert span._data.get("ai.streaming") is True - assert span._data.get("gen_ai.usage.input_tokens") == 10 - assert span._data.get("gen_ai.usage.output_tokens") == 20 - assert span._data.get("gen_ai.usage.total_tokens") == 30 + assert ( + span._data.get(SPANDATA.GEN_AI_RESPONSE_TEXT) + == "[{\"text\": \"{'test': 'data','more': 'json'}\", \"type\": \"text\"}]" + ) + assert span._data.get(SPANDATA.GEN_AI_USAGE_INPUT_TOKENS) == 10 + assert span._data.get(SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS) == 20 + assert span._data.get(SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS) == 30