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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,77 @@ | ||
| from functools import wraps | ||
|
|
||
| import sentry_sdk.utils | ||
| from sentry_sdk import start_span | ||
| from sentry_sdk.tracing import Span | ||
| from sentry_sdk.utils import ContextVar | ||
| from sentry_sdk._types import TYPE_CHECKING | ||
|
|
||
| if TYPE_CHECKING: | ||
| from typing import Optional, Callable, Any | ||
|
|
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| _ai_pipeline_name = ContextVar("ai_pipeline_name", default=None) | ||
|
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|
|
||
| def set_ai_pipeline_name(name): | ||
| # type: (Optional[str]) -> None | ||
| _ai_pipeline_name.set(name) | ||
|
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||
|
|
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| def get_ai_pipeline_name(): | ||
| # type: () -> Optional[str] | ||
| return _ai_pipeline_name.get() | ||
|
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||
|
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| def ai_track(description, **span_kwargs): | ||
| # type: (str, Any) -> Callable[..., Any] | ||
| def decorator(f): | ||
| # type: (Callable[..., Any]) -> Callable[..., Any] | ||
| @wraps(f) | ||
| def wrapped(*args, **kwargs): | ||
| # type: (Any, Any) -> Any | ||
| curr_pipeline = _ai_pipeline_name.get() | ||
| op = span_kwargs.get("op", "ai.run" if curr_pipeline else "ai.pipeline") | ||
| with start_span(description=description, op=op, **span_kwargs) as span: | ||
| if curr_pipeline: | ||
| span.set_data("ai.pipeline.name", curr_pipeline) | ||
| return f(*args, **kwargs) | ||
| else: | ||
| _ai_pipeline_name.set(description) | ||
| try: | ||
| res = f(*args, **kwargs) | ||
| except Exception as e: | ||
| event, hint = sentry_sdk.utils.event_from_exception( | ||
| e, | ||
| client_options=sentry_sdk.get_client().options, | ||
| mechanism={"type": "ai_monitoring", "handled": False}, | ||
| ) | ||
| sentry_sdk.capture_event(event, hint=hint) | ||
| raise e from None | ||
| finally: | ||
| _ai_pipeline_name.set(None) | ||
| return res | ||
|
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||
| return wrapped | ||
|
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| return decorator | ||
|
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|
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| def record_token_usage( | ||
| span, prompt_tokens=None, completion_tokens=None, total_tokens=None | ||
| ): | ||
| # type: (Span, Optional[int], Optional[int], Optional[int]) -> None | ||
| ai_pipeline_name = get_ai_pipeline_name() | ||
| if ai_pipeline_name: | ||
| span.set_data("ai.pipeline.name", ai_pipeline_name) | ||
| if prompt_tokens is not None: | ||
| span.set_measurement("ai_prompt_tokens_used", value=prompt_tokens) | ||
| if completion_tokens is not None: | ||
| span.set_measurement("ai_completion_tokens_used", value=completion_tokens) | ||
| if ( | ||
| total_tokens is None | ||
| and prompt_tokens is not None | ||
| and completion_tokens is not None | ||
| ): | ||
| total_tokens = prompt_tokens + completion_tokens | ||
| if total_tokens is not None: | ||
| span.set_measurement("ai_total_tokens_used", total_tokens) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,32 @@ | ||
| from sentry_sdk._types import TYPE_CHECKING | ||
|
|
||
| if TYPE_CHECKING: | ||
| from typing import Any | ||
|
|
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| from sentry_sdk.tracing import Span | ||
| from sentry_sdk.utils import logger | ||
|
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||
|
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| def _normalize_data(data): | ||
| # type: (Any) -> Any | ||
|
|
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| # convert pydantic data (e.g. OpenAI v1+) to json compatible format | ||
| if hasattr(data, "model_dump"): | ||
| try: | ||
| return data.model_dump() | ||
| except Exception as e: | ||
| 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 isinstance(data, dict): | ||
| return {k: _normalize_data(v) for (k, v) in data.items()} | ||
| return data | ||
|
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|
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| def set_data_normalized(span, key, value): | ||
| # type: (Span, str, Any) -> None | ||
| normalized = _normalize_data(value) | ||
| span.set_data(key, normalized) |
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