439 lines
14 KiB
Python
439 lines
14 KiB
Python
from functools import wraps
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from typing import TYPE_CHECKING
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import sentry_sdk
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from sentry_sdk.ai.monitoring import record_token_usage
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from sentry_sdk.ai.utils import (
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set_data_normalized,
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normalize_message_roles,
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truncate_and_annotate_messages,
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get_start_span_function,
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)
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from sentry_sdk.consts import OP, SPANDATA, SPANSTATUS
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from sentry_sdk.integrations import _check_minimum_version, DidNotEnable, Integration
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from sentry_sdk.scope import should_send_default_pii
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from sentry_sdk.tracing_utils import set_span_errored
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from sentry_sdk.utils import (
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capture_internal_exceptions,
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event_from_exception,
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package_version,
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safe_serialize,
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)
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try:
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try:
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from anthropic import NotGiven
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except ImportError:
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NotGiven = None
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try:
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from anthropic import Omit
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except ImportError:
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Omit = None
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from anthropic.resources import AsyncMessages, Messages
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if TYPE_CHECKING:
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from anthropic.types import MessageStreamEvent
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except ImportError:
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raise DidNotEnable("Anthropic not installed")
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if TYPE_CHECKING:
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from typing import Any, AsyncIterator, Iterator
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from sentry_sdk.tracing import Span
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class AnthropicIntegration(Integration):
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identifier = "anthropic"
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origin = f"auto.ai.{identifier}"
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def __init__(self, include_prompts=True):
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# type: (AnthropicIntegration, bool) -> None
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self.include_prompts = include_prompts
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@staticmethod
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def setup_once():
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# type: () -> None
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version = package_version("anthropic")
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_check_minimum_version(AnthropicIntegration, version)
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Messages.create = _wrap_message_create(Messages.create)
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AsyncMessages.create = _wrap_message_create_async(AsyncMessages.create)
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def _capture_exception(exc):
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# type: (Any) -> None
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set_span_errored()
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event, hint = event_from_exception(
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exc,
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client_options=sentry_sdk.get_client().options,
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mechanism={"type": "anthropic", "handled": False},
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)
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sentry_sdk.capture_event(event, hint=hint)
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def _get_token_usage(result):
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# type: (Messages) -> tuple[int, int]
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"""
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Get token usage from the Anthropic response.
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"""
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input_tokens = 0
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output_tokens = 0
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if hasattr(result, "usage"):
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usage = result.usage
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if hasattr(usage, "input_tokens") and isinstance(usage.input_tokens, int):
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input_tokens = usage.input_tokens
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if hasattr(usage, "output_tokens") and isinstance(usage.output_tokens, int):
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output_tokens = usage.output_tokens
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return input_tokens, output_tokens
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def _collect_ai_data(event, model, input_tokens, output_tokens, content_blocks):
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# type: (MessageStreamEvent, str | None, int, int, list[str]) -> tuple[str | None, int, int, list[str]]
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"""
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Collect model information, token usage, and collect content blocks from the AI streaming response.
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"""
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with capture_internal_exceptions():
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if hasattr(event, "type"):
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if event.type == "message_start":
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usage = event.message.usage
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input_tokens += usage.input_tokens
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output_tokens += usage.output_tokens
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model = event.message.model or model
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elif event.type == "content_block_start":
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pass
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elif event.type == "content_block_delta":
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if hasattr(event.delta, "text"):
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content_blocks.append(event.delta.text)
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elif hasattr(event.delta, "partial_json"):
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content_blocks.append(event.delta.partial_json)
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elif event.type == "content_block_stop":
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pass
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elif event.type == "message_delta":
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output_tokens += event.usage.output_tokens
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return model, input_tokens, output_tokens, content_blocks
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def _set_input_data(span, kwargs, integration):
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# type: (Span, dict[str, Any], AnthropicIntegration) -> None
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"""
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Set input data for the span based on the provided keyword arguments for the anthropic message creation.
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"""
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messages = kwargs.get("messages")
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if (
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messages is not None
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and len(messages) > 0
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and should_send_default_pii()
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and integration.include_prompts
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):
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normalized_messages = []
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for message in messages:
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if (
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message.get("role") == "user"
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and "content" in message
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and isinstance(message["content"], (list, tuple))
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):
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for item in message["content"]:
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if item.get("type") == "tool_result":
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normalized_messages.append(
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{
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"role": "tool",
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"content": {
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"tool_use_id": item.get("tool_use_id"),
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"output": item.get("content"),
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},
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}
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)
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else:
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normalized_messages.append(message)
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role_normalized_messages = normalize_message_roles(normalized_messages)
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scope = sentry_sdk.get_current_scope()
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messages_data = truncate_and_annotate_messages(
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role_normalized_messages, span, scope
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)
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if messages_data is not None:
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set_data_normalized(
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span, SPANDATA.GEN_AI_REQUEST_MESSAGES, messages_data, unpack=False
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)
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set_data_normalized(
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span, SPANDATA.GEN_AI_RESPONSE_STREAMING, kwargs.get("stream", False)
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)
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kwargs_keys_to_attributes = {
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"max_tokens": SPANDATA.GEN_AI_REQUEST_MAX_TOKENS,
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"model": SPANDATA.GEN_AI_REQUEST_MODEL,
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"temperature": SPANDATA.GEN_AI_REQUEST_TEMPERATURE,
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"top_k": SPANDATA.GEN_AI_REQUEST_TOP_K,
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"top_p": SPANDATA.GEN_AI_REQUEST_TOP_P,
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}
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for key, attribute in kwargs_keys_to_attributes.items():
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value = kwargs.get(key)
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if value is not None and _is_given(value):
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set_data_normalized(span, attribute, value)
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# Input attributes: Tools
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tools = kwargs.get("tools")
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if tools is not None and _is_given(tools) and len(tools) > 0:
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set_data_normalized(
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span, SPANDATA.GEN_AI_REQUEST_AVAILABLE_TOOLS, safe_serialize(tools)
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)
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def _set_output_data(
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span,
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integration,
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model,
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input_tokens,
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output_tokens,
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content_blocks,
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finish_span=False,
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):
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# type: (Span, AnthropicIntegration, str | None, int | None, int | None, list[Any], bool) -> None
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"""
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Set output data for the span based on the AI response."""
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span.set_data(SPANDATA.GEN_AI_RESPONSE_MODEL, model)
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if should_send_default_pii() and integration.include_prompts:
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output_messages = {
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"response": [],
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"tool": [],
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} # type: (dict[str, list[Any]])
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for output in content_blocks:
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if output["type"] == "text":
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output_messages["response"].append(output["text"])
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elif output["type"] == "tool_use":
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output_messages["tool"].append(output)
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if len(output_messages["tool"]) > 0:
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set_data_normalized(
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span,
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SPANDATA.GEN_AI_RESPONSE_TOOL_CALLS,
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output_messages["tool"],
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unpack=False,
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)
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if len(output_messages["response"]) > 0:
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set_data_normalized(
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span, SPANDATA.GEN_AI_RESPONSE_TEXT, output_messages["response"]
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)
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record_token_usage(
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span,
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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)
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if finish_span:
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span.__exit__(None, None, None)
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def _sentry_patched_create_common(f, *args, **kwargs):
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# type: (Any, *Any, **Any) -> Any
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integration = kwargs.pop("integration")
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if integration is None:
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return f(*args, **kwargs)
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if "messages" not in kwargs:
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return f(*args, **kwargs)
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try:
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iter(kwargs["messages"])
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except TypeError:
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return f(*args, **kwargs)
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model = kwargs.get("model", "")
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span = get_start_span_function()(
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op=OP.GEN_AI_CHAT,
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name=f"chat {model}".strip(),
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origin=AnthropicIntegration.origin,
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)
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span.__enter__()
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_set_input_data(span, kwargs, integration)
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result = yield f, args, kwargs
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with capture_internal_exceptions():
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if hasattr(result, "content"):
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input_tokens, output_tokens = _get_token_usage(result)
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content_blocks = []
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for content_block in result.content:
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if hasattr(content_block, "to_dict"):
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content_blocks.append(content_block.to_dict())
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elif hasattr(content_block, "model_dump"):
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content_blocks.append(content_block.model_dump())
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elif hasattr(content_block, "text"):
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content_blocks.append({"type": "text", "text": content_block.text})
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_set_output_data(
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span=span,
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integration=integration,
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model=getattr(result, "model", None),
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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content_blocks=content_blocks,
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finish_span=True,
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)
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# Streaming response
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elif hasattr(result, "_iterator"):
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old_iterator = result._iterator
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def new_iterator():
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# type: () -> Iterator[MessageStreamEvent]
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model = None
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input_tokens = 0
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output_tokens = 0
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content_blocks = [] # type: list[str]
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for event in old_iterator:
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model, input_tokens, output_tokens, content_blocks = (
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_collect_ai_data(
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event, model, input_tokens, output_tokens, content_blocks
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)
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)
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yield event
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_set_output_data(
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span=span,
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integration=integration,
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model=model,
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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content_blocks=[{"text": "".join(content_blocks), "type": "text"}],
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finish_span=True,
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)
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async def new_iterator_async():
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# type: () -> AsyncIterator[MessageStreamEvent]
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model = None
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input_tokens = 0
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output_tokens = 0
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content_blocks = [] # type: list[str]
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async for event in old_iterator:
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model, input_tokens, output_tokens, content_blocks = (
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_collect_ai_data(
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event, model, input_tokens, output_tokens, content_blocks
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)
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)
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yield event
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_set_output_data(
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span=span,
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integration=integration,
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model=model,
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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content_blocks=[{"text": "".join(content_blocks), "type": "text"}],
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finish_span=True,
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)
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if str(type(result._iterator)) == "<class 'async_generator'>":
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result._iterator = new_iterator_async()
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else:
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result._iterator = new_iterator()
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else:
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span.set_data("unknown_response", True)
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span.__exit__(None, None, None)
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return result
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def _wrap_message_create(f):
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# type: (Any) -> Any
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def _execute_sync(f, *args, **kwargs):
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# type: (Any, *Any, **Any) -> Any
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gen = _sentry_patched_create_common(f, *args, **kwargs)
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try:
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f, args, kwargs = next(gen)
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except StopIteration as e:
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return e.value
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try:
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try:
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result = f(*args, **kwargs)
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except Exception as exc:
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_capture_exception(exc)
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raise exc from None
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return gen.send(result)
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except StopIteration as e:
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return e.value
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@wraps(f)
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def _sentry_patched_create_sync(*args, **kwargs):
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# type: (*Any, **Any) -> Any
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integration = sentry_sdk.get_client().get_integration(AnthropicIntegration)
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kwargs["integration"] = integration
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try:
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return _execute_sync(f, *args, **kwargs)
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finally:
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span = sentry_sdk.get_current_span()
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if span is not None and span.status == SPANSTATUS.ERROR:
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with capture_internal_exceptions():
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span.__exit__(None, None, None)
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return _sentry_patched_create_sync
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def _wrap_message_create_async(f):
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# type: (Any) -> Any
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async def _execute_async(f, *args, **kwargs):
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# type: (Any, *Any, **Any) -> Any
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gen = _sentry_patched_create_common(f, *args, **kwargs)
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try:
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f, args, kwargs = next(gen)
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except StopIteration as e:
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return await e.value
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try:
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try:
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result = await f(*args, **kwargs)
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except Exception as exc:
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_capture_exception(exc)
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raise exc from None
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return gen.send(result)
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except StopIteration as e:
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return e.value
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@wraps(f)
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async def _sentry_patched_create_async(*args, **kwargs):
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# type: (*Any, **Any) -> Any
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integration = sentry_sdk.get_client().get_integration(AnthropicIntegration)
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kwargs["integration"] = integration
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try:
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return await _execute_async(f, *args, **kwargs)
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finally:
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span = sentry_sdk.get_current_span()
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if span is not None and span.status == SPANSTATUS.ERROR:
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with capture_internal_exceptions():
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span.__exit__(None, None, None)
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return _sentry_patched_create_async
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def _is_given(obj):
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# type: (Any) -> bool
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"""
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Check for givenness safely across different anthropic versions.
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"""
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if NotGiven is not None and isinstance(obj, NotGiven):
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return False
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if Omit is not None and isinstance(obj, Omit):
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return False
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return True
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