ref(anthropic): Skip accumulation logic for unexpected types in streamed response

sentry_sdk/integrations/anthropic.py

@@ -37,8 +37,18 @@
     except ImportError:
         Omit = None
 
+    from anthropic import Stream, AsyncStream
     from anthropic.resources import AsyncMessages, Messages
 
+    from anthropic.types import (
+        MessageStartEvent,
+        MessageDeltaEvent,
+        MessageStopEvent,
+        ContentBlockStartEvent,
+        ContentBlockDeltaEvent,
+        ContentBlockStopEvent,
+    )
+
     if TYPE_CHECKING:
         from anthropic.types import MessageStreamEvent, TextBlockParam
 except ImportError:
@@ -49,6 +59,8 @@
     from sentry_sdk.tracing import Span
     from sentry_sdk._types import TextPart
 
+    from anthropic.types import RawMessageStreamEvent
+
 
 class _RecordedUsage:
     output_tokens: int = 0
@@ -338,6 +350,129 @@ def _set_input_data(
         span.set_data(SPANDATA.GEN_AI_REQUEST_AVAILABLE_TOOLS, safe_serialize(tools))
 
 
+def _wrap_synchronous_message_iterator(
+    iterator: "Iterator[RawMessageStreamEvent]",
+    span: "Span",
+    integration: "AnthropicIntegration",
+) -> "Iterator[RawMessageStreamEvent]":
+    """
+    Sets information received while iterating the response stream on the AI Client Span.
+    Responsible for closing the AI Client Span.
+    """
+
+    model = None
+    usage = _RecordedUsage()
+    content_blocks: "list[str]" = []
+
+    for event in iterator:
+        if not isinstance(
+            event,
+            (
+                MessageStartEvent,
+                MessageDeltaEvent,
+                MessageStopEvent,
+                ContentBlockStartEvent,
+                ContentBlockDeltaEvent,
+                ContentBlockStopEvent,
+            ),
+        ):
+            yield event
+            continue
+
+        (
+            model,
+            usage,
+            content_blocks,
+        ) = _collect_ai_data(
+            event,
+            model,
+            usage,
+            content_blocks,
+        )
+        yield event
+
+    # Anthropic's input_tokens excludes cached/cache_write tokens.
+    # Normalize to total input tokens for correct cost calculations.
+    total_input = (
+        usage.input_tokens
+        + (usage.cache_read_input_tokens or 0)
+        + (usage.cache_write_input_tokens or 0)
+    )
+
+    _set_output_data(
+        span=span,
+        integration=integration,
+        model=model,
+        input_tokens=total_input,
+        output_tokens=usage.output_tokens,
+        cache_read_input_tokens=usage.cache_read_input_tokens,
+        cache_write_input_tokens=usage.cache_write_input_tokens,
+        content_blocks=[{"text": "".join(content_blocks), "type": "text"}],
+        finish_span=True,
+    )
+
+
+async def _wrap_asynchronous_message_iterator(
+    iterator: "AsyncIterator[RawMessageStreamEvent]",
+    span: "Span",
+    integration: "AnthropicIntegration",
+) -> "AsyncIterator[RawMessageStreamEvent]":
+    """
+    Sets information received while iterating the response stream on the AI Client Span.
+    Responsible for closing the AI Client Span.
+    """
+    model = None
+    usage = _RecordedUsage()
+    content_blocks: "list[str]" = []
+
+    async for event in iterator:
+        if not isinstance(
+            event,
+            (
+                MessageStartEvent,
+                MessageDeltaEvent,
+                MessageStopEvent,
+                ContentBlockStartEvent,
+                ContentBlockDeltaEvent,
+                ContentBlockStopEvent,
+            ),
+        ):
+            yield event
+            continue
+
+        (
+            model,
+            usage,
+            content_blocks,
+        ) = _collect_ai_data(
+            event,
+            model,
+            usage,
+            content_blocks,
+        )
+        yield event
+
+    # Anthropic's input_tokens excludes cached/cache_write tokens.
+    # Normalize to total input tokens for correct cost calculations.
+    total_input = (
+        usage.input_tokens
+        + (usage.cache_read_input_tokens or 0)
+        + (usage.cache_write_input_tokens or 0)
+    )
+
+    _set_output_data(
+        span=span,
+        integration=integration,
+        model=model,
+        input_tokens=total_input,
+        output_tokens=usage.output_tokens,
+        cache_read_input_tokens=usage.cache_read_input_tokens,
+        cache_write_input_tokens=usage.cache_write_input_tokens,
+        content_blocks=[{"text": "".join(content_blocks), "type": "text"}],
+        finish_span=True,
+    )
+
+
 def _set_output_data(
     span: "Span",
     integration: "AnthropicIntegration",
@@ -415,6 +550,18 @@ def _sentry_patched_create_common(f: "Any", *args: "Any", **kwargs: "Any") -> "A
 
     result = yield f, args, kwargs
 
+    if isinstance(result, Stream):
+        result._iterator = _wrap_synchronous_message_iterator(
+            result._iterator, span, integration
+        )
+        return result
+
+    if isinstance(result, AsyncStream):
+        result._iterator = _wrap_asynchronous_message_iterator(
+            result._iterator, span, integration
+        )
+        return result
+
     with capture_internal_exceptions():
         if hasattr(result, "content"):
             (
@@ -444,92 +591,6 @@ def _sentry_patched_create_common(f: "Any", *args: "Any", **kwargs: "Any") -> "A
                 content_blocks=content_blocks,
                 finish_span=True,
             )
-
-        # Streaming response
-        elif hasattr(result, "_iterator"):
-            old_iterator = result._iterator
-
-            def new_iterator() -> "Iterator[MessageStreamEvent]":
-                model = None
-                usage = _RecordedUsage()
-                content_blocks: "list[str]" = []
-
-                for event in old_iterator:
-                    (
-                        model,
-                        usage,
-                        content_blocks,
-                    ) = _collect_ai_data(
-                        event,
-                        model,
-                        usage,
-                        content_blocks,
-                    )
-                    yield event
-
-                # Anthropic's input_tokens excludes cached/cache_write tokens.
-                # Normalize to total input tokens for correct cost calculations.
-                total_input = (
-                    usage.input_tokens
-                    + (usage.cache_read_input_tokens or 0)
-                    + (usage.cache_write_input_tokens or 0)
-                )
-
-                _set_output_data(
-                    span=span,
-                    integration=integration,
-                    model=model,
-                    input_tokens=total_input,
-                    output_tokens=usage.output_tokens,
-                    cache_read_input_tokens=usage.cache_read_input_tokens,
-                    cache_write_input_tokens=usage.cache_write_input_tokens,
-                    content_blocks=[{"text": "".join(content_blocks), "type": "text"}],
-                    finish_span=True,
-                )
-
-            async def new_iterator_async() -> "AsyncIterator[MessageStreamEvent]":
-                model = None
-                usage = _RecordedUsage()
-                content_blocks: "list[str]" = []
-
-                async for event in old_iterator:
-                    (
-                        model,
-                        usage,
-                        content_blocks,
-                    ) = _collect_ai_data(
-                        event,
-                        model,
-                        usage,
-                        content_blocks,
-                    )
-                    yield event
-
-                # Anthropic's input_tokens excludes cached/cache_write tokens.
-                # Normalize to total input tokens for correct cost calculations.
-                total_input = (
-                    usage.input_tokens
-                    + (usage.cache_read_input_tokens or 0)
-                    + (usage.cache_write_input_tokens or 0)
-                )
-
-                _set_output_data(
-                    span=span,
-                    integration=integration,
-                    model=model,
-                    input_tokens=total_input,
-                    output_tokens=usage.output_tokens,
-                    cache_read_input_tokens=usage.cache_read_input_tokens,
-                    cache_write_input_tokens=usage.cache_write_input_tokens,
-                    content_blocks=[{"text": "".join(content_blocks), "type": "text"}],
-                    finish_span=True,
-                )
-
-            if str(type(result._iterator)) == "<class 'async_generator'>":
-                result._iterator = new_iterator_async()
-            else:
-                result._iterator = new_iterator()
-
         else:
             span.set_data("unknown_response", True)
             span.__exit__(None, None, None)