feat(openai): Set system instruction attribute for Completions API

sentry_sdk/integrations/openai.py

@@ -1,5 +1,6 @@
 import sys
 from functools import wraps
+from collections.abc import Iterable
 
 import sentry_sdk
 from sentry_sdk import consts
@@ -25,7 +26,6 @@
 if TYPE_CHECKING:
     from typing import (
         Any,
-        Iterable,
         List,
         Optional,
         Callable,
@@ -34,8 +34,10 @@
         Union,
     )
     from sentry_sdk.tracing import Span
+    from sentry_sdk._types import TextPart
 
-    from openai.types.responses import ResponseInputParam
+    from openai.types.responses import ResponseInputParam, ResponseInputItemParam
+    from openai import Omit
 
 try:
     try:
@@ -52,7 +54,11 @@
     from openai.resources import Embeddings, AsyncEmbeddings
 
     if TYPE_CHECKING:
-        from openai.types.chat import ChatCompletionMessageParam, ChatCompletionChunk
+        from openai.types.chat import (
+            ChatCompletionMessageParam,
+            ChatCompletionChunk,
+            ChatCompletionSystemMessageParam,
+        )
 except ImportError:
     raise DidNotEnable("OpenAI not installed")
 
@@ -193,6 +199,45 @@ def _calculate_token_usage(
     )
 
 
+def _is_system_instruction_completions(message: "ChatCompletionMessageParam") -> bool:
+    return isinstance(message, dict) and message.get("role") == "system"
+
+
+def _get_system_instructions_completions(
+    messages: "Iterable[ChatCompletionMessageParam]",
+) -> "list[ChatCompletionMessageParam]":
+    if not isinstance(messages, Iterable):
+        return []
+
+    return [
+        message for message in messages if _is_system_instruction_completions(message)
+    ]
+
+
+def _transform_system_instructions(
+    system_instructions: "list[ChatCompletionSystemMessageParam]",
+) -> "list[TextPart]":
+    instruction_text_parts: "list[TextPart]" = []
+
+    for instruction in system_instructions:
+        if not isinstance(instruction, dict):
+            continue
+
+        content = instruction.get("content")
+
+        if isinstance(content, str):
+            instruction_text_parts.append({"type": "text", "content": content})
+
+        elif isinstance(content, list):
+            for part in content:
+                if isinstance(part, dict) and part.get("type") == "text":
+                    text = part.get("text", "")
+                    if text:
+                        instruction_text_parts.append({"type": "text", "content": text})
+
+    return instruction_text_parts
+
+
 def _get_input_messages(
     kwargs: "dict[str, Any]",
 ) -> "Optional[Union[Iterable[Any], list[str]]]":
@@ -270,17 +315,48 @@ def _set_completions_api_input_data(
     kwargs: "dict[str, Any]",
     integration: "OpenAIIntegration",
 ) -> None:
-    messages: "Optional[Union[Iterable[ChatCompletionMessageParam], list[str]]]" = (
-        _get_input_messages(kwargs)
+    messages: "Optional[Union[str, Iterable[ChatCompletionMessageParam]]]" = kwargs.get(
+        "messages"
     )
 
-    if (
-        messages is not None
-        and len(messages) > 0  # type: ignore
-        and should_send_default_pii()
-        and integration.include_prompts
-    ):
-        normalized_messages = normalize_message_roles(messages)  # type: ignore
+    if not should_send_default_pii() or not integration.include_prompts:
+        set_data_normalized(span, SPANDATA.GEN_AI_OPERATION_NAME, "responses")
+        _commmon_set_input_data(span, kwargs)
+        return
+
+    if messages is None:
+        set_data_normalized(span, SPANDATA.GEN_AI_OPERATION_NAME, "chat")
+        _commmon_set_input_data(span, kwargs)
+        return
+
+    system_instructions = _get_system_instructions_completions(messages)
+    if len(system_instructions) > 0:
+        set_data_normalized(
+            span,
+            SPANDATA.GEN_AI_SYSTEM_INSTRUCTIONS,
+            _transform_system_instructions(system_instructions),
+            unpack=False,
+        )
+
+    if isinstance(messages, str):
+        normalized_messages = normalize_message_roles([messages])  # type: ignore
+        scope = sentry_sdk.get_current_scope()
+        messages_data = truncate_and_annotate_messages(normalized_messages, span, scope)
+        if messages_data is not None:
+            set_data_normalized(
+                span, SPANDATA.GEN_AI_REQUEST_MESSAGES, messages_data, unpack=False
+            )
+        set_data_normalized(span, SPANDATA.GEN_AI_OPERATION_NAME, "chat")
+        _commmon_set_input_data(span, kwargs)
+        return
+
+    non_system_messages = [
+        message
+        for message in messages
+        if not _is_system_instruction_completions(message)
+    ]
+    if len(non_system_messages) > 0:
+        normalized_messages = normalize_message_roles(non_system_messages)  # type: ignore
         scope = sentry_sdk.get_current_scope()
         messages_data = truncate_and_annotate_messages(normalized_messages, span, scope)
         if messages_data is not None: