fix: extract model from response for OpenAI stored prompts

When using OpenAI stored prompts, the model is defined in the OpenAI
dashboard rather than passed in the API request. This change adds a
fallback to extract the model from the response object when not
provided in kwargs.

Fixes PostHog/posthog#42861

Co-Authored-By: Claude Opus 4.5 <[email protected]>

Author: andrewm4894

posthog/ai/openai/openai.py

@@ -124,14 +124,23 @@ def _create_streaming(
         start_time = time.time()
         usage_stats: TokenUsage = TokenUsage()
         final_content = []
+        model_from_response: Optional[str] = None
         response = self._original.create(**kwargs)
 
         def generator():
             nonlocal usage_stats
             nonlocal final_content  # noqa: F824
+            nonlocal model_from_response
 
             try:
                 for chunk in response:
+                    # Extract model from response object in chunk (for stored prompts)
+                    if hasattr(chunk, "response") and chunk.response:
+                        if model_from_response is None and hasattr(
+                            chunk.response, "model"
+                        ):
+                            model_from_response = chunk.response.model
+
                     # Extract usage stats from chunk
                     chunk_usage = extract_openai_usage_from_chunk(chunk, "responses")
 
@@ -161,6 +170,7 @@ def generator():
                     latency,
                     output,
                     None,  # Responses API doesn't have tools
+                    model_from_response,
                 )
 
         return generator()
@@ -177,6 +187,7 @@ def _capture_streaming_event(
         latency: float,
         output: Any,
         available_tool_calls: Optional[List[Dict[str, Any]]] = None,
+        model_from_response: Optional[str] = None,
     ):
         from posthog.ai.types import StreamingEventData
         from posthog.ai.openai.openai_converter import (
@@ -189,9 +200,12 @@ def _capture_streaming_event(
         formatted_input = format_openai_streaming_input(kwargs, "responses")
         sanitized_input = sanitize_openai_response(formatted_input)
 
+        # Use model from kwargs, fallback to model from response
+        model = kwargs.get("model") or model_from_response or "unknown"
+
         event_data = StreamingEventData(
             provider="openai",
-            model=kwargs.get("model", "unknown"),
+            model=model,
             base_url=str(self._client.base_url),
             kwargs=kwargs,
             formatted_input=sanitized_input,
@@ -320,6 +334,7 @@ def _create_streaming(
         usage_stats: TokenUsage = TokenUsage()
         accumulated_content = []
         accumulated_tool_calls: Dict[int, Dict[str, Any]] = {}
+        model_from_response: Optional[str] = None
         if "stream_options" not in kwargs:
             kwargs["stream_options"] = {}
         kwargs["stream_options"]["include_usage"] = True
@@ -329,9 +344,14 @@ def generator():
             nonlocal usage_stats
             nonlocal accumulated_content  # noqa: F824
             nonlocal accumulated_tool_calls
+            nonlocal model_from_response
 
             try:
                 for chunk in response:
+                    # Extract model from chunk (Chat Completions chunks have model field)
+                    if model_from_response is None and hasattr(chunk, "model"):
+                        model_from_response = chunk.model
+
                     # Extract usage stats from chunk
                     chunk_usage = extract_openai_usage_from_chunk(chunk, "chat")
 
@@ -376,6 +396,7 @@ def generator():
                     accumulated_content,
                     tool_calls_list,
                     extract_available_tool_calls("openai", kwargs),
+                    model_from_response,
                 )
 
         return generator()
@@ -393,6 +414,7 @@ def _capture_streaming_event(
         output: Any,
         tool_calls: Optional[List[Dict[str, Any]]] = None,
         available_tool_calls: Optional[List[Dict[str, Any]]] = None,
+        model_from_response: Optional[str] = None,
     ):
         from posthog.ai.types import StreamingEventData
         from posthog.ai.openai.openai_converter import (
@@ -405,9 +427,12 @@ def _capture_streaming_event(
         formatted_input = format_openai_streaming_input(kwargs, "chat")
         sanitized_input = sanitize_openai(formatted_input)
 
+        # Use model from kwargs, fallback to model from response
+        model = kwargs.get("model") or model_from_response or "unknown"
+
         event_data = StreamingEventData(
             provider="openai",
-            model=kwargs.get("model", "unknown"),
+            model=model,
             base_url=str(self._client.base_url),
             kwargs=kwargs,
             formatted_input=sanitized_input,

posthog/ai/openai/openai_async.py

@@ -128,14 +128,23 @@ async def _create_streaming(
         start_time = time.time()
         usage_stats: TokenUsage = TokenUsage()
         final_content = []
+        model_from_response: Optional[str] = None
         response = await self._original.create(**kwargs)
 
         async def async_generator():
             nonlocal usage_stats
             nonlocal final_content  # noqa: F824
+            nonlocal model_from_response
 
             try:
                 async for chunk in response:
+                    # Extract model from response object in chunk (for stored prompts)
+                    if hasattr(chunk, "response") and chunk.response:
+                        if model_from_response is None and hasattr(
+                            chunk.response, "model"
+                        ):
+                            model_from_response = chunk.response.model
+
                     # Extract usage stats from chunk
                     chunk_usage = extract_openai_usage_from_chunk(chunk, "responses")
 
@@ -166,6 +175,7 @@ async def async_generator():
                     latency,
                     output,
                     extract_available_tool_calls("openai", kwargs),
+                    model_from_response,
                 )
 
         return async_generator()
@@ -182,13 +192,17 @@ async def _capture_streaming_event(
         latency: float,
         output: Any,
         available_tool_calls: Optional[List[Dict[str, Any]]] = None,
+        model_from_response: Optional[str] = None,
     ):
         if posthog_trace_id is None:
             posthog_trace_id = str(uuid.uuid4())
 
+        # Use model from kwargs, fallback to model from response
+        model = kwargs.get("model") or model_from_response
+
         event_properties = {
             "$ai_provider": "openai",
-            "$ai_model": kwargs.get("model"),
+            "$ai_model": model,
             "$ai_model_parameters": get_model_params(kwargs),
             "$ai_input": with_privacy_mode(
                 self._client._ph_client,
@@ -350,6 +364,7 @@ async def _create_streaming(
         usage_stats: TokenUsage = TokenUsage()
         accumulated_content = []
         accumulated_tool_calls: Dict[int, Dict[str, Any]] = {}
+        model_from_response: Optional[str] = None
 
         if "stream_options" not in kwargs:
             kwargs["stream_options"] = {}
@@ -360,9 +375,14 @@ async def async_generator():
             nonlocal usage_stats
             nonlocal accumulated_content  # noqa: F824
             nonlocal accumulated_tool_calls
+            nonlocal model_from_response
 
             try:
                 async for chunk in response:
+                    # Extract model from chunk (Chat Completions chunks have model field)
+                    if model_from_response is None and hasattr(chunk, "model"):
+                        model_from_response = chunk.model
+
                     # Extract usage stats from chunk
                     chunk_usage = extract_openai_usage_from_chunk(chunk, "chat")
                     if chunk_usage:
@@ -405,6 +425,7 @@ async def async_generator():
                     accumulated_content,
                     tool_calls_list,
                     extract_available_tool_calls("openai", kwargs),
+                    model_from_response,
                 )
 
         return async_generator()
@@ -422,13 +443,17 @@ async def _capture_streaming_event(
         output: Any,
         tool_calls: Optional[List[Dict[str, Any]]] = None,
         available_tool_calls: Optional[List[Dict[str, Any]]] = None,
+        model_from_response: Optional[str] = None,
     ):
         if posthog_trace_id is None:
             posthog_trace_id = str(uuid.uuid4())
 
+        # Use model from kwargs, fallback to model from response
+        model = kwargs.get("model") or model_from_response
+
         event_properties = {
             "$ai_provider": "openai",
-            "$ai_model": kwargs.get("model"),
+            "$ai_model": model,
             "$ai_model_parameters": get_model_params(kwargs),
             "$ai_input": with_privacy_mode(
                 self._client._ph_client,

posthog/ai/utils.py

@@ -285,7 +285,7 @@ def call_llm_and_track_usage(
 
         event_properties = {
             "$ai_provider": provider,
-            "$ai_model": kwargs.get("model"),
+            "$ai_model": kwargs.get("model") or getattr(response, "model", None),
             "$ai_model_parameters": get_model_params(kwargs),
             "$ai_input": with_privacy_mode(
                 ph_client, posthog_privacy_mode, sanitized_messages
@@ -396,7 +396,7 @@ async def call_llm_and_track_usage_async(
 
         event_properties = {
             "$ai_provider": provider,
-            "$ai_model": kwargs.get("model"),
+            "$ai_model": kwargs.get("model") or getattr(response, "model", None),
             "$ai_model_parameters": get_model_params(kwargs),
             "$ai_input": with_privacy_mode(
                 ph_client, posthog_privacy_mode, sanitized_messages