fix(recording): endpoint resolution (#3013)

# What does this PR do?


## Test Plan

llama_stack/testing/inference_recorder.py

@@ -217,55 +217,21 @@ class ResponseStorage:
         return cast(dict[str, Any], data)
 
 
-async def _patched_inference_method(original_method, self, client_type, method_name=None, *args, **kwargs):
+async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs):
     global _current_mode, _current_storage
 
     if _current_mode == InferenceMode.LIVE or _current_storage is None:
         # Normal operation
         return await original_method(self, *args, **kwargs)
 
-    # Get base URL and endpoint based on client type
+    # Get base URL based on client type
     if client_type == "openai":
         base_url = str(self._client.base_url)
-
-        # Determine endpoint based on the method's module/class path
-        method_str = str(original_method)
-        if "chat.completions" in method_str:
-            endpoint = "/v1/chat/completions"
-        elif "embeddings" in method_str:
-            endpoint = "/v1/embeddings"
-        elif "completions" in method_str:
-            endpoint = "/v1/completions"
-        else:
-            # Fallback - try to guess from the self object
-            if hasattr(self, "_resource") and hasattr(self._resource, "_resource"):
-                resource_name = getattr(self._resource._resource, "_resource", "unknown")
-                if "chat" in str(resource_name):
-                    endpoint = "/v1/chat/completions"
-                elif "embeddings" in str(resource_name):
-                    endpoint = "/v1/embeddings"
-                else:
-                    endpoint = "/v1/completions"
-            else:
-                endpoint = "/v1/completions"
-
     elif client_type == "ollama":
         # Get base URL from the client (Ollama client uses host attribute)
         base_url = getattr(self, "host", "http://localhost:11434")
         if not base_url.startswith("http"):
             base_url = f"http://{base_url}"
-
-        # Determine endpoint based on method name
-        if method_name == "generate":
-            endpoint = "/api/generate"
-        elif method_name == "chat":
-            endpoint = "/api/chat"
-        elif method_name == "embed":
-            endpoint = "/api/embeddings"
-        elif method_name == "list":
-            endpoint = "/api/tags"
-        else:
-            endpoint = f"/api/{method_name}"
     else:
         raise ValueError(f"Unknown client type: {client_type}")
 
@@ -366,14 +332,18 @@ def patch_inference_clients():
     # Create patched methods for OpenAI client
     async def patched_chat_completions_create(self, *args, **kwargs):
         return await _patched_inference_method(
-            _original_methods["chat_completions_create"], self, "openai", *args, **kwargs
+            _original_methods["chat_completions_create"], self, "openai", "/v1/chat/completions", *args, **kwargs
         )
 
     async def patched_completions_create(self, *args, **kwargs):
-        return await _patched_inference_method(_original_methods["completions_create"], self, "openai", *args, **kwargs)
+        return await _patched_inference_method(
+            _original_methods["completions_create"], self, "openai", "/v1/completions", *args, **kwargs
+        )
 
     async def patched_embeddings_create(self, *args, **kwargs):
-        return await _patched_inference_method(_original_methods["embeddings_create"], self, "openai", *args, **kwargs)
+        return await _patched_inference_method(
+            _original_methods["embeddings_create"], self, "openai", "/v1/embeddings", *args, **kwargs
+        )
 
     # Apply OpenAI patches
     AsyncChatCompletions.create = patched_chat_completions_create
@@ -383,30 +353,32 @@ def patch_inference_clients():
     # Create patched methods for Ollama client
     async def patched_ollama_generate(self, *args, **kwargs):
         return await _patched_inference_method(
-            _original_methods["ollama_generate"], self, "ollama", "generate", *args, **kwargs
+            _original_methods["ollama_generate"], self, "ollama", "/api/generate", *args, **kwargs
         )
 
     async def patched_ollama_chat(self, *args, **kwargs):
         return await _patched_inference_method(
-            _original_methods["ollama_chat"], self, "ollama", "chat", *args, **kwargs
+            _original_methods["ollama_chat"], self, "ollama", "/api/chat", *args, **kwargs
         )
 
     async def patched_ollama_embed(self, *args, **kwargs):
         return await _patched_inference_method(
-            _original_methods["ollama_embed"], self, "ollama", "embed", *args, **kwargs
+            _original_methods["ollama_embed"], self, "ollama", "/api/embeddings", *args, **kwargs
         )
 
     async def patched_ollama_ps(self, *args, **kwargs):
-        return await _patched_inference_method(_original_methods["ollama_ps"], self, "ollama", "ps", *args, **kwargs)
+        return await _patched_inference_method(
+            _original_methods["ollama_ps"], self, "ollama", "/api/ps", *args, **kwargs
+        )
 
     async def patched_ollama_pull(self, *args, **kwargs):
         return await _patched_inference_method(
-            _original_methods["ollama_pull"], self, "ollama", "pull", *args, **kwargs
+            _original_methods["ollama_pull"], self, "ollama", "/api/pull", *args, **kwargs
         )
 
     async def patched_ollama_list(self, *args, **kwargs):
         return await _patched_inference_method(
-            _original_methods["ollama_list"], self, "ollama", "list", *args, **kwargs
+            _original_methods["ollama_list"], self, "ollama", "/api/tags", *args, **kwargs
         )
 
     # Apply Ollama patches