chore(recorder, tests): add support for openai /v1/models

llama_stack/testing/inference_recorder.py

@@ -7,6 +7,7 @@
 from __future__ import annotations  # for forward references
 
 import hashlib
+import inspect
 import json
 import os
 from collections.abc import Generator
@@ -198,16 +199,11 @@ def _model_identifiers_digest(endpoint: str, response: dict[str, Any]) -> str:
 
         Supported endpoints:
         - '/api/tags' (Ollama): response body has 'models': [ { name/model/digest/id/... }, ... ]
-        - '/v1/models' (OpenAI): response body has 'data': [ { id: ... }, ... ]
+        - '/v1/models' (OpenAI): response body is: [ { id: ... }, ... ]
         Returns a list of unique identifiers or None if structure doesn't match.
         """
-        body = response["body"]
-        if endpoint == "/api/tags":
-            items = body.get("models")
-            idents = [m.model for m in items]
-        else:
-            items = body.get("data")
-            idents = [m.id for m in items]
+        items = response["body"]
+        idents = [m.model if endpoint == "/api/tags" else m.id for m in items]
         return sorted(set(idents))
 
     identifiers = _extract_model_identifiers()
@@ -219,28 +215,22 @@ def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]])
     seen: dict[str, dict[str, Any]] = {}
     for rec in records:
         body = rec["response"]["body"]
-        if endpoint == "/api/tags":
-            items = body.models
-        elif endpoint == "/v1/models":
-            items = body.data
-        else:
-            items = []
-
-        for m in items:
-            if endpoint == "/v1/models":
+        if endpoint == "/v1/models":
+            for m in body:
                 key = m.id
-            else:
+                seen[key] = m
+        elif endpoint == "/api/tags":
+            for m in body.models:
                 key = m.model
-            seen[key] = m
+                seen[key] = m
 
     ordered = [seen[k] for k in sorted(seen.keys())]
     canonical = records[0]
     canonical_req = canonical.get("request", {})
     if isinstance(canonical_req, dict):
         canonical_req["endpoint"] = endpoint
-    if endpoint == "/v1/models":
-        body = {"data": ordered, "object": "list"}
-    else:
+    body = ordered
+    if endpoint == "/api/tags":
         from ollama import ListResponse
 
         body = ListResponse(models=ordered)
@@ -252,7 +242,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
 
     if _current_mode == InferenceMode.LIVE or _current_storage is None:
         # Normal operation
-        return await original_method(self, *args, **kwargs)
+        if inspect.iscoroutinefunction(original_method):
+            return await original_method(self, *args, **kwargs)
+        else:
+            return original_method(self, *args, **kwargs)
 
     # Get base URL based on client type
     if client_type == "openai":
@@ -282,7 +275,7 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
         if recording:
             response_body = recording["response"]["body"]
 
-            if recording["response"].get("is_streaming", False):
+            if recording["response"].get("is_streaming", False) or endpoint == "/v1/models":
 
                 async def replay_stream():
                     for chunk in response_body:
@@ -300,7 +293,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
             )
 
     elif _current_mode == InferenceMode.RECORD:
-        response = await original_method(self, *args, **kwargs)
+        if inspect.iscoroutinefunction(original_method):
+            response = await original_method(self, *args, **kwargs)
+        else:
+            response = original_method(self, *args, **kwargs)
 
         request_data = {
             "method": method,
@@ -314,7 +310,7 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
         # Determine if this is a streaming request based on request parameters
         is_streaming = body.get("stream", False)
 
-        if is_streaming:
+        if is_streaming or endpoint == "/v1/models":
             # For streaming responses, we need to collect all chunks immediately before yielding
             # This ensures the recording is saved even if the generator isn't fully consumed
             chunks = []
@@ -380,10 +376,19 @@ def patch_inference_clients():
             _original_methods["embeddings_create"], self, "openai", "/v1/embeddings", *args, **kwargs
         )
 
-    async def patched_models_list(self, *args, **kwargs):
-        return await _patched_inference_method(
-            _original_methods["models_list"], self, "openai", "/v1/models", *args, **kwargs
-        )
+    def patched_models_list(self, *args, **kwargs):
+        import asyncio
+        import concurrent.futures
+
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future = executor.submit(
+                lambda: asyncio.run(
+                    _patched_inference_method(
+                        _original_methods["models_list"], self, "openai", "/v1/models", *args, **kwargs
+                    )
+                )
+            )
+            return future.result()
 
     # Apply OpenAI patches
     AsyncChatCompletions.create = patched_chat_completions_create