refactor: unify stream and non-stream impls for responses (#2388)

The non-streaming version is just a small layer on top of the streaming
version - just pluck off the final `response.completed` event and return
that as the response!

This PR also includes a couple other changes which I ended up making
while working on it on a flight:
- changes to `ollama` so it does not pull embedding models
unconditionally
- a small fix to library client to make the stream and non-stream cases
a bit more symmetric

tests/unit/providers/agents/meta_reference/test_openai_responses.py

@@ -80,6 +80,37 @@ def openai_responses_impl(mock_inference_api, mock_tool_groups_api, mock_tool_ru
     )
 
 
+async def fake_stream(fixture: str = "simple_chat_completion.yaml"):
+    value = load_chat_completion_fixture(fixture)
+    yield ChatCompletionChunk(
+        id=value.id,
+        choices=[
+            Choice(
+                index=0,
+                delta=ChoiceDelta(
+                    content=c.message.content,
+                    role=c.message.role,
+                    tool_calls=[
+                        ChoiceDeltaToolCall(
+                            index=0,
+                            id=t.id,
+                            function=ChoiceDeltaToolCallFunction(
+                                name=t.function.name,
+                                arguments=t.function.arguments,
+                            ),
+                        )
+                        for t in (c.message.tool_calls or [])
+                    ],
+                ),
+            )
+            for c in value.choices
+        ],
+        created=1,
+        model=value.model,
+        object="chat.completion.chunk",
+    )
+
+
 @pytest.mark.asyncio
 async def test_create_openai_response_with_string_input(openai_responses_impl, mock_inference_api):
     """Test creating an OpenAI response with a simple string input."""
@@ -88,8 +119,7 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
     model = "meta-llama/Llama-3.1-8B-Instruct"
 
     # Load the chat completion fixture
-    mock_chat_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
-    mock_inference_api.openai_chat_completion.return_value = mock_chat_completion
+    mock_inference_api.openai_chat_completion.return_value = fake_stream()
 
     # Execute
     result = await openai_responses_impl.create_openai_response(
@@ -104,7 +134,7 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
         messages=[OpenAIUserMessageParam(role="user", content="What is the capital of Ireland?", name=None)],
         response_format=OpenAIResponseFormatText(),
         tools=None,
-        stream=False,
+        stream=True,
         temperature=0.1,
     )
     openai_responses_impl.responses_store.store_response_object.assert_called_once()
@@ -121,20 +151,15 @@ async def test_create_openai_response_with_string_input_with_tools(openai_respon
     input_text = "What is the capital of Ireland?"
     model = "meta-llama/Llama-3.1-8B-Instruct"
 
-    # Load the chat completion fixtures
-    tool_call_completion = load_chat_completion_fixture("tool_call_completion.yaml")
-    tool_response_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
-
     mock_inference_api.openai_chat_completion.side_effect = [
-        tool_call_completion,
-        tool_response_completion,
+        fake_stream("tool_call_completion.yaml"),
+        fake_stream(),
     ]
 
     openai_responses_impl.tool_groups_api.get_tool.return_value = Tool(
         identifier="web_search",
         provider_id="client",
         toolgroup_id="web_search",
-        tool_host="client",
         description="Search the web for information",
         parameters=[
             ToolParameter(name="query", parameter_type="string", description="The query to search for", required=True)
@@ -189,7 +214,7 @@ async def test_create_openai_response_with_tool_call_type_none(openai_responses_
     input_text = "How hot it is in San Francisco today?"
     model = "meta-llama/Llama-3.1-8B-Instruct"
 
-    async def fake_stream():
+    async def fake_stream_toolcall():
         yield ChatCompletionChunk(
             id="123",
             choices=[
@@ -212,7 +237,7 @@ async def test_create_openai_response_with_tool_call_type_none(openai_responses_
             object="chat.completion.chunk",
         )
 
-    mock_inference_api.openai_chat_completion.return_value = fake_stream()
+    mock_inference_api.openai_chat_completion.return_value = fake_stream_toolcall()
 
     # Execute
     result = await openai_responses_impl.create_openai_response(
@@ -271,7 +296,7 @@ async def test_create_openai_response_with_multiple_messages(openai_responses_im
     ]
     model = "meta-llama/Llama-3.1-8B-Instruct"
 
-    mock_inference_api.openai_chat_completion.return_value = load_chat_completion_fixture("simple_chat_completion.yaml")
+    mock_inference_api.openai_chat_completion.return_value = fake_stream()
 
     # Execute
     await openai_responses_impl.create_openai_response(
@@ -399,9 +424,7 @@ async def test_create_openai_response_with_instructions(openai_responses_impl, m
     model = "meta-llama/Llama-3.1-8B-Instruct"
     instructions = "You are a geography expert. Provide concise answers."
 
-    # Load the chat completion fixture
-    mock_chat_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
-    mock_inference_api.openai_chat_completion.return_value = mock_chat_completion
+    mock_inference_api.openai_chat_completion.return_value = fake_stream()
 
     # Execute
     await openai_responses_impl.create_openai_response(
@@ -440,8 +463,7 @@ async def test_create_openai_response_with_instructions_and_multiple_messages(
     model = "meta-llama/Llama-3.1-8B-Instruct"
     instructions = "You are a geography expert. Provide concise answers."
 
-    mock_chat_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
-    mock_inference_api.openai_chat_completion.return_value = mock_chat_completion
+    mock_inference_api.openai_chat_completion.return_value = fake_stream()
 
     # Execute
     await openai_responses_impl.create_openai_response(
@@ -499,8 +521,8 @@ async def test_create_openai_response_with_instructions_and_previous_response(
 
     model = "meta-llama/Llama-3.1-8B-Instruct"
     instructions = "You are a geography expert. Provide concise answers."
-    mock_chat_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
-    mock_inference_api.openai_chat_completion.return_value = mock_chat_completion
+
+    mock_inference_api.openai_chat_completion.return_value = fake_stream()
 
     # Execute
     await openai_responses_impl.create_openai_response(
@@ -674,8 +696,8 @@ async def test_store_response_uses_rehydrated_input_with_previous_response(
 
     current_input = "Now what is 3+3?"
     model = "meta-llama/Llama-3.1-8B-Instruct"
-    mock_chat_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
-    mock_inference_api.openai_chat_completion.return_value = mock_chat_completion
+
+    mock_inference_api.openai_chat_completion.return_value = fake_stream()
 
     # Execute - Create response with previous_response_id
     result = await openai_responses_impl.create_openai_response(
@@ -732,9 +754,7 @@ async def test_create_openai_response_with_text_format(
     input_text = "How hot it is in San Francisco today?"
     model = "meta-llama/Llama-3.1-8B-Instruct"
 
-    # Load the chat completion fixture
-    mock_chat_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
-    mock_inference_api.openai_chat_completion.return_value = mock_chat_completion
+    mock_inference_api.openai_chat_completion.return_value = fake_stream()
 
     # Execute
     _result = await openai_responses_impl.create_openai_response(