fix(provider): preserve litellm usage details (#2246)

tests/unit_tests/provider/test_litellmchat.py

@@ -115,6 +115,15 @@ class TestExtractUsage:
         assert result['prompt_tokens'] == 0
         assert result['completion_tokens'] == 0
 
+    def test_extract_usage_without_provider_usage(self):
+        """Missing provider usage is not treated as authoritative zero usage."""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        response = Mock()
+        response.usage = None
+
+        assert requester._extract_usage(response) is None
+
 
 class TestNormalizeUsage:
     """Test _normalize_usage helper covering real-world usage shapes"""
@@ -131,6 +140,22 @@ class TestNormalizeUsage:
         )
         assert result == {'prompt_tokens': 12, 'completion_tokens': 8, 'total_tokens': 20}
 
+    def test_preserves_token_details(self):
+        """Provider token details such as cache counters are preserved."""
+        result = litellmchat.LiteLLMRequester._normalize_usage(
+            {
+                'prompt_tokens': 12,
+                'completion_tokens': 8,
+                'total_tokens': 20,
+                'prompt_tokens_details': {'cached_tokens': 7},
+                'completion_tokens_details': {'reasoning_tokens': 3},
+            }
+        )
+
+        assert result['prompt_tokens'] == 12
+        assert result['prompt_tokens_details'] == {'cached_tokens': 7}
+        assert result['completion_tokens_details'] == {'reasoning_tokens': 3}
+
     def test_missing_total_is_derived(self):
         """When total_tokens is absent/zero it is derived from prompt + completion"""
         usage = Mock()
@@ -166,9 +191,7 @@ class TestInvokeLLMStreamUsage:
         if has_choice:
             choice = Mock()
             delta = Mock()
-            delta.model_dump = Mock(
-                return_value={'role': 'assistant', 'content': content, 'tool_calls': tool_calls}
-            )
+            delta.model_dump = Mock(return_value={'role': 'assistant', 'content': content, 'tool_calls': tool_calls})
             choice.delta = delta
             choice.finish_reason = finish_reason
             chunk.choices = [choice]
@@ -313,7 +336,8 @@ class TestInvokeLLMStreamUsage:
 
         with patch.object(litellmchat, 'acompletion', new=AsyncMock(side_effect=lambda **kw: _aiter())):
             collected = [
-                chunk async for chunk in requester.invoke_llm_stream(
+                chunk
+                async for chunk in requester.invoke_llm_stream(
                     query=query,
                     model=model,
                     messages=messages,
@@ -788,7 +812,9 @@ class TestInvokeRerank:
         with patch('httpx.AsyncClient', return_value=mock_client):
             # arerank must NOT be called on the openai-compatible path
             with patch.object(
-                litellmchat, 'arerank', new_callable=AsyncMock,
+                litellmchat,
+                'arerank',
+                new_callable=AsyncMock,
                 side_effect=AssertionError('arerank must not be used for openai-compatible provider'),
             ):
                 results = await requester.invoke_rerank(
@@ -1068,8 +1094,7 @@ class TestScanModels:
 
         with patch.object(litellmchat.litellm, 'supports_function_calling') as mock_supports_function_calling:
             mock_supports_function_calling.side_effect = (
-                lambda model, custom_llm_provider=None: model == 'moonshot/kimi-k2.6'
-                and custom_llm_provider is None
+                lambda model, custom_llm_provider=None: model == 'moonshot/kimi-k2.6' and custom_llm_provider is None
             )
 
             assert requester._supports_function_calling('kimi-k2.6') is True