fix(types/utils.py): support passing prompt cache usage stats in usage object

Passes deepseek prompt caching values through to end user

litellm/tests/test_completion_cost.py

@@ -1007,3 +1007,20 @@ def test_completion_cost_anthropic():
 
     print(input_cost)
     print(output_cost)
+
+
+def test_completion_cost_deepseek():
+    litellm.set_verbose = True
+    model_name = "deepseek/deepseek-chat"
+    messages = [{"role": "user", "content": "Hey, how's it going?"}]
+    try:
+        response_1 = litellm.completion(model=model_name, messages=messages)
+        response_2 = litellm.completion(model=model_name, messages=messages)
+        # Add any assertions here to check the response
+        print(response_2)
+        assert response_2.usage.prompt_cache_hit_tokens is not None
+        assert response_2.usage.prompt_cache_miss_tokens is not None
+    except litellm.APIError as e:
+        pass
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")

litellm/types/utils.py

@@ -5,7 +5,7 @@ from enum import Enum
 from typing import Dict, List, Literal, Optional, Tuple, Union
 
 from openai._models import BaseModel as OpenAIObject
-from pydantic import ConfigDict
+from pydantic import ConfigDict, Field, PrivateAttr
 from typing_extensions import Dict, Required, TypedDict, override
 
 from ..litellm_core_utils.core_helpers import map_finish_reason
@@ -445,16 +445,28 @@ class Choices(OpenAIObject):
 
 
 class Usage(OpenAIObject):
+    prompt_cache_hit_tokens: Optional[int] = Field(default=None)
+    prompt_cache_miss_tokens: Optional[int] = Field(default=None)
+    prompt_tokens: Optional[int] = Field(default=None)
+    completion_tokens: Optional[int] = Field(default=None)
+    total_tokens: Optional[int] = Field(default=None)
+
     def __init__(
-        self, prompt_tokens=None, completion_tokens=None, total_tokens=None, **params
+        self,
+        prompt_tokens: Optional[int] = None,
+        completion_tokens: Optional[int] = None,
+        total_tokens: Optional[int] = None,
+        prompt_cache_hit_tokens: Optional[int] = None,
+        prompt_cache_miss_tokens: Optional[int] = None,
     ):
-        super(Usage, self).__init__(**params)
+        data = {
-        if prompt_tokens:
+            "prompt_tokens": prompt_tokens,
-            self.prompt_tokens = prompt_tokens
+            "completion_tokens": completion_tokens,
-        if completion_tokens:
+            "total_tokens": total_tokens,
-            self.completion_tokens = completion_tokens
+            "prompt_cache_hit_tokens": prompt_cache_hit_tokens,
-        if total_tokens:
+            "prompt_cache_miss_tokens": prompt_cache_miss_tokens,
-            self.total_tokens = total_tokens
+        }
+        super().__init__(**data)
 
     def __contains__(self, key):
         # Define custom behavior for the 'in' operator

litellm/utils.py

@@ -5825,6 +5825,8 @@ def convert_to_model_response_object(
                 model_response_object.usage.completion_tokens = response_object["usage"].get("completion_tokens", 0)  # type: ignore
                 model_response_object.usage.prompt_tokens = response_object["usage"].get("prompt_tokens", 0)  # type: ignore
                 model_response_object.usage.total_tokens = response_object["usage"].get("total_tokens", 0)  # type: ignore
+                model_response_object.usage.prompt_cache_hit_tokens = response_object["usage"].get("prompt_cache_hit_tokens", None)  # type: ignore
+                model_response_object.usage.prompt_cache_miss_tokens = response_object["usage"].get("prompt_cache_miss_tokens", None)  # type: ignore
 
             if "created" in response_object:
                 model_response_object.created = response_object["created"] or int(