Merge pull request #1557 from BerriAI/litellm_emit_spend_logs

feat(utils.py): emit response cost as part of logs

docs/my-website/docs/proxy/custom_pricing.md

@@ -2,14 +2,48 @@ import Image from '@theme/IdealImage';
 
 # Custom Pricing - Sagemaker, etc. 
 
-Use this to register custom pricing (cost per token or cost per second) for models. 
+Use this to register custom pricing for models. 
+
+There's 2 ways to track cost: 
+- cost per token
+- cost per second
+
+By default, the response cost is accessible in the logging object via `kwargs["response_cost"]` on success (sync + async). [**Learn More**](../observability/custom_callback.md)
 
 ## Quick Start 
 
-Register custom pricing for sagemaker completion + embedding models. 
+Register custom pricing for sagemaker completion model. 
 
 For cost per second pricing, you **just** need to register `input_cost_per_second`. 
 
+```python
+# !pip install boto3 
+from litellm import completion, completion_cost 
+
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+os.environ["AWS_REGION_NAME"] = ""
+
+
+def test_completion_sagemaker():
+    try:
+        print("testing sagemaker")
+        response = completion(
+            model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+            input_cost_per_second=0.000420,
+        )
+        # Add any assertions here to check the response
+        print(response)
+        cost = completion_cost(completion_response=response)
+        print(cost)
+    except Exception as e:
+        raise Exception(f"Error occurred: {e}")
+
+```
+
+### Usage with OpenAI Proxy Server
+
 **Step 1: Add pricing to config.yaml**
 ```yaml
 model_list:
@@ -31,4 +65,44 @@ litellm /path/to/config.yaml
 
 **Step 3: View Spend Logs**
 
-<Image img={require('../../img/spend_logs_table.png')} />
+<Image img={require('../../img/spend_logs_table.png')} />
+
+## Cost Per Token 
+
+```python
+# !pip install boto3 
+from litellm import completion, completion_cost 
+
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+os.environ["AWS_REGION_NAME"] = ""
+
+
+def test_completion_sagemaker():
+    try:
+        print("testing sagemaker")
+        response = completion(
+            model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+            input_cost_per_token=0.005,
+            output_cost_per_token=1,
+        )
+        # Add any assertions here to check the response
+        print(response)
+        cost = completion_cost(completion_response=response)
+        print(cost)
+    except Exception as e:
+        raise Exception(f"Error occurred: {e}")
+
+```
+
+### Usage with OpenAI Proxy Server
+
+```yaml
+model_list:
+  - model_name: sagemaker-completion-model
+    litellm_params:
+      model: sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4
+      input_cost_per_token: 0.000420 # 👈 key change
+      output_cost_per_token: 0.000420 # 👈 key change
+```

docs/my-website/sidebars.js

@@ -139,13 +139,13 @@ const sidebars = {
           "items": [
             "proxy/call_hooks",
             "proxy/rules",
-            "proxy/custom_pricing"
           ]
         },
         "proxy/deploy", 
         "proxy/cli", 
       ]
     },
+    "proxy/custom_pricing",
     "routing",
     "rules",
     "set_keys",

litellm/llms/openai.py

@@ -706,15 +706,16 @@ class OpenAIChatCompletion(BaseLLM):
 
             ## COMPLETION CALL
             response = openai_client.images.generate(**data, timeout=timeout)  # type: ignore
+            response = response.model_dump()  # type: ignore
             ## LOGGING
             logging_obj.post_call(
-                input=input,
+                input=prompt,
                 api_key=api_key,
                 additional_args={"complete_input_dict": data},
                 original_response=response,
             )
             # return response
-            return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="image_generation")  # type: ignore
+            return convert_to_model_response_object(response_object=response, model_response_object=model_response, response_type="image_generation")  # type: ignore
         except OpenAIError as e:
             exception_mapping_worked = True
             raise e

litellm/main.py

@@ -83,6 +83,7 @@ from litellm.utils import (
     TextCompletionResponse,
     TextChoices,
     EmbeddingResponse,
+    ImageResponse,
     read_config_args,
     Choices,
     Message,
@@ -2987,6 +2988,7 @@ def image_generation(
         else:
             model = "dall-e-2"
             custom_llm_provider = "openai"  # default to dall-e-2 on openai
+        model_response._hidden_params["model"] = model
         openai_params = [
             "user",
             "request_timeout",

litellm/proxy/proxy_server.py

@@ -570,13 +570,8 @@ async def track_cost_callback(
         litellm_params = kwargs.get("litellm_params", {}) or {}
         proxy_server_request = litellm_params.get("proxy_server_request") or {}
         user_id = proxy_server_request.get("body", {}).get("user", None)
-        if "complete_streaming_response" in kwargs:
+        if "response_cost" in kwargs:
-            # for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost
+            response_cost = kwargs["response_cost"]
-            completion_response = kwargs["complete_streaming_response"]
-            response_cost = litellm.completion_cost(
-                completion_response=completion_response
-            )
-
             user_api_key = kwargs["litellm_params"]["metadata"].get(
                 "user_api_key", None
             )
@@ -585,31 +580,6 @@ async def track_cost_callback(
                 "user_api_key_user_id", None
             )
 
-            verbose_proxy_logger.info(
-                f"streaming response_cost {response_cost}, for user_id {user_id}"
-            )
-            if user_api_key and (
-                prisma_client is not None or custom_db_client is not None
-            ):
-                await update_database(
-                    token=user_api_key,
-                    response_cost=response_cost,
-                    user_id=user_id,
-                    kwargs=kwargs,
-                    completion_response=completion_response,
-                    start_time=start_time,
-                    end_time=end_time,
-                )
-        elif kwargs["stream"] == False:  # for non streaming responses
-            response_cost = litellm.completion_cost(
-                completion_response=completion_response
-            )
-            user_api_key = kwargs["litellm_params"]["metadata"].get(
-                "user_api_key", None
-            )
-            user_id = user_id or kwargs["litellm_params"]["metadata"].get(
-                "user_api_key_user_id", None
-            )
             verbose_proxy_logger.info(
                 f"response_cost {response_cost}, for user_id {user_id}"
             )
@@ -625,6 +595,10 @@ async def track_cost_callback(
                     start_time=start_time,
                     end_time=end_time,
                 )
+        else:
+            raise Exception(
+                f"Model not in litellm model cost map. Add custom pricing - https://docs.litellm.ai/docs/proxy/custom_pricing"
+            )
     except Exception as e:
         verbose_proxy_logger.debug(f"error in tracking cost callback - {str(e)}")
 

litellm/tests/test_custom_callback_input.py

@@ -74,6 +74,7 @@ class CompletionCustomHandler(
 
     def log_post_api_call(self, kwargs, response_obj, start_time, end_time):
         try:
+            print(f"kwargs: {kwargs}")
             self.states.append("post_api_call")
             ## START TIME
             assert isinstance(start_time, datetime)
@@ -149,7 +150,14 @@ class CompletionCustomHandler(
             ## END TIME
             assert isinstance(end_time, datetime)
             ## RESPONSE OBJECT
-            assert isinstance(response_obj, litellm.ModelResponse)
+            assert isinstance(
+                response_obj,
+                (
+                    litellm.ModelResponse,
+                    litellm.EmbeddingResponse,
+                    litellm.ImageResponse,
+                ),
+            )
             ## KWARGS
             assert isinstance(kwargs["model"], str)
             assert isinstance(kwargs["messages"], list) and isinstance(
@@ -170,12 +178,14 @@ class CompletionCustomHandler(
             )
             assert isinstance(kwargs["additional_args"], (dict, type(None)))
             assert isinstance(kwargs["log_event_type"], str)
+            assert isinstance(kwargs["response_cost"], (float, type(None)))
         except:
             print(f"Assertion Error: {traceback.format_exc()}")
             self.errors.append(traceback.format_exc())
 
     def log_failure_event(self, kwargs, response_obj, start_time, end_time):
         try:
+            print(f"kwargs: {kwargs}")
             self.states.append("sync_failure")
             ## START TIME
             assert isinstance(start_time, datetime)
@@ -262,6 +272,7 @@ class CompletionCustomHandler(
             assert isinstance(kwargs["additional_args"], (dict, type(None)))
             assert isinstance(kwargs["log_event_type"], str)
             assert kwargs["cache_hit"] is None or isinstance(kwargs["cache_hit"], bool)
+            assert isinstance(kwargs["response_cost"], (float, type(None)))
         except:
             print(f"Assertion Error: {traceback.format_exc()}")
             self.errors.append(traceback.format_exc())
@@ -545,8 +556,9 @@ async def test_async_chat_bedrock_stream():
 
 # asyncio.run(test_async_chat_bedrock_stream())
 
-# Text Completion 
+# Text Completion
-        
+
+
 ## Test OpenAI text completion + Async
 @pytest.mark.asyncio
 async def test_async_text_completion_openai_stream():
@@ -585,6 +597,7 @@ async def test_async_text_completion_openai_stream():
     except Exception as e:
         pytest.fail(f"An exception occurred: {str(e)}")
 
+
 # EMBEDDING
 ## Test OpenAI + Async
 @pytest.mark.asyncio
@@ -762,6 +775,52 @@ async def test_async_embedding_azure_caching():
     assert len(customHandler_caching.states) == 4  # pre, post, success, success
 
 
-# asyncio.run(
+# Image Generation
-#     test_async_embedding_azure_caching()
+
-# )
+
+# ## Test OpenAI + Sync
+# def test_image_generation_openai():
+#     try:
+#         customHandler_success = CompletionCustomHandler()
+#         customHandler_failure = CompletionCustomHandler()
+#         litellm.callbacks = [customHandler_success]
+
+#         litellm.set_verbose = True
+
+#         response = litellm.image_generation(
+#             prompt="A cute baby sea otter", model="dall-e-3"
+#         )
+
+#         print(f"response: {response}")
+#         assert len(response.data) > 0
+
+#         print(f"customHandler_success.errors: {customHandler_success.errors}")
+#         print(f"customHandler_success.states: {customHandler_success.states}")
+#         assert len(customHandler_success.errors) == 0
+#         assert len(customHandler_success.states) == 3  # pre, post, success
+#         # test failure callback
+#         litellm.callbacks = [customHandler_failure]
+#         try:
+#             response = litellm.image_generation(
+#                 prompt="A cute baby sea otter", model="dall-e-4"
+#             )
+#         except:
+#             pass
+#         print(f"customHandler_failure.errors: {customHandler_failure.errors}")
+#         print(f"customHandler_failure.states: {customHandler_failure.states}")
+#         assert len(customHandler_failure.errors) == 0
+#         assert len(customHandler_failure.states) == 3  # pre, post, failure
+#     except litellm.RateLimitError as e:
+#         pass
+#     except litellm.ContentPolicyViolationError:
+#         pass  # OpenAI randomly raises these errors - skip when they occur
+#     except Exception as e:
+#         pytest.fail(f"An exception occurred - {str(e)}")
+
+
+# test_image_generation_openai()
+## Test OpenAI + Async
+
+## Test Azure + Sync
+
+## Test Azure + Async

litellm/tests/test_key_generate_dynamodb.py

@@ -184,9 +184,11 @@ def test_call_with_user_over_budget(custom_db_client):
     # 5. Make a call with a key over budget, expect to fail
     setattr(litellm.proxy.proxy_server, "custom_db_client", custom_db_client)
     setattr(litellm.proxy.proxy_server, "master_key", "sk-1234")
-    from litellm._logging import verbose_proxy_logger
+    from litellm._logging import verbose_proxy_logger, verbose_logger
     import logging
 
+    litellm.set_verbose = True
+    verbose_logger.setLevel(logging.DEBUG)
     verbose_proxy_logger.setLevel(logging.DEBUG)
     try:
 
@@ -234,6 +236,7 @@ def test_call_with_user_over_budget(custom_db_client):
                             "user_api_key_user_id": user_id,
                         }
                     },
+                    "response_cost": 0.00002,
                 },
                 completion_response=resp,
             )
@@ -306,6 +309,7 @@ def test_call_with_user_over_budget_stream(custom_db_client):
                             "user_api_key_user_id": user_id,
                         }
                     },
+                    "response_cost": 0.00002,
                 },
                 completion_response=ModelResponse(),
             )

litellm/tests/test_key_generate_prisma.py

@@ -260,6 +260,7 @@ def test_call_with_user_over_budget(prisma_client):
                             "user_api_key_user_id": user_id,
                         }
                     },
+                    "response_cost": 0.00002,
                 },
                 completion_response=resp,
                 start_time=datetime.now(),
@@ -335,6 +336,7 @@ def test_call_with_user_over_budget_stream(prisma_client):
                             "user_api_key_user_id": user_id,
                         }
                     },
+                    "response_cost": 0.00002,
                 },
                 completion_response=ModelResponse(),
                 start_time=datetime.now(),

litellm/utils.py

@@ -1064,6 +1064,27 @@ class Logging:
             self.model_call_details["log_event_type"] = "successful_api_call"
             self.model_call_details["end_time"] = end_time
             self.model_call_details["cache_hit"] = cache_hit
+            ## if model in model cost map - log the response cost
+            ## else set cost to None
+            verbose_logger.debug(f"Model={self.model}; result={result}")
+            if result is not None and (
+                isinstance(result, ModelResponse)
+                or isinstance(result, EmbeddingResponse)
+            ):
+                try:
+                    self.model_call_details["response_cost"] = litellm.completion_cost(
+                        completion_response=result,
+                    )
+                    verbose_logger.debug(
+                        f"Model={self.model}; cost={self.model_call_details['response_cost']}"
+                    )
+                except litellm.NotFoundError as e:
+                    verbose_logger.debug(
+                        f"Model={self.model} not found in completion cost map."
+                    )
+                    self.model_call_details["response_cost"] = None
+            else:  # streaming chunks + image gen.
+                self.model_call_details["response_cost"] = None
 
             if litellm.max_budget and self.stream:
                 time_diff = (end_time - start_time).total_seconds()
@@ -1077,7 +1098,7 @@ class Logging:
 
             return start_time, end_time, result
         except Exception as e:
-            print_verbose(f"[Non-Blocking] LiteLLM.Success_Call Error: {str(e)}")
+            raise Exception(f"[Non-Blocking] LiteLLM.Success_Call Error: {str(e)}")
 
     def success_handler(
         self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs

model_prices_and_context_window.json

@@ -906,6 +906,14 @@
         "litellm_provider": "bedrock",
         "mode": "chat"
     },
+    "amazon.titan-embed-text-v1": {
+        "max_tokens": 8192, 
+        "output_vector_size": 1536,
+        "input_cost_per_token": 0.0000001,
+        "output_cost_per_token": 0.0,
+        "litellm_provider": "bedrock", 
+        "mode": "embedding"
+    },
     "anthropic.claude-v1": {
         "max_tokens": 100000, 
         "max_output_tokens": 8191,