Add cost summary to client.py (microsoft#812)

* init commit

* add doc, notebook and test

* fix test

* update

* update

* update

* update

autogen/oai/client.py

@@ -36,6 +36,8 @@ class OpenAIWrapper:
     cache_path_root: str = ".cache"
     extra_kwargs = {"cache_seed", "filter_func", "allow_format_str_template", "context", "api_version"}
     openai_kwargs = set(inspect.getfullargspec(OpenAI.__init__).kwonlyargs)
+    total_usage_summary: Dict = None
+    actual_usage_summary: Dict = None
 
     def __init__(self, *, config_list: List[Dict] = None, **base_config):
         """
@@ -233,14 +235,15 @@ def yes_or_no_filter(context, response):
                     # Try to get the response from cache
                     key = get_key(params)
                     response = cache.get(key, None)
+                    if response is not None:
+                        self._update_usage_summary(response, use_cache=True)
                     if response is not None:
                         # check the filter
                         pass_filter = filter_func is None or filter_func(context=context, response=response)
                         if pass_filter or i == last:
                             # Return the response if it passes the filter or it is the last client
                             response.config_id = i
                             response.pass_filter = pass_filter
-                            response.cost = self.cost(response)
                             return response
                         continue  # filter is not passed; try the next config
             try:
@@ -250,6 +253,9 @@ def yes_or_no_filter(context, response):
                 if i == last:
                     raise
             else:
+                # add cost calculation before caching not matter filter is passed or not
+                response.cost = self.cost(response)
+                self._update_usage_summary(response, use_cache=False)
                 if cache_seed is not None:
                     # Cache the response
                     with diskcache.Cache(f"{self.cache_path_root}/{cache_seed}") as cache:
@@ -261,25 +267,9 @@ def yes_or_no_filter(context, response):
                     # Return the response if it passes the filter or it is the last client
                     response.config_id = i
                     response.pass_filter = pass_filter
-                    response.cost = self.cost(response)
                     return response
                 continue  # filter is not passed; try the next config
 
-    def cost(self, response: Union[ChatCompletion, Completion]) -> float:
-        """Calculate the cost of the response."""
-        model = response.model
-        if model not in oai_price1k:
-            # TODO: add logging to warn that the model is not found
-            return 0
-
-        n_input_tokens = response.usage.prompt_tokens
-        n_output_tokens = response.usage.completion_tokens
-        tmp_price1K = oai_price1k[model]
-        # First value is input token rate, second value is output token rate
-        if isinstance(tmp_price1K, tuple):
-            return (tmp_price1K[0] * n_input_tokens + tmp_price1K[1] * n_output_tokens) / 1000
-        return tmp_price1K * (n_input_tokens + n_output_tokens) / 1000
-
     def _completions_create(self, client, params):
         completions = client.chat.completions if "messages" in params else client.completions
         # If streaming is enabled, has messages, and does not have functions, then
@@ -342,6 +332,105 @@ def _completions_create(self, client, params):
             response = completions.create(**params)
         return response
 
+    def _update_usage_summary(self, response: ChatCompletion | Completion, use_cache: bool) -> None:
+        """Update the usage summary.
+
+        Usage is calculated no mattter filter is passed or not.
+        """
+
+        def update_usage(usage_summary):
+            if usage_summary is None:
+                usage_summary = {"total_cost": response.cost}
+            else:
+                usage_summary["total_cost"] += response.cost
+
+            usage_summary[response.model] = {
+                "cost": usage_summary.get(response.model, {}).get("cost", 0) + response.cost,
+                "prompt_tokens": usage_summary.get(response.model, {}).get("prompt_tokens", 0)
+                + response.usage.prompt_tokens,
+                "completion_tokens": usage_summary.get(response.model, {}).get("completion_tokens", 0)
+                + response.usage.completion_tokens,
+                "total_tokens": usage_summary.get(response.model, {}).get("total_tokens", 0)
+                + response.usage.total_tokens,
+            }
+            return usage_summary
+
+        self.total_usage_summary = update_usage(self.total_usage_summary)
+        if not use_cache:
+            self.actual_usage_summary = update_usage(self.actual_usage_summary)
+
+    def print_usage_summary(self, mode: Union[str, List[str]] = ["actual", "total"]) -> None:
+        """Print the usage summary."""
+
+        def print_usage(usage_summary, usage_type="total"):
+            word_from_type = "including" if usage_type == "total" else "excluding"
+            if usage_summary is None:
+                print("No actual cost incurred (all completions are using cache).", flush=True)
+                return
+
+            print(f"Usage summary {word_from_type} cached usage: ", flush=True)
+            print(f"Total cost: {round(usage_summary['total_cost'], 5)}", flush=True)
+            for model, counts in usage_summary.items():
+                if model == "total_cost":
+                    continue  #
+                print(
+                    f"* Model '{model}': cost: {round(counts['cost'], 5)}, prompt_tokens: {counts['prompt_tokens']}, completion_tokens: {counts['completion_tokens']}, total_tokens: {counts['total_tokens']}",
+                    flush=True,
+                )
+
+        if self.total_usage_summary is None:
+            print('No usage summary. Please call "create" first.', flush=True)
+            return
+
+        if isinstance(mode, list):
+            if len(mode) == 0 or len(mode) > 2:
+                raise ValueError(f'Invalid mode: {mode}, choose from "actual", "total", ["actual", "total"]')
+            if "actual" in mode and "total" in mode:
+                mode = "both"
+            elif "actual" in mode:
+                mode = "actual"
+            elif "total" in mode:
+                mode = "total"
+
+        print("-" * 100, flush=True)
+        if mode == "both":
+            print_usage(self.actual_usage_summary, "actual")
+            print()
+            if self.total_usage_summary != self.actual_usage_summary:
+                print_usage(self.total_usage_summary, "total")
+            else:
+                print(
+                    "All completions are non-cached: the total cost with cached completions is the same as actual cost.",
+                    flush=True,
+                )
+        elif mode == "total":
+            print_usage(self.total_usage_summary, "total")
+        elif mode == "actual":
+            print_usage(self.actual_usage_summary, "actual")
+        else:
+            raise ValueError(f'Invalid mode: {mode}, choose from "actual", "total", ["actual", "total"]')
+        print("-" * 100, flush=True)
+
+    def clear_usage_summary(self) -> None:
+        """Clear the usage summary."""
+        self.total_usage_summary = None
+        self.actual_usage_summary = None
+
+    def cost(self, response: Union[ChatCompletion, Completion]) -> float:
+        """Calculate the cost of the response."""
+        model = response.model
+        if model not in oai_price1k:
+            # TODO: add logging to warn that the model is not found
+            return 0
+
+        n_input_tokens = response.usage.prompt_tokens
+        n_output_tokens = response.usage.completion_tokens
+        tmp_price1K = oai_price1k[model]
+        # First value is input token rate, second value is output token rate
+        if isinstance(tmp_price1K, tuple):
+            return (tmp_price1K[0] * n_input_tokens + tmp_price1K[1] * n_output_tokens) / 1000
+        return tmp_price1K * (n_input_tokens + n_output_tokens) / 1000
+
     @classmethod
     def extract_text_or_function_call(cls, response: ChatCompletion | Completion) -> List[str]:
         """Extract the text or function calls from a completion or chat response.