update openai model support

flaml/autogen/agent/assistant_agent.py

@@ -39,6 +39,7 @@ def receive(self, message, sender):
             self._conversations[sender.name] = [{"content": self._system_message, "role": "system"}]
         super().receive(message, sender)
         responses = oai.ChatCompletion.create(messages=self._conversations[sender.name], **self._config)
+        # TODO: handle function_call
         response = oai.ChatCompletion.extract_text(responses)[0]
         self._send(response, sender)
 

flaml/autogen/oai/completion.py

@@ -45,12 +45,16 @@ class Completion(openai_Completion):
     # set of models that support chat completion
     chat_models = {
         "gpt-3.5-turbo",
-        "gpt-3.5-turbo-0301",
+        "gpt-3.5-turbo-0301",  # deprecate in Sep
+        "gpt-3.5-turbo-0613",
+        "gpt-3.5-turbo-16k",
         "gpt-35-turbo",
         "gpt-4",
         "gpt-4-32k",
-        "gpt-4-32k-0314",
-        "gpt-4-0314",
+        "gpt-4-32k-0314",  # deprecate in Sep
+        "gpt-4-0314",  # deprecate in Sep
+        "gpt-4-0613",
+        "gpt-4-32k-0613",
     }
 
     # price per 1k tokens
@@ -62,13 +66,17 @@ class Completion(openai_Completion):
         "code-davinci-002": 0.1,
         "text-davinci-002": 0.02,
         "text-davinci-003": 0.02,
-        "gpt-3.5-turbo": 0.002,
-        "gpt-3.5-turbo-0301": 0.002,
+        "gpt-3.5-turbo": (0.0015, 0.002),
+        "gpt-3.5-turbo-0301": (0.0015, 0.002),  # deprecate in Sep
+        "gpt-3.5-turbo-0613": (0.0015, 0.002),
+        "gpt-3.5-turbo-16k": (0.003, 0.004),
         "gpt-35-turbo": 0.002,
         "gpt-4": (0.03, 0.06),
-        "gpt-4-0314": (0.03, 0.06),
         "gpt-4-32k": (0.06, 0.12),
-        "gpt-4-32k-0314": (0.06, 0.12),
+        "gpt-4-0314": (0.03, 0.06),  # deprecate in Sep
+        "gpt-4-32k-0314": (0.06, 0.12),  # deprecate in Sep
+        "gpt-4-0613": (0.03, 0.06),
+        "gpt-4-32k-0613": (0.06, 0.12),
     }
 
     default_search_space = {
@@ -386,7 +394,7 @@ def _eval(cls, config: dict, prune=True, eval_only=False):
                         result["cost"] = cost
                         return result
                     # evaluate the quality of the responses
-                    responses = cls.extract_text(response)
+                    responses = cls.extract_text_or_function_call(response)
                     usage = response["usage"]
                     n_input_tokens = usage["prompt_tokens"]
                     n_output_tokens = usage.get("completion_tokens", 0)
@@ -898,7 +906,7 @@ def eval_func(responses, **data):
             response = cls.create(data_i, use_cache, **config)
             cost += response["cost"]
             # evaluate the quality of the responses
-            responses = cls.extract_text(response)
+            responses = cls.extract_text_or_function_call(response)
             if eval_func is not None:
                 metrics = eval_func(responses, **data_i)
             elif hasattr(cls, "_eval_func"):
@@ -991,6 +999,21 @@ def extract_text(cls, response: dict) -> List[str]:
             return [choice["text"] for choice in choices]
         return [choice["message"].get("content", "") for choice in choices]
 
+    @classmethod
+    def extract_text_or_function_call(cls, response: dict) -> List[str]:
+        """Extract the text or function calls from a completion or chat response.
+
+        Args:
+            response (dict): The response from OpenAI API.
+
+        Returns:
+            A list of function calls in the responses.
+        """
+        choices = response["choices"]
+        if "text" in choices[0]:
+            return [choice["text"] for choice in choices]
+        return [choice["message"].get("content") or choice["message"].get("function_call", "") for choice in choices]
+
     @classmethod
     @property
     def logged_history(cls) -> Dict:

setup.py

@@ -127,8 +127,8 @@
             "pytorch-forecasting>=0.9.0",
         ],
         "benchmark": ["catboost>=0.26", "psutil==5.8.0", "xgboost==1.3.3", "pandas==1.1.4"],
-        "openai": ["openai==0.27.4", "diskcache"],
-        "autogen": ["openai==0.27.4", "diskcache", "docker"],
+        "openai": ["openai==0.27.8", "diskcache"],
+        "autogen": ["openai==0.27.8", "diskcache", "docker"],
         "synapse": [
             "joblibspark>=0.5.0",
             "optuna==2.8.0",

test/autogen/test_assistant_agent.py

@@ -11,10 +11,10 @@ def test_gpt35(human_input_mode="NEVER", max_consecutive_auto_reply=5):
         import openai
     except ImportError:
         return
-    config_list = oai.config_list_from_models(key_file_path=KEY_LOC, model_list=["gpt-3.5-turbo"])
+    config_list = oai.config_list_from_models(key_file_path=KEY_LOC, model_list=["gpt-3.5-turbo-0613"])
     assistant = AssistantAgent(
         "coding_agent",
-        request_timeout=600,
+        # request_timeout=600,
         seed=40,
         max_tokens=1024,
         config_list=config_list,

website/docs/Use-Cases/Auto-Generation.md

@@ -368,14 +368,14 @@ Set `compact=False` in `start_logging()` to switch.
     },
 }
 ```
-It can be seen that the individual API call history contain redundant information of the conversation. For a long conversation the degree of redundancy is high.
+It can be seen that the individual API call history contains redundant information of the conversation. For a long conversation the degree of redundancy is high.
 The compact history is more efficient and the individual API call history contains more details.
 
 ### Other Utilities
 
 - a [`cost`](../reference/autogen/oai/completion#cost) function to calculate the cost of an API call.
 - a [`test`](../reference/autogen/oai/completion#test) function to conveniently evaluate the configuration over test data.
-- a [`extract_text`](../reference/autogen/oai/completion#extract_text) function to extract the text from a completion or chat response.
+- an [`extract_text_or_function_call`](../reference/autogen/oai/completion#extract_text_or_function_call) function to extract the text or function call from a completion or chat response.
 
 
 ## Agents (Experimental)