add openai.Completion support

metagpt/provider/base_gpt_api.py

@@ -36,6 +36,10 @@ class BaseGPTAPI(BaseChatbot):
         rsp = self.completion(message)
         return self.get_choice_text(rsp)
 
+    def ask_nonchat(self, prompt: str) -> str:
+        rsp = self.nonchat_completion(prompt)
+        return self.get_nonchat_choice_text(rsp)
+
     async def aask(self, msg: str, system_msgs: Optional[list[str]] = None) -> str:
         if system_msgs:
             message = self._system_msgs(system_msgs) + [self._user_msg(msg)]
@@ -89,6 +93,10 @@ class BaseGPTAPI(BaseChatbot):
         ]
         """
 
+    @abstractmethod
+    def nonchat_completion(self, prompt: str) -> dict:
+        """ for openai.Completion request """
+
     @abstractmethod
     async def acompletion(self, messages: list[dict]):
         """Asynchronous version of completion
@@ -108,6 +116,10 @@ class BaseGPTAPI(BaseChatbot):
         """Required to provide the first text of choice"""
         return rsp.get("choices")[0]["message"]["content"]
 
+    def get_nonchat_choice_text(self, rsp: dict) -> str:
+        """ for openai.Completion """
+        return rsp.get("choices")[0]["text"]
+
     def messages_to_prompt(self, messages: list[dict]):
         """[{"role": "user", "content": msg}] to user: <msg> etc."""
         return '\n'.join([f"{i['role']}: {i['content']}" for i in messages])

metagpt/provider/openai_api.py

@@ -205,6 +205,17 @@ class OpenAIGPTAPI(BaseGPTAPI, RateLimiter):
         kwargs.update(kwargs_mode)
         return kwargs
 
+    def _nonchat_cons_kwargs(self, prompt: str) -> dict:
+        kwargs = {
+            "model": self.model,
+            "prompt": prompt,
+            "max_tokens": self.get_max_tokens(prompt),  # TODO adapt if auto_max_tokens is True
+            "n": 1,
+            "stop": None,
+            "temperature": 0.3
+        }
+        return kwargs
+
     async def _achat_completion(self, messages: list[dict]) -> dict:
         rsp = await self.llm.ChatCompletion.acreate(**self._cons_kwargs(messages))
         self._update_costs(rsp.get("usage"))
@@ -220,6 +231,14 @@ class OpenAIGPTAPI(BaseGPTAPI, RateLimiter):
         #     messages = self.messages_to_dict(messages)
         return self._chat_completion(messages)
 
+    def _nonchat_completion(self, prompt: str) -> dict:
+        rsp = self.llm.Completion.create(**self._nonchat_cons_kwargs(prompt))
+        self._update_costs(rsp.get("usage"))
+        return rsp
+
+    def nonchat_completion(self, prompt: str) -> dict:
+        return self._nonchat_completion(prompt)
+
     async def acompletion(self, messages: list[dict]) -> dict:
         # if isinstance(messages[0], Message):
         #     messages = self.messages_to_dict(messages)
@@ -249,7 +268,7 @@ class OpenAIGPTAPI(BaseGPTAPI, RateLimiter):
                 usage["completion_tokens"] = completion_tokens
                 return usage
             except Exception as e:
-                logger.error("usage calculation failed! {e}")
+                logger.error(f"usage calculation failed! {e}")
         else:
             return usage
 
@@ -286,7 +305,7 @@ class OpenAIGPTAPI(BaseGPTAPI, RateLimiter):
                 completion_tokens = int(usage["completion_tokens"])
                 self._cost_manager.update_cost(prompt_tokens, completion_tokens, self.model)
             except Exception as e:
-                logger.error("updating costs failed! {e}")
+                logger.error(f"updating costs failed! {e}")
 
     def get_costs(self) -> Costs:
         return self._cost_manager.get_costs()

metagpt/utils/token_counter.py

@@ -11,6 +11,7 @@ ref3: https://github.com/hwchase17/langchain/blob/master/langchain/chat_models/o
 import tiktoken
 
 TOKEN_COSTS = {
+    "gpt-3.5-turbo-instruct": {"prompt": 0.0015, "completion": 0.002},
     "gpt-3.5-turbo": {"prompt": 0.0015, "completion": 0.002},
     "gpt-3.5-turbo-0301": {"prompt": 0.0015, "completion": 0.002},
     "gpt-3.5-turbo-0613": {"prompt": 0.0015, "completion": 0.002},
@@ -26,6 +27,7 @@ TOKEN_COSTS = {
 
 
 TOKEN_MAX = {
+    "gpt-3.5-turbo-instruct": 4096,
     "gpt-3.5-turbo": 4096,
     "gpt-3.5-turbo-0301": 4096,
     "gpt-3.5-turbo-0613": 4096,
@@ -48,6 +50,7 @@ def count_message_tokens(messages, model="gpt-3.5-turbo-0613"):
         print("Warning: model not found. Using cl100k_base encoding.")
         encoding = tiktoken.get_encoding("cl100k_base")
     if model in {
+        "gpt-3.5-turbo-instruct",
         "gpt-3.5-turbo-0613",
         "gpt-3.5-turbo-16k-0613",
         "gpt-4-0314",