refactor: Refactor proxy LLM (#1064)

dbgpt/model/adapter/base.py

@@ -152,6 +152,17 @@ class LLMModelAdapter(ABC):
         except Exception:
             return "\n"
 
+    def get_prompt_roles(self) -> List[str]:
+        """Get the roles of the prompt
+
+        Returns:
+            List[str]: The roles of the prompt
+        """
+        roles = [ModelMessageRoleType.HUMAN, ModelMessageRoleType.AI]
+        if self.support_system_message:
+            roles.append(ModelMessageRoleType.SYSTEM)
+        return roles
+
     def transform_model_messages(
         self, messages: List[ModelMessage], convert_to_compatible_format: bool = False
     ) -> List[Dict[str, str]]:
@@ -185,7 +196,7 @@ class LLMModelAdapter(ABC):
             # We will not do any transform in the future
             return self._transform_to_no_system_messages(messages)
         else:
-            return ModelMessage.to_openai_messages(
+            return ModelMessage.to_common_messages(
                 messages, convert_to_compatible_format=convert_to_compatible_format
             )
 
@@ -216,7 +227,7 @@ class LLMModelAdapter(ABC):
         Returns:
             List[Dict[str, str]]: The transformed model messages
         """
-        openai_messages = ModelMessage.to_openai_messages(messages)
+        openai_messages = ModelMessage.to_common_messages(messages)
         system_messages = []
         return_messages = []
         for message in openai_messages:
@@ -394,6 +405,9 @@ class LLMModelAdapter(ABC):
         conv.set_system_message("".join(can_use_systems))
         return conv
 
+    def apply_conv_template(self) -> bool:
+        return self.model_type() != ModelType.PROXY
+
     def model_adaptation(
         self,
         params: Dict,
@@ -414,7 +428,11 @@ class LLMModelAdapter(ABC):
         params["convert_to_compatible_format"] = convert_to_compatible_format
 
         # Some model context to dbgpt server
-        model_context = {"prompt_echo_len_char": -1, "has_format_prompt": False}
+        model_context = {
+            "prompt_echo_len_char": -1,
+            "has_format_prompt": False,
+            "echo": params.get("echo", True),
+        }
         if messages:
             # Dict message to ModelMessage
             messages = [
@@ -422,6 +440,11 @@ class LLMModelAdapter(ABC):
                 for m in messages
             ]
             params["messages"] = messages
+        params["string_prompt"] = ModelMessage.messages_to_string(messages)
+
+        if not self.apply_conv_template():
+            # No need to apply conversation template, now for proxy LLM
+            return params, model_context
 
         new_prompt = self.get_str_prompt(
             params, messages, tokenizer, prompt_template, convert_to_compatible_format
@@ -442,7 +465,6 @@ class LLMModelAdapter(ABC):
         # TODO remote bos token and eos token from tokenizer_config.json of model
         prompt_echo_len_char = len(new_prompt.replace("</s>", "").replace("<s>", ""))
         model_context["prompt_echo_len_char"] = prompt_echo_len_char
-        model_context["echo"] = params.get("echo", True)
         model_context["has_format_prompt"] = True
         params["prompt"] = new_prompt