refactor : memory clear section function logic

server/sources/vllm_handler.py

@@ -1,74 +0,0 @@
-from .generator import GeneratorLLM
-from vllm import LLM, SamplingParams
-import logging
-from typing import List, Dict
-
-class Vllm(GeneratorLLM):
-    def __init__(self):
-        """
-        Handle generation using vLLM.
-        """
-        super().__init__()
-        self.logger = logging.getLogger(__name__)
-        self.llm = None 
-        
-    def convert_history_to_prompt(self, history: List[Dict[str, str]]) -> str:
-        """
-        Convert OpenAI-format history to a single prompt string for vLLM.
-        """
-        prompt = ""
-        for message in history:
-            role = message["role"]
-            content = message["content"]
-            if role == "system":
-                prompt += f"System: {content}\n"
-            elif role == "user":
-                prompt += f"User: {content}\n"
-            elif role == "assistant":
-                prompt += f"Assistant: {content}\n"
-        prompt += "Assistant: "
-        return prompt
-
-    def generate(self, history: List[Dict[str, str]]):
-        """
-        Generate response using vLLM from OpenAI-format message history.
-        
-        Args:
-            history: List of dictionaries in OpenAI format [{"role": "user", "content": "..."}, ...]
-        """
-        self.logger.info(f"Using {self.model} for generation with vLLM")
-        if self.llm is None:
-            self.llm = LLM(model=self.model)
-        
-        try:
-            with self.state.lock:
-                self.state.is_generating = True
-                self.state.last_complete_sentence = ""
-                self.state.current_buffer = ""
-
-            prompt = self.convert_history_to_prompt(history)
-            
-            sampling_params = SamplingParams(
-                temperature=0.7,
-                max_tokens=512,
-                gpu_memory_utilization=0.5,
-                stream=True  # Enable streaming
-            )
-            outputs = self.llm.generate(prompt, sampling_params, use_tqdm=False)
-            for output in outputs:
-                content = output.outputs[0].text
-                with self.state.lock:
-                    if '.' in content:
-                        self.logger.info(self.state.current_buffer)
-                    self.state.current_buffer += content
-            with self.state.lock:
-                self.logger.info(f"Final output: {self.state.current_buffer}")
-
-        except Exception as e:
-            self.logger.error(f"Error during generation: {str(e)}")
-            raise e
-            
-        finally:
-            self.logger.info("Generation complete")
-            with self.state.lock:
-                self.state.is_generating = False