eval_agent/base_agent.py

from openai import OpenAI
import json
# from vllm import LLM, SamplingParams
import requests

class BaseAgent:
    def __init__(self, system_prompt="", use_history=True, temp=0.5, top_p=0.95):
        self.use_history = use_history
        self.client = OpenAI()
        self.system = system_prompt
        self.temp = temp
        self.top_p = top_p
        self.input_tokens_count = 0
        self.output_tokens_count = 0
        self.messages = []
        if self.system:
            self.messages.append({"role": "system", "content": system_prompt})
    
    
    def __call__(self, message, parse=False):
        self.messages.append({"role": "user", "content": message})
        result = self.generate(message, parse)
        self.messages.append({"role": "assistant", "content": result})

        if parse:
            try:
                result = self.parse_json(result)
            except:
                raise Exception("Error content is list below:\n", result)
            
        return result
        
    
    
    def generate(self, message, json_format):
        if self.use_history:
            input_messages = self.messages
        else:
            input_messages = [
                {"role": "system", "content": self.system},
                {"role": "user", "content": message}
            ]
            
        
        if json_format:
            response = self.client.chat.completions.create(
                model="gpt-4o-2024-08-06", # gpt-4
                messages=input_messages,
                temperature=self.temp,
                top_p=self.top_p,
                response_format = { "type": "json_object" }
                )
        else:
            response = self.client.chat.completions.create(
                model="gpt-4o-2024-08-06", # gpt-4
                messages=input_messages,
                temperature=self.temp,
                top_p=self.top_p,
                )
        self.update_tokens_count(response)
        return response.choices[0].message.content
    
    
    def parse_json(self, response):
        return json.loads(response)

    
    def add(self, message: dict):
        self.messages.append(message)
    
    
    def update_tokens_count(self, response):
        self.input_tokens_count += response.usage.prompt_tokens
        self.output_tokens_count += response.usage.completion_tokens
    
    
    def show_usage(self):
        print(f"Total input tokens used: {self.input_tokens_count}\nTotal output tokens used: {self.output_tokens_count}")
        

class BaseAgent_SFT:
    def __init__(self, system_prompt="", use_history=True, temp=0, top_p=1, model_name_or_path="http://0.0.0.0:12333/v1/chat/completions"):
        self.use_history = use_history
        if not model_name_or_path.startswith("http"):
            self.client = LLM(model=model_name_or_path, tokenizer=model_name_or_path, gpu_memory_utilization=0.5, tensor_parallel_size=1)
            self.api = False
        else:
            self.client = model_name_or_path
            self.model_name = "eval-agent"
            self.api = True
        self.system = system_prompt
        self.temp = temp
        self.top_p = top_p
        self.input_tokens_count = 0
        self.output_tokens_count = 0
        self.messages = []
        if self.system:
            self.messages.append({"role": "system", "content": system_prompt})
    
    
    def __call__(self, message):
        self.messages.append({"role": "user", "content": message})
        result = self.generate(message)
        self.messages.append({"role": "assistant", "content": result})

        return result
        
    
    def generate(self, message):
        if self.use_history:
            input_messages = self.messages
        else:
            input_messages = [
                {"role": "system", "content": self.system},
                {"role": "user", "content": message}
            ]
            
        if self.api:
            payload = {
                "model": self.model_name,
                "messages": input_messages,
                "max_tokens": 1024,
                "temperature": self.temp,
                "top_p": self.top_p,
                "stream": False
            }
            
            for _ in range(3):
                try:
                    response = requests.post(self.client, json=payload, timeout=120)
                    response.raise_for_status()
                    result = response.json()
                    return result["choices"][0]["message"]["content"]
                
                except requests.exceptions.RequestException as e:
                    print(f"❌ API request failed: {e}")
                    continue
                
                except (KeyError, IndexError) as e:
                    print(f"❌ Unexpected response format: {e}")
                    continue
            return None
        else:
            response = self.client.generate(
                input_messages,
                sampling_params=SamplingParams(
                    max_tokens=1024,
                    temperature=self.temp,
                    top_p=self.top_p,
                    n=1,
                ),
            )
            return response[0].outputs[0].text

class BaseAgent_Open:
    def __init__(self, system_prompt="", use_history=True, temp=0, top_p=1, model_name_or_path="Qwen/Qwen2.5-3B-Instruct"):
        self.use_history = use_history
        self.client = LLM(model=model_name_or_path, tokenizer=model_name_or_path, gpu_memory_utilization=0.5, tensor_parallel_size=1)
        self.tokenizer = self.client.get_tokenizer()
        self.system = system_prompt
        self.temp = temp
        self.top_p = top_p
        self.messages = []
        if self.system:
            self.messages.append({"role": "system", "content": system_prompt})
    
    
    def __call__(self, message):
        self.messages.append({"role": "user", "content": message})
        result = self.generate(message)
        self.messages.append({"role": "assistant", "content": result})

        return result
        
    
    def generate(self, message):
        if self.use_history:
            input_messages = self.messages
        else:
            input_messages = [
                {"role": "system", "content": self.system},
                {"role": "user", "content": message}
            ]
        
        # Convert messages to string using tokenizer's chat template
        prompt = self.tokenizer.apply_chat_template(
            input_messages,
            tokenize=False,
            add_generation_prompt=True
        )
        
        response = self.client.generate(
            prompt,
            sampling_params=SamplingParams(
                max_tokens=1024,
                temperature=self.temp,
                top_p=self.top_p,
                n=1,
            ),
        )
        return response[0].outputs[0].text