252 lines
9.2 KiB
Python
252 lines
9.2 KiB
Python
"""
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DeepSeek V3模型智能体 - 带调试功能
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负责API调用、内容重写等执行型任务
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"""
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import json
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import logging
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from typing import Dict, List, Any, Optional
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from openai import OpenAI
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from config import Config
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from app.agents.prompts import get_prompt
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from app.utils.debug_logger import ai_debug_logger
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logger = logging.getLogger(__name__)
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class V3Agent:
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"""V3模型智能体"""
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def __init__(self, api_key: str = None):
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self.api_key = api_key or Config.DEEPSEEK_API_KEY
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base_url = Config.DEEPSEEK_BASE_URL
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# 火山引擎 ARK 平台使用不同的模型名称
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if 'volces.com' in base_url:
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self.model = "deepseek-v3-241226" # 火山引擎的 V3 模型名称
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else:
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self.model = Config.V3_MODEL
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=base_url
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)
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def _call_api(self, prompt: str, temperature: float = 0.3,
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max_tokens: int = 4096, functions: List[Dict] = None) -> Any:
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"""调用V3 API"""
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try:
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messages = [{"role": "user", "content": prompt}]
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kwargs = {
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"model": self.model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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# 如果提供了functions，添加function calling参数
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if functions:
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kwargs["functions"] = functions
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kwargs["function_call"] = "auto"
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logger.info(f"调用V3 API: temperature={temperature}, max_tokens={max_tokens}, functions={bool(functions)}")
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response = self.client.chat.completions.create(**kwargs)
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# 准备响应内容
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if functions and response.choices[0].message.function_call:
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result = {
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"function_call": {
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"name": response.choices[0].message.function_call.name,
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"arguments": json.loads(response.choices[0].message.function_call.arguments)
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}
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}
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response_text = json.dumps(result, ensure_ascii=False)
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else:
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result = response.choices[0].message.content.strip()
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response_text = result
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# 记录调试日志
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ai_debug_logger.log_api_call(
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model=self.model,
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agent_type="V3",
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method=self._get_caller_method(),
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prompt=prompt,
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response=response_text,
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temperature=temperature,
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max_tokens=max_tokens,
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metadata={
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"has_functions": bool(functions),
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"function_count": len(functions) if functions else 0,
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"prompt_tokens": response.usage.prompt_tokens if hasattr(response, 'usage') else None,
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"completion_tokens": response.usage.completion_tokens if hasattr(response, 'usage') else None
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}
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)
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return result
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except Exception as e:
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logger.error(f"V3 API调用失败: {e}")
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ai_debug_logger.log_api_call(
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model=self.model,
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agent_type="V3",
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method=self._get_caller_method(),
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prompt=prompt,
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response=f"ERROR: {str(e)}",
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temperature=temperature,
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max_tokens=max_tokens,
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metadata={"error": str(e)}
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)
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raise
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def _get_caller_method(self) -> str:
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"""获取调用方法名"""
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import inspect
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frame = inspect.currentframe()
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if frame and frame.f_back and frame.f_back.f_back:
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return frame.f_back.f_back.f_code.co_name
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return "unknown"
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def generate_search_queries(self, subtopic: str, explanation: str,
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related_questions: List[str], count: int) -> List[str]:
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"""生成搜索查询"""
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prompt = get_prompt("generate_search_queries",
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subtopic=subtopic,
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explanation=explanation,
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related_questions=', '.join(related_questions),
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count=count)
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result = self._call_api(prompt, temperature=0.7)
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# 解析结果为列表
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queries = [q.strip() for q in result.split('\n') if q.strip()]
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# 去除可能的序号
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queries = [q.lstrip('0123456789.-) ') for q in queries]
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# 记录解析后的查询
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logger.debug(f"生成了{len(queries)}个搜索查询")
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return queries[:count]
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def generate_refined_queries(self, key_info: str, detail_needed: str) -> List[str]:
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"""生成细化搜索查询"""
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prompt = get_prompt("generate_refined_queries",
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key_info=key_info,
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detail_needed=detail_needed)
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result = self._call_api(prompt, temperature=0.7)
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queries = [q.strip() for q in result.split('\n') if q.strip()]
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queries = [q.lstrip('0123456789.-) ') for q in queries]
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logger.debug(f"为'{key_info}'生成了{len(queries)}个细化查询")
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return queries[:3]
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def rewrite_hallucination(self, hallucinated_content: str,
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original_sources: str) -> str:
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"""重写幻觉内容"""
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prompt = get_prompt("rewrite_hallucination",
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hallucinated_content=hallucinated_content,
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original_sources=original_sources)
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rewritten = self._call_api(prompt, temperature=0.3)
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# 记录幻觉修正
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logger.info(f"修正幻觉内容: 原始长度={len(hallucinated_content)}, 修正后长度={len(rewritten)}")
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return rewritten
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def call_tavily_search(self, query: str, max_results: int = 10) -> Dict[str, Any]:
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"""
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调用Tavily搜索API（通过function calling）
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注意：这是一个示例实现，实际的Tavily调用会在search_service.py中
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"""
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# 定义Tavily搜索function
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tavily_function = {
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"name": "tavily_search",
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"description": "Search the web using Tavily API",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The search query"
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},
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"max_results": {
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"type": "integer",
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"description": "Maximum number of results",
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"default": 10
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},
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"search_depth": {
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"type": "string",
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"enum": ["basic", "advanced"],
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"default": "advanced"
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}
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},
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"required": ["query"]
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}
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}
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prompt = f"Please search for information about: {query}"
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result = self._call_api(prompt, functions=[tavily_function])
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# 如果返回的是function call，提取参数
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if isinstance(result, dict) and "function_call" in result:
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return result["function_call"]["arguments"]
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# 否则返回默认参数
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return {
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"query": query,
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"max_results": max_results,
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"search_depth": "advanced"
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}
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def format_search_results(self, results: List[Dict[str, Any]]) -> str:
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"""格式化搜索结果为结构化文本"""
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formatted = []
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for i, result in enumerate(results, 1):
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formatted.append(f"{i}. 标题: {result.get('title', 'N/A')}")
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formatted.append(f" URL: {result.get('url', 'N/A')}")
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formatted.append(f" 摘要: {result.get('snippet', 'N/A')}")
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if result.get('score'):
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formatted.append(f" 相关度: {result.get('score', 0):.2f}")
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formatted.append("")
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return '\n'.join(formatted)
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def extract_key_points(self, text: str, max_points: int = 5) -> List[str]:
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"""从文本中提取关键点"""
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prompt = f"""
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请从以下文本中提取最多{max_points}个关键点：
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{text}
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每个关键点独占一行，简洁明了。
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"""
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result = self._call_api(prompt, temperature=0.5)
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points = [p.strip() for p in result.split('\n') if p.strip()]
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points = [p.lstrip('0123456789.-) ') for p in points]
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logger.debug(f"从文本中提取了{len(points)}个关键点")
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return points[:max_points]
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def summarize_content(self, content: str, max_length: int = 200) -> str:
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"""总结内容"""
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prompt = f"""
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请将以下内容总结为不超过{max_length}字的摘要：
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{content}
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要求：保留关键信息，语言流畅。
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"""
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summary = self._call_api(prompt, temperature=0.5)
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logger.debug(f"内容总结: 原始长度={len(content)}, 摘要长度={len(summary)}")
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return summary |