137 lines
5.1 KiB
Python
137 lines
5.1 KiB
Python
"""Legacy LLMClient facade backed by AgentRuntime."""
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import json
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import re
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from pathlib import Path
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from typing import Optional, Dict, Any, List
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from ..config import Config
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from ..adapters.llm.agent_runtime import AgentRuntime, NeedAgentResponse
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from ..agent_engine.json_schema import object_schema
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class LLMClient:
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"""Compatibility wrapper for older services.
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New business code should call AgentRuntime directly. This class remains so
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legacy UI/report code can be migrated incrementally without direct SDK use.
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"""
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def __init__(
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self,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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model: Optional[str] = None
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):
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self.api_key = api_key
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self.base_url = base_url or Config.LLM_BASE_URL
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self.model = model or Config.LLM_MODEL_NAME
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legacy_run_dir = Path(Config.MIROFISH_RUNS_DIR) / "legacy-ui"
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legacy_run_dir.mkdir(parents=True, exist_ok=True)
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self.runtime = AgentRuntime(run_dir=str(legacy_run_dir))
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def chat(
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self,
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messages: List[Dict[str, str]],
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temperature: float = 0.7,
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max_tokens: int = 4096,
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response_format: Optional[Dict] = None
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) -> str:
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"""
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发送聊天请求
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Args:
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messages: 消息列表
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temperature: 温度参数
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max_tokens: 最大token数
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response_format: 响应格式（如JSON模式）
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Returns:
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模型响应文本
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"""
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if response_format and response_format.get("type") == "json_object":
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max_tokens = max(max_tokens, 8192)
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structured_input = {
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"response_format": response_format,
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"legacy_client": True,
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}
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user_prompt = "\n".join(message.get("content", "") for message in messages if message.get("role") == "user")
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system_prompt = "\n".join(message.get("content", "") for message in messages if message.get("role") == "system")
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result = self.runtime.run_task(
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run_id="legacy-ui",
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task_type="validate_json_output" if response_format else "answer_followup_question",
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stage="legacy_llm_client",
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expected_schema=object_schema({"text": {"type": "string"}}, ["text"]),
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structured_input=structured_input,
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system_prompt=system_prompt,
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user_prompt=user_prompt,
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)
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if result.status == "need_agent_response":
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raise NeedAgentResponse(result)
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if result.status != "ok":
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raise RuntimeError(result.error or "LLM provider failed")
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content = (result.output or {}).get("text")
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if content is None and result.output:
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content = json.dumps(result.output, ensure_ascii=False)
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content = content or ""
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# 部分模型（如MiniMax M2.5）会在content中包含<think>思考内容，需要移除
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content = re.sub(r'<think>[\s\S]*?</think>', '', content).strip()
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return content
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def chat_json(
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self,
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messages: List[Dict[str, str]],
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temperature: float = 0.3,
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max_tokens: int = 4096
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) -> Dict[str, Any]:
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"""
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发送聊天请求并返回JSON
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Args:
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messages: 消息列表
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temperature: 温度参数
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max_tokens: 最大token数
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Returns:
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解析后的JSON对象
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"""
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structured_input = {
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"response_format": {"type": "json_object"},
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"legacy_client": True,
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}
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user_prompt = "\n".join(message.get("content", "") for message in messages if message.get("role") == "user")
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system_prompt = "\n".join(message.get("content", "") for message in messages if message.get("role") == "system")
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result = self.runtime.run_task(
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run_id="legacy-ui",
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task_type="validate_json_output",
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stage="legacy_llm_client",
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expected_schema={"type": "object"},
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structured_input=structured_input,
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system_prompt=system_prompt,
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user_prompt=user_prompt,
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)
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if result.status == "need_agent_response":
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raise NeedAgentResponse(result)
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if result.status != "ok":
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raise RuntimeError(result.error or "LLM provider failed")
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if isinstance(result.output, dict):
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return result.output
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response = json.dumps(result.output, ensure_ascii=False)
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# 清理markdown代码块标记
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cleaned_response = response.strip()
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cleaned_response = re.sub(r'^```(?:json)?\s*\n?', '', cleaned_response, flags=re.IGNORECASE)
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cleaned_response = re.sub(r'\n?```\s*$', '', cleaned_response)
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cleaned_response = cleaned_response.strip()
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try:
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return json.loads(cleaned_response)
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except json.JSONDecodeError:
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raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}")
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