""" LLM客户端封装 统一使用OpenAI格式调用 """ import json import re from typing import Optional, Dict, Any, List from openai import OpenAI from ..config import Config class LLMClient: """LLM客户端""" def __init__( self, api_key: Optional[str] = None, base_url: Optional[str] = None, model: Optional[str] = None ): self.api_key = api_key or Config.LLM_API_KEY self.base_url = base_url or Config.LLM_BASE_URL self.model = model or Config.LLM_MODEL_NAME if not self.api_key: raise ValueError("LLM_API_KEY 未配置") self.client = OpenAI( api_key=self.api_key, base_url=self.base_url ) def _stub_key(self, messages: list[dict]) -> str: user_msg = next((m["content"] for m in reversed(messages) if m.get("role") == "user"), "") sys_msg = next((m["content"] for m in messages if m.get("role") == "system"), "") # Allow callers to embed an explicit stub_key=... token for chunk in user_msg.split(): if chunk.startswith("stub_key="): return chunk[len("stub_key="):] import hashlib return hashlib.sha256((sys_msg + "|" + user_msg).encode("utf-8")).hexdigest()[:12] def _stub_response(self, messages: list[dict]) -> str: import json as _json return _json.dumps(self._stub_response_json(messages), ensure_ascii=False) def _stub_response_json(self, messages: list[dict]) -> dict: import hashlib, json as _json sys_msg = next((m["content"] for m in messages if m.get("role") == "system"), "") usr_msg = next((m["content"] for m in reversed(messages) if m.get("role") == "user"), "") h = hashlib.sha256((sys_msg + "|" + usr_msg).encode("utf-8")).hexdigest() seed = int(h[:8], 16) rng = (seed % 5) + 1 # Longitudinal Likert (12 items) if all(tok in usr_msg for tok in ("stk_1", "gov_1", "mkt_1", "clm_1")): ids = ["stk_1","stk_2","stk_3","gov_1","gov_2","gov_3", "mkt_1","mkt_2","mkt_3","clm_1","clm_2","clm_3"] return {"responses": {k: ((seed >> (i*3)) % 5) + 1 for i, k in enumerate(ids)}, "confidence": {k: 0.6 for k in ids}, "open_comment": f"stub:{h[:8]}"} # Diversity Q-sort: 24 statements + 6 axes, forced distribution 2,3,4,6,4,3,2 if "st_01" in usr_msg and "ax_pres_extr" in usr_msg: buckets = [-3]*2 + [-2]*3 + [-1]*4 + [0]*6 + [1]*4 + [2]*3 + [3]*2 stmts = [f"st_{i+1:02d}" for i in range(24)] # shuffle deterministically order = sorted(range(24), key=lambda i: (h[i % len(h)], i)) placements = {stmts[i]: buckets[order.index(i)] for i in range(24)} return { "placements": placements, "likert_axes": {a: ((seed >> (j*3)) % 7) + 1 for j, a in enumerate( ["ax_pres_extr","ax_loc_eu","ax_sci_trad", "ax_ind_col","ax_short_long","ax_mkt_reg"])}, } # Scenario: S1..S4 × 4 dims if all(s in usr_msg for s in ("S1:", "S2:", "S3:", "S4:")): return {"ratings": {sid: { "desirability": ((seed >> (i*3)) % 7) + 1, "plausibility": ((seed >> (i*3+1)) % 7) + 1, "impact_on_my_group": ((seed >> (i*3+2)) % 7) + 1, "fairness": ((seed >> (i*3+4)) % 7) + 1, "if_woke_up_response": f"act-{sid}-{h[:4]}", } for i, sid in enumerate(["S1","S2","S3","S4"])}} # Delphi R1: q1..q4 free text if "q1" in usr_msg and "q2" in usr_msg and "Bewerten" not in usr_msg and "Sie sehen" not in usr_msg: return {"answers": {qid: f"stub-themes-{qid}-{h[:4]}" for qid in ("q1","q2","q3","q4")}} # Delphi theme extraction (no in-character system prompt) if "extract distinct thematic codes" in sys_msg: return {"themes": [{"theme_id": f"theme_{i}", "label": f"Thema {i}"} for i in range(5)]} # Delphi R2 (rate) or R3 (revise) if "Bewerten Sie jedes Thema" in usr_msg or "Sie sehen unten" in usr_msg \ or "Rate each theme" in usr_msg or "Below are the anonymised" in usr_msg: theme_ids = [f"theme_{i}" for i in range(5)] out = {"ratings": {tid: {"importance": ((seed >> (i*2)) % 5) + 1, "plausibility": ((seed >> (i*2+1)) % 5) + 1} for i, tid in enumerate(theme_ids)}} if "Sie sehen unten" in usr_msg or "Below are the anonymised" in usr_msg: out["justification"] = "stub-revision" return out # Fallback return {"stub_key": h[:12], "value": rng} def chat( self, messages: List[Dict[str, str]], temperature: float = 0.7, max_tokens: int = 4096, response_format: Optional[Dict] = None ) -> str: """ 发送聊天请求 Args: messages: 消息列表 temperature: 温度参数 max_tokens: 最大token数 response_format: 响应格式（如JSON模式） Returns: 模型响应文本 """ from app.config import Config if getattr(Config, "LLM_STUB_MODE", False): return self._stub_response(messages) kwargs = { "model": self.model, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, } if response_format: kwargs["response_format"] = response_format response = self.client.chat.completions.create(**kwargs) content = response.choices[0].message.content # 部分模型（如MiniMax M2.5）会在content中包含思考内容，需要移除 content = re.sub(r'[\s\S]*?', '', content).strip() return content def chat_json( self, messages: List[Dict[str, str]], temperature: float = 0.3, max_tokens: int = 4096 ) -> Dict[str, Any]: """ 发送聊天请求并返回JSON Args: messages: 消息列表 temperature: 温度参数 max_tokens: 最大token数 Returns: 解析后的JSON对象 """ from app.config import Config if getattr(Config, "LLM_STUB_MODE", False): return self._stub_response_json(messages) response = self.chat( messages=messages, temperature=temperature, max_tokens=max_tokens, response_format={"type": "json_object"} ) # 清理markdown代码块标记 cleaned_response = response.strip() cleaned_response = re.sub(r'^```(?:json)?\s*\n?', '', cleaned_response, flags=re.IGNORECASE) cleaned_response = re.sub(r'\n?```\s*$', '', cleaned_response) cleaned_response = cleaned_response.strip() try: return json.loads(cleaned_response) except json.JSONDecodeError: raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}")