188 lines
7.1 KiB
Python
188 lines
7.1 KiB
Python
"""
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LLM客户端封装
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统一使用OpenAI格式调用
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"""
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import json
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import re
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from typing import Optional, Dict, Any, List
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from openai import OpenAI
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from ..config import Config
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class LLMClient:
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"""LLM客户端"""
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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 or Config.LLM_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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if not self.api_key:
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raise ValueError("LLM_API_KEY 未配置")
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url
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)
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def _stub_key(self, messages: list[dict]) -> str:
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user_msg = next((m["content"] for m in reversed(messages) if m.get("role") == "user"), "")
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sys_msg = next((m["content"] for m in messages if m.get("role") == "system"), "")
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# Allow callers to embed an explicit stub_key=... token
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for chunk in user_msg.split():
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if chunk.startswith("stub_key="):
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return chunk[len("stub_key="):]
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import hashlib
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return hashlib.sha256((sys_msg + "|" + user_msg).encode("utf-8")).hexdigest()[:12]
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def _stub_response(self, messages: list[dict]) -> str:
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import json as _json
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return _json.dumps(self._stub_response_json(messages), ensure_ascii=False)
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def _stub_response_json(self, messages: list[dict]) -> dict:
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import hashlib, json as _json
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sys_msg = next((m["content"] for m in messages if m.get("role") == "system"), "")
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usr_msg = next((m["content"] for m in reversed(messages) if m.get("role") == "user"), "")
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h = hashlib.sha256((sys_msg + "|" + usr_msg).encode("utf-8")).hexdigest()
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seed = int(h[:8], 16)
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rng = (seed % 5) + 1
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# Longitudinal Likert (12 items)
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if all(tok in usr_msg for tok in ("stk_1", "gov_1", "mkt_1", "clm_1")):
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ids = ["stk_1","stk_2","stk_3","gov_1","gov_2","gov_3",
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"mkt_1","mkt_2","mkt_3","clm_1","clm_2","clm_3"]
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return {"responses": {k: ((seed >> (i*3)) % 5) + 1 for i, k in enumerate(ids)},
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"confidence": {k: 0.6 for k in ids},
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"open_comment": f"stub:{h[:8]}"}
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# Diversity Q-sort: 24 statements + 6 axes, forced distribution 2,3,4,6,4,3,2
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if "st_01" in usr_msg and "ax_pres_extr" in usr_msg:
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buckets = [-3]*2 + [-2]*3 + [-1]*4 + [0]*6 + [1]*4 + [2]*3 + [3]*2
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stmts = [f"st_{i+1:02d}" for i in range(24)]
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# shuffle deterministically
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order = sorted(range(24), key=lambda i: (h[i % len(h)], i))
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placements = {stmts[i]: buckets[order.index(i)] for i in range(24)}
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return {
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"placements": placements,
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"likert_axes": {a: ((seed >> (j*3)) % 7) + 1 for j, a in enumerate(
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["ax_pres_extr","ax_loc_eu","ax_sci_trad",
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"ax_ind_col","ax_short_long","ax_mkt_reg"])},
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}
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# Scenario: S1..S4 × 4 dims
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if all(s in usr_msg for s in ("S1:", "S2:", "S3:", "S4:")):
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return {"ratings": {sid: {
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"desirability": ((seed >> (i*3)) % 7) + 1,
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"plausibility": ((seed >> (i*3+1)) % 7) + 1,
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"impact_on_my_group": ((seed >> (i*3+2)) % 7) + 1,
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"fairness": ((seed >> (i*3+4)) % 7) + 1,
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"if_woke_up_response": f"act-{sid}-{h[:4]}",
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} for i, sid in enumerate(["S1","S2","S3","S4"])}}
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# Delphi R1: q1..q4 free text
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if "q1" in usr_msg and "q2" in usr_msg and "Bewerten" not in usr_msg and "Sie sehen" not in usr_msg:
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return {"answers": {qid: f"stub-themes-{qid}-{h[:4]}" for qid in ("q1","q2","q3","q4")}}
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# Delphi theme extraction (no in-character system prompt)
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if "extract distinct thematic codes" in sys_msg:
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return {"themes": [{"theme_id": f"theme_{i}", "label": f"Thema {i}"} for i in range(5)]}
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# Delphi R2 (rate) or R3 (revise)
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if "Bewerten Sie jedes Thema" in usr_msg or "Sie sehen unten" in usr_msg \
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or "Rate each theme" in usr_msg or "Below are the anonymised" in usr_msg:
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theme_ids = [f"theme_{i}" for i in range(5)]
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out = {"ratings": {tid: {"importance": ((seed >> (i*2)) % 5) + 1,
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"plausibility": ((seed >> (i*2+1)) % 5) + 1}
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for i, tid in enumerate(theme_ids)}}
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if "Sie sehen unten" in usr_msg or "Below are the anonymised" in usr_msg:
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out["justification"] = "stub-revision"
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return out
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# Fallback
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return {"stub_key": h[:12], "value": rng}
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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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from app.config import Config
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if getattr(Config, "LLM_STUB_MODE", False):
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return self._stub_response(messages)
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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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if response_format:
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kwargs["response_format"] = response_format
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response = self.client.chat.completions.create(**kwargs)
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content = response.choices[0].message.content
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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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from app.config import Config
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if getattr(Config, "LLM_STUB_MODE", False):
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return self._stub_response_json(messages)
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response = self.chat(
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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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)
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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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