288 lines
11 KiB
Python
288 lines
11 KiB
Python
from __future__ import annotations
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import asyncio
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import json
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import logging
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import re
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import time
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from typing import Any
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from openai import AsyncOpenAI
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logger = logging.getLogger(__name__)
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class LLMClient:
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def __init__(self, api_key: str, base_url: str, model: str, max_concurrent: int = 10):
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self.client = AsyncOpenAI(api_key=api_key, base_url=base_url)
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self.model = model
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self._is_minimax = "minimax" in (base_url or "").lower()
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self.semaphore = asyncio.Semaphore(max_concurrent)
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self.total_tokens = 0
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self.total_calls = 0
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self._lock = asyncio.Lock()
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# Per-purpose call stats — useful when a sim stalls and you need to
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# know which LLM call category is slow. Reset by the engine at the
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# start of each sim if you want per-sim stats.
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self._purpose_stats: dict[str, dict[str, float]] = {}
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async def _track(self, usage):
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if usage:
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async with self._lock:
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self.total_tokens += usage.total_tokens or 0
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self.total_calls += 1
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async def _record_purpose(self, purpose: str | None, elapsed: float, ok: bool):
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if not purpose:
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return
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async with self._lock:
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st = self._purpose_stats.setdefault(
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purpose, {"calls": 0, "errors": 0, "total_ms": 0.0}
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)
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st["calls"] += 1
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st["total_ms"] += elapsed * 1000
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if not ok:
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st["errors"] += 1
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async def generate(
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self,
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system: str,
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user: str,
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json_mode: bool = False,
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max_tokens: int = 4096,
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retries: int = 3,
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purpose: str | None = None,
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) -> str:
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kwargs: dict = {
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"model": self.model,
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"messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": user},
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],
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"temperature": 0.5 if json_mode else 1.0,
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}
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if self._is_minimax:
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# MiniMax M3 enables thinking by default. In structured-output
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# calls that can consume the completion budget before a visible
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# message.content is produced. MiniMax-specific fields must be
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# sent through extra_body when using the OpenAI Python SDK.
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kwargs["max_completion_tokens"] = max_tokens
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kwargs["extra_body"] = {"thinking": {"type": "disabled"}}
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else:
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kwargs["max_tokens"] = max_tokens
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if json_mode and not self._is_minimax:
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# MiniMax's newer M-series models do not reliably implement the
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# OpenAI json_object response_format. They still receive the
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# explicit JSON schema in the prompt and are parsed defensively.
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kwargs["response_format"] = {"type": "json_object"}
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t0 = time.monotonic()
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for attempt in range(retries):
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try:
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async with self.semaphore:
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response = await self.client.chat.completions.create(**kwargs)
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await self._track(response.usage)
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if not response.choices:
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logger.warning(
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"LLM 返回空 choices（用途=%s，响应ID=%s，模型=%s）",
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purpose or "-", getattr(response, "id", "-"), self.model,
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)
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content = ""
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finish = "no_choices"
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message = None
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else:
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choice = response.choices[0]
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message = choice.message
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content = getattr(message, "content", None) or ""
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finish = getattr(choice, "finish_reason", "unknown")
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# A few OpenAI-compatible providers return content parts
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# instead of a plain string. Normalize text parts here.
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if isinstance(content, list):
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content = "".join(
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part.get("text", "") if isinstance(part, dict)
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else getattr(part, "text", "") or ""
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for part in content
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)
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if not content:
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reasoning = getattr(message, "reasoning_content", None) if message else None
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reasoning_details = getattr(message, "reasoning_details", None) if message else None
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refusal = getattr(message, "refusal", None) if message else None
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usage = getattr(response, "usage", None)
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logger.warning(
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"LLM 返回空内容（用途=%s，结束原因=%s，完成令牌=%s，推理内容长度=%s，推理详情=%s，拒答=%s）",
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purpose or "-",
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finish,
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getattr(usage, "completion_tokens", "?"),
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len(reasoning or ""),
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bool(reasoning_details),
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bool(refusal),
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)
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if attempt < retries - 1:
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wait = 2 ** attempt
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logger.warning(
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"LLM 返回空内容，将在%d秒后重试（用途=%s，第%d/%d次）",
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wait, purpose or "-", attempt + 1, retries,
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)
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await asyncio.sleep(wait)
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continue
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await self._record_purpose(purpose, time.monotonic() - t0, ok=False)
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return ""
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await self._record_purpose(purpose, time.monotonic() - t0, ok=True)
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if purpose:
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logger.debug(
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"LLM 调用成功（用途=%s，令牌数=%s，耗时=%.2f秒）",
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purpose,
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getattr(response.usage, "total_tokens", "?"),
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time.monotonic() - t0,
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)
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return content.strip()
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except Exception as e:
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if attempt == retries - 1:
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await self._record_purpose(purpose, time.monotonic() - t0, ok=False)
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logger.error(
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"LLM 调用重试 %d 次后仍失败（用途=%s，耗时=%.2f秒）：%s",
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retries, purpose or "-", time.monotonic() - t0, e,
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)
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raise
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wait = 2 ** attempt
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logger.warning(
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"LLM 调用失败（用途=%s，第%d次尝试，%d秒后重试，已耗时=%.2f秒）：%s",
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purpose or "-", attempt + 1, wait, time.monotonic() - t0, e,
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)
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await asyncio.sleep(wait)
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return ""
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async def generate_batch(
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self,
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prompts: list[tuple[str, str]],
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json_mode: bool = False,
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max_tokens: int = 1000,
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purpose: str | None = None,
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sub_purpose: str | None = None,
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) -> list[str]:
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# `purpose` is the batch label (e.g. "decision"); `sub_purpose` lets
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# us split stats by an inner dimension (e.g. "active/react").
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tasks = [
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self.generate(
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system, user,
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json_mode=json_mode, max_tokens=max_tokens,
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purpose=sub_purpose or purpose,
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)
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for system, user in prompts
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]
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results = await asyncio.gather(*tasks, return_exceptions=True)
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return [r if isinstance(r, str) else "" for r in results]
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async def generate_light(self, system: str, user: str, max_tokens: int = 100) -> str:
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return await self.generate(system, user, json_mode=False, max_tokens=max_tokens)
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def get_stats(self) -> dict:
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return {
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"total_tokens": self.total_tokens,
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"total_calls": self.total_calls,
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"by_purpose": dict(self._purpose_stats),
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}
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def parse_json(text: str, purpose: str | None = None) -> Any:
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"""Parse a model response that was requested as JSON.
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Providers occasionally return prose, truncated JSON, or a markdown code
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block even when ``response_format=json_object`` was requested. Keep the
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permissive recovery logic, but make a final failure actionable: include
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the call purpose, response length, and a bounded raw preview in the
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simulation log.
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"""
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text = (text or "").strip()
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label = purpose or "未标注用途"
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if not text:
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logger.error("JSON 解析失败（用途=%s）：LLM 返回内容为空", label)
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return {}
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# Strip modern LLM "thinking" blocks (Anthropic, DeepSeek, etc.) that may
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# appear either before or around the JSON payload.
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text = re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL)
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# Markdown code fence: ```json ... ``` or ``` ... ```
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if text.startswith("```"):
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match = re.search(r"```(?:json)?\s*\n?(.*?)```", text, re.DOTALL)
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if match:
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text = match.group(1).strip()
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# Try the whole text first.
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try:
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return json.loads(text)
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except json.JSONDecodeError:
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pass
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# Otherwise find the first balanced { ... } or [ ... ] substring that parses.
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def _balanced_at(start: int) -> int | None:
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open_ch, other_ch = ("{", "}") if text[start] == "{" else (("[", "]") if text[start] == "[" else (None, None))
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if open_ch is None:
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return None
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depth = 0
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in_str = False
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esc = False
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for j in range(start, len(text)):
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c = text[j]
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if in_str:
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if esc:
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esc = False
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elif c == "\\":
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esc = True
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elif c == '"':
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in_str = False
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continue
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if c == '"':
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in_str = True
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elif c == open_ch:
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depth += 1
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elif c == other_ch:
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depth -= 1
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if depth == 0:
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return j
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return None
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for opener in ("{", "["):
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i = 0
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while True:
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idx = text.find(opener, i)
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if idx == -1:
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break
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end = _balanced_at(idx)
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if end is not None:
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try:
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return json.loads(text[idx : end + 1])
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except json.JSONDecodeError:
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pass
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i = idx + 1
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# Last-ditch: trim from the end while we can find a parseable prefix.
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for i in range(len(text) - 1, -1, -1):
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if text[i] in "]}":
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try:
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return json.loads(text[: i + 1])
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except json.JSONDecodeError:
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continue
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open_braces = text.count("{") - text.count("}")
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open_brackets = text.count("[") - text.count("]")
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repaired = text + ("}" * max(0, open_braces)) + ("]" * max(0, open_brackets))
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try:
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return json.loads(repaired)
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except json.JSONDecodeError:
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pass
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preview = text[:1000].replace("\r", "\\r").replace("\n", "\\n")
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logger.error(
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"JSON 解析失败（用途=%s，返回长度=%d）：内容不是有效 JSON 或已被截断；原始返回片段=%s%s",
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label,
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len(text),
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preview,
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"……" if len(text) > 1000 else "",
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)
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return {}
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