remove rule ai

src/classes/ai.py

@@ -1,19 +1,15 @@
 """
-NPC AI的类。
-这里指的不是LLM或者Machine Learning，而是NPC的决策机制
-分为两类：规则AI和LLM AI
+NPC AI 的类。
+这里指的是 NPC 的决策机制。
 """
 from __future__ import annotations
 from abc import ABC, abstractmethod
 from typing import TYPE_CHECKING
-import random
 
 from src.classes.world import World
-from src.classes.region import Region
-from src.classes.root import get_essence_types_for_root
 from src.classes.event import Event, NULL_EVENT
 from src.utils.llm import get_ai_prompt_and_call_llm_async
-from src.classes.typings import ACTION_NAME, ACTION_PARAMS, ACTION_PAIR, ACTION_NAME_PARAMS_PAIRS
+from src.classes.typings import ACTION_NAME_PARAMS_PAIRS
 from src.utils.config import CONFIG
 from src.classes.actions import ACTION_INFOS_STR
 
@@ -34,8 +30,8 @@ class AI(ABC):
     async def decide(self, world: World, avatars_to_decide: list[Avatar]) -> dict[Avatar, tuple[ACTION_NAME_PARAMS_PAIRS, str, str, Event]]:
         """
         决定做什么，同时生成对应的事件。
-        一个ai支持批量生成多个avatar的动作。
-        这对LLM AI节省时间和token非常有意义。
+        一个 AI 支持批量生成多个 avatar 的动作。
+        这对 LLM AI 节省时间和 token 非常有意义。
         """
         results = {}
         max_decide_num = CONFIG.ai.max_decide_num
@@ -43,68 +39,12 @@ class AI(ABC):
             results.update(await self._decide(world, avatars_to_decide[i:i+max_decide_num]))
 
         for avatar, result in list(results.items()):
-            # 兼容：RuleAI 返回单动作，LLMAI 返回动作链
-            if result and isinstance(result[0], list):
-                action_name_params_pairs, avatar_thinking, objective = result  # type: ignore
-            else:
-                action_name, action_params, avatar_thinking, objective = result  # type: ignore
-                action_name_params_pairs = [(action_name, action_params)]
+            action_name_params_pairs, avatar_thinking, objective = result  # type: ignore
             # 不在决策阶段生成开始事件，提交阶段统一触发
             results[avatar] = (action_name_params_pairs, avatar_thinking, objective, NULL_EVENT)
 
         return results
 
-class RuleAI(AI):
-    """
-    规则AI（批量接口，内部逐个决策）
-    """
-
-    def __decide(self, world: World, avatar: "Avatar", regions: list[Region]) -> tuple[ACTION_NAME, ACTION_PARAMS, str, str]:
-        """
-        单个 Avatar 的决策逻辑。
-        先做一个简单的：
-        1. 找到自己灵根对应的最好的区域
-        2. 检测自己是否在最好的区域
-        3. 如果不在，则移动到最好的区域
-        4. 如果已经到达最好的区域，则进行修炼
-        5. 如果需要突破境界了，则突破境界
-        """
-        if random.random() < 0.1:
-            return ("Play", {}, "", "放松一下，缓解修行压力")
-
-        best_region = self.get_best_region_for_avatar(avatar, regions)
-
-        if avatar.is_in_region(best_region):
-            if avatar.cultivation_progress.can_break_through():
-                return ("Breakthrough", {}, "", "尽快突破到更高境界")
-            else:
-                return ("Cultivate", {}, "", "稳步提升修为")
-        else:
-            return ("MoveToRegion", {"region": best_region.name}, "", f"前往{best_region.name}修行")
-
-    async def _decide(self, world: World, avatars_to_decide: list[Avatar]) -> dict[Avatar, tuple[ACTION_NAME, ACTION_PARAMS, str, str]]:
-        """
-        决策逻辑：批量接口的实现上，逐个 Avatar 调用 __decide 进行独立决策，
-        以保持规则AI的可控性与可测试性。
-        """
-        results: dict[Avatar, tuple[ACTION_NAME, ACTION_PARAMS, str, str]] = {}
-        regions: list[Region] = list(world.map.regions.values())
-
-        for avatar in avatars_to_decide:
-            results[avatar] = self.__decide(world, avatar, regions)
-
-        return results
-
-    def get_best_region_for_avatar(self, avatar: "Avatar", regions: list[Region]) -> Region:
-        """
-        根据avatar的灵根找到最适合的区域
-        """
-        essence_types = get_essence_types_for_root(avatar.root)
-        def best_density(region: Region) -> int:
-            return max((region.essence.get_density(et) for et in essence_types), default=0)
-        region_with_best_essence = max(regions, key=best_density)
-        return region_with_best_essence
-
 class LLMAI(AI):
     """
     LLM AI
@@ -154,5 +94,4 @@ class LLMAI(AI):
             results[avatar] = (pairs, avatar_thinking, objective)
         return results
 
-llm_ai = LLMAI()
-rule_ai = RuleAI()
+llm_ai = LLMAI()

src/sim/simulator.py

@@ -7,7 +7,7 @@ from src.classes.age import Age
 from src.classes.cultivation import Realm
 from src.classes.world import World
 from src.classes.event import Event, is_null_event
-from src.classes.ai import llm_ai, rule_ai
+from src.classes.ai import llm_ai
 from src.utils.names import get_random_name
 from src.utils.config import CONFIG
 from src.run.log import get_logger
@@ -29,10 +29,7 @@ class Simulator:
                 avatars_to_decide.append(avatar)
         if not avatars_to_decide:
             return
-        if CONFIG.ai.mode == "llm":
-            ai = llm_ai
-        else:
-            ai = rule_ai
+        ai = llm_ai
         decide_results = await ai.decide(self.world, avatars_to_decide)
         for avatar, result in decide_results.items():
             action_name_params_pairs, avatar_thinking, objective, _event = result

static/config.yml

@@ -1,9 +1,9 @@
 llm:
   # 填入litellm支持的model name和key
-  model_name: "your-model-name"
-  fast_model_name: "your-fast-model-name"
-  key: "your-api-key"
-  base_url: "your-base-url-of-llm"
+  key: "your-api-key" # 目前需要的是阿里的qwen api
+  model_name: "openai/qwen-plus"
+  fast_model_name: "openai/qwen-flash"
+  base_url: "https://dashscope.aliyuncs.com/compatible-mode/v1"
   max_parse_retries: 3
 
 paths:
@@ -11,7 +11,6 @@ paths:
   game_configs: static/game_configs/
 
 ai:
-  mode: "llm" # "rule" or "llm"
   max_decide_num: 4
 
 game: