故事引擎接入模糊搜索，提供缺省帮助

OlivOS-Team · Jan 4, 2024 · 077deb0 · 077deb0
1 parent 02f83ff
commit 077deb0
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Showing 2 changed files with 67 additions and 11 deletions.
diff --git a/OlivaStoryCore/msgCustom.py b/OlivaStoryCore/msgCustom.py
@@ -28,7 +28,8 @@
     'strStoryCoreStoryTall': '{tStoryCoreResult}\n\n{tStoryCoreSelection}',
     'strStoryCoreStoryTallNone': '故事不存在',
     'strStoryCoreStoryTallBreak': '故事中断了',
-    'strStoryCoreStoryTallEnd': '故事结束了'
+    'strStoryCoreStoryTallEnd': '故事结束了',
+    'strStoryCoreStoryRecommend': '未找到故事，您可能想要的是：\n{tStoryCoreRecommend}'
 }
 
 dictStrConst = {}
@@ -37,9 +38,27 @@
 
 dictTValue = {
     'tStoryCoreResult': 'N/A',
-    'tStoryCoreSelection': 'N/A'
+    'tStoryCoreSelection': 'N/A',
+    'tStoryCoreRecommend': 'N/A'
 }
 
-dictHelpDocTemp = {}
+dictHelpDocTemp = {
+    'story': '''故事引擎模块:
+[.story [故事名称]]    开启对应的故事
+[.story end]    结束故事''',
+
+    'OlivaStoryCore': '''[OlivaStoryCore]
+OlivaStory核心模块
+本模块为青果跑团掷骰机器人(OlivaDice)的故事引擎模块，新一代文游引擎，它的设计初衷是为了让骰主能够更加方便地进行文游的设计，以文游的方式进行带团。
+核心开发者: lunzhiPenxil仑质
+[.help OlivaStoryCore更新] 查看本模块更新日志
+注: 本模块为可选模块。''',
+
+    'OlivaStoryCore更新': '''[OlivaStoryCore]
+3.0.1: 优化体验
+3.0.0: 初始化项目''',
+
+    '故事引擎': '&story',
+}
 
 dictUserConfigNoteDefault = {}
diff --git a/OlivaStoryCore/msgReply.py b/OlivaStoryCore/msgReply.py
@@ -249,14 +249,29 @@ def unity_reply(plugin_event, Proc):
                     storyName = tmp_story_name,
                     chatToken = tmp_chat_token
                 )
-                tmp_reply_str = getStoryTall(
-                    plugin_event = plugin_event,
-                    dictStrCustom = dictStrCustom,
-                    dictTValue = dictTValue,
-                    nodeData = tmp_nodeData,
-                    flagIsStart = True,
-                    noteList = tmp_noteList
-                )
+                if tmp_nodeData is not None:
+                    tmp_reply_str = getStoryTall(
+                        plugin_event = plugin_event,
+                        dictStrCustom = dictStrCustom,
+                        dictTValue = dictTValue,
+                        nodeData = tmp_nodeData,
+                        flagIsStart = True,
+                        noteList = tmp_noteList
+                    )
+                else:
+                    tmp_reply_str = None
+                    searchList = getStorySearchList(tmp_story_name, tmp_botHash)
+                    if len(searchList) > 0:
+                        dictTValue['tStoryCoreRecommend'] = '\n'.join([f'[.story {storyName_this}]' for storyName_this in searchList])
+                        tmp_reply_str = OlivaDiceCore.msgCustomManager.formatReplySTR(dictStrCustom['strStoryCoreStoryRecommend'], dictTValue)
+                    else:
+                        tmp_reply_str = OlivaDiceCore.msgCustomManager.formatReplySTR(dictStrCustom['strStoryCoreStoryTallNone'], dictTValue)
+                    if tmp_reply_str != None:
+                        replyMsg(plugin_event, tmp_reply_str)
+            else:
+                tmp_reply_str = OlivaDiceCore.helpDoc.getHelp('story', plugin_event.bot_info.hash)
+                if tmp_reply_str != None:
+                    replyMsg(plugin_event, tmp_reply_str)
         else:
             tmp_platform = plugin_event.platform['platform']
             tmp_botHash = plugin_event.bot_info.hash
@@ -483,3 +498,25 @@ def getStoryTall(
             except Exception as e:
                 traceback.print_exc()
     return res
+
+def getStorySearchList(storyName_search:str, botHash:str):
+    res = []
+    tmp_RecommendRank_list = []
+    if botHash in OlivaStoryCore.storyEngine.storyList:
+        for storyName_this in OlivaStoryCore.storyEngine.storyList[botHash]:
+            tmp_RecommendRank_list.append([
+                OlivaDiceCore.helpDoc.getRecommendRank(
+                    storyName_search,
+                    storyName_this
+                ),
+                storyName_this
+            ])
+        tmp_RecommendRank_list.sort(key = lambda x : x[0])
+    tmp_count_max = min(8, len(tmp_RecommendRank_list))
+    count = 0
+    while count < tmp_count_max:
+        if tmp_RecommendRank_list[count][0] < 1000:
+            if len(tmp_RecommendRank_list[count][1]) < 25:
+                res.append(tmp_RecommendRank_list[count][1])
+        count += 1
+    return res