修复服务器bug

AIGC/AICore.py

@@ -1,21 +1,47 @@
+import logging
+from typing import Tuple
 import requests
 from ollama import Client, ResponseError
 import tiktoken
+import random
+from Utils.AIGCLog import AIGCLog
 
 class AICore:
     modelMaxTokens = 128000
     # 初始化 DeepSeek 使用的 Tokenizer (cl100k_base)
     encoder = tiktoken.get_encoding("cl100k_base")
+    logger = AIGCLog(name = "AIGC", log_file = "aigc.log")
 
-     
 
     def __init__(self, model):
          #初始化ollama客户端
         self.ollamaClient = Client(host='http://localhost:11434', headers={'x-some-header': 'some-value'})
+        self.modelName = model
         response = self.ollamaClient.show(model)
         modelMaxTokens = response.modelinfo['qwen2.context_length']
         
     def getPromptToken(self, prompt)-> int: 
         tokens = self.encoder.encode(prompt)
         return len(tokens)
-   
+    
+    def generateAI(self, promptStr: str) -> Tuple[bool, str]:
+        try:
+            response = self.ollamaClient.generate(
+                model = self.modelName,
+                stream = False,
+                prompt = promptStr,
+                options={
+                    "temperature": random.uniform(1.0, 1.5),
+                    "repeat_penalty": 1.2,  # 抑制重复
+                    "top_p": random.uniform(0.7, 0.95),
+                    "num_ctx": 4096,           # 上下文长度
+                }
+            )
+            return True, response.response
+        except ResponseError as e:
+            if e.status_code == 503:
+                print("🔄 服务不可用，5秒后重试...")    
+            return False,"ollama 服务不可用"
+        except Exception as e:
+            print(f"🔥 未预料错误: {str(e)}")
+            return False, "未预料错误"

AIGC/DatabaseHandle.py

@@ -103,17 +103,18 @@ class DatabaseHandle:
             conn.commit()
             return cursor.lastrowid
         
-    def get_chats_by_character_id(self, character_id: int) -> list:
+    def get_chats_by_character_id(self, character_id: str) -> list:
         """
         根据角色ID查询聊天记录（target_id为空时返回全部数据）
         :param target_id: 目标角色ID（None时返回全部记录）
         :return: 聊天记录字典列表
         """
+    
+        sorted_ids = sorted(character_id.split(","), key=int)  # 按数值升序
+        normalized_param = ",".join(sorted_ids)
         with self._get_connection() as conn:
             cursor = conn.cursor()
-            sql = "SELECT * FROM chat_records WHERE ',' || character_ids || ',' LIKE '%,' || ? || ',%'"
+            sql = "SELECT * FROM chat_records WHERE character_ids = ?"
-            params = (str(character_id))
+            cursor.execute(sql, (normalized_param,))
-            cursor.execute(sql, params)
-            # 转换结果为字典列表
             columns = [col[0] for col in cursor.description]
             return [dict(zip(columns, row)) for row in cursor.fetchall()]

AIGC/StartCommand.bat

@@ -2,7 +2,7 @@
 chcp 65001 > nul
 set OLLAMA_MODEL=deepseek-r1:7b
 rem 启动Ollama服务
-start "Ollama DeepSeek" cmd /k ollama run %OLLAMA_MODEL%
+start "Ollama DeepSeek" cmd /k ollama serve
 
 rem 检测11434端口是否就绪
 echo 等待Ollama服务启动...

AIGC/main.py

@@ -26,11 +26,15 @@ parser.add_argument('--model', type=str, default='deepseek-r1:1.5b',
 args = parser.parse_args()
 logger.log(logging.INFO, f"使用的模型是 {args.model}")
 
-maxAIRegerateCount = 5
+maxAIRegerateCount = 5 #最大重新生成次数
+regenerateCount = 1 #当前重新生成次数
+totalAIGenerateCount = 1 #客户端生成AI响应总数
+currentGenerateCount = 0 #当前生成次数
 lastPrompt = ""
-
+character_id1 = 0
+character_id2 = 0
 aicore = AICore(args.model)
-
+database = DatabaseHandle()
 
 async def heartbeat(websocket: WebSocket):
     pass
@@ -48,6 +52,18 @@ async def senddata(websocket: WebSocket,  protocol: dict):
         json_string = json.dumps(protocol, ensure_ascii=False)
         await websocket.send_text(json_string)
 
+async def sendprotocol(websocket: WebSocket,  cmd: str, status: int, message: str, data: str):
+    # 将AI响应发送回UE5
+    protocol = {}
+    protocol["cmd"] = cmd
+    protocol["status"] = status
+    protocol["message"] = message
+    protocol["data"] = data
+
+    if websocket.client_state == WebSocketState.CONNECTED:
+        json_string = json.dumps(protocol, ensure_ascii=False)
+        await websocket.send_text(json_string)
+
 # WebSocket路由处理
 @app.websocket("/ws/{client_id}")
 async def websocket_endpoint(websocket: WebSocket, client_id: str):
@@ -62,20 +78,7 @@ async def websocket_endpoint(websocket: WebSocket, client_id: str):
             data = await websocket.receive_text()
             logger.log(logging.INFO, f"收到UE5消息 [{client_id}]: {data}")
             await process_protocol_json(data, websocket)
-
+                         
-            # success, prompt = process_prompt(data)
-            # global lastPrompt
-            # lastPrompt = prompt
-            # # 调用AI生成响应
-            # if(success):
-            #     asyncio.create_task(generateAIChat(prompt, websocket))
-            #     await senddata(websocket, 0, [])
-            # else:
-            #     await senddata(websocket, -1, [])
-           
-            
-           
-    
     except WebSocketDisconnect:
         #manager.disconnect(client_id)
         logger.log(logging.WARNING, f"UE5客户端主动断开 [{client_id}]")
@@ -95,6 +98,72 @@ async def handle_characterlist(client: WebSocket):
     protocol["data"] = json.dumps(characterforUE)
     await senddata(client, protocol)
 
+async def generate_aichat(promptStr: str, client: WebSocket| None = None):
+    dynamic_token = str(int(time.time() % 1000)) 
+    promptStr = f""" 
+        [动态标识码:{dynamic_token}]
+    """ + promptStr
+    logger.log(logging.INFO, "prompt:" + promptStr)
+    starttime = time.time()
+    success, response, = aicore.generateAI(promptStr)
+    if(success):
+        logger.log(logging.INFO, "接口调用耗时 :" + str(time.time() - starttime))
+        logger.log(logging.INFO, "AI生成" + response)
+        #处理ai输出内容
+        think_remove_text = re.sub(r'<think>.*?</think>', '', response, flags=re.DOTALL)
+        pattern = r".*<format>(.*?)</format>"  # .* 吞掉前面所有字符，定位最后一组
+        match = re.search(pattern, think_remove_text, re.DOTALL)
+        if not match:
+            #生成内容格式错误
+            if await reGenerateAIChat(lastPrompt, client):
+                pass
+            else:
+                #超过重新生成次数
+                logger.log(logging.ERROR, "请更换prompt,或者升级模型大小")
+                await sendprotocol(client, "AiChatGenerate", 0, "请更换prompt,或者升级模型大小", "")
+        else:
+            #生成内容格式正确
+            core_dialog = match.group(1).strip()
+            dialog_lines = [line.strip() for line in core_dialog.split('\n') if line.strip()]
+            if len(dialog_lines) != 4:
+                #生成内容格式错误
+                if await reGenerateAIChat(lastPrompt, client):
+                    pass
+                else:
+                    logger.log(logging.ERROR, "请更换prompt，或者升级模型大小")
+                await sendprotocol(client, "AiChatGenerate", 0, "请更换prompt，或者升级模型大小", "")
+            else:
+                logger.log(logging.INFO, "AI的输出正确：\n" + core_dialog)
+                global regenerateCount
+                regenerateCount = 0
+                #保存数据到数据库
+                database.add_chat({"character_ids":f"{character_id1},{character_id2}","chat":f"{" ".join(dialog_lines)}"})
+
+                await sendprotocol(client, "AiChatGenerate", 1, "AI生成成功", "|".join(dialog_lines))
+    else:
+        await sendprotocol(client, "AiChatGenerate", -1, "调用ollama服务失败", "")
+    
+async def handle_aichat_generate(client: WebSocket, aichat_data:str):
+    ### 处理ai prompt###
+    success, prompt = process_prompt(aichat_data)
+    global lastPrompt
+    lastPrompt = prompt
+    
+    # 调用AI生成响应
+    if(success):
+        #asyncio.create_task(generateAIChat(prompt, client))
+        global currentGenerateCount
+        while currentGenerateCount < totalAIGenerateCount:
+            currentGenerateCount += 1
+            await generate_aichat(prompt, client)
+        
+        currentGenerateCount = 0
+        #全部生成完成
+        await sendprotocol(client, "AiChatGenerate", 2, "AI生成成功", "")
+    else:
+        #prompt生成失败
+        await sendprotocol(client, "AiChatGenerate", -1, "prompt convert failed", "")
+
 async def handle_addcharacter(client: WebSocket, chracterJson: str):
     ### 添加角色到数据库 ###
     character_info = json.loads(chracterJson)
@@ -113,6 +182,8 @@ async def process_protocol_json(json_str: str, client: WebSocket):
             await handle_characterlist(client)
         elif cmd == "AddCharacter":
             await handle_addcharacter(client, data)
+        elif cmd == "AiChatGenerate":
+            await handle_aichat_generate(client, data)
 
     except json.JSONDecodeError as e:
         print(f"JSON解析错误: {e}")
@@ -121,42 +192,65 @@ async def process_protocol_json(json_str: str, client: WebSocket):
 def process_prompt(promptFromUE: str) -> Tuple[bool, str]:
     try:
         data = json.loads(promptFromUE)
+        global maxAIRegerateCount
         # 提取数据
-        dialog_scene = data["dialogContent"]["dialogScene"]
+        dialog_scene = data["dialogScene"]
-        persons = data["persons"]
+        global totalAIGenerateCount
+        totalAIGenerateCount = data["generateCount"]
+        persons = data["characterName"]
         
         assert len(persons) == 2
-        for person in persons:
+        characterInfo1 = database.get_character_byname(persons[0])
-            print(f"  姓名: {person['name']}, 职业: {person['job']}")
+        characterInfo2 = database.get_character_byname(persons[1])
-    
+        global character_id1, character_id2
+        character_id1 = characterInfo1[0]["id"]
+        character_id2 = characterInfo2[0]["id"]
+        chat_history = database.get_chats_by_character_id(str(character_id1) +  "," + str(character_id2))
+        #整理对话记录
+        result = result = '\n'.join([item['chat'] for item in chat_history])
+      
+
         prompt = f"""
-                你是一个游戏NPC对话生成器。请严格按以下要求生成两个路人NPC（{persons[0]["name"]}和{persons[1]["name"]}）的日常对话：
+            #你是一个游戏NPC对话生成器。请严格按以下要求生成两个角色的日常对话
-                1. 生成【2轮完整对话】，每轮包含双方各一次发言（共4句）
+            #对话的背景
-                2. 对话场景：{dialog_scene}
+            {dialog_scene}
-                3. 角色设定：
+            1. 生成【2轮完整对话】，每轮包含双方各一次发言（共4句）
-                {persons[0]["name"]}：{persons[0]["job"]}
+            2.角色设定
-                {persons[1]["name"]}：{persons[1]["job"]}
+             {characterInfo1[0]["name"]}: {{
-                4. 对话要求：
+                "姓名": {characterInfo1[0]["name"]},
-                * 每轮对话需自然衔接，体现生活细节
+                "年龄": {characterInfo1[0]["age"]},
-                * 避免任务指引或玩家交互内容
+                "性格": {characterInfo1[0]["personality"]},
-                * 结尾保持对话未完成感
+                "职业": {characterInfo1[0]["profession"]},
-                5. 输出格式：
+                "背景": {characterInfo1[0]["characterBackground"]},
-                <format>
+                "语言风格": {characterInfo1[0]["chat_style"]}
-                {persons[0]["name"]}：[第一轮发言] 
+            }},
-                {persons[1]["name"]}：[第一轮回应] 
+             {characterInfo2[0]["name"]}: {{
-                {persons[0]["name"]}：[第二轮发言] 
+                "姓名": {characterInfo2[0]["name"]},
-                {persons[1]["name"]}：[第二轮回应]
+                "年龄": {characterInfo2[0]["age"]},
-                </format>
+                "性格": {characterInfo2[0]["personality"]},
-                
+                "职业": {characterInfo2[0]["profession"]},
-                6.重要！若未按此格式输出，请重新生成直至完全符合
+                "背景": {characterInfo2[0]["characterBackground"]},
+                "语言风格": {characterInfo2[0]["chat_style"]}
+            }}
+            3.参考的历史对话内容
+            {result}
+            4.输出格式：
+            <format>
+            张三：[第一轮发言] 
+            李明：[第一轮回应] 
+            张三：[第二轮发言] 
+            李明：[第二轮回应]
+            </format>
+            5.重要！若未按此格式输出，请重新生成直至完全符合
         """
         return True, prompt
     
     except json.JSONDecodeError as e:
         print(f"JSON解析错误: {e}")
         return False, ""
-    except KeyError as e:
+    except Exception as e:
-        print(f"缺少必要字段: {e}")
+        print(f"发生错误：{type(e).__name__} - {e}") 
+        return False, ""
         
     
 
@@ -173,91 +267,15 @@ def run_webserver():
         log_level="info"
     )
 
-async def generateAIChat(promptStr: str, websocket: WebSocket| None = None):
-    #动态标识吗 防止重复输入导致的结果重复
-    dynamic_token = str(int(time.time() % 1000)) 
-    promptStr = f""" 
-        [动态标识码:{dynamic_token}]
-    """ + promptStr
-    logger.log(logging.INFO, "prompt:" + promptStr)
-    starttime = time.time()
-    receivemessage=[
-            {"role": "system", "content": promptStr}
-        ]
-    try:
-
-        # response = ollamaClient.chat(
-        #     model = args.model,
-        #     stream = False,
-        #     messages = receivemessage,
-        #     options={
-        #         "temperature": random.uniform(1.0, 1.5),
-        #         "repeat_penalty": 1.2,  # 抑制重复
-        #         "top_p": random.uniform(0.7, 0.95),
-        #         "num_ctx": 4096,           # 上下文长度
-        #         "seed": int(time.time() * 1000) % 1000000 
-        #     }
-        # )
-        response = ollamaClient.generate(
-            model = args.model,
-            stream = False,
-            prompt = promptStr,
-            options={
-                "temperature": random.uniform(1.0, 1.5),
-                "repeat_penalty": 1.2,  # 抑制重复
-                "top_p": random.uniform(0.7, 0.95),
-                "num_ctx": 4096,           # 上下文长度
-            }
-        )
-
-    except ResponseError as e:
-        if e.status_code == 503:
-            print("🔄 服务不可用，5秒后重试...")    
-        return await senddata(websocket, -1, messages=["ollama 服务不可用"])
-    except Exception as e:
-        print(f"🔥 未预料错误: {str(e)}")
-        return await senddata(websocket, -1, messages=["未预料错误"])
-    logger.log(logging.INFO, "接口调用耗时 :" + str(time.time() - starttime))
-    #aiResponse = response['message']['content']
-    aiResponse = response['response']
-    logger.log(logging.INFO, "AI生成" + aiResponse)
-    #处理ai输出内容
-    think_remove_text = re.sub(r'<think>.*?</think>', '', aiResponse, flags=re.DOTALL)
-    pattern = r".*<format>(.*?)</format>"  # .* 吞掉前面所有字符，定位最后一组
-    match = re.search(pattern, think_remove_text, re.DOTALL)
-    
-    if not match:
-        if await reGenerateAIChat(lastPrompt, websocket):
-            pass
-        else:
-            logger.log(logging.ERROR, "请更换prompt，或者升级模型大小")
-            await senddata(websocket, -1, messages=["请更换prompt，或者升级模型大小"])
-        
-    else:
-        core_dialog = match.group(1).strip()
-        dialog_lines = [line for line in core_dialog.split('\n') if line.strip()]
-        if len(dialog_lines) != 4:
-            if await reGenerateAIChat(lastPrompt, websocket):
-                pass
-            else:
-                logger.log(logging.ERROR, "请更换prompt，或者升级模型大小")
-                await senddata(websocket, -1, messages=["请更换prompt，或者升级模型大小"])
-        else:
-            logger.log(logging.INFO, "AI的输出正确：\n" + core_dialog)
-            global regenerateCount
-            regenerateCount = 0
-            await senddata(websocket, 1,  dialog_lines)
-
-regenerateCount = 1
 async def reGenerateAIChat(prompt: str, websocket: WebSocket):
     global regenerateCount
     if regenerateCount < maxAIRegerateCount:
         regenerateCount += 1
         logger.log(logging.ERROR, f"AI输出格式不正确，重新进行生成 {regenerateCount}/{maxAIRegerateCount}")
-        await senddata(websocket, 2, messages=["ai生成格式不正确， 重新进行生成"])
+        await sendprotocol(websocket, "AiChatGenerate", 0, "ai生成格式不正确， 重新进行生成", "")
         await asyncio.sleep(0)
-        prompt = prompt + "补充：上一次的输出格式错误，严格执行prompt中第5条的输出格式要求"
+        prompt = prompt + "补充：上一次的输出格式错误，严格执行prompt中的输出格式要求"
-        await generateAIChat(prompt, websocket)
+        await generate_aichat(prompt, websocket)
         return True
     else:
         regenerateCount = 0
@@ -272,19 +290,18 @@ if __name__ == "__main__":
 
     
     #Test database
-    database = DatabaseHandle()
+    
    
     
-    id = database.add_character({"name":"李明","age":30,"personality":"活泼健谈","profession":"产品经理"
+    # id = database.add_character({"name":"李明","age":30,"personality":"活泼健谈","profession":"产品经理"
-                            ,"characterBackground":"公司资深产品经理","chat_style":"热情"})
+    #                         ,"characterBackground":"公司资深产品经理","chat_style":"热情"})
     
-    characters = database.get_character_byname("")
+    #characters = database.get_character_byname("")
     #chat_id = database.add_chat({"character_ids":"1,2","chat":"张三：[第一轮发言] 李明：[第一轮回应] 张三：[第二轮发言] 李明：[第二轮回应"})
-    chat = database.get_chats_by_character_id(3)
+    #chat = database.get_chats_by_character_id(3)
-    if id == 0:
+    #
-        logger.log(logging.ERROR, f"角色 张三已经添加到数据库")
     # Test AI
-    aicore.getPromptToken("测试功能")
+    #aicore.getPromptToken("测试功能")
     # asyncio.run(
     #     generateAIChat(promptStr = f"""
     #         #你是一个游戏NPC对话生成器。请严格按以下要求生成两个角色的日常对话