修复服务器bug
This commit is contained in:
parent
23b62b60a5
commit
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@ -1,21 +1,47 @@
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import logging
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from typing import Tuple
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import requests
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from ollama import Client, ResponseError
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import tiktoken
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import random
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from Utils.AIGCLog import AIGCLog
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class AICore:
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modelMaxTokens = 128000
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# 初始化 DeepSeek 使用的 Tokenizer (cl100k_base)
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encoder = tiktoken.get_encoding("cl100k_base")
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logger = AIGCLog(name = "AIGC", log_file = "aigc.log")
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def __init__(self, model):
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#初始化ollama客户端
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self.ollamaClient = Client(host='http://localhost:11434', headers={'x-some-header': 'some-value'})
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self.modelName = model
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response = self.ollamaClient.show(model)
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modelMaxTokens = response.modelinfo['qwen2.context_length']
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def getPromptToken(self, prompt)-> int:
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tokens = self.encoder.encode(prompt)
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return len(tokens)
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def generateAI(self, promptStr: str) -> Tuple[bool, str]:
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try:
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response = self.ollamaClient.generate(
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model = self.modelName,
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stream = False,
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prompt = promptStr,
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options={
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"temperature": random.uniform(1.0, 1.5),
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"repeat_penalty": 1.2, # 抑制重复
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"top_p": random.uniform(0.7, 0.95),
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"num_ctx": 4096, # 上下文长度
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}
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)
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return True, response.response
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except ResponseError as e:
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if e.status_code == 503:
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print("🔄 服务不可用，5秒后重试...")
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return False,"ollama 服务不可用"
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except Exception as e:
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print(f"🔥 未预料错误: {str(e)}")
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return False, "未预料错误"
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@ -103,17 +103,18 @@ class DatabaseHandle:
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conn.commit()
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return cursor.lastrowid
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def get_chats_by_character_id(self, character_id: int) -> list:
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def get_chats_by_character_id(self, character_id: str) -> list:
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"""
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根据角色ID查询聊天记录（target_id为空时返回全部数据）
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:param target_id: 目标角色ID（None时返回全部记录）
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:return: 聊天记录字典列表
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"""
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sorted_ids = sorted(character_id.split(","), key=int) # 按数值升序
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normalized_param = ",".join(sorted_ids)
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with self._get_connection() as conn:
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cursor = conn.cursor()
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sql = "SELECT * FROM chat_records WHERE ',' || character_ids || ',' LIKE '%,' || ? || ',%'"
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params = (str(character_id))
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cursor.execute(sql, params)
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# 转换结果为字典列表
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sql = "SELECT * FROM chat_records WHERE character_ids = ?"
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cursor.execute(sql, (normalized_param,))
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columns = [col[0] for col in cursor.description]
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return [dict(zip(columns, row)) for row in cursor.fetchall()]
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@ -2,7 +2,7 @@
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chcp 65001 > nul
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set OLLAMA_MODEL=deepseek-r1:7b
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rem 启动Ollama服务
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start "Ollama DeepSeek" cmd /k ollama run %OLLAMA_MODEL%
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start "Ollama DeepSeek" cmd /k ollama serve
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rem 检测11434端口是否就绪
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echo 等待Ollama服务启动...
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BIN
AIGC/data.db
BIN
AIGC/data.db
Binary file not shown.
277
AIGC/main.py
277
AIGC/main.py
@ -26,11 +26,15 @@ parser.add_argument('--model', type=str, default='deepseek-r1:1.5b',
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args = parser.parse_args()
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logger.log(logging.INFO, f"使用的模型是 {args.model}")
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maxAIRegerateCount = 5
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maxAIRegerateCount = 5 #最大重新生成次数
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regenerateCount = 1 #当前重新生成次数
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totalAIGenerateCount = 1 #客户端生成AI响应总数
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currentGenerateCount = 0 #当前生成次数
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lastPrompt = ""
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character_id1 = 0
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character_id2 = 0
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aicore = AICore(args.model)
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database = DatabaseHandle()
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async def heartbeat(websocket: WebSocket):
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pass
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@ -48,6 +52,18 @@ async def senddata(websocket: WebSocket, protocol: dict):
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json_string = json.dumps(protocol, ensure_ascii=False)
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await websocket.send_text(json_string)
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async def sendprotocol(websocket: WebSocket, cmd: str, status: int, message: str, data: str):
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# 将AI响应发送回UE5
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protocol = {}
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protocol["cmd"] = cmd
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protocol["status"] = status
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protocol["message"] = message
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protocol["data"] = data
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if websocket.client_state == WebSocketState.CONNECTED:
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json_string = json.dumps(protocol, ensure_ascii=False)
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await websocket.send_text(json_string)
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# WebSocket路由处理
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@app.websocket("/ws/{client_id}")
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async def websocket_endpoint(websocket: WebSocket, client_id: str):
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@ -62,20 +78,7 @@ async def websocket_endpoint(websocket: WebSocket, client_id: str):
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data = await websocket.receive_text()
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logger.log(logging.INFO, f"收到UE5消息 [{client_id}]: {data}")
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await process_protocol_json(data, websocket)
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# success, prompt = process_prompt(data)
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# global lastPrompt
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# lastPrompt = prompt
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# # 调用AI生成响应
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# if(success):
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# asyncio.create_task(generateAIChat(prompt, websocket))
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# await senddata(websocket, 0, [])
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# else:
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# await senddata(websocket, -1, [])
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except WebSocketDisconnect:
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#manager.disconnect(client_id)
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logger.log(logging.WARNING, f"UE5客户端主动断开 [{client_id}]")
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@ -95,6 +98,72 @@ async def handle_characterlist(client: WebSocket):
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protocol["data"] = json.dumps(characterforUE)
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await senddata(client, protocol)
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async def generate_aichat(promptStr: str, client: WebSocket| None = None):
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dynamic_token = str(int(time.time() % 1000))
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promptStr = f"""
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[动态标识码:{dynamic_token}]
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""" + promptStr
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logger.log(logging.INFO, "prompt:" + promptStr)
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starttime = time.time()
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success, response, = aicore.generateAI(promptStr)
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if(success):
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logger.log(logging.INFO, "接口调用耗时 :" + str(time.time() - starttime))
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logger.log(logging.INFO, "AI生成" + response)
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#处理ai输出内容
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think_remove_text = re.sub(r'<think>.*?</think>', '', response, flags=re.DOTALL)
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pattern = r".*<format>(.*?)</format>" # .* 吞掉前面所有字符，定位最后一组
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match = re.search(pattern, think_remove_text, re.DOTALL)
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if not match:
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#生成内容格式错误
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if await reGenerateAIChat(lastPrompt, client):
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pass
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else:
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#超过重新生成次数
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logger.log(logging.ERROR, "请更换prompt,或者升级模型大小")
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await sendprotocol(client, "AiChatGenerate", 0, "请更换prompt,或者升级模型大小", "")
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else:
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#生成内容格式正确
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core_dialog = match.group(1).strip()
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dialog_lines = [line.strip() for line in core_dialog.split('\n') if line.strip()]
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if len(dialog_lines) != 4:
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#生成内容格式错误
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if await reGenerateAIChat(lastPrompt, client):
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pass
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else:
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logger.log(logging.ERROR, "请更换prompt，或者升级模型大小")
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await sendprotocol(client, "AiChatGenerate", 0, "请更换prompt，或者升级模型大小", "")
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else:
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logger.log(logging.INFO, "AI的输出正确：\n" + core_dialog)
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global regenerateCount
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regenerateCount = 0
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#保存数据到数据库
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database.add_chat({"character_ids":f"{character_id1},{character_id2}","chat":f"{" ".join(dialog_lines)}"})
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await sendprotocol(client, "AiChatGenerate", 1, "AI生成成功", "|".join(dialog_lines))
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else:
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await sendprotocol(client, "AiChatGenerate", -1, "调用ollama服务失败", "")
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async def handle_aichat_generate(client: WebSocket, aichat_data:str):
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### 处理ai prompt###
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success, prompt = process_prompt(aichat_data)
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global lastPrompt
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lastPrompt = prompt
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# 调用AI生成响应
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if(success):
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#asyncio.create_task(generateAIChat(prompt, client))
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global currentGenerateCount
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while currentGenerateCount < totalAIGenerateCount:
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currentGenerateCount += 1
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await generate_aichat(prompt, client)
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currentGenerateCount = 0
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#全部生成完成
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await sendprotocol(client, "AiChatGenerate", 2, "AI生成成功", "")
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else:
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#prompt生成失败
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await sendprotocol(client, "AiChatGenerate", -1, "prompt convert failed", "")
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async def handle_addcharacter(client: WebSocket, chracterJson: str):
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### 添加角色到数据库 ###
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character_info = json.loads(chracterJson)
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@ -113,6 +182,8 @@ async def process_protocol_json(json_str: str, client: WebSocket):
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await handle_characterlist(client)
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elif cmd == "AddCharacter":
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await handle_addcharacter(client, data)
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elif cmd == "AiChatGenerate":
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await handle_aichat_generate(client, data)
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except json.JSONDecodeError as e:
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print(f"JSON解析错误: {e}")
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@ -121,42 +192,65 @@ async def process_protocol_json(json_str: str, client: WebSocket):
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def process_prompt(promptFromUE: str) -> Tuple[bool, str]:
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try:
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data = json.loads(promptFromUE)
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global maxAIRegerateCount
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# 提取数据
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dialog_scene = data["dialogContent"]["dialogScene"]
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persons = data["persons"]
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dialog_scene = data["dialogScene"]
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global totalAIGenerateCount
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totalAIGenerateCount = data["generateCount"]
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persons = data["characterName"]
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assert len(persons) == 2
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for person in persons:
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print(f" 姓名: {person['name']}, 职业: {person['job']}")
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characterInfo1 = database.get_character_byname(persons[0])
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characterInfo2 = database.get_character_byname(persons[1])
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global character_id1, character_id2
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character_id1 = characterInfo1[0]["id"]
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character_id2 = characterInfo2[0]["id"]
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chat_history = database.get_chats_by_character_id(str(character_id1) + "," + str(character_id2))
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#整理对话记录
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result = result = '\n'.join([item['chat'] for item in chat_history])
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prompt = f"""
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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：
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1. 生成【2轮完整对话】，每轮包含双方各一次发言（共4句）
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2. 对话场景：{dialog_scene}
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3. 角色设定：
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{persons[0]["name"]}：{persons[0]["job"]}
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{persons[1]["name"]}：{persons[1]["job"]}
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4. 对话要求：
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* 每轮对话需自然衔接，体现生活细节
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* 避免任务指引或玩家交互内容
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* 结尾保持对话未完成感
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5. 输出格式：
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<format>
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{persons[0]["name"]}：[第一轮发言]
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{persons[1]["name"]}：[第一轮回应]
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{persons[0]["name"]}：[第二轮发言]
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{persons[1]["name"]}：[第二轮回应]
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</format>
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6.重要！若未按此格式输出，请重新生成直至完全符合
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#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
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#对话的背景
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{dialog_scene}
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1. 生成【2轮完整对话】，每轮包含双方各一次发言（共4句）
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2.角色设定
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{characterInfo1[0]["name"]}: {{
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"姓名": {characterInfo1[0]["name"]},
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"年龄": {characterInfo1[0]["age"]},
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"性格": {characterInfo1[0]["personality"]},
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"职业": {characterInfo1[0]["profession"]},
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"背景": {characterInfo1[0]["characterBackground"]},
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"语言风格": {characterInfo1[0]["chat_style"]}
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}},
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{characterInfo2[0]["name"]}: {{
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"姓名": {characterInfo2[0]["name"]},
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"年龄": {characterInfo2[0]["age"]},
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"性格": {characterInfo2[0]["personality"]},
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"职业": {characterInfo2[0]["profession"]},
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"背景": {characterInfo2[0]["characterBackground"]},
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"语言风格": {characterInfo2[0]["chat_style"]}
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}}
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3.参考的历史对话内容
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{result}
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4.输出格式：
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<format>
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张三：[第一轮发言]
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李明：[第一轮回应]
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张三：[第二轮发言]
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李明：[第二轮回应]
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</format>
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5.重要！若未按此格式输出，请重新生成直至完全符合
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"""
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return True, prompt
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except json.JSONDecodeError as e:
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print(f"JSON解析错误: {e}")
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return False, ""
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except KeyError as e:
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print(f"缺少必要字段: {e}")
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except Exception as e:
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print(f"发生错误：{type(e).__name__} - {e}")
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return False, ""
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@ -173,91 +267,15 @@ def run_webserver():
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log_level="info"
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)
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async def generateAIChat(promptStr: str, websocket: WebSocket| None = None):
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#动态标识吗 防止重复输入导致的结果重复
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dynamic_token = str(int(time.time() % 1000))
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promptStr = f"""
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[动态标识码:{dynamic_token}]
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""" + promptStr
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logger.log(logging.INFO, "prompt:" + promptStr)
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starttime = time.time()
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receivemessage=[
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{"role": "system", "content": promptStr}
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]
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try:
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# response = ollamaClient.chat(
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# model = args.model,
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# stream = False,
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# messages = receivemessage,
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# options={
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# "temperature": random.uniform(1.0, 1.5),
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# "repeat_penalty": 1.2, # 抑制重复
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# "top_p": random.uniform(0.7, 0.95),
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# "num_ctx": 4096, # 上下文长度
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# "seed": int(time.time() * 1000) % 1000000
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# }
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# )
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response = ollamaClient.generate(
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model = args.model,
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stream = False,
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prompt = promptStr,
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options={
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"temperature": random.uniform(1.0, 1.5),
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"repeat_penalty": 1.2, # 抑制重复
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"top_p": random.uniform(0.7, 0.95),
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"num_ctx": 4096, # 上下文长度
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}
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)
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except ResponseError as e:
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if e.status_code == 503:
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print("🔄 服务不可用，5秒后重试...")
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return await senddata(websocket, -1, messages=["ollama 服务不可用"])
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except Exception as e:
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print(f"🔥 未预料错误: {str(e)}")
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return await senddata(websocket, -1, messages=["未预料错误"])
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logger.log(logging.INFO, "接口调用耗时 :" + str(time.time() - starttime))
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#aiResponse = response['message']['content']
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aiResponse = response['response']
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logger.log(logging.INFO, "AI生成" + aiResponse)
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#处理ai输出内容
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think_remove_text = re.sub(r'<think>.*?</think>', '', aiResponse, flags=re.DOTALL)
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pattern = r".*<format>(.*?)</format>" # .* 吞掉前面所有字符，定位最后一组
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match = re.search(pattern, think_remove_text, re.DOTALL)
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if not match:
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if await reGenerateAIChat(lastPrompt, websocket):
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pass
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else:
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logger.log(logging.ERROR, "请更换prompt，或者升级模型大小")
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await senddata(websocket, -1, messages=["请更换prompt，或者升级模型大小"])
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else:
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core_dialog = match.group(1).strip()
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dialog_lines = [line for line in core_dialog.split('\n') if line.strip()]
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if len(dialog_lines) != 4:
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if await reGenerateAIChat(lastPrompt, websocket):
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pass
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else:
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logger.log(logging.ERROR, "请更换prompt，或者升级模型大小")
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await senddata(websocket, -1, messages=["请更换prompt，或者升级模型大小"])
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else:
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logger.log(logging.INFO, "AI的输出正确：\n" + core_dialog)
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global regenerateCount
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regenerateCount = 0
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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"
|
||||
await generateAIChat(prompt, websocket)
|
||||
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 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"
|
||||
,"characterBackground":"公司资深产品经理","chat_style":"热情"})
|
||||
# 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"})
|
||||
|
||||
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)
|
||||
if id == 0:
|
||||
logger.log(logging.ERROR, f"角色 张三已经添加到数据库")
|
||||
#chat = database.get_chats_by_character_id(3)
|
||||
#
|
||||
# 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
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user