#!/usr/bin/env python # -*- encoding: utf-8 -*- ''' @File : spark_llm.py @Time : 2023/10/16 18:53:26 @Author : Logan Zou @Version : 1.0 @Contact : loganzou0421@163.com @License : (C)Copyright 2017-2018, Liugroup-NLPR-CASIA @Desc : 基于讯飞星火大模型自定义 LLM 类 ''' from langchain.llms.base import LLM from typing import Any, List, Mapping, Optional, Dict, Union, Tuple from pydantic import Field from llm.self_llm import Self_LLM import json import requests from langchain.callbacks.manager import CallbackManagerForLLMRun import _thread as thread import base64 import datetime import hashlib import hmac import json from urllib.parse import urlparse import ssl from datetime import datetime from time import mktime from urllib.parse import urlencode from wsgiref.handlers import format_date_time import websocket # 使用websocket_client import queue class Spark_LLM(Self_LLM): # 讯飞星火大模型的自定义 LLM # URL url : str = "wss://spark-api.xf-yun.com/v1/x1" # v3.1 版本 # APPID appid : str = None # APISecret api_secret : str = None # Domain domain :str = "x1" # v3 版本 # max_token max_tokens : int = 4096 # model name model : str = "Spark-X1" # 对话历史 text: list[str] = [] # 添加类型注解 def __init__(self, model: str = "Spark-X1", temperature: float = 0.0, appid: str = None, api_secret: str = None, api_key: str = None): super().__init__() self.temperature = temperature self.appid = appid self.api_secret = api_secret self.api_key = api_key self.text = [] def getText(self, role, content): jsoncon = {} jsoncon["role"] = role jsoncon["content"] = content self.text.append(jsoncon) return self.text def _call(self, prompt : str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any): if self.api_key == None or self.appid == None or self.api_secret == None: # 三个 Key 均存在才可以正常调用 print("请填入 Key") raise ValueError("Key 不存在") # 将 Prompt 填充到星火格式 print("正在准备问题...") question = self.getText("user", prompt) # 发起请求 try: print("正在调用星火大模型...") response = spark_main(self.appid, self.api_key, self.api_secret, self.url, self.domain, question, self.temperature, self.max_tokens) # 保存助手的回复 print("收到模型回复，正在保存...") self.getText("assistant", response) print("Spark_LLM._call 执行完成") return response except Exception as e: print(f"请求失败: {str(e)}") print("请求失败") return "请求失败" @property def _llm_type(self) -> str: return "spark" answer = "" class Ws_Param(object): # 初始化 def __init__(self, APPID, APIKey, APISecret, Spark_url): self.APPID = APPID self.APIKey = APIKey self.APISecret = APISecret self.host = urlparse(Spark_url).netloc self.path = urlparse(Spark_url).path self.Spark_url = Spark_url # 自定义 self.temperature = 0 self.max_tokens = 2048 # 生成url def create_url(self): # 生成RFC1123格式的时间戳 now = datetime.now() date = format_date_time(mktime(now.timetuple())) # 拼接字符串 signature_origin = "host: " + self.host + "\n" signature_origin += "date: " + date + "\n" signature_origin += "GET " + self.path + " HTTP/1.1" # 进行hmac-sha256进行加密 signature_sha = hmac.new(self.APISecret.encode('utf-8'), signature_origin.encode('utf-8'), digestmod=hashlib.sha256).digest() signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding='utf-8') authorization_origin = f'api_key="{self.APIKey}", algorithm="hmac-sha256", headers="host date request-line", signature="{signature_sha_base64}"' authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode(encoding='utf-8') # 将请求的鉴权参数组合为字典 v = { "authorization": authorization, "date": date, "host": self.host } # 拼接鉴权参数，生成url url = self.Spark_url + '?' + urlencode(v) return url # 收到websocket错误的处理 def on_error(ws, error): print("### error:", error) # 收到websocket关闭的处理 def on_close(ws,one,two): print(" ") # 收到websocket连接建立的处理 def on_open(ws): thread.start_new_thread(run, (ws,)) def run(ws, *args): try: data = json.dumps(gen_params(appid=ws.appid, domain=ws.domain, question=ws.question)) ws.send(data) except Exception as e: print(f"发送数据时出错: {str(e)}") ws.close() # 收到websocket消息的处理 def on_message(ws, message): try: data = json.loads(message) code = data['header']['code'] content = '' if code != 0: print(f'请求错误: {code}, {data}') ws.close() else: choices = data["payload"]["choices"] status = choices["status"] text = choices['text'][0] if ('reasoning_content' in text and '' != text['reasoning_content']): reasoning_content = text["reasoning_content"] print(reasoning_content, end="") global isFirstcontent isFirstcontent = True if('content' in text and '' != text['content']): content = text["content"] if(True == isFirstcontent): print("\n*******************以上为思维链内容，模型回复内容如下********************\n") print(content, end="") isFirstcontent = False global answer answer += content if status == 2: ws.close() except Exception as e: print(f"处理消息时出错: {str(e)}") ws.close() def gen_params(appid, domain, question, temperature=1.2, max_tokens=32768): """ 通过appid和用户的提问来生成请参数 """ data = { "header": { "app_id": appid, "uid": "1234" }, "parameter": { "chat": { "domain": domain, "temperature": temperature, "max_tokens": max_tokens } }, "payload": { "message": { "text": question } } } return data def spark_main(appid, api_key, api_secret, Spark_url, domain, question, temperature, max_tokens): print("spark_main 开始执行...") # 验证连接参数 if not all([appid, api_key, api_secret]): raise ValueError("缺少必要的认证参数：appid, api_key, api_secret") global answer answer = "" # 重置全局变量 global isFirstcontent isFirstcontent = False print("正在创建 WebSocket 连接...") wsParam = Ws_Param(appid, api_key, api_secret, Spark_url) websocket.enableTrace(False) # 关闭详细日志 wsUrl = wsParam.create_url() ws = websocket.WebSocketApp(wsUrl, on_message=on_message, on_error=on_error, on_close=on_close, on_open=on_open) ws.appid = appid ws.question = question ws.domain = domain print("正在启动 WebSocket 连接...") # 设置超时时间 ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE}) print("spark_main 执行完成") return answer