llm/spark_llm.py

#!/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