import base64 import datetime import hashlib import hmac import json import ssl import websocket import threading from toolbox import get_conf, get_pictures_list, encode_image from loguru import logger from urllib.parse import urlparse from datetime import datetime from time import mktime from urllib.parse import urlencode from wsgiref.handlers import format_date_time timeout_bot_msg = '[Local Message] Request timeout. Network error.' class Ws_Param(object): # 初始化 def __init__(self, APPID, APIKey, APISecret, gpt_url): self.APPID = APPID self.APIKey = APIKey self.APISecret = APISecret self.host = urlparse(gpt_url).netloc self.path = urlparse(gpt_url).path self.gpt_url = gpt_url # 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.gpt_url + '?' + urlencode(v) # 此处打印出建立连接时候的url,参考本demo的时候可取消上方打印的注释，比对相同参数时生成的url与自己代码生成的url是否一致 return url class SparkRequestInstance(): def __init__(self): XFYUN_APPID, XFYUN_API_SECRET, XFYUN_API_KEY = get_conf('XFYUN_APPID', 'XFYUN_API_SECRET', 'XFYUN_API_KEY') if XFYUN_APPID == '00000000' or XFYUN_APPID == '': raise RuntimeError('请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET') self.appid = XFYUN_APPID self.api_secret = XFYUN_API_SECRET self.api_key = XFYUN_API_KEY self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat" self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat" self.gpt_url_v3 = "ws://spark-api.xf-yun.com/v3.1/chat" self.gpt_url_v35 = "wss://spark-api.xf-yun.com/v3.5/chat" self.gpt_url_img = "wss://spark-api.cn-huabei-1.xf-yun.com/v2.1/image" self.gpt_url_v4 = "wss://spark-api.xf-yun.com/v4.0/chat" self.time_to_yield_event = threading.Event() self.time_to_exit_event = threading.Event() self.result_buf = "" def generate(self, inputs, llm_kwargs, history, system_prompt, use_image_api=False): llm_kwargs = llm_kwargs history = history system_prompt = system_prompt import _thread as thread thread.start_new_thread(self.create_blocking_request, (inputs, llm_kwargs, history, system_prompt, use_image_api)) while True: self.time_to_yield_event.wait(timeout=1) if self.time_to_yield_event.is_set(): yield self.result_buf if self.time_to_exit_event.is_set(): return self.result_buf def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt, use_image_api): if llm_kwargs['llm_model'] == 'sparkv2': gpt_url = self.gpt_url_v2 elif llm_kwargs['llm_model'] == 'sparkv3': gpt_url = self.gpt_url_v3 elif llm_kwargs['llm_model'] == 'sparkv3.5': gpt_url = self.gpt_url_v35 elif llm_kwargs['llm_model'] == 'sparkv4': gpt_url = self.gpt_url_v4 else: gpt_url = self.gpt_url file_manifest = [] if use_image_api and llm_kwargs.get('most_recent_uploaded'): if llm_kwargs['most_recent_uploaded'].get('path'): file_manifest = get_pictures_list(llm_kwargs['most_recent_uploaded']['path']) if len(file_manifest) > 0: logger.info('正在使用讯飞图片理解API') gpt_url = self.gpt_url_img wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, gpt_url) websocket.enableTrace(False) wsUrl = wsParam.create_url() # 收到websocket连接建立的处理 def on_open(ws): import _thread as thread thread.start_new_thread(run, (ws,)) def run(ws, *args): data = json.dumps(gen_params(ws.appid, *ws.all_args, file_manifest)) ws.send(data) # 收到websocket消息的处理 def on_message(ws, message): data = json.loads(message) code = data['header']['code'] if code != 0: logger.error(f'请求错误: {code}, {data}') self.result_buf += str(data) ws.close() self.time_to_exit_event.set() else: choices = data["payload"]["choices"] status = choices["status"] content = choices["text"][0]["content"] ws.content += content self.result_buf += content if status == 2: ws.close() self.time_to_exit_event.set() self.time_to_yield_event.set() # 收到websocket错误的处理 def on_error(ws, error): logger.error("error:", error) self.time_to_exit_event.set() # 收到websocket关闭的处理 def on_close(ws, *args): self.time_to_exit_event.set() # websocket ws = websocket.WebSocketApp(wsUrl, on_message=on_message, on_error=on_error, on_close=on_close, on_open=on_open) ws.appid = self.appid ws.content = "" ws.all_args = (inputs, llm_kwargs, history, system_prompt) ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE}) def generate_message_payload(inputs, llm_kwargs, history, system_prompt, file_manifest): conversation_cnt = len(history) // 2 messages = [] if file_manifest: base64_images = [] for image_path in file_manifest: base64_images.append(encode_image(image_path)) for img_s in base64_images: if img_s not in str(messages): messages.append({"role": "user", "content": img_s, "content_type": "image"}) else: messages = [{"role": "system", "content": system_prompt}] if conversation_cnt: for index in range(0, 2*conversation_cnt, 2): what_i_have_asked = {} what_i_have_asked["role"] = "user" what_i_have_asked["content"] = history[index] what_gpt_answer = {} what_gpt_answer["role"] = "assistant" what_gpt_answer["content"] = history[index+1] if what_i_have_asked["content"] != "": if what_gpt_answer["content"] == "": continue if what_gpt_answer["content"] == timeout_bot_msg: continue messages.append(what_i_have_asked) messages.append(what_gpt_answer) else: messages[-1]['content'] = what_gpt_answer['content'] what_i_ask_now = {} what_i_ask_now["role"] = "user" what_i_ask_now["content"] = inputs messages.append(what_i_ask_now) return messages def gen_params(appid, inputs, llm_kwargs, history, system_prompt, file_manifest): """ 通过appid和用户的提问来生成请参数 """ domains = { "spark": "general", "sparkv2": "generalv2", "sparkv3": "generalv3", "sparkv3.5": "generalv3.5", "sparkv4": "4.0Ultra" } domains_select = domains[llm_kwargs['llm_model']] if file_manifest: domains_select = 'image' data = { "header": { "app_id": appid, "uid": "1234" }, "parameter": { "chat": { "domain": domains_select, "temperature": llm_kwargs["temperature"], "random_threshold": 0.5, "max_tokens": 4096, "auditing": "default" } }, "payload": { "message": { "text": generate_message_payload(inputs, llm_kwargs, history, system_prompt, file_manifest) } } } return data