from toolbox import get_conf, get_pictures_list, encode_image 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 import threading, 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.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 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: print('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: print(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): print("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", } 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