218 lines
8.2 KiB
Python
218 lines
8.2 KiB
Python
from toolbox import get_conf, get_pictures_list, encode_image
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import base64
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import datetime
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import hashlib
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import hmac
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import json
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from urllib.parse import urlparse
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import ssl
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from datetime import datetime
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from time import mktime
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from urllib.parse import urlencode
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from wsgiref.handlers import format_date_time
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import websocket
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import threading, time
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timeout_bot_msg = '[Local Message] Request timeout. Network error.'
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class Ws_Param(object):
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# 初始化
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def __init__(self, APPID, APIKey, APISecret, gpt_url):
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self.APPID = APPID
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self.APIKey = APIKey
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self.APISecret = APISecret
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self.host = urlparse(gpt_url).netloc
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self.path = urlparse(gpt_url).path
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self.gpt_url = gpt_url
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# 生成url
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def create_url(self):
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# 生成RFC1123格式的时间戳
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now = datetime.now()
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date = format_date_time(mktime(now.timetuple()))
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# 拼接字符串
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signature_origin = "host: " + self.host + "\n"
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signature_origin += "date: " + date + "\n"
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signature_origin += "GET " + self.path + " HTTP/1.1"
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# 进行hmac-sha256进行加密
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signature_sha = hmac.new(self.APISecret.encode('utf-8'), signature_origin.encode('utf-8'), digestmod=hashlib.sha256).digest()
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signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding='utf-8')
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authorization_origin = f'api_key="{self.APIKey}", algorithm="hmac-sha256", headers="host date request-line", signature="{signature_sha_base64}"'
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authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode(encoding='utf-8')
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# 将请求的鉴权参数组合为字典
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v = {
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"authorization": authorization,
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"date": date,
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"host": self.host
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}
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# 拼接鉴权参数，生成url
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url = self.gpt_url + '?' + urlencode(v)
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# 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
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return url
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class SparkRequestInstance():
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def __init__(self):
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XFYUN_APPID, XFYUN_API_SECRET, XFYUN_API_KEY = get_conf('XFYUN_APPID', 'XFYUN_API_SECRET', 'XFYUN_API_KEY')
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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')
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self.appid = XFYUN_APPID
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self.api_secret = XFYUN_API_SECRET
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self.api_key = XFYUN_API_KEY
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self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat"
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self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat"
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self.gpt_url_v3 = "ws://spark-api.xf-yun.com/v3.1/chat"
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self.gpt_url_img = "wss://spark-api.cn-huabei-1.xf-yun.com/v2.1/image"
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self.time_to_yield_event = threading.Event()
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self.time_to_exit_event = threading.Event()
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self.result_buf = ""
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def generate(self, inputs, llm_kwargs, history, system_prompt, use_image_api=False):
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llm_kwargs = llm_kwargs
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history = history
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system_prompt = system_prompt
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import _thread as thread
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thread.start_new_thread(self.create_blocking_request, (inputs, llm_kwargs, history, system_prompt, use_image_api))
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while True:
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self.time_to_yield_event.wait(timeout=1)
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if self.time_to_yield_event.is_set():
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yield self.result_buf
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if self.time_to_exit_event.is_set():
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return self.result_buf
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def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt, use_image_api):
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if llm_kwargs['llm_model'] == 'sparkv2':
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gpt_url = self.gpt_url_v2
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elif llm_kwargs['llm_model'] == 'sparkv3':
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gpt_url = self.gpt_url_v3
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else:
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gpt_url = self.gpt_url
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file_manifest = []
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if use_image_api and llm_kwargs.get('most_recent_uploaded'):
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if llm_kwargs['most_recent_uploaded'].get('path'):
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file_manifest = get_pictures_list(llm_kwargs['most_recent_uploaded']['path'])
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if len(file_manifest) > 0:
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print('正在使用讯飞图片理解API')
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gpt_url = self.gpt_url_img
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wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, gpt_url)
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websocket.enableTrace(False)
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wsUrl = wsParam.create_url()
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# 收到websocket连接建立的处理
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def on_open(ws):
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import _thread as thread
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thread.start_new_thread(run, (ws,))
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def run(ws, *args):
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data = json.dumps(gen_params(ws.appid, *ws.all_args, file_manifest))
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ws.send(data)
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# 收到websocket消息的处理
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def on_message(ws, message):
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data = json.loads(message)
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code = data['header']['code']
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if code != 0:
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print(f'请求错误: {code}, {data}')
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self.result_buf += str(data)
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ws.close()
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self.time_to_exit_event.set()
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else:
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choices = data["payload"]["choices"]
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status = choices["status"]
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content = choices["text"][0]["content"]
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ws.content += content
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self.result_buf += content
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if status == 2:
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ws.close()
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self.time_to_exit_event.set()
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self.time_to_yield_event.set()
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# 收到websocket错误的处理
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def on_error(ws, error):
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print("error:", error)
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self.time_to_exit_event.set()
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# 收到websocket关闭的处理
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def on_close(ws, *args):
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self.time_to_exit_event.set()
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# websocket
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ws = websocket.WebSocketApp(wsUrl, on_message=on_message, on_error=on_error, on_close=on_close, on_open=on_open)
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ws.appid = self.appid
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ws.content = ""
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ws.all_args = (inputs, llm_kwargs, history, system_prompt)
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ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE})
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def generate_message_payload(inputs, llm_kwargs, history, system_prompt, file_manifest):
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conversation_cnt = len(history) // 2
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messages = []
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if file_manifest:
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base64_images = []
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for image_path in file_manifest:
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base64_images.append(encode_image(image_path))
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for img_s in base64_images:
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if img_s not in str(messages):
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messages.append({"role": "user", "content": img_s, "content_type": "image"})
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else:
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messages = [{"role": "system", "content": system_prompt}]
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if conversation_cnt:
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for index in range(0, 2*conversation_cnt, 2):
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what_i_have_asked = {}
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what_i_have_asked["role"] = "user"
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what_i_have_asked["content"] = history[index]
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what_gpt_answer = {}
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what_gpt_answer["role"] = "assistant"
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what_gpt_answer["content"] = history[index+1]
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if what_i_have_asked["content"] != "":
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if what_gpt_answer["content"] == "": continue
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if what_gpt_answer["content"] == timeout_bot_msg: continue
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messages.append(what_i_have_asked)
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messages.append(what_gpt_answer)
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else:
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messages[-1]['content'] = what_gpt_answer['content']
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what_i_ask_now = {}
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what_i_ask_now["role"] = "user"
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what_i_ask_now["content"] = inputs
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messages.append(what_i_ask_now)
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return messages
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def gen_params(appid, inputs, llm_kwargs, history, system_prompt, file_manifest):
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"""
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通过appid和用户的提问来生成请参数
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"""
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domains = {
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"spark": "general",
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"sparkv2": "generalv2",
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"sparkv3": "generalv3",
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}
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domains_select = domains[llm_kwargs['llm_model']]
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if file_manifest: domains_select = 'image'
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data = {
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"header": {
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"app_id": appid,
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"uid": "1234"
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},
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"parameter": {
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"chat": {
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"domain": domains_select,
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"temperature": llm_kwargs["temperature"],
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"random_threshold": 0.5,
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"max_tokens": 4096,
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"auditing": "default"
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}
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},
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"payload": {
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"message": {
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"text": generate_message_payload(inputs, llm_kwargs, history, system_prompt, file_manifest)
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}
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}
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}
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return data
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