注入火山引擎大模型的接口代码
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binary-husky
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37f15185b6
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627d739720
@ -198,6 +198,12 @@ ZHIPUAI_API_KEY = ""
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ZHIPUAI_MODEL = "chatglm_turbo"
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# # 火山引擎YUNQUE大模型
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# YUNQUE_SECRET_KEY = ""
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# YUNQUE_ACCESS_KEY = ""
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# YUNQUE_MODEL = ""
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# Claude API KEY
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ANTHROPIC_API_KEY = ""
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@ -594,6 +594,23 @@ if "deepseekcoder" in AVAIL_LLM_MODELS: # deepseekcoder
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})
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except:
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print(trimmed_format_exc())
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# if "skylark" in AVAIL_LLM_MODELS:
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# try:
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# from .bridge_skylark2 import predict_no_ui_long_connection as skylark_noui
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# from .bridge_skylark2 import predict as skylark_ui
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# model_info.update({
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# "skylark": {
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# "fn_with_ui": skylark_ui,
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# "fn_without_ui": skylark_noui,
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# "endpoint": None,
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# "max_token": 4096,
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# "tokenizer": tokenizer_gpt35,
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# "token_cnt": get_token_num_gpt35,
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# }
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# })
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# except:
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# print(trimmed_format_exc())
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# <-- 用于定义和切换多个azure模型 -->
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AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY")
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67
request_llms/bridge_skylark2.py
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67
request_llms/bridge_skylark2.py
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@ -0,0 +1,67 @@
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import time
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from toolbox import update_ui, get_conf, update_ui_lastest_msg
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from toolbox import check_packages, report_exception
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model_name = '云雀大模型'
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def validate_key():
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YUNQUE_SECRET_KEY = get_conf("YUNQUE_SECRET_KEY")
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if YUNQUE_SECRET_KEY == '': return False
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return True
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def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
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"""
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⭐ 多线程方法
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函数的说明请见 request_llms/bridge_all.py
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"""
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watch_dog_patience = 5
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response = ""
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if validate_key() is False:
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raise RuntimeError('请配置YUNQUE_SECRET_KEY')
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from .com_skylark2api import YUNQUERequestInstance
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sri = YUNQUERequestInstance()
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for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
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if len(observe_window) >= 1:
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observe_window[0] = response
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if len(observe_window) >= 2:
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if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
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return response
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def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
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"""
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⭐ 单线程方法
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函数的说明请见 request_llms/bridge_all.py
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"""
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chatbot.append((inputs, ""))
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yield from update_ui(chatbot=chatbot, history=history)
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# 尝试导入依赖，如果缺少依赖，则给出安装建议
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try:
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check_packages(["zhipuai"])
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except:
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yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade zhipuai```。",
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chatbot=chatbot, history=history, delay=0)
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return
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if validate_key() is False:
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yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置HUOSHAN_API_KEY", chatbot=chatbot, history=history, delay=0)
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return
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if additional_fn is not None:
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from core_functional import handle_core_functionality
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inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
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# 开始接收回复
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from .com_skylark2api import YUNQUERequestInstance
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sri = YUNQUERequestInstance()
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for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
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chatbot[-1] = (inputs, response)
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yield from update_ui(chatbot=chatbot, history=history)
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# 总结输出
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if response == f"[Local Message] 等待{model_name}响应中 ...":
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response = f"[Local Message] {model_name}响应异常 ..."
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history.extend([inputs, response])
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yield from update_ui(chatbot=chatbot, history=history)
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95
request_llms/com_skylark2api.py
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95
request_llms/com_skylark2api.py
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@ -0,0 +1,95 @@
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from toolbox import get_conf
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import threading
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import logging
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import os
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timeout_bot_msg = '[Local Message] Request timeout. Network error.'
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#os.environ['VOLC_ACCESSKEY'] = ''
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#os.environ['VOLC_SECRETKEY'] = ''
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class YUNQUERequestInstance():
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def __init__(self):
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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):
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# import _thread as thread
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from volcengine.maas import MaasService, MaasException
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maas = MaasService('maas-api.ml-platform-cn-beijing.volces.com', 'cn-beijing')
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YUNQUE_SECRET_KEY, YUNQUE_ACCESS_KEY,YUNQUE_MODEL = get_conf("YUNQUE_SECRET_KEY", "YUNQUE_ACCESS_KEY","YUNQUE_MODEL")
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maas.set_ak(YUNQUE_ACCESS_KEY) #填写 VOLC_ACCESSKEY
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maas.set_sk(YUNQUE_SECRET_KEY) #填写 'VOLC_SECRETKEY'
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self.result_buf = ""
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req = {
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"model": {
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"name": YUNQUE_MODEL,
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"version": "1.0", # use default version if not specified.
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},
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"parameters": {
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"max_new_tokens": 4000, # 输出文本的最大tokens限制
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"min_new_tokens": 1, # 输出文本的最小tokens限制
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"temperature": llm_kwargs['temperature'], # 用于控制生成文本的随机性和创造性，Temperature值越大随机性越大，取值范围0~1
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"top_p": llm_kwargs['top_p'], # 用于控制输出tokens的多样性，TopP值越大输出的tokens类型越丰富，取值范围0~1
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"top_k": 0, # 选择预测值最大的k个token进行采样，取值范围0-1000，0表示不生效
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"max_prompt_tokens": 4000, # 最大输入 token 数，如果给出的 prompt 的 token 长度超过此限制，取最后 max_prompt_tokens 个 token 输入模型。
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},
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"messages": self.generate_message_payload(inputs, llm_kwargs, history, system_prompt)
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}
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response = maas.stream_chat(req)
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for resp in response:
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self.result_buf += resp.choice.message.content
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yield self.result_buf
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'''
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for event in response.events():
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if event.event == "add":
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self.result_buf += event.data
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yield self.result_buf
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elif event.event == "error" or event.event == "interrupted":
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raise RuntimeError("Unknown error:" + event.data)
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elif event.event == "finish":
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yield self.result_buf
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break
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else:
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raise RuntimeError("Unknown error:" + str(event))
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logging.info(f'[raw_input] {inputs}')
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logging.info(f'[response] {self.result_buf}')
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'''
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return self.result_buf
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def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
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from volcengine.maas import ChatRole
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conversation_cnt = len(history) // 2
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messages = [{"role": ChatRole.USER, "content": system_prompt},
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{"role": ChatRole.ASSISTANT, "content": "Certainly!"}]
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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"] = ChatRole.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"] = ChatRole.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"] == "":
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continue
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if what_gpt_answer["content"] == timeout_bot_msg:
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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"] = ChatRole.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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