184 lines
6.4 KiB
Python
184 lines
6.4 KiB
Python
import time
|
||
|
||
import openai
|
||
from colorama import init, Fore
|
||
from loguru import logger
|
||
from robowaiter.llm_client.tool_register import get_tools, dispatch_tool
|
||
import requests
|
||
import json
|
||
from collections import deque
|
||
|
||
import urllib3
|
||
import copy
|
||
init(autoreset=True)
|
||
from robowaiter.utils import get_root_path
|
||
import os
|
||
import re
|
||
from robowaiter.llm_client.single_round import single_round
|
||
########################################
|
||
# 该文件实现了与大模型的通信以及工具调用
|
||
########################################
|
||
|
||
# 忽略https的安全性警告
|
||
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
|
||
|
||
base_url = "https://45.125.46.134:25344" # 本地部署的地址,或者使用你访问模型的API地址
|
||
|
||
root_path = get_root_path()
|
||
# load test questions
|
||
file_path = os.path.join(root_path,"robowaiter/llm_client/data/fix_questions.txt")
|
||
|
||
functions = get_tools()
|
||
|
||
fix_questions_dict = {}
|
||
no_reply_functions = ["create_sub_task"]
|
||
|
||
with open(file_path,'r',encoding="utf-8") as f:
|
||
#读取所有行
|
||
lines = f.read().strip()
|
||
sections = re.split(r'\n\s*\n', lines)
|
||
for s in sections:
|
||
x = s.strip().splitlines()
|
||
if len(x) == 2:
|
||
fix_questions_dict[x[0]] = {
|
||
"answer": x[1],
|
||
"function": None
|
||
}
|
||
else:
|
||
fix_questions_dict[x[0]] = {
|
||
"answer": x[1],
|
||
"function": x[2],
|
||
"args": x[3]
|
||
}
|
||
|
||
|
||
role_system = [{
|
||
"role": "system",
|
||
"content": "你是RoboWaiter,一个由HPCL团队开发的机器人服务员，你在咖啡厅工作。接受顾客的指令并调用工具函数来完成各种服务任务。如果顾客问你们这里有什么，或者想要点单，你说我们咖啡厅提供咖啡，水，点心，酸奶等食物。如果顾客不需要你了，你就回到吧台招待。如果顾客叫你去做某事，你回复：好的，我马上去做这件事。",
|
||
}]
|
||
|
||
def new_history(max_length=7):
|
||
history = deque(maxlen=max_length)
|
||
|
||
return history
|
||
|
||
def new_response():
|
||
return {'choices': [{'index': 0, 'message':{} }]}
|
||
|
||
def parse_fix_question(question):
|
||
response = new_response()
|
||
fix_ans = fix_questions_dict[question]
|
||
if not fix_ans['function']: #简单对话
|
||
message = {'role': 'assistant', 'content': fix_ans["answer"], 'name': None,
|
||
'function_call': None}
|
||
else:
|
||
func = fix_ans["function"]
|
||
args = fix_ans["args"]
|
||
# tool_response = dispatch_tool(function_call["name"], json.loads(args))
|
||
# logger.info(f"Tool Call Response: {tool_response}")
|
||
message = {'role': 'assistant',
|
||
'content': f"\n <|assistant|> {func}({args})\n ```python\ntool_call(goal={args})\n```",
|
||
'name': None,
|
||
'function_call': {'name': func, 'arguments': args}}
|
||
|
||
response["choices"][0]["message"] = message
|
||
return response
|
||
|
||
def get_response(sentence, history, allow_function_call = True):
|
||
if sentence:
|
||
history.append({"role": "user", "content": sentence})
|
||
|
||
if sentence in fix_questions_dict:
|
||
time.sleep(2)
|
||
return True, parse_fix_question(sentence)
|
||
|
||
params = dict(model="RoboWaiter")
|
||
params['messages'] = role_system + list(history)
|
||
if allow_function_call:
|
||
params["functions"] = functions
|
||
|
||
|
||
response = requests.post(f"{base_url}/v1/chat/completions", json=params, stream=False, verify=False)
|
||
decoded_line = response.json()
|
||
return False, decoded_line
|
||
|
||
def deal_response(response, history, func_map=None ):
|
||
if response["choices"][0]["message"].get("function_call"):
|
||
function_call = response["choices"][0]["message"]["function_call"]
|
||
logger.info(f"Function Call Response: {function_call}")
|
||
|
||
function_name = function_call["name"]
|
||
function_args = json.loads(function_call["arguments"])
|
||
if func_map:
|
||
tool_response = func_map[function_name](**function_args)
|
||
else:
|
||
try:
|
||
tool_response = dispatch_tool(function_call["name"], function_args)
|
||
logger.info(f"Tool Call Response: {tool_response}")
|
||
except:
|
||
logger.info(f"重试工具调用")
|
||
# tool_response = dispatch_tool(function_call["name"], function_args)
|
||
return function_name,None
|
||
|
||
return_message = response["choices"][0]["message"]
|
||
|
||
history.append(return_message)
|
||
t = {
|
||
"role": "function",
|
||
"name": function_call["name"],
|
||
"content": str(tool_response), # 调用函数返回结果
|
||
}
|
||
|
||
history.append(t)
|
||
return function_call["name"], tool_response
|
||
|
||
else:
|
||
return_message = response["choices"][0]["message"]
|
||
reply = return_message["content"]
|
||
|
||
history.append(return_message)
|
||
logger.info(f"Final Reply: \n{reply}")
|
||
|
||
return False, reply
|
||
|
||
|
||
def ask_llm(question,history, func_map=None, retry=3):
|
||
fixed, response = get_response(question, history)
|
||
|
||
function_call,result = deal_response(response, history, func_map)
|
||
if function_call:
|
||
if question in fix_questions_dict:
|
||
if fix_questions_dict[question]['function'] in no_reply_functions:
|
||
reply = fix_questions_dict[question]["answer"]
|
||
result = single_round(reply,
|
||
"你是机器人服务员，请把以下句子换一种表述方式对顾客说，但是意思不变，尽量简短：\n")
|
||
else:
|
||
reply = fix_questions_dict[question]["answer"]
|
||
result = single_round(f"你是机器人服务员，顾客想知道{question}, 你的具身场景查询返回的是{result},把返回的英文名词翻译成中文,请把按照以下句子对顾客说，{reply}, 尽量简短。\n")
|
||
|
||
message = {'role': 'assistant', 'content': result, 'name': None,
|
||
'function_call': None}
|
||
history.append(message)
|
||
|
||
else:
|
||
fixed, response = get_response(None, history,allow_function_call=False)
|
||
_,result = deal_response(response, history, func_map)
|
||
|
||
|
||
print(f'{len(history)}条历史记录:')
|
||
for x in history:
|
||
print(x)
|
||
return function_call, result
|
||
|
||
if __name__ == "__main__":
|
||
question = input("\n顾客：")
|
||
history = new_history()
|
||
n = 1
|
||
max_retry = 2
|
||
|
||
while question != 'end':
|
||
function_call, return_message = ask_llm(question,history)
|
||
|
||
|
||
question = input("\n顾客：")
|