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增加利用百度UNIT实现智能对话插件 (#642)

* 利用百度UNIT实现智能对话插件

* 更新参数

* 增加BDunit配置参数模板
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zhanws GitHub vor 1 Jahr
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4 geänderte Dateien mit 316 neuen und 0 gelöschten Zeilen
  1. +14
    -0
      plugins/bdunit/README.md
  2. +0
    -0
      plugins/bdunit/__init__.py
  3. +297
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      plugins/bdunit/bdunit.py
  4. +5
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      plugins/bdunit/config.json.template

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plugins/bdunit/README.md Datei anzeigen

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利用百度UNIT实现智能对话
1.解决问题:chatgpt无法处理的指令,交给百度UNIT处理如:天气,日期时间,数学运算等
2.如问时间:现在几点钟,今天几号
3.如问天气:明天广州天气怎么样,这个周末深圳会不会下雨
4.如问数学运算:23+45=多少,100-23=多少,35转化为二进制是多少?
大家可以在百度UNIT官网上自己创建应用,申请百度机器人,可以把预先训练好的模型导入到自己的应用中,
see https://ai.baidu.com/unit/home#/home?track=61fe1b0d3407ce3face1d92cb5c291087095fc10c8377aaf
https://console.bce.baidu.com/ai平台申请
然后在百度UNIT官网上获取应用的API Key和Secret Key,然后在config.json中配置
{
"service_id": "s...", #"机器人ID"
"api_key": "",
"secret_key": ""
}

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plugins/bdunit/__init__.py Datei anzeigen


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plugins/bdunit/bdunit.py Datei anzeigen

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# encoding:utf-8
import json
import os
import uuid
import requests
from bridge.context import ContextType
from bridge.reply import Reply, ReplyType
from common.log import logger
import plugins
from plugins import *
from uuid import getnode as get_mac


"""利用百度UNIT实现智能对话
如果命中意图,返回意图对应的回复,否则返回继续交付给下个插件处理
"""


@plugins.register(name="BDunit", desc="Baidu unit bot system", version="0.1", author="jackson", desire_priority=0)
class BDunit(Plugin):
def __init__(self):
super().__init__()
try:
curdir = os.path.dirname(__file__)
config_path = os.path.join(curdir, "config.json")
conf = None
if not os.path.exists(config_path):
conf = {"service_id": "", "api_key": "",
"secret_key": ""}
with open(config_path, "w") as f:
json.dump(conf, f, indent=4)
else:
with open(config_path, "r") as f:
conf = json.load(f)
self.service_id = conf["service_id"]
self.api_key = conf["api_key"]
self.secret_key = conf["secret_key"]
self.access_token = self.get_token()
self.handlers[Event.ON_HANDLE_CONTEXT] = self.on_handle_context
logger.info("[BDunit] inited")
except Exception as e:
logger.warn(
"BDunit init failed: %s, ignore " % e)

def on_handle_context(self, e_context: EventContext):

if e_context['context'].type != ContextType.TEXT:
return

content = e_context['context'].content
logger.debug("[BDunit] on_handle_context. content: %s" % content)
parsed = self.getUnit2(content)
intent = self.getIntent(parsed)
if intent: # 找到意图
logger.debug("[BDunit] Baidu_AI Intent= %s", intent)
reply = Reply()
reply.type = ReplyType.TEXT
reply.content = self.getSay(parsed)
e_context['reply'] = reply
e_context.action = EventAction.BREAK_PASS # 事件结束,并跳过处理context的默认逻辑
else:
e_context.action = EventAction.CONTINUE # 事件继续,交付给下个插件或默认逻辑

def get_help_text(self, **kwargs):
help_text = "本插件会处理询问实时日期时间,天气,数学运算等问题,这些技能由您的百度智能对话UNIT决定\n"
return help_text

def get_token(self):
"""获取访问百度UUNIT 的access_token
#param api_key: UNIT apk_key
#param secret_key: UNIT secret_key
Returns:
string: access_token
"""
url = "https://aip.baidubce.com/oauth/2.0/token?client_id={}&client_secret={}&grant_type=client_credentials".format(
self.api_key, self.secret_key)
payload = ""
headers = {
'Content-Type': 'application/json',
'Accept': 'application/json'
}

response = requests.request("POST", url, headers=headers, data=payload)

# print(response.text)
return response.json()['access_token']

def getUnit(self, query):
"""
NLU 解析version 3.0
:param query: 用户的指令字符串
:returns: UNIT 解析结果。如果解析失败,返回 None
"""

url = (
'https://aip.baidubce.com/rpc/2.0/unit/service/v3/chat?access_token='
+ self.access_token
)
request = {"query": query, "user_id": str(
get_mac())[:32], "terminal_id": "88888"}
body = {
"log_id": str(uuid.uuid1()),
"version": "3.0",
"service_id": self.service_id,
"session_id": str(uuid.uuid1()),
"request": request,
}
try:
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=body, headers=headers)
return json.loads(response.text)
except Exception:
return None

def getUnit2(self, query):
"""
NLU 解析 version 2.0

:param query: 用户的指令字符串
:returns: UNIT 解析结果。如果解析失败,返回 None
"""
url = (
"https://aip.baidubce.com/rpc/2.0/unit/service/chat?access_token="
+ self.access_token
)
request = {"query": query, "user_id": str(get_mac())[:32]}
body = {
"log_id": str(uuid.uuid1()),
"version": "2.0",
"service_id": self.service_id,
"session_id": str(uuid.uuid1()),
"request": request,
}
try:
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=body, headers=headers)
return json.loads(response.text)
except Exception:
return None

def getIntent(self, parsed):
"""
提取意图

:param parsed: UNIT 解析结果
:returns: 意图数组
"""
if (
parsed
and "result" in parsed
and "response_list" in parsed["result"]
):
try:
return parsed["result"]["response_list"][0]["schema"]["intent"]
except Exception as e:
logger.warning(e)
return ""
else:
return ""

def hasIntent(self, parsed, intent):
"""
判断是否包含某个意图

:param parsed: UNIT 解析结果
:param intent: 意图的名称
:returns: True: 包含; False: 不包含
"""
if (
parsed
and "result" in parsed
and "response_list" in parsed["result"]
):
response_list = parsed["result"]["response_list"]
for response in response_list:
if (
"schema" in response
and "intent" in response["schema"]
and response["schema"]["intent"] == intent
):
return True
return False
else:
return False

def getSlots(self, parsed, intent=""):
"""
提取某个意图的所有词槽

:param parsed: UNIT 解析结果
:param intent: 意图的名称
:returns: 词槽列表。你可以通过 name 属性筛选词槽,
再通过 normalized_word 属性取出相应的值
"""
if (
parsed
and "result" in parsed
and "response_list" in parsed["result"]
):
response_list = parsed["result"]["response_list"]
if intent == "":
try:
return parsed["result"]["response_list"][0]["schema"]["slots"]
except Exception as e:
logger.warning(e)
return []
for response in response_list:
if (
"schema" in response
and "intent" in response["schema"]
and "slots" in response["schema"]
and response["schema"]["intent"] == intent
):
return response["schema"]["slots"]
return []
else:
return []

def getSlotWords(self, parsed, intent, name):
"""
找出命中某个词槽的内容

:param parsed: UNIT 解析结果
:param intent: 意图的名称
:param name: 词槽名
:returns: 命中该词槽的值的列表。
"""
slots = self.getSlots(parsed, intent)
words = []
for slot in slots:
if slot["name"] == name:
words.append(slot["normalized_word"])
return words

def getSayByConfidence(self, parsed):
"""
提取 UNIT 置信度最高的回复文本

:param parsed: UNIT 解析结果
:returns: UNIT 的回复文本
"""
if (
parsed
and "result" in parsed
and "response_list" in parsed["result"]
):
response_list = parsed["result"]["response_list"]
answer = {}
for response in response_list:
if (
"schema" in response
and "intent_confidence" in response["schema"]
and (
not answer
or response["schema"]["intent_confidence"]
> answer["schema"]["intent_confidence"]
)
):
answer = response
return answer["action_list"][0]["say"]
else:
return ""

def getSay(self, parsed, intent=""):
"""
提取 UNIT 的回复文本

:param parsed: UNIT 解析结果
:param intent: 意图的名称
:returns: UNIT 的回复文本
"""
if (
parsed
and "result" in parsed
and "response_list" in parsed["result"]
):
response_list = parsed["result"]["response_list"]
if intent == "":
try:
return response_list[0]["action_list"][0]["say"]
except Exception as e:
logger.warning(e)
return ""
for response in response_list:
if (
"schema" in response
and "intent" in response["schema"]
and response["schema"]["intent"] == intent
):
try:
return response["action_list"][0]["say"]
except Exception as e:
logger.warning(e)
return ""
return ""
else:
return ""

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plugins/bdunit/config.json.template Datei anzeigen

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{
"service_id": "s...",
"api_key": "",
"secret_key": ""
}

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