ConversationAgent
Project description
說明
不需資料庫之對話腳本代理。
- 透過"ConversationAgent.LibStage.gen_agent"方法來建置機器人
- 透過"ConversationAgent.to_bot"方式與機器人溝通
- 該方法需要三個參數
- agent代理物件: gen_agent 產生
- text: 使用者輸入內容，字串內容
- data: 過場資訊，預設使用
{}空字典，第二次與之後溝通應該戴上to_bot回傳的資料。
- 該方法會回傳機器人回應與過場資訊，下次溝通保留該過場資訊在進行溝通。
- 該方法需要三個參數
Quick start
from ConversationAgent.LibStage import gen_agent
import ConversationAgent
bot = {
"__MAIN_STAGES__": [
{
"stage_type": "__QA_STAGE__",
"qa_threshold": 1,
"says": {
"sys_welcome": "歡迎句",
"sys_refuse": "拒絕句",
"sys_complete": "完成句"
},
"corpus": {
"早安": "1",
"午安": "2",
"晚安": "3"
},
"__SAVED_NAME__": {
"__QA_RESPOND__": "QA_r1",
"__QA_RESPOND_THRESHOLD__": "QA_th",
"__QA_RESPOND_QUESTION__": "QA_q1",
"__QA_RESPOND_SCORE__": "QA_s1",
"__RUNNING_CORPUS__": "QA_c1",
},
"DISSABLE_WELCOME": False
}
]
}
print(f"\n" * 5)
agent = gen_agent(bot)
data = {}
reply_text, data = ConversationAgent.to_bot(agent, "哈囉", data)
print(f"reply_text: {reply_text}， ")
reply_text, data = ConversationAgent.to_bot(agent, "哈囉", data)
print(f"reply_text: {reply_text}， ")
reply_text, data = ConversationAgent.to_bot(agent, "早安", data)
print(f"reply_text: {reply_text}， ")
Stage 種類
RE_STAGE
RE_STAGE 採用stage_type為__RE_STAGE__，是用於最基礎的對話階段，由兩個主要結構構成：
-
says: 用來設定該階段的回應句，回應句有三種類型
- 歡迎句: 第一次到該階段時，機器人會回應該句子。(可依需求關閉功能，
DISSABLE_WELCOME設為True就關閉，預設為False。) - 拒絕句: 當沒有滿足抓取到所有
is_fits部分所要求的變數時，機器人會回應該句子。 - 完成句: 以上都完成時，機器人會回應該句子。(可透過
%%包裹變數名稱，並以空格前後相隔後，調用該變數。)
- 歡迎句: 第一次到該階段時，機器人會回應該句子。(可依需求關閉功能，
-
is_fits: 透過
正規表達式(regular expression)從使用者的輸入句子來抓取變數，該變數會儲存起來提供給完成句和SWITCH_STAGE使用。
{
"stage_type": "__RE_STAGE__",
"says": {
"sys_welcome": "歡迎句",
"sys_refuse": "拒絕句",
"sys_complete": "完成句 %%YOUSAYS%% "
},
"is_fits": [
[".*", "YOUSAYS"]
],
"DISSABLE_WELCOME": False
}
SWITCH_STAGE
SWITCH_STAGE 採用stage_type為__LIB_SWITCH_STAGE__，用於在Agent不同路線切換，主要結構是stages_filter。 stages_filter用來設定切換路線的條件，用[]可包含帶多種條件多路線，每一條件單位由變數名稱、限定數值和 切換路線三部分組成。
以下說明主要四種設置方式:
- 無條件設定:
[ ["*",True,"_新路線1_"] ] - 單一條件設定:
[ ["_VAR_","VALUE1","_新路線1_"], ["_VAR_","VALUE2","_新路線2_"] ] - 多條件設定:
[ [["_VAR1_","_VAR2_"],["VALUE1","VALUE2"],"_新路線1_"], [["_VAR1_","_VAR2_"],["VALUE3","VALUE4"],"_新路線2_"], ] - 混合條件設定:
[ ["_VAR1_","VALUE1","_新路線1_"], [["_VAR1_","_VAR2_"],["VALUE3","VALUE4"],"_新路線2_"], ["*",True,"_新路線3_"] ]
**儲存變數方式是透過RE_STAGE的 is_fits來執行。
範例：
{
"stage_type": "__LIB_SWITCH_STAGE__",
"stages_filter": [
["VAR","我想要的數值","_成功路線_"],,
["*",True,"_失敗路線_"]
]
}
QA_STAGE
QA_STAGE 採用stage_type為__QA_STAGE__，是通過相似度來決定回應的一種階段，主要有三個部分的組成。
-
says: 用來設定該階段的回應句，回應句有三種類型
- 歡迎句: 第一次到該階段時，機器人會回應該句子。(可依需求關閉功能，
DISSABLE_WELCOME設為True就關閉，預設為False。) - 拒絕句: 當相似分數低於
qa_threshold時，機器人會回應該句子。 - 完成句: 以上都完成時，機器人會回應該句子。(可透過
%%包裹變數名稱，並以空格前後相隔後，調用該變數。)
- 歡迎句: 第一次到該階段時，機器人會回應該句子。(可依需求關閉功能，
-
corpus: 使用者的輸入會與該字典的所有
key進行比對，並儲存相關結果，相關結果包含：__QA_RESPOND_QUESTION__: 相似值最高的 key__QA_RESPOND__: 相似值最高的 key 對應之 value__QA_RESPOND_SCORE__: 相似值最高的數值__RUNNING_CORPUS__: 該次測試時使用的 corpus__QA_RESPOND_THRESHOLD__: 該次測試使用的 threshold
-
__SAVED_NAME__: 設定儲存之變數的名稱，方便使用。
{
"stage_type": "__QA_STAGE__",
"qa_threshold": 1,
"says": {
"sys_welcome": "歡迎句",
"sys_refuse": "拒絕句",
"sys_complete": "完成句"
},
"corpus": {
"早安": "1",
"午安": "2",
"晚安": "3"
},
"__SAVED_NAME__": {
QAStage.__QA_RESPOND__: "QA_r1",
QAStage.__QA_RESPOND_THRESHOLD__: "QA_th",
QAStage.__QA_RESPOND_QUESTION__: "QA_q1",
QAStage.__QA_RESPOND_SCORE__: "QA_s1",
QAStage.__RUNNING_CORPUS__: "QA_c1",
},
"DISSABLE_WELCOME": False
}
Example
購票系統範例
from ConversationAgent.LibStage import gen_multi_agent, QAStage
import ConversationAgent
bot_json = {
"__MAIN_STAGES__": [
{
"stage_type": "__RE_STAGE__",
"question": {
"sys_reply_q1": "請問是要做哪種票種呢？",
"sys_reply_q2": "請說『月票』或是『單程票』",
"sys_reply_complete": "好的，將開始訂購 %%set_level%% "
},
"is_fits": [
[
"(月票|1280|長期票|定期票)+",
"set_level"
],
[
"(單程票|單程|一次)+",
"set_level"
]
]
},
{
"stage_type": "Switch",
"stages_filter": [
[
"set_level",
"月票",
"_月票_"
],
[
"set_level",
"1280",
"_月票_"
],
[
"set_level",
"長期票",
"_月票_"
],
[
"set_level",
"定期票",
"_月票_"
],
[
"set_level",
"單程票",
"_單程票_"
],
[
"set_level",
"單程",
"_單程票_"
],
[
"set_level",
"一次",
"_單程票_"
]
]
}
],
"_月票_": [
{
"stage_type": "__RE_STAGE__",
"question": {
"sys_reply_q1": "月票的價格為 1280元，是否確認？",
"sys_reply_q2": "月票的價格為 1280元，是否確認？請回答『是』或『否』",
"sys_reply_complete": "好的，確認您使用 %%set_level%% 車廂的意願為 『 %%user_status%% 』，\n 感謝您的使用。\n "
},
"is_fits": [
[
"(是|好的|好|沒問題)+$",
"user_status"
],
[
"(否|不|不行|不要|不好)+$",
"user_status"
]
]
}
],
"_單程票_": [
{
"stage_type": "__RE_STAGE__",
"question": {
"sys_reply_q1": "",
"sys_reply_q2": "",
"sys_reply_complete": "如果要訂購單程票，請使用票卷機，感謝您的使用。\n "
},
"is_fits": [],
"__DISSABLE_Q1__": True
}
]
}
data = {}
agent = gen_multi_agent(bot_json)
#
text = "hi"
reply_text, data = ConversationAgent.mock_client_once(agent, text, data)
print(f"data: {data}")
print(f"reply_text: {reply_text}")
#
text = "月票"
reply_text, data = ConversationAgent.mock_client_once(agent, text, data)
print(f"data: {data}")
print(f"reply_text: {reply_text}")
#
text = "好"
reply_text, data = ConversationAgent.mock_client_once(agent, text, data)
print(f"data: {data}")
print(f"reply_text: {reply_text}")
購票+問答系統範例
from ConversationAgent.LibStage import gen_multi_agent, QAStage
import ConversationAgent
bot_json = {
"__MAIN_STAGES__": [
{
"stage_type": "__RE_STAGE__",
"question": {
"sys_reply_q1": "哈囉請問要做什麼？ 目前提供『問答』和『訂票』服務",
"sys_reply_q2": "目前只提供『問答』和『訂票』服務喔",
"sys_reply_complete": "好的，將開始 『 %%selected_service%% 』 "
},
"is_fits": [
[
"(問答|問題|詢問)+",
"selected_service"
],
[
"(訂票|票價|買票)+",
"selected_service"
]
]
},
{
"stage_type": "Switch",
"stages_filter": [
[
"selected_service",
"訂票",
"_訂票_"
],
[
"selected_service",
"買票",
"_訂票_"
],
[
"selected_service",
"票價",
"_訂票_"
],
[
"selected_service",
"問答",
"_問答_"
],
[
"selected_service",
"問題",
"_問答_"
],
[
"selected_service",
"詢問",
"_問答_"
]
]
}
],
"_問答_": [
{
"stage_type": "__QA_STAGE__",
"corpus": {
"廁所在哪裡": "這裡沒有廁所",
"詢問處在哪裡": "這裡沒有詢問處",
"診所在哪裡": "這裡沒有診所",
},
"question": {
"sys_reply_q1": "請問有什麼問題呢？",
"sys_reply_q2": "",
"sys_reply_complete": "我有 %%__QA_RESPOND_SCORE__%% 的信心覺得您要問：<br> \n %%__QA_RESPOND_QUESTION__%% <br> \n 答案是 %%__QA_RESPOND__%% "
},
"is_fits": []
}
],
"_訂票_": [
{
"stage_type": "__RE_STAGE__",
"question": {
"sys_reply_q1": "請問是要做哪種票種呢？",
"sys_reply_q2": "請說『月票』或是『單程票』",
"sys_reply_complete": "好的，將開始訂購 %%set_level%% "
},
"is_fits": [
[
"(月票|1280|長期票|定期票)+",
"set_level"
],
[
"(單程票|單程|一次)+",
"set_level"
]
]
},
{
"stage_type": "Switch",
"stages_filter": [
[
"set_level",
"月票",
"_月票_"
],
[
"set_level",
"1280",
"_月票_"
],
[
"set_level",
"長期票",
"_月票_"
],
[
"set_level",
"定期票",
"_月票_"
],
[
"set_level",
"單程票",
"_單程票_"
],
[
"set_level",
"單程",
"_單程票_"
],
[
"set_level",
"一次",
"_單程票_"
]
]
}
],
"_月票_": [
{
"stage_type": "__RE_STAGE__",
"question": {
"sys_reply_q1": "月票的價格為 1280元，是否確認？",
"sys_reply_q2": "月票的價格為 1280元，是否確認？請回答『是』或『否』",
"sys_reply_complete": "好的，確認您使用 %%set_level%% 車廂的意願為 『 %%user_status%% 』，\n 感謝您的使用。\n "
},
"is_fits": [
[
"(是|好的|好|沒問題)+$",
"user_status"
],
[
"(否|不|不行|不要|不好)+$",
"user_status"
]
]
}
],
"_單程票_": [
{
"stage_type": "__RE_STAGE__",
"question": {
"sys_reply_q1": "",
"sys_reply_q2": "",
"sys_reply_complete": "如果要訂購單程票，請使用票卷機，感謝您的使用。\n "
},
"is_fits": [],
"__DISSABLE_Q1__": True
}
]
}
data = {}
agent = gen_multi_agent(bot_json)
#
text = "hi"
reply_text, data = ConversationAgent.mock_client_once(agent, text, data)
print(f"data: {data}")
print(f"reply_text: {reply_text}")
#
text = "我要月票"
reply_text, data = ConversationAgent.mock_client_once(agent, text, data)
print(f"data: {data}")
print(f"reply_text: {reply_text}")
#
text = "好"
reply_text, data = ConversationAgent.mock_client_once(agent, text, data)
print(f"data: {data}")
print(f"reply_text: {reply_text}")
如何客製化類別：以購票+問答系統為例子
from ConversationAgent.LibStage import __LIB_STAGES__
from ConversationAgent.LibStage import QAStage
import requests
"""
##########
__NEW QAWorkerSTAGE__
##########
"""
__NEW_QUESTIONANSWER__ = "NEW_QUESTIONANSWER"
class QAWorkerSTAGE(QAStage):
def __init__(self,data):
super(QAWorkerSTAGE, self).__init__(data)
self.similar_method = data.get(self.__SIMILAR_METHOD__, "worker_api")
self.__NLPCORESERVER__ = "http://52.147.71.0:8000"
def __request_similar_api__(self,text,corpus):
res = requests.post(url=f"{self.__NLPCORESERVER__}/jobs/{self.similar_method}", json={
"sentence": [
text
],
"corpus": corpus})
return res.json()
# 增加自訂義的類別
__LIB_STAGES__[__NEW_QUESTIONANSWER__] = QAWorkerSTAGE
"""
##########
Bot
##########
"""
bot_json = {
"__MAIN_STAGES__": [
{
"stage_type": "__RE_STAGE__",
"question": {
"sys_reply_q1": "哈囉請問要做什麼？ 目前提供『問答』和『訂票』服務",
"sys_reply_q2": "目前只提供『問答』和『訂票』服務喔",
"sys_reply_complete": "好的，將開始 『 %%selected_service%% 』 "
},
"is_fits": [
[
"(問答|問題|詢問)+",
"selected_service"
],
[
"(訂票|票價|買票)+",
"selected_service"
]
]
},
{
"stage_type": "Switch",
"stages_filter": [
[
"selected_service",
"訂票",
"_訂票_"
],
[
"selected_service",
"買票",
"_訂票_"
],
[
"selected_service",
"票價",
"_訂票_"
],
[
"selected_service",
"問答",
"_問答_"
],
[
"selected_service",
"問題",
"_問答_"
],
[
"selected_service",
"詢問",
"_問答_"
]
]
}
],
"_問答_": [
{
"stage_type": __NEW_QUESTIONANSWER__,
"corpus": {
"廁所在哪裡": "這裡沒有廁所",
"詢問處在哪裡": "這裡沒有詢問處",
"診所在哪裡": "這裡沒有診所",
},
"question": {
"sys_reply_q1": "請問有什麼問題呢？",
"sys_reply_q2": "",
"sys_reply_complete": "我有 %%__QA_RESPOND_SCORE__%% 的信心覺得您要問：<br> \n %%__QA_RESPOND_QUESTION__%% <br> \n 答案是 %%__QA_RESPOND__%% "
},
"is_fits": []
}
],
"_訂票_": [
{
"stage_type": "__RE_STAGE__",
"question": {
"sys_reply_q1": "請問是要做哪種票種呢？",
"sys_reply_q2": "請說『月票』或是『單程票』",
"sys_reply_complete": "好的，將開始訂購 %%set_level%% "
},
"is_fits": [
[
"(月票|1280|長期票|定期票)+",
"set_level"
],
[
"(單程票|單程|一次)+",
"set_level"
]
]
},
{
"stage_type": "Switch",
"stages_filter": [
[
"set_level",
"月票",
"_月票_"
],
[
"set_level",
"1280",
"_月票_"
],
[
"set_level",
"長期票",
"_月票_"
],
[
"set_level",
"定期票",
"_月票_"
],
[
"set_level",
"單程票",
"_單程票_"
],
[
"set_level",
"單程",
"_單程票_"
],
[
"set_level",
"一次",
"_單程票_"
]
]
}
],
"_月票_": [
{
"stage_type": "__RE_STAGE__",
"question": {
"sys_reply_q1": "月票的價格為 1280元，是否確認？",
"sys_reply_q2": "月票的價格為 1280元，是否確認？請回答『是』或『否』",
"sys_reply_complete": "好的，確認您使用 %%set_level%% 車廂的意願為 『 %%user_status%% 』，\n 感謝您的使用。\n "
},
"is_fits": [
[
"(是|好的|好|沒問題)+$",
"user_status"
],
[
"(否|不|不行|不要|不好)+$",
"user_status"
]
]
}
],
"_單程票_": [
{
"stage_type": "__RE_STAGE__",
"question": {
"sys_reply_q1": "",
"sys_reply_q2": "",
"sys_reply_complete": "如果要訂購單程票，請使用票卷機，感謝您的使用。\n "
},
"is_fits": [],
"__DISSABLE_Q1__": True
}
]
}
data = {}
agent = gen_multi_agent(bot_json)
#
text = "hi"
reply_text, data = ConversationAgent.mock_client_once(agent, text, data)
print(f"data: {data}")
print(f"reply_text: {reply_text}")
#
text = "我有點問題"
reply_text, data = ConversationAgent.mock_client_once(agent, text, data)
print(f"data: {data}")
print(f"reply_text: {reply_text}")
#
text = "附近有廁所嗎"
reply_text, data = ConversationAgent.mock_client_once(agent, text, data)
print(f"data: {data}")
print(f"reply_text: {reply_text}")
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