ton/YiemAgent
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update

Browse Source
This commit is contained in:
narawat lamaiin
2024-12-09 20:48:45 +07:00
parent 4f1280daa3
commit 9aef993813
47 changed files with 16623 additions and 12554 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
test/etc.jl
View File

@@ -1,9 +1,9 @@
using GeneralUtils
response = "trajectory_evaluation:\nThe trajectory is correct so far. The thought accurately reflects the user's question, and the action taken is a valid attempt to retrieve data from the database that matches the specified criteria.\n\nanswer_evaluation:\nThe observation provides information about two red wines from Bordeaux rive droite in France, which partially answers the question. However, it does not provide a complete answer as it only lists the wine names and characteristics, but does not explicitly state whether there are any other wines that match the criteria.\n\naccepted_as_answer: No\n\nscore: 6\nThe trajectory is mostly correct, but the observation does not fully address the question.\n\nsuggestion: Consider adding more filters or parameters to the database query to retrieve a complete list of wines that match the specified criteria."
responsedict = GeneralUtils.textToDict(response,
["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"],
rightmarker=":", symbolkey=true)
using GeneralUtils
response = "trajectory_evaluation:\nThe trajectory is correct so far. The thought accurately reflects the user's question, and the action taken is a valid attempt to retrieve data from the database that matches the specified criteria.\n\nanswer_evaluation:\nThe observation provides information about two red wines from Bordeaux rive droite in France, which partially answers the question. However, it does not provide a complete answer as it only lists the wine names and characteristics, but does not explicitly state whether there are any other wines that match the criteria.\n\naccepted_as_answer: No\n\nscore: 6\nThe trajectory is mostly correct, but the observation does not fully address the question.\n\nsuggestion: Consider adding more filters or parameters to the database query to retrieve a complete list of wines that match the specified criteria."
responsedict = GeneralUtils.textToDict(response,
["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"],
rightmarker=":", symbolkey=true)

318
test/prompttest.jl
View File

@@ -1,159 +1,159 @@
using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures
thoughtDict = OrderedDict(
:Question=> "Hello, I would like a get a bottle of wine",
:Thought_1=> "The customer wants to buy a bottle of wine, but we need more information about their preferences.",
:Action_1=> Dict(
:name=> "chatbox",
:input=> "What occasion are you buying the wine for?",
),
:Observation_1=> "We are having a wedding pary this weekend.",
:Thought_2=> "A wedding party is a great occasion to have a good bottle of wine.",
:Action_2=> Dict(
:name=> "chatbox",
:input=> "What type of food will you be serving with the wine?",
),
:Observation_2=> "I think it is Thai dishes",
:Thought_3=> "Now that I know the occasion and food, I need to ask about the budget.",
:Action_3=> Dict(
:name=> "chatbox",
:input=> "What is your budget for this wine?",
),
:Observation_3=> "50 bucks",
:Thought_4=> "With a budget of \$50, we have a wide range of options. Now that I know it's a wedding party and Thai dishes, I need to ask about the type of wine they prefer.",
:Action_4=> Dict(
:name=> "chatbox",
:input=> "What type of wine are you looking for? (Red, White, Sparkling, Rose, Dessert, Fortified)",
),
:Observation_4=> "Sparkling please.",
:Thought_5=> "Now that I know the occasion, food, budget and preferred type of wine, it's time to check our inventory for the best matching wine.",
:Action_5=> Dict(
:name=> "winestock",
:input=> "wine with budget \$50, Thai dishes, sparkling, wedding party",
),
:Observation_5=> "I found the following wine in stock {1 : Zena Crown Vista, 2 : Schrader Cabernet Sauvignon}",
:Thought_6=> "Now that I have all the information, it's time to recommend a wine that fits their preferences.",
:Action_6=> Dict(
:name=> "recommendation",
:input=> "I recommend Zena Crown Vista for its sparkling and affordable price.",
),
:Observation_6=> "I don't like it. Do you have another option?",
)
_thoughtJsonStr = JSON3.write(thoughtDict)
thoughtJsonStr = _thoughtJsonStr[1:end-1] # remove } at the end
# @show thoughtJsonStr
_, latestThoughtIndice = GeneralUtils.findHighestIndexKey(thoughtDict, "Thought")
nextThoughtIndice = latestThoughtIndice + 1
_prompt =
"""
You are a helpful sommelier working for a wine store.
Your goal is to reccommend the best wine from your inventory that match the user preferences.
You must follow the following criteria:
1) Get to know what occasion the user is buying wine for
2) Get to know what food the user will have with wine
3) Get to know how much the user willing to spend
4) Get to know type of wine the user is looking for e.g. Red, White, Sparkling, Rose, Dessert, Fortified
5) Get to know what characteristics of wine the user is looking for
e.g. tannin, sweetness, intensity, acidity
6) Check your inventory for the best wine that match the user preference
7) Recommend wine to the user
You should only respond with interleaving Thought, Action, Observation steps.
Thought can reason about the current situation, and Action can be three types:
1) winestock[query], which you can use to find wine in your inventory. The more input data the better.
2) chatbox[text], which you can use to interact with the user.
3) recommendation[answer], which returns your wine reccommendation to the user.
You should only respond in JSON format as describe below:
{
"Thought": "your reasoning",
"Action": {"name": "action to take", "input": "Action input"},
"Observation": "result of the action"
}
Here are some examples:
{
"Question": "I would like to buy a sedan with 8 seats.",
"Thought_1": "Our showroom carries various vehicle model. But I'm not sure whether we have a models that fits the user demand, I need to check our inventory.",
"Action_1": {"name": "inventory", "input": "sedan with 8 seats."},
"Observation_1": "Several model has 8 seats. Available color are black, red green"
}
{
"Thought_2": "I have to ask the user what color he likes.",
"Action_2": {"name": "chatbox", "input": "Which color do you like?"}
"Observation_2": "I'll take black."
}
{
"Thought_3": "There is only one model that fits the user preference. It's Yiem model A",
"Action_3": {"name": "recommendation", "input": "I recommend a Yiem model A"}
}
Let's begin!
$(JSON3.write(thoughtDict))
{Thought_$nextThoughtIndice
"""
prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
@show prompt
msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing,
:receiverId => nothing,
:getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing,
:replyToMsgId => nothing,
:msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/loadbalancer/requestingservice",
:receiverName => "text2textinstruct",
:replyTopic => nothing,
:senderName => "decisionMaker",
:senderSelfnote => nothing,
:senderId => "testingSessionID",
:timeStamp => "2024-05-04T08:06:23.561"
)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> prompt,
)
)
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
thoughtJsonStr = _response[:response][:text]
using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures
thoughtDict = OrderedDict(
:Question=> "Hello, I would like a get a bottle of wine",
:Thought_1=> "The customer wants to buy a bottle of wine, but we need more information about their preferences.",
:Action_1=> Dict(
:name=> "chatbox",
:input=> "What occasion are you buying the wine for?",
),
:Observation_1=> "We are having a wedding pary this weekend.",
:Thought_2=> "A wedding party is a great occasion to have a good bottle of wine.",
:Action_2=> Dict(
:name=> "chatbox",
:input=> "What type of food will you be serving with the wine?",
),
:Observation_2=> "I think it is Thai dishes",
:Thought_3=> "Now that I know the occasion and food, I need to ask about the budget.",
:Action_3=> Dict(
:name=> "chatbox",
:input=> "What is your budget for this wine?",
),
:Observation_3=> "50 bucks",
:Thought_4=> "With a budget of \$50, we have a wide range of options. Now that I know it's a wedding party and Thai dishes, I need to ask about the type of wine they prefer.",
:Action_4=> Dict(
:name=> "chatbox",
:input=> "What type of wine are you looking for? (Red, White, Sparkling, Rose, Dessert, Fortified)",
),
:Observation_4=> "Sparkling please.",
:Thought_5=> "Now that I know the occasion, food, budget and preferred type of wine, it's time to check our inventory for the best matching wine.",
:Action_5=> Dict(
:name=> "winestock",
:input=> "wine with budget \$50, Thai dishes, sparkling, wedding party",
),
:Observation_5=> "I found the following wine in stock {1 : Zena Crown Vista, 2 : Schrader Cabernet Sauvignon}",
:Thought_6=> "Now that I have all the information, it's time to recommend a wine that fits their preferences.",
:Action_6=> Dict(
:name=> "recommendation",
:input=> "I recommend Zena Crown Vista for its sparkling and affordable price.",
),
:Observation_6=> "I don't like it. Do you have another option?",
)
_thoughtJsonStr = JSON3.write(thoughtDict)
thoughtJsonStr = _thoughtJsonStr[1:end-1] # remove } at the end
# @show thoughtJsonStr
_, latestThoughtIndice = GeneralUtils.findHighestIndexKey(thoughtDict, "Thought")
nextThoughtIndice = latestThoughtIndice + 1
_prompt =
"""
You are a helpful sommelier working for a wine store.
Your goal is to reccommend the best wine from your inventory that match the user preferences.
You must follow the following criteria:
1) Get to know what occasion the user is buying wine for
2) Get to know what food the user will have with wine
3) Get to know how much the user willing to spend
4) Get to know type of wine the user is looking for e.g. Red, White, Sparkling, Rose, Dessert, Fortified
5) Get to know what characteristics of wine the user is looking for
e.g. tannin, sweetness, intensity, acidity
6) Check your inventory for the best wine that match the user preference
7) Recommend wine to the user
You should only respond with interleaving Thought, Action, Observation steps.
Thought can reason about the current situation, and Action can be three types:
1) winestock[query], which you can use to find wine in your inventory. The more input data the better.
2) chatbox[text], which you can use to interact with the user.
3) recommendation[answer], which returns your wine reccommendation to the user.
You should only respond in JSON format as describe below:
{
"Thought": "your reasoning",
"Action": {"name": "action to take", "input": "Action input"},
"Observation": "result of the action"
}
Here are some examples:
{
"Question": "I would like to buy a sedan with 8 seats.",
"Thought_1": "Our showroom carries various vehicle model. But I'm not sure whether we have a models that fits the user demand, I need to check our inventory.",
"Action_1": {"name": "inventory", "input": "sedan with 8 seats."},
"Observation_1": "Several model has 8 seats. Available color are black, red green"
}
{
"Thought_2": "I have to ask the user what color he likes.",
"Action_2": {"name": "chatbox", "input": "Which color do you like?"}
"Observation_2": "I'll take black."
}
{
"Thought_3": "There is only one model that fits the user preference. It's Yiem model A",
"Action_3": {"name": "recommendation", "input": "I recommend a Yiem model A"}
}
Let's begin!
$(JSON3.write(thoughtDict))
{Thought_$nextThoughtIndice
"""
prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
@show prompt
msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing,
:receiverId => nothing,
:getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing,
:replyToMsgId => nothing,
:msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/loadbalancer/requestingservice",
:receiverName => "text2textinstruct",
:replyTopic => nothing,
:senderName => "decisionMaker",
:senderSelfnote => nothing,
:senderId => "testingSessionID",
:timeStamp => "2024-05-04T08:06:23.561"
)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> prompt,
)
)
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
thoughtJsonStr = _response[:response][:text]

174
test/prompttest_1.jl
View File

@@ -1,87 +1,87 @@
using Revise # remove when this package is completed
using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, DataStructures
using Base.Threads
# ---------------------------------------------- 100 --------------------------------------------- #
config = copy(JSON3.read("config.json"))
instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1"
client, connection = MakeConnection(config[:mqttServerInfo][:broker],
config[:mqttServerInfo][:port])
receiveUserMsgChannel = Channel{Dict}(4)
receiveInternalMsgChannel = Channel{Dict}(4)
msgMeta = GeneralUtils.generate_msgMeta(
"N/A",
replyTopic = config[:servicetopic][:mqtttopic] # ask frontend reply to this instance_chat_topic
)
agentConfig = Dict(
:mqttServerInfo=> config[:mqttServerInfo],
:receivemsg=> Dict(
:prompt=> config[:servicetopic][:mqtttopic], # topic to receive prompt i.e. frontend send msg to this topic
:internal=> instanceInternalTopic,
),
:externalservice=> config[:externalservice],
)
# Instantiate an agent
tools=Dict( # update input format
"askbox"=> Dict(
:description => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
:input => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
:output => "" ,
:func => nothing,
),
# "winestock"=> Dict(
# :description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
# :input => """<input>Input is a JSON-formatted string that contains a detailed and precise search query.</input><input example>{\"wine type\": \"rose\", \"price\": \"max 35\", \"sweetness level\": \"sweet\", \"intensity level\": \"light bodied\", \"Tannin level\": \"low\", \"Acidity level\": \"low\"}</input example>""",
# :output => """<output>Output are wines that match the search query in JSON format.""",
# :func => ChatAgent.winestock,
# ),
"finalanswer"=> Dict(
:description => "<tool description>Useful for when you are ready to recommend wines to the user.</tool description>",
:input => """<input format>{\"finalanswer\": \"some text\"}.</input format><input example>{\"finalanswer\": \"I recommend Zena Crown Vista\"}</input example>""",
:output => "" ,
:func => nothing,
),
)
a = YiemAgent.sommelier(
receiveUserMsgChannel,
receiveInternalMsgChannel,
agentConfig,
name= "assistant",
id= "testingSessionID", # agent instance id
tools=tools,
)
input =
OrderedDict{Symbol, Any}(:question => "Hello, I would like a get a bottle of wine", :thought_1 => "It's great that the user is looking for a bottle of wine. To give them a personalized recommendation, I need to know more about their preferences.", :action_1 => Dict{Symbol, Any}(:name => "chatbox", :input => "What occasion are you planning to use this wine for?"), :observation_1 => "We are holding a wedding party", :thought_2 => "A wedding party is a great occasion for a special bottle of wine. I need to know what type of food will be served, and how much the user is willing to spend.", :action_2 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of food will you be serving at the wedding?"), :observation_2 => "It will be Thai dishes.", :thought_3 => "The type of wine that pairs well with Thai dishes is usually a crisp and refreshing white wine, but I also need to consider the budget and personal preferences.", :action_3 => Dict{Symbol, Any}(:name => "chatbox", :input => "How much are you willing to spend on this bottle of wine?"), :observation_3 => "I would spend up to 50 bucks.", :thought_4 => "I have a good idea of the occasion, food, and budget. Now I need to know what type of wine the user is looking for.", :action_4 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of wine are you usually looking for? Red, White, Sparkling, Rose, Dessert or Fortified?"), :observation_4 => "I like full-bodied Red wine with low tannin.", :thought_5 => "Now that I have all the necessary information, I can start searching for a suitable wine in our inventory.", :action_5 => Dict{Symbol, Any}(:name => "winestock", :input => "red wine with low tannins"), :observation_5 => "I found the following wines in our stock: \n{\n 1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n", :thought_6 => "Now that I have the information about the wine, it's time to make a recommendation.", :action_6 => Dict{Symbol, Any}(:name => "recommendbox", :input => "El Enemigo Cabernet Franc 2019"), :observation_6 => "I don't like the one you recommend. I want dry wine.")
result = YiemAgent.jsoncorrection(a, input)
using Revise # remove when this package is completed
using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, DataStructures
using Base.Threads
# ---------------------------------------------- 100 --------------------------------------------- #
config = copy(JSON3.read("config.json"))
instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1"
client, connection = MakeConnection(config[:mqttServerInfo][:broker],
config[:mqttServerInfo][:port])
receiveUserMsgChannel = Channel{Dict}(4)
receiveInternalMsgChannel = Channel{Dict}(4)
msgMeta = GeneralUtils.generate_msgMeta(
"N/A",
replyTopic = config[:servicetopic][:mqtttopic] # ask frontend reply to this instance_chat_topic
)
agentConfig = Dict(
:mqttServerInfo=> config[:mqttServerInfo],
:receivemsg=> Dict(
:prompt=> config[:servicetopic][:mqtttopic], # topic to receive prompt i.e. frontend send msg to this topic
:internal=> instanceInternalTopic,
),
:externalservice=> config[:externalservice],
)
# Instantiate an agent
tools=Dict( # update input format
"askbox"=> Dict(
:description => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
:input => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
:output => "" ,
:func => nothing,
),
# "winestock"=> Dict(
# :description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
# :input => """<input>Input is a JSON-formatted string that contains a detailed and precise search query.</input><input example>{\"wine type\": \"rose\", \"price\": \"max 35\", \"sweetness level\": \"sweet\", \"intensity level\": \"light bodied\", \"Tannin level\": \"low\", \"Acidity level\": \"low\"}</input example>""",
# :output => """<output>Output are wines that match the search query in JSON format.""",
# :func => ChatAgent.winestock,
# ),
"finalanswer"=> Dict(
:description => "<tool description>Useful for when you are ready to recommend wines to the user.</tool description>",
:input => """<input format>{\"finalanswer\": \"some text\"}.</input format><input example>{\"finalanswer\": \"I recommend Zena Crown Vista\"}</input example>""",
:output => "" ,
:func => nothing,
),
)
a = YiemAgent.sommelier(
receiveUserMsgChannel,
receiveInternalMsgChannel,
agentConfig,
name= "assistant",
id= "testingSessionID", # agent instance id
tools=tools,
)
input =
OrderedDict{Symbol, Any}(:question => "Hello, I would like a get a bottle of wine", :thought_1 => "It's great that the user is looking for a bottle of wine. To give them a personalized recommendation, I need to know more about their preferences.", :action_1 => Dict{Symbol, Any}(:name => "chatbox", :input => "What occasion are you planning to use this wine for?"), :observation_1 => "We are holding a wedding party", :thought_2 => "A wedding party is a great occasion for a special bottle of wine. I need to know what type of food will be served, and how much the user is willing to spend.", :action_2 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of food will you be serving at the wedding?"), :observation_2 => "It will be Thai dishes.", :thought_3 => "The type of wine that pairs well with Thai dishes is usually a crisp and refreshing white wine, but I also need to consider the budget and personal preferences.", :action_3 => Dict{Symbol, Any}(:name => "chatbox", :input => "How much are you willing to spend on this bottle of wine?"), :observation_3 => "I would spend up to 50 bucks.", :thought_4 => "I have a good idea of the occasion, food, and budget. Now I need to know what type of wine the user is looking for.", :action_4 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of wine are you usually looking for? Red, White, Sparkling, Rose, Dessert or Fortified?"), :observation_4 => "I like full-bodied Red wine with low tannin.", :thought_5 => "Now that I have all the necessary information, I can start searching for a suitable wine in our inventory.", :action_5 => Dict{Symbol, Any}(:name => "winestock", :input => "red wine with low tannins"), :observation_5 => "I found the following wines in our stock: \n{\n 1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n", :thought_6 => "Now that I have the information about the wine, it's time to make a recommendation.", :action_6 => Dict{Symbol, Any}(:name => "recommendbox", :input => "El Enemigo Cabernet Franc 2019"), :observation_6 => "I don't like the one you recommend. I want dry wine.")
result = YiemAgent.jsoncorrection(a, input)

238
test/prompttest_2.jl
View File

@@ -1,119 +1,119 @@
using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures, LibPQ
using SQLLLM
# _prompt =
# """
# You are a helpful assistant.
# answer the following question:
# From the following CSV text:
# "{\"tabledescription\":[\"The customer table stores information about customers. It includes details such as first name, last name, display name, username, password, gender, country, telephone number, email, birthdate, additional_search_term, other attributes (in JSON format) and a description.\",\"The wine table stores information about different wines. It includes details namely id, name, brand, manufacturer, region, country, wine_type, grape_variety, serving_temperature, intensity, sweetness, tannin, acidity, fizziness, additional_search_term, other attributes (in JSON format) and a description.\",\"The wine_food table represents the association between wines and food items. It estab" ⋯ 477 bytes ⋯ "ed to retailer names, usernames, passwords, addresses, contact persons, telephone numbers, email addresses, additional_search_term, other attributes (in JSON format) and a description.\",\"The retailer_wine table represents the relationship between retailers and wines. It stores information about the wines available from which retailers, including vintage, their price, and the currency.\",\"The retailer_food table represents the relationship between retailers and food items. It stores information about the food items available from which retailers, including their price and the currency.\"],\"tablename\":[\"customer\",\"wine\",\"wine_food\",\"food\",\"retailer\",\"retailer_wine\",\"retailer_food\"]}"
# What is the description of table wine?
# """
# prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
# @show prompt
# msgMeta = Dict(:requestResponse => nothing,
# :msgPurpose => nothing,
# :receiverId => nothing,
# :getPost => nothing,
# :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
# :acknowledgestatus => nothing,
# :replyToMsgId => nothing,
# :msgFormatVersion => nothing,
# :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
# :sendTopic => "/loadbalancer/requestingservice",
# :receiverName => "text2textinstruct",
# :replyTopic => nothing,
# :senderName => "decisionMaker",
# :senderSelfnote => nothing,
# :senderId => "testingSessionID",
# :timeStamp => "2024-05-04T08:06:23.561"
# )
# outgoingMsg = Dict(
# :msgMeta=> msgMeta,
# :payload=> Dict(
# :text=> prompt,
# )
# )
# _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
# result = _response[:response][:text]
DBconnection = LibPQ.Connection("host=192.168.88.12 port=5432 dbname=yiem_wine_assistant user=yiem password=yiem@Postgres_0.0")
tableinfo, df1, df2, df3 = SQLLLM.tableinfo(DBconnection, "wine")
_prompt =
"""
You are a helpful assistant helping to answer user question from a database table.
$tableinfo
Are there any chardonnay?
"""
prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
@show prompt
msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing,
:receiverId => nothing,
:getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing,
:replyToMsgId => nothing,
:msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/loadbalancer/requestingservice",
:receiverName => "text2textinstruct",
:replyTopic => nothing,
:senderName => "decisionMaker",
:senderSelfnote => nothing,
:senderId => "testingSessionID",
:timeStamp => "2024-05-04T08:06:23.561"
)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> prompt,
)
)
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
result2 = _response[:response][:text]
using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures, LibPQ
using SQLLLM
# _prompt =
# """
# You are a helpful assistant.
# answer the following question:
# From the following CSV text:
# "{\"tabledescription\":[\"The customer table stores information about customers. It includes details such as first name, last name, display name, username, password, gender, country, telephone number, email, birthdate, additional_search_term, other attributes (in JSON format) and a description.\",\"The wine table stores information about different wines. It includes details namely id, name, brand, manufacturer, region, country, wine_type, grape_variety, serving_temperature, intensity, sweetness, tannin, acidity, fizziness, additional_search_term, other attributes (in JSON format) and a description.\",\"The wine_food table represents the association between wines and food items. It estab" ⋯ 477 bytes ⋯ "ed to retailer names, usernames, passwords, addresses, contact persons, telephone numbers, email addresses, additional_search_term, other attributes (in JSON format) and a description.\",\"The retailer_wine table represents the relationship between retailers and wines. It stores information about the wines available from which retailers, including vintage, their price, and the currency.\",\"The retailer_food table represents the relationship between retailers and food items. It stores information about the food items available from which retailers, including their price and the currency.\"],\"tablename\":[\"customer\",\"wine\",\"wine_food\",\"food\",\"retailer\",\"retailer_wine\",\"retailer_food\"]}"
# What is the description of table wine?
# """
# prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
# @show prompt
# msgMeta = Dict(:requestResponse => nothing,
# :msgPurpose => nothing,
# :receiverId => nothing,
# :getPost => nothing,
# :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
# :acknowledgestatus => nothing,
# :replyToMsgId => nothing,
# :msgFormatVersion => nothing,
# :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
# :sendTopic => "/loadbalancer/requestingservice",
# :receiverName => "text2textinstruct",
# :replyTopic => nothing,
# :senderName => "decisionMaker",
# :senderSelfnote => nothing,
# :senderId => "testingSessionID",
# :timeStamp => "2024-05-04T08:06:23.561"
# )
# outgoingMsg = Dict(
# :msgMeta=> msgMeta,
# :payload=> Dict(
# :text=> prompt,
# )
# )
# _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
# result = _response[:response][:text]
DBconnection = LibPQ.Connection("host=192.168.88.12 port=5432 dbname=yiem_wine_assistant user=yiem password=yiem@Postgres_0.0")
tableinfo, df1, df2, df3 = SQLLLM.tableinfo(DBconnection, "wine")
_prompt =
"""
You are a helpful assistant helping to answer user question from a database table.
$tableinfo
Are there any chardonnay?
"""
prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
@show prompt
msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing,
:receiverId => nothing,
:getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing,
:replyToMsgId => nothing,
:msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/loadbalancer/requestingservice",
:receiverName => "text2textinstruct",
:replyTopic => nothing,
:senderName => "decisionMaker",
:senderSelfnote => nothing,
:senderId => "testingSessionID",
:timeStamp => "2024-05-04T08:06:23.561"
)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> prompt,
)
)
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
result2 = _response[:response][:text]

412
test/runtest.jl
View File

@@ -1,227 +1,255 @@
using Revise # remove when this package is completed
using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, LibPQ, Base64, DataFrames
using Revise
using JSON, JSON3, MQTTClient, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
using YiemAgent, GeneralUtils
using Base.Threads
# ---------------------------------------------- 100 --------------------------------------------- #
config = copy(JSON3.read("config.json"))
# instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1"
# client, connection = MakeConnection(config[:mqttServerInfo][:broker],
# config[:mqttServerInfo][:port])
receiveUserMsgChannel = Channel{Dict}(4)
# receiveInternalMsgChannel = Channel{Dict}(4)
# println(typeof(connection))
# msgMeta = GeneralUtils.generate_msgMeta(
# "N/A",
# replyTopic = config[:servicetopic][:mqtttopic] # ask frontend reply to this instance_chat_topic
# )
# load config
config = copy(JSON3.read("../mountvolume/config.json"))
function executeSQL(sql::T) where {T<:AbstractString}
DBconnection = LibPQ.Connection("host=192.168.88.12 port=5432 dbname=yiem_wine_assistant user=yiem password=yiem@Postgres_0.0")
DBconnection = LibPQ.Connection("host=192.168.88.12 port=10201 dbname=wineDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
result = LibPQ.execute(DBconnection, sql)
close(DBconnection)
return result
end
function executeSQLVectorDB(sql)
DBconnection = LibPQ.Connection("host=192.168.88.12 port=10203 dbname=SQLVectorDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
result = LibPQ.execute(DBconnection, sql)
close(DBconnection)
return result
end
function text2textInstructLLM(prompt::String)
msgMeta = GeneralUtils.generate_msgMeta(
config[:externalservice][:text2textinstruct][:mqtttopic];
msgPurpose= "inference",
senderName= "yiemagent",
senderId= string(uuid4()),
receiverName= "text2textinstruct",
mqttBrokerAddress= config[:mqttServerInfo][:broker],
mqttBrokerPort= config[:mqttServerInfo][:port],
msgPurpose="inference",
senderName="yiemagent",
senderId=string(uuid4()),
receiverName="text2textinstruct",
mqttBrokerAddress=config[:mqttServerInfo][:broker],
mqttBrokerPort=config[:mqttServerInfo][:port],
)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> prompt,
:kwargs=> Dict(
:max_tokens=> 2048,
:stop=> ["<|eot_id|>"],
:temperature=> 0.2,
)
:msgMeta => msgMeta,
:payload => Dict(
:text => prompt,
:kwargs => Dict(
:num_ctx => 16384,
:temperature => 0.2,
)
)
)
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120)
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
response = _response[:response][:text]
return response
end
function executeSQLVectorDB(sql)
DBconnection = LibPQ.Connection("host=192.168.88.12 port=5433 dbname=SQLVectorDB user=yiemtechnologies@gmail.com password=yiem@Postgres_0.0")
result = LibPQ.execute(DBconnection, sql)
close(DBconnection)
return result
end
function addSQLVectorDB(state)
# get embedding of the query
query = [state[:thoughtHistory][:question]]
# get text embedding from a LLM service
function getEmbedding(text::T) where {T<:AbstractString}
msgMeta = GeneralUtils.generate_msgMeta(
config[:externalservice][:text2textinstruct][:mqtttopic];
msgPurpose= "embedding",
senderName= "yiemagent",
senderId= string(uuid4()),
receiverName= "text2textinstruct",
mqttBrokerAddress= config[:mqttServerInfo][:broker],
mqttBrokerPort= config[:mqttServerInfo][:port],
msgPurpose="embedding",
senderName="yiemagent",
senderId=string(uuid4()),
receiverName="text2textinstruct",
mqttBrokerAddress=config[:mqttServerInfo][:broker],
mqttBrokerPort=config[:mqttServerInfo][:port],
)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> query
)
:msgMeta => msgMeta,
:payload => Dict(
:text => [text] # must be a vector of string
)
)
response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
embedding = response[:response][:embeddings][1]
embedding = response[:response][:embeddings]
return embedding
end
function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
vectorDB::Function; limit::Integer=1
)::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
# get embedding from LLM service
embedding = getEmbedding(text)[1]
# check whether there is close enough vector already store in vectorDB. if no, add, else skip
sql =
"""
SELECT *, embedding <-> '$embedding' as distance
FROM sql_statement_repository
ORDER BY distance LIMIT 1;
"""
response = executeSQLVectorDB(sql)
sql = """
SELECT *, $embeddingColumnName <-> '$embedding' as distance
FROM $tablename
ORDER BY distance LIMIT $limit;
"""
response = vectorDB(sql)
df = DataFrame(response)
return df
end
function similarSQLVectorDB(query; maxdistance::Integer=100)
tablename = "sqlllm_decision_repository"
# get embedding of the query
df = findSimilarTextFromVectorDB(query, tablename,
"function_input_embedding", executeSQLVectorDB)
row, col = size(df)
distance = row == 0 ? Inf : df[1, :distance]
if row != 0 && distance < maxdistance
# if there is usable SQL, return it.
output_b64 = df[1, :function_output_base64] # pick the closest match
output_str = String(base64decode(output_b64))
rowid = df[1, :id]
println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
return (dict=output_str, distance=distance)
else
println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
return (dict=nothing, distance=nothing)
end
end
function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=1) where {T1<:AbstractString, T2<:AbstractString}
tablename = "sqlllm_decision_repository"
# get embedding of the query
# query = state[:thoughtHistory][:question]
df = findSimilarTextFromVectorDB(query, tablename,
"function_input_embedding", executeSQLVectorDB)
row, col = size(df)
distance = row == 0 ? Inf : df[1, :distance]
if row == 0 || distance > 10 # no close enough SQL stored in the database
latestKey, _ = GeneralUtils.findHighestIndexKey(state[:thoughtHistory], :action_input)
_sqlStatement = state[:thoughtHistory][latestKey]
if occursin("SELECT", _sqlStatement) # make sure it is an SQL statement before adding into DB
sqlStatementBase64 = base64encode(_sqlStatement)
sqlStatement = replace(_sqlStatement, "'"=>"")
sql =
"""
INSERT INTO sql_statement_repository (question, sql_statement, sql_statement_base64, embedding) VALUES ('$query', '$sqlStatement', '$sqlStatementBase64', '$embedding');
"""
_ = executeSQLVectorDB(sql)
println("--> added new SQL statement to vectorDB ", @__FILE__, " ", @__LINE__)
println(sqlStatement)
end
if row == 0 || distance > maxdistance # no close enough SQL stored in the database
query_embedding = getEmbedding(query)[1]
query = replace(query, "'" => "")
sql_base64 = base64encode(SQL)
sql_ = replace(SQL, "'" => "")
sql = """
INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
"""
println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
println(sql)
_ = executeSQLVectorDB(sql)
end
end
function querySQLVectorDB(state)
# provide similarSQL at the first time thinking only
latestKey, _ = GeneralUtils.findHighestIndexKey(state[:thoughtHistory], :action_input)
if latestKey === nothing
# get embedding of the query
query = [state[:thoughtHistory][:question]]
msgMeta = GeneralUtils.generate_msgMeta(
config[:externalservice][:text2textinstruct][:mqtttopic];
msgPurpose= "embedding",
senderName= "yiemagent",
senderId= string(uuid4()),
receiverName= "text2textinstruct",
mqttBrokerAddress= config[:mqttServerInfo][:broker],
mqttBrokerPort= config[:mqttServerInfo][:port],
)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> query
)
)
response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
embedding = response[:response][:embeddings][1]
# check whether there is close enough vector already store in vectorDB. if no, add, else skip
sql =
"""
SELECT *, embedding <-> '$embedding' as distance
FROM sql_statement_repository
ORDER BY distance LIMIT 1;
"""
response = executeSQLVectorDB(sql)
df = DataFrame(response)
row, col = size(df)
distance = row == 0 ? Inf : df[1, :distance]
if row != 0 && distance < 100
# if there is usable SQL, return it.
sqlStatementBase64 = df[1, :sql_statement_base64]
sqlStatement = String(base64decode(sqlStatementBase64))
println("--> getting SQL statement from vectorDB ", @__FILE__, " ", @__LINE__)
println(sqlStatement)
return sqlStatement
else
return nothing
end
function similarSommelierDecision(recentevents::T1; maxdistance::Integer=5
)::Union{AbstractDict, Nothing} where {T1<:AbstractString}
tablename = "sommelier_decision_repository"
# find similar
println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
df = findSimilarTextFromVectorDB(recentevents, tablename,
"function_input_embedding", executeSQLVectorDB)
row, col = size(df)
distance = row == 0 ? Inf : df[1, :distance]
if row != 0 && distance < maxdistance
# if there is usable decision, return it.
rowid = df[1, :id]
println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
output_b64 = df[1, :function_output_base64] # pick the closest match
_output_str = String(base64decode(output_b64))
output = copy(JSON3.read(_output_str))
return output
else
println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
return nothing
end
return nothing
end
# Instantiate an agent
function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
) where {T1<:AbstractString, T2<:AbstractDict}
tablename = "sommelier_decision_repository"
# find similar
df = findSimilarTextFromVectorDB(recentevents, tablename,
"function_input_embedding", executeSQLVectorDB)
row, col = size(df)
distance = row == 0 ? Inf : df[1, :distance]
if row == 0 || distance > maxdistance # no close enough SQL stored in the database
recentevents_embedding = a.func[:getEmbedding](recentevents)[1]
recentevents = replace(recentevents, "'" => "")
decision_json = JSON3.write(decision)
decision_base64 = base64encode(decision_json)
decision = replace(decision_json, "'" => "")
sql = """
INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
"""
println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
println(sql)
_ = executeSQLVectorDB(sql)
else
println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
end
end
sessionId = "12345"
externalFunction = (
getEmbedding=getEmbedding,
text2textInstructLLM=text2textInstructLLM,
executeSQL=executeSQL,
similarSQLVectorDB=similarSQLVectorDB,
insertSQLVectorDB=insertSQLVectorDB,
similarSommelierDecision=similarSommelierDecision,
insertSommelierDecision=insertSommelierDecision,
)
a = YiemAgent.sommelier(
text2textInstructLLM,
executeSQL,
querySQLVectorDB,
addSQLVectorDB;
name= "Jene",
id= "tempId", # agent instance id
externalFunction;
name="Ton",
id=sessionId, # agent instance id
retailername="Yiem",
)
function main()
for i in 1:10
userinput = ""
for i in 1:3
if userinput == ""
println("")
println("--> user input:")
userinput = readline()
else
break
end
end
response = YiemAgent.conversation(a, Dict(:text=> userinput))
println("")
println("--> assistant response: \n", response)
end
while true
println("your respond: ")
user_answer = readline()
response = YiemAgent.conversation(a, Dict(:text=> user_answer))
println("\n$response")
end
main()
"""
I'm joining a graduation party this evening. I want to get a bottle of white wine from the US to celebrate. I'm ok with any price range.
Well, the party is small casual with close friends and no food serving.
I'm open to suggestion since I have no specific idea.
I'm ok with any region.
The input is instructions on how you want the presentation to be conducted.
"""
wines =
"""
Summary: This table contains two wine records, both from the United States, with white wine types, moderate sweetness (2), and high intensity (5).
More details: 1) wine_id: add9824f-81b0-47da-a08a-ee20498bc6c8, wine_name: Belle Cote Chardonnay, brand: Peter Michael, manufacturer: Peter Michael, region: Californian, country: United States, wine_type: white, grape_variety: Chardonnay, serving_temperature: 11 to 13 Celsius, intensity: 5, sweetness: 2, tannin: missing, acidity: 3, fizziness: missing, tasting_notes: oak, butter, vanilla, cream, oil, lemon curd, pear, peach, apple
2) wine_id: ff9a494c-e916-44c4-9385-1c18b23aa825, wine_name: Ma Belle-Fille Chardonnay, brand: Peter Michael, manufacturer: Peter Michael, region: Californian, country: United States, wine_type: white, grape_variety: Chardonnay, serving_temperature: 11 to 13 Celsius, intensity: 5, sweetness: 2, tannin: missing, acidity: 3, fizziness: missing, tasting_notes: oak, butter, vanilla, cream, banana, cheese, apricot, peach, apple
"""
# response = YiemAgent.conversation(a, Dict(:text=> "newtopic",) )
# response = YiemAgent.conversation(a, Dict(:text=> "Hello, I would like a get a bottle of wine."))
# println("---> YiemAgent: ", response)
# response = YiemAgent.conversation(a, Dict(:text=> "I'm having a graduation party this evening. I'll pay at most 30 bucks."))
# response = YiemAgent.conversation(a, Dict(:text=> "I want to get a French red wine under 100."))
@@ -229,60 +257,16 @@ More details: 1) wine_id: add9824f-81b0-47da-a08a-ee20498bc6c8, wine_name: Belle
# # input = "query=\"off dry, medium tannin, French Rosé\""
# input = "Search the database for wine type: white, country: France, sweetness level: 1"
# YiemAgent.winestock(a, input)
# input = "French dry white wines with medium body"
# input = "query=\"medium-bodied dry white wine\""
# # input = "the customer is looking for a medium-bodied, dry white wine."
# result = YiemAgent.checkinventory(a, input)
error("test done")
function run_with_timeout(f, args...; timeout=5)
result = Ref{Any}()
task = Threads.@spawn try
result[] = f(args...)
catch e
println("Task interrupted: ", e)
end
Timer(timeout) do _
if !istaskdone(task)
schedule(task, InterruptException())
println("Task did not complete in time. Aborting.")
else
println("Task completed within the timeout.")
end
end
return result[]
end
# Example function that takes arguments and returns a value
function example_function(x, y)
sleep(10) # Simulate a long-running task
return x + y
end
# Example usage
result = run_with_timeout(example_function, 3, 4; timeout=5)
println("Result: ", result)
a = `$"hello\nworld"`
a = $"hello\nworld"
a = """$("hello\nworld")"""

192
test/test_1.jl
View File

@@ -1,192 +0,0 @@
using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures
# ---------------------------------------------- 100 --------------------------------------------- #
msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing,
:receiverId => nothing,
:getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing,
:replyToMsgId => "dummyid",
:msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/testingSessionID",
:receiverName => "wineassistant",
:replyTopic => nothing,
:senderName => "test_1",
:senderSelfnote => nothing,
:senderId => nothing,
:timeStamp => "2024-05-04T08:06:23.561"
)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "We are holding a wedding party",
:select=> nothing,
:reward=> 0,
:isterminal=> false,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "It will be Thai dishes.",
:select=> nothing,
:reward=> 0,
:isterminal=> false,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "I would spend up to 50 bucks.",
:select=> nothing,
:reward=> 0,
:isterminal=> false,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "I like dry, full-bodied red wine with low tannin.",
:select=> nothing,
:reward=> 0,
:isterminal=> false,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "What do you have?",
:select=> nothing,
:reward=> 0,
:isterminal=> false,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "Dry wine please.",
:select=> nothing,
:reward=> 0,
:isterminal=> false,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "You did not gave me any choice.",
:select=> nothing,
:reward=> -1,
:isterminal=> false,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "You don't need to ask me. Just tell me already!",
:select=> nothing,
:reward=> -1,
:isterminal=> false,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "I like dry wine with fruity flavors.",
:select=> nothing,
:reward=> 0,
:isterminal=> false,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "OK, I'll take it.",
:select=> 1,
:reward=> 1,
:isterminal=> true,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "Yes.",
:select=> nothing,
:reward=> 0,
:isterminal=> false,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> "I like dry wine with medium acidity.",
:select=> nothing,
:reward=> 0,
:isterminal=> false,
)
)
result = GeneralUtils.sendMqttMsg(outgoingMsg)

34
test/test_2.jl
View File

@@ -1,34 +0,0 @@
import requests
# URL of the API endpoint
url = 'https://api.yiem.cc/wine/agent/sommelier/prompt/apiv1'
# Data to be sent in the JSON request
data = {
'sid': 'dummySID',
'txt': 'hello'
}
# Sending the POST request
response = requests.post(url, json=data)
# Displaying the response
print('Status Code:', response.status_code)
print('Response JSON:', response.json())