update
This commit is contained in:
narawat lamaiin
102
src/interface.jl
102
src/interface.jl
@@ -320,21 +320,57 @@ function reflector()
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"""
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You are an advanced reasoning agent that can improve based on self refection.
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You will be given the information related to the previous help you've done for a user.
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You were unsuccessful in helping the user either because you guessed the wrong answer with Finish[<answer>], or you used up your set number of reasoning steps.
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You were unsuccessful in helping the user either because you guessed the wrong answer with Finish[answer], or you used up your set number of reasoning steps.
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In a few sentences, Diagnose a possible reason for failure and devise a new, concise, high level plan that aims to mitigate the same failure.
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Use complete sentences.
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Here are some examples:
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Previous Trial:
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{
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"question": "Hello, I would like a get a bottle of wine",
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"thought_1": "A customer wants to buy a bottle of wine. Before making a recommendation, I need to know more about their preferences.",
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"action_1": {"name": "chatbox", "input": "What is the occasion for which you're buying this wine?"},
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"observation_1": "We are holding a wedding party",
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"thought_2": "A wedding party, that's a great occasion! The customer might be looking for a celebratory drink. Let me ask some more questions to narrow down the options.",
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"action_2": {"name": "chatbox", "input": "What type of food will you be serving at the wedding?"},
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"observation_2": "It will be Thai dishes.",
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"thought_3": "With Thai food, I should recommend a wine that complements its spicy and savory flavors. And since it's a celebratory occasion, the customer might prefer a full-bodied wine.",
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"action_3": {"name": "chatbox", "input": "What is your budget for this bottle of wine?"},
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"observation_3": "I would spend up to 50 bucks.",
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"thought_4": "Now that I have some more information, it's time to narrow down the options.",
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"action_4": {"name": "winestock", "input": "red wine with full body, pairs well with spicy food, budget \$50"},
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"observation_4": "I found the following wines in our stock: \n{\n 1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n",
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"thought_5": "Now that I have a list of potential wines, I need to know more about the customer's taste preferences.",
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"action_5": {"name": "chatbox", "input": "What type of wine characteristics are you looking for? (e.g. t.e.g. tannin level, sweetness, intensity, acidity)"},
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"observation_5": "I like full-bodied Red wine with low tannin.",
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"thought_6": "Now that I have more information about the customer's preferences, it's time to make a recommendation.",
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"action_6": {"name": "recommendbox", "input": "El Enemigo Cabernet Franc 2019"},
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"observation_6": "I don't like the one you recommend. I want dry wine."
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}
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Reflection: I searched one of the prime ministers involved in the signing, then attemted to answer right away. I should have searched each of the prime ministers, then looked up 'death' on each of their pages in order to get more information before answering.
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Question: The Rome Protocols were signed by three Prime Ministers one of which was assassinated as part of what?
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Thought 1: I need to search Rome Protocols, find the three Prime Ministers, then find what they were assassinated as part of.
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Action 1: Search[Rome Protocols]
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@@ -386,6 +422,52 @@ function reflector()
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Previous trial:
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{trajectory}Reflection:
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"""
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# apply LLM specific instruct format
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externalService = a.config[:externalservice][:text2textinstruct]
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llminfo = externalService[:llminfo]
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prompt =
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if llminfo[:name] == "llama3instruct"
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formatLLMtext_llama3instruct("system", _prompt)
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else
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error("llm model name is not defied yet $(@__LINE__)")
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end
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msgMeta = GeneralUtils.generate_msgMeta(
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a.config[:externalservice][:text2textinstruct][:mqtttopic],
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senderName= "progressValueEstimator",
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senderId= a.id,
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receiverName= "text2textinstruct",
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mqttBroker= a.config[:mqttServerInfo][:broker],
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mqttBrokerPort= a.config[:mqttServerInfo][:port],
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)
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outgoingMsg = Dict(
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:msgMeta=> msgMeta,
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:payload=> Dict(
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:text=> prompt,
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:kwargs=> Dict(
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:max_tokens=> 512,
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:stop=> ["<|eot_id|>"],
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)
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)
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)
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_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
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_thoughtJsonStr = _response[:response][:text]
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# expectedJsonExample =
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# """
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# Here is an expected JSON format:
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# {"evaluation": "...", "score": "..."}
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# """
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# resultJsonStr = jsoncorrection(a, _thoughtJsonStr, expectedJsonExample)
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# resultDict = copy(JSON3.read(resultJsonStr))
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# return resultDict[:evaluation], resultDict[:score]
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return _thoughtJsonStr
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end
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@@ -137,7 +137,7 @@ result = GeneralUtils.sendMqttMsg(outgoingMsg)
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outgoingMsg = Dict(
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:msgMeta=> msgMeta,
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:payload=> Dict(
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:text=> "Yep.",
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:text=> "I don't have any thing specific",
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:select=> nothing,
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:reward=> 0,
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:isterminal=> false,
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130
test/test_2.jl
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130
test/test_2.jl
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@@ -0,0 +1,130 @@
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using Revise # remove when this package is completed
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using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, DataStructures
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using Base.Threads
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# ---------------------------------------------- 100 --------------------------------------------- #
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config = copy(JSON3.read("config.json"))
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instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1"
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client, connection = MakeConnection(config[:mqttServerInfo][:broker],
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config[:mqttServerInfo][:port])
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receiveUserMsgChannel = Channel{Dict}(4)
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receiveInternalMsgChannel = Channel{Dict}(4)
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msgMeta = GeneralUtils.generate_msgMeta(
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"N/A",
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replyTopic = config[:servicetopic][:mqtttopic] # ask frontend reply to this instance_chat_topic
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)
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agentConfig = Dict(
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:mqttServerInfo=> config[:mqttServerInfo],
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:receivemsg=> Dict(
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:prompt=> config[:servicetopic][:mqtttopic], # topic to receive prompt i.e. frontend send msg to this topic
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:internal=> instanceInternalTopic,
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),
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:externalservice=> config[:externalservice],
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)
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# Instantiate an agent
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tools=Dict( # update input format
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"askbox"=> Dict(
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: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>",
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: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>""",
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:output => "" ,
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:func => nothing,
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),
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# "winestock"=> Dict(
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# :description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
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# :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>""",
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# :output => """<output>Output are wines that match the search query in JSON format.""",
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# :func => ChatAgent.winestock,
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# ),
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"finalanswer"=> Dict(
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:description => "<tool description>Useful for when you are ready to recommend wines to the user.</tool description>",
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:input => """<input format>{\"finalanswer\": \"some text\"}.</input format><input example>{\"finalanswer\": \"I recommend Zena Crown Vista\"}</input example>""",
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:output => "" ,
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:func => nothing,
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),
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)
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a = YiemAgent.sommelier(
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receiveUserMsgChannel,
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receiveInternalMsgChannel,
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agentConfig,
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name= "assistant",
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id= "testingSessionID", # agent instance id
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tools=tools,
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)
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input =
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OrderedDict{Symbol, Any}(
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:question => "Hello, I would like a get a bottle of wine",
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:thought_1 => "A customer wants to buy a bottle of wine. Before making a recommendation, I need to know more about their preferences.",
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:action_1 => Dict{Symbol, Any}(
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:name => "chatbox",
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:input => "What is the occasion for which you're buying this wine?"
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),
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:observation_1 => "We are holding a wedding party",
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:thought_2 => "A wedding party, that's a great occasion! The customer might be looking for a celebratory drink. Let me ask some more questions to narrow down the options.",
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:action_2 => Dict{Symbol, Any}(
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:name => "chatbox",
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:input => "What type of food will you be serving at the wedding?"
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),
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:observation_2 => "It will be Thai dishes.",
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:thought_3 => "With Thai food, I should recommend a wine that complements its spicy and savory flavors. And since it's a celebratory occasion, the customer might prefer a full-bodied wine.",
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:action_3 => Dict{Symbol, Any}(
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:name => "chatbox",
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:input => "What is your budget for this bottle of wine?"
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),
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:observation_3 => "I would spend up to 50 bucks.",
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:thought_4 => "Now that I have some more information, it's time to narrow down the options.",
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:action_4 => Dict{Symbol, Any}(
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:name => "winestock",
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:input => "red wine with full body, pairs well with spicy food, budget \$50"),
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:observation_4 => "I found the following wines in our stock: \n{\n 1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n",
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:thought_5 => "Now that I have a list of potential wines, I need to know more about the customer's taste preferences.",
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:action_5 => Dict{Symbol, Any}(
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:name => "chatbox",
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:input => "What type of wine characteristics are you looking for? (e.g. t.e.g. tannin level, sweetness, intensity, acidity)"
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),
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:observation_5 => "I like full-bodied Red wine with low tannin.",
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:thought_6 => "Now that I have more information about the customer's preferences, it's time to make a recommendation.",
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:action_6 => Dict{Symbol, Any}(
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:name => "recommendbox",
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:input => "El Enemigo Cabernet Franc 2019"
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),
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:observation_6 => "I don't like the one you recommend. I want dry wine."
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)
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result = YiemAgent.reflector(a, input)
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