update
This commit is contained in:
narawat lamaiin
128
src/interface.jl
128
src/interface.jl
@@ -174,23 +174,59 @@ function decisionMaker(a::T)::Dict{Symbol, Any} where {T<:agent}
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# {"thought"
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# """
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# systemmsg =
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# """
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# You are a helpful sommelier working for a wine store.
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# Your task is to help the user choose the best wine that match the user preferences from your inventory.
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# You are also eager to improve your helpfulness.
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# You must follow the following guidelines:
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# - Get to know how much the user willing to spend
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# - Get to know type of wine the user is looking for e.g. red, white, sparkling, rose, dessert, fortified
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# - Get to know what occasion the user is buying wine for
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# - Get to know what characteristics of wine the user is looking for e.g. tannin, sweetness, intensity, acidity
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# - Get to know what food the user will have with wine
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# At each round of conversation, the user will give you the current situation:
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# Context: ...
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# Your earlier conversation with the user: ...
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# You should then respond to the user with interleaving Thought, Plan, Action and Observation:
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# - thought:
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# 1) State your reasoning about the current situation.
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# - plan: Based on the current situation, what would you do to complete the task? Be specific.
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# - action (Must be aligned with your plan): Can be one of the following functions:
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# 1) CHATBOX[text], which you can use to talk with the user. "text" is in verbal English.
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# 2) WINESTOCK[query], which you can use to find info about wine in your inventory. "query" is a search term in verbal English.
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# - observation: result of the action.
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# You should only respond in format as described below:
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# thought: ...
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# plan: ...
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# action_name: ...
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# action_input: ...
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# observation: ...
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# Let's begin!
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# """
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systemmsg =
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"""
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You are a helpful sommelier working for a wine store.
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Your task is to help the user choose the best wine that match the user preferences from your inventory.
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You are also eager to improve your helpfulness.
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You must follow the following guidelines:
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At each round of conversation, the user will give you the current situation:
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Context: ...
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Your earlier conversation with the user: ...
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You should follow the following guidelines:
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- Get to know how much the user willing to spend
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- Get to know type of wine the user is looking for e.g. red, white, sparkling, rose, dessert, fortified
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- Get to know what occasion the user is buying wine for
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- Get to know what characteristics of wine the user is looking for e.g. tannin, sweetness, intensity, acidity
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- Get to know what food the user will have with wine
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At each round of conversation, the user will give you the current situation:
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Context: ...
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Your earlier conversation with the user: ...
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You should then respond to the user with interleaving Thought, Plan, Action and Observation:
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- thought:
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1) State your reasoning about the current situation.
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@@ -213,7 +249,7 @@ function decisionMaker(a::T)::Dict{Symbol, Any} where {T<:agent}
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usermsg =
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"""
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Context: None
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Your earlier conversation with the user: $(chatHistoryToString(a))
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Your earlier conversation with the user: $(vectorOfDictToText(a.chathistory))
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"""
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_prompt =
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@@ -902,10 +938,11 @@ function conversation(a::T, userinput::Dict) where {T<:agent}
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# add usermsg to a.chathistory
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addNewMessage(a, "user", userinput[:text])
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thought = think(a)
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think(a)
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# thought will be added to chat model via context
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chatresponse = generatechat(a, thought)
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chatresponse = generatechat(a)
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addNewMessage(a, "assistant", chatresponse)
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return chatresponse
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end
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@@ -1024,8 +1061,12 @@ function think(a::T) where {T<:agent}
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isterminal::Bool = haskey(response, :isterminal) ? response[:isterminal] : false
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errormsg::Union{AbstractString, Nothing} = haskey(response, :errormsg) ? response[:errormsg] : nothing
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success::Bool = haskey(response, :success) ? response[:success] : false
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a.shortmem
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return result
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if actionname == "CHATBOX"
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a.memory[:chatbox] = result
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else
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push!(a.memory[:shortmem], Dict(Symbol(actionname)=> result))
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end
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end
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@@ -1049,27 +1090,22 @@ julia>
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# Signature
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"""
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function generatechat(a::T1, input::T2) where {T1<:agent, T2<:AbstractString}
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function generatechat(a::T) where {T<:agent}
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systemmsg =
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"""
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You are a helpful sommelier working for a wine store.
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Your task is to help the user choose the best wine that match the user preferences from your inventory.
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You are also eager to improve your helpfulness.
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You must follow the following guidelines:
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- Get to know how much the user willing to spend
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- Get to know type of wine the user is looking for e.g. red, white, sparkling, rose, dessert, fortified
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- Get to know what occasion the user is buying wine for
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- Get to know what characteristics of wine the user is looking for e.g. tannin, sweetness, intensity, acidity
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- Get to know what food the user will have with wine
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At each round of conversation, the user will give you:
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At each round of conversation, the user will give you the current situation:
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Context: ...
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Your thoughts: Your current thinking in your mind
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Your earlier conversation with the user: ...
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You must follow the following guidelines (if the user interrupts, prioritize the user):
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- Do not recommend specific wine before you checked your inventory.
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You should then respond to the user with:
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- chat: what do you want to say to the user
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- chat: what do you want to say to the user based on the current situation
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You should only respond in format as described below:
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chat: ...
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@@ -1077,11 +1113,13 @@ function generatechat(a::T1, input::T2) where {T1<:agent, T2<:AbstractString}
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Let's begin!
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"""
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context = length(a.memory[:shortmem]) > 0 ? vectorOfDictToText(a.memory[:shortmem], withkey=false) : "None"
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usermsg =
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"""
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Context: None
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Your thoughts: $input
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Your earlier conversation with the user: $(chatHistoryToString(a))
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Context: $context
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Your earlier conversation with the user: $(vectorOfDictToText(a.chathistory))
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Your thoughts: $(a.memory[:chatbox])
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"""
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_prompt =
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@@ -1099,31 +1137,31 @@ function generatechat(a::T1, input::T2) where {T1<:agent, T2<:AbstractString}
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for attempt in 1:5
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try
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response = text2textInstructLLM(prompt)
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response = a.text2textInstructLLM(prompt)
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responsedict = GeneralUtils.textToDict(response,
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["chat"],
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rightmarker=":", symbolkey=true)
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# check if dict has all required value
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evaluationtext::AbstractString = responsedict[:evaluation]
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responsedict[:score] = parse(Int, responsedict[:score]) # convert string "5" into integer 5
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score::Integer = responsedict[:score]
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accepted_as_answer::AbstractString = responsedict[:accepted_as_answer]
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suggestion::AbstractString = responsedict[:suggestion]
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# add to state here instead to in transition() because the latter causes julia extension crash (a bug in julia extension)
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state[:evaluation] = responsedict[:evaluation]
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state[:evaluationscore] = responsedict[:score]
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state[:accepted_as_answer] = responsedict[:accepted_as_answer]
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state[:suggestion] = responsedict[:suggestion]
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# mark as terminal state when the answer is achieved
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if accepted_as_answer == "Yes"
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state[:isterminal] = true
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state[:reward] = 1
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for i ∈ [:chat]
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if length(JSON3.write(responsedict[i])) == 0
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error("$i is empty ", @__LINE__)
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end
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end
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return responsedict[:score]
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# check if there are more than 1 key per categories
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for i ∈ [:chat]
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matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
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if length(matchkeys) > 1
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error("generatechat has more than one key per categories")
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end
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end
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result = responsedict[:chat]
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#[WORKING] delete chat
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a.memory[:chatbox] = ""
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return result
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catch e
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io = IOBuffer()
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showerror(io, e)
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@@ -1134,7 +1172,7 @@ function generatechat(a::T1, input::T2) where {T1<:agent, T2<:AbstractString}
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println("")
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end
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end
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error("evaluator failed to generate an evaluation")
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error("generatechat failed to generate an evaluation")
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