update
This commit is contained in:
narawat lamaiin
@@ -2,7 +2,7 @@
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julia_version = "1.10.4"
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manifest_format = "2.0"
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project_hash = "03625e2270b5f9b2a2b6b43af674dcefbd8f4f9d"
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project_hash = "bf6c32becbc917fa1c33558e7aa59c1aac5237e3"
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[[deps.AliasTables]]
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deps = ["PtrArrays", "Random"]
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@@ -647,7 +647,7 @@ uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
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version = "0.7.0"
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[[deps.SQLLLM]]
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deps = ["CSV", "CondaPkg", "DataFrames", "DataStructures", "Dates", "DispatchDoctor", "FileIO", "FormatCorrector", "GeneralUtils", "HTTP", "JSON3", "LLMMCTS", "LibPQ", "MQTTClient", "PrettyPrinting", "PythonCall", "Random", "Revise", "Tables", "URIs", "UUIDs"]
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deps = ["CSV", "CondaPkg", "DataFrames", "DataStructures", "Dates", "DispatchDoctor", "FileIO", "FormatCorrector", "GeneralUtils", "HTTP", "JSON3", "LLMMCTS", "LibPQ", "MQTTClient", "PrettyPrinting", "PythonCall", "Random", "Revise", "StatsBase", "Tables", "URIs", "UUIDs"]
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path = "/appfolder/app/privatejuliapkg/SQLLLM"
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uuid = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
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version = "0.1.0"
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@@ -18,5 +18,6 @@ PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
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SQLLLM = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
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Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
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URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
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UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
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1595
src/interface BACKUP 2.jl
Normal file
1595
src/interface BACKUP 2.jl
Normal file
File diff suppressed because it is too large
Load Diff
268
src/interface.jl
268
src/interface.jl
@@ -3,7 +3,7 @@ module interface
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export addNewMessage, conversation, decisionMaker, evaluator, reflector
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# isterminal,
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using JSON3, DataStructures, Dates, UUIDs, HTTP, Random, MQTTClient, PrettyPrinting
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using JSON3, DataStructures, Dates, UUIDs, HTTP, Random, MQTTClient, PrettyPrinting, Serialization
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using GeneralUtils, LLMMCTS
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using ..type, ..util, ..llmfunction
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@@ -80,7 +80,7 @@ julia> config = Dict(
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julia> output_thoughtDict = Dict(
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:thought_1 => "The customer wants to buy a bottle of wine. This is a good start!",
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:action_1 => Dict{Symbol, Any}(
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:action=>"Chatbox",
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:action=>"CHATBOX",
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:input=>"What occasion are you buying the wine for?"
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),
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:observation_1 => ""
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@@ -93,7 +93,6 @@ julia> output_thoughtDict = Dict(
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- [] implement RAG to pull similar experience
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- [] use customerinfo
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- [] user storeinfo
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- BUG LLM recommend wine before check inventory
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# Signature
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"""
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@@ -143,7 +142,7 @@ function decisionMaker(a::T)::Dict{Symbol, Any} where {T<:agent}
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# You should only respond with interleaving Thought, Action, Observation steps.
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# Thought can reason about the current situation, and Action can be three types:
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# 1) winestock[query], which you can use to find wine in your inventory. The more input data the better.
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# 2) chatbox[text], which you can use to interact with the user.
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# 2) CHATBOX[text], which you can use to interact with the user.
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# After each observation, provide the next Thought and next Action.
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# You should only respond in JSON format as describe below:
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@@ -162,7 +161,7 @@ function decisionMaker(a::T)::Dict{Symbol, Any} where {T<:agent}
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# }
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# {
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# "thought": "I have a few color for the user to choose from. I will ask him what color he likes.",
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# "action": {"name": "chatbox", "input": "Which color do you like?"}
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# "action": {"name": "CHATBOX", "input": "Which color do you like?"}
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# "observation": "I'll take black."
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# }
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@@ -210,42 +209,43 @@ function decisionMaker(a::T)::Dict{Symbol, Any} where {T<:agent}
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# Let's begin!
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# """
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# QandA = generatequestion(a, text2textInstructLLM)
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systemmsg =
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"""
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You are a website-based, attentive, and polite sommelier working for an online wine store. You are currently talking with the user.
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Your task is to help the user choose the best wine from your inventory that matches the user preferences.
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You are a internet-based, polite sommelier working for an online wine store.
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You are currently talking with the user.
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Your goal is to recommend the best wines from your inventory that match the user's preferences.
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Your current task is to decide what action to take so that you can achieve your goal.
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Definitions:
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"observation" is result of the preceding immediate action.
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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 conversation with the user: ...
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At each round of conversation, you will be given the current situation:
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Your ongoing conversation with the user: ...
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I found the best matched wines from inventory: ...
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You MUST follow the following guidelines:
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- Generally speaking, your inventory has some wines from France, the United States, Australia, Spain, and Italy but you won't know which wines are in stock until you check your inventory.
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- Use the "get to know the user's preferences, then check inventory" strategy to help the user, as there are many wines in the inventory.
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- After recommending wines to the user, ask if there is anything else you can help with, but do not offer any extra services. If the user doesn't need anything else, say thank you and goodbye.
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- Generally speaking, your inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know which wines your store carries until you check your inventory.
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- Use the "understand-then-check" inventory strategy to understand the user, as there are many wines in the inventory.
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- Do not ask the user about wine's flavor e.g. floral, citrusy, nutty or some thing similar.
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- After the user chose the wine, end the conversation politely.
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You should follow the following guidelines as you see fit:
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- If the user interrupts, prioritize the user.
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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 will be served with wine.
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- If you don't already know, find out the user's budget.
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- If you don't already know, find out the type of wine the user is looking for, such as red, white, sparkling, rose, dessert, fortified.
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- If you don't already know, find out the occasion for which the user is buying wine.
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- If you don't already know, find out the characteristics of wine the user is looking for, such as tannin, sweetness, intensity, acidity.
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- If you don't already know, find out what food will be served with wine.
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You should then respond to the user with interleaving Thought, Plan, Action and Observation:
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You should then respond to the user with interleaving Thought, Plan, Action:
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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, state a complete plan to complete the task. Be specific.
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- action_name (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) CHECKINVENTORY[query], which you can use to check info about wine in your inventory. "query" is a search term in verbal English.
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- action_name (Must be aligned with your plan): The name of the action which can be one of the following functions:
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1) CHATBOX [input], which you can use to generate conversation in order to communicate with the user. The input is your intention for the talk. Be specific.
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2) CHECKINVENTORY [input], which you can use to check info about wine in your inventory. The input is a search term in verbal English.
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Good query example: black car, a stereo, 200 mile range, electric motor.
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Good query example: How many car brand are from Asia?
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- action_input: input to the action
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- action_input: input details of the action
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- mentioning_wine: Are you mentioning specific wine name to the user? Can be "Yes" or "No"
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You should only respond in format as described below:
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@@ -257,25 +257,26 @@ function decisionMaker(a::T)::Dict{Symbol, Any} where {T<:agent}
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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) : "DO not recommending wine because inventory has not been searched yet"
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context =
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if length(a.memory[:shortmem]) > 0
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vectorOfDictToText(a.memory[:shortmem], withkey=false)
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x = vectorOfDictToText(a.memory[:shortmem], withkey=false)
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x = split(x, "More details:")
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y = x[2]
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"I have searched the inventory and this is what I found: $y"
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else
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"None"
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""
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end
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chathistory = vectorOfDictToText(a.chathistory)
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checkinventory_flag = ""
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errornote = ""
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response = nothing # placeholder for show when error msg show up
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for attempt in 1:10
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usermsg =
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"""
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Context: $context
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Your conversation with the user: $chathistory)
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$checkinventory_flag
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$context
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$errornote
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"""
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_prompt =
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@@ -296,14 +297,19 @@ function decisionMaker(a::T)::Dict{Symbol, Any} where {T<:agent}
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responsedict = GeneralUtils.textToDict(response,
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["thought", "plan", "action_name", "action_input", "mentioning_wine"],
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rightmarker=":", symbolkey=true)
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# if occursin('[', responsedict[:action_name])
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# action_input = GeneralUtils.getStringBetweenCharacters(responsedict[:action_name], '[', ']')
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# action_name = string(split(responsedict[:action_name], '[')[1])
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# end
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if responsedict[:action_name] ∉ ["CHATBOX", "CHECKINVENTORY"]
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error("decisionMaker didn't use the given functions ", @__LINE__)
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if responsedict[:action_name] ∉ ["RECOMMEMDBOX", "CHATBOX", "CHECKINVENTORY"]
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errornote = "You must use the given functions"
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error("You must use the given functions ", @__FILE__, " ", @__LINE__)
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end
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for i ∈ [:thought, :plan, :action_name]
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if length(responsedict[i]) == 0
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error("$i is empty ", @__LINE__)
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error("$i is empty ", @__FILE__, " ", @__LINE__)
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end
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end
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@@ -323,15 +329,19 @@ function decisionMaker(a::T)::Dict{Symbol, Any} where {T<:agent}
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isMemEmpty = isempty(a.memory[:shortmem])
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if occursin("Yes", responsedict[:mentioning_wine]) && isMemEmpty &&
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responsedict[:action_name] != "CHECKINVENTORY"
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checkinventory_flag = "Note: You must check your inventory before recommending wine to the user."
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errornote = "Note: You must check your inventory before recommending wine to the user."
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error( "You must check your inventory before recommending wine")
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else
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checkinventory_flag = ""
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errornote = ""
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end
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delete!(responsedict, :mentioning_wine)
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return responsedict
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# if length(a.memory[:shortmem]) > 0 && responsedict[:action_name] != "RECOMMEMDBOX"
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# errornote = "Note: You have found the best matched wines for the user. Use R them."
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# error("found wines but not recommending")
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# end
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return responsedict
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catch e
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io = IOBuffer()
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showerror(io, e)
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@@ -390,7 +400,7 @@ end
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# You should only respond with interleaving Thought, Action, Observation steps.
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# Thought can reason about the current situation, and Action can be three types:
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# 1) winestock[query], which you can use to find wine in your inventory. The more input data the better.
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# 2) chatbox[text], which you can use to interact with the user.
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# 2) CHATBOX[text], which you can use to interact with the user.
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# After each observation, provide the next Thought and next Action.
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# You should only respond in JSON format as describe below:
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@@ -409,7 +419,7 @@ end
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# }
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# {
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# "thought": "I have a few color for the user to choose from. I will ask him what color he likes.",
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# "action": {"name": "chatbox", "input": "Which color do you like?"}
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# "action": {"name": "CHATBOX", "input": "Which color do you like?"}
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# "observation": "I'll take black."
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# }
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@@ -472,7 +482,7 @@ end
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# thought::AbstractString = thoughtDict[:thought]
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# actionname::AbstractString = thoughtDict[:action][:name]
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# actioninput::AbstractString = thoughtDict[:action][:input]
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# if actionname ∈ ["winestock", "chatbox", "recommendbox"]
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# if actionname ∈ ["winestock", "CHATBOX", "recommendbox"]
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# # LLM use available function
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# elseif thought == ""
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# error("DecisionMaker has no thought")
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@@ -562,7 +572,7 @@ function evaluator(config::T1, state::T2
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"action_1": {"name": "inventory", "input": "pen with 4 color and a pencil."},
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"observation_1": "I found {1: "Pilot Dr. grip 4-in-1 pen", 2: "Rotting pencil"}",
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"thought_2": "Ok, I have what the user is asking. Let's tell the user.",
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"action_2": {"name": "chatbox", "input": "Yes, we do have a Pilot Dr. grip 4-in-1 pen and a Rotting pencil"},
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"action_2": {"name": "CHATBOX", "input": "Yes, we do have a Pilot Dr. grip 4-in-1 pen and a Rotting pencil"},
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"observation_1": "This is not what I wanted."
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}
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assistant:
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@@ -690,15 +700,15 @@ function reflector(config::T1, state::T2)::String where {T1<:AbstractDict, T2<:A
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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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"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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"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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"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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@@ -706,7 +716,7 @@ function reflector(config::T1, state::T2)::String where {T1<:AbstractDict, T2<:A
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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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"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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@@ -898,7 +908,7 @@ end
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# actioninput = bestNextState[:thoughtHistory][latestActionKey][:input]
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# # transition
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# if actionname == "chatbox"
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# if actionname == "CHATBOX"
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# # add usermsg to a.chathistory
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# addNewMessage(a, "assistant", actioninput)
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# return actioninput
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@@ -980,18 +990,16 @@ 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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actionname, result = think(a) #[WORKING] need to wrapped in a while loop to let it think UNTIL it use chatbox. Right now it has unsync bewteen think() and generatechat()
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# -------- use dummy memory to check generatechat() for halucination (checking inventory) -------- #
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mem = deepcopy(a.memory)
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if actionname == "CHATBOX"
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mem[:chatbox] = result
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else
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push!(mem[:shortmem], Dict(Symbol(actionname)=> result))
|
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# use dummy memory to check generatechat() for halucination (checking inventory)
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for i in 1:3
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actionname, result = think(a)
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if actionname == "CHATBOX"
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break
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end
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end
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||||
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||||
# thought will be added to chat model via context
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chatresponse = generatechat(mem, a.chathistory, a.text2textInstructLLM)
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chatresponse = generatechat(a.memory, a.chathistory, a.text2textInstructLLM)
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# some time LLM said to user that it (checking inventory) but it is not.
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# if chatresponse want to check inventory but think() didn't checkinventory then do it
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@@ -1004,14 +1012,7 @@ function conversation(a::T, userinput::Dict) where {T<:agent}
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# generate chatresponse again because we have force inventory check
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chatresponse = generatechat(a.memory, a.chathistory, a.text2textInstructLLM)
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else
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if actionname == "CHATBOX"
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# skip
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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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# since chatresponse does not halucinate i.e. no (check inventory), it does not need
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||||
# to regenerate again and con be use directly
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end
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@@ -1064,6 +1065,12 @@ function think(a::T)::NamedTuple{(:actionname, :result), Tuple{String, String}}
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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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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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||||
return (actionname=actionname, result=result)
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end
|
||||
|
||||
@@ -1217,19 +1224,17 @@ julia>
|
||||
function generatechat(memory::Dict, chathistory::Vector, text2textInstructLLM::Function)
|
||||
systemmsg =
|
||||
"""
|
||||
You are a website-based, attentive, and polite sommelier working for an online wine store. You are currently talking with the user.
|
||||
Your task is to help the user choose the best wine from your inventory that matches the user preferences.
|
||||
You are a website-based, polite sommelier working for an online wine store.
|
||||
You are currently talking with the user.
|
||||
Your task is to understand their preferences and then recommend the best wines from your inventory that match those preferences.
|
||||
|
||||
At each round of conversation, the user will give you the current situation:
|
||||
At each round of conversation, you will be given the current situation:
|
||||
Context: ...
|
||||
Your earlier conversation with the user: ...
|
||||
Your ongoing conversation with the user: ...
|
||||
Your current thoughts in your mind: ...
|
||||
|
||||
You must follow the following guidelines:
|
||||
- Your thoughts matter.
|
||||
|
||||
You should then respond to the user with:
|
||||
- chat: what do you want to say to the user based on the current situation
|
||||
- chat: Your conversation with the user according to your thoughts.
|
||||
- mentioning_wine: Are you mentioning specific wine name to the user? Can be "Yes" or "No"
|
||||
|
||||
You should only respond in format as described below:
|
||||
@@ -1239,18 +1244,27 @@ function generatechat(memory::Dict, chathistory::Vector, text2textInstructLLM::F
|
||||
Let's begin!
|
||||
"""
|
||||
|
||||
context_1 = length(memory[:shortmem]) > 0 ? vectorOfDictToText(memory[:shortmem], withkey=false) : "None"
|
||||
context =
|
||||
if length(memory[:shortmem]) > 0
|
||||
x = vectorOfDictToText(memory[:shortmem], withkey=false)
|
||||
x = split(x, "More details:")
|
||||
y = x[2]
|
||||
"I have searched the inventory and this is what I found: $y"
|
||||
else
|
||||
""
|
||||
end
|
||||
|
||||
chathistory = vectorOfDictToText(chathistory)
|
||||
checkinventory_flag = ""
|
||||
errornote = ""
|
||||
response = nothing # placeholder for show when error msg show up
|
||||
|
||||
for attempt in 1:5
|
||||
usermsg =
|
||||
"""
|
||||
Context: $context_1
|
||||
Your earlier conversation with the user: $chathistory)
|
||||
Your thoughts: $(memory[:chatbox])
|
||||
$checkinventory_flag
|
||||
Your conversation with the user: $chathistory)
|
||||
$context
|
||||
Your thoughts: $(memory[:CHATBOX])
|
||||
$errornote
|
||||
"""
|
||||
|
||||
_prompt =
|
||||
@@ -1292,14 +1306,14 @@ function generatechat(memory::Dict, chathistory::Vector, text2textInstructLLM::F
|
||||
# check if LLM recommend wine before checking inventory
|
||||
isMemEmpty = isempty(memory[:shortmem])
|
||||
if occursin("Yes", responsedict[:mentioning_wine]) && isMemEmpty
|
||||
checkinventory_flag = "Note: You must check your inventory before recommending wine to the user."
|
||||
errornote = "Note: You must check your inventory before recommending wine to the user."
|
||||
error( "You must check your inventory before recommending wine")
|
||||
else
|
||||
checkinventory_flag = ""
|
||||
errornote = ""
|
||||
end
|
||||
|
||||
memory[:CHATBOX] = "" # delete content because it no longer used.
|
||||
delete!(responsedict, :mentioning_wine)
|
||||
|
||||
result = responsedict[:chat]
|
||||
|
||||
return result
|
||||
@@ -1317,6 +1331,92 @@ function generatechat(memory::Dict, chathistory::Vector, text2textInstructLLM::F
|
||||
end
|
||||
|
||||
|
||||
# function generatequestion(a, text2textInstructLLM::Function)::String
|
||||
|
||||
# systemmsg =
|
||||
# """
|
||||
# You are a helpful sommelier that generate multiple questions about the current situation.
|
||||
|
||||
# At each round of conversation, you will be given the current situation:
|
||||
# User query: What's the user preferences about wine?
|
||||
# Your work progress: ...
|
||||
|
||||
# You must follow the following guidelines:
|
||||
# 1) Ask at least three questions but no more than five.
|
||||
# 2) Your question should be specific, self-contained and not require any additional context.
|
||||
# 3) Do not generate any question or comments at the end.
|
||||
|
||||
# You should then respond to the user with:
|
||||
# - Reasoning: State your detailed reasoning of the current situation
|
||||
# - Q: Your question
|
||||
# - A: Your answer to the question.
|
||||
|
||||
|
||||
# You must only respond in format as described below:
|
||||
# Reasoning: ...
|
||||
# Q 1: ...
|
||||
# A 1: ...
|
||||
# Q 2: ...
|
||||
# A 2: ...
|
||||
# Q 3: ...
|
||||
# A 3: ...
|
||||
# ...
|
||||
|
||||
# Let's begin!
|
||||
# """
|
||||
|
||||
# workprogress = ""
|
||||
# for (k, v) in state[:thoughtHistory]
|
||||
# if k ∉ [:query]
|
||||
# workprogress *= "$k: $v\n"
|
||||
# end
|
||||
# end
|
||||
|
||||
# usermsg =
|
||||
# """
|
||||
# $(context[:tablelist])
|
||||
# User query: $(state[:thoughtHistory][:question])
|
||||
# Your work progress: $workprogress
|
||||
# """
|
||||
|
||||
# _prompt =
|
||||
# [
|
||||
# Dict(:name=> "system", :text=> systemmsg),
|
||||
# Dict(:name=> "user", :text=> usermsg)
|
||||
# ]
|
||||
|
||||
# # put in model format
|
||||
# prompt = GeneralUtils.formatLLMtext(_prompt, "llama3instruct")
|
||||
# prompt *=
|
||||
# """
|
||||
# <|start_header_id|>assistant<|end_header_id|>
|
||||
# """
|
||||
# response = nothing # store for show when error msg show up
|
||||
# for attempt in 1:10
|
||||
# try
|
||||
# response = text2textInstructLLM(prompt)
|
||||
# q_number = count("Q ", response)
|
||||
# if q_number < 3
|
||||
# error("too few questions only $q_number questions are generated ", @__FILE__, " ", @__LINE__)
|
||||
# end
|
||||
# println("--> generatequestion ", @__FILE__, " ", @__LINE__)
|
||||
# pprintln(response)
|
||||
# return response
|
||||
# catch e
|
||||
# io = IOBuffer()
|
||||
# showerror(io, e)
|
||||
# errorMsg = String(take!(io))
|
||||
# st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
|
||||
# println("")
|
||||
# println("Attempt $attempt. Error occurred: $errorMsg\n$st")
|
||||
# println("")
|
||||
# end
|
||||
# end
|
||||
# error("generatequestion failed to generate a thought ", response)
|
||||
# end
|
||||
|
||||
|
||||
|
||||
|
||||
# """
|
||||
|
||||
@@ -1349,7 +1449,7 @@ end
|
||||
# labeled by environmental observations about the situation, thoughts that can reason about
|
||||
# the current situation and actions that can be three types:
|
||||
# 1) winestock[query], which you can use to find wine in your inventory.
|
||||
# 2) chatbox[text], which you can use to interact with the user.
|
||||
# 2) CHATBOX[text], which you can use to interact with the user.
|
||||
# 3) recommendbox[answer], which returns your wine recommendation to the user.
|
||||
|
||||
# Given a question and a trajectory, evaluate its correctness and provide your reasoning and
|
||||
@@ -1381,7 +1481,7 @@ end
|
||||
# "action_1": {"name": "inventory", "input": "pen with 4 color and a pencil."},
|
||||
# "observation_1": "I found {1: "Pilot Dr. grip 4-in-1 pen", 2: "Rotting pencil"}",
|
||||
# "thought_2": "Ok, I have what the user is asking. Let's tell the user.",
|
||||
# "action_2": {"name": "chatbox", "input": "Yes, we do have a Pilot Dr. grip 4-in-1 pen and a Rotting pencil"},
|
||||
# "action_2": {"name": "CHATBOX", "input": "Yes, we do have a Pilot Dr. grip 4-in-1 pen and a Rotting pencil"},
|
||||
# "observation_1": "This is not what I wanted."
|
||||
# }
|
||||
# {"evaluation": "This trajectory is incorrect as my search term should be related to a 4-colors pen with a pencil in it,
|
||||
|
||||
@@ -345,6 +345,7 @@ function extractWineAttributes_1(a::T1, input::T2
|
||||
- occasion: ...
|
||||
- food_to_be_paired_with_wine: food that the user will be served with wine
|
||||
- country: wine's country of origin
|
||||
- region: wine's region of origin such as Burgundy, Napa Valley
|
||||
- grape variety: a single name of grape used to make wine.
|
||||
- flavors: Names of items that the wine tastes like.
|
||||
- aromas: wine's aroma
|
||||
@@ -356,6 +357,7 @@ function extractWineAttributes_1(a::T1, input::T2
|
||||
occasion: ...
|
||||
food_to_be_paired_with_wine: ...
|
||||
country: ...
|
||||
region: ...
|
||||
grape_variety: ...
|
||||
flavors: ...
|
||||
aromas: ...
|
||||
@@ -363,29 +365,29 @@ function extractWineAttributes_1(a::T1, input::T2
|
||||
Let's begin!
|
||||
"""
|
||||
|
||||
# chathistory = vectorOfDictToText(a.chathistory)
|
||||
|
||||
usermsg =
|
||||
"""
|
||||
User's query: $input
|
||||
"""
|
||||
|
||||
_prompt =
|
||||
[
|
||||
Dict(:name=> "system", :text=> systemmsg),
|
||||
Dict(:name=> "user", :text=> usermsg)
|
||||
]
|
||||
|
||||
# put in model format
|
||||
prompt = GeneralUtils.formatLLMtext(_prompt, "llama3instruct")
|
||||
prompt *=
|
||||
"""
|
||||
<|start_header_id|>assistant<|end_header_id|>
|
||||
"""
|
||||
|
||||
attributes = ["reasoning", "wine_type", "price", "occasion", "food_to_be_paired_with_wine", "country", "grape_variety", "flavors", "aromas"]
|
||||
attributes = ["reasoning", "wine_type", "price", "occasion", "food_to_be_paired_with_wine", "country", "region", "grape_variety", "flavors", "aromas"]
|
||||
errornote = ""
|
||||
for attempt in 1:5
|
||||
|
||||
usermsg =
|
||||
"""
|
||||
User's query: $input
|
||||
$errornote
|
||||
"""
|
||||
|
||||
_prompt =
|
||||
[
|
||||
Dict(:name=> "system", :text=> systemmsg),
|
||||
Dict(:name=> "user", :text=> usermsg)
|
||||
]
|
||||
|
||||
# put in model format
|
||||
prompt = GeneralUtils.formatLLMtext(_prompt, "llama3instruct")
|
||||
prompt *=
|
||||
"""
|
||||
<|start_header_id|>assistant<|end_header_id|>
|
||||
"""
|
||||
|
||||
try
|
||||
response = a.text2textInstructLLM(prompt)
|
||||
responsedict = GeneralUtils.textToDict(response, attributes, rightmarker=":", symbolkey=true)
|
||||
@@ -397,8 +399,20 @@ function extractWineAttributes_1(a::T1, input::T2
|
||||
end
|
||||
|
||||
#[PENDING] check if grape_variety has more than 1 name
|
||||
if length(split(responsedict[:grape_variety], ",")) > 1
|
||||
error("multiple name in grape_variety is not allowed")
|
||||
x = length(split(responsedict[:grape_variety], ",")) * length(split(responsedict[:grape_variety], "/"))
|
||||
if x > 1
|
||||
errornote = "only a single name in grape_variety is allowed"
|
||||
error("only a single grape_variety name is allowed")
|
||||
end
|
||||
x = length(split(responsedict[:country], ",")) * length(split(responsedict[:country], "/"))
|
||||
if x > 1
|
||||
errornote = "only a single name in country is allowed"
|
||||
error("only a single country name is allowed")
|
||||
end
|
||||
x = length(split(responsedict[:region], ",")) * length(split(responsedict[:region], "/"))
|
||||
if x > 1
|
||||
errornote = "only a single name in region is allowed"
|
||||
error("only a single region name is allowed")
|
||||
end
|
||||
|
||||
responsedict[:flavors] = replace(responsedict[:flavors], "notes"=>"")
|
||||
|
||||
9
test/etc.jl
Normal file
9
test/etc.jl
Normal file
@@ -0,0 +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)
|
||||
|
||||
|
||||
@@ -88,9 +88,9 @@ end
|
||||
main()
|
||||
|
||||
"""
|
||||
I'm having a graduation party this evening. I have unlimited budget. I want a bottle of dry red wine.
|
||||
It will be a casual party with no food serving.
|
||||
I'm open to suggestion since I have no specific idea about wine other than I like full bodied wine from France.
|
||||
I'm joining a graduation party this evening. I have unlimited budget. I want a bottle of dry red wine.
|
||||
Well, it is a small casual party with close friend and no food serving.
|
||||
I'm open to suggestion since I have no specific idea about wine but I like full bodied wine from France.
|
||||
The latter one seems nice.
|
||||
|
||||
"""
|
||||
|
||||
Reference in New Issue
Block a user