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@@ -1,7 +1,7 @@
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name = "YiemAgent"
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uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
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authors = ["narawat lamaiin <narawat@outlook.com>"]
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version = "0.1.1"
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version = "0.1.3"
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[deps]
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DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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@@ -22,6 +22,6 @@ UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
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[compat]
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DataFrames = "1.7.0"
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GeneralUtils = "0.1.0"
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GeneralUtils = "0.1, 0.2"
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LLMMCTS = "0.1.2"
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SQLLLM = "0.2.0"
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1109
src/interface.jl
1109
src/interface.jl
File diff suppressed because it is too large
Load Diff
@@ -1,7 +1,8 @@
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module llmfunction
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export virtualWineUserChatbox, jsoncorrection, checkinventory, # recommendbox,
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virtualWineUserRecommendbox, userChatbox, userRecommendbox, extractWineAttributes_1
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virtualWineUserRecommendbox, userChatbox, userRecommendbox, extractWineAttributes_1,
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extractWineAttributes_2, paraphrase
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using HTTP, JSON3, URIs, Random, PrettyPrinting, UUIDs, Dates
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using GeneralUtils, SQLLLM
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@@ -290,20 +291,20 @@ julia> result = checkinventory(agent, input)
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function checkinventory(a::T1, input::T2
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) where {T1<:agent, T2<:AbstractString}
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println("\n~~~ checkinventory order: $input ", @__FILE__, " ", @__LINE__)
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println("\n~~~ checkinventory order: $input ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
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wineattributes_1 = extractWineAttributes_1(a, input)
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wineattributes_2 = extractWineAttributes_2(a, input)
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_inventoryquery = "retailer name: $(a.retailername), $wineattributes_1, $wineattributes_2"
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inventoryquery = "Retrieves winery, wine_name, vintage, region, country, wine_type, grape, serving_temperature, sweetness, intensity, tannin, acidity, tasting_notes, price and currency of wines that match the following criteria - {$_inventoryquery}"
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println("~~~ checkinventory input: $inventoryquery ", @__FILE__, " ", @__LINE__)
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println("~~~ checkinventory input: $inventoryquery ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
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# add suppport for similarSQLVectorDB
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textresult, rawresponse = SQLLLM.query(inventoryquery, a.func[:executeSQL],
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a.func[:text2textInstructLLM],
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insertSQLVectorDB=a.func[:insertSQLVectorDB],
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similarSQLVectorDB=a.func[:similarSQLVectorDB])
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println("\n~~~ checkinventory result ", @__FILE__, " ", @__LINE__)
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println("\n~~~ checkinventory result ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
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println(textresult)
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return (result=textresult, rawresponse=rawresponse, success=true, errormsg=nothing)
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@@ -325,234 +326,67 @@ julia>
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# TODO
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- [] update docstring
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- [x] implement the function
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- implement the function
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# Signature
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"""
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# function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<:AbstractString}
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# systemmsg =
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# """
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# As a helpful sommelier, your task is to extract the user information from the user's query as much as possible to fill out user's preference form.
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# At each round of conversation, the user will give you the current situation:
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# User's query: ...
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# You must follow the following guidelines:
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# 1) If specific information required in the preference form is not available in the query or there isn't any, mark with "NA" to indicate this.
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# Additionally, words like 'any' or 'unlimited' mean no information is available.
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# 2) Do not generate other comments.
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# You should then respond to the user with the following points:
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# - reasoning: state your understanding of the current situation
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# - wine_name: name of the wine
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# - winery: name of the winery
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# - vintage: the year of the wine
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# - region: a region in a country where the wine is produced, such as Burgundy, Napa Valley, etc
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# - country: a country where the wine is produced. Can be "Austria", "Australia", "France", "Germany", "Italy", "Portugal", "Spain", "United States"
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# - wine_type: can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
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# - grape_variety: the name of the primary grape used to make the wine
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# - tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
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# - wine_price: price of wine. For example, up to 100, less than 100, 20 to 100, 30-79.95
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# - occasion: the occasion the user is having the wine for
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# - food_to_be_paired_with_wine: food that the user will be served with the wine such as poultry, fish, steak, etc
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# You should only respond in the user's preference form as described below:
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# reasoning: ...
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# winery: ...
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# wine_name: ...
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# vintage: ...
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# region: ...
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# country: ...
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# wine_type: ...
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# grape_variety: ...
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# tasting_notes: ...
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# wine_price: ...
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# occasion: ...
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# food_to_be_paired_with_wine: ...
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# Here are some example:
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# User's query: red, Chenin Blanc, Riesling, under 20
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# reasoning: ...
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# winery: NA
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# wine_name: NA
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# vintage: NA
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# region: NA
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# country: NA
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# wine_type: red
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# grape_variety: Chenin Blanc, Riesling
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# tasting_notes: NA
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# wine_price: under 20
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# occasion: NA
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# food_to_be_paired_with_wine: NA
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# User's query: Domaine du Collier Saumur Blanc 2019, France, white, Chenin Blanc
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# reasoning: ...
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# winery: Domaine du Collier
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# wine_name: Saumur Blanc
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# vintage: 2019
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# region: Saumur
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# country: France
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# wine_type: white
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# grape_variety: Chenin Blanc
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# tasting_notes: NA
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# wine_price: 109
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# occasion: NA
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# food_to_be_paired_with_wine: NA
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# Let's begin!
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# """
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# attributes = ["reasoning", "winery", "wine_name", "vintage", "region", "country", "wine_type", "grape_variety", "tasting_notes", "wine_price", "occasion", "food_to_be_paired_with_wine"]
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# errornote = ""
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# maxattempt = 5
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# for attempt in 1:maxattempt
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# usermsg =
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# """
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# User's query: $input
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# $errornote
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# """
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# _prompt =
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# [
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# Dict(:name=> "system", :text=> systemmsg),
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# Dict(:name=> "user", :text=> usermsg)
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# ]
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# # put in model format
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# prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
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# prompt *=
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# """
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# <|start_header_id|>assistant<|end_header_id|>
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# """
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# try
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# response = a.func[:text2textInstructLLM](prompt)
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# response = GeneralUtils.remove_french_accents(response)
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# # check wheter all attributes are in the response
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# for word in attributes
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# if !occursin(word, response)
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# error("$word attribute is missing")
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# end
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# end
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# responsedict = GeneralUtils.textToDict(response, attributes, rightmarker=":", symbolkey=true)
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# for i ∈ attributes
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# if length(JSON3.write(responsedict[Symbol(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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# #check if the following attributes has more than 1 name
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# # responsedict[:grape_variety] = split(responsedict[:grape_variety], ',')[1]
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# # responsedict[:grape_variety] = split(responsedict[:grape_variety], '/')[1]
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# responsedict[:country] = split(responsedict[:country], ',')[1]
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# responsedict[:country] = split(responsedict[:country], '/')[1]
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# responsedict[:region] = split(responsedict[:region], ',')[1]
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# responsedict[:region] = split(responsedict[:region], '/')[1]
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# delete!(responsedict, :reasoning)
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# delete!(responsedict, :tasting_notes)
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# delete!(responsedict, :occasion)
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# delete!(responsedict, :food_to_be_paired_with_wine)
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# # check if winery, wine_name, region, country, wine_type, grape_variety are in the query because sometime AI halucinates
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# for i in [:grape_variety, :winery, :wine_name, :region]
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# result = check_key_in_input(input, responsedict, attempt, maxattempt, i)
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# if result === nothing
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# # nothing wrong
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# elseif result == "NA"
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# responsedict[i] = "NA"
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# else
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# errornote = result
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# error(errornote)
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# end
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# end
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# # remove (some text)
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# for (k, v) in responsedict
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# _v = replace(v, r"\(.*?\)" => "")
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# responsedict[k] = _v
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# end
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# result = ""
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# for (k, v) in responsedict
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# # some time LLM generate text with "(some comment)". this line removes it
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# if !occursin("NA", v) && v != "" && !occursin("none", v) && !occursin("None", v)
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# result *= "$k: $v, "
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# end
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||||
# end
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# #[PENDING] remove halucination. "highend dry white wine" --> "wine_type: white, occasion: special occasion, food_to_be_paired_with_wine: seafood, fish, country: France, Italy, USA, grape_variety: Chardonnay, Sauvignon Blanc, Pinot Grigio\nwine_notes: citrus, green apple, floral"
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# result = result[1:end-2] # remove the ending ", "
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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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# errorMsg = String(take!(io))
|
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# st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
|
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# println("")
|
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# println("Attempt $attempt. Error occurred: $errorMsg\n$st ", @__FILE__, " ", @__LINE__)
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# println("")
|
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# end
|
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# end
|
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# error("wineattributes_wordToNumber() failed to get a response")
|
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# end
|
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function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<:AbstractString}
|
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|
||||
systemmsg =
|
||||
"""
|
||||
As a helpful sommelier, your task is to extract the user information from the user's query as much as possible to fill out user's preference form.
|
||||
|
||||
At each round of conversation, the user will give you the current situation:
|
||||
At each round of conversation, the user will give you the following:
|
||||
User's query: ...
|
||||
|
||||
You must follow the following guidelines:
|
||||
1) If specific information required in the preference form is not available in the query or there isn't any, mark with "NA" to indicate this.
|
||||
- If specific information required in the preference form is not available in the query or there isn't any, mark with "NA" to indicate this.
|
||||
Additionally, words like 'any' or 'unlimited' mean no information is available.
|
||||
2) Do not generate other comments.
|
||||
- Do not generate other comments.
|
||||
|
||||
You should then respond to the user with the following points:
|
||||
- reasoning: state your understanding of the current situation
|
||||
- wine_name: name of the wine
|
||||
- winery: name of the winery
|
||||
- vintage: the year of the wine
|
||||
- region: a region (NOT a country) where the wine is produced, such as Burgundy, Napa Valley, etc
|
||||
- country: a country where the wine is produced. Can be "Austria", "Australia", "France", "Germany", "Italy", "Portugal", "Spain", "United States"
|
||||
- wine_type: can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
|
||||
- grape_variety: the name of the primary grape used to make the wine
|
||||
- tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
|
||||
- wine_price: price of wine. For example, up to 100, less than 100, 20 to 100, 30-79.95
|
||||
- occasion: the occasion the user is having the wine for
|
||||
- food_to_be_paired_with_wine: food that the user will be served with the wine such as poultry, fish, steak, etc
|
||||
|
||||
You should then respond to the user with:
|
||||
Comprehension: state your understanding of the current situation
|
||||
Wine_name: name of the wine
|
||||
Winery: name of the winery
|
||||
Vintage: the year of the wine
|
||||
Region: a region (NOT a country) where the wine is produced, such as Burgundy, Napa Valley, etc
|
||||
Country: a country where the wine is produced. Can be "Austria", "Australia", "France", "Germany", "Italy", "Portugal", "Spain", "United States"
|
||||
Wine_type: can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
|
||||
Grape_varietal: the name of the primary grape used to make the wine
|
||||
Tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
|
||||
Wine_price: price range of wine.
|
||||
Occasion: the occasion the user is having the wine for
|
||||
Food_to_be_paired_with_wine: food that the user will be served with the wine such as poultry, fish, steak, etc
|
||||
|
||||
You should only respond in the user's preference form (JSON) as described below:
|
||||
{"reasoning": ..., "winery": ..., "wine_name": ..., "vintage": ..., "region": ..., "country": ..., "wine_type": ..., "grape_variety": ..., "tasting_notes": ..., "wine_price": ..., "occasion": ..., "food_to_be_paired_with_wine": ...}
|
||||
You should only respond in format as described below:
|
||||
Comprehension: ...
|
||||
Wine_name: ...
|
||||
Winery: ...
|
||||
Vintage: ...
|
||||
Region: ...
|
||||
Country: ...
|
||||
Wine_type:
|
||||
Grape_varietal: ...
|
||||
Tasting_notes: ...
|
||||
Wine_price: ...
|
||||
Occasion: ...
|
||||
Food_to_be_paired_with_wine: ...
|
||||
|
||||
Here are some example:
|
||||
User's query: red, Chenin Blanc, Riesling, under 20
|
||||
{"reasoning": ..., "winery": "NA", "wine_name": "NA", "vintage": "NA", "region": "NA", "country": "NA", "wine_type": "red", "grape_variety": "Chenin Blanc, Riesling", "tasting_notes": "NA", "wine_price": "under 20", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}
|
||||
User's query: red, Chenin Blanc, Riesling, 20 USD
|
||||
{"reasoning": ..., "winery": "NA", "wine_name": "NA", "vintage": "NA", "region": "NA", "country": "NA", "wine_type": "red, white", "grape_varietal": "Chenin Blanc, Riesling", "tasting_notes": "NA", "wine_price": "0-20", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}
|
||||
|
||||
User's query: Domaine du Collier Saumur Blanc 2019, France, white, Chenin Blanc
|
||||
{"reasoning": ..., "winery": "Domaine du Collier", "wine_name": "Saumur Blanc", "vintage": "2019", "region": "Saumur", "country": "France", "wine_type": "white", "grape_variety": "Chenin Blanc", "tasting_notes": "NA", "wine_price": "109", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}
|
||||
{"reasoning": ..., "winery": "Domaine du Collier", "wine_name": "Saumur Blanc", "vintage": "2019", "region": "Saumur", "country": "France", "wine_type": "white", "grape_varietal": "Chenin Blanc", "tasting_notes": "NA", "wine_price": "NA", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}
|
||||
|
||||
Let's begin!
|
||||
"""
|
||||
|
||||
attributes = ["reasoning", "winery", "wine_name", "vintage", "region", "country", "wine_type", "grape_variety", "tasting_notes", "wine_price", "occasion", "food_to_be_paired_with_wine"]
|
||||
header = ["Comprehension:", "Wine_name:", "Winery:", "Vintage:", "Region:", "Country:", "Wine_type:", "Grape_varietal:", "Tasting_notes:", "Wine_price:", "Occasion:", "Food_to_be_paired_with_wine:"]
|
||||
dictkey = ["comprehension", "wine_name", "winery", "vintage", "region", "country", "wine_type", "grape_varietal", "tasting_notes", "wine_price", "occasion", "food_to_be_paired_with_wine"]
|
||||
errornote = ""
|
||||
maxattempt = 5
|
||||
for attempt in 1:maxattempt
|
||||
|
||||
|
||||
for attempt in 1:5
|
||||
usermsg =
|
||||
"""
|
||||
User's query: $input
|
||||
@@ -566,76 +400,116 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
|
||||
]
|
||||
|
||||
# put in model format
|
||||
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
|
||||
prompt *=
|
||||
"""
|
||||
<|start_header_id|>assistant<|end_header_id|>
|
||||
"""
|
||||
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
|
||||
response = a.func[:text2textInstructLLM](prompt)
|
||||
response = GeneralUtils.remove_french_accents(response)
|
||||
|
||||
try
|
||||
response = a.func[:text2textInstructLLM](prompt)
|
||||
response = GeneralUtils.remove_french_accents(response)
|
||||
|
||||
# check wheter all attributes are in the response
|
||||
for word in attributes
|
||||
if !occursin(word, response)
|
||||
error("$word attribute is missing")
|
||||
end
|
||||
# check wheter all attributes are in the response
|
||||
checkFlag = false
|
||||
for word in header
|
||||
if !occursin(word, response)
|
||||
errornote = "$word attribute is missing in previous attempts"
|
||||
println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
|
||||
checkFlag = true
|
||||
break
|
||||
end
|
||||
end
|
||||
checkFlag == true ? continue : nothing
|
||||
|
||||
# check whether response has all header
|
||||
detected_kw = GeneralUtils.detect_keyword(header, response)
|
||||
if sum(values(detected_kw)) < length(header)
|
||||
errornote = "\nYiemAgent extractWineAttributes_1() response does not have all header"
|
||||
continue
|
||||
elseif sum(values(detected_kw)) > length(header)
|
||||
errornote = "\nYiemAgent extractWineAttributes_1() response has duplicated header"
|
||||
continue
|
||||
end
|
||||
responsedict = GeneralUtils.textToDict(response, header;
|
||||
dictKey=dictkey, symbolkey=true)
|
||||
|
||||
responsedict = copy(JSON3.read(response))
|
||||
delete!(responsedict, :comprehension)
|
||||
delete!(responsedict, :tasting_notes)
|
||||
delete!(responsedict, :occasion)
|
||||
delete!(responsedict, :food_to_be_paired_with_wine)
|
||||
|
||||
delete!(responsedict, :reasoning)
|
||||
delete!(responsedict, :tasting_notes)
|
||||
delete!(responsedict, :occasion)
|
||||
delete!(responsedict, :food_to_be_paired_with_wine)
|
||||
println(@__FILE__, " ", @__LINE__)
|
||||
pprintln(responsedict)
|
||||
|
||||
# check if winery, wine_name, region, country, wine_type, grape_variety are in the query because sometime AI halucinates
|
||||
for i in [:grape_variety, :winery, :wine_name, :region]
|
||||
content = responsedict[i]
|
||||
if occursin(",", content)
|
||||
content = split(content, ",") # sometime AI generates multiple values e.g. "Chenin Blanc, Riesling"
|
||||
content = strip.(content)
|
||||
# check if winery, wine_name, region, country, wine_type, grape_varietal's value are in the query because sometime AI halucinates
|
||||
checkFlag = false
|
||||
for i in dictkey
|
||||
j = Symbol(i)
|
||||
if j ∉ [:comprehension, :tasting_notes, :occasion, :food_to_be_paired_with_wine]
|
||||
# in case j is wine_price it needs to be checked differently because its value is ranged
|
||||
if j == :wine_price
|
||||
if responsedict[:wine_price] != "NA"
|
||||
# check whether wine_price is in ranged number
|
||||
if !occursin('-', responsedict[:wine_price])
|
||||
errornote = "wine_price must be a range number"
|
||||
println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
|
||||
checkFlag = true
|
||||
break
|
||||
end
|
||||
|
||||
# check whether max wine_price is in the input
|
||||
pricerange = split(responsedict[:wine_price], '-')
|
||||
minprice = pricerange[1]
|
||||
maxprice = pricerange[end]
|
||||
if !occursin(maxprice, input)
|
||||
responsedict[:wine_price] = "NA"
|
||||
end
|
||||
# price range like 100-100 is not good
|
||||
if minprice == maxprice
|
||||
errornote = "wine_price with minimum equals to maximum is not valid"
|
||||
println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
|
||||
checkFlag = true
|
||||
break
|
||||
end
|
||||
end
|
||||
else
|
||||
content = [content]
|
||||
end
|
||||
content = responsedict[j]
|
||||
if typeof(content) <: AbstractVector
|
||||
content = strip.(content)
|
||||
elseif occursin(',', content)
|
||||
content = split(content, ",") # sometime AI generates multiple values e.g. "Chenin Blanc, Riesling"
|
||||
content = strip.(content)
|
||||
else
|
||||
content = [content]
|
||||
end
|
||||
|
||||
for x in content
|
||||
if !occursin("NA", responsedict[i]) && !occursin(x, input)
|
||||
errornote = "$x is not mentioned in the user query, you must only use the info from the query."
|
||||
error(errornote)
|
||||
for x in content #check whether price are mentioned in the input
|
||||
if !occursin("NA", responsedict[j]) && !occursin(x, input)
|
||||
errornote = "$x is not mentioned in the user query, you must only use the info from the query."
|
||||
println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
|
||||
checkFlag == true
|
||||
break
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
# remove (some text)
|
||||
for (k, v) in responsedict
|
||||
_v = replace(v, r"\(.*?\)" => "")
|
||||
responsedict[k] = _v
|
||||
end
|
||||
|
||||
result = ""
|
||||
for (k, v) in responsedict
|
||||
# some time LLM generate text with "(some comment)". this line removes it
|
||||
if !occursin("NA", v) && v != "" && !occursin("none", v) && !occursin("None", v)
|
||||
result *= "$k: $v, "
|
||||
end
|
||||
end
|
||||
|
||||
#[PENDING] remove halucination. "highend dry white wine" --> "wine_type: white, occasion: special occasion, food_to_be_paired_with_wine: seafood, fish, country: France, Italy, USA, grape_variety: Chardonnay, Sauvignon Blanc, Pinot Grigio\nwine_notes: citrus, green apple, floral"
|
||||
|
||||
result = result[1:end-2] # remove the ending ", "
|
||||
|
||||
return result
|
||||
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 ", @__FILE__, " ", @__LINE__)
|
||||
println("")
|
||||
end
|
||||
checkFlag == true ? continue : nothing # skip the rest code if true
|
||||
|
||||
# remove (some text)
|
||||
for (k, v) in responsedict
|
||||
_v = replace(v, r"\(.*?\)" => "")
|
||||
responsedict[k] = _v
|
||||
end
|
||||
|
||||
result = ""
|
||||
for (k, v) in responsedict
|
||||
# some time LLM generate text with "(some comment)". this line removes it
|
||||
if !occursin("NA", v) && v != "" && !occursin("none", v) && !occursin("None", v)
|
||||
result *= "$k: $v, "
|
||||
end
|
||||
end
|
||||
|
||||
#[PENDING] remove halucination. "highend dry white wine" --> "wine_type: white, occasion: special occasion, food_to_be_paired_with_wine: seafood, fish, country: France, Italy, USA, grape_varietal: Chardonnay, Sauvignon Blanc, Pinot Grigio\nwine_notes: citrus, green apple, floral"
|
||||
|
||||
result = result[1:end-2] # remove the ending ", "
|
||||
|
||||
return result
|
||||
end
|
||||
error("wineattributes_wordToNumber() failed to get a response")
|
||||
end
|
||||
@@ -643,12 +517,13 @@ end
|
||||
"""
|
||||
# TODO
|
||||
- [PENDING] "French dry white wines with medium bod" the LLM does not recognize sweetness. use LLM self questioning to solve.
|
||||
- [PENDING] French Syrah, Viognier, under 100. LLM extract intensiry of 3-5. why?
|
||||
"""
|
||||
function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<:AbstractString}
|
||||
|
||||
conversiontable =
|
||||
"""
|
||||
Conversion Table:
|
||||
<Conversion Table>
|
||||
Intensity level:
|
||||
1 to 2: May correspond to "light-bodied" or a similar description.
|
||||
2 to 3: May correspond to "med light bodied", "medium light" or a similar description.
|
||||
@@ -673,10 +548,9 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
|
||||
3 to 4: May correspond to "medium acidity" or a similar description.
|
||||
4 to 5: May correspond to "semi high acidity" or a similar description.
|
||||
4 to 5: May correspond to "high acidity" or a similar description.
|
||||
</Conversion Table>
|
||||
"""
|
||||
|
||||
# chathistory = vectorOfDictToText(a.chathistory)
|
||||
|
||||
systemmsg =
|
||||
"""
|
||||
As an helpful sommelier, your task is to fill out the user's preference form based on the corresponding words from the user's query.
|
||||
@@ -688,106 +562,241 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
|
||||
The preference form requires the following information:
|
||||
sweetness, acidity, tannin, intensity
|
||||
|
||||
You must follow the following guidelines:
|
||||
<You must follow the following guidelines>
|
||||
1) If specific information required in the preference form is not available in the query or there isn't any, mark with 'NA' to indicate this.
|
||||
Additionally, words like 'any' or 'unlimited' mean no information is available.
|
||||
2) Use the conversion table to convert the descriptive word level of sweetness, intensity, tannin, and acidity into a corresponding integer.
|
||||
3) Do not generate other comments.
|
||||
</You must follow the following guidelines>
|
||||
|
||||
You should then respond to the user with the following points:
|
||||
- reasoning: State your understanding of the current situation
|
||||
- sweetness: ( S ), where ( S ) represents integers indicating the range of sweetness levels. Example: 1-2
|
||||
- acidity: ( A ), where ( A ) represents integers indicating the range of acidity level. Example: 3-5
|
||||
- tannin: ( T ), where ( T ) represents integers indicating the range of tannin level. Example: 1-3
|
||||
- intensity: ( I ), where ( I ) represents integers indicating the range of intensity level. Example: 2-4
|
||||
- notes: Anything you want to add
|
||||
<You should then respond to the user with>
|
||||
Sweetness_keyword: The exact keywords in the user's query describing the sweetness level of the wine.
|
||||
Sweetness: ( S ), where ( S ) represents integers indicating the range of sweetness levels. Example: 1-2
|
||||
Acidity_keyword: The exact keywords in the user's query describing the acidity level of the wine.
|
||||
Acidity: ( A ), where ( A ) represents integers indicating the range of acidity level. Example: 3-5
|
||||
Tannin_keyword: The exact keywords in the user's query describing the tannin level of the wine.
|
||||
Tannin: ( T ), where ( T ) represents integers indicating the range of tannin level. Example: 1-3
|
||||
Intensity_keyword: The exact keywords in the user's query describing the intensity level of the wine.
|
||||
Intensity: ( I ), where ( I ) represents integers indicating the range of intensity level. Example: 2-4
|
||||
</You should then respond to the user with>
|
||||
|
||||
You should only respond in the form as described below:
|
||||
reasoning: ...
|
||||
sweetness: ...
|
||||
acidity: ...
|
||||
tannin: ...
|
||||
intensity: ...
|
||||
notes: ...
|
||||
<You should only respond in format as described below>
|
||||
Sweetness_keyword: ...
|
||||
Sweetness: ...
|
||||
Acidity_keyword: ...
|
||||
Acidity: ...
|
||||
Tannin_keyword: ...
|
||||
Tannin: ...
|
||||
Intensity_keyword: ...
|
||||
Intensity: ...
|
||||
</You should only respond in format as described below>
|
||||
|
||||
<Here are some examples>
|
||||
User's query: I want a wine with a medium-bodied, low acidity, medium tannin.
|
||||
Sweetness_keyword: NA
|
||||
Sweetness: NA
|
||||
Acidity_keyword: low acidity
|
||||
Acidity: 1-2
|
||||
Tannin_keyword: medium tannin
|
||||
Tannin: 3-4
|
||||
Intensity_keyword: medium-bodied
|
||||
Intensity: 3-4
|
||||
|
||||
User's query: German red wine, under 100, pairs with spicy food
|
||||
Sweetness_keyword: NA
|
||||
Sweetness: NA
|
||||
Acidity_keyword: NA
|
||||
Acidity: NA
|
||||
Tannin_keyword: NA
|
||||
Tannin: NA
|
||||
Intensity_keyword: NA
|
||||
Intensity: NA
|
||||
</Here are some examples>
|
||||
|
||||
Let's begin!
|
||||
"""
|
||||
header = ["Sweetness_keyword:", "Sweetness:", "Acidity_keyword:", "Acidity:", "Tannin_keyword:", "Tannin:", "Intensity_keyword:", "Intensity:"]
|
||||
dictkey = ["sweetness_keyword", "sweetness", "acidity_keyword", "acidity", "tannin_keyword", "tannin", "intensity_keyword", "intensity"]
|
||||
errornote = ""
|
||||
|
||||
# chathistory = vectorOfDictToText(a.chathistory)
|
||||
|
||||
usermsg =
|
||||
"""
|
||||
$conversiontable
|
||||
User's query: $input
|
||||
"""
|
||||
|
||||
_prompt =
|
||||
[
|
||||
Dict(:name=> "system", :text=> systemmsg),
|
||||
Dict(:name=> "user", :text=> usermsg)
|
||||
]
|
||||
|
||||
# put in model format
|
||||
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
|
||||
prompt *=
|
||||
for attempt in 1:10
|
||||
usermsg =
|
||||
"""
|
||||
<|start_header_id|>assistant<|end_header_id|>
|
||||
$conversiontable
|
||||
User's query: $input
|
||||
$errornote
|
||||
"""
|
||||
|
||||
attributes = ["reasoning", "sweetness", "acidity", "tannin", "intensity", "notes"]
|
||||
_prompt =
|
||||
[
|
||||
Dict(:name=> "system", :text=> systemmsg),
|
||||
Dict(:name=> "user", :text=> usermsg)
|
||||
]
|
||||
|
||||
# put in model format
|
||||
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
|
||||
|
||||
response = a.func[:text2textInstructLLM](prompt)
|
||||
|
||||
# check whether response has all header
|
||||
detected_kw = GeneralUtils.detect_keyword(header, response)
|
||||
if sum(values(detected_kw)) < length(header)
|
||||
errornote = "\nYiemAgent extractWineAttributes_2() response does not have all header"
|
||||
continue
|
||||
elseif sum(values(detected_kw)) > length(header)
|
||||
errornote = "\nYiemAgent extractWineAttributes_2() response has duplicated header"
|
||||
continue
|
||||
end
|
||||
|
||||
responsedict = GeneralUtils.textToDict(response, header;
|
||||
dictKey=dictkey, symbolkey=true)
|
||||
|
||||
# check whether each describing keyword is in the input to prevent halucination
|
||||
for i in ["sweetness", "acidity", "tannin", "intensity"]
|
||||
keyword = Symbol(i * "_keyword") # e.g. sweetness_keyword
|
||||
value = responsedict[keyword]
|
||||
if value != "NA" && !occursin(value, input)
|
||||
errornote = "WARNING. Keyword $keyword: $value does not appear in the input. You must use information from the input only"
|
||||
println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
|
||||
continue
|
||||
end
|
||||
|
||||
# if value == "NA" then responsedict[i] = "NA"
|
||||
# e.g. if sweetness_keyword == "NA" then sweetness = "NA"
|
||||
if value == "NA"
|
||||
responsedict[Symbol(i)] = "NA"
|
||||
end
|
||||
end
|
||||
|
||||
# some time LLM not put integer range
|
||||
for (k, v) in responsedict
|
||||
if !occursin("keyword", string(k))
|
||||
if v !== "NA" && (!occursin('-', v) || length(v) > 5)
|
||||
errornote = "WARNING: The non-range value {$k: $v} is not allowed. It should be specified in a range format, i.e. min-max."
|
||||
println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
|
||||
continue
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
# some time LLM says NA-2. Need to convert NA to 1
|
||||
for (k, v) in responsedict
|
||||
if occursin("NA", v) && occursin("-", v)
|
||||
new_v = replace(v, "NA"=>"1")
|
||||
responsedict[k] = new_v
|
||||
end
|
||||
end
|
||||
|
||||
result = ""
|
||||
for (k, v) in responsedict
|
||||
# some time LLM generate text with "(some comment)". this line removes it
|
||||
if !occursin("NA", v)
|
||||
result *= "$k: $v, "
|
||||
end
|
||||
end
|
||||
result = result[1:end-2] # remove the ending ", "
|
||||
|
||||
return result
|
||||
end
|
||||
error("wineattributes_wordToNumber() failed to get a response")
|
||||
end
|
||||
|
||||
|
||||
function paraphrase(text2textInstructLLM::Function, text::String)
|
||||
systemmsg =
|
||||
"""
|
||||
Your name: N/A
|
||||
Your vision:
|
||||
- You are a helpful assistant who help the user to paraphrase their text.
|
||||
Your mission:
|
||||
- To help paraphrase the user's text
|
||||
Mission's objective includes:
|
||||
- To help paraphrase the user's text
|
||||
Your responsibility includes:
|
||||
1) To help paraphrase the user's text
|
||||
Your responsibility does NOT includes:
|
||||
1) N/A
|
||||
Your profile:
|
||||
- N/A
|
||||
Additional information:
|
||||
- N/A
|
||||
|
||||
At each round of conversation, you will be given the following information:
|
||||
Text: The user's given text
|
||||
|
||||
You MUST follow the following guidelines:
|
||||
- N/A
|
||||
|
||||
You should follow the following guidelines:
|
||||
- N/A
|
||||
|
||||
You should then respond to the user with:
|
||||
1) Paraphrase: Paraphrased text
|
||||
|
||||
You should only respond in format as described below:
|
||||
Paraphrase: ...
|
||||
|
||||
Let's begin!
|
||||
"""
|
||||
|
||||
errornote = ""
|
||||
response = nothing # placeholder for show when error msg show up
|
||||
|
||||
|
||||
for attempt in 1:10
|
||||
usermsg = """
|
||||
Text: $text
|
||||
$errornote
|
||||
"""
|
||||
|
||||
_prompt =
|
||||
[
|
||||
Dict(:name => "system", :text => systemmsg),
|
||||
Dict(:name => "user", :text => usermsg)
|
||||
]
|
||||
|
||||
# put in model format
|
||||
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
|
||||
prompt *= """
|
||||
<|start_header_id|>assistant<|end_header_id|>
|
||||
"""
|
||||
|
||||
for attempt in 1:5
|
||||
try
|
||||
response = a.func[:text2textInstructLLM](prompt)
|
||||
responsedict = GeneralUtils.textToDict(response, attributes, rightmarker=":", symbolkey=true)
|
||||
response = text2textInstructLLM(prompt)
|
||||
# sometime the model response like this "here's how I would respond: ..."
|
||||
if occursin("respond:", response)
|
||||
errornote = "You don't need to intro your response"
|
||||
error("\n~~~ paraphrase() response contain : ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
|
||||
end
|
||||
response = GeneralUtils.remove_french_accents(response)
|
||||
response = replace(response, '*'=>"")
|
||||
response = replace(response, '$' => "USD")
|
||||
response = replace(response, '`' => "")
|
||||
response = GeneralUtils.remove_french_accents(response)
|
||||
|
||||
for i ∈ attributes
|
||||
if length(JSON3.write(responsedict[Symbol(i)])) == 0
|
||||
error("$i is empty ", @__LINE__)
|
||||
header = ["Paraphrase:"]
|
||||
dictkey = ["paraphrase"]
|
||||
responsedict = GeneralUtils.textToDict(response, header;
|
||||
dictKey=dictkey, symbolkey=true)
|
||||
|
||||
for i ∈ [:paraphrase]
|
||||
if length(JSON3.write(responsedict[i])) == 0
|
||||
error("$i is empty ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
|
||||
end
|
||||
end
|
||||
|
||||
delete!(responsedict, :reasoning)
|
||||
delete!(responsedict, :notes) # LLM traps. so it can add useless info here like comments.
|
||||
|
||||
# some time LLM think the user mentioning acidity and tannin but actually didn't
|
||||
for (k, v) in responsedict
|
||||
if k ∈ [:acidity, :tannin] && !occursin(string(k), input)
|
||||
responsedict[k] = "NA"
|
||||
# check if there are more than 1 key per categories
|
||||
for i ∈ [:paraphrase]
|
||||
matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
|
||||
if length(matchkeys) > 1
|
||||
error("paraphrase() has more than one key per categories")
|
||||
end
|
||||
end
|
||||
|
||||
# remove (some text)
|
||||
for (k, v) in responsedict
|
||||
_v = replace(v, r"\(.*?\)" => "")
|
||||
responsedict[k] = _v
|
||||
end
|
||||
println("\n~~~ paraphrase() ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
|
||||
pprintln(Dict(responsedict))
|
||||
|
||||
# some time LLM not put integer range
|
||||
for (k, v) in responsedict
|
||||
responsedict[k] = v
|
||||
if length(v) > 5
|
||||
error("non-range is not allowed. $k $v")
|
||||
end
|
||||
end
|
||||
|
||||
# some time LLM says NA-2. Need to convert NA to 1
|
||||
for (k, v) in responsedict
|
||||
if occursin("NA", v) && occursin("-", v)
|
||||
new_v = replace(v, "NA"=>"1")
|
||||
responsedict[k] = new_v
|
||||
end
|
||||
end
|
||||
|
||||
result = ""
|
||||
for (k, v) in responsedict
|
||||
# some time LLM generate text with "(some comment)". this line removes it
|
||||
if !occursin("NA", v)
|
||||
result *= "$k: $v, "
|
||||
end
|
||||
end
|
||||
|
||||
result = result[1:end-2] # remove the ending ", "
|
||||
result = responsedict[:paraphrase]
|
||||
|
||||
return result
|
||||
catch e
|
||||
@@ -795,153 +804,13 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
|
||||
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("")
|
||||
println("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
|
||||
end
|
||||
end
|
||||
error("wineattributes_wordToNumber() failed to get a response")
|
||||
error("generatechat failed to generate a response")
|
||||
end
|
||||
|
||||
|
||||
# function recommendbox(a::T1, input::T2)::String where {T1<:agent, T2<:AbstractString}
|
||||
# error("recommendbox")
|
||||
# systemmsg =
|
||||
# """
|
||||
# As an helpful sommelier, your task is to fill out the user's preference form based on the corresponding words from the user's query.
|
||||
|
||||
# At each round of conversation, the user will give you the current situation:
|
||||
# User's query: ...
|
||||
|
||||
# The preference form requires the following information:
|
||||
# wine_type, price, occasion, food_to_be_paired_with_wine, country, grape_variety, flavors, aromas.
|
||||
|
||||
# You must follow the following guidelines:
|
||||
# 1) If specific information required in the preference form is not available in the query or there isn't any, mark with 'NA' to indicate this.
|
||||
# Additionally, words like 'any' or 'unlimited' mean no information is available.
|
||||
# 2) Use the conversion table to convert the descriptive word level of sweetness, intensity, tannin, and acidity into a corresponding integer.
|
||||
# 3) Do not generate other comments.
|
||||
|
||||
# You should then respond to the user with the following points:
|
||||
# - reasoning: State your understanding of the current situation
|
||||
# - wine_type: Can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
|
||||
# - price: Must be an integer representing the cost of the wine.
|
||||
# - 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
|
||||
|
||||
# You should only respond in the form as described below:
|
||||
# reasoning: ...
|
||||
# wine_type: ...
|
||||
# price: ...
|
||||
# occasion: ...
|
||||
# food_to_be_paired_with_wine: ...
|
||||
# country: ...
|
||||
# region: ...
|
||||
# grape_variety: ...
|
||||
# flavors: ...
|
||||
# aromas: ...
|
||||
|
||||
# Let's begin!
|
||||
# """
|
||||
|
||||
# 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
|
||||
# $errornote
|
||||
# """
|
||||
|
||||
# _prompt =
|
||||
# [
|
||||
# Dict(:name=> "system", :text=> systemmsg),
|
||||
# Dict(:name=> "user", :text=> usermsg)
|
||||
# ]
|
||||
|
||||
# # put in model format
|
||||
# prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
|
||||
# prompt *=
|
||||
# """
|
||||
# <|start_header_id|>assistant<|end_header_id|>
|
||||
# """
|
||||
|
||||
# try
|
||||
# response = a.func[:text2textInstructLLM](prompt)
|
||||
# responsedict = GeneralUtils.textToDict(response, attributes, rightmarker=":", symbolkey=true)
|
||||
|
||||
# for i ∈ attributes
|
||||
# if length(JSON3.write(responsedict[Symbol(i)])) == 0
|
||||
# error("$i is empty ", @__LINE__)
|
||||
# end
|
||||
# end
|
||||
|
||||
# #[PENDING] check if the following attributes has more than 1 name
|
||||
# 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
|
||||
|
||||
# # check if grape_variety is mentioned in the input
|
||||
# if responsedict[:grape_variety] != "NA" && !occursin(responsedict[:grape_variety], input)
|
||||
# error("$(responsedict[:grape_variety]) is not mentioned in the input")
|
||||
# end
|
||||
|
||||
# responsedict[:flavors] = replace(responsedict[:flavors], "notes"=>"")
|
||||
# delete!(responsedict, :reasoning)
|
||||
# delete!(responsedict, :tasting_notes)
|
||||
# delete!(responsedict, :flavors)
|
||||
# delete!(responsedict, :aromas)
|
||||
|
||||
# # remove (some text)
|
||||
# for (k, v) in responsedict
|
||||
# _v = replace(v, r"\(.*?\)" => "")
|
||||
# responsedict[k] = _v
|
||||
# end
|
||||
|
||||
# result = ""
|
||||
# for (k, v) in responsedict
|
||||
# # some time LLM generate text with "(some comment)". this line removes it
|
||||
# if !occursin("NA", v) && v != "" && !occursin("none", v) && !occursin("None", v)
|
||||
# result *= "$k: $v, "
|
||||
# end
|
||||
# end
|
||||
|
||||
# #[PENDING] remove halucination. "highend dry white wine" --> "wine_type: white, occasion: special occasion, food_to_be_paired_with_wine: seafood, fish, country: France, Italy, USA, grape_variety: Chardonnay, Sauvignon Blanc, Pinot Grigio\nwine_notes: citrus, green apple, floral"
|
||||
|
||||
# result = result[1:end-2] # remove the ending ", "
|
||||
|
||||
# return result
|
||||
# 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("wineattributes_wordToNumber() failed to get a response")
|
||||
# end
|
||||
|
||||
|
||||
""" Attemp to correct LLM response's incorrect JSON response.
|
||||
|
||||
@@ -1133,7 +1002,7 @@ end
|
||||
# state[:isterminal] = true
|
||||
# state[:reward] = 1
|
||||
# end
|
||||
# println("--> 5 Evaluator ", @__FILE__, " ", @__LINE__)
|
||||
# println("--> 5 Evaluator ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
|
||||
# pprintln(Dict(responsedict))
|
||||
# return responsedict[:score]
|
||||
# catch e
|
||||
|
||||
38
src/type.jl
38
src/type.jl
@@ -11,8 +11,8 @@ abstract type agent end
|
||||
|
||||
|
||||
mutable struct companion <: agent
|
||||
name::String # agent name
|
||||
id::String # agent id
|
||||
systemmsg::Union{String, Nothing}
|
||||
maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized
|
||||
|
||||
""" Memory
|
||||
@@ -34,8 +34,8 @@ end
|
||||
function companion(
|
||||
text2textInstructLLM::Function
|
||||
;
|
||||
name::String= "Assistant",
|
||||
id::String= string(uuid4()),
|
||||
systemmsg::Union{String, Nothing}= nothing,
|
||||
maxHistoryMsg::Integer= 20,
|
||||
chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(),
|
||||
)
|
||||
@@ -48,13 +48,13 @@ function companion(
|
||||
)
|
||||
|
||||
newAgent = companion(
|
||||
name,
|
||||
id,
|
||||
maxHistoryMsg,
|
||||
chathistory,
|
||||
memory,
|
||||
text2textInstructLLM
|
||||
)
|
||||
id,
|
||||
systemmsg,
|
||||
maxHistoryMsg,
|
||||
chathistory,
|
||||
memory,
|
||||
text2textInstructLLM
|
||||
)
|
||||
|
||||
return newAgent
|
||||
end
|
||||
@@ -146,7 +146,6 @@ mutable struct sommelier <: agent
|
||||
"""
|
||||
chathistory::Vector{Dict{Symbol, Any}}
|
||||
memory::Dict{Symbol, Any}
|
||||
|
||||
func # NamedTuple of functions
|
||||
end
|
||||
|
||||
@@ -179,14 +178,17 @@ function sommelier(
|
||||
# ),
|
||||
)
|
||||
|
||||
memory = Dict{Symbol, Any}(
|
||||
:chatbox=> "",
|
||||
:shortmem=> OrderedDict{Symbol, Any}(),
|
||||
:events=> Vector{Dict{Symbol, Any}}(),
|
||||
:state=> Dict{Symbol, Any}(
|
||||
:wine_presented_to_user=> "None",
|
||||
),
|
||||
)
|
||||
memory = Dict{Symbol, Any}(
|
||||
:chatbox=> "",
|
||||
:shortmem=> OrderedDict{Symbol, Any}(
|
||||
:available_wine=> [],
|
||||
:found_wine=> [], # used by decisionMaker(). This is to prevent decisionMaker() keep presenting the same wines
|
||||
),
|
||||
:events=> Vector{Dict{Symbol, Any}}(),
|
||||
:state=> Dict{Symbol, Any}(
|
||||
),
|
||||
:recap=> OrderedDict{Symbol, Any}(),
|
||||
)
|
||||
|
||||
newAgent = sommelier(
|
||||
name,
|
||||
|
||||
53
src/util.jl
53
src/util.jl
@@ -1,6 +1,7 @@
|
||||
module util
|
||||
|
||||
export clearhistory, addNewMessage, vectorOfDictToText, eventdict, noises
|
||||
export clearhistory, addNewMessage, chatHistoryToText, eventdict, noises, createTimeline,
|
||||
availableWineToText
|
||||
|
||||
using UUIDs, Dates, DataStructures, HTTP, JSON3
|
||||
using GeneralUtils
|
||||
@@ -106,7 +107,7 @@ function addNewMessage(a::T1, name::String, text::T2;
|
||||
error("name is not in agent.availableRole $(@__LINE__)")
|
||||
end
|
||||
|
||||
#[] summarize the oldest 10 message
|
||||
#[PENDING] summarize the oldest 10 message
|
||||
if length(a.chathistory) > maximumMsg
|
||||
summarize(a.chathistory)
|
||||
else
|
||||
@@ -138,7 +139,7 @@ julia> GeneralUtils.vectorOfDictToText(vecd, withkey=true)
|
||||
```
|
||||
# Signature
|
||||
"""
|
||||
function vectorOfDictToText(vecd::Vector; withkey=true)::String
|
||||
function chatHistoryToText(vecd::Vector; withkey=true)::String
|
||||
# Initialize an empty string to hold the final text
|
||||
text = ""
|
||||
|
||||
@@ -169,11 +170,34 @@ function vectorOfDictToText(vecd::Vector; withkey=true)::String
|
||||
end
|
||||
|
||||
|
||||
function availableWineToText(vecd::Vector)::String
|
||||
# Initialize an empty string to hold the final text
|
||||
rowtext = ""
|
||||
# Loop through each dictionary in the input vector
|
||||
for (i, d) in enumerate(vecd)
|
||||
# Iterate over all key-value pairs in the dictionary
|
||||
temp = []
|
||||
for (k, v) in d
|
||||
# Append the formatted string to the text variable
|
||||
t = "$k:$v"
|
||||
push!(temp, t)
|
||||
end
|
||||
_rowtext = join(temp, ',')
|
||||
rowtext *= "$i) $_rowtext "
|
||||
end
|
||||
|
||||
return rowtext
|
||||
end
|
||||
|
||||
|
||||
|
||||
function eventdict(;
|
||||
event_description::Union{String, Nothing}=nothing,
|
||||
timestamp::Union{DateTime, Nothing}=nothing,
|
||||
subject::Union{String, Nothing}=nothing,
|
||||
action_or_dialogue::Union{String, Nothing}=nothing,
|
||||
thought::Union{AbstractDict, Nothing}=nothing,
|
||||
actionname::Union{String, Nothing}=nothing, # "CHAT", "CHECKINVENTORY", "PRESENTBOX", etc
|
||||
actioninput::Union{String, Nothing}=nothing,
|
||||
location::Union{String, Nothing}=nothing,
|
||||
equipment_used::Union{String, Nothing}=nothing,
|
||||
material_used::Union{String, Nothing}=nothing,
|
||||
@@ -184,7 +208,9 @@ function eventdict(;
|
||||
:event_description=> event_description,
|
||||
:timestamp=> timestamp,
|
||||
:subject=> subject,
|
||||
:action_or_dialogue=> action_or_dialogue,
|
||||
:thought=> thought,
|
||||
:actionname=> actionname,
|
||||
:actioninput=> actioninput,
|
||||
:location=> location,
|
||||
:equipment_used=> equipment_used,
|
||||
:material_used=> material_used,
|
||||
@@ -194,6 +220,23 @@ function eventdict(;
|
||||
end
|
||||
|
||||
|
||||
function createTimeline(memory::T1; skiprecent::Integer=0) where {T1<:AbstractVector}
|
||||
events = memory[1:end-skiprecent]
|
||||
|
||||
timeline = ""
|
||||
for (i, event) in enumerate(events)
|
||||
if event[:outcome] === nothing
|
||||
timeline *= "$i) $(event[:subject])> $(event[:actioninput])\n"
|
||||
else
|
||||
timeline *= "$i) $(event[:subject])> $(event[:actioninput]) $(event[:outcome])\n"
|
||||
end
|
||||
end
|
||||
|
||||
return timeline
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
# """ Convert a single chat dictionary into LLM model instruct format.
|
||||
|
||||
|
||||
@@ -31,26 +31,46 @@
|
||||
"description": "organization name"
|
||||
},
|
||||
"externalservice": {
|
||||
"text2textinstruct": {
|
||||
"mqtttopic": "/loadbalancer/requestingservice",
|
||||
"description": "text to text service with instruct LLM",
|
||||
"llminfo": {
|
||||
"name": "llama3instruct"
|
||||
}
|
||||
},
|
||||
"virtualWineCustomer_1": {
|
||||
"mqtttopic": "/virtualenvironment/winecustomer",
|
||||
"description": "text to text service with instruct LLM that act as wine customer",
|
||||
"llminfo": {
|
||||
"name": "llama3instruct"
|
||||
}
|
||||
},
|
||||
"text2textchat": {
|
||||
"mqtttopic": "/loadbalancer/requestingservice",
|
||||
"description": "text to text service with instruct LLM",
|
||||
"llminfo": {
|
||||
"name": "llama3instruct"
|
||||
}
|
||||
}
|
||||
"loadbalancer": {
|
||||
"mqtttopic": "/loadbalancer/requestingservice",
|
||||
"description": "text to text service with instruct LLM"
|
||||
},
|
||||
"text2textinstruct": {
|
||||
"mqtttopic": "/loadbalancer/requestingservice",
|
||||
"description": "text to text service with instruct LLM",
|
||||
"llminfo": {
|
||||
"name": "llama3instruct"
|
||||
}
|
||||
},
|
||||
"virtualWineCustomer_1": {
|
||||
"mqtttopic": "/virtualenvironment/winecustomer",
|
||||
"description": "text to text service with instruct LLM that act as wine customer",
|
||||
"llminfo": {
|
||||
"name": "llama3instruct"
|
||||
}
|
||||
},
|
||||
"text2textchat": {
|
||||
"mqtttopic": "/loadbalancer/requestingservice",
|
||||
"description": "text to text service with instruct LLM",
|
||||
"llminfo": {
|
||||
"name": "llama3instruct"
|
||||
}
|
||||
},
|
||||
"wineDB" : {
|
||||
"description": "A wine database connection info for LibPQ client",
|
||||
"host": "192.168.88.12",
|
||||
"port": 10201,
|
||||
"dbname": "wineDB",
|
||||
"user": "yiemtechnologies",
|
||||
"password": "yiemtechnologies@Postgres_0.0"
|
||||
},
|
||||
"SQLVectorDB" : {
|
||||
"description": "A wine database connection info for LibPQ client",
|
||||
"host": "192.168.88.12",
|
||||
"port": 10203,
|
||||
"dbname": "SQLVectorDB",
|
||||
"user": "yiemtechnologies",
|
||||
"password": "yiemtechnologies@Postgres_0.0"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,9 +0,0 @@
|
||||
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)
|
||||
|
||||
|
||||
0
test/runtests.jl
Normal file
0
test/runtests.jl
Normal file
@@ -1,272 +1,292 @@
|
||||
using Revise
|
||||
using JSON, JSON3, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
|
||||
using YiemAgent, GeneralUtils
|
||||
using Base.Threads
|
||||
|
||||
# ---------------------------------------------- 100 --------------------------------------------- #
|
||||
|
||||
|
||||
|
||||
# load config
|
||||
config = JSON3.read("./test/config.json")
|
||||
# config = copy(JSON3.read("../mountvolume/config.json"))
|
||||
|
||||
|
||||
function executeSQL(sql::T) where {T<:AbstractString}
|
||||
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],
|
||||
)
|
||||
|
||||
outgoingMsg = Dict(
|
||||
:msgMeta => msgMeta,
|
||||
:payload => Dict(
|
||||
:text => prompt,
|
||||
:kwargs => Dict(
|
||||
:num_ctx => 16384,
|
||||
:temperature => 0.2,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
|
||||
response = _response[:response][:text]
|
||||
|
||||
return response
|
||||
end
|
||||
|
||||
# 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],
|
||||
)
|
||||
|
||||
outgoingMsg = Dict(
|
||||
:msgMeta => msgMeta,
|
||||
:payload => Dict(
|
||||
:text => [text] # must be a vector of string
|
||||
)
|
||||
)
|
||||
response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
|
||||
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 *, $embeddingColumnName <-> '$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 > 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 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
|
||||
end
|
||||
|
||||
|
||||
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 $distance ", @__FILE__, " ", @__LINE__)
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
sessionId = "12345"
|
||||
|
||||
externalFunction = (
|
||||
getEmbedding=getEmbedding,
|
||||
text2textInstructLLM=text2textInstructLLM,
|
||||
executeSQL=executeSQL,
|
||||
similarSQLVectorDB=similarSQLVectorDB,
|
||||
insertSQLVectorDB=insertSQLVectorDB,
|
||||
similarSommelierDecision=similarSommelierDecision,
|
||||
insertSommelierDecision=insertSommelierDecision,
|
||||
)
|
||||
|
||||
|
||||
|
||||
a = YiemAgent.sommelier(
|
||||
externalFunction;
|
||||
name="Ton",
|
||||
id=sessionId, # agent instance id
|
||||
retailername="Yiem",
|
||||
)
|
||||
|
||||
while true
|
||||
println("your respond: ")
|
||||
user_answer = readline()
|
||||
response = YiemAgent.conversation(a, Dict(:text=> user_answer))
|
||||
println("\n$response")
|
||||
end
|
||||
|
||||
|
||||
# response = YiemAgent.conversation(a, Dict(:text=> "I want to get a French red wine under 100."))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
using Revise
|
||||
using JSON, JSON3, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
|
||||
using YiemAgent, GeneralUtils
|
||||
using Base.Threads
|
||||
|
||||
# ---------------------------------------------- 100 --------------------------------------------- #
|
||||
|
||||
|
||||
|
||||
# load config
|
||||
config = JSON3.read("/appfolder/app/dev/YiemAgent/test/config.json")
|
||||
# config = copy(JSON3.read("../mountvolume/config.json"))
|
||||
|
||||
|
||||
function executeSQL(sql::T) where {T<:AbstractString}
|
||||
host = config[:externalservice][:wineDB][:host]
|
||||
port = config[:externalservice][:wineDB][:port]
|
||||
dbname = config[:externalservice][:wineDB][:dbname]
|
||||
user = config[:externalservice][:wineDB][:user]
|
||||
password = config[:externalservice][:wineDB][:password]
|
||||
DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
|
||||
result = LibPQ.execute(DBconnection, sql)
|
||||
close(DBconnection)
|
||||
return result
|
||||
end
|
||||
|
||||
function executeSQLVectorDB(sql)
|
||||
host = config[:externalservice][:SQLVectorDB][:host]
|
||||
port = config[:externalservice][:SQLVectorDB][:port]
|
||||
dbname = config[:externalservice][:SQLVectorDB][:dbname]
|
||||
user = config[:externalservice][:SQLVectorDB][:user]
|
||||
password = config[:externalservice][:SQLVectorDB][:password]
|
||||
DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
|
||||
result = LibPQ.execute(DBconnection, sql)
|
||||
close(DBconnection)
|
||||
return result
|
||||
end
|
||||
|
||||
function text2textInstructLLM(prompt::String; maxattempt=3)
|
||||
msgMeta = GeneralUtils.generate_msgMeta(
|
||||
config[:externalservice][:loadbalancer][:mqtttopic];
|
||||
msgPurpose="inference",
|
||||
senderName="yiemagent",
|
||||
senderId=sessionId,
|
||||
receiverName="text2textinstruct_small",
|
||||
mqttBrokerAddress=config[:mqttServerInfo][:broker],
|
||||
mqttBrokerPort=config[:mqttServerInfo][:port],
|
||||
)
|
||||
|
||||
outgoingMsg = Dict(
|
||||
:msgMeta => msgMeta,
|
||||
:payload => Dict(
|
||||
:text => prompt,
|
||||
:kwargs => Dict(
|
||||
:num_ctx => 16384,
|
||||
:temperature => 0.2,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
response = nothing
|
||||
for attempts in 1:maxattempt
|
||||
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=300, maxattempt=maxattempt)
|
||||
payload = _response[:response]
|
||||
if _response[:success] && payload[:text] !== nothing
|
||||
response = _response[:response][:text]
|
||||
break
|
||||
else
|
||||
println("\n<text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
|
||||
pprintln(outgoingMsg)
|
||||
println("</text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
|
||||
sleep(3)
|
||||
end
|
||||
end
|
||||
|
||||
return response
|
||||
end
|
||||
|
||||
# get text embedding from a LLM service
|
||||
function getEmbedding(text::T) where {T<:AbstractString}
|
||||
msgMeta = GeneralUtils.generate_msgMeta(
|
||||
config[:externalservice][:loadbalancer][:mqtttopic];
|
||||
msgPurpose="embedding",
|
||||
senderName="yiemagent",
|
||||
senderId=sessionId,
|
||||
receiverName="text2textinstruct_small",
|
||||
mqttBrokerAddress=config[:mqttServerInfo][:broker],
|
||||
mqttBrokerPort=config[:mqttServerInfo][:port],
|
||||
)
|
||||
|
||||
outgoingMsg = Dict(
|
||||
:msgMeta => msgMeta,
|
||||
:payload => Dict(
|
||||
:text => [text] # must be a vector of string
|
||||
)
|
||||
)
|
||||
response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120)
|
||||
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 *, $embeddingColumnName <-> '$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)
|
||||
# println(df[1, [:id, :function_output]])
|
||||
row, col = size(df)
|
||||
distance = row == 0 ? Inf : df[1, :distance]
|
||||
# distance = 100 # CHANGE this is for testing only
|
||||
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__, " $(Dates.now())")
|
||||
return (dict=output_str, distance=distance)
|
||||
else
|
||||
println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
|
||||
return (dict=nothing, distance=nothing)
|
||||
end
|
||||
end
|
||||
|
||||
function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=3) 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 > 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__, " $(Dates.now())")
|
||||
# println(sql)
|
||||
_ = executeSQLVectorDB(sql)
|
||||
end
|
||||
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
|
||||
end
|
||||
|
||||
|
||||
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 $distance ", @__FILE__, " ", @__LINE__)
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
sessionId = "12345"
|
||||
|
||||
externalFunction = (
|
||||
getEmbedding=getEmbedding,
|
||||
text2textInstructLLM=text2textInstructLLM,
|
||||
executeSQL=executeSQL,
|
||||
similarSQLVectorDB=similarSQLVectorDB,
|
||||
insertSQLVectorDB=insertSQLVectorDB,
|
||||
similarSommelierDecision=similarSommelierDecision,
|
||||
insertSommelierDecision=insertSommelierDecision,
|
||||
)
|
||||
|
||||
|
||||
|
||||
a = YiemAgent.sommelier(
|
||||
externalFunction;
|
||||
name="Ton",
|
||||
id=sessionId, # agent instance id
|
||||
retailername="Yiem",
|
||||
)
|
||||
|
||||
while true
|
||||
println("your respond: ")
|
||||
user_answer = readline()
|
||||
response = YiemAgent.conversation(a, Dict(:text=> user_answer))
|
||||
println("\n$response")
|
||||
end
|
||||
|
||||
|
||||
# response = YiemAgent.conversation(a, Dict(:text=> "I want to get a French red wine under 100."))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user