module llmfunction

export  virtualWineUserChatbox, jsoncorrection, checkinventory, # recommendbox,
        virtualWineUserRecommendbox, userChatbox, userRecommendbox, extractWineAttributes_1,
        extractWineAttributes_2, paraphrase

using HTTP, JSON3, URIs, Random, PrettyPrinting, UUIDs, Dates
using GeneralUtils, SQLLLM
using ..type, ..util

# ---------------------------------------------- 100 --------------------------------------------- #


""" Chatbox for chatting with virtual wine customer.

# Arguments
  - `a::T1`
    one of Yiem's agent
  - `input::T2`
    text to be send to virtual wine customer
  
# Return
  - `response::String`
    response of virtual wine customer
# Example
```jldoctest
julia> 
```

# TODO
  - [] update docstring
  - [] add reccommend() to compare wine

# Signature
"""
function virtualWineUserRecommendbox(a::T1, input
  )::Union{Tuple{String, Number, Number, Bool}, Tuple{String, Nothing, Number, Bool}} where {T1<:agent}

  # put in model format
  virtualWineCustomer = a.config[:externalservice][:virtualWineCustomer_1]
  llminfo = virtualWineCustomer[:llminfo]
  prompt = 
  if llminfo[:name] == "llama3instruct"
    formatLLMtext_llama3instruct("assistant", input)
  else
    error("llm model name is not defied yet $(@__LINE__)")
  end

  # send formatted input to user using GeneralUtils.sendReceiveMqttMsg
  msgMeta = GeneralUtils.generate_msgMeta(
    virtualWineCustomer[:mqtttopic],
    senderName= "virtualWineUserRecommendbox",
    senderId= a.id,
    receiverName= "virtualWineCustomer",
    mqttBroker= a.config[:mqttServerInfo][:broker],
    mqttBrokerPort= a.config[:mqttServerInfo][:port],
    msgId = "dummyid" #CHANGE remove after testing finished
    )

  outgoingMsg = Dict(
    :msgMeta=> msgMeta,
    :payload=> Dict(
      :text=> prompt,
    )
  )

  result = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120)
  response = result[:response]

  return (response[:text], response[:select], response[:reward], response[:isterminal])
end



""" Chatbox for chatting with virtual wine customer.

# Arguments
  - `a::T1`
    one of Yiem's agent
  - `input::T2`
    text to be send to virtual wine customer
  
# Return
  - `response::String`
    response of virtual wine customer
# Example
```jldoctest
julia> 
```

# TODO
  - [] update docs 
  - [x] write a prompt for virtual customer 

# Signature
"""
function virtualWineUserChatbox(config::T1, input::T2, virtualCustomerChatHistory
  )::Union{Tuple{String, Number, Number, Bool}, Tuple{String, Nothing, Number, Bool}} where {T1<:AbstractDict, T2<:AbstractString}

  previouswines = 
  """
  You have the following wines previously:

  """

  systemmsg = 
  """
  You find yourself in a well-stocked wine store, engaged in a conversation with the store's knowledgeable sommelier. 
  You're on a quest to find a bottle of wine that aligns with your specific preferences and requirements. 
  
  The ideal wine you're seeking should meet the following criteria:
  1. It should fit within your budget.
  2. It should be suitable for the occasion you're planning.
  3. It should pair well with the food you intend to serve.
  4. It should be of a particular type of wine you prefer.
  5. It should possess certain characteristics, including:
    - The level of sweetness.
    - The intensity of its flavor.
    - The amount of tannin it contains.
    - Its acidity level.

  Here's the criteria details:
  {
    "budget": 50,
    "occasion": "graduation ceremony",
    "food pairing": "Thai food",
    "type of wine": "red",
    "wine sweetness level": "dry",
    "wine intensity level": "full-bodied",
    "wine tannin level": "low",
    "wine acidity level": "medium", 
  }

  You should only respond with "text", "select", "reward", "isterminal" steps.
  "text" is your conversation.
  "select" is an integer. Choose an option when presented with choices, or leave it null if none of the options satisfy you or if no choices are available.
  "reward" is an integer, it can be three number:
    1) 1 if you find the right wine.
    2) 0 if you don’t find the ideal wine.
    3) -1 if you’re dissatisfied with the sommelier’s response.
  "isterminal" can be false if you still want to talk with the sommelier, true otherwise.

  You should only respond in JSON format as describe below:
  {
    "text": "your conversation",
    "select": null,
    "reward": 0,
    "isterminal": false
  }

  Here are some examples:

  sommelier: "What's your budget?
  you:
  {
    "text": "My budget is 30 USD.",
    "select": null,
    "reward": 0,
    "isterminal": false
  }

  sommelier: "The first option is Zena Crown and the second one is Buano Red."
  you:
  {
    "text": "I like the 2nd option.",
    "select": 2,
    "reward": 1,
    "isterminal": true
  }

  Let's begin!
  """

  pushfirst!(virtualCustomerChatHistory, Dict(:name=> "system", :text=> systemmsg))
  
  # replace the :user key in chathistory to allow the virtual wine customer AI roleplay
  chathistory::Vector{Dict{Symbol, Any}} = Vector{Dict{Symbol, Any}}()
  for i in virtualCustomerChatHistory
    newdict = Dict()
    newdict[:name] =
    if i[:name] == "user"
      "you"
    elseif i[:name] == "assistant"
      "sommelier"
    else
      i[:name]
    end

    newdict[:text] = i[:text]
    push!(chathistory, newdict)
  end

  push!(chathistory, Dict(:name=> "assistant", :text=> input))

  # put in model format
  prompt = formatLLMtext(chathistory, "llama3instruct")
  prompt *=
    """
    <|start_header_id|>you<|end_header_id|>
    {"text"
    """

  pprint(prompt)
  externalService = config[:externalservice][:text2textinstruct]

  # send formatted input to user using GeneralUtils.sendReceiveMqttMsg
  msgMeta = GeneralUtils.generate_msgMeta(
    externalService[:mqtttopic],
    senderName= "virtualWineUserChatbox",
    senderId= string(uuid4()),
    receiverName= "text2textinstruct",
    mqttBroker= config[:mqttServerInfo][:broker],
    mqttBrokerPort= config[:mqttServerInfo][:port],
    msgId = string(uuid4()) #CHANGE remove after testing finished
  )

  outgoingMsg = Dict(
    :msgMeta=> msgMeta,
    :payload=> Dict(
      :text=> prompt,
    )
  )

  attempt = 0
  for attempt in 1:5
    try
      response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120)
      _responseJsonStr = response[:response][:text]
      expectedJsonExample =
      """
      Here is an expected JSON format:
      {
        "text": "...",
        "select": "...",
        "reward": "...",
        "isterminal": "..."
      }
      """
      responseJsonStr = jsoncorrection(config, _responseJsonStr, expectedJsonExample)
      responseDict = copy(JSON3.read(responseJsonStr)) 

      text::AbstractString = responseDict[:text]
      select::Union{Nothing, Number} = responseDict[:select] == "null" ? nothing : responseDict[:select]
      reward::Number = responseDict[:reward]
      isterminal::Bool = responseDict[:isterminal]

      if text != ""
        # pass test
      else
        error("virtual customer not answer correctly")
      end
      
      return (text, select, reward, isterminal)
    catch e
      io = IOBuffer()
      showerror(io, e)
      errorMsg = String(take!(io))
      st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
      println("")
      @warn "Error occurred: $errorMsg\n$st"
      println("")
    end
  end
  error("virtualWineUserChatbox failed to get a response")
end

""" Search wine in stock.

# Arguments
  - `a::T1` 
    one of ChatAgent's agent.
  - `input::T2`
# Return
  A JSON string of available wine

# Example
```jldoctest
julia> using ChatAgent
julia> agent = ChatAgent.agentReflex("Jene")
julia> input = "{\"food\": \"pizza\", \"occasion\": \"anniversary\"}"
julia> result = checkinventory(agent, input)
"{"wine 1": {\"Winery\": \"Pichon Baron\", \"wine name\": \"Pauillac (Grand Cru Classé)\", \"grape variety\": \"Cabernet Sauvignon\", \"year\": 2010, \"price\": \"125 USD\", \"stock ID\": \"ar-17\"}, }"
```

# TODO
  - [] update docs
  - [x] implement the function

# Signature
"""
function checkinventory(a::T1, input::T2
  ) where {T1<:agent, T2<:AbstractString}

  println("\ncheckinventory order: $input ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
  wineattributes_1 = extractWineAttributes_1(a, input)
  wineattributes_2 = extractWineAttributes_2(a, input)

  _inventoryquery = "retailer name: $(a.retailername), $wineattributes_1, $wineattributes_2"
  inventoryquery = "Retrieves winery, wine_name, vintage, region, country, wine_type, grape, serving_temperature, sweetness, intensity, tannin, acidity, tasting_notes, price and currency of wines that match the following criteria - {$_inventoryquery}"
  println("\ncheckinventory input: $inventoryquery ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
  # add suppport for similarSQLVectorDB
  textresult, rawresponse = SQLLLM.query(inventoryquery, a.func[:executeSQL], 
                        a.func[:text2textInstructLLM],
                        insertSQLVectorDB=a.func[:insertSQLVectorDB], 
                        similarSQLVectorDB=a.func[:similarSQLVectorDB])

  println("\ncheckinventory result ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
  println(textresult)

  return (result=textresult, rawresponse=rawresponse, success=true, errormsg=nothing)
end


"""

# Arguments
  - `v::Integer`
    dummy variable
    
# Return

# Example
```jldoctest
julia> 
```

# TODO
  - [] update docstring
  - implement the function

# Signature
"""
function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<:AbstractString}

  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 following:
  User's query: ...

  You must follow the following guidelines:
    - 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.
    - Do not generate other comments.

  You should then respond to the user with:
    Thought: 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 format as described below:
  Thought: ...
  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, 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, Merlot
  {"reasoning": ..., "winery": "Domaine du Collier", "wine_name": "Saumur Blanc", "vintage": "2019", "region": "Saumur", "country": "France", "wine_type": "white", "grape_varietal": "Merlot", "tasting_notes": "NA", "wine_price": "NA", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}

  Let's begin!
  """

  header = ["Thought:", "Wine_name:", "Winery:", "Vintage:", "Region:", "Country:", "Wine_type:", "Grape_varietal:", "Tasting_notes:", "Wine_price:", "Occasion:", "Food_to_be_paired_with_wine:"]
  dictkey = ["thought", "wine_name", "winery", "vintage", "region", "country", "wine_type", "grape_varietal", "tasting_notes", "wine_price", "occasion", "food_to_be_paired_with_wine"]
  errornote = ""
  
  for attempt in 1:10
    #[WORKING] I should add generatequestion()

    if attempt > 1
      println("\nYiemAgent extractWineAttributes_1() attempt $attempt/10 ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    end

    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="qwen")
    response = a.func[:text2textInstructLLM](prompt)
    response = GeneralUtils.remove_french_accents(response)

    # 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 ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
        checkFlag = true
        break
      end
    end
    checkFlag == true ? continue : nothing
    
    # check whether response has all header
    detected_kw = GeneralUtils.detect_keyword(header, response)
    if 0 ∈ values(detected_kw)
      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)

    delete!(responsedict, :thought)
    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_varietal's value are in the query because sometime AI halucinates
    checkFlag = false
    for i in dictkey
      j = Symbol(i)
      if j ∉ [:thought, :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("ERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
              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("ERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
              checkFlag = true
              break
            end
          end
        else
          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  #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("ERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
          #     checkFlag == true
          #     break
          #   end
          # end
        end
      end
    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

"""
# 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>
  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.
    3 to 4: May correspond to "medium bodied" or a similar description.
    4 to 5: May correspond to "med full bodied", "medium full" or a similar description.
    4 to 5: May correspond to "full bodied" or a similar description.
  Sweetness level:
    1 to 2: May correspond to "dry", "no sweet" or a similar description.
    2 to 3: May correspond to "off dry", "less sweet" or a similar description.
    3 to 4: May correspond to "semi sweet" or a similar description.
    4 to 5: May correspond to "sweet" or a similar description.
    4 to 5: May correspond to "very sweet" or a similar description.
  Tannin level:
    1 to 2: May correspond to "low tannin" or a similar description.
    2 to 3: May correspond to "semi low tannin" or a similar description.
    3 to 4: May correspond to "medium tannin" or a similar description.
    4 to 5: May correspond to "semi high tannin" or a similar description.
    4 to 5: May correspond to "high tannin" or a similar description.
  Acidity level:
    1 to 2: May correspond to "low acidity" or a similar description.
    2 to 3: May correspond to "semi low acidity" or a similar description.
    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>
  """

  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:
  Conversion Table: ...
  User's query: ...

  The preference form requires the following information:
  sweetness, acidity, tannin, intensity

  <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>
    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 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 = ""

  for attempt in 1:10
    usermsg =
    """
    $conversiontable
    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="qwen")

    response = a.func[:text2textInstructLLM](prompt)
    
    # check whether response has all header
    detected_kw = GeneralUtils.detect_keyword(header, response)
    if 0 ∈ values(detected_kw)
      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 ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
        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 ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
          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:
    Paraphrase: Paraphrased text

    You should only respond in format as described below:
    Paraphrase: ...

    Let's begin!
  """

  header = ["Paraphrase:"]
  dictkey = ["paraphrase"]

  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="qwen")

    try
      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("\nparaphrase() response contain : ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
      end
      response = GeneralUtils.remove_french_accents(response)
      response = replace(response, '*'=>"")
      response = replace(response, '$' => "USD")
      response = replace(response, '`' => "")
      response = GeneralUtils.remove_french_accents(response)

      # check whether response has all header
      detected_kw = GeneralUtils.detect_keyword(header, response)
      if 0 ∈ values(detected_kw)
        errornote = "\nYiemAgent paraphrase() response does not have all header"
        continue
      elseif sum(values(detected_kw)) > length(header)
        errornote = "\nnYiemAgent paraphrase() response has duplicated header"
        continue
      end

      responsedict = GeneralUtils.textToDict(response, header; 
                                            dictKey=dictkey, symbolkey=true)

      for i ∈ [:paraphrase]
        if length(JSON3.write(responsedict[i])) == 0
          error("$i is empty ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
        end
      end

      # 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

      println("\nparaphrase() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
      pprintln(Dict(responsedict))

      result = responsedict[:paraphrase]

      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("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    end
  end
  error("paraphrase() failed to generate a response")
end



""" Attemp to correct LLM response's incorrect JSON response.

# Arguments
  - `a::T1`
    one of Yiem's agent
  - `input::T2`
    text to be send to virtual wine customer
  
# Return
  - `correctjson::String`
    corrected json string  

# Example
```jldoctest
julia> 
```

# Signature
"""
function jsoncorrection(config::T1, input::T2, correctJsonExample::T3;
  maxattempt::Integer=3
  ) where {T1<:AbstractDict, T2<:AbstractString, T3<:AbstractString}

  incorrectjson = deepcopy(input)
  correctjson = nothing

  for attempt in 1:maxattempt
    try
      d = copy(JSON3.read(incorrectjson))
      correctjson = incorrectjson
      return correctjson
    catch e
      @warn "Attempting to correct JSON string. Attempt $attempt"
      e = """$e"""
      if occursin("EOF", e)
        e = split(e, "EOF")[1] * "EOF"
      end
      incorrectjson = deepcopy(input)
      _prompt =
      """
      Your goal are:
      1) Use the expected JSON format as a guideline to check why the given JSON string failed to load and provide a corrected version that can be loaded by Python's json.load function.
      2) Provide Corrected JSON string only. Do not provide any other info.

      $correctJsonExample

      Let's begin!
      Given JSON string: $incorrectjson
      The given JSON string failed to load previously because: $e
      Corrected JSON string:
      """

      # apply LLM specific instruct format
      externalService = config[:externalservice][:text2textinstruct]
      llminfo = externalService[:llminfo]
      prompt = 
      if llminfo[:name] == "llama3instruct"
        formatLLMtext_llama3instruct("system", _prompt)
      else
        error("llm model name is not defied yet $(@__LINE__)")
      end

      # send formatted input to user using GeneralUtils.sendReceiveMqttMsg
      msgMeta = GeneralUtils.generate_msgMeta(
        externalService[:mqtttopic],
        senderName= "jsoncorrection",
        senderId= string(uuid4()),
        receiverName= "text2textinstruct",
        mqttBroker= config[:mqttServerInfo][:broker],
        mqttBrokerPort= config[:mqttServerInfo][:port],
      )

      outgoingMsg = Dict(
        :msgMeta=> msgMeta,
        :payload=> Dict(
          :text=> prompt,
          :kwargs=> Dict(
            :max_tokens=> 512,
            :stop=> ["<|eot_id|>"],
            )
          )
        )
      result = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120)
      incorrectjson = result[:response][:text]
    end
  end
end


# function isrecommend(state::T1, text2textInstructLLM::Function
#   ) where {T1<:AbstractDict}

#   systemmsg = 
#   """
#   You are a helpful assistant that analyzes agent's trajectories to find solutions and observations (i.e., the results of actions) to answer the user's questions.

#   Definitions:
#   "question" is the user's question.
#   "thought" is step-by-step reasoning about the current situation.
#   "plan" is what to do to complete the task from the current situation.
#   “action_name” is the name of the action taken, which can be one of the following functions:
#       1) CHATBOX[text], which you can use to talk with the user. "text" is in verbal English. 
#       2) WINESTOCK[query], which you can use to find info about wine in your inventory. "query" is a search term in verbal English. The best query must includes "budget", "type of wine", "characteristics of wine" and "food pairing".
#   "action_input" is the input to the action
#   "observation" is result of the preceding immediate action.

#   At each round of conversation, the user will give you:
#   Context: ...
#   Trajectories: ...
    
#   You should then respond to the user with:
#   1) trajectory_evaluation:
#     - Analyze the trajectories of a solution to answer the user's original question. 
#       Then given a question and a trajectory, evaluate its correctness and provide your reasoning and
#       analysis in detail. Focus on the latest thought, action, and observation. 
#       Incomplete trajectories can be correct if the thoughts and actions so far are correct, 
#       even if the answer is not found yet. Do not generate additional thoughts or actions.
#   2) answer_evaluation: Focus only on the matter mentioned in the question and analyze how the latest observation addresses the question.
#   3) accepted_as_answer: Decide whether the latest observation's content answers the question. The possible responses are either 'Yes' or 'No.'
#     Bad example (The observation didn't answers the question):
#       question: Find cars with 4 wheels.
#       observation: There are 2 cars in the table.
#     Good example (The observation answers the question): 
#       question: Find cars with a stereo.
#       observation: There are 1 cars in the table. 1) brand: Toyota, model: yaris, color: black.
#   4) score: Correctness score s where s is a single integer between 0 to 9. 
#       - 0 means the trajectories are incorrect.
#       - 9 means the trajectories are correct, and the observation's content directly answers the question.
#   5) suggestion: if accepted_as_answer is "No", provide suggestion.

#   You should only respond in format as described below:
#   trajectory_evaluation: ...
#   answer_evaluation: ...
#   accepted_as_answer: ...
#   score: ...
#   suggestion: ...

#   Let's begin!
#   """

#   thoughthistory = ""
#   for (k, v) in state[:thoughtHistory]
#     thoughthistory *= "$k: $v\n" 
#   end

#   usermsg =
#   """
#   Context: None
#   Trajectories: $thoughthistory
#   """

#   _prompt = 
#   [
#     Dict(:name=> "system", :text=> systemmsg),
#     Dict(:name=> "user", :text=> usermsg)
#   ]

#   # put in model format
#   prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
#   prompt *=
#     """
#     <|start_header_id|>assistant<|end_header_id|>
#     """

#   for attempt in 1:5
#     try
#       response = text2textInstructLLM(prompt)
#       responsedict = GeneralUtils.textToDict(response,
#             ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"], 
#             rightmarker=":", symbolkey=true)

#       # check if dict has all required value
#       trajectoryevaluation_text::AbstractString = responsedict[:trajectory_evaluation]
#       answerevaluation_text::AbstractString = responsedict[:answer_evaluation]
#       responsedict[:score] = parse(Int, responsedict[:score]) # convert string "5" into integer 5
#       score::Integer = responsedict[:score]
#       accepted_as_answer::AbstractString = responsedict[:accepted_as_answer]
#       suggestion::AbstractString = responsedict[:suggestion]

#       # add to state here instead to in transition() because the latter causes julia extension crash (a bug in julia extension)
#       state[:evaluation] = "$(responsedict[:trajectory_evaluation]) $(responsedict[:answer_evaluation])" 
#       state[:evaluationscore] = responsedict[:score] 
#       state[:accepted_as_answer] = responsedict[:accepted_as_answer]
#       state[:suggestion] = responsedict[:suggestion]

#       # mark as terminal state when the answer is achieved
#       if accepted_as_answer == "Yes"
#         state[:isterminal] = true
#         state[:reward] = 1
#       end
#       println("--> 5 Evaluator ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
#       pprintln(Dict(responsedict))
#       return responsedict[:score]
#     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("evaluator failed to generate an evaluation")
# end








































end # module llmfunction