YiemAgent/src/llmfunction.jl

module llmfunction

export  virtualWineUserChatbox, jsoncorrection, checkinventory,
        virtualWineUserRecommendbox, userChatbox, userRecommendbox

using HTTP, JSON3, URIs, Random, PrettyPrinting, UUIDs
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
  )::NamedTuple{(:result, :success, :errormsg), Tuple{String, Bool, Union{String, Nothing}}} where {T1<:agent, T2<:AbstractString}
  println("--> checkinventory input: $input ", @__FILE__, " ", @__LINE__)
  wineattributes_1 = extractWineAttributes_1(a, input)
  wineattributes_2 = extractWineAttributes_2(a, input)

  inventoryquery = "$wineattributes_1, $wineattributes_2"
  println("--> checkinventory: $inventoryquery ", @__FILE__, " ", @__LINE__)
  result = SQLLLM.query(inventoryquery, a.executeSQL, a.text2textInstructLLM)
  return (result=result, success=true, errormsg=nothing)
end


"""

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

# Example
```jldoctest
julia> 
```

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

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

  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
  - 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: ...
  grape_variety: ...
  flavors: ...
  aromas: ...

  Let's begin!
  """

  # chathistory = vectorOfDictToText(a.chathistory)

  usermsg =
  """
  User's query: $input
  """

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

  # put in model format
  prompt = GeneralUtils.formatLLMtext(_prompt, "llama3instruct")
  prompt *=
    """
    <|start_header_id|>assistant<|end_header_id|>
    """

  attributes = ["reasoning", "wine_type", "price", "occasion", "food_to_be_paired_with_wine", "country", "grape_variety", "flavors", "aromas"]
  errornote = ""
  for attempt in 1:5
    try
      response = a.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 grape_variety has more than 1 name
      if length(split(responsedict[:grape_variety], ",")) > 1
        error("multiple name in grape_variety is not allowed")
      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

"""
# TODO
 - [PENDING] "French dry white wines with medium bod" the LLM does not recognize sweetness. use LLM self questioning to solve.
"""
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.
  """

  # 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.

  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 should then respond to the user with the following points:
  - reasoning: State your understanding of the current situation
  - sweetness: S where S are integers represent the range of sweetness levels
      Example: 1-2
  - acidity: D where D are integers represent the range of acidity level
      Example: 3-5
  - tannin: T where T are integers represent the range of tannin level
      Example: 1-3
  - intensity: I where I are integers represent the range of intensity level
      Example: 2-4
  - notes: Anything you want to add

  You should only respond in the form as described below:
  reasoning: ...
  sweetness: ...
  acidity: ...
  tannin: ...
  intensity: ...
  notes: ...

  Let's begin!
  """

  # 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, "llama3instruct")
  prompt *=
    """
    <|start_header_id|>assistant<|end_header_id|>
    """

  attributes = ["reasoning", "sweetness", "acidity", "tannin", "intensity", "notes"]

  for attempt in 1:5
    try
      response = a.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

      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"
        end
      end

      # remove (some text)
      for (k, v) in responsedict
        _v = replace(v, r"\(.*?\)" => "")
        responsedict[k] = _v
      end

      # 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 ", "

      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.

# 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

# [WORKING] check whether  
# 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, "llama3instruct")
#   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__)
#       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