module interface
    

export agentReact, agentReflex,
        addNewMessage, clearMessage, removeLatestMsg, conversation, directconversation, 
        writeEvaluationGuideline, grading, analyze, selfReflext, 
        formulateUserresponse, extractinfo, updateEnvState, chat_mistral_openorca, 
        recap

using JSON3, DataStructures, Dates, UUIDs, HTTP, Random
using CommUtils, GeneralUtils
using ..type, ..utils

# ---------------------------------------------------------------------------- #
#                              pythoncall setting                              #
# ---------------------------------------------------------------------------- #
# Ref: https://github.com/JuliaPy/PythonCall.jl/issues/252
# by setting the following variables, PythonCall.jl will use:
#     1. system's python and packages installed by system (via apt install)
# or  2. conda python and packages installed by conda
# if these setting are not set (comment out), PythonCall will use its own python and packages that
# installed by CondaPkg.jl (from env_preparation.jl)
# ENV["JULIA_CONDAPKG_BACKEND"] = "Null"      # set condapkg backend = none
# systemPython = split(read(`which python`, String), "\n")[1]     # system's python path
# ENV["JULIA_PYTHONCALL_EXE"] = systemPython  # find python location with $> which python  ex. raw"/root/conda/bin/python"

# using PythonCall
# const py_agents = PythonCall.pynew()
# const py_llms = PythonCall.pynew()
# function __init__()
#     # PythonCall.pycopy!(py_cv2, pyimport("cv2"))

#     # equivalent to     from urllib.request import urlopen    in python
#     PythonCall.pycopy!(py_agents, pyimport("langchain.agents"))
#     PythonCall.pycopy!(py_llms, pyimport("langchain.llms"))
# end

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


""" Add new message to agent.

  Arguments:

  Return:

```jldoctest
julia> addNewMessage(agent1, "user", "Where should I go to buy snacks")
```
"""
function addNewMessage(a::T1, role::String, content::T2) where {T1<:agent, T2<:AbstractString}
  if role ∉ a.availableRole   # guard against typo
    error("role is not in agent.availableRole $(@__LINE__)")
  end

  # check whether user messages exceed limit
  userMsg = 0
  for i in a.messages
    if i[:role] == "user"
      userMsg += 1
    end
  end
  messageleft = 0

  if userMsg > a.maxUserMsg # delete all conversation
    clearMessage(a)
    messageleft = a.maxUserMsg
  else
    userMsg += 1
    d = Dict(:role=> role, :content=> content, :timestamp=> Dates.now())
    push!(a.messages, d)
    messageleft = a.maxUserMsg - userMsg
  end

  return messageleft
end

function clearMessage(a::T)  where {T<:agent}
  for i in eachindex(a.messages)
    if length(a.messages) > 0 
      pop!(a.messages)
    else
      break
    end
  end
  memory::Dict{Symbol, Any} = Dict(
    :shortterm=> OrderedDict{String, Any}(),
    :longterm=> OrderedDict{String, Any}(),
    :log=> OrderedDict{String, Any}(),  # span from user stimulus -> multiples attempts -> final respond
  )
  @show a.messages
end

function removeLatestMsg(a::T)  where {T<:agent}
  if length(a.messages) > 1
    pop!(a.messages)
  end
end

function chat_mistral_openorca(a::agentReflex)
  """
    general prompt format:

    "
    <|system|>
    {role}
    {tools}
    {thinkingFormat}
    {context}
    <|im_end|>
    <|im_start|>user
    {usermsg}
    <|im_end|>
    <|im_start|>assistant

    "

    Note:
      {context} = 
        "
        {earlierConversation}
        {env state}
        {shortterm memory}
        {longterm memory}
        "
  """

  conversation = messagesToString(a.messages)

  aboutYourself =
  """
    Your name is $(a.agentName)
    $(a.roles[a.role])  
  """

  prompt = 
  """
    <|system|>
      <About yourself>
      $aboutYourself
      </About yourself>
    </s>
    $conversation
    <|assistant|>
  """
  
  response = sendReceivePrompt(a, prompt, timeout=180, stopword=["<|", "</"])
  response = split(response, "<|")[1]
  response = split(response, "</")[1]

  return response
end


function planner_mistral_openorca(a::agentReflex)
  """
    general prompt format:

    "
    <|system|>
    {role}
    {tools}
    {thinkingFormat}
    <|im_end|>
    {context}
    <|im_start|>user
    {usermsg}
    <|im_end|>
    <|im_start|>assistant

    "

    Note:
      {context} = 
        "
        {earlierConversation}
        {env state}
        {shortterm memory}
        {longterm memory}
        "
  """

  conversation = messagesToString(a.messages)
  toollines = ""
  for (toolname, v) in a.tools
    if toolname ∉ [""]
      # toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
      toolline = "$toolname: $(v[:description])\n"
      toollines *= toolline
    end
  end

  # skip objective and plan because LLM is going to generate new plan
  shorttermMemory = dictToString(a.memory[:shortterm], skiplist=["Objective:", "Plan 1:"])

  aboutYourself =
  """
    Your name is $(a.agentName)
    $(a.roles[a.role])
    $(a.roleSpecificInstruction[a.role])
  """
  
  assistant_plan_prompt =
    """
    <|system|>
      <About yourself>
      $aboutYourself
      </About yourself>
      <You have access to the following tools>
      $toollines
      </You have access to the following tools
      <Your earlier work>
      $shorttermMemory
      </Your earlier work>
      <Your job>
      Plan: first you should always think about your conversation with the user and your earlier work thoroughly then extract and devise a complete, task by task, plan to achieve your objective (pay attention to correct numeral calculation and commonsense).
      P.S.1 each task of the plan should be a single action.
      </Your job>
      <Example>
      1. Ask the user about how many miles per day they drive
      2. Ask the user about what stuff they usually carry with
      3. Ask the user about preferred type of car they want to buy (sedan, sport, SUV, etc)
      8. Ask the user about their price range
      9. Use inventory tool to find cars that match the user's preferences and are within their price range
      10. Use finalanswer tool to present the recommended car to the user.
      </Example
    </s>
    $conversation
    <|assistant|>
    Plan: 
    """
# WORKING provide ecmaple that show good planning
  plan = sendReceivePrompt(a, assistant_plan_prompt, max_tokens=1024, temperature=0.1,
                          timeout=180, stopword=["<|user|>", "</"])
  plan = split(plan, "<|")[1]
  plan = split(plan, "</")[1]
  # plan = split(plan, "\n\n")[1]

  return plan
end

""" Update the current plan.
"""
function updatePlan(a::agentReflex)
  # conversation = messagesToString_nomark(a.messages)
  
  toollines = ""
  for (toolname, v) in a.tools
    if toolname ∉ ["askbox"]
      toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
      toollines *= toolline
    end
  end

  work = dictToString(a.memory[:shortterm])

  prompt =
  """
    <|system|>
    $(a.roles[a.role])
    Request the user’s input for the following info initially, and use alternative sources of information only if they are unable to provide it:
      - occasion
      - type of food ask the user
      - user's personal taste of wine
      - ambient temperature at the serving location
      - wine price range
      - wines we have in stock (use tools to get the info)
    You provide a personalized recommendation of up to two wines based on the user's info above, and you describe the benefits of each wine in detail.
    
    You have access to the following tools:
    $toollines

    Your work:
    $work

    Your job is to update the plan using available info from your work.
    P.S. do not update if no info available.

    For example:
    Plan: 1. Ask the user for their food type.
    Obs: It will be Thai dishes.
    Updated plan: 1. Ask the user for their food type (Thai dishes).
    </s>
    Updated plan: 
  """

  result = sendReceivePrompt(a, prompt, max_tokens=1024, temperature=0.1)
  @show updatedPlan = result
  a.memory[:shortterm]["Plan 1:"] = result
  
end

# function selfAwareness(a::agentReflex)
#   getonlykeys = ["Actinput", "Obs"]
#   worknoplan = similar(a.memory[:shortterm])
#   for (k, v) in a.memory[:shortterm]
#     count = 0
#     for i in getonlykeys
#       if occursin(i, k)
#         count += 1
#       end
#     end
#     if count != 0
#       worknoplan[k] = v 
#     end
#   end

#   work = dictToString(worknoplan)

#   aboutYourself =
#   """
#     Your name is $(a.agentName)
#     $(a.roles[a.role])  
#   """

#   prompt = 
#   """
#     <|system|>
#       <About yourself>
#       $aboutYourself
#       </About yourself>
#       <Your work>
#       $work
#       </your work
#       <Your plan>
#       $(a.memory[:shortterm]["Plan 1:"])
#       </Your plan>
#       <Your job> 
#       Use the following format:
#       What I know: based on observed results, repeat all the details of what you have been gathered.
#       Current progress: focus on checking your progress against the plan.
#       What I am missing: describe in detail what you are missing.
#       </Your job>
#     </s>
#     <|assistant|>
#     What I know:
#   """
#   response = sendReceivePrompt(a, prompt, max_tokens=1024, temperature=0.4, timeout=180,
#                                   stopword=["/n/n", "END", "End", "Obs", "<|", "</"])
#   @show response

#   return response
# end

function selfAwareness(a::agentReflex)
  getonlykeys = ["Actinput", "Obs"]
  worknoplan = similar(a.memory[:shortterm])
  for (k, v) in a.memory[:shortterm]
    count = 0
    for i in getonlykeys
      if occursin(i, k)
        count += 1
      end
    end
    if count != 0
      worknoplan[k] = v 
    end
  end

  work = dictToString(worknoplan)

  aboutYourself =
  """
    Your name is $(a.agentName)
    $(a.roles[a.role])

  """

  # prompt = 
  # """
  #   <|system|>
  #     <About yourself>
  #     $aboutYourself
  #     $(a.roleSpecificInstruction[a.role])
  #     </About yourself>
  #     <Your earlier work>
  #     $work
  #     </Your earlier work>
      
  #     <Your plan>
  #     $(a.memory[:shortterm]["Plan 1:"])
  #     </Your plan>
  #     <Your job> 
  #     Use the following format strictly:
  #     What do I know: based on observed results, repeat all the information you got.
  #     Info matching: using JSON format, explicitly state what information matches which variable name in my plan.
  #     What am I missing: based on Info match, describe in detail what you are still missing based on the plan.
  #     P.S. do not mention any toolnames
  #     </Your job>
  #     <Example>
  #     What do I know:
  #       - The user is buying an electric SUV car. 
  #     Info matching: {"car type": "SUV", "engine": "electric motor", "color": "N/A", "financing": "N/A"}
  #     What am I missing: 
  #     - The user's preferred color
  #     - The user's financing method
  #     </Example>
  #   </s>
  #   <|assistant|>
  #   What I know:
  # """

  prompt = 
  """
    <|system|>
      <About yourself>
      $aboutYourself
      $(a.roleSpecificInstruction[a.role])
      </About yourself>
      <Your earlier work>
      $work
      </Your earlier work>
      
      <Your plan>
      $(a.memory[:shortterm]["Plan 1:"])
      </Your plan>
      <Your job> 
      Use the following format strictly:
      What I know: based on observed results, repeat all the information you got
      Info match: using JSON format, explicitly state what information matches which variable name in my plan
      What am I missing: based on Info match, describe in detail what you are still missing based on the plan
      P.S. do not mention any toolnames
      </Your job>
      <Example>
      user response:
        - EV could mean electric vehicle
      What do I know:
        - The user is buying an electric SUV car. 
      Info matching: {"car type": "SUV", "engine type": "electric motor", "color": "not know yet", "financing": "not know yet"}
      What am I missing: 
      - The user's preferred color
      - The user's financing method
      </Example>
    </s>
    <|assistant|>
    What I know:
  """
  response = sendReceivePrompt(a, prompt, max_tokens=1024, temperature=0.4, timeout=180,
                                  stopword=["/n/n", "END", "End", "Obs", "<|", "</"])
  response = split(response, "<|")[1]
  response = split(response, "</")[1]
  @show response

  return response
end

function actor_mistral_openorca(a::agentReflex, selfaware=nothing)
  getonlykeys = ["Actinput", "Obs"]
  worknoplan = similar(a.memory[:shortterm])
  for (k, v) in a.memory[:shortterm]
    count = 0
    for i in getonlykeys
      if occursin(i, k)
        count += 1
      end
    end
    if count != 0
      worknoplan[k] = v 
    end
  end

  work = dictToString(worknoplan)
  
  
  
  """
    general prompt format:

    "
    <|system|>
    {role}
    {tools}
    {thinkingFormat}
    <|im_end|>
    {context}
    <|im_start|>user
    {usermsg}
    <|im_end|>
    <|im_start|>assistant

    "

    Note:
      {context} = 
        "
        {earlierConversation}
        {env state}
        {shortterm memory}
        {longterm memory}
        "
  """

  toolslist = []
  toolnames = ""
  toollines = ""
  for (toolname, v) in a.tools
    toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
    toollines *= toolline
    toolnames *= "$toolname, "
    push!(toolslist, toolname)
  end

  println("")
  @show actor_selfaware = selfaware

  thought = "Thought: you should always think about what to do according to the plan (pay attention to correct numeral calculation and commonsense and do one thing at a time.)"
  if selfaware !== nothing
    
    # aware = "Self-awareness: based on the recap, assess your progress against the plan then identify areas where you need to address."
    # aware = "Self-awareness: based on the recap and the plan, state your current understanding of the matter in details then identify areas where you need to address."
    # aware = "Self-awareness: Based on Obs, review your progress against the plan. Then, describe in detail the results you have achieved so far. Finally, describe in detail what you are missing. (focus on your actions and their results)"
    # aware = "Self-awareness: Based on action's input and observed results, check your progress against the plan. Then, repeat all the details of what you have been gathered. Finally, describe in detail what you are missing."
    thought = 
    "Self-awareness: $selfaware
    Thought: based on self-awareness, you should always think about what to do next by prioritizing what you missed first. (P.S. 1) let's think a single step. 2) pay attention to correct numeral calculation and commonsense.)
    "
  end
                        # your should request the missing information first before making a decision
  aboutYourself =
  """
    Your name is $(a.agentName)
    $(a.roles[a.role])  
  """

  winestocksearchresult = nothing
  if haskey(a.memory, :winestocksearchresult) && a.memory[:winestockResult] !== nothing
    winestocksearchresult = 
    """
    <winestock search result>
    $(a.memory[:winestocksearchresult])
    </winestock search result>
    """
  else
    winestocksearchresult = "\n"
  end

  prompt = 
  """
    <|system|>
      <About yourself>
      $aboutYourself
      </About yourself>
      <You have access to the following tools>
      $toollines
      </You have access to the following tools>
      <Your plan>
      $(a.memory[:shortterm]["Plan 1:"])
      </Your plan>

      <Your job> 
      Use the following format:
      $thought
      Act: based on your thought what action to choose?, must be one of [{toolnames}].
      Actinput: your input to the action (pay attention to the tool's input)
      Obs: observed result of the action
      </Your job>
      <Example>
      Thought: based on self-awareness, I think he also need to know whether there are any charging station near by his house. I should search the internet to get this info.
      Act: internetsearch
      Actinput: {\"internetsearch\": \"EV charging station near Bangkok\"}
      </Example>
    </s>
    <|assistant|>
    $work
    "Thought: "
  """
  prompt = replace(prompt, "{toolnames}" => toolnames)

  println("")
  @show actor_prompt = prompt

  response = nothing
  chunkedtext = nothing
  latestTask = nothing

  tempcounter = 0.2
  seed = nothing
  while true  # while Thought or Act is empty, run actor again
    # tempcounter += 0.2
    @show tempcounter
    response = sendReceivePrompt(a, prompt, max_tokens=1024, temperature=0.4, timeout=300,
                                stopword=["Thought:", "Obs:", "<|system|>", "</s>", "<|end|>"],
                                seed=seed)
    response = splittext(response, ["/n/n", "END", "End","obs", "Obs", "<|im_end|>"])
    response = split(response, "<|")[1]
    response = split(response, "</")[1]
    response = split(response, "Thought:")[end]
    
    latestTask = shortMemLatestTask(a.memory[:shortterm]) +1

    if occursin("Thought", response) == false
      response = "Thought:" * response
    end

    headerToDetect = ["Plan:", "Self-awareness:", "Thought:", 
                      "Act:", "Actinput:", "Obs:", 
                      "Answer:", "Conclusion:", "Summary:"]
    
    # replace headers with headers with correct attempt and task number
    response = replaceHeaders(response, headerToDetect, latestTask)  
    response = split(response, "<|")[1]
    response = split(response, "</")[1]
    
    # sometime LLM use wrong keyword. use regex to detect "actinput5:" and replace it with "Actinput"
    regexmatch = match(r"actinput\d+:", response)
    respone = regexmatch !== nothing ? response = replace(response, match=>"Actinput:") : response
    response = replace(response, "actinput:"=>"Actinput:")

    println("")
    @show response

    headerToDetect = ["Plan $(a.attempt):",
                      "Self-awareness $latestTask:", 
                      "Thought $latestTask:", 
                      "Act $latestTask:", 
                      "Actinput $latestTask:", 
                      "Obs $latestTask:",
                      "Check $latestTask:",]
    headers = detectCharacters(response, headerToDetect)
    chunkedtext = chunktext(response, headers) 

    # assuming length more than 10 character means LLM has valid thinking
    check_1 = haskey(chunkedtext, "Thought $latestTask:")
    check_2 = haskey(chunkedtext, "Act $latestTask:")
    check_3 = haskey(chunkedtext, "Actinput $latestTask:")

    # check for a valid toolname
    check_4 = false
    for i in toolslist
      if occursin(i, chunkedtext["Act $latestTask:"])
        check_4 = true
        break
      end
    end

    # check for empty Thought
    check_5 = length(chunkedtext["Thought $latestTask:"]) > 5
    # check for empty Actinput
    check_6 = nothing
    try
      check_6 = length(chunkedtext["Actinput $latestTask:"]) > 5  
    catch
      println("")
      @show response
      println("")
      @show chunkedtext
      a.memory[:chunkedtext] = chunkedtext
    end
    

    # check whether the act has valid json
    check_7 = true
    if occursin('{', response)
      try
        act = GeneralUtils.getStringBetweenCharacters(response, '{', '}', endCharLocation="end")
        act = JSON3.read(act)
        check_7 = true
      catch
        check_7 = false
      end
    end

    # print all check_1 to check_6
    println("check_1: $check_1, check_2: $check_2, check_3: $check_3, check_4: $check_4, 
            check_5: $check_5, check_6: $check_6, check_7: $check_7")
    if check_1 && check_2 && check_3 && check_4 && check_5 && check_6 && check_7
      
      #TODO paraphrase selfaware
      break
    end
    @show retrying_actor = response
  end
  
  toolname = toolNameBeingCalled(chunkedtext["Act $latestTask:"], a.tools)

  # change trailing number to continue a.memory[:shortterm]
  headerToDetect = ["Question:", "Plan:", "Self-awareness:", "Thought:", 
                      "Act:", "Actinput:", "Obs:", "...", 
                      "Answer:", "Conclusion:", "Summary:"]
  response = replaceHeaders(response, headerToDetect, latestTask)
  println("")
  @show actor_response_1 = response
  headerToDetect = ["Plan $(a.attempt):",
                      "Thought $latestTask:", 
                      "Act $latestTask:", 
                      "Actinput $latestTask:", 
                      "Obs $latestTask:",
                      "Check $latestTask:",]
  headers = detectCharacters(response, headerToDetect)
  chunkedtext = chunktext(response, headers)
  println("")
  @show chunkedtext
  
  
  toolinput = chunkedtext["Actinput $latestTask:"]


  # because tools has JSON input but sometime LLM output is not JSON, we need to check.
  if occursin("{", toolinput)
    act = GeneralUtils.getStringBetweenCharacters(response, '{', '}', endCharLocation="end")
    act = copy(JSON3.read(act))
    chunkedtext["Actinput $latestTask:"] = JSON3.write(act[Symbol(toolname)])
    toolinput = act[Symbol(toolname)]
  end
  

  chunkedtext["Act $latestTask:"] = toolname
  
  return (toolname=toolname, toolinput=toolinput, chunkedtext=chunkedtext, selfaware=selfaware)
end


"""
  Chat with llm.
  
  ```jldoctest
  julia> using JSON3, UUIDs, Dates, FileIO, CommUtils, ChatAgent
  julia> mqttClientSpec = (
                            clientName= "someclient",   # name of this client
                            clientID= "$(uuid4())",
                            broker= "mqtt.yiem.cc", 
                            pubtopic= (imgAI="img/api/v0.0.1/gpu/request",
                                      txtAI="txt/api/v0.1.0/gpu/request"),
                            subtopic= (imgAI="agent/api/v0.1.0/img/response",
                                      txtAI="agent/api/v0.1.0/txt/response"),
                            keepalive= 30,
                          )
  julia> msgMeta = Dict(
                        :msgPurpose=> "updateStatus",
                        :from=> "agent",       
                        :to=> "llmAI",
                        :requestresponse=> "request",
                        :sendto=> "",      # destination topic
                        :replyTo=> "agent/api/v0.1.0/txt/response",     # requester ask responseer to send reply to this topic
                        :repondToMsgId=> "",   # responseer is responseing to this msg id
                        :taskstatus=> "",       # "complete", "fail", "waiting" or other status
                        :timestamp=> Dates.now(),
                        :msgId=> "$(uuid4())",
                      )
  julia> newAgent = ChatAgent.agentReact(
                                        "Jene",
                                        mqttClientSpec, 
                                        role=:assistant_react,
                                        msgMeta=msgMeta
                                        )
  julia> response = ChatAgent.conversation(newAgent, "Hi! how are you?")
  ```
# """
function conversation(a::agentReflex, usermsg::String; attemptlimit::Int=3)
  a.attemptlimit = attemptlimit
  workstate = nothing
  response = nothing

  _ = addNewMessage(a, "user", usermsg)
  isuseplan = isUsePlans(a)
  # newinfo = extractinfo(a, usermsg)
  # a.env = newinfo !== nothing ? updateEnvState(a, newinfo) : a.env
  @show isuseplan

  if isuseplan # use plan before responding
    if haskey(a.memory[:shortterm], "User:") == false #TODO should change role if user want to buy wine.
      a.memory[:shortterm]["User:"] = usermsg
    end
    workstate, response = work(a)
  end

  # if LLM using askbox, use returning msg form askbox as conversation response
  if workstate == "askbox" || workstate == "formulatedUserResponse"
    #TODO paraphrase msg so that it is human friendlier word.
  else
    response = chat_mistral_openorca(a)
    response = split(response, "\n\n")[1]
    response = split(response, "\n\n")[1]
  end
  
  response = removeTrailingCharacters(response)
  _ = addNewMessage(a, "assistant", response)

  return response
end

"""
  Continuously run llm functions except when llm is getting Answer: or askbox.
  There are many work() depend on thinking mode.
""" 
function work(a::agentReflex)
  workstate = nothing
  response = nothing
  
  # user answering LLM -> Obs
  if length(a.memory[:shortterm]) > 1
    latestTask = shortMemLatestTask(a.memory[:shortterm])
    if haskey(a.memory[:shortterm], "Act $latestTask:")
      if occursin("askbox", a.memory[:shortterm]["Act $latestTask:"])
        a.memory[:shortterm]["Obs $latestTask:"] = "(user response) " * a.messages[end][:content]
      end
    end
  end
  
  while true  # Work loop
    objective = nothing

    # make new plan
    if !haskey(a.memory[:shortterm], "Plan 1:")
      plan = planner_mistral_openorca(a)
      a.memory[:shortterm]["Plan $(a.attempt):"] = plan
      a.memory[:log]["Plan $(a.attempt):"] = plan
      a.task = 1  # reset because new plan is created
      println("")
      @show plan
      println("")
      @show a.attempt
    end

    if a.attempt <= a.attemptlimit
      toolname = nothing
      toolinput = nothing

      # enter actor loop
      actorstate, msgToUser = actor(a)

      if actorstate == "askbox"
        response = msgToUser
        workstate = actorstate
        break
      elseif actorstate == "formulateFinalResponse"
        println("all tasks done")

        response = formulateUserresponse(a)

        println("")
        formulatedresponse = response
        @show formulatedresponse
        
        a.memory[:shortterm]["response $(a.attempt):"] = response
        a.memory[:log]["response $(a.attempt):"] = response

        # evaluate. if score > 6/10 good enough.
        guideline = writeEvaluationGuideline(a)

        println("")
        @show guideline

        score = grading(a, guideline, response)
        @show score
        if score > 5 # good enough answer
          println("")
          formulatedresponse_final = response
          @show formulatedresponse_final
          workstate = "formulatedUserResponse"
          a.memory[:shortterm] = OrderedDict{String, Any}()
          a.memory[:log] = OrderedDict{String, Any}()
          break
        else  # self evaluate and reflect then try again
          analysis = analyze(a)
          println("")
          @show analysis
          
          lessonwithcontext = selfReflext(a, analysis)
          
          println("")
          @show lessonwithcontext

          headerToDetect = ["Lesson:", "Context:", ]
          headers = detectCharacters(lessonwithcontext, headerToDetect)
          chunkedtext = chunktext(lessonwithcontext, headers)

          a.memory[:longterm][chunkedtext["Context:"]] = chunkedtext["Lesson:"]
          a.attempt += 1
          a.task = 0
          a.memory[:shortterm] = OrderedDict{String, Any}()
          a.memory[:log] = OrderedDict{String, Any}()
          println("")
          println("RETRY $(a.attempt +1)")
          println("")
        end
      else
        error("undefied condition, actorstate $actorstate $(@__LINE__)")
        break
      end
    else
      error("attempt limit reach")
      break
    end
  end

  # good enough answer
  return  workstate, response
end


"""
  Actor function. 
  
  Arguments:
    a, one of ChatAgent's agent.
    plan, a task by task plan to response
  
  Return:
    case 1) if actor complete the plan successfully.
      actorState = "all tasks done" inidicates that all task in plan were done.
      msgToUser = nothing.
    case 2) if actor needs to talk to user for more context
    actorState = "askbox"
    msgToUser = "message from assistant to user"

"""
function actor(a::agentReflex)

  actorState = nothing
  msgToUser = nothing

  
    # totaltasks = checkTotalTaskInPlan(a)
  

  while true  # Actor loop
    # check whether the current task is completed, skip evaluation if memory has only "Plan 1:"
    # taskrecap = ""
    # if length(keys(a.memory[:shortterm])) != 1
    #   taskrecap = recap(a)
    # end
    # println("")
    # @show taskrecap
    latestTask = shortMemLatestTask(a.memory[:shortterm]) +1
    println(">>> working")
    # work
    selfaware = nothing
    if length(a.memory[:shortterm]) > 2 # must have User:, Plan:, Thought:, Act:, Actinput: already
      selfaware = selfAwareness(a)
      println("")
      @show selfaware
    end

    actorResult = actor_mistral_openorca(a, selfaware)
    println("")
    toolname, toolinput, chunkedtext, selfaware = actorResult
    @show toolname
    @show toolinput
    println(typeof(toolinput))
    println("")

    addShortMem!(a.memory[:shortterm], chunkedtext)
    println("")

    if toolname == "askbox"  # chat with user
      msgToUser = toolinput
      actorState = toolname
      break
    elseif toolname == "finalanswer"
      println(">>> already done")
      actorState = "formulateFinalResponse"
      break
    else  # function call
      f = a.tools[toolname][:func]                
      toolresult = f(a, actorResult)
      @show toolresult
      if toolname == ""
        a.memory[:shortterm]["Obs $latestTask:"] = "I found wines in <winestock search result>"
        a.memory[:winestockResult] = toolresult
        a.memory[:log]["Obs $latestTask:"] = "winestock search done"
      else
        a.memory[:shortterm]["Obs $latestTask:"] = toolresult
        a.memory[:log]["Obs $latestTask:"] = toolresult
      end
    end




    
  end

  return actorState, msgToUser
end



""" Write evaluation guideline.

  Arguments:
    a, one of ChatAgent's agent.
    usermsg, stimulus e.g. question, task and etc.

  Return:
    An evaluation guideline used to guage AI's work.

  Example:

  ```jldoctest
  julia> using ChatAgent, CommUtils
  julia> agent = ChatAgent.agentReflex("Jene")
  julia> usermsg = "What's AMD latest product?"
  "
  julia> evaluationGuideLine = writeEvaluationGuideline(agent, usermsg)
  ``` 
"""
function writeEvaluationGuideline(a::agentReflex)
  prompt =
  """
    <|system|>
    $(a.roles[a.role])
      <You have access to the following tools>
      askbox: Useful for when you need to ask a customer for more context. Input should be a conversation to customer.
      wikisearch: Useful for when you need to search an encyclopedia Input is keywords and not a question.
      </You have access to the following tools>
      <Your job>
      1. Write an evaluation guideline for wine recommendation in order to be able to evaluate your response.
      2. An example of what the response should be.
      </Your job>
    </s>
    <|assistant|>

  """

  response = sendReceivePrompt(a, prompt)
  return response
end



""" Determine a score out of 10 according to evaluation guideline.

  Arguments:
    a, one of ChatAgent's agent.
    guidelines, an evaluation guideline.
    shorttermMemory, a short term memory that logs what happened.

  Return:
    A score out of 10 based on guideline.

  Example:

  ```jldoctest
  julia> using ChatAgent, CommUtils
  julia> agent = ChatAgent.agentReflex("Jene")
  julia> shorttermMemory = OrderedDict{String, Any}(
    "user"        => "What's the latest AMD GPU?",
    "Plan 1:"     => "   To answer this question, I will need to search for the latest AMD GPU using the wikisearch tool.\n",
    "Act 1:"      => " wikisearch\n",
    "Actinput 1:" => " amd gpu latest\n",
    "Obs 1:"      => "No info available for your search query.",
    "Act 2:"      => "  wikisearch\n",
    "Actinput 2:" => "  amd graphics card latest\n",
    "Obs 2:"      => "No info available for your search query.")
  julia> guideline = "\nEvaluation Guideline:\n1. Check if the user's question has been understood correctly.\n2. Evaluate the tasks taken to provide the information requested by the user.\n3. Assess whether the correct tools were used for the task.\n4. Determine if the user's request was successfully fulfilled.\n5. Identify any potential improvements or alternative approaches that could be used in the future.\n\nThe response should include:\n1. A clear understanding of the user's question.\n2. The tasks taken to provide the information requested by the user.\n3. An evaluation of whether the correct tools were used for the task.\n4. A confirmation or explanation if the user's request was successfully fulfilled.\n5. Any potential improvements or alternative approaches that could be used in the future."
  julia> score = grading(agent, guideline, shorttermMemory)
  ```
"""
function grading(a, guideline::T, text::T)  where {T<:AbstractString}
  prompt =
  """
    <|system|>
      <You have access to the following tools>
      askbox: Useful for when you need to ask a customer for more context. Input should be a conversation to customer.
      wikisearch: Useful for when you need to search an encyclopedia Input is keywords and not a question.
      </You have access to the following tools>
      <Guideline>
      $guideline
      </Guideline>
      <Your response>
      $text
      </Your response>

      <Your job>
      Evaluate your response using the evaluation guideline then give yourself a score out of 9 for your response.
      </Your job>
      <Example>
      {"Evaluate": "My response is detailed with good comparison between options.", "Score": 6}
      </Example>
    </s>
    <|assistant|>
    {
  """
  println("")
  prompt_grading = prompt
  @show prompt_grading
  println("")
  score = nothing
  while true
    response = sendReceivePrompt(a, prompt, timeout=180)
    try
      response = "{" * split(response, "}")[1] * "}"
      @show response
      @show jsonresponse = JSON3.read(response)
      score = jsonresponse["Score"]
      break
    catch
      println("retry grading")
    end
  end

  return score
end



""" Analize work.

  Arguments:
    a, one of ChatAgent's agent.

  Return:
    A report of analized work.

  Example:

  ```jldoctest
  julia> using ChatAgent, CommUtils
  julia> agent = ChatAgent.agentReflex("Jene")
  julia> shorttermMemory = OrderedDict{String, Any}(
    "user:"        => "What's the latest AMD GPU?",
    "Plan 1:"     => "   To answer this question, I will need to search for the latest AMD GPU using the wikisearch tool.\n",
    "Act 1:"      => " wikisearch\n",
    "Actinput 1:" => " amd gpu latest\n",
    "Obs 1:"      => "No info available for your search query.",
    "Act 2:"      => "  wikisearch\n",
    "Actinput 2:" => "  amd graphics card latest\n",
    "Obs 2:"      => "No info available for your search query.")
  julia> report = analyze(agent, shorttermMemory)
  ```
"""
function analyze(a)
  shorttermMemory = dictToString(a.memory[:shortterm])
  prompt =
  """
    <|system|>
      <you have access to the following tools>
      askbox: Useful for when you need to ask a customer for more context. Input should be a conversation to customer.
      wikisearch: Useful for when you need to search an encyclopedia Input is keywords and not a question.
      </you have access to the following tools>
      <your earlier work>
      $shorttermMemory
      </your earlier work>
      <your job>
      You job is to do each of the following in detail to analize your work.
      1. What happened?
      2. List all relationships, each with cause and effect.
      3. Look at each relationship, figure out why it behaved that way.
      4. What could you do to improve the response?
      </your job>
    </s>
    <|assistant|>
    
  """

  response = sendReceivePrompt(a, prompt, max_tokens=1024, timeout=180)

  return response
end


""" Write a lesson drawn from evaluation.

  Arguments:
    a, one of ChatAgent's agent.
    report, a report resulted from analyzing shorttermMemory

  Return:
    A lesson.

  Example:

  ```jldoctest
  julia> using ChatAgent, CommUtils
  julia> agent = ChatAgent.agentReflex("Jene")
  julia> report = 
    "What happened: I tried to search for AMD's latest product using the wikisearch tool, 
    but no information was available in the search results.
    Cause and effect relationships:
      1. Searching \"AMD latest product\" -> No info available.
      2. Searching \"most recent product release\" -> No info available.
      3. Searching \"latest product\" -> No info available.
    Analysis of each relationship:
      1. The search for \"AMD latest product\" did not provide any information because the wikisearch tool could not find relevant results for that query.
      2. The search for \"most recent product release\" also did not yield any results, indicating that there might be no recent product releases available or that the information is not accessible through the wikisearch tool.
      3. The search for \"latest product\" similarly resulted in no information being found, suggesting that either the latest product is not listed on the encyclopedia or it is not easily identifiable using the wikisearch tool.
    Improvements: To improve the response, I could try searching for AMD's products on a different 
      source or search engine to find the most recent product release. Additionally, I could ask 
      the user for more context or clarify their question to better understand what they are 
      looking for."
  julia> lesson = selfReflext(agent, report)
  ```
"""
function selfReflext(a, analysis::T) where {T<:AbstractString}
  prompt =
  """
    <|system|>
      <You have access to the following tools>
      askbox: Useful for when you need to ask a customer for more context. Input should be a conversation to customer.
      wikisearch: Useful for when you need to search an encyclopedia Input is keywords and not a question.
      </You have access to the following tools>
      <your report>
      $analysis
      </your report>
      <your job are>
      1. Lesson: what lesson could you learn from your report?.
      2. Context: what is the context this lesson could apply to?
      </your job are>
    </s>
    <|assistant|>
    
  """

  response = sendReceivePrompt(a, prompt, max_tokens=1024)
  return response
end


""" Formulate a response from work for user's stimulus.

  Arguments:
    a, one of ChatAgent's agent.

  Return:
    A response for user's stimulus.

  Example:
  ```jldoctest
  julia> using ChatAgent, CommUtils
  julia> agent = ChatAgent.agentReflex("Jene")
  julia> shorttermMemory = OrderedDict{String, Any}(
    "user:"        => "What's the latest AMD GPU?",
    "Plan 1:"     => "   To answer this question, I will need to search for the latest AMD GPU using the wikisearch tool.\n",
    "Act 1:"      => " wikisearch\n",
    "Actinput 1:" => " amd gpu latest\n",
    "Obs 1:"      => "No info available for your search query.",
    "Act 2:"      => "  wikisearch\n",
    "Actinput 2:" => "  amd graphics card latest\n",
    "Obs 2:"      => "No info available for your search query.")

  julia> report = formulateUserresponse(agent, shorttermMemory)
  ```
"""
function formulateUserresponse(a)
  conversation = messagesToString_nomark(a.messages, addressAIas="I")
  work = dictToString(a.memory[:shortterm])

  prompt =
  """
    <|system|>
      <symbol meaning>
      Plan: a plan  
      Thought: your thought
      Act: the action you took
      Actinput: the input to the action
      Obs: the result of the action
      </symbol meaning>

      <your talk with the user>
      $conversation
      </your talk with the user>

      <your earlier work>
      $work
      </your earlier work>
    
      <your job>      
      Based on your talk with the user and your work, present a response that compares and justifies each option in great detail.
      </your job>   
    </s>
    <|assistant|>
    Recommendation:
  """
  response = sendReceivePrompt(a, prompt, max_tokens=1024, timeout=300)
  return response
end

# function formulateUserresponse(a)
#   conversation = messagesToString_nomark(a.messages, addressAIas="I")
#   work = dictToString(a.memory[:shortterm])

#   prompt =
#   """
#     <|system|>
#     Symbol:
#     Plan: a plan
#     Thought: your thought
#     Act: the action you took
#     Actinput: the input to the action
#     Obs: the result of the action

#     Your talk with the user:
#     $conversation

#     Your work:
#     $work
    
#     From your talk with the user and your work, formulate a response for the user .
#     </s>
#     <|assistant|>
#     response:
#   """
#   response = sendReceivePrompt(a, prompt)
#   return response
# end


""" Extract important info from text into key-value pair text.

  Arguments:
    a, one of ChatAgent's agent.
    text, a text you want to extract info

  Return:
    key-value pair text.

  Example:
  ```jldoctest
  julia> using ChatAgent
  julia> agent = ChatAgent.agentReflex("Jene")
  julia> text = "We are holding a wedding party at the beach."
  julia> extract(agent, text)
  "location=beach, event=wedding party"
  ```
"""
function extractinfo(a, text::T)  where {T<:AbstractString}
  # determine whether there are any important info in an input text
  prompt =
  """
    <|system|>
      <user's message>
      $text
      </user's message>
      <your job>
      Determine whether there are important info in the user's message. Answer: {Yes/No/Not sure}
      </your job>
    </s>
    Answer: 
  """
  response = sendReceivePrompt(a, prompt, temperature=0.0)
  if occursin("Yes", response)
    prompt =
    """
      <|system|>
        <user's message>
        $text
        </user's message>
        <your job>
        Extract important info from the user's message into keys and values using this format: key=value,.
        p.s.1 you can extract many key-value pairs.
        </your job>
      </s>

    """

    response = sendReceivePrompt(a, prompt, temperature=0.0)
    return response
  else
    return nothing
  end
end 


""" Update important info from key-value pair text into another key-value pair text.

  Arguments:
    a, one of ChatAgent's agent
    text, a key-value pair text

  Return:
    updated key-value pair text

  Example:
  ```jldoctest
  julia> using ChatAgent
  julia> agent = ChatAgent.agentReflex("Jene")
  julia> currentinfo = "location=beach, event=wedding party"
  julia> newinfo = "wine_type=full body, dry and medium tannin\nprice_range=50 dollars"
  julia> updateEnvState(agent, currentinfo, newinfo)
  "  location=beach, event=wedding party, wine_type=full body, dry and medium tannin, price_range=50 dollars"
  ```
"""
function updateEnvState(a, newinfo)
  prompt =
  """
    <|im_start|>system
    Current state:
    $(a.env)

    New info:
    $newinfo
    
    Your job is to update or add information from new info into the current state which use key-value format.
    <|im_end|>
    Updated Current State:\n
  """

  response = sendReceivePrompt(a, prompt, temperature=0.0)
  return response
end



""" Determine whether LLM should go to next task.

  Arguments:
    a, one of ChatAgent's agent.

  Return:
    "Yes" or "no" decision to go next task.

  Example:
  ```jldoctest
  julia> using ChatAgent, CommUtils
  julia> agent = ChatAgent.agentReflex("Jene")
  julia> shorttermMemory = OrderedDict{String, Any}(
    "user:"        => "What's the latest AMD GPU?",
    "Plan 1:"     => "   To answer this question, I will need to search for the latest AMD GPU using the wikisearch tool.\n",
    "Act 1:"      => " wikisearch\n",
    "Actinput 1:" => " amd gpu latest\n",
    "Obs 1:"      => "No info available for your search query.",
    "Act 2:"      => "  wikisearch\n",
    "Actinput 2:" => "  amd graphics card latest\n",
    "Obs 2:"      => "No info available for your search query.")

  julia> decision = checkTaskCompletion(agent)
  "Yes"
  ```
"""
function checkTaskCompletion(a)
  @show a.memory[:shortterm]["Plan 1:"]
  # stimulus = a.memory[:shortterm]["user:"]
  work = dictToString(a.memory[:shortterm])

  prompt =
  """
    <|system|>
      <Symbol meaning>
      Plan: a plan
      Thought: your thought
      Act: the action you took
      Actinput: the input to the action
      Obs: the result of the action
      </Symbol meaning>

      <Your earlier work>
      $work
      </Your earlier work>
      <Your job>
      Check whether each task of your plan has been completed.
      </Your job>

      <Example 1>
        Task 1 of the plan: Ask user about their preferred topping of a pizza.
        Obs: I love Malvasia.
        assistant: After checking all my work's observed results, I can't find any relevant info that the user tell me what is their preferred topping in pizza. Thus, task 1 isn't done yet.
        Task 2 of the plan: Ask user if they have any preferred type of car. 
        Obs: I like a semi truck. 
        assistant: After checking all my work's observed results, I found that the user like a semi truck. Thus, task 2 is done.
        Task 3 of the plan: How much you are looking to spend for a new house?
        Obs: 50K THB. 
        assistant: After checking all my work's observed results, I found that the user have a budget of 50,000 Baht. Thus, task 3 is done.
      </Example 1>

      Let's think step by step.
    </s>
    <|assistant|> After 
  """
  response = nothing
  _response = nothing
  _response = sendReceivePrompt(a, prompt, max_tokens=1024)
  @show checkTaskCompletion_raw = _response
  _response = split(_response, "</")[1]
  _response = split(_response, "\n\n")[1]
  # response = "I " * split(_response, "{")[1]  # sometime response have more than 1 {answer: done}

  decision = nothing
  # if occursin("done", response)
  #   decision = true
  # else
  #   decision = false
  # end
  
  return decision, response
end


function recap(a)
  # stimulus = a.memory[:shortterm]["user:"]
  getonlykeys = ["Actinput", "Obs"]
  worknoplan = similar(a.memory[:shortterm])
  for (k, v) in a.memory[:shortterm]
    count = 0
    for i in getonlykeys
      if occursin(i, k)
        count += 1
      end
    end
    if count != 0
      worknoplan[k] = v 
    end
  end

  work = dictToString(worknoplan)

  toolnames = ""
  toollines = ""
  for (toolname, v) in a.tools
    toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
    toollines *= toolline
    toolnames *= "$toolname, "
  end

  # prompt =
  # """
  #   <|system|>
  #     <Symbol meaning>
  #     Plan: a plan
  #     Thought: your thought
  #     Act: the action you took
  #     Actinput: the input to the action
  #     Obs: the result of the action
  #     </Symbol meaning>

  #     <Your earlier work>
  #     $work
  #     </Your earlier work>
  #     <Your job>
  #     Recap: list all your observed results in detail
  #     </Your job>

  #     Let's think step by step.
  #   </s>
  #   <|assistant|>
  #   Recap: 
  # """



  prompt =
  """
    <|system|>
    $(a.roles[a.role])
      <Symbol meaning>
      Plan: a plan
      Thought: your thought
      Act: the action you took
      Actinput: the input to the action
      Obs: the result of the action
      </Symbol meaning>
      <You have access to the following tools>
      $toollines
      </You have access to the following tools>
      <Your earlier work>
      $work
      </Your earlier work>
      <Your job>
      Extract info: extract each info in details from your earlier work according to the Actinput context.
      </Your job>

      Let's think step by step.
    </s>
    <|assistant|>
    Extracted info: 
  """
  aware = "Self-awareness: map the info from the recap to the plan's tasks then state your mapping."
  response = sendReceivePrompt(a, prompt, max_tokens=1024, temperature=0.0)
  response = split(response, "</")[1]
  response = split(response, "<|")[1]
  response = split(response, "\n\n")[1]
  
  return response
end

""" Direct conversation is not an agent, messages does not pass through logic loop
  but goes directly to LLM.
"""
function directconversation(a::agentReflex, usermsg::String)
  response = nothing

  _ = addNewMessage(a, "user", usermsg)

  response = chat_mistral_openorca(a)
  response = removeTrailingCharacters(response)
  _ = addNewMessage(a, "assistant", response)
  return response
end
















































end # module