module interface
    

export agentReact, agentReflex,
        addNewMessage, clearMessage, removeLatestMsg, conversation, writeEvaluationGuideline,
        grading, analyze, selfReflext, actor_mistral_openorca2, formulateUserRespond

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

# ---------------------------------------------------------------------------- #
#                              pythoncall setting                              #
# ---------------------------------------------------------------------------- #
# Ref: https://github.com/JuliaPy/PythonCall.jl/issues/252
# by setting the following variables, PythonCall will use system python or conda python and 
# packages installed by system or conda
# if these setting are not set (comment out), PythonCall will use its own python and package that
# installed by CondaPkg (from env_preparation.jl)
# ENV["JULIA_CONDAPKG_BACKEND"] = "Null"
# systemPython = split(read(`which python`, String), "\n")[1]
# 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.

  Args:

  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) > 1 # system instruction will NOT be deleted
      pop!(a.messages)
    else
      break
    end
  end
end

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

# function generatePrompt_mistral_openorca(a::T, usermsg::String, role::Symbol) where {T<:agent}
#   prompt = 
#   """
#     <|im_start|>system
#     {systemMsg}
#     <|im_end|>
#     Here are the context for the question:
#     {context}
#   """
#   prompt = replace(prompt, "{systemMsg}" => a.roles[role])
  
#   toolnames = ""
#   toollines = ""
#   for (toolname, v) in a.tools
#     toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
#     toollines *= toolline
#     toolnames *= "$toolname,"
#   end
#   prompt = replace(prompt, "{toolnames}" => toolnames)
#   prompt = replace(prompt, "{tools}" => toollines)

#   prompt = replace(prompt, "{context}" => a.context)

#   prompt *= "<|im_start|>user\n" * usermsg * "\n<|im_end|>\n"
#   prompt *= "<|im_start|>assistant\n"

#   return prompt
# end

# function generatePrompt_mistral_openorca(a::T, usermsg::String,
#           thinkingMode::Symbol=:nothinking) where {T<:agent}

#   prompt = 
#   """
#     <|im_start|>system
#     {systemMsg}
#     You have access to the following tools:
#     {tools}
#     {thinkingMode}
#     <|im_end|>
#     Here are the context for the question:
#     {context}
#   """
#   prompt = replace(prompt, "{systemMsg}" => a.roles[a.role])
#   prompt = replace(prompt, "{thinkingMode}" => a.thinkingMode[thinkingMode])
#   toolnames = ""
#   toollines = ""
#   for (toolname, v) in a.tools
#     toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
#     toollines *= toolline
#     toolnames *= "$toolname,"
#   end
#   prompt = replace(prompt, "{toolnames}" => toolnames)
#   prompt = replace(prompt, "{tools}" => toollines)

#   prompt = replace(prompt, "{context}" => a.context)

#   prompt *= "<|im_start|>user\nQuestion: " * usermsg * "\n<|im_end|>\n"
#   prompt *= "<|im_start|>assistant\n"
  
#   return prompt
# end


function generatePrompt_mistral_openorca(a::T, usermsg::String,
          thinkingMode::Symbol=:nothinking) where {T<:agent}

  prompt = 
  """
    <|im_start|>system
    {systemMsg}
    {tools}
    {thinkingMode}
    <|im_end|>
    Here are the context for the stimulus:
    {context}
  """
  prompt = replace(prompt, "{systemMsg}" => a.roles[a.role])
  prompt = replace(prompt, "{thinkingMode}" => a.thinkingMode[thinkingMode])
  toolnames = ""
  toollines = ""
  for (toolname, v) in a.tools
    toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
    toollines *= toolline
    toolnames *= "$toolname,"
  end
  prompt = replace(prompt, "{toolnames}" => toolnames)

  prompt = replace(prompt, "{context}" => a.context)

  prompt *= "<|im_start|>user\nStimulus: " * usermsg * "\n<|im_end|>\n"
  prompt *= "<|im_start|>assistant\n"
  
  return prompt
end

function chat_mistral_openorca(a::agentReflex, usermsg::String)
  """
    general prompt format:

    "
    <|im_start|>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}
        "
  """

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

  """
  prompt = replace(prompt, "{role}" => a.roles[a.role])
  prompt = replace(prompt, "{thinkingFormat}" => "")

  context = 
  """
  {earlierConversation}
  {env state}
  {longterm memory}
  """
  context = replace(context, "{earlierConversation}" => "My earlier talk with the user:\n$(a.earlierConversation)")
  context = replace(context, "{env state}" => "")
  context = replace(context, "{longterm memory}" => "")

  prompt = replace(prompt, "{context}" => context)

  prompt = replace(prompt, "{usermsg}" => "Stimulus: $usermsg")
  
  return prompt
end


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

    "
    <|im_start|>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}
        "
  """

  prompt = 
  """
    <|im_start|>system
    {role}
    {roleSpecificKnowledge}
    {tools}
    {thinkingFormat}
    {context}
    <|im_end|>
    <|im_start|>user
    {usermsg}
    <|im_end|>
    <|im_start|>assistant
    Plan:
  """
  prompt = replace(prompt, "{role}" => a.roles[a.role])
  prompt = replace(prompt, "{thinkingFormat}" => a.thinkingFormat[:planner])
  roleSpecificKnowledge = 
  """
    Info you need from the user to be able to help them selecting their best wine:
      - type of food
      - occasion
      - user's personal taste of wine
      - wine price range
      - ambient temperature at the serving location
      - wines we have in stock
      You job is to 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.
  """
  prompt = replace(prompt, "{roleSpecificKnowledge}" => roleSpecificKnowledge)
  toolnames = ""
  toollines = ""
  for (toolname, v) in a.tools
    toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
    toollines *= toolline
    toolnames *= "$toolname,"
  end
  
  prompt = replace(prompt, "{toolnames}" => toolnames)
  prompt = replace(prompt, "{tools}" => "You have access to the following tools:\n$toollines")
  
  context = 
    """
    {earlierConversation}
    {env state}
    {longterm memory}
    """
  context = replace(context, "{earlierConversation}" => "My earlier talk with the user:\n$(a.earlierConversation)")
  context = replace(context, "{env state}" => "")
  context = replace(context, "{longterm memory}" => "")

  prompt = replace(prompt, "{context}" => context)

  # initialize short term memory
  prompt = replace(prompt, "{usermsg}" => "Stimulus: $(a.memory[:shortterm]["user:"])")

  return prompt
end


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

    "
    <|im_start|>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}
        "
  """

  mark = "$(a.step)"

  prompt = 
  """
    <|im_start|>system
    {role} 
    {tools}
    {thinkingFormat}
    {context}
    <|im_end|>
    {shorttermMemory}
    Thought $(a.step): 
  """
  
  prompt = replace(prompt, "{role}" => a.roles[a.role])
  prompt = replace(prompt, "{thinkingFormat}" => a.thinkingFormat[:actor])
  prompt = replace(prompt, "{step}" => a.step)
  
  s = shortMemoryToString(a.memory[:shortterm], ["user:", "Plan 1:"])
  prompt = replace(prompt, "{shorttermMemory}" => s)

  toolnames = ""
  toollines = ""
  for (toolname, v) in a.tools
    toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
    toollines *= toolline
    toolnames *= "$toolname, "
  end
  prompt = replace(prompt, "{toolnames}" => toolnames)
  prompt = replace(prompt, "{tools}" => "You have access to the following tools:\n$toollines")
  
  context = 
  """
  {env state}
  {longterm memory}
  {plan}
  """
  # context = replace(context, "{earlierConversation}" => "My earlier talk with the user:\n$(a.earlierConversation)")
  context = replace(context, "{env state}" => "")
  context = replace(context, "{longterm memory}" => "")
  context = replace(context, "{plan}" => "My plan:\n$(a.memory[:shortterm]["Plan $(a.attempt):"])")
  prompt = replace(prompt, "{context}" => context)

  return prompt
end



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

  if a.thought != "nothing" # continue thought
    _ = addNewMessage(a, "user", usermsg)
    a.thought *= "Obs $(a.thinkinground): $usermsg\n"
    prompt = a.thought
    respond = work(a, prompt)
  else  # new thought
    thinkingmode = chooseThinkingMode(a, usermsg)
    @show thinkingmode
    if thinkingmode == :no_thinking 
      a.context = conversationSummary(a)  #TODO should be long conversation before use summary because it leaves out details
      _ = addNewMessage(a, "user", usermsg)
      prompt = generatePrompt_mistral_openorca(a, usermsg, thinkingmode)
      @show prompt
      respond = sendReceivePrompt(a, prompt)
      respond = split(respond, "<|im_end|>")[1]
      respond = replace(respond, "\n" => "")
      _ = addNewMessage(a, "assistant", respond)
      @show respond
    elseif thinkingmode == :thinking
      a.context = conversationSummary(a)
      _ = addNewMessage(a, "user", usermsg)
      prompt = generatePrompt_mistral_openorca(a, usermsg, thinkingmode)
      respond = work(a, prompt)
    else
      error("undefined condition thinkingmode = $thinkingmode $(@__LINE__)")
    end
  end

  return respond
end

"""
  Continuously run llm functions except when llm is getting Answer: or chatbox.
  There are many work() depend on thinking mode.
"""
function work(a::T, prompt::String, maxround::Int=3) where {T<:agent}
  respond = nothing
  while true
    a.thinkinground += 1
    @show a.thinkinground
    toolname = nothing
    toolinput = nothing

    if a.thinkinground > a.thinkingroundlimit
      a.thought *= "Thought $(a.thinkinground): I think I know the answer."
      prompt = a.thought
    end

    @show prompt
    respond = sendReceivePrompt(a, prompt)

    headerToDetect = nothing
    if a.thinkinground == 1
      try 
        respond = split(respond, "Obs:")[1]  
        headerToDetect = ["Question:", "Plan:", "Thought:", "Act:", "Actinput:", "Obs:", "...", "Answer:", 
                        "Conclusion:", "Summary:"]
      catch 
      end
    else
      try 
        respond = split(respond, "Obs $(a.thinkinground):")[1]  
        headerToDetect = ["Question $(a.thinkinground):", "Plan $(a.thinkinground):", 
                        "Thought $(a.thinkinground):", "Act $(a.thinkinground):", 
                        "Actinput $(a.thinkinground):", "Obs $(a.thinkinground):", 
                        "...", "Answer:", 
                        "Conclusion:", "Summary:"]
      catch 
      end
    end
    @show respond
    headers = detectCharacters(respond, headerToDetect)
    chunkedtext = chunktext(respond, headers)

    Answer = findDetectedCharacter(headers, "Answer:")
    AnswerInd = length(Answer) != 0 ? Answer[1] : nothing
    Act = findDetectedCharacter(headers, "Act $(a.thinkinground):")
    if length(Answer) == 1 && length(Act) == 0
      a.thought = "nothing" # assignment finished, no more thought
      a.context = "nothing"
      a.thinkinground = 0
      respond = chunkedtext[AnswerInd][:body]
      respond = replace(respond, "<|im_end|>"=>"")
      _ = addNewMessage(a, "assistant", respond)
      break
    else

      # check for tool being called
      ActHeader = a.thinkinground == 1 ? "Act:" : "Act $(a.thinkinground):"
      if length(findDetectedCharacter(headers, ActHeader)) != 0 # check whether there is Act: in a respond
        ActInd = findDetectedCharacter(headers, ActHeader)[1]
        toolname = toolNameBeingCalled(chunkedtext[ActInd][:body], a.tools)
      end
      ActinputHeader = a.thinkinground == 1 ? "Actinput:" : "Actinput $(a.thinkinground):"
      if length(findDetectedCharacter(headers, ActinputHeader)) != 0 # check whether there is Actinput: in a respond
        ActinputInd = findDetectedCharacter(headers, ActinputHeader)[1]
        toolinput = chunkedtext[ActinputInd][:body]
      end
      
      # clean up
      if occursin(" \"", toolinput)
        toolinput = GeneralUtils.getStringBetweenCharacters(toolinput, " \"", "\"\n")
      else
        toolinput = GeneralUtils.getStringBetweenCharacters(toolinput, " ", "\n")
      end
      @show toolname 
      @show toolinput
      if toolname === nothing || toolinput === nothing
        println("toolname $toolname toolinput $toolinput retry thinking")
        a.thinkinground -= 1
        continue
      end

      if a.thought == "nothing"
        thought = ""
        for i in chunkedtext
          header = i[:header]
          header = replace(header, ":"=>" $(a.thinkinground):") # add number so that llm not confused
          body = i[:body]
          thought *= "$header $body"
        end
        a.thought = prompt * thought
      else
        a.thought *= respond
      end


      if toolname == "chatbox"  # chat with user
        a.thought *= toolinput
        respond = toolinput
        _ = addNewMessage(a, "assistant", respond)
        break
      else  # function call
        f = a.tools[Symbol(toolname)][:func]
        _result = f(toolinput)
        if _result != "No info available."  #TODO for use with wikisearch(). Not good for other tools
          _result = makeSummary(a, _result)
        end
        result = "Obs $(a.thinkinground): $_result\n"
        a.thought *= result
        prompt = a.thought
      end
    end
  end
  @show respond
  return respond
end

function conversation(a::agentReflex, usermsg::String; attemptlimit::Int=3)
  a.attemptlimit = attemptlimit
  respond = nothing

  # determine thinking mode
  a.thinkingmode = chooseThinkingMode(a, usermsg)
  @show a.thinkingmode

  if a.thinkingmode == :no_thinking
    a.earlierConversation = conversationSummary(a)  #TODO should be long conversation before use summary because it leaves out details
    _ = addNewMessage(a, "user", usermsg)
    prompt = chat_mistral_openorca(a, usermsg)
    println("")
    @show prompt
    respond = sendReceivePrompt(a, prompt)
    respond = split(respond, "<|im_end|>")[1]
    respond = replace(respond, "\n" => "")
    _ = addNewMessage(a, "assistant", respond)
    println("")
    @show respond
  else
    respond = work(a, usermsg)
  end

  return respond
end


function work(a::agentReflex, usermsg::String)
  respond = nothing

  if a.thinkingmode == :new_thinking
    a.earlierConversation = conversationSummary(a)
    _ = addNewMessage(a, "user", usermsg)
    a.memory[:shortterm]["user:"] = usermsg
    a.memory[:log]["user:"] = usermsg
    a.newplan = true
  elseif a.thinkingmode == :continue_thinking #TODO
    println("continue_thinking!!")
    _ = addNewMessage(a, "user", usermsg)
    a.memory[:shortterm]["Obs $(a.step):"] = usermsg
    a.memory[:log]["Obs $(a.step):"] = usermsg
  else
    error("undefined condition thinkingmode = $thinkingmode $(@__LINE__)")
  end

  while true  # Work loop
    # plan
    if a.attempt <= a.attemptlimit
      toolname = nothing
      toolinput = nothing
      if a.newplan == true
        a.attempt += 1
        a.step = 0
        prompt_plan = planner_mistral_openorca(a)
        println("")
        @show prompt_plan
        respond = sendReceivePrompt(a, prompt_plan, max_tokens=1024)
        println("")
        plan_raw = respond
        @show plan_raw
        # sometimes LLM add not-need word I don't want
        plan = splittext(respond, ["Step 1", "<|im_end|>", "Response", "Execution", 
                                  "Result", "Recommendation", "My response"])
        # plan = replace(plan, "Plan:"=>"")
        println("")
        @show plan
        
        a.newplan = false
        a.memory[:shortterm]["Plan $(a.attempt):"] = plan
        a.memory[:log]["Plan $(a.attempt):"] = plan
      end
      
      @show a.attempt
      @show a.memory[:shortterm]["user:"]

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

      if actorstate == "chatbox"
        respond = msgToUser
        break
      elseif actorstate == "all steps done" ||  actorstate == "formulateUserRespond"
        println("all steps done")

        respond = formulateUserRespond(a)

        println("")
        formulatedRespond = respond
        @show formulatedRespond
        
        a.memory[:shortterm]["Respond $(a.attempt):"] = respond
        a.memory[:log]["Respond $(a.attempt):"] = respond

        # evaluate. if score < 8/10 try again.
        guideline = writeEvaluationGuideline(a, a.memory[:shortterm]["user:"])
        @show guideline
        score = grading(a, guideline, respond)
        @show score
        if score >= 8 # good enough answer
          @show a.memory[:shortterm]
          a.memory[:shortterm] = OrderedDict{String, Any}()
          a.memory[:log] = OrderedDict{String, Any}()
          break
        else  # self evaluate and reflect then try again
          analysis = analyze(a)
          @show analysis
          lessonwithcontext = selfReflext(a, analysis)
          @show lessonwithcontext

          newdict = OrderedDict()

          a.memory[:shortterm] = keepOnlyKeys(a.memory[:shortterm], ["user:"])

          #TODO add lesson and context into longterm memory
          headerToDetect = ["Lesson:", "Context:", ]
          headers = detectCharacters(lessonwithcontext, headerToDetect)
          chunkedtext = chunktext(lessonwithcontext, headers)
          @show chunkedtext
          a.memory[:longterm][chunkedtext["Context:"]] = chunkedtext["Lesson:"]
          a.newplan = true
          println("")
        end
      else
        error("undefied condition, actorstate $actorstate $(@__LINE__)")
        break
      end
    else  #TODO attempt limit reached, force AI to answer
      error("attempt limit reach")
      break
    end
  end

  # good enough answer


  # communicates with user
  _ = addNewMessage(a, "assistant", respond)
  return  respond
end


# function evaluate()
  
# end



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

"""
function actor(a::agentReflex)
  actorState = nothing
  msgToUser = nothing

  totalsteps = checkTotalStepInPlan(a)

  while true  # Actor loop
    if a.step == 0
      a.step = 1
    else
      decision, reason = goNogo(a)
      println("")
      @show decision
      @show reason
      # a.memory[:shortterm]["Check $(a.step):"] = reason
      if decision == "Yes"   # in case there is a cancel, go straight to evaluation
        a.step += 1
      elseif decision == "No"
        # repeat the latest step
        a.memory[:shortterm] = removeHeaders(a.memory[:shortterm], a.step, ["Plan"])
        a.memory[:log] = removeHeaders(a.memory[:log], a.step, ["Plan"])
        println("repeating step $(a.step)")
      elseif decision == "formulateUserRespond"
        actorState = "formulateUserRespond"
        msgToUser = nothing
        break
      else
        error("undefined condition decision = $decision $(@__LINE__)")
      end
    end
    

    @show a.step
    if a.step < totalsteps

      prompt_actor = actor_mistral_openorca(a)
      println("")
      @show prompt_actor
      respond = sendReceivePrompt(a, prompt_actor)
      respond = splittext(respond, ["Obs", "<|im_end|>"])
      respond_actor_raw = respond
      println("")
      @show respond_actor_raw
      if !occursin("Thought", respond)
        respond = "Thought: " * respond
      end

      headerToDetect = ["Question:", "Plan:", "Thought:", 
                        "Act:", "Actinput:", "Obs:", "...", 
                        "Answer:", "Conclusion:", "Summary:"]
      
      # replace headers with headers with correct attempt and step number
      respond = replaceHeaders(respond, headerToDetect, a.step)  
      
      headers = detectCharacters(respond, headerToDetect)

      respond_actor = respond
      println("")
      @show respond_actor

      headerToDetect = ["Plan $(a.attempt):", 
                        "Thought $(a.step):", 
                        "Act $(a.step):", 
                        "Actinput $(a.step):", 
                        "Obs $(a.step):",
                        "Check $(a.step):",]
      headers = detectCharacters(respond, headerToDetect)
      chunkedtext = chunktext(respond, headers)
      @show chunkedtext

      # add to memory
      a.memory[:shortterm] = addShortMem!(a.memory[:shortterm], chunkedtext)
      a.memory[:log] = addShortMem!(a.memory[:log], chunkedtext)

      toolname = toolNameBeingCalled(chunkedtext["Act $(a.step):"], a.tools)
      toolinput = chunkedtext["Actinput $(a.step):"]
      @show toolname
      @show toolinput

      if toolname == "chatbox"  # chat with user
        msgToUser = toolinput
        actorState = toolname
        break
      elseif toolname == "formulateUserRespond"
        msgToUser = toolinput
        actorState = toolname
        break
      else  # function call
        f = a.tools[Symbol(toolname)][:func]
        toolresult = f(a, toolinput)
        @show toolresult
        a.memory[:shortterm]["Obs $(a.step):"] = toolresult
        a.memory[:log]["Obs $(a.step):"] = toolresult
      end
    else  #TODO finish all steps
      actorState = "all steps done"
      msgToUser = nothing
      break
    end
  end

  return actorState, msgToUser
end



""" Write evaluation guideline.

Args:
  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, usermsg::T)  where {T<:AbstractString}
  prompt =
  """
    <|im_start|>system
    You have access to the following tools:
    chatbox: 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.

    Your work:
    $usermsg
    
    Your job are:
    1. Write an evaluation guideline for your work in order to be able to evaluate your respond.
    2. An example of what the respond should be.
    <|im_end|>
  """

  respond = sendReceivePrompt(a, prompt)
  return respond
end



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

Args:
  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 steps 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 respond should include:\n1. A clear understanding of the user's question.\n2. The steps 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)
2
```
"""
function grading(a, guideline::T, text::T)  where {T<:AbstractString}
  prompt =
  """
    <|im_start|>system
    You have access to the following tools:
    chatbox: 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.

    $guideline

    Your respond: $text

    You job are:
    1. Evaluate your respond using the evaluation guideline and an example respond.
    2. Give yourself a score out of 10 for your respond. 

    Use the following format to answer: 
    {Evaluation} Score {}/10.
    <|im_end|>
  """
  println("")
  prompt_grading = prompt
  @show prompt_grading

  respond = sendReceivePrompt(a, prompt)

  println("")
  respond_grading = respond
  @show respond_grading

  _score = split(respond[end-5:end], "/")[1]
  _score = split(_score, " ")[end]
  score = parse(Int, _score)
  return score
end



""" Analize work.

Args:
  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)
```
""" #WORKING analyze sometime result in empty string ""
function analyze(a)
  shorttermMemory = shortMemoryToString(a.memory[:shortterm], ["user:"])
  prompt =
  """
    <|im_start|>system
    You have access to the following tools:
    chatbox: 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.

    Your work:
    $shorttermMemory

    You job is to do each of the following steps 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 respond?
    <|im_end|>
    <|im_start|>assistant
    
  """

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

  return respond
end


""" Write a lesson drawn from evaluation.

Args:
  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 =
  """
    <|im_start|>system
    You have access to the following tools:
    chatbox: 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.

    Your report:
    $analysis
    
    Your job are:
    1. Lesson: what lesson could you learn from your report?.
    2. Context: what is the context this lesson could apply to?
    <|im_end|>
  """

  respond = sendReceivePrompt(a, prompt, max_tokens=2048)
  return respond
end


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

Args:
  a, one of ChatAgent's agent.

Return:
  A respond 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 = formulateUserRespond(agent, shorttermMemory)
```
"""
function formulateUserRespond(a)
  stimulus = a.memory[:shortterm]["user:"]

  work = shortMemoryToString(a.memory[:shortterm], ["user:"])

  prompt =
  """
    <|im_start|>system
    Symbol:
    Stimulus: the input user gives to you and you must respond
    Plan: a plan
    Thought: your thought
    Act: the action you took
    Actinput: the input to the action
    Obs: the result of the action

    Stimulus:
    $stimulus

    Your work:
    $work
    
    From your work, formulate a respond for user's stimulus.
    <|im_end|>
    Respond: 
  """
  respond = sendReceivePrompt(a, prompt)
  return respond
end



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

Args:
  a, one of ChatAgent's agent.

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

# 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 = goNogo(agent)
"Yes"
```
""" #BUG sometime AI ready to formulate respond before all step are completed
function goNogo(a)
  stimulus = a.memory[:shortterm]["user:"]
  work = shortMemoryToString(a.memory[:shortterm], ["user:"])

  # prompt =
  # """
  #   <|im_start|>system
  #   Symbol meaning:
  #   Stimulus: the input user gives to you and you must respond
  #   Plan: a plan
  #   Thought: your thought
  #   Act: the action you took
  #   Actinput: the input to the action
  #   Obs: the result of the action

  #   Stimulus:
  #   $stimulus

  #   Your work:
  #   $work
    
  #   From your work, you job is to decide what to do next by choosing one of the following choices:
  #   If you are ready to do the next step of the plan say, "{Yes}". And what is the rationale behind the decision?
  #   If you need to repeat the latest step say, "{No}". And what is the rationale behind the decision?
  #   If you are ready to formulate a final respond to user original stimulus say, {formulateUserRespond}. And what is the rationale behind the decision?
  #   <|im_end|>
  # """

  prompt =
  """
    <|im_start|>system
    Symbol meaning:
    Stimulus: the input user gives to you and you must respond
    Plan: a plan
    Thought: your thought
    Act: the action you took
    Actinput: the input to the action
    Obs: the result of the action

    Stimulus:
    $stimulus

    Your work:
    $work
    
    Your job is to check whether step $(a.step) of your work is completed according to the plan and choose only one of the following choices.
    choice 1: If you are ready to do the next step of the plan say, "{Yes}". And what is the rationale behind the decision?
    choice 2: If you need to repeat the latest step say, "{No}". And what is the rationale behind the decision?
    <|im_end|>
    <|im_start|>assistant

  """

  respond = sendReceivePrompt(a, prompt)
  
  println("")
  goNogo_raw = respond
  @show goNogo_raw

  decision = nothing
  reason = nothing
  if occursin("Yes", respond)
    decision = "Yes"
  elseif occursin("No", respond)
    decision = "No"
  elseif occursin("formulateUserRespond", respond)
    decision = "formulateUserRespond"
  else
    error("undefied condition, decision $decision $(@__LINE__)")
  end

  startInd = findfirst(decision, respond)[end] +2

  if occursin(":", respond[startInd:end]) # check for ":" after decision cha
    startInd2 = findnext(":", respond, startInd)[end]+1
    reason = respond[startInd2:end]
  else
    reason = respond[startInd:end]
  end

  return decision, reason
end
























end # module