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
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.

  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
  a.memory[:shortterm] = OrderedDict{String, Any}()
  a.memory[:log] = OrderedDict{String, Any}()
  @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:

    "
    <|im_start|>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)

  prompt = 
  """
    <|system|>
    $(a.roles[a.role])
    Your earlier talk with the user:
    $(a.earlierConversation)
    </s>
    $conversation
    <|assistant|>
  """
  
  response = sendReceivePrompt(a, prompt)
  response = split(response, "<|im_end|>")[1]

  return response
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}
        "
  """

  conversation = messagesToString(a.messages)
  toollines = ""
  for (toolname, v) in a.tools
    if toolname ∉ [""]
      toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\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:"])
  
  assistant_plan_prompt =
    """
    <|system|>
    $(a.roles[a.role])
    The required info you need for wine recommendation:
        - type of food: ask the user
        - occasion: ask the user
        - type of wine (Rose, White, Red and Sparkling): ask the user
        - user's personal taste of wine characteristic: ask the user
        - wine price range: ask the user
        - ambient temperature at the serving location: ask the user
        - wines we have in stock
    
    You have access to the following tools:
    $toollines

    Your earlier work:
    $shorttermMemory

    Your job is to do the following:
    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.
    </s>
    $conversation
    <|assistant|>
    Plan: 
    """

  plan = sendReceivePrompt(a, assistant_plan_prompt, max_tokens=512, temperature=0.1)
  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 ∉ ["chatbox"]
      toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
      toollines *= toolline
    end
  end

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

  prompt =
  """
    <|system|>
    $(a.roles[a.role])
    The required info you need for wine recommendation:
        - wine price range: ask the user
        - user's personal taste of wine: ask the user
        - type of food: ask the user
        - occasion: ask the user
        - ambient temperature at the serving location: ask the user
        - wines we have in stock
    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=512, temperature=0.1)
  @show updatedPlan = result
  a.memory[:shortterm]["Plan 1:"] = result
  
end

function actor_mistral_openorca(a::agentReflex, taskrecap="")
  """
    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}
        "
  """
  #BUG BIG output from winestock cause recap() to fail. 
  toolnames = ""
  toollines = ""
  for (toolname, v) in a.tools
    toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
    toollines *= toolline
    toolnames *= "$toolname, "
  end

  # shorttermMemory = dictToString(a.memory[:shortterm], skiplist=["user:"])

  # conversation = conversationSummary(a)
  # println("")
  # @show conversationSum = conversation
  
  # context = 
  # """
  # Your talk with the user:
  # $conversation
  # """

  start = "Thought"
  aware = ""
  if taskrecap != ""
    start = "Aware"
    aware = "Aware: check recap against the plan about current situation"
  end

  winestockResult = ""
  if a.winestockResult != ""
    winestockResult =
    """
    <winestock search result>
    $(a.winestockResult)
    </winestock search result>
    """
  end

  prompt = 
  """
    <|system|>
    $(a.roles[a.role])
      <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 earlier work's recap>
      $taskrecap
      </Your earlier work's recap>
      $winestockResult
      <Your job> 
      Use the following format:
      $aware
      Thought: based on the plan and the recap of the plan, what to do? (pay attention to correct numeral calculation, commonsense. ask the user one by one question.)
      Act: an action to take based on your thought, must be one of [{toolnames}]
      Actinput: your input to the action based on your thought (pay attention to the tool's input)
      Obs: observed result of the action
      </Your job>
    </s>
    <|assistant|>
    $start: 
  """
  prompt = replace(prompt, "{toolnames}" => toolnames)

  println("")
  @show actor_prompt = prompt

  response = nothing
  chunkedtext = nothing
  latestTask = nothing

  tempcounter = 0.2
  while true  # while Thought or Act is empty, run actor again
    # tempcounter += 0.1
    @show tempcounter
    response = sendReceivePrompt(a, prompt, max_tokens=1024, temperature=tempcounter)
    response = splittext(response, ["Obs", "<|im_end|>"])
    
    latestTask = shortMemLatestTask(a.memory[:shortterm]) +1
    
    if start == "Thought"
      response = "Thought $latestTask: " * response
    else
      response = "Aware $latestTask: " * response
    end

    headerToDetect = ["Question:", "Plan:", "Aware:", "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]
    
    headers = detectCharacters(response, headerToDetect)

    println("")
    

    headerToDetect = ["Plan $(a.attempt):",
                      "Aware $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
    @show iskey_Thought = haskey(chunkedtext, "Thought $latestTask:")
    @show iskey_Act = haskey(chunkedtext, "Act $latestTask:")
    @show iskey_Actinput = haskey(chunkedtext, "Actinput $latestTask:")
    if iskey_Thought && iskey_Act && iskey_Actinput
      if chunkedtext["Thought $latestTask:"] != " " && chunkedtext["Act $latestTask:"] != " " &&
        length(chunkedtext["Actinput $latestTask:"]) > 5
        break
      end
    end
  end
  
  toolname = toolNameBeingCalled(chunkedtext["Act $latestTask:"], a.tools)
  toolinput = chunkedtext["Actinput $latestTask:"]

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

  return toolname, toolinput, chunkedtext
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

  # a.earlierConversation = conversationSummary(a)
  _ = 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
    workstate, response = work(a)
  end

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

  return response
end

"""
  Continuously run llm functions except when llm is getting Answer: or chatbox.
  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]) != 0
    latestTask = shortMemLatestTask(a.memory[:shortterm])
    if haskey(a.memory[:shortterm], "Act $latestTask:")
      if occursin("chatbox", a.memory[:shortterm]["Act $latestTask:"])
        a.memory[:shortterm]["Obs $latestTask:"] = 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 == "chatbox"
        response = msgToUser
        workstate = actorstate
        break
      elseif actorstate == "all tasks done"
        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 = "chatbox"
    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
    toolname, toolinput, chunkedtext = actor_mistral_openorca(a, taskrecap)
    println("")
    @show toolname
    @show toolinput
    println("")

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

    if toolname == "chatbox"  # chat with user
      msgToUser = toolinput
      actorState = toolname
      break
    elseif toolname == "noaction"
      println(">>> already done")
    else  # function call
      f = a.tools[toolname][:func]
      toolresult = f(a, toolinput)
      @show toolresult
      # if toolname == "winestock"
      #   a.winestockResult = toolresult
      #   a.memory[:shortterm]["Obs $latestTask:"] = "winestock search done"
      #   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 =
  """
    <|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:
    $(a.memory[:shortterm]["Objective:"])
    
    Your job are:
    1. Write an evaluation guideline for your work in order to be able to evaluate your response.
    2. An example of what the response should be.
    <|im_end|>
  """

  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)
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 response: $text

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

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

  response = sendReceivePrompt(a, prompt)

  println("")
  response_grading = response
  @show response_grading

  _score = split(response[end-5:end], "/")[1]
  _score = split(_score, " ")[end]
  score = parse(Int, _score)
  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 =
  """
    <|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 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?
    <|im_end|>
    <|im_start|>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 =
  """
    <|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|>
  """

  response = sendReceivePrompt(a, prompt, max_tokens=2048)
  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:
    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 =
  """
    <|im_start|>system
    User's message:
    $text
    
    Your job is determine whether there are important info in the user's message. Answer: {Yes/No/Not sure}
    <|im_end|>
    Answer: 
  """
  response = sendReceivePrompt(a, prompt, temperature=0.0)
  if occursin("Yes", response)
    prompt =
    """
      <|im_start|>system
      User's message:
      $text
      
      Your job is to 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.
      <|im_end|>

    """

    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=512)
  @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)

  # 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>

      <Your earlier work>
      $work
      </Your earlier work>
      <Your job>
      Extract info: extract all info in details from your earlier work.
      </Your job>

      Let's think step by step.
    </s>
    <|assistant|>
    Extracted info: 
  """

  response = sendReceivePrompt(a, prompt, max_tokens=512, 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