module type
  
export agent, sommelier, companion

using Dates, UUIDs, DataStructures, JSON3
using GeneralUtils

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

abstract type agent end


mutable struct companion <: agent
  name::String  # agent name
  id::String    # agent id
  maxHistoryMsg::Integer  # e.g. 21th and earlier messages will get summarized

  """ Memory
    Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3
    NO "system" message in chathistory because I want to add it at the inference time
    chathistory= [
      Dict(:name=>"user", :text=> "Wassup!", :timestamp=> Dates.now()),
      Dict(:name=>"assistant", :text=> "Hi I'm your assistant.", :timestamp=> Dates.now()),
    ]

  """
  chathistory::Vector{Dict{Symbol, Any}}
  memory::Dict{Symbol, Any}

  # communication function
  text2textInstructLLM::Function
end

function companion(
  text2textInstructLLM::Function
  ;
  name::String= "Assistant",
  id::String= string(uuid4()),
  maxHistoryMsg::Integer= 20,
  chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(),
  )

  memory = Dict{Symbol, Any}(
              :chatbox=> "",
              :shortmem=> Vector{Dict{Symbol, String}}(),
              :events=> Vector{Dict{Symbol, Any}}()
            )

  newAgent = companion(
    name,
    id,
    maxHistoryMsg,
    chathistory,
    memory,
    text2textInstructLLM
    )
  
  return newAgent
end



""" A sommelier agent.

# Arguments
  - `mqttClient::Client`
    MQTTClient's client
  - `msgMeta::Dict{Symbol, Any}`
    A dict contain info about a message.
  - `config::Dict{Symbol, Any}`
    Config info for an agent. Contain mqtt topic for internal use and other info.
  
# Keyword Arguments
  - `name::String`
    Agent's name
  - `id::String`
    Agent's ID
  - `tools::Dict{Symbol, Any}`
    Agent's tools
  - `maxHistoryMsg::Integer`
    max history message

# Return
  - `nothing`

# Example
```jldoctest
julia> using YiemAgent, MQTTClient, GeneralUtils
julia> msgMeta = GeneralUtils.generate_msgMeta(
                "N/A", 
                replyTopic = "/testtopic/prompt"
                )  
julia> tools= Dict(
          :chatbox=>Dict(
            :name => "chatbox",
            :description => "Useful only for when you need to ask the user for more info or context. Do not ask the user their own question.",
            :input => "Input should be a text.",
            :output => "" ,
            :func => nothing,
          ),
        )
julia> agentConfig = Dict(
          :receiveprompt=>Dict(
            :mqtttopic=> "/testtopic/prompt", # topic to receive prompt i.e. frontend send msg to this topic
          ),
          :receiveinternal=>Dict(  
            :mqtttopic=> "/testtopic/internal", # receive topic for model's internal 
          ),
          :text2text=>Dict(
            :mqtttopic=> "/text2text/receive",
          ),
        )
julia> client, connection = MakeConnection("test.mosquitto.org", 1883)
julia> agent = YiemAgent.bsommelier(
                  client,
                  msgMeta,
                  agentConfig, 
                  name= "assistant",
                  id= "555",  # agent instance id
                  tools=tools,
                )
```

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

# Signature
"""
mutable struct sommelier <: agent
  name::String  # agent name
  id::String    # agent id
  tools::Dict   
  maxHistoryMsg::Integer  # e.g. 21th and earlier messages will get summarized

  """ Memory
    Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3
    NO "system" message in chathistory because I want to add it at the inference time
    chathistory= [
      Dict(:name=>"user", :text=> "Wassup!", :timestamp=> Dates.now()),
      Dict(:name=>"assistant", :text=> "Hi I'm your assistant.", :timestamp=> Dates.now()),
    ]

  """
  chathistory::Vector{Dict{Symbol, Any}}
  memory::Dict{Symbol, Any}

  # communication function
  text2textInstructLLM::Function
  executeSQL::Function
  querySQLVectorDB::Function
  addSQLVectorDB::Function
end

function sommelier(
  text2textInstructLLM::Function,
  executeSQL::Function,
  querySQLVectorDB::Function,
  addSQLVectorDB::Function
  ;
  name::String= "Assistant",
  id::String= string(uuid4()),
  maxHistoryMsg::Integer= 20,
  chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(),
  )

  tools = Dict( # update input format
      "chatbox"=> Dict(
        :description => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
        :input => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
        :output => "" ,
      ),
      "winestock"=> Dict(
        :description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
        :input => """<input>Input is a JSON-formatted string that contains a detailed and precise search query.</input><input example>{\"wine type\": \"rose\", \"price\": \"max 35\", \"sweetness level\": \"sweet\", \"intensity level\": \"light bodied\", \"Tannin level\": \"low\", \"Acidity level\": \"low\"}</input example>""",
        :output => """<output>Output are wines that match the search query in JSON format.""",
      ),
      # "finalanswer"=> Dict(
      #   :description => "<tool description>Useful for when you are ready to recommend wines to the user.</tool description>",
      #   :input => """<input format>{\"finalanswer\": \"some text\"}.</input format><input example>{\"finalanswer\": \"I recommend Zena Crown Vista\"}</input example>""",
      #   :output => "" ,
      #   :func => nothing,
      # ),
    )

  memory = Dict{Symbol, Any}(
              :chatbox=> "",
              :shortmem=> Vector{Dict{Symbol, String}}(),
              :events=> Vector{Dict{Symbol, Any}}()
            )

  newAgent = sommelier(
    name,
    id,
    tools,
    maxHistoryMsg,
    chathistory,
    memory,
    text2textInstructLLM,
    executeSQL,
    querySQLVectorDB,
    addSQLVectorDB
    )
  
  return newAgent
end


























































end # module type