update

Reviewed-on: #1

Project.toml

@@ -1,7 +1,7 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
-version = "0.1.2"
+version = "0.1.3"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

src/interface.jl

@@ -1,6 +1,6 @@
 module interface
 
-export addNewMessage, conversation, decisionMaker, evaluator, reflector, generatechat,
+export addNewMessage, conversation, decisionMaker, reflector, generatechat,
   generalconversation, detectWineryName, generateSituationReport
 
 using JSON3, DataStructures, Dates, UUIDs, HTTP, Random, PrettyPrinting, Serialization,
@@ -55,6 +55,8 @@ end
     config
   - `state::T2`
     a game state
+
+# Keyword Arguments
   
 # Return        
   - `thoughtDict::Dict`
@@ -90,8 +92,6 @@ julia> output_thoughtDict = Dict(
 
 # TODO
   - [] update docstring
-  - [x] implement the function
-  - [] implement RAG to pull similar experience
   - [] use customerinfo
   - [] user storeinfo
 
@@ -182,11 +182,10 @@ function decisionMaker(a::T; recent::Integer=5)::Dict{Symbol,Any} where {T<:agen
       1) Make an informed decision about what you need to do to achieve the goal
       2) Thanks the user when they don't need any further assistance and invite them to comeback next time
 
-      Your responsibility excludes:
-      1) Asking or guiding the user to make an order or purchase
-      2) Processing sales orders or engaging in any other sales-related activities
-      3) Answering questions beyond just recommendations.
-      4) Offering additional services beyond just recommendations.
+      Your responsibility does NOT includes:
+      1) Requesting the user to place an order, make a purchase, or confirm the order. These are the job of our sales team at the store.
+      2) Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
+      3) Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.
 
       At each round of conversation, you will be given the current situation:
       Your recent events: latest 5 events of the situation
@@ -195,7 +194,7 @@ function decisionMaker(a::T; recent::Integer=5)::Dict{Symbol,Any} where {T<:agen
       You must follow the following guidelines:
       - Generally speaking, your inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know exactly until you check your inventory.
       - All wines in your inventory are always in stock.
-      - Engage in conversation to indirectly investigate the customer's intention, budget and preferences before checking your inventory.
+      - Approach each customer with open-ended questions to understand their preferences, budget, and occasion. This will help you guide the conversation naturally while gathering essential insights. Once you have this information, you can efficiently check your inventory for the best match.
       - Do not ask the user about wine's flavor e.g. floral, citrusy, nutty or some thing similar as these terms cannot be used to search the database.
       - Once the user has selected their wine, ask the user if they need any further assistance. Do not offer any additional services. If the user doesn't need any further assistance, say goodbye and invite them to come back next time.
       - Medium and full-bodied red wines should not be paired with spicy foods.
@@ -204,11 +203,10 @@ function decisionMaker(a::T; recent::Integer=5)::Dict{Symbol,Any} where {T<:agen
       - When searching an inventory, search as broadly as possible based on the information you have gathered so far.
       - Encourage the customer to explore different options and try new things.
       - Sometimes, the item a user desires might not be available in your inventory. In such cases, inform the user that the item is unavailable and suggest an alternative instead.
-      - If a customer requests information about discounts, quantity, rewards programs, promotions, delivery options, boxes, gift wrapping, packaging, or personalized messages, please inform them that they can contact our sales team at the store.
-      - Do not discuss other stores with the user except for your own.
 
       For your information:
-      - vintage 0 means non-vintage.
+      - Your store carries only wine.
+      - Vintage 0 means non-vintage.
 
       You should then respond to the user with interleaving Understanding, Reasoning, Plan, Action:
       1) Understanding: 
@@ -221,7 +219,7 @@ function decisionMaker(a::T; recent::Integer=5)::Dict{Symbol,Any} where {T<:agen
           - CHATBOX which you can use to talk with the user. The input is your intentions for the dialogue. Be specific.
           - CHECKINVENTORY which you can use to check info about wine you want in your inventory. The input is a search term in verbal English.
               Good query example: white wine, full-bodied, France, less than 2000 USD. 
-          - ENDCONVERSATION which you can use when you believe the user has concluded their interaction, to properly end the conversation with them. Input is "NA".
+          - ENDCONVERSATION which you can use when the user has finished their conversation with you, so that you can properly end the conversation. Input is "NA".
       5) Action_input: input of the action
 
       You should only respond in format as described below:
@@ -233,7 +231,7 @@ function decisionMaker(a::T; recent::Integer=5)::Dict{Symbol,Any} where {T<:agen
 
       Let's begin!
     """
-
+    
     chathistory = chatHistoryToText(a.chathistory)
 
     # check if winename in shortmem occurred in chathistory. if not, skip decision and imediately use PRESENTBOX
@@ -271,7 +269,7 @@ function decisionMaker(a::T; recent::Integer=5)::Dict{Symbol,Any} where {T<:agen
         push!(winenames, name)
       end
       availableWineName = join(winenames, ',')
-      "You found information about the following wines in your inventory: $availableWineName"
+      "Available wines you've found in your inventory so far: $availableWineName"
     else
       ""
     end
@@ -296,15 +294,13 @@ function decisionMaker(a::T; recent::Integer=5)::Dict{Symbol,Any} where {T<:agen
           Dict(:name => "user", :text => usermsg)
         ]
 
-      # put in model format
-      prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-      prompt *= """
-                <|start_header_id|>assistant<|end_header_id|>
-                """
+      # change qwen format       put in model format
+      prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
 
       response = a.func[:text2textInstructLLM](prompt)
       response = GeneralUtils.remove_french_accents(response)
-      response = replace(response, '*'=>"")
+      response = replace(response, "**"=>"")
+      response = replace(response, "***"=>"")
       response = replace(response, "<|eot_id|>"=>"")
 
       # check if response contain more than one functions from ["CHATBOX", "CHECKINVENTORY", "ENDCONVERSATION"]
@@ -320,9 +316,10 @@ function decisionMaker(a::T; recent::Integer=5)::Dict{Symbol,Any} where {T<:agen
         continue
       end
 
-      responsedict = GeneralUtils.textToDict(response,
-        ["Understanding", "Reasoning", "Plan", "Action_name", "Action_input"],
-        rightmarker=":", symbolkey=true, lowercasekey=true)
+      header = ["Understanding:", "Reasoning:", "Plan:", "Action_name:", "Action_input:"]
+      dictkey = ["understanding", "reasoning", "plan", "action_name", "action_input"]
+      responsedict = GeneralUtils.textToDict(response, header; 
+        dictKey=dictkey, symbolkey=true)
 
       if responsedict[:action_name] ∉ ["CHATBOX", "CHECKINVENTORY", "ENDCONVERSATION"]
         errornote = "You must use the given functions"
@@ -396,284 +393,280 @@ function decisionMaker(a::T; recent::Integer=5)::Dict{Symbol,Any} where {T<:agen
 end
 
 
-""" Assigns a scalar value to each new child node to be used for selec-
-tion and backpropagation. This value effectively quantifies the agent's progress in task completion,
-serving as a heuristic to steer the search algorithm towards the most promising regions of the tree.
+# """ Assigns a scalar value to each new child node to be used for selec-
+# tion and backpropagation. This value effectively quantifies the agent's progress in task completion,
+# serving as a heuristic to steer the search algorithm towards the most promising regions of the tree.
 
-# Arguments
-  - `a::T1`
-    one of Yiem's agent
-  - `state::T2`
-    a game state  
+# # Arguments
+#   - `a::T1`
+#     one of Yiem's agent
+#   - `state::T2`
+#     a game state  
 
-# Return
-  - `evaluation::Tuple{String, Integer}`
-    evaluation and score
+# # Return
+#   - `evaluation::Tuple{String, Integer}`
+#     evaluation and score
 
-# Example
-```jldoctest
-julia> 
-```
+# # Example
+# ```jldoctest
+# julia> 
+# ```
 
-# Signature
-"""
-function evaluator(config::T1, state::T2
-)::Tuple{String,Integer} where {T1<:AbstractDict,T2<:AbstractDict}
+# # Signature
+# """
+# function evaluator(config::T1, state::T2
+# )::Tuple{String,Integer} where {T1<:AbstractDict,T2<:AbstractDict}
 
-  systemmsg = 
-  """
-    Analyze the trajectories of a solution to a question answering task. The trajectories are 
-    labeled by environmental observations about the situation, thoughts that can reason about 
-    the current situation and actions that can be three types:
-    1) CHECKINVENTORY[query], which you can use to find wine in your inventory.
-    2) CHATBOX[text], which you can use to interact with the user.
+#   systemmsg = 
+#   """
+#     Analyze the trajectories of a solution to a question answering task. The trajectories are 
+#     labeled by environmental observations about the situation, thoughts that can reason about 
+#     the current situation and actions that can be three types:
+#     1) CHECKINVENTORY[query], which you can use to find wine in your inventory.
+#     2) CHATBOX[text], which you can use to interact with the user.
 
-    Given a question and a trajectory, evaluate its correctness and provide your reasoning and
-    analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories 
-    can be correct if the thoughts and actions so far are correct, even if the answer is not found 
-    yet. Do not generate additional thoughts or actions. Then ending with the correctness score s 
-    where s is an integer from 0 to 10.
+#     Given a question and a trajectory, evaluate its correctness and provide your reasoning and
+#     analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories 
+#     can be correct if the thoughts and actions so far are correct, even if the answer is not found 
+#     yet. Do not generate additional thoughts or actions. Then ending with the correctness score s 
+#     where s is an integer from 0 to 10.
 
-    You should only respond in JSON format as describe below:
-    {"evaluation": "your evaluation", "score": "your evaluation score"}
+#     You should only respond in JSON format as describe below:
+#     {"evaluation": "your evaluation", "score": "your evaluation score"}
 
-    Here are some examples:
-    user:
-    {
-      "question": "I'm looking for a sedan with an automatic driving feature.",
-      "thought_1": "I have many types of sedans in my inventory, each with diverse features.",
-      "thought_2": "But there is only 1 model that has the feature customer wanted.",
-      "thought_3": "I should check our inventory first to see if we have it.", 
-      "action_1": {"name": "inventory", "input": "Yiem model A"},
-      "observation_1": "Yiem model A is in stock."
-    }
-    assistant
-    {
-      "evaluation": "This trajectory is correct as it is reasonable to check an inventory for info provided in the question.
-                                      It is also better to have simple searches corresponding to a single entity, making this the best action.",
-      "score": 10
-    }
+#     Here are some examples:
+#     user:
+#     {
+#       "question": "I'm looking for a sedan with an automatic driving feature.",
+#       "thought_1": "I have many types of sedans in my inventory, each with diverse features.",
+#       "thought_2": "But there is only 1 model that has the feature customer wanted.",
+#       "thought_3": "I should check our inventory first to see if we have it.", 
+#       "action_1": {"name": "inventory", "input": "Yiem model A"},
+#       "observation_1": "Yiem model A is in stock."
+#     }
+#     assistant
+#     {
+#       "evaluation": "This trajectory is correct as it is reasonable to check an inventory for info provided in the question.
+#                                       It is also better to have simple searches corresponding to a single entity, making this the best action.",
+#       "score": 10
+#     }
 
-    user:
-    {
-      "question": "Do you have an all-in-one pen with 4 colors and a pencil for sale?",
-      "thought_1": "Let me check our inventory first to see if I have it.",
-      "action_1": {"name": "inventory", "input": "pen with 4 color and a pencil."},
-      "observation_1": "I found {1: "Pilot Dr. grip 4-in-1 pen", 2: "Rotting pencil"}",
-      "thought_2": "Ok, I have what the user is asking. Let's tell the user.", 
-      "action_2": {"name": "CHATBOX", "input": "Yes, we do have a Pilot Dr. grip 4-in-1 pen and a Rotting pencil"},
-      "observation_1": "This is not what I wanted."
-    }
-    assistant:
-    {
-      "evaluation": "This trajectory is incorrect as my search term should be related to a 4-colors pen with a pencil in it,
-                    not a pen and a pencil seperately. A better search term should have been a 4-colors pen with a pencil, all-in-one.",
-      "score": 0
-    }
+#     user:
+#     {
+#       "question": "Do you have an all-in-one pen with 4 colors and a pencil for sale?",
+#       "thought_1": "Let me check our inventory first to see if I have it.",
+#       "action_1": {"name": "inventory", "input": "pen with 4 color and a pencil."},
+#       "observation_1": "I found {1: "Pilot Dr. grip 4-in-1 pen", 2: "Rotting pencil"}",
+#       "thought_2": "Ok, I have what the user is asking. Let's tell the user.", 
+#       "action_2": {"name": "CHATBOX", "input": "Yes, we do have a Pilot Dr. grip 4-in-1 pen and a Rotting pencil"},
+#       "observation_1": "This is not what I wanted."
+#     }
+#     assistant:
+#     {
+#       "evaluation": "This trajectory is incorrect as my search term should be related to a 4-colors pen with a pencil in it,
+#                     not a pen and a pencil seperately. A better search term should have been a 4-colors pen with a pencil, all-in-one.",
+#       "score": 0
+#     }
 
-    Let's begin!
-  """
+#     Let's begin!
+#   """
 
-  usermsg = """
-            $(JSON3.write(state[:thoughtHistory]))
-            """
+#   usermsg = """
+#             $(JSON3.write(state[:thoughtHistory]))
+#             """
 
-  chathistory =
-    [
-      Dict(:name => "system", :text => systemmsg),
-      Dict(:name => "user", :text => usermsg)
-    ]
+#   chathistory =
+#     [
+#       Dict(:name => "system", :text => systemmsg),
+#       Dict(:name => "user", :text => usermsg)
+#     ]
 
-  # put in model format
-  prompt = formatLLMtext(chathistory, "llama3instruct")
-  prompt *= """
-            <|start_header_id|>assistant<|end_header_id|>
-            {
-            """
+#   # put in model format
+#   prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
 
-  pprint(prompt)
-  externalService = config[:externalservice][:text2textinstruct]
+#   pprint(prompt)
+#   externalService = config[:externalservice][:text2textinstruct]
 
 
-  # apply LLM specific instruct format
-  externalService = config[:externalservice][:text2textinstruct]
+#   # apply LLM specific instruct format
+#   externalService = config[:externalservice][:text2textinstruct]
 
-  msgMeta = GeneralUtils.generate_msgMeta(
-    externalService[:mqtttopic],
-    senderName="evaluator",
-    senderId=string(uuid4()),
-    receiverName="text2textinstruct",
-    mqttBroker=config[:mqttServerInfo][:broker],
-    mqttBrokerPort=config[:mqttServerInfo][:port],
-  )
+#   msgMeta = GeneralUtils.generate_msgMeta(
+#     externalService[:mqtttopic],
+#     senderName="evaluator",
+#     senderId=string(uuid4()),
+#     receiverName="text2textinstruct",
+#     mqttBroker=config[:mqttServerInfo][:broker],
+#     mqttBrokerPort=config[:mqttServerInfo][:port],
+#   )
 
-  outgoingMsg = Dict(
-    :msgMeta => msgMeta,
-    :payload => Dict(
-      :text => prompt,
-      :kwargs => Dict(
-        :max_tokens => 512,
-        :stop => ["<|eot_id|>"],
-      )
-    )
-  )
+#   outgoingMsg = Dict(
+#     :msgMeta => msgMeta,
+#     :payload => Dict(
+#       :text => prompt,
+#       :kwargs => Dict(
+#         :max_tokens => 512,
+#         :stop => ["<|eot_id|>"],
+#       )
+#     )
+#   )
 
-  for attempt in 1:5
-    try
-      response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
-      _responseJsonStr = response[:response][:text]
-      expectedJsonExample = """
-                            Here is an expected JSON format:
-                            {"evaluation": "...", "score": "..."}
-                            """
-      responseJsonStr = jsoncorrection(config, _responseJsonStr, expectedJsonExample)
-      evaluationDict = copy(JSON3.read(responseJsonStr))
+#   for attempt in 1:5
+#     try
+#       response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
+#       _responseJsonStr = response[:response][:text]
+#       expectedJsonExample = """
+#                             Here is an expected JSON format:
+#                             {"evaluation": "...", "score": "..."}
+#                             """
+#       responseJsonStr = jsoncorrection(config, _responseJsonStr, expectedJsonExample)
+#       evaluationDict = copy(JSON3.read(responseJsonStr))
 
-      # check if dict has all required value
-      dummya::AbstractString = evaluationDict[:evaluation]
-      dummyb::Integer = evaluationDict[:score]
+#       # check if dict has all required value
+#       dummya::AbstractString = evaluationDict[:evaluation]
+#       dummyb::Integer = evaluationDict[:score]
 
-      return (evaluationDict[:evaluation], evaluationDict[:score])
-    catch e
-      io = IOBuffer()
-      showerror(io, e)
-      errorMsg = String(take!(io))
-      st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-      println("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
-    end
-  end
-  error("evaluator failed to generate an evaluation")
-end
+#       return (evaluationDict[:evaluation], evaluationDict[:score])
+#     catch e
+#       io = IOBuffer()
+#       showerror(io, e)
+#       errorMsg = String(take!(io))
+#       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
+#       println("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+#     end
+#   end
+#   error("evaluator failed to generate an evaluation")
+# end
 
 
-"""
+# """
 
-# Arguments
+# # Arguments
   
-# Return
+# # Return
 
-# Example
-```jldoctest
-julia> 
-```
+# # Example
+# ```jldoctest
+# julia> 
+# ```
 
-# TODO
-  - [] update docstring
-  - [x] implement the function
-  - [x] add try block. check result that it is expected before returning
+# # TODO
+#   - [] update docstring
+#   - [x] implement the function
+#   - [x] add try block. check result that it is expected before returning
 
-# Signature
-"""
-function reflector(config::T1, state::T2)::String where {T1<:AbstractDict,T2<:AbstractDict}
-  # https://github.com/andyz245/LanguageAgentTreeSearch/blob/main/hotpot/hotpot.py
+# # Signature
+# """
+# function reflector(config::T1, state::T2)::String where {T1<:AbstractDict,T2<:AbstractDict}
+#   # https://github.com/andyz245/LanguageAgentTreeSearch/blob/main/hotpot/hotpot.py
 
-  _prompt = 
-  """
-    You are a helpful sommelier working for a wine store. 
-    Your goal is to recommend the best wine from your inventory that match the user preferences.
-    You will be given a question and a trajectory of the previous help you've done for a user.
-    You were unsuccessful in helping the user either because you guessed the wrong answer with Finish[answer], or you didn't know the user enough.
-    In a few sentences, Diagnose a possible reason for failure and devise a new, concise, high level plan that aims to mitigate the same failure. 
-    Use complete sentences.  
+#   _prompt = 
+#   """
+#     You are a helpful sommelier working for a wine store. 
+#     Your goal is to recommend the best wine from your inventory that match the user preferences.
+#     You will be given a question and a trajectory of the previous help you've done for a user.
+#     You were unsuccessful in helping the user either because you guessed the wrong answer with Finish[answer], or you didn't know the user enough.
+#     In a few sentences, Diagnose a possible reason for failure and devise a new, concise, high level plan that aims to mitigate the same failure. 
+#     Use complete sentences.  
 
-    You should only respond in JSON format as describe below:
-    {"reflection": "your relection"}
+#     You should only respond in JSON format as describe below:
+#     {"reflection": "your relection"}
 
-    Here are some examples:
-    Previous Trial:
-    {
-      "question": "Hello, I would like a get a bottle of wine", 
-      "thought_1": "A customer wants to buy a bottle of wine. Before making a recommendation, I need to know more about their preferences.", 
-      "action_1": {"name": "CHATBOX", "input": "What is the occasion for which you're buying this wine?"},
-      "observation_1": "We are holding a wedding party",
+#     Here are some examples:
+#     Previous Trial:
+#     {
+#       "question": "Hello, I would like a get a bottle of wine", 
+#       "thought_1": "A customer wants to buy a bottle of wine. Before making a recommendation, I need to know more about their preferences.", 
+#       "action_1": {"name": "CHATBOX", "input": "What is the occasion for which you're buying this wine?"},
+#       "observation_1": "We are holding a wedding party",
 
-      "thought_2": "A wedding party, that's a great occasion! The customer might be looking for a celebratory drink. Let me ask some more questions to narrow down the options.",
-      "action_2": {"name": "CHATBOX", "input": "What type of food will you be serving at the wedding?"},
-      "observation_2": "It will be Thai dishes.", 
+#       "thought_2": "A wedding party, that's a great occasion! The customer might be looking for a celebratory drink. Let me ask some more questions to narrow down the options.",
+#       "action_2": {"name": "CHATBOX", "input": "What type of food will you be serving at the wedding?"},
+#       "observation_2": "It will be Thai dishes.", 
 
-      "thought_3": "With Thai food, I should recommend a wine that complements its spicy and savory flavors. And since it's a celebratory occasion, the customer might prefer a full-bodied wine.",
-      "action_3": {"name": "CHATBOX", "input": "What is your budget for this bottle of wine?"},
-      "observation_3": "I would spend up to 50 bucks.",
+#       "thought_3": "With Thai food, I should recommend a wine that complements its spicy and savory flavors. And since it's a celebratory occasion, the customer might prefer a full-bodied wine.",
+#       "action_3": {"name": "CHATBOX", "input": "What is your budget for this bottle of wine?"},
+#       "observation_3": "I would spend up to 50 bucks.",
 
-      "thought_4": "Now that I have some more information, it's time to narrow down the options.", 
-      "action_4": {"name": "winestock", "input": "red wine with full body, pairs well with spicy food, budget \$50"},
-      "observation_4": "I found the following wines in our stock: \n{\n  1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n", 
+#       "thought_4": "Now that I have some more information, it's time to narrow down the options.", 
+#       "action_4": {"name": "winestock", "input": "red wine with full body, pairs well with spicy food, budget \$50"},
+#       "observation_4": "I found the following wines in our stock: \n{\n  1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n", 
 
-      "thought_5": "Now that I have a list of potential wines, I need to know more about the customer's taste preferences.",
-      "action_5": {"name": "CHATBOX", "input": "What type of wine characteristics are you looking for? (e.g. tannin level, sweetness, intensity, acidity)"},
-      "observation_5": "I like full-bodied red wine with low tannin.", 
+#       "thought_5": "Now that I have a list of potential wines, I need to know more about the customer's taste preferences.",
+#       "action_5": {"name": "CHATBOX", "input": "What type of wine characteristics are you looking for? (e.g. tannin level, sweetness, intensity, acidity)"},
+#       "observation_5": "I like full-bodied red wine with low tannin.", 
 
-      "thought_6": "Now that I have more information about the customer's preferences, it's time to make a recommendation.",
-      "action_6": {"name": "recommendbox", "input": "El Enemigo Cabernet Franc 2019"},
-      "observation_6": "I don't like the one you recommend. I want dry wine."
-    }
+#       "thought_6": "Now that I have more information about the customer's preferences, it's time to make a recommendation.",
+#       "action_6": {"name": "recommendbox", "input": "El Enemigo Cabernet Franc 2019"},
+#       "observation_6": "I don't like the one you recommend. I want dry wine."
+#     }
 
-    {
-      "reflection": "I asked the user about the occasion, food type, and budget, and then searched for wine in the inventory right away. However, I should have asked the user for the specific wine type and their preferences in order to gather more information before making a recommendation."
-    }
+#     {
+#       "reflection": "I asked the user about the occasion, food type, and budget, and then searched for wine in the inventory right away. However, I should have asked the user for the specific wine type and their preferences in order to gather more information before making a recommendation."
+#     }
 
-    Let's begin!
+#     Let's begin!
 
-    Previous trial:
-    $(JSON3.write(state[:thoughtHistory]))
-    {"reflection"
-  """
+#     Previous trial:
+#     $(JSON3.write(state[:thoughtHistory]))
+#     {"reflection"
+#   """
 
-  # apply LLM specific instruct format
-  externalService = config[:externalservice][:text2textinstruct]
-  llminfo = externalService[:llminfo]
-  prompt =
-    if llminfo[:name] == "llama3instruct"
-      formatLLMtext_llama3instruct("system", _prompt)
-    else
-      error("llm model name is not defied yet $(@__LINE__)")
-    end
+#   # apply LLM specific instruct format
+#   externalService = config[:externalservice][:text2textinstruct]
+#   llminfo = externalService[:llminfo]
+#   prompt =
+#     if llminfo[:name] == "llama3instruct"
+#       formatLLMtext_llama3instruct("system", _prompt)
+#     else
+#       error("llm model name is not defied yet $(@__LINE__)")
+#     end
 
-  msgMeta = GeneralUtils.generate_msgMeta(
-    a.config[:externalservice][:text2textinstruct][:mqtttopic],
-    senderName="reflector",
-    senderId=string(uuid4()),
-    receiverName="text2textinstruct",
-    mqttBroker=config[:mqttServerInfo][:broker],
-    mqttBrokerPort=config[:mqttServerInfo][:port],
-  )
+#   msgMeta = GeneralUtils.generate_msgMeta(
+#     a.config[:externalservice][:text2textinstruct][:mqtttopic],
+#     senderName="reflector",
+#     senderId=string(uuid4()),
+#     receiverName="text2textinstruct",
+#     mqttBroker=config[:mqttServerInfo][:broker],
+#     mqttBrokerPort=config[:mqttServerInfo][:port],
+#   )
 
-  outgoingMsg = Dict(
-    :msgMeta => msgMeta,
-    :payload => Dict(
-      :text => prompt,
-      :kwargs => Dict(
-        :max_tokens => 512,
-        :stop => ["<|eot_id|>"],
-      )
-    )
-  )
+#   outgoingMsg = Dict(
+#     :msgMeta => msgMeta,
+#     :payload => Dict(
+#       :text => prompt,
+#       :kwargs => Dict(
+#         :max_tokens => 512,
+#         :stop => ["<|eot_id|>"],
+#       )
+#     )
+#   )
 
-  for attempt in 1:5
-    try
-      response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
-      _responseJsonStr = response[:response][:text]
-      expectedJsonExample = """
-                            Here is an expected JSON format:
-                            {"reflection": "..."}
-                            """
-      responseJsonStr = jsoncorrection(config, _responseJsonStr, expectedJsonExample)
-      reflectionDict = copy(JSON3.read(responseJsonStr))
+#   for attempt in 1:5
+#     try
+#       response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
+#       _responseJsonStr = response[:response][:text]
+#       expectedJsonExample = """
+#                             Here is an expected JSON format:
+#                             {"reflection": "..."}
+#                             """
+#       responseJsonStr = jsoncorrection(config, _responseJsonStr, expectedJsonExample)
+#       reflectionDict = copy(JSON3.read(responseJsonStr))
 
-      # check if dict has all required value
-      dummya::AbstractString = reflectionDict[:reflection]
+#       # check if dict has all required value
+#       dummya::AbstractString = reflectionDict[:reflection]
 
-      return reflectionDict[:reflection]
-    catch e
-      io = IOBuffer()
-      showerror(io, e)
-      errorMsg = String(take!(io))
-      st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-      println("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
-    end
-  end
-  error("reflector failed to generate a thought")
-end
+#       return reflectionDict[:reflection]
+#     catch e
+#       io = IOBuffer()
+#       showerror(io, e)
+#       errorMsg = String(take!(io))
+#       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
+#       println("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+#     end
+#   end
+#   error("reflector failed to generate a thought")
+# end
 
 
 """ Chat with llm.
@@ -862,13 +855,6 @@ function think(a::T)::NamedTuple{(:actionname, :result),Tuple{String,String}} wh
         actionname=actionname,
         actioninput=chatresponse,
       )
-
-      # eventdict(;
-      #   event_description="the assistant talks to the user.",
-      #   timestamp=Dates.now(),
-      #   subject="assistant",
-      #   actioninput=chatresponse,
-      # )
     )
     result = chatresponse
 
@@ -936,11 +922,10 @@ function generatechat(a::sommelier, thoughtDict)
     Your responsibility includes:
     1) Given the situation, convey your thoughts to the user.
 
-    Your responsibility excludes:
-      1) Asking or guiding the user to make an order or purchase
-      2) Processing sales orders or engaging in any other sales-related activities
-      3) Answering questions beyond just recommendations.
-      4) Offering additional services beyond just recommendations.
+    Your responsibility does NOT includes:
+      1) Requesting the user to place an order, make a purchase, or confirm the order. These are the job of our sales team at the store.
+      2) Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
+      3) Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.
 
     At each round of conversation, you will be given the current situation:
     Your ongoing conversation with the user: ...
@@ -949,13 +934,13 @@ function generatechat(a::sommelier, thoughtDict)
 
     You MUST follow the following guidelines:
     - Do not offer additional services you didn't thought.
+    - Focus on plan.
 
     You should follow the following guidelines:
     - Focus on the latest conversation.
     - If the user interrupts, prioritize the user
     - Be honest
     - Medium and full-bodied red wines should not be paired with spicy foods.
-    - Do not discuss other stores with the user except for your own.
 
     You should then respond to the user with:
     1) Chat: Given the situation, How would you respond to the user to express your thoughts honestly and keep the conversation going smoothly?
@@ -969,6 +954,9 @@ function generatechat(a::sommelier, thoughtDict)
     Let's begin!
   """
 
+  header = ["Chat:"]
+  dictkey = ["chat"]
+
   # a.memory[:shortmem][:available_wine] is a vector of dictionary
   context =
     if length(a.memory[:shortmem][:available_wine]) != 0
@@ -981,11 +969,21 @@ function generatechat(a::sommelier, thoughtDict)
   errornote = ""
   response = nothing # placeholder for show when error msg show up
 
+  yourthought = "$(thoughtDict[:understanding]) $(thoughtDict[:reasoning]) $(thoughtDict[:plan])"
+  yourthought1 = nothing
+
   for attempt in 1:10
+
+    if attempt > 1 # use to prevent LLM generate the same respond over and over
+      yourthought1 = paraphrase(a.func[:text2textInstructLLM], yourthought)
+    else
+      yourthought1 = yourthought
+    end
+
     usermsg = """
               Your ongoing conversation with the user: $chathistory
               Contex: $context
-              Your thoughts: $(thoughtDict[:understanding]) $(thoughtDict[:reasoning]) $(thoughtDict[:plan]) 
+              Your thoughts: $yourthought1
               $errornote
               """
 
@@ -996,10 +994,7 @@ function generatechat(a::sommelier, thoughtDict)
       ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-    prompt *= """
-              <|start_header_id|>assistant<|end_header_id|>
-              """
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
 
     try
       response = a.func[:text2textInstructLLM](prompt)
@@ -1014,22 +1009,27 @@ function generatechat(a::sommelier, thoughtDict)
       response = replace(response, '`' => "")
       response = replace(response, "<|eot_id|>"=>"")
       response = GeneralUtils.remove_french_accents(response)
-      responsedict = GeneralUtils.textToDict(response, ["Chat"],
-        rightmarker=":", symbolkey=true, lowercasekey=true)
 
-      for i ∈ [:chat]
-        if length(JSON3.write(responsedict[i])) == 0
-          error("$i is empty ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
-        end
+      # check whether response has all header
+      detected_kw = GeneralUtils.detect_keyword(header, response)
+      if sum(values(detected_kw)) < length(header)
+        errornote = "\nSQL decisionMaker() response does not have all header"
+        continue
+      elseif sum(values(detected_kw)) > length(header)
+        errornote = "\nSQL decisionMaker() response has duplicated header"
+        continue
       end
 
-      # check if there are more than 1 key per categories
-      for i ∈ [:chat]
-        matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
-        if length(matchkeys) > 1
-          error("generatechat has more than one key per categories")
-        end
-      end
+      responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)
+
+      # # check if there are more than 1 key per categories
+      # for i ∈ Symbol.(dictkey)
+      #   matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
+      #   if length(matchkeys) > 1
+      #     error("generatechat has more than one key per categories")
+      #   end
+      # end
 
       # check if Context: is in chat
       if occursin("Context:", responsedict[:chat])
@@ -1121,10 +1121,7 @@ function generatechat(a::companion)
       ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-    prompt *= """
-              <|start_header_id|>assistant<|end_header_id|>
-              """
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
 
     response = a.text2textInstructLLM(prompt)
 
@@ -1146,9 +1143,10 @@ function generatequestion(a, text2textInstructLLM::Function; recent=nothing)::St
     Your responsibility includes:
     1) Ask yourself what to do about the current situation
 
-    Your responsibility does not include:
-    1) Processing sales orders or engaging in any other sales-related activities.
-    2) Answering questions and offering additional services beyond just recommendations.
+    Your responsibility does NOT includes:
+      1) Requesting the user to place an order, make a purchase, or confirm the order. These are the job of our sales team at the store.
+      2) Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
+      3) Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.
 
     At each round of conversation, you will be given the current situation:
     Recap: recap of what has happened so far
@@ -1180,7 +1178,7 @@ function generatequestion(a, text2textInstructLLM::Function; recent=nothing)::St
         - State your understanding about the current situation
     2) Q: Given the situation, "ask yourself" at least five, but no more than ten, questions
     3) A: Given the situation, "answer to yourself" the best you can
-        - Do not generate any text after the last answer.
+        - Do not generate any extra text after you finish answering all questions
 
     You must only respond in format as described below:
     Understanding: ...
@@ -1231,10 +1229,10 @@ function generatequestion(a, text2textInstructLLM::Function; recent=nothing)::St
 
     Let's begin!
   """
-
+  
   context =
     if length(a.memory[:shortmem][:available_wine]) != 0
-      "Wines previously found in your inventory: $(availableWineToText(a.memory[:shortmem][:available_wine]))"
+      "Available wines you've found in your inventory so far: $(availableWineToText(a.memory[:shortmem][:available_wine]))"
     else
       "N/A"
     end
@@ -1292,13 +1290,36 @@ function generatequestion(a, text2textInstructLLM::Function; recent=nothing)::St
       ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-    prompt *= """
-              <|start_header_id|>assistant<|end_header_id|>
-              """
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
 
     try
       response = text2textInstructLLM(prompt)
+      # make sure generatequestion() don't have wine name that is not from retailer inventory
+      # check whether an agent recommend wines before checking inventory or recommend wines 
+      # outside its inventory
+      # ask LLM whether there are any winery mentioned in the response
+      mentioned_winery = detectWineryName(a, response)
+      if mentioned_winery != "None"
+        mentioned_winery = String.(strip.(split(mentioned_winery, ",")))
+
+        # check whether the wine is in event
+        isWineInEvent = false
+        for winename in mentioned_winery
+          for event in a.memory[:events]
+            if event[:outcome] !== nothing && occursin(winename, event[:outcome])
+              isWineInEvent = true
+              break
+            end
+          end
+        end
+
+        # if wine is mentioned but not in timeline or shortmem,
+        # then the agent is not supposed to recommend the wine
+        if isWineInEvent == false
+          errornote = "Previously, You mentioned wines that is not in your inventory which is not allowed."
+          error("Previously, You mentioned wines that is not in your inventory which is not allowed.")
+        end
+      end
 
       # sometime LLM generate more than 1 Understanding:
       understanding_number = count("Understanding:", response)
@@ -1318,9 +1339,10 @@ function generatequestion(a, text2textInstructLLM::Function; recent=nothing)::St
         error("no answer found in the response ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
       end
 
-      responsedict = GeneralUtils.textToDict(response,
-        ["Understanding", "Q1"],
-        rightmarker=":", symbolkey=true, lowercasekey=true)
+      header = ["Understanding:", "Q1:"]
+      dictkey = ["understanding", "q1"]
+      responsedict = GeneralUtils.textToDict(response, header; 
+        dictKey=dictkey, symbolkey=true)
       response = "Q1: " * responsedict[:q1]
       println("\n~~~ generatequestion ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
       pprintln(response)
@@ -1350,6 +1372,9 @@ function generateSituationReport(a, text2textInstructLLM::Function; skiprecent::
     Events timeline: ...
     Context: ...
 
+    You should follow the following guidelines:
+    - Use the word "user" and "assistant" instead of their name in the report
+
     You should then respond to the user with:
     event: a detailed summary for each event without exaggerated details.
     
@@ -1368,6 +1393,9 @@ function generateSituationReport(a, text2textInstructLLM::Function; skiprecent::
     Let's begin!
   """
 
+  header = ["Event_$i:" for i in eachindex(a.memory[:events])]
+  dictkey = lowercase.(["Event_$i" for i in eachindex(a.memory[:events])])
+
   if length(a.memory[:events]) <= skiprecent
     return nothing
   end
@@ -1393,15 +1421,11 @@ function generateSituationReport(a, text2textInstructLLM::Function; skiprecent::
       ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-    prompt *= """
-              <|start_header_id|>assistant<|end_header_id|>
-              """
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
 
     response = text2textInstructLLM(prompt)
-    eventheader = ["Event_$i" for i in eachindex(a.memory[:events])]
-    responsedict = GeneralUtils.textToDict(response, eventheader, 
-                      rightmarker=":", symbolkey=true)
+    responsedict = GeneralUtils.textToDict(response, header; 
+                    dictKey=dictkey, symbolkey=true)
     
     println("\n~~~ generateSituationReport() ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
     pprintln(response)
@@ -1450,18 +1474,17 @@ function detectWineryName(a, text)
         ]
 
       # put in model format
-      prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-      prompt *= """
-                <|start_header_id|>assistant<|end_header_id|>
-                """
+      prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
 
       try
         response = a.func[:text2textInstructLLM](prompt)
         println("\n~~~ detectWineryName() ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
         pprintln(response)
 
-        responsedict = GeneralUtils.textToDict(response, ["winery_names"],
-          rightmarker=":", symbolkey=true, lowercasekey=true)
+        header = ["Winery_names:"]
+        dictkey = ["winery_names"]
+        responsedict = GeneralUtils.textToDict(response, header; 
+          dictKey=dictkey, symbolkey=true)
 
         result = responsedict[:winery_names]
 

src/llmfunction.jl

@@ -2,7 +2,7 @@ module llmfunction
 
 export  virtualWineUserChatbox, jsoncorrection, checkinventory, # recommendbox,
         virtualWineUserRecommendbox, userChatbox, userRecommendbox, extractWineAttributes_1,
-        extractWineAttributes_2
+        extractWineAttributes_2, paraphrase
 
 using HTTP, JSON3, URIs, Random, PrettyPrinting, UUIDs, Dates
 using GeneralUtils, SQLLLM
@@ -307,8 +307,6 @@ function checkinventory(a::T1, input::T2
   println("\n~~~ checkinventory result ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
   println(textresult)
 
-  #[WORKING] when rawresponse is nothing, AI get errors
-
   return (result=textresult, rawresponse=rawresponse, success=true, errormsg=nothing)
 end
 
@@ -328,7 +326,7 @@ julia>
 
 # TODO
   - [] update docstring
-  - [x] implement the function
+  - implement the function
 
 # Signature
 """
@@ -338,31 +336,41 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
   """
   As a helpful sommelier, your task is to extract the user information from the user's query as much as possible to fill out user's preference form.
 
-  At each round of conversation, the user will give you the current situation:
+  At each round of conversation, the user will give you the following:
   User's query: ...
 
   You must follow the following guidelines:
-    1) If specific information required in the preference form is not available in the query or there isn't any, mark with "NA" to indicate this.
+    - If specific information required in the preference form is not available in the query or there isn't any, mark with "NA" to indicate this.
       Additionally, words like 'any' or 'unlimited' mean no information is available.
-    2) Do not generate other comments.
+    - Do not generate other comments.
 
-  You should then respond to the user with the following points:
-  - reasoning: state your understanding of the current situation
-  - wine_name: name of the wine
-  - winery: name of the winery
-  - vintage: the year of the wine
-  - region: a region (NOT a country) where the wine is produced, such as Burgundy, Napa Valley, etc
-  - country: a country where the wine is produced. Can be "Austria", "Australia", "France", "Germany", "Italy", "Portugal", "Spain", "United States"
-  - wine_type: can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
-  - grape_varietal: the name of the primary grape used to make the wine
-  - tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
-  - wine_price: price range of wine.
-  - occasion: the occasion the user is having the wine for
-  - food_to_be_paired_with_wine: food that the user will be served with the wine such as poultry, fish, steak, etc
-  
+  You should then respond to the user with:
+    Comprehension: state your understanding of the current situation
+    Wine_name: name of the wine
+    Winery: name of the winery
+    Vintage: the year of the wine
+    Region: a region (NOT a country) where the wine is produced, such as Burgundy, Napa Valley, etc
+    Country: a country where the wine is produced. Can be "Austria", "Australia", "France", "Germany", "Italy", "Portugal", "Spain", "United States"
+    Wine_type: can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
+    Grape_varietal: the name of the primary grape used to make the wine
+    Tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
+    Wine_price: price range of wine.
+    Occasion: the occasion the user is having the wine for
+    Food_to_be_paired_with_wine: food that the user will be served with the wine such as poultry, fish, steak, etc
 
-  You should only respond in the user's preference form (JSON) as described below:
-  {"reasoning": ..., "winery": ..., "wine_name": ..., "vintage": ..., "region": ..., "country": ..., "wine_type": ..., "grape_varietal": ..., "tasting_notes": ..., "wine_price": ..., "occasion": ..., "food_to_be_paired_with_wine": ...}
+  You should only respond in format as described below:
+  Comprehension: ...
+  Wine_name: ...
+  Winery: ...
+  Vintage: ...
+  Region: ...
+  Country: ...
+  Wine_type:
+  Grape_varietal: ...
+  Tasting_notes: ...
+  Wine_price: ...
+  Occasion: ...
+  Food_to_be_paired_with_wine: ...
   
   Here are some example:
   User's query: red, Chenin Blanc, Riesling, 20 USD
@@ -374,7 +382,8 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
   Let's begin!
   """
 
-  attributes = ["reasoning", "winery", "wine_name", "vintage", "region", "country", "wine_type", "grape_varietal", "tasting_notes", "wine_price", "occasion", "food_to_be_paired_with_wine"]
+  header = ["Comprehension:", "Wine_name:", "Winery:", "Vintage:", "Region:", "Country:", "Wine_type:", "Grape_varietal:", "Tasting_notes:", "Wine_price:", "Occasion:", "Food_to_be_paired_with_wine:"]
+  dictkey = ["comprehension", "wine_name", "winery", "vintage", "region", "country", "wine_type", "grape_varietal", "tasting_notes", "wine_price", "occasion", "food_to_be_paired_with_wine"]
   errornote = ""
   
   for attempt in 1:5
@@ -391,18 +400,13 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
     ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-    prompt *=
-      """
-      <|start_header_id|>assistant<|end_header_id|>
-      """
-
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
     response = a.func[:text2textInstructLLM](prompt)
     response = GeneralUtils.remove_french_accents(response)
 
     # check wheter all attributes are in the response
     checkFlag = false
-    for word in attributes
+    for word in header
       if !occursin(word, response)
         errornote = "$word attribute is missing in previous attempts"
         println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
@@ -411,12 +415,20 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
       end
     end
     checkFlag == true ? continue : nothing
+    
+    # check whether response has all header
+    detected_kw = GeneralUtils.detect_keyword(header, response)
+    if sum(values(detected_kw)) < length(header)
+      errornote = "\nYiemAgent extractWineAttributes_1() response does not have all header"
+      continue
+    elseif sum(values(detected_kw)) > length(header)
+      errornote = "\nYiemAgent extractWineAttributes_1() response has duplicated header"
+      continue
+    end
+    responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)
 
-    responsedict = copy(JSON3.read(response))
-
-    # convert 
-
-    delete!(responsedict, :reasoning)
+    delete!(responsedict, :comprehension)
     delete!(responsedict, :tasting_notes)
     delete!(responsedict, :occasion)
     delete!(responsedict, :food_to_be_paired_with_wine)
@@ -426,9 +438,9 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
 
     # check if winery, wine_name, region, country, wine_type, grape_varietal's value are in the query because sometime AI halucinates
     checkFlag = false
-    for i in attributes
+    for i in dictkey
       j = Symbol(i)
-      if j ∉ [:reasoning, :tasting_notes, :occasion, :food_to_be_paired_with_wine]
+      if j ∉ [:comprehension, :tasting_notes, :occasion, :food_to_be_paired_with_wine]
         # in case j is wine_price it needs to be checked differently because its value is ranged
         if j == :wine_price
           if responsedict[:wine_price] != "NA" 
@@ -441,10 +453,19 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
             end
 
             # check whether max wine_price is in the input
-            maxprice = split(responsedict[:wine_price], '-')[end]
+            pricerange = split(responsedict[:wine_price], '-')
+            minprice = pricerange[1]
+            maxprice = pricerange[end]
             if !occursin(maxprice, input)
               responsedict[:wine_price] = "NA"
             end
+            # price range like 100-100 is not good
+            if minprice == maxprice
+              errornote = "wine_price with minimum equals to maximum is not valid"
+              println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+              checkFlag = true
+              break
+            end
           end
         else
           content = responsedict[j]
@@ -457,7 +478,7 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
             content = [content]
           end
 
-          for x in content  #BUG why x is "0-1500"
+          for x in content  #check whether price are mentioned in the input
             if !occursin("NA", responsedict[j]) && !occursin(x, input)
               errornote = "$x is not mentioned in the user query, you must only use the info from the query."
               println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
@@ -468,7 +489,7 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
         end
       end
     end
-    checkFlag == true ? continue : nothing
+    checkFlag == true ? continue : nothing  # skip the rest code if true
 
     # remove (some text)
     for (k, v) in responsedict
@@ -502,7 +523,7 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
 
   conversiontable =
   """
-  Conversion Table:
+  <Conversion Table>
   Intensity level:
     1 to 2: May correspond to "light-bodied" or a similar description.
     2 to 3: May correspond to "med light bodied", "medium light" or a similar description.
@@ -527,6 +548,7 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
     3 to 4: May correspond to "medium acidity" or a similar description.
     4 to 5: May correspond to "semi high acidity" or a similar description.
     4 to 5: May correspond to "high acidity" or a similar description.
+  </Conversion Table>
   """
 
   systemmsg =
@@ -540,67 +562,64 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
   The preference form requires the following information:
   sweetness, acidity, tannin, intensity
 
-  You must follow the following guidelines:
+  <You must follow the following guidelines>
     1) If specific information required in the preference form is not available in the query or there isn't any, mark with 'NA' to indicate this.
       Additionally, words like 'any' or 'unlimited' mean no information is available.
     2) Use the conversion table to convert the descriptive word level of sweetness, intensity, tannin, and acidity into a corresponding integer.
     3) Do not generate other comments.
+  </You must follow the following guidelines>
 
-  You should then respond to the user with the following points:
-  - sweetness_keyword: The exact keywords in the user's query describing the sweetness level of the wine.
-  - sweetness: ( S ), where ( S ) represents integers indicating the range of sweetness levels. Example: 1-2
-  - acidity_keyword: The exact keywords in the user's query describing the acidity level of the wine.
-  - acidity: ( A ), where ( A ) represents integers indicating the range of acidity level. Example: 3-5
-  - tannin_keyword: The exact keywords in the user's query describing the tannin level of the wine.
-  - tannin: ( T ), where ( T ) represents integers indicating the range of tannin level. Example: 1-3
-  - intensity_keyword: The exact keywords in the user's query describing the intensity level of the wine.
-  - intensity: ( I ), where ( I ) represents integers indicating the range of intensity level. Example: 2-4
+  <You should then respond to the user with>
+    Sweetness_keyword: The exact keywords in the user's query describing the sweetness level of the wine.
+    Sweetness: ( S ), where ( S ) represents integers indicating the range of sweetness levels. Example: 1-2
+    Acidity_keyword: The exact keywords in the user's query describing the acidity level of the wine.
+    Acidity: ( A ), where ( A ) represents integers indicating the range of acidity level. Example: 3-5
+    Tannin_keyword: The exact keywords in the user's query describing the tannin level of the wine.
+    Tannin: ( T ), where ( T ) represents integers indicating the range of tannin level. Example: 1-3
+    Intensity_keyword: The exact keywords in the user's query describing the intensity level of the wine.
+    Intensity: ( I ), where ( I ) represents integers indicating the range of intensity level. Example: 2-4
+  </You should then respond to the user with>
 
-  You should only respond in the form (JSON) as described below:
-  {
-    "sweetness_keyword": ...,
-    "sweetness": ...,
-    "acidity_keyword": ...,
-    "acidity": ...,
-    "tannin_keyword": ...,
-    "tannin": ...,
-    "intensity_keyword": ...,
-    "intensity": ...
-  }
+  <You should only respond in format as described below>
+    Sweetness_keyword: ...
+    Sweetness: ...
+    Acidity_keyword: ...
+    Acidity: ...
+    Tannin_keyword: ...
+    Tannin: ...
+    Intensity_keyword: ...
+    Intensity: ...
+  </You should only respond in format as described below>
 
-  Here are some examples:
-  User's query: I want a wine with a medium-bodied, low acidity, medium tannin.
-  {
-    "sweetness_keyword": "NA",
-    "sweetness": "NA",
-    "acidity_keyword": "low acidity",
-    "acidity": "1-2",
-    "tannin_keyword": "medium tannin",
-    "tannin": "3-4",
-    "intensity_keyword": "medium-bodied",
-    "intensity": "3-4"
-  }
-
-
-  User's query: German red wine, under 100, pairs with spicy food
-  {
-    "sweetness_keyword": "NA",
-    "sweetness": "NA",
-    "acidity_keyword": "NA",
-    "acidity": "NA",
-    "tannin_keyword": "NA",
-    "tannin": "NA",
-    "intensity_keyword": "NA",
-    "intensity": "NA"
-  }
+  <Here are some examples>
+    User's query: I want a wine with a medium-bodied, low acidity, medium tannin.
+    Sweetness_keyword: NA
+    Sweetness: NA
+    Acidity_keyword: low acidity
+    Acidity: 1-2
+    Tannin_keyword: medium tannin
+    Tannin: 3-4
+    Intensity_keyword: medium-bodied
+    Intensity: 3-4
 
+    User's query: German red wine, under 100, pairs with spicy food
+    Sweetness_keyword: NA
+    Sweetness: NA
+    Acidity_keyword: NA
+    Acidity: NA
+    Tannin_keyword: NA
+    Tannin: NA
+    Intensity_keyword: NA
+    Intensity: NA
+  </Here are some examples>
 
   Let's begin!
   """
-
+  header = ["Sweetness_keyword:", "Sweetness:", "Acidity_keyword:", "Acidity:", "Tannin_keyword:", "Tannin:", "Intensity_keyword:", "Intensity:"]
+  dictkey = ["sweetness_keyword", "sweetness", "acidity_keyword", "acidity", "tannin_keyword", "tannin", "intensity_keyword", "intensity"]
   errornote = ""
 
-  for attempt in 1:5
+  for attempt in 1:10
     usermsg =
     """
     $conversiontable
@@ -615,14 +634,22 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
     ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-    prompt *=
-      """
-      <|start_header_id|>assistant<|end_header_id|>
-      """
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
 
     response = a.func[:text2textInstructLLM](prompt)
-    responsedict = copy(JSON3.read(response))
+    
+    # check whether response has all header
+    detected_kw = GeneralUtils.detect_keyword(header, response)
+    if sum(values(detected_kw)) < length(header)
+      errornote = "\nYiemAgent extractWineAttributes_2() response does not have all header"
+      continue
+    elseif sum(values(detected_kw)) > length(header)
+      errornote = "\nYiemAgent extractWineAttributes_2() response has duplicated header"
+      continue
+    end
+
+    responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)
 
     # check whether each describing keyword is in the input to prevent halucination
     for i in ["sweetness", "acidity", "tannin", "intensity"]
@@ -675,168 +702,113 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
 end
 
 
-# function concept(a::sommelier, thoughtDict)
-#   systemmsg = 
-#   """
-#     Your name: N/A
-#     Situation:
-#       - You are a helpful assistant
-#     Your vision: 
-#       - This is a good opportunity to help the user
-#     Your mission:
-#       - To describe the concept of a conversation
-#     Mission's objective includes:
-#       - To 
-#     Your responsibility includes:
-#       1) Given the situation, convey your thoughts to the user.
-#     Your responsibility excludes:
-#       1) Asking or guiding the user to make a purchase
-#       2) Processing sales orders or engaging in any other sales-related activities
-#       3) Answering questions and offering additional services beyond just recommendations, such as delivery, box, gift wrapping, personalized messages. Customers can reach out to our sales at the store.
-#     Your profile:
-#       - You are a young professional in a big company.
-#       - You are avid party goer
-#       - You like beer.
-#       - You know nothing about wine.
-#       - You have a budget of 1500usd.
-#     Additional information:
-#       - your boss like spicy food.
-#       - your boss is a middle-aged man.
+function paraphrase(text2textInstructLLM::Function, text::String)
+  systemmsg = 
+  """
+    Your name: N/A
+    Your vision: 
+      - You are a helpful assistant who help the user to paraphrase their text.
+    Your mission:
+      - To help paraphrase the user's text
+    Mission's objective includes:
+      - To help paraphrase the user's text
+    Your responsibility includes:
+      1) To help paraphrase the user's text
+    Your responsibility does NOT includes:
+      1) N/A
+    Your profile:
+      - N/A
+    Additional information:
+      - N/A
 
-#     At each round of conversation, you will be given the following information:
-#     Your ongoing conversation with the user: ...
-#     Context: ...
-#     Your thoughts: Your current thoughts in your mind
+    At each round of conversation, you will be given the following information:
+    Text: The user's given text 
 
-#     You MUST follow the following guidelines:
-#     - Do not offer additional services you didn't thought.
+    You MUST follow the following guidelines:
+    - N/A
 
-#     You should follow the following guidelines:
-#     - Focus on the latest conversation.
-#     - If the user interrupts, prioritize the user
-#     - Medium and full-bodied red wines should not be paired with spicy foods.
+    You should follow the following guidelines:
+    - N/A
 
-#     You should then respond to the user with:
-#     1) Chat: Given the situation, How would you respond to the user to express your thoughts honestly and keep the conversation going smoothly?
+    You should then respond to the user with:
+    1) Paraphrase: Paraphrased text
 
-#     You should only respond in format as described below:
-#     Chat: ...
+    You should only respond in format as described below:
+    Paraphrase: ...
 
-#     Here are some examples of response format:
-#     Chat: "I see. Let me think about it. I'll get back to you with my recommendation."
+    Let's begin!
+  """
 
-#     Let's begin!
-#   """
+  errornote = ""
+  response = nothing # placeholder for show when error msg show up
 
-#   # a.memory[:shortmem][:available_wine] is a dataframe.
-#   context =
-#     if haskey(a.memory[:shortmem], :available_wine)
-#       "Available wines $(GeneralUtils.dfToString(a.memory[:shortmem][:available_wine]))"
-#     else
-#       "None"
-#     end
 
-#   chathistory = vectorOfDictToText(a.chathistory)
-#   errornote = ""
-#   response = nothing # placeholder for show when error msg show up
+  for attempt in 1:10
+    usermsg = """
+              Text: $text
+              $errornote
+              """
 
-#   for attempt in 1:10
-#     usermsg = """
-#               Your ongoing conversation with the user: $chathistory
-#               Contex: $context
-#               Your thoughts: $(thoughtDict[:understanding]) $(thoughtDict[:reasoning]) $(thoughtDict[:plan]) 
-#               $errornote
-#               """
+    _prompt =
+      [
+        Dict(:name => "system", :text => systemmsg),
+        Dict(:name => "user", :text => usermsg)
+      ]
 
-#     _prompt =
-#       [
-#         Dict(:name => "system", :text => systemmsg),
-#         Dict(:name => "user", :text => usermsg)
-#       ]
+    # put in model format
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+    prompt *= """
+              <|start_header_id|>assistant<|end_header_id|>
+              """
 
-#     # put in model format
-#     prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-#     prompt *= """
-#               <|start_header_id|>assistant<|end_header_id|>
-#               """
+    try
+      response = text2textInstructLLM(prompt)
+      # sometime the model response like this "here's how I would respond: ..."
+      if occursin("respond:", response)
+        errornote = "You don't need to intro your response"
+        error("\n~~~ paraphrase() response contain : ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+      end
+      response = GeneralUtils.remove_french_accents(response)
+      response = replace(response, '*'=>"")
+      response = replace(response, '$' => "USD")
+      response = replace(response, '`' => "")
+      response = GeneralUtils.remove_french_accents(response)
 
-#     try
-#       response = a.func[:text2textInstructLLM](prompt)
-#       # sometime the model response like this "here's how I would respond: ..."
-#       if occursin("respond:", response)
-#         errornote = "You don't need to intro your response"
-#         error("generatechat() response contain : ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
-#       end
-#       response = GeneralUtils.remove_french_accents(response)
-#       response = replace(response, '*'=>"")
-#       response = replace(response, '$' => "USD")
-#       response = replace(response, '`' => "")
-#       response = GeneralUtils.remove_french_accents(response)
-#       responsedict = GeneralUtils.textToDict(response, ["Chat"],
-#         rightmarker=":", symbolkey=true, lowercasekey=true)
+      header = ["Paraphrase:"]
+      dictkey = ["paraphrase"]
+      responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)
 
-#       for i ∈ [:chat]
-#         if length(JSON3.write(responsedict[i])) == 0
-#           error("$i is empty ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
-#         end
-#       end
+      for i ∈ [:paraphrase]
+        if length(JSON3.write(responsedict[i])) == 0
+          error("$i is empty ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+        end
+      end
 
-#       # check if there are more than 1 key per categories
-#       for i ∈ [:chat]
-#         matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
-#         if length(matchkeys) > 1
-#           error("generatechat has more than one key per categories")
-#         end
-#       end
+      # check if there are more than 1 key per categories
+      for i ∈ [:paraphrase]
+        matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
+        if length(matchkeys) > 1
+          error("paraphrase() has more than one key per categories")
+        end
+      end
 
-#       # check if Context: is in chat
-#       if occursin("Context:", responsedict[:chat])
-#         error("Context: is in text. This is not allowed")
-#       end
+      println("\n~~~ paraphrase() ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+      pprintln(Dict(responsedict))
 
-#       println("\n~~~ generatechat() ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
-#       pprintln(Dict(responsedict))
+      result = responsedict[:paraphrase]
 
-#       # check whether an agent recommend wines before checking inventory or recommend wines 
-#       # outside its inventory
-#       # ask LLM whether there are any winery mentioned in the response
-#       mentioned_winery = detectWineryName(a, responsedict[:chat])
-#       if mentioned_winery != "None"
-#         mentioned_winery = String.(strip.(split(mentioned_winery, ",")))
-
-#         # check whether the wine is in event
-#         isWineInEvent = false
-#         for winename in mentioned_winery
-#           for event in a.memory[:events]
-#             if event[:outcome] !== nothing && occursin(winename, event[:outcome])
-#               isWineInEvent = true
-#               break
-#             end
-#           end
-#         end
-
-#         # if wine is mentioned but not in timeline or shortmem,
-#         # then the agent is not supposed to recommend the wine
-#         if isWineInEvent == false
-
-#           errornote = "Previously: You recommend a wine that is not in your inventory which is not allowed."
-#           error("Previously: You recommend a wine that is not in your inventory which is not allowed.")
-#         end
-#       end
-
-#       result = responsedict[:chat]
-
-#       return result
-#     catch e
-#       io = IOBuffer()
-#       showerror(io, e)
-#       errorMsg = String(take!(io))
-#       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-#       println("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
-#     end
-#   end
-#   error("generatechat failed to generate a response")
-# end
+      return result
+    catch e
+      io = IOBuffer()
+      showerror(io, e)
+      errorMsg = String(take!(io))
+      st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
+      println("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+    end
+  end
+  error("generatechat failed to generate a response")
+end
 
 
 

src/util.jl

@@ -107,7 +107,7 @@ function addNewMessage(a::T1, name::String, text::T2;
     error("name is not in agent.availableRole $(@__LINE__)")
   end
 
-  #[WORKING] summarize the oldest 10 message
+  #[PENDING] summarize the oldest 10 message
   if length(a.chathistory) > maximumMsg
     summarize(a.chathistory)
   else

test/config.json

@@ -31,26 +31,46 @@
       "description": "organization name"
   },
   "externalservice": {
-      "text2textinstruct": {
-          "mqtttopic": "/loadbalancer/requestingservice",
-          "description": "text to text service with instruct LLM",
-          "llminfo": {
-              "name": "llama3instruct"
-          }
-      },
-      "virtualWineCustomer_1": {
-          "mqtttopic": "/virtualenvironment/winecustomer",
-          "description": "text to text service with instruct LLM that act as wine customer",
-          "llminfo": {
-              "name": "llama3instruct"
-          }
-      },
-      "text2textchat": {
-          "mqtttopic": "/loadbalancer/requestingservice",
-          "description": "text to text service with instruct LLM",
-          "llminfo": {
-              "name": "llama3instruct"
-          }
-      }
+    "loadbalancer": {
+      "mqtttopic": "/loadbalancer/requestingservice",
+      "description": "text to text service with instruct LLM"
+    },
+    "text2textinstruct": {
+        "mqtttopic": "/loadbalancer/requestingservice",
+        "description": "text to text service with instruct LLM",
+        "llminfo": {
+            "name": "llama3instruct"
+        }
+    },
+    "virtualWineCustomer_1": {
+        "mqtttopic": "/virtualenvironment/winecustomer",
+        "description": "text to text service with instruct LLM that act as wine customer",
+        "llminfo": {
+            "name": "llama3instruct"
+        }
+    },
+    "text2textchat": {
+        "mqtttopic": "/loadbalancer/requestingservice",
+        "description": "text to text service with instruct LLM",
+        "llminfo": {
+            "name": "llama3instruct"
+        }
+    },
+    "wineDB" : {
+      "description": "A wine database connection info for LibPQ client",
+      "host": "192.168.88.12",
+      "port": 10201,
+      "dbname": "wineDB",
+      "user": "yiemtechnologies",
+      "password": "yiemtechnologies@Postgres_0.0"
+    },
+    "SQLVectorDB" : {
+      "description": "A wine database connection info for LibPQ client",
+      "host": "192.168.88.12",
+      "port": 10203,
+      "dbname": "SQLVectorDB",
+      "user": "yiemtechnologies",
+      "password": "yiemtechnologies@Postgres_0.0"
+    }
   }
 }

test/etc.jl

@@ -1,9 +0,0 @@
-using GeneralUtils
-
-response = "trajectory_evaluation:\nThe trajectory is correct so far. The thought accurately reflects the user's question, and the action taken is a valid attempt to retrieve data from the database that matches the specified criteria.\n\nanswer_evaluation:\nThe observation provides information about two red wines from Bordeaux rive droite in France, which partially answers the question. However, it does not provide a complete answer as it only lists the wine names and characteristics, but does not explicitly state whether there are any other wines that match the criteria.\n\naccepted_as_answer: No\n\nscore: 6\nThe trajectory is mostly correct, but the observation does not fully address the question.\n\nsuggestion: Consider adding more filters or parameters to the database query to retrieve a complete list of wines that match the specified criteria."
-
-responsedict = GeneralUtils.textToDict(response,
-            ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"], 
-            rightmarker=":", symbolkey=true)
-
-

test/test_1.jl

@@ -1,272 +1,292 @@
-using Revise
-using JSON, JSON3, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
-using YiemAgent, GeneralUtils
-using Base.Threads
-
-# ---------------------------------------------- 100 --------------------------------------------- #
-
-
-
-# load config
-config = JSON3.read("./test/config.json")
-# config = copy(JSON3.read("../mountvolume/config.json"))
-
-
-function executeSQL(sql::T) where {T<:AbstractString}
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10201 dbname=wineDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-function executeSQLVectorDB(sql)
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10203 dbname=SQLVectorDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-function text2textInstructLLM(prompt::String)
-  msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:text2textinstruct][:mqtttopic];
-    msgPurpose="inference",
-    senderName="yiemagent",
-    senderId=string(uuid4()),
-    receiverName="text2textinstruct",
-    mqttBrokerAddress=config[:mqttServerInfo][:broker],
-    mqttBrokerPort=config[:mqttServerInfo][:port],
-  )
-
-  outgoingMsg = Dict(
-    :msgMeta => msgMeta,
-    :payload => Dict(
-      :text => prompt,
-      :kwargs => Dict(
-        :num_ctx => 16384,
-        :temperature => 0.2,
-      )
-    )
-  )
-
-  _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
-  response = _response[:response][:text]
-
-  return response
-end
-
-# get text embedding from a LLM service
-function getEmbedding(text::T) where {T<:AbstractString}
-  msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:text2textinstruct][:mqtttopic];
-    msgPurpose="embedding",
-    senderName="yiemagent",
-    senderId=string(uuid4()),
-    receiverName="text2textinstruct",
-    mqttBrokerAddress=config[:mqttServerInfo][:broker],
-    mqttBrokerPort=config[:mqttServerInfo][:port],
-  )
-
-  outgoingMsg = Dict(
-    :msgMeta => msgMeta,
-    :payload => Dict(
-      :text => [text] # must be a vector of string
-    )
-  )
-  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
-  embedding = response[:response][:embeddings]
-  return embedding
-end
-
-function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
-   vectorDB::Function; limit::Integer=1
-   )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
-
-  # get embedding from LLM service
-  embedding = getEmbedding(text)[1] 
-
-  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-  sql = """
-        SELECT *, $embeddingColumnName <-> '$embedding' as distance
-        FROM $tablename
-        ORDER BY distance LIMIT $limit;
-        """
-  response = vectorDB(sql)
-  df = DataFrame(response)
-  return df
-end
-
-
-function similarSQLVectorDB(query; maxdistance::Integer=100)
-  tablename = "sqlllm_decision_repository"
-  # get embedding of the query
-  df = findSimilarTextFromVectorDB(query, tablename, 
-                                  "function_input_embedding", executeSQLVectorDB)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]  
-  if row != 0 && distance < maxdistance
-    # if there is usable SQL, return it.
-    output_b64 = df[1, :function_output_base64]   # pick the closest match
-    output_str = String(base64decode(output_b64))
-    rowid = df[1, :id]
-    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
-    return (dict=output_str, distance=distance)
-  else
-    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
-    return (dict=nothing, distance=nothing)
-  end
-end
-
-
-function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=1) where {T1<:AbstractString, T2<:AbstractString}
-  tablename = "sqlllm_decision_repository"
-  # get embedding of the query
-  # query = state[:thoughtHistory][:question]
-  df = findSimilarTextFromVectorDB(query, tablename, 
-                                  "function_input_embedding", executeSQLVectorDB)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]
-  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-    query_embedding = getEmbedding(query)[1]
-    query = replace(query, "'" => "")
-    sql_base64 = base64encode(SQL)
-    sql_ = replace(SQL, "'" => "")
-
-    sql = """
-          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
-          """
-    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
-    println(sql)
-    _ = executeSQLVectorDB(sql)
-  end
-end
-
-
-function similarSommelierDecision(recentevents::T1; maxdistance::Integer=5
-  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
-  tablename = "sommelier_decision_repository"
-  # find similar
-  println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
-  df = findSimilarTextFromVectorDB(recentevents, tablename, 
-                                  "function_input_embedding", executeSQLVectorDB)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]
-  if row != 0 && distance < maxdistance
-    # if there is usable decision, return it.
-    rowid = df[1, :id]
-    println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
-    output_b64 = df[1, :function_output_base64]   # pick the closest match
-    _output_str = String(base64decode(output_b64))
-    output = copy(JSON3.read(_output_str))
-    return output
-  else
-    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
-    return nothing
-  end
-end
-
-
-function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
-  ) where {T1<:AbstractString, T2<:AbstractDict}
-  tablename = "sommelier_decision_repository"
-  # find similar
-  df = findSimilarTextFromVectorDB(recentevents, tablename, 
-                                  "function_input_embedding", executeSQLVectorDB)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]
-  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-    recentevents_embedding = a.func[:getEmbedding](recentevents)[1]
-    recentevents = replace(recentevents, "'" => "")
-    decision_json = JSON3.write(decision)
-    decision_base64 = base64encode(decision_json)
-    decision = replace(decision_json, "'" => "")
-    
-    sql = """
-          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
-          """
-    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
-    println(sql)
-    _ = executeSQLVectorDB(sql)
-  else
-    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
-  end
-end
-
-
-sessionId = "12345"
-
-externalFunction = (
-    getEmbedding=getEmbedding,
-    text2textInstructLLM=text2textInstructLLM,
-    executeSQL=executeSQL,
-    similarSQLVectorDB=similarSQLVectorDB,
-    insertSQLVectorDB=insertSQLVectorDB,
-    similarSommelierDecision=similarSommelierDecision,
-    insertSommelierDecision=insertSommelierDecision,
-  )
-
-
-
-a = YiemAgent.sommelier(
-  externalFunction;
-  name="Ton",
-  id=sessionId,  # agent instance id
-  retailername="Yiem",
-)
-
-while true
-  println("your respond: ")
-  user_answer = readline()
-  response = YiemAgent.conversation(a, Dict(:text=> user_answer))
-  println("\n$response")
-end
-
-
-# response = YiemAgent.conversation(a, Dict(:text=> "I want to get a French red wine under 100."))
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+using Revise
+using JSON, JSON3, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
+using YiemAgent, GeneralUtils
+using Base.Threads
+
+# ---------------------------------------------- 100 --------------------------------------------- #
+
+
+
+# load config
+config = JSON3.read("/appfolder/app/dev/YiemAgent/test/config.json")
+# config = copy(JSON3.read("../mountvolume/config.json"))
+
+
+function executeSQL(sql::T) where {T<:AbstractString}
+  host = config[:externalservice][:wineDB][:host]
+  port = config[:externalservice][:wineDB][:port]
+  dbname = config[:externalservice][:wineDB][:dbname]
+  user = config[:externalservice][:wineDB][:user]
+  password = config[:externalservice][:wineDB][:password]
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
+  result = LibPQ.execute(DBconnection, sql)
+  close(DBconnection)
+  return result
+end
+
+function executeSQLVectorDB(sql)
+  host = config[:externalservice][:SQLVectorDB][:host]
+  port = config[:externalservice][:SQLVectorDB][:port]
+  dbname = config[:externalservice][:SQLVectorDB][:dbname]
+  user = config[:externalservice][:SQLVectorDB][:user]
+  password = config[:externalservice][:SQLVectorDB][:password]
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
+  result = LibPQ.execute(DBconnection, sql)
+  close(DBconnection)
+  return result
+end
+
+function text2textInstructLLM(prompt::String; maxattempt=3)
+  msgMeta = GeneralUtils.generate_msgMeta(
+    config[:externalservice][:loadbalancer][:mqtttopic];
+    msgPurpose="inference",
+    senderName="yiemagent",
+    senderId=sessionId,
+    receiverName="text2textinstruct_small",
+    mqttBrokerAddress=config[:mqttServerInfo][:broker],
+    mqttBrokerPort=config[:mqttServerInfo][:port],
+  )
+
+  outgoingMsg = Dict(
+    :msgMeta => msgMeta,
+    :payload => Dict(
+      :text => prompt,
+      :kwargs => Dict(
+        :num_ctx => 16384,
+        :temperature => 0.2,
+      )
+    )
+  )
+
+  response = nothing
+  for attempts in 1:maxattempt
+    _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=300, maxattempt=maxattempt)
+    payload = _response[:response]
+    if _response[:success] && payload[:text] !== nothing
+      response = _response[:response][:text]
+      break
+    else
+      println("\n<text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      pprintln(outgoingMsg)
+      println("</text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      sleep(3)
+    end
+  end
+
+  return response
+end
+
+# get text embedding from a LLM service
+function getEmbedding(text::T) where {T<:AbstractString}
+  msgMeta = GeneralUtils.generate_msgMeta(
+    config[:externalservice][:loadbalancer][:mqtttopic];
+    msgPurpose="embedding",
+    senderName="yiemagent",
+    senderId=sessionId,
+    receiverName="text2textinstruct_small",
+    mqttBrokerAddress=config[:mqttServerInfo][:broker],
+    mqttBrokerPort=config[:mqttServerInfo][:port],
+  )
+
+  outgoingMsg = Dict(
+    :msgMeta => msgMeta,
+    :payload => Dict(
+      :text => [text] # must be a vector of string
+    )
+  )
+  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120)
+  embedding = response[:response][:embeddings]
+  return embedding
+end
+
+function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
+  vectorDB::Function; limit::Integer=1
+  )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
+  # get embedding from LLM service
+  embedding = getEmbedding(text)[1]
+  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
+  sql = """
+        SELECT *, $embeddingColumnName <-> '$embedding' as distance
+        FROM $tablename
+        ORDER BY distance LIMIT $limit;
+        """
+  response = vectorDB(sql)
+  df = DataFrame(response)
+  return df
+end
+
+function similarSQLVectorDB(query; maxdistance::Integer=100)
+  tablename = "sqlllm_decision_repository"
+  # get embedding of the query
+  df = findSimilarTextFromVectorDB(query, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  # println(df[1, [:id, :function_output]])
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]  
+  # distance = 100  # CHANGE this is for testing only
+  if row != 0 && distance < maxdistance
+    # if there is usable SQL, return it.
+    output_b64 = df[1, :function_output_base64]   # pick the closest match
+    output_str = String(base64decode(output_b64))
+    rowid = df[1, :id]
+    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    return (dict=output_str, distance=distance)
+  else
+    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    return (dict=nothing, distance=nothing)
+  end
+end
+
+function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
+  tablename = "sqlllm_decision_repository"
+  # get embedding of the query
+  # query = state[:thoughtHistory][:question]
+  df = findSimilarTextFromVectorDB(query, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]
+  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+    query_embedding = getEmbedding(query)[1]
+    query = replace(query, "'" => "")
+    sql_base64 = base64encode(SQL)
+    sql_ = replace(SQL, "'" => "")
+
+    sql = """
+          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
+          """
+    # println("\n~~~ added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    # println(sql)
+    _ = executeSQLVectorDB(sql)
+  end
+end
+
+
+function similarSommelierDecision(recentevents::T1; maxdistance::Integer=5
+  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
+  tablename = "sommelier_decision_repository"
+  # find similar
+  println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]
+  if row != 0 && distance < maxdistance
+    # if there is usable decision, return it.
+    rowid = df[1, :id]
+    println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
+    output_b64 = df[1, :function_output_base64]   # pick the closest match
+    _output_str = String(base64decode(output_b64))
+    output = copy(JSON3.read(_output_str))
+    return output
+  else
+    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+    return nothing
+  end
+end
+
+
+function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
+  ) where {T1<:AbstractString, T2<:AbstractDict}
+  tablename = "sommelier_decision_repository"
+  # find similar
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]
+  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+    recentevents_embedding = a.func[:getEmbedding](recentevents)[1]
+    recentevents = replace(recentevents, "'" => "")
+    decision_json = JSON3.write(decision)
+    decision_base64 = base64encode(decision_json)
+    decision = replace(decision_json, "'" => "")
+    
+    sql = """
+          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
+          """
+    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
+    println(sql)
+    _ = executeSQLVectorDB(sql)
+  else
+    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+  end
+end
+
+
+sessionId = "12345"
+
+externalFunction = (
+    getEmbedding=getEmbedding,
+    text2textInstructLLM=text2textInstructLLM,
+    executeSQL=executeSQL,
+    similarSQLVectorDB=similarSQLVectorDB,
+    insertSQLVectorDB=insertSQLVectorDB,
+    similarSommelierDecision=similarSommelierDecision,
+    insertSommelierDecision=insertSommelierDecision,
+  )
+
+
+
+a = YiemAgent.sommelier(
+  externalFunction;
+  name="Ton",
+  id=sessionId,  # agent instance id
+  retailername="Yiem",
+)
+
+while true
+  println("your respond: ")
+  user_answer = readline()
+  response = YiemAgent.conversation(a, Dict(:text=> user_answer))
+  println("\n$response")
+end
+
+
+# response = YiemAgent.conversation(a, Dict(:text=> "I want to get a French red wine under 100."))
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