update

Reviewed-on: #4

Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.12.6"
 manifest_format = "2.0"
-project_hash = "5b5e1c071ff66b72aeed7d8a4316829e2407ae1a"
+project_hash = "95dc0193a18325ca5b1e37deab8108d1b35915db"
 
 [[deps.Accessors]]
 deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "MacroTools"]
@@ -38,12 +38,6 @@ version = "1.1.3"
 uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"
 version = "1.1.2"
 
-[[deps.Arrow]]
-deps = ["ArrowTypes", "BitIntegers", "CodecLz4", "CodecZstd", "ConcurrentUtilities", "DataAPI", "Dates", "EnumX", "Mmap", "PooledArrays", "SentinelArrays", "StringViews", "Tables", "TimeZones", "TranscodingStreams", "UUIDs"]
-git-tree-sha1 = "4a69a3eadc1f7da78d950d1ef270c3a62c1f7e01"
-uuid = "69666777-d1a9-59fb-9406-91d4454c9d45"
-version = "2.8.1"
-
 [[deps.ArrowTypes]]
 deps = ["Sockets", "UUIDs"]
 git-tree-sha1 = "404265cd8128a2515a81d5eae16de90fdef05101"
@@ -58,12 +52,6 @@ version = "1.11.0"
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 version = "1.11.0"
 
-[[deps.BitIntegers]]
-deps = ["Random"]
-git-tree-sha1 = "091d591a060e43df1dd35faab3ca284925c48e46"
-uuid = "c3b6d118-76ef-56ca-8cc7-ebb389d030a1"
-version = "0.3.7"
-
 [[deps.BufferedStreams]]
 git-tree-sha1 = "6863c5b7fc997eadcabdbaf6c5f201dc30032643"
 uuid = "e1450e63-4bb3-523b-b2a4-4ffa8c0fd77d"
@@ -96,24 +84,12 @@ git-tree-sha1 = "40956acdbef3d8c7cc38cba42b56034af8f8581a"
 uuid = "6c391c72-fb7b-5838-ba82-7cfb1bcfecbf"
 version = "0.3.4"
 
-[[deps.CodecLz4]]
-deps = ["Lz4_jll", "TranscodingStreams"]
-git-tree-sha1 = "d58afcd2833601636b48ee8cbeb2edcb086522c2"
-uuid = "5ba52731-8f18-5e0d-9241-30f10d1ec561"
-version = "0.4.6"
-
 [[deps.CodecZlib]]
 deps = ["TranscodingStreams", "Zlib_jll"]
 git-tree-sha1 = "962834c22b66e32aa10f7611c08c8ca4e20749a9"
 uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
 version = "0.7.8"
 
-[[deps.CodecZstd]]
-deps = ["TranscodingStreams", "Zstd_jll"]
-git-tree-sha1 = "da54a6cd93c54950c15adf1d336cfd7d71f51a56"
-uuid = "6b39b394-51ab-5f42-8807-6242bab2b4c2"
-version = "0.8.7"
-
 [[deps.CommonSolve]]
 git-tree-sha1 = "99ee296f88c12485402e37c2fd025f95ae097637"
 uuid = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
@@ -148,12 +124,6 @@ weakdeps = ["InverseFunctions"]
     [deps.CompositionsBase.extensions]
     CompositionsBaseInverseFunctionsExt = "InverseFunctions"
 
-[[deps.ConcurrentUtilities]]
-deps = ["Serialization", "Sockets"]
-git-tree-sha1 = "21d088c496ea22914fe80906eb5bce65755e5ec8"
-uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb"
-version = "2.5.1"
-
 [[deps.ConstructionBase]]
 git-tree-sha1 = "b4b092499347b18a015186eae3042f72267106cb"
 uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
@@ -436,7 +406,9 @@ version = "1.21.3+0"
 
 [[deps.LLMMCTS]]
 deps = ["GeneralUtils", "JSON", "PrettyPrinting"]
-path = "../LLMMCTS"
+git-tree-sha1 = "3dff98131dfa79be8c9bd84fc51cb0ba1832c472"
+repo-rev = "main"
+repo-url = "https://git.yiem.cc/ton/LLMMCTS"
 uuid = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
 version = "0.1.5"
 
@@ -522,12 +494,6 @@ git-tree-sha1 = "3733419e9a71156b389f3e331672d2e95436783f"
 uuid = "6f1432cf-f94c-5a45-995e-cdbf5db27b0b"
 version = "3.6.2"
 
-[[deps.Lz4_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "191686b1ac1ea9c89fc52e996ad15d1d241d1e33"
-uuid = "5ced341a-0733-55b8-9ab6-a4889d929147"
-version = "1.10.1+0"
-
 [[deps.MacroTools]]
 git-tree-sha1 = "1e0228a030642014fe5cfe68c2c0a818f9e3f522"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
@@ -794,11 +760,11 @@ version = "0.7.0"
 
 [[deps.SQLLLM]]
 deps = ["CSV", "DataFrames", "DataStructures", "Dates", "FileIO", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "PrettyPrinting", "Random", "Revise", "StatsBase", "Tables", "URIs", "UUIDs"]
-git-tree-sha1 = "997602ed56a285ac29d74c91bb57bc5faeadfad6"
+git-tree-sha1 = "8f264038c55c5bea069cccbdc057c56e27899c42"
 repo-rev = "main"
 repo-url = "https://git.yiem.cc/ton/SQLLLM"
 uuid = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
-version = "0.2.5"
+version = "0.2.6"
 
 [[deps.SQLStrings]]
 git-tree-sha1 = "55de0530689832b1d3d43491ee6b67bd54d3323c"
@@ -902,11 +868,6 @@ git-tree-sha1 = "d05693d339e37d6ab134c5ab53c29fce5ee5d7d5"
 uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e"
 version = "0.4.4"
 
-[[deps.StringViews]]
-git-tree-sha1 = "f2dcb92855b31ad92fe8f079d4f75ac57c93e4b8"
-uuid = "354b36f9-a18e-4713-926e-db85100087ba"
-version = "1.3.7"
-
 [[deps.StructTypes]]
 deps = ["Dates", "UUIDs"]
 git-tree-sha1 = "159331b30e94d7b11379037feeb9b690950cace8"
@@ -1022,10 +983,10 @@ uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
 version = "1.6.1"
 
 [[deps.YiemAgent]]
-deps = ["CSV", "DataFrames", "DataStructures", "Dates", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SQLLLM", "Serialization", "URIs", "UUIDs", "msghandler"]
+deps = ["CSV", "DataFrames", "DataStructures", "Dates", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "Serialization", "URIs", "UUIDs"]
 path = "."
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.4.0"
+version = "0.4.1"
 
 [[deps.Zlib_jll]]
 deps = ["Libdl"]
@@ -1049,14 +1010,6 @@ git-tree-sha1 = "011b0a7331b41c25524b64dc42afc9683ee89026"
 uuid = "a9144af2-ca23-56d9-984f-0d03f7b5ccf8"
 version = "1.0.21+0"
 
-[[deps.msghandler]]
-deps = ["Arrow", "Base64", "DataFrames", "Dates", "GeneralUtils", "HTTP", "JSON", "NATS", "PrettyPrinting", "Revise", "UUIDs"]
-git-tree-sha1 = "db16f76f72bd4fa2a87e34ef47bb787204e1f888"
-repo-rev = "main"
-repo-url = "https://git.yiem.cc/ton/msghandler"
-uuid = "f2724d33-f338-4a57-b9f8-1be882570d10"
-version = "0.5.7"
-
 [[deps.nghttp2_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

Project.toml

@@ -1,6 +1,6 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.4.0"
+version = "0.4.2"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
 
 [deps]
@@ -21,7 +21,6 @@ SQLLLM = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
-msghandler = "f2724d33-f338-4a57-b9f8-1be882570d10"
 
 [compat]
 CSV = "0.10.15"
@@ -29,6 +28,6 @@ DataFrames = "1.7.0"
 GeneralUtils = "0.4.9"
 HTTP = "2.4.0"
 JSON = "1.6.1"
+LLMMCTS = "0.1.5"
 NATS = "0.1.0"
-SQLLLM = "0.2.5"
+SQLLLM = "0.2.6"
-msghandler = "0.5.6"

etc.jl

@@ -1,93 +1,13 @@
-using DataStructures
-
-function dictify2(x; keytype::Type=Any, sort_order::Union{Nothing, Vector}=nothing)
-    # Dict-like objects
-    if x isa AbstractDict
-        out = OrderedDict{keytype, Any}()
-        
-        # 1. Process and normalize all keys from the input dictionary
-        processed_dict = OrderedDict{keytype, Any}()
-        for (k, v) in x
-            if keytype === String
-                newk = string(k)
-            elseif keytype === Symbol
-                newk = Symbol(string(k))
-            else
-                newk = k
-            end
-            processed_dict[newk] = dictify(v; keytype=keytype, sort_order=sort_order)
-        end
-
-        # 2. If a sort order is specified, apply it
-        if !isnothing(sort_order)
-            # Normalize the sort_order elements to match the requested keytype
-            normalized_order = map(sort_order) do tk
-                if keytype === String
-                    return string(tk)
-                elseif keytype === Symbol
-                    return Symbol(string(tk))
-                else
-                    return tk
-                end
-            end
-
-            # First, insert keys that match the requested order
-            for target_key in normalized_order
-                if haskey(processed_dict, target_key)
-                    out[target_key] = processed_dict[target_key]
-                end
-            end
-
-            # Then, append any remaining keys that weren't in the sort_order
-            for (k, v) in processed_dict
-                if !haskey(out, k)
-                    out[k] = v
-                end
-            end
-        else
-            # If no sort order is given, just use the processed dict
-            out = processed_dict
-        end
-
-        return out
-
-    # Arrays / vectors: map elements recursively
-    elseif x isa AbstractArray
-        return [dictify(element; keytype=keytype, sort_order=sort_order) for element in x]
-    
-    # Everything else: return as-is
-    else
-        return x
-    end
-end
-
-
-
-
-function dict_to_string_html2(d::AbstractDict; indent_level=1, indent_str=" ")
-  lines = String[]
-  padding = indent_str ^ indent_level
-  
-  # Sort keys for predictable, clean output
-  for k in keys(d)
-      v = d[k]
-      
-      if v isa AbstractDict
-          # Open tag, recurse for children, then close tag
-          push!(lines, "$padding<$k>")
-          ind_level = indent_level + 1
-          push!(lines, dict_to_string_html(v; indent_level=ind_level, indent_str=indent_str))
-          push!(lines, "$padding</$k>")
-      else
-          # Leaf node: put key and value on a single line
-          push!(lines, "$padding<$k>$v</$k>")
-      end
-  end
-  return join(lines, "\n")
-end
-
 
 
 
+d = Dict(
+  "hello"=> 555,
+  "world"=> Dict(
+    "name"=> "ton"
+  )
+)
 
+x = 55
 
+@info "YiemAgent think() 1 " d x @__LINE__

src/interface.jl

@@ -60,212 +60,22 @@ end
 # Keyword Arguments
   
 # Return        
-  - `thoughtDict::Dict`
+  - `thoughtdict::Dict`
 
 # Example
 ```jldoctest
-julia>  config = Dict(
+julia> result = decisionMaker(agent)
-           "mqttServerInfo" => Dict(
-             "description" => "mqtt server info", 
-             "port" => 1883, 
-             "broker" => "mqtt.yiem.cc"
-           ),
-           "externalservice" => Dict(
-               "text2textinstruct" => Dict(
-                 "mqtttopic" => "/loadbalancer/requestingservice", 
-                 "description" => "text to text service with instruct LLM", 
-                 "llminfo" => Dict(
-                   "name" => "llama3instruct"
-                 )
-               ),
-           )
-         )
 
-julia> output_thoughtDict = Dict(
+OrderedDict{String, Any} with 4 entries:
-        "thought_1"     => "The customer wants to buy a bottle of wine. This is a good start!",
+  "plan"          => "The user provided an image of a sparkling white wine (Asolo Prosecco Bella Principessa from Italy) and requested a search for similar wines in the inventory. According to store guidelines, I must st…
-        "action_1"      => Dict{String, Any}(
+  "action_name"   => "CHECK_WINE"
-                            "action"=>"CHAT_BOX", 
+  "action_input"  => "Sparkling white wine from Italy"
-                            "input"=>"What occasion are you buying the wine for?"
+  "action_result" => "1) winery: Terrazze dell Etna, wine_name: Rose Brut.
-                            ),
-        "observation_1" => ""
-        )
 ```
-  - [] update docstring
-  - [] use customerinfo
-  - [] user storeinfo
-
 """
-# function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
-#   ) where {T<:agent}
-#   println("\nExecuting YiemAgent decisionMaker()")
-#   # lessonDict = copy(JSON.parsefile("lesson.json"))
-
-#   # lesson =
-#   # if isempty(lessonDict)
-#   #   ""
-#   # else
-#   #   lessons = Dict{String, Any}()
-#   #   for (k, v) in lessonDict
-#   #     lessons[k] = lessonDict[k][:lesson]
-#   #   end
-
-#   #   """
-#   #   You have attempted to help the user before and failed, either because your reasoning for the 
-#   #   recommendation was incorrect or your response did not exactly match the user expectation. 
-#   #   The following lesson(s) give a plan to avoid failing to help the user in the same way you 
-#   #   did previously. Use them to improve your strategy to help the user.
-
-#   #   Here are some lessons in JSON format:
-#   #   $(JSON.json(lessons))
-
-#   #   When providing the thought and action for the current trial, that into account these failed 
-#   #   trajectories and make sure not to repeat the same mistakes and incorrect answers. 
-#   #   """
-#   # end
-
-#   # recentevents_ind = GeneralUtils.recentElementsIndex(
-#   #   length(a.memory["events"]), recentevents; includelatest=true)
-
-#     systemmsg = 
-#     """
-#     # Store Policy
-#     - Generally speaking, the store inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know exactly until you check your inventory.
-#     - If you found wines in the store's database, they are in stock.
-#     - You can only recommend wines that are currently in our inventory
-#     - Before searching the database for wine, ensure you have at least the following information: 1) budget, 2) wine type, and 3) occasion. Additional details are always helpful. If the user is unsure, provide relevant information and gather insights to make reasonable inferences.
-#     - Ask the user one question at a time.
-#     - 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, if you haven't already, ask the user whether they need any further assistance. Do not offer any additional services.
-#     - Only end the conversation when the user explicitly intends to do so. When ending, ensure a polite farewell and an invitation to return in the future.
-#     - Spicy foods should be paired only with light red wines.
-#     - We do not sell organic, sustainable, gluten-free, and sulfite-free wine. Inform the user imediately if they are looking for these types of wines. Do not sell our wines as such.
-#     - Gift box, gift card, and custom messages are available. Inform the user to contact our sales team.
-
-#     # Store Guidelines
-#     - Greeting the customer warmly by ask them how could you help. Do not ask any other questions during this greeting.
-#     - Customer may provide images for you to look up.
-#     - Encourage the customer to explore different options and try new things.
-#     - If you are unable to locate the desired item in the database after 2 attempts, it may not be available in your inventory. In such cases, inform the user that the item is unavailable and suggest an alternative instead.
-#     - Your store carries only wine.
-#     - Vintage 0 means non-vintage.
-#     - Start searching the database as broadly as possible within the given information boundary to maximize the chances of finding. Avoid unnecessary parameters unless specified by the user. Refine the search subsequently.    
-
-#     # Situation
-#     Your customer is coming into the store
-
-#     # Role
-#     Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store. You are working under your mentor supervision.
-
-#     # Objective
-#     1. Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
-#     2. Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
-
-#     # Responsibility Includes
-#     1. According to the store's policy and guidelines, make an informed decision about what you need to do to achieve the objective
-#     2. Keep the conversation with the customer going smoothly
-#     3. Obey your mentor's suggestions.
-
-#     # Responsibility Does NOT Include
-
-#     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.
-
-#     # You should then respond to the user with interleaving plan, action_name, action_input
-#       1) plan: Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
-#       2) action_name: (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
-#       3) action_input: The input to the action you are about to perform according to your plan.
-#       After the action is executed you gets "action_result". It is the output from the action you selected.
-
-#     # You should only respond in JSON format as described below
-#       "plan": "...",
-#       "action_name": "...",
-#       "action_input": "..."
-
-#     # Available Actions
-#     - **CHAT_BOX** which you can use to talk with the user.
-#     - **CHECK_WINE** allows you to check information about wines you want in your inventory's database. The input is text that specify supported search criteria includeing: retailer_name, wine price, winery, name, vintage, region, country, type, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
-#         - Example query 1: "Dry, full-bodied red wine from 1) region: Burgundy, country: France or 2) region: Tuscany, country: Italy. Grape varietal: Merlot or Syrah. price 100 to 1000 USD."
-#         - Example query 2: "Red or white wine, medium tannin, price under 700 USD"
-#         - Example query 3: "white wine, region: Tuscany or Bordeaux, country: Italy or France
-#     - **PRESENT_WINE_GUIDELINE** which you can use to check the store guidelines about how to present wines you have found to the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
-#     - **END_CONVER_GUIDELINE** which you can use to check the store guidelines about how to end the conversation with the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
-#     """
-    
-
-#   requiredKeys = ["plan", "action_name", "action_input"]
-#   context = 
-#     """
-#     <internal_context_for_assistant>
-#       <thought_history>
-#         $(GeneralUtils.dict_to_string_html(a.memory["shortmem"]))
-#       </thought_history>
-#     </internal_context_for_assistant>
-
-#     """
-
-#   # add context to text of the latest message (in the front). 
-#   # use for loop because in openai format, each msg may contain both text and image. 
-#   for d in a.chathistory[end]["content"]
-#     if d["type"] == "text"
-#       d["text"] = context * d["text"]
-#       break
-#     end
-#   end
-#   errornote = "N/A"
-#   response = nothing # placeholder for show when error msg show up
-
-#   for attempt in 1:maxattempt
-#     if attempt > 1
-#       println("\nYiemAgent decisionMaker() attempt $attempt/$maxattempt ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#     end
-    
-#     msg = Dict(
-#       "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
-#       "messages" => a.chathistory,
-#       "temperature" => 0.7
-#     )
-
-#     response = a.context.text2textInstructLLM(a.id, msg)
-#     response = GeneralUtils.clean_json_response(response)
-
-#     response = GeneralUtils.remove_french_accents(response)
-#     think, response = GeneralUtils.extractthink(response)
-#     response = String(split(response, ", observation")[1])  # in case LLM generate observation key which it isn't supposed to
-#     response = strip(response)
-#     responsedict = nothing
-#     try
-#       _responsedict = JSON.parse(response)
-#       responsedict = GeneralUtils.dictify(_responsedict; keytype=String, sort_order=requiredKeys)
-#     catch
-#       println("\nERROR YiemAgent decisionMaker() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue
-#     end
-
-#     # check whether all answer's key points are in responsedict
-#     ispass, errormsg = GeneralUtils.checkAgentResponse_JSON(responsedict, requiredKeys)
-#     if !ispass
-#       errornote = errormsg
-#       println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-#       continue
-#     end
-
-#     if responsedict["action_name"] ∉ ["CHAT_BOX", "CHECK_WINE", "PRESENT_WINE_GUIDELINE", "END_CONVER_GUIDELINE"]
-#       errornote = "Your previous attempt didn't use the given functions"
-#       println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)--> $(responsedict["action_name"])", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue
-#     end
-
-#     # println("\nYiem decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#     # pprintln(responsedict)
-
-#     return responsedict
-#   end
-#   error("DecisionMaker failed to generate a thought ", response)
-# end 
 function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
   ) where {T<:agent}
-  println("\nExecuting YiemAgent decisionMaker()")
+
   # lessonDict = copy(JSON.parsefile("lesson.json"))
 
   # lesson =
@@ -298,9 +108,9 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
   context = 
     """
     <internal_context_for_assistant>
-      <thought_history>
+      <assistant_action_history>
         $(GeneralUtils.dict_to_string_html(a.memory["shortmem"]))
-      </thought_history>
+      </assistant_action_history>
     </internal_context_for_assistant>
     """
 
@@ -328,11 +138,11 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
 
     response = a.context.text2textInstructLLM(a.id, msg)
     response = GeneralUtils.clean_json_response(response)
-
     response = GeneralUtils.remove_french_accents(response)
     think, response = GeneralUtils.extractthink(response)
     response = String(split(response, ", observation")[1])  # in case LLM generate observation key which it isn't supposed to
     response = strip(response)
+
     responsedict = nothing
     if occursin(requiredKeys[2], response) 
       try
@@ -345,26 +155,31 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
 
     # fall back to normal text because LLM default to natural chat when it didn't use action_call
     else  
+      try
         responsedict = OrderedDict(
           "plan"=> "I will talk to the user",
           "action_name"=> "CHAT_BOX",
           "action_input"=> response[2:end-1]  # remove { } at the front and back that added by clean_json_response
           )
+      catch e
+        println("\nERROR YiemAgent decisionMaker(). $e --(not qualify response)-> $response", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+        continue
+      end
     end
 
     # check whether all answer's key points are in responsedict
     ispass, errormsg = GeneralUtils.checkAgentResponse_JSON(responsedict, requiredKeys)
     if !ispass
       errornote = errormsg
-      println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)-> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
       continue
     end
 
-    if responsedict["action_name"] ∉ ["CHAT_BOX", "CHECK_WINE", "PRESENT_WINE_GUIDELINE", "END_CONVER_GUIDELINE"]
+    # if responsedict["action_name"] ∉ ["CHAT_BOX", "CHECK_WINE", "PRESENT_WINE_GUIDELINE", "END_CONVER_GUIDELINE"]
-      errornote = "Your previous attempt didn't use the given functions"
+    #   errornote = "Your previous attempt didn't use the given functions"
-      println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)--> $(responsedict["action_name"])", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    #   println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)--> $(responsedict["action_name"])", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      continue
+    #   continue
-    end
+    # end
 
     # println("\nYiem decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
     # pprintln(responsedict)
@@ -459,9 +274,9 @@ function evaluator(a::T1, timeline, decisiondict, evaluateecontext
     context =
     """
     <context>
-      <trajectory>
+      <assistant_trajectories>
       $timeline
-      </trajectory>
+      </assistant_trajectories>
       <evaluatee_context>
       $evaluateecontext
       </evaluatee_context>
@@ -510,36 +325,11 @@ function evaluator(a::T1, timeline, decisiondict, evaluateecontext
 
     println("\nEvaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
     pprintln(Dict(responsedict))
-
-    # # read sessionId
-    # sessionid = a.id
-    # # save to filename ./log/decisionlog.txt
-    # println("saving SQLLLM evaluator() to disk")
-    # filename = "agent_evaluator_log_$(sessionid[:id]).json"
-    # filepath = "/appfolder/app/log/$filename"
-    # # check whether there is a file path exists before writing to it
-    # if !isfile(filepath)
-    #   decisionlist = [responsedict]
-    #   println("Creating file $filepath")
-    #   open(filepath, "w") do io
-    #     JSON.pretty(io, decisionlist)
-    #   end
-    # else
-    #   # read the file and append new data
-    #   decisionlist = copy(JSON.parsefile(filepath))
-    #   push!(decisionlist, responsedict)
-    #   println("Appending new data to file $filepath")
-    #   open(filepath, "w") do io
-    #     JSON.pretty(io, decisionlist)
-    #   end
-    # end
-
     return responsedict
   end
   error("Evaluator failed to generate an evaluation, Response: \n$response\n<|End of error|>")
 end
 
-
 """ Chat with llm.
 
 # Example userinput
@@ -564,92 +354,56 @@ message => Dict(
     ]
   )
 
-# ---------------------------------------------- 100 --------------------------------------------- #
 """
 function conversation(a::sommelier; userinput::Union{Dict{String, Any}, JSON.Object{String, Any}},
-                      maximumMsg=50)
+                      maximumMsg=50, max_think_loop::Integer=3)
+  
+  @info "YiemAgent conversation() 1" @__LINE__
   userinput = GeneralUtils.dictify(userinput; keytype=String, sort_order=["text"])
 
   # find text in usermsg
   usertext = nothing
-  text_position = nothing
   for (i, d) in enumerate(userinput["content"])
     if d["type"] == "text"
+      d["text"] = GeneralUtils.remove_french_accents(d["text"])
       usertext = d["text"]
-      text_position = i
     end
   end
 
-  # place holder
-  action_name = nothing
-  result = nothing
-  chatresponse = nothing
-
   if usertext == "newtopic"
     clearhistory(a)
     return "Okay. What shall we talk about?"
   else
-    userinput["content"][text_position]["text"] = GeneralUtils.remove_french_accents(usertext)
+    @info "YiemAgent conversation() 2" @__LINE__
     # add usermsg to a.chathistory but how do I handle images?
     addNewMessage(a, "user", userinput; maximumMsg=maximumMsg)
 
     # thinking loop until AI wants to communicate with the user
-    chatresponse = nothing
+    loopcount = 0
-    while chatresponse === nothing
+    while true
-      action_name, result = think(a)
+      loopcount += 1
-      if action_name ∈ ["CHAT_BOX"]
+      @info "YiemAgent conversation() 2-0 count $loopcount" @__LINE__
-        chatresponse = result
+      thoughtdict, _ = think(a)
-      end
+      if thoughtdict["action_name"] ∈ ["CHAT_BOX"]
-    end
+        @info "YiemAgent conversation() 2-1" @__LINE__
         assistant_response = Dict{String, Any}(
           "role" => "assistant",
-      "content" => [Dict("type" => "text", "text" => chatresponse),]
+          "content" => [Dict("type" => "text", "text" => thoughtdict["action_input"]),]
         )
         addNewMessage(a, "assistant", assistant_response; maximumMsg=maximumMsg)
+        return thoughtdict["action_input"]
+      end
 
-    return chatresponse
+      if loopcount > max_think_loop
+        @info "YiemAgent conversation() 2-2" @__LINE__
+        r = generatechat(a)
+        @info "YiemAgent conversation() 2-3" @__LINE__
+        return r
+      end
+    end
   end
 end
 
-function conversation(a::Union{companion, virtualcustomer}, userinput::Dict; 
-                      converPartnerName::Union{String, Nothing}=nothing,
-                      maximumMsg=50)
-
-  chatresponse = nothing
-
-  if userinput["text"] == "newtopic"
-    clearhistory(a)
-    return "Okay. What shall we talk about?"
-  else
-    # add usermsg to a.chathistory
-    addNewMessage(a, "user", userinput["text"]; maximumMsg=maximumMsg)
-
-    # add user activity to events memory
-    push!(a.memory["events"],
-      eventdict(;
-        event_description="the user talks to the assistant.",
-        timestamp=Dates.now(),
-        subject="user",
-        action_name="CHAT_BOX",
-        action_input=userinput["text"],
-      )
-    )
-    chatresponse = generatechat(a; converPartnerName=converPartnerName, recentEventNum=20)
-
-    addNewMessage(a, "assistant", chatresponse; maximumMsg=maximumMsg)
-
-    push!(a.memory["events"],
-      eventdict(;
-        event_description="the assistant talks to the user.",
-        timestamp=Dates.now(),
-        subject="assistant",
-        action_name="CHAT_BOX",
-        action_input=chatresponse,
-      )
-    )
-    return chatresponse
-  end
-end
 
 """
 # Arguments
@@ -662,41 +416,58 @@ julia>
 ```
 
 """
-function think(a::T) where {T<:agent}
+function think(a::T)::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
   # a.memory[:recap] = generateSituationReport(a, a.context["text"2textInstructLLM]; skiprecent=0)
-  thoughtDict = decisionMaker(a)
+  thoughtdict = decisionMaker(a)
+  @info "YiemAgent think() 1" @__LINE__
+  # pprintln(thoughtdict)
 
-  println("\n--- YiemAgent think() 1 ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+  result_raw = nothing
-  pprintln(thoughtDict)
+  if thoughtdict["action_name"] ∈ ["CHAT_BOX"]
-  println("---")
+    @info "YiemAgent think() 2" @__LINE__
+    thoughtdict, result_raw = chatbox!(a, thoughtdict)
     
-  # # map action and input() to llm function
+  elseif thoughtdict["action_name"] == "END_CONVER_GUIDELINE"
-  # response =
+    @info "YiemAgent think() 3" @__LINE__
-  #   if thoughtDict["action_name"] == "CHAT_BOX" || thoughtDict["action_name"] == "END_CONVER_GUIDELINE"
+    thoughtdict, result_raw = end_conversation_guideline!(a, thoughtdict)
-  #     (result=thoughtDict["plan"], errormsg=nothing, success=true)
-  #   elseif thoughtDict["action_name"] == "CHECK_WINE"
-  #     checkwine(a, thoughtDict["action_input"])
-  #   elseif thoughtDict["action_name"] == "PRESENT_WINE_GUIDELINE"
-  #     (result=thoughtDict["action_input"], errormsg=nothing, success=true)
-  #   else
-  #     error("undefined LLM function. Requesting $(thoughtDict["action_name"])")
-  #   end
     
-  # # this section allow LLM functions above to have different return values.
+  elseif thoughtdict["action_name"] ∈ ["WINE_PRESENTATION_GUIDELINE"]
-  # result = haskey(response, "result") ? response["result"] : nothing
+    @info "YiemAgent think() 4" @__LINE__
-  # rawresponse = haskey(response, "rawresponse") ? response["rawresponse"] : nothing
+    thoughtdict, result_raw =  wine_presentation_guideline!(a, thoughtdict)
-  # select = haskey(response, "select") ? response["select"] : nothing
-  # reward::Integer = haskey(response, "reward") ? response["reward"] : 0
-  # isterminal::Bool = haskey(response, "isterminal") ? response["isterminal"] : false
-  # errormsg::Union{AbstractString,Nothing} = haskey(response, "errormsg") ? response["errormsg"] : nothing
-  # success::Bool = haskey(response, "success") ? response["success"] : false
     
-  result = nothing
-  if thoughtDict["action_name"] ∈ ["CHAT_BOX"]
-    result = thoughtDict["action_input"]
-  elseif thoughtDict["action_name"] == "END_CONVER_GUIDELINE"
 
-    # add guideline in to context
+  elseif thoughtdict["action_name"] == "CHECK_WINE" 
+    @info "YiemAgent think() 5" @__LINE__
+    thoughtdict, result_raw = checkwine!(a, thoughtdict)
+
+  else
+    @info "YiemAgent think() 6" @__LINE__
+    error("condition is not defined ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+  end
+
+  max_ind =
+    if length(a.memory["shortmem"]) == 0
+      0
+    else
+      k = keys(a.memory["shortmem"])
+      maximum(parse.(Int, k))
+    end
+  a.memory["shortmem"]["$(max_ind + 1)"] = thoughtdict
+
+  @info "YiemAgent think() 7" @__LINE__
+  pprintln(thoughtdict)
+  return (thoughtdict=thoughtdict, result_raw=result_raw)
+end
+
+function chatbox!(a::T, thoughtdict::AbstractDict
+  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
+  thoughtdict["action_result"] = "Action result is the next user dialogue."
+  return (thoughtdict=thoughtdict, result_raw=nothing)
+end
+
+function end_conversation_guideline!(a::T, thoughtdict::AbstractDict
+  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
+  
   guideline =
     """
     <end_conversation_guideline>
@@ -708,19 +479,14 @@ function think(a::T) where {T<:agent}
       </store_info>
     </end_conversation_guideline>
     """
-    thoughtDict["action_result"] = guideline
+  thoughtdict["action_result"] = guideline
-    max_ind =
+
-      if length(a.memory["shortmem"]) == 0
+  return (thoughtdict=thoughtdict, result_raw=nothing)
-        0
-      else
-        k = keys(a.memory["shortmem"])
-        maximum(parse.(Int, k))
 end
-    a.memory["shortmem"]["$(max_ind + 1)"] = thoughtDict
 
-  elseif thoughtDict["action_name"] ∈ ["PRESENT_WINE_GUIDELINE"]    #WORKING
+function wine_presentation_guideline!(a::T, thoughtdict::AbstractDict
+  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
   
-    # add guideline in to context
   guideline =
     """
     <wine_presentation_guideline>
@@ -757,296 +523,195 @@ function think(a::T) where {T<:agent}
       </conversion_table>
     </wine_presentation_guideline>
     """
-    thoughtDict["action_result"] = guideline
+  thoughtdict["action_result"] = guideline
-    max_ind =
-      if length(a.memory["shortmem"]) == 0
-        0
-      else
-        k = keys(a.memory["shortmem"])
-        maximum(parse.(Int, k))
-      end
-    a.memory["shortmem"]["$(max_ind + 1)"] = thoughtDict
 
-  elseif thoughtDict["action_name"] == "CHECK_WINE"  
+  return (thoughtdict=thoughtdict, result_raw=nothing)
-    result = checkwine(a, thoughtDict["action_input"])
-
-    thoughtDict["action_result"] = result[:result_str]
-    max_ind =
-      if length(a.memory["shortmem"]) == 0
-        0
-      else
-        k = keys(a.memory["shortmem"])
-        maximum(parse.(Int, k))
-      end
-    a.memory["shortmem"]["$(max_ind + 1)"] = thoughtDict
-  else
-    error("condition is not defined ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  end
-
-    println("\n--- YiemAgent think() 2 ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    pprintln(thoughtDict)
-    println("---")
-  return (action_name=thoughtDict["action_name"], result=result)
 end
 
 
-function presentbox(a::sommelier, thoughtDict; maxtattempt::Integer=10, recentevents::Integer=10)
+#PENDING
-  recentchat_ind = GeneralUtils.recentElementsIndex(length(a.chathistory), recentevents; 
+function generatechat(a::T; recentevents::Integer=20, maxattempt=10
-                                                    includelatest=true)
+  )::String where {T<:agent}
+
+  # lessonDict = copy(JSON.parsefile("lesson.json"))
+
+  # lesson =
+  # if isempty(lessonDict)
+  #   ""
+  # else
+  #   lessons = Dict{String, Any}()
+  #   for (k, v) in lessonDict
+  #     lessons[k] = lessonDict[k][:lesson]
+  #   end
+
+  #   """
+  #   You have attempted to help the user before and failed, either because your reasoning for the 
+  #   recommendation was incorrect or your response did not exactly match the user expectation. 
+  #   The following lesson(s) give a plan to avoid failing to help the user in the same way you 
+  #   did previously. Use them to improve your strategy to help the user.
+
+  #   Here are some lessons in JSON format:
+  #   $(JSON.json(lessons))
+
+  #   When providing the thought and action for the current trial, that into account these failed 
+  #   trajectories and make sure not to repeat the same mistakes and incorrect answers. 
+  #   """
+  # end
+
+  # recentevents_ind = GeneralUtils.recentElementsIndex(
+  #   length(a.memory["events"]), recentevents; includelatest=true)
 
   systemmsg = 
     """
-  <situation>
+    # store_policy
-    You have checked the inventory and found wines that may match what the user wants.
+      - Generally speaking, the store inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know exactly until you check your inventory.
-  </situation>
+      - If you found wines in the store's database, they are in stock.
-  <Your role>
+      - You can only recommend wines that are currently in our inventory
-    Your name is $(a.name). You are a helpful English-speaking assistant, acting as a polite, website-based sommelier for $(a.retailername)'s wine store.
+      - Before searching the database for wine, ensure you have at least the following information: 1) budget, 2) wine type, and 3) occasion. Additional details are always helpful. If the user is unsure, provide relevant information and gather insights to make reasonable inferences.
-  </Your role>
+      - Ask the user one question at a time.
-  <objective>
+      - 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.
-    Present the wines to the user in a way that keep the conversation smooth and engaging.
+      - Once the user has selected their wine, if you haven't already, ask the user whether they need any further assistance. Do not offer any additional services.
-  </objective>
+      - Only end the conversation when the user explicitly intends to do so. When ending, ensure a polite farewell and an invitation to return in the future.
+      - Spicy foods should be paired only with light red wines.
+      - We do not sell organic, sustainable, gluten-free, and sulfite-free wine. Inform the user immediately if they are looking for these types of wines. Do not sell our wines as such.
+      - Gift box, gift card, and custom messages are available. Inform the user to contact our sales team.
 
+    # store_guidelines
+      - Greeting the customer warmly by ask them how could you help. Do not ask any other questions during this greeting.
+      - Customer may provide images for you to look up.
+      - Encourage the customer to explore different options and try new things.
+      - If you are unable to locate the desired item in the database after 2 attempts, it may not be available in your inventory. In such cases, inform the user that the item is unavailable and suggest an alternative instead.
+      - Your store carries only wine.
+      - Vintage 0 means non-vintage.
+      - Start searching the database as broadly as possible within the given information boundary to maximize the chances of finding. Avoid unnecessary parameters unless specified by the user. Refine the search subsequently.
 
+    # situation
+      You are continuing the conversation with the user.
 
+    # your role
+      Your name is $(a.name). You are a helpful sommelier for website-based $(a.retailername)'s wine store. You are working under your mentor supervision.
 
+    # objective
+      - Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
+      - Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
 
+    # your responsibility includes
+      - According to the store's policy and guidelines, continuing conversation with the customer using CHAT_BOX action.
+      - Keep the conversation with the customer going smoothly
 
+    # your responsibility does NOT includes
+      - 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.
+      - Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
+      - 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.
 
+    # you should then respond to the user with interleaving plan, action_name, action_input
+      1) **plan**, Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
+      2) **action_name**, (Typically corresponds to the execution of the first step in your plan). Must be "CHAT_BOX
+      3) **action_input**, Dialogue you want to chat with the user according to your plan.
+      After the action is executed you gets "action_result". It is the output from the action you selected.
 
-  <At each round of conversation, you will be given the following information>
+    # you should only respond in JSON format as described below
-    Name of the wines that needs to be introduced: name of wines you are going to introduce to the user
+      "plan": "...",
-    Database search result: the result of a database search using SQL commands you have found so far
+      "action_name": "...",
-  </At each round of conversation, you will be given the following information>
+      "action_input": "..."
-  <You should follow the following guidelines>
-    
-  </You should follow the following guidelines>
-  <You should then respond to the user with>
-    dialogue: Your presentation to the user
-  </You should then respond to the user with>
-  <You should only respond in format as described below>
-  {
-    "dialogue": "..."
-  }
-  </You should only respond in format as described below>
-
-  Let's begin!
     """
-  requiredKeys = [:dialogue]
-  database_search_result = 
-      if length(a.memory["shortmem"][:db_search_result]) != 0
-        availableWineToText(a.memory["shortmem"][:db_search_result])
-      else
-        "N/A"
-      end
   
-  # chathistory = chatHistoryToText(a.chathistory)
+  system_msg = Dict(
-  errornote = "N/A"
+      "role" => "system",
-  response = nothing # placeholder for show when error msg show up
+      "content" => [
+        Dict("type" => "text", "text" => systemmsg),
+      ]
+    )
 
+  chathistory = deepcopy(a.chathistory[2:end])
+  pushfirst!(chathistory, system_msg)
 
-
+  requiredKeys = ["plan", "action_name", "action_input"]
-  # yourthought = "$(thoughtDict[:thought]) $(thoughtDict["plan"])"
-  # yourthought1 = nothing
-
-  for attempt in 1:maxtattempt
-
   context = 
     """
-    <context>
+    <internal_context_for_assistant>
-    Name of the wines that needs to be introduced: $(thoughtDict["action_input"])
+      <assistant_action_history>
-    $(a.memory["shortmem"]["scratchpad"])
+        $(GeneralUtils.dict_to_string_html(a.memory["shortmem"]))
-    P.S. $errornote
+      </assistant_action_history>
-    </context>
+    </internal_context_for_assistant>
     """
 
-    unformatPrompt =
+  # add context to text of the latest message (in the front). 
-      [
+  # use for loop because in openai format, each msg may contain both text and image. 
-        Dict("name" => "system", "text" => systemmsg),
+  for d in chathistory[end]["content"]
-      ]
+    if d["type"] == "text"
-
+      d["text"] = context * d["text"]
-    unformatPrompt = vcat(unformatPrompt, recentchat)
-    # put in model format
-    prompt = GeneralUtils.formatLLMtext(unformatPrompt, a.llmFormatName)
-    # add info
-    prompt = prompt * context
-
-    response = a.context.text2textInstructLLM(prompt; senderId=a.id)
-    response = GeneralUtils.deFormatLLMtext(response, a.llmFormatName)
-    response = GeneralUtils.remove_french_accents(response)
-    # response = replace(response, '$'=>"USD")
-    think, response = GeneralUtils.extractthink(response)
-
-    response = replace(response, '*'=>"")
-    response = replace(response, '$' => "USD")
-    response = replace(response, '`' => "")
-    response = replace(response, "<|eot_id|>"=>"")
-    
-    responsedict = nothing
-    try
-      responsedict = copy(JSON.parsefile(response))  
-    catch
-      println("\nERROR YiemAgent presentbox() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      continue
-    end
-
-    # check whether all answer's key points are in responsedict
-    ispass, errormsg = checkAgentResponse_JSON(responsedict, requiredKeys)
-    if !ispass
-      errornote = errormsg
-      println("\nERROR YiemAgent presentbox() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-      continue
-    end
-
-    # check if Context: is in dialogue
-    if occursin("Context:", responsedict["dialogue"])
-      errornote = "Your previous response contains 'Context:' which is not allowed"
-      println("\nERROR YiemAgent presentbox() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      continue
-    end
-
-    println("\nYiemAgent presentbox() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    pprintln(Dict(responsedict))
-
-    # 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["dialogue"])
-    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["observation"] !== nothing && occursin(winename, event["observation"])
-            isWineInEvent = true
       break
     end
   end
+  errornote = "N/A"
+  response = nothing # placeholder for show when error msg show up
+
+  for attempt in 1:maxattempt
+    if attempt > 1
+      println("\nYiemAgent generatechat() attempt $attempt/$maxattempt ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
     end
     
-      # if wine is mentioned but not in timeline or shortmem,
+    msg = Dict(
-      # then the agent is not supposed to recommend the wine
+      "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
-      if isWineInEvent == false
+      "messages" => a.chathistory,
-        errornote = "Your previous response recommended wines that is not in your inventory which is not allowed"
+      "temperature" => 0.7
-        println("\nERROR YiemAgent presentbox() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    )
-        s = """
-        Perfect choice! The Colgin Tychson Hill Vineyard Cabernet Sauvignon (2014) is an excellent match for your criteria. Here's why: 
 
-        Boldness & Flavor: This wine delivers intense blackberry, black cherry, and dark fruit notes, layered with vanilla, oak, and earthy undertones. Its high intensity (rated 5/5) ensures a rich, full-bodied experience that's both powerful and balanced. 
+    response = a.context.text2textInstructLLM(a.id, msg)
+    response = GeneralUtils.clean_json_response(response)
+    response = GeneralUtils.remove_french_accents(response)
+    think, response = GeneralUtils.extractthink(response)
+    response = String(split(response, ", observation")[1])  # in case LLM generate observation key which it isn't supposed to
     
-        Family-Owned Legacy: Produced by Colgin Cellars, a renowned Napa Valley family winery, this vintage reflects their commitment to quality and tradition. While not a limited-edition release, it's a highly regarded, consistently excellent Cabernet Sauvignon. 
+    response = strip(response)
+    @show response
 
-        Gift-Ready & Affordable: Priced at USD144 (well under your USD250 budget), it comes in a sleek, gift-ready box—perfect for impressing friends or loved ones. 
+    responsedict = nothing
-        
+    if occursin(requiredKeys[2], response) 
-        Why I Recommend It: It perfectly balances your desire for bold fruit, oak, and a presentable format without sacrificing quality. If you're curious about alternatives, the 2017 Hunter Glenn Cabernet (also USD159) shares similar intensity but lacks specific tasting notes. However, the 2014 Tychson Hill is a more complete match for your criteria. Enjoy your selection!"""
+      try
+        _responsedict = JSON.parse(response)
+        responsedict = GeneralUtils.dictify(_responsedict; keytype=String, sort_order=requiredKeys)
+      catch
+        println("\nERROR YiemAgent generatechat() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        continue
+      end
 
+    # fall back to normal text because LLM default to natural chat when it didn't use action_call
+    else  
+      try
+        responsedict = OrderedDict(
+          "plan"=> "I will talk to the user",
+          "action_name"=> "CHAT_BOX",
+          "action_input"=> response[2:end-1]  # remove { } at the front and back that added by clean_json_response
+          )
+      catch e
+        println("\nERROR YiemAgent decisionMaker(). $e --(not qualify response)-> $response", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
         continue
       end
     end
 
-    result = responsedict["dialogue"]
+    # check whether all answer's key points are in responsedict
-    return result
+    ispass, errormsg = GeneralUtils.checkAgentResponse_JSON(responsedict, requiredKeys)
-  end
+    if !ispass
-  error("presentbox() failed to generate a response")
+      errornote = errormsg
+      println("\nERROR YiemAgent generatechat() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      continue
     end
 
-# function endconversation(a::sommelier, thoughtDict; maxattempt::Integer=10)
+    # if responsedict["action_name"] ∉ ["CHAT_BOX", "CHECK_WINE", "PRESENT_WINE_GUIDELINE", "END_CONVER_GUIDELINE"]
-#   text = 
+    #   errornote = "Your previous attempt didn't use the given functions"
-#   """
+    #   println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)--> $(responsedict["action_name"])", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#   ---
-  
-#   """
-#   requiredKeys = ["dialogue"]
-
-#   system_msg = Dict(
-#     "role" => "system",
-#     "content" => [
-#       Dict("type" => "text", "text" => systemmsg),
-#     ]
-#   )
-
-#   for attempt in 1:maxattempt
-
-#     unformatPrompt =
-#       [
-#         Dict("name" => "system", "text" => systemmsg),
-#       ]
-
-#     # put in model format
-#     prompt = GeneralUtils.formatLLMtext(unformatPrompt, a.llmFormatName)
-#     # add info
-#     prompt = prompt * context
-
-#     response = a.context.text2textInstructLLM(prompt; senderId=a.id)
-#     response = GeneralUtils.deFormatLLMtext(response, a.llmFormatName)
-#     response = GeneralUtils.remove_french_accents(response)
-#     # response = replace(response, '$'=>"USD")
-#     think, response = GeneralUtils.extractthink(response)
-    
-#     responsedict = nothing
-#     try
-#       responsedict = copy(JSON.parsefile(response))  
-#     catch
-#       println("\nERROR YiemAgent generatechat() failed to parse response: $response", @__FILE__, ":", @__LINE__, " $(Dates.now())")
     #   continue
     # end
 
-#     # check whether all answer's key points are in responsedict
+    # println("\nYiem decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#     ispass, errormsg = checkAgentResponse_JSON(responsedict, requiredKeys)
+    # pprintln(responsedict)
-#     if !ispass
-#       errornote = errormsg
-#       println("\nERROR YiemAgent generatechat() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-#       continue
-#     end
 
-#     # sometime the model response like this "here's how I would respond: ..."
+    return responsedict["action_input"]
-#     if occursin("respond:", response)
+  end
-#       errornote = "Your previous response contains 'response:' which is not allowed"
+  error("YiemAgent generatechat() failed to generate a thought ", response)
-#       println("\nERROR YiemAgent generatechat() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+end
-#       continue
-#     elseif occursin("Your thoughts:", response) || occursin("your thoughts:", response)
-#       errornote = "You don't need to put 'Your thoughts:' in your response"
-#       println("\nERROR YiemAgent generatechat() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue
-#     end
-#     response = GeneralUtils.remove_french_accents(response)
-#     response = replace(response, '*'=>"")
-#     response = replace(response, '$' => "USD")
-#     response = replace(response, '`' => "")
-#     response = replace(response, "<|eot_id|>"=>"")
-
-#     # 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["observation"] !== nothing && occursin(winename, event["observation"])
-#             isWineInEvent = true
-#             break
-#           end
-#         end
-#       end
-
-#       # then the agent is not supposed to recommend the wine
-#       if isWineInEvent == false
-#         errornote = "You recommended wines that are not in your inventory before. Please only recommend wines that you have previously found in your inventory."        
-#         println("\nERROR YiemAgent generatechat() $errornote $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#         continue
-#       end
-#     end
-#     result = responsedict["dialogue"]
-
-#     return result
-#   end
-#   error("generatechat failed to generate a response")
-# end
 
 
 function generatequestion(a, text2textInstructLLM::Function, timeline)::String

src/llmfunction.jl

@@ -1,10 +1,10 @@
 module llmfunction
 
-export  virtualWineUserChatbox, jsoncorrection, checkwine, # recommendbox,
+export  virtualWineUserChatbox, jsoncorrection, checkwine!, # recommendbox,
         virtualWineUserRecommendbox, userChatbox, userRecommendbox, extractWineAttributes_1,
         extractWineAttributes_2, paraphrase
 
-using HTTP, JSON, URIs, Random, PrettyPrinting, UUIDs, Dates, DataFrames
+using HTTP, JSON, URIs, Random, PrettyPrinting, UUIDs, Dates, DataFrames, DataStructures
 using GeneralUtils, SQLLLM
 using ..type, ..util
 
@@ -269,7 +269,7 @@ end
 # Arguments
   - `a::T1` 
     one of ChatAgent's agent.
-  - `input::T2`
+  - `thoughtdict::AbstractDict`
 # Return
   A JSON string of available wine
 
@@ -282,52 +282,13 @@ julia> result = checkinventory(agent, input)
 "{"wine 1": {\"Winery\": \"Pichon Baron\", \"wine name\": \"Pauillac (Grand Cru Classé)\", \"grape variety\": \"Cabernet Sauvignon\", \"year\": 2010, \"price\": \"125 USD\", \"stock ID\": \"ar-17\"}, }"
 ```
 """
-function checkwine(a::T1, input::T2; maxattempt::Int=3
+function checkwine!(a::T, thoughtdict::AbstractDict
-  ) where {T1<:agent, T2<:AbstractString}
+  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
 
-  println("\ncheckinventory order: $input ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+  println("\ncheckinventory order: $(thoughtdict["action_input"]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  wineattributes_1 = extractWineAttributes_1(a, input)
+  wineattributes_1 = extractWineAttributes_1(a, thoughtdict["action_input"])
-  wineattributes_2 = extractWineAttributes_2(a, input)
+  wineattributes_2 = extractWineAttributes_2(a, thoughtdict["action_input"])
 
-  # placeholder
-  # textresult = nothing
-  # rawresponse = nothing
-  
-  # for i in 1:maxattempt
-
-  #   #CHANGE if you want to add retailer name
-  #   # _inventoryquery = "retailer name: $(a.retailername), $wineattributes_1, $wineattributes_2"
-  #   _inventoryquery = "$wineattributes_1, $wineattributes_2"
-    
-  #   retrieve_attributes = ["winery", "wine_name", "wine_id", "vintage", "region", "country", "wine_type", "grape", "serving_temperature", "sweetness", "intensity", "tannin", "acidity", "tasting_notes", "price", "currency"]
-  #   inventoryquery = "Retrieves $retrieve_attributes of wines that match the following criteria - {$_inventoryquery}"
-  #   println("\ncheckinventory input: $inventoryquery ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  #   # add suppport for similarSQLVectorDB
-  #   textresult, result_raw = SQLLLM.query(
-  #                         inventoryquery, 
-  #                         a.context.executeSQL, 
-  #                         a.context.text2textInstructLLM;
-  #                         insertSQLVectorDB=a.context.insertSQLVectorDB, 
-  #                         similarSQLVectorDB=a.context.similarSQLVectorDB,
-  #                         llmFormatName="qwen3")
-
-  #   # check if all of retrieve_attributes appears in textresult
-  #   isin = [occursin(x, textresult) for x in retrieve_attributes]
-  #   # check if rawresponse type is DataFrame so that I can check for column
-  #   if typeof(result_raw) == DataFrame && 
-  #     !occursin("The resulting table has 0 row", textresult) &&
-  #     !all(isin)
-      
-  #     errornote = "Not all of $retrieve_attributes appear in search result"
-  #     println("\nERROR YiemAgent checkwine() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  #     continue
-  #   else
-  #     break
-  #   end
-  # end
-
-  #CHANGE if you want to add retailer name
-    # _inventoryquery = "retailer name: $(a.retailername), $wineattributes_1, $wineattributes_2"
   retrieve_attributes = ["winery", "wine_name", "wine_id", "vintage", "region", "country", "wine_type", "grape", "serving_temperature", "sweetness", "intensity", "tannin", "acidity", "tasting_notes", "price", "currency"]
   _inventoryquery = "$wineattributes_1, $wineattributes_2"
   inventoryquery = "Retrieves $retrieve_attributes of wines that match the following criteria - {$_inventoryquery}"
@@ -340,15 +301,11 @@ function checkwine(a::T1, input::T2; maxattempt::Int=3
                         insertSQLVectorDB=a.context.insertSQLVectorDB, 
                         similarSQLVectorDB=a.context.similarSQLVectorDB,
                         llmFormatName="qwen3")
-  # println("\n--- YiemAgent checkwine() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+  thoughtdict["action_result"] = textresult
-  # println(textresult)
-  # println(result_raw)
-  # println("---")
 
-  return (result_str=textresult, result_raw=result_raw, success=true, errormsg=nothing)
+  return (thoughtdict=thoughtdict, result_raw=result_raw)
 end
 
-
 """
 
 # Arguments

src/type.jl

@@ -223,10 +223,9 @@ function sommelier(
     context,
     llmFormatName
     )
-
   systemmsg = 
     """
-    <store_policy>
+    # store_policy
       - Generally speaking, the store inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know exactly until you check your inventory.
       - If you found wines in the store's database, they are in stock.
       - You can only recommend wines that are currently in our inventory
@@ -238,8 +237,8 @@ function sommelier(
       - Spicy foods should be paired only with light red wines.
       - We do not sell organic, sustainable, gluten-free, and sulfite-free wine. Inform the user imediately if they are looking for these types of wines. Do not sell our wines as such.
       - Gift box, gift card, and custom messages are available. Inform the user to contact our sales team.
-    </store_policy>
+
-    <store_guidelines>
+    # store_guidelines
       - Greeting the customer warmly by ask them how could you help. Do not ask any other questions during this greeting.
       - Customer may provide images for you to look up.
       - Encourage the customer to explore different options and try new things.
@@ -247,47 +246,46 @@ function sommelier(
       - Your store carries only wine.
       - Vintage 0 means non-vintage.
       - Start searching the database as broadly as possible within the given information boundary to maximize the chances of finding. Avoid unnecessary parameters unless specified by the user. Refine the search subsequently.
-    </store_guidelines>
+
-    <situation>
+    # situation
-      Your customer is coming into the store
+      You are having conversation with a customer.
-    </situation>
+
-    <your role>
+    # your role
-      Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store. You are working under your mentor supervision.
+      Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store.
-    </your role>
+
-    <objective>
+    # objective
-      1) Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
+      - Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
-      2) Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
+      - Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
-    </objective>
+
-    <your responsibility includes>
+    # your responsibility includes
-      1) According to the store's policy and guidelines, make an informed decision about what you need to do to achieve the objective
+      - According to the store's policy and guidelines, and make an informed decision about what available_actions you need to use to achieve the objective.
-      2) Keep the conversation with the customer going smoothly
+      - Keep the conversation with the customer going smoothly
-      2) Obey your mentor's suggestions.
+
-    </your responsibility includes>
+    # your responsibility does NOT includes
-    <your responsibility does NOT includes>
+      - 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.
-      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.
+      - Processing sales orders or engaging in any other sales-related activities. 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.
+      - 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.
-      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.
+
-    </your responsibility does NOT includes>
+    # you should then respond to the user with interleaving plan, action_name, action_input
-    <you should then respond to the user with interleaving plan, action_name, action_input>
+      1) **plan**, Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
-      1) plan: Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
+      2) **action_name**, (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
-      2) action_name: (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
+      3) **action_input**, The input to the action you are about to perform according to your plan.
-      3) action_input: The input to the action you are about to perform according to your plan.
       After the action is executed you gets "action_result". It is the output from the action you selected.
-    </you should then respond to the user with interleaving plan, action_name, action_input>
+
-    <you should only respond in JSON format as described below>
+    # you should only respond in JSON format as described below
       "plan": "...",
       "action_name": "...",
       "action_input": "..."
-    </you should only respond in JSON format as described below>
+
-    <available_actions>
+    # available actions
-      - CHAT_BOX which you can use to talk with the user.
+      **CHAT_BOX**, which you can use to talk with the user. The input is dialogue you want to chat with the user according to your plan.
-      - CHECK_WINE allows you to check information about wines you want in your inventory's database. The input is text that specify supported search criteria includeing: retailer_name, wine price, winery, name, vintage, region, country, type, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
+      **CHECK_WINE**, allows you to check information about wines you want in your inventory's database. The input is text that specify supported search criteria includeing: retailer_name, wine price, winery, name, vintage, region, country, type, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
         Example query 1: "Dry, full-bodied red wine from 1) region: Burgundy, country: France or 2) region: Tuscany, country: Italy. Grape varietal: Merlot or Syrah. price 100 to 1000 USD."
         Example query 2: "Red or white wine, medium tannin, price under 700 USD"
         Example query 3: "white wine, region: Tuscany or Bordeaux, country: Italy or France
-      - PRESENT_WINE_GUIDELINE which you can use to check the store guidelines about how to present wines you have found to the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
+      **WINE_PRESENTATION_GUIDELINE**, which you can use to check the store guidelines about how to present wines you have found to the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
-      - END_CONVER_GUIDELINE which you can use to check the store guidelines about how to end the conversation with the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
+      **END_CONVER_GUIDELINE**, which you can use to check the store guidelines about how to end the conversation with the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
-    </available_actions>
+
     """
     
   system_msg = Dict(