update

src/interface.jl

@@ -76,35 +76,7 @@ OrderedDict{String, Any} with 4 entries:
 function decisionMaker(a::T; recentevents::Integer=20, maxattempt=3
   ) where {T<:agent}
   @info "YiemAgent decisionMaker() start " @__LINE__
-  # 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)
-
-  requiredKeys = ["plan", "action_name", "action_input"]
   context = 
     """
     <internal_context_for_assistant>
@@ -173,7 +145,7 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=3
           "properties"=> Dict(
             "think"=> Dict(
               "type"=> "string",
-              "description"=> "your thought"
+              "description"=> "Your step-by-step reasoning process. Explain why you are choosing this action."
               ),
             "action_name"=> Dict(
               "type"=> "string",
@@ -181,8 +153,13 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=3
               "description"=> "one of the available actions"
               ),
             "action_input"=> Dict(
-              "type"=> "string",
-              "description"=> "action's input"
+              "type"=> "object",
+              "properties"=> Dict(
+                "dialogue"=> Dict("type"=> "string", "description"=> "for CHAT_BOX"),
+                "query"=> Dict("type"=> "string", "description"=> "for SEARCH_WINE_DATABASE"),
+                "present_guide"=> Dict("type"=> "null", "description"=> "for WINE_PRESENTATION_GUIDELINE"),
+                "endconv_guide"=> Dict("type"=> "null", "description"=> "for END_CONVER_GUIDELINE"),
+                )
               ),
           ),
           "required"=> ["think", "action_name", "action_input"],
@@ -208,6 +185,13 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=3
     end
 
     responsedict = JSON.parse(response)
+    # println("\nYiem decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    # pprintln(responsedict)
+
+    # for decision that use a single action. make it simpler
+    for (k, v) in responsedict["action_input"]
+      responsedict["action_input"] = v
+    end
 
     if occursin("CHAT_BOX", responsedict["action_input"])
       println("\nERROR YiemAgent decisionMaker() --(not qualify response)> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
@@ -220,8 +204,8 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=3
       continue
     end
 
-    # println("\nYiem decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    # pprintln(responsedict)
+    println("\nYiem decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    pprintln(responsedict)
     @info "YiemAgent decisionMaker() end " @__LINE__
     return responsedict
   end

src/type.jl

@@ -201,18 +201,18 @@ function sommelier(
 
     # you should then respond to the user with interleaving think, action_name, action_input in JSON format
       1) "think", Your step-by-step reasoning process. Explain why you are choosing this action.
-      2) "action_name", Can be one of the available actions
+      2) "action_name", Can be one of the available actions. Typically corresponds to the execution of the first step in your thought
       3) "action_input", The input to the action you are about to perform.
       After the action is executed you gets "action_result". It is the output from the action you selected.
 
     # available actions
-      "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.
+      "CHAT_BOX", which you can use to talk with the user. The input is dialogue you want to say with the user.
       "SEARCH_WINE_DATABASE", allows you to search information about wines you want in your inventory's database. The input is strictly supported search term including: retailer_name, wine price, winery, name, vintage, region, country, type of wine, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
         Example query 1: "Dry, full-bodied red wine from Burgundy, France. Grape varietal could be 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 from Tuscany, Italy or Bordeaux, France
-      "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.
+      "WINE_PRESENTATION_GUIDELINE", store guidelines about how to present wines to the user appropriately. The input is "null" keyword.
+      "END_CONVER_GUIDELINE", store guidelines about how to end the conversation with the user appropriately. The input is "null" keyword.
     """
     
   system_msg = Dict(