update

src/interface.jl

@@ -121,7 +121,8 @@ function decisionMaker(a::T1, state::T2) where {T1<:agent, T2<:AbstractDict}
     "Question": "I would like to buy a sedan",
     "Thought_1": "I have many car in my inventory suitable for several usage scenarios",
     "Thought_2": "It would be better if I know what the user intend to do with his car",
-    "Thought_3": "
+    "Thought_3": "I will ask the user what is the intended usecase",
+    "Action_1": "Chatbox[What will you use it for?]"
   }
 
   $reflect
@@ -131,7 +132,28 @@ function decisionMaker(a::T1, state::T2) where {T1<:agent, T2<:AbstractDict}
 
   prompt =  formatLLMtext_llama3instruct("system", _prompt)
 
-  thought = iterativeprompting(a, prompt, syntaxcheck_json)
+  msgMeta = GeneralUtils.generate_msgMeta(
+    a.config[:externalService][:text2textinstruct],
+    senderName= "iterativeprompting",
+    senderId= a.id,
+    receiverName= "text2textinstruct",
+    mqttBroker= a.config[:mqttServerInfo][:broker],
+    mqttBrokerPort= a.config[:mqttServerInfo][:port],
+  )
+
+  outgoingMsg = Dict(
+    :msgMeta=> msgMeta,
+    :payload=> Dict(
+      :text=> prompt,
+    )
+  )
+  @show outgoingMsg
+  thought = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
+
+
+
+
+  # thought = iterativeprompting(a, prompt, syntaxcheck_json)
 
 
 
@@ -310,7 +332,7 @@ function conversation(a::T, userinput::Dict) where {T<:agent}
         )  
       )
       bestplan = runMCTS(a, initialState, decisionMaker, stateValueEstimator, reflector,
-                        3, 10, 1000, 1.0)
+                        isterminal, 3, 10, 1000, 1.0)
       error("---> bestplan")
 
       # actor loop(bestplan)

src/mcts.jl

@@ -345,7 +345,8 @@ function runMCTS(
   isterminal::Function,
   n::Integer, 
   maxDepth::Integer, 
-  maxIterations::Integer, w::Float64) where {T1<:agent}
+  maxIterations::Integer, 
+  w::Float64) where {T1<:agent}
   
   statetype = typeof(initialState)
   root = MCTSNode(initialState, 0, 0.0, Dict{statetype, MCTSNode}())