update

src/interface.jl

@@ -217,7 +217,7 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
 
     # put in model format
     prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
-    response = text2textInstructLLM(prompt)
+    response = text2textInstructLLM(prompt) #BUG response === nothing
 
     # LLM tends to generate observation given that it is in the input
     response = 
@@ -812,7 +812,7 @@ function evaluator(state::T1, text2textInstructLLM::Function;
       question: Find cars with a stereo.
       observation: There are 1 cars in the table. 1) brand: Toyota, model: yaris, color: black.
   4) Score: Correctness score s where s is a single integer between 0 to 9. 
-      Score guideline:
+      For example:
       - 0 indicates that both the trajectory is incorrect, failed or errors and the observation is incorrect or failed
       - 4 indicates that the trajectory are correct but the observation is incorrect or failed
       - 8 indicates that both the trajectory are correct, and the observation's content directly answers the question.
@@ -1034,7 +1034,7 @@ function reflector(config::T1, state::T2)::String where {T1<:AbstractDict, T2<:A
       )
     )
 
-  for attempt in 1:5
+  for attempt in 1:10
     try
       response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
       _responseJsonStr = response[:response][:text]
@@ -1280,7 +1280,8 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
   _, _, resultState = LLMMCTS.runMCTS(initialstate, transition, transitionargs;
                       horizontalSampleExpansionPhase=3, 
                       horizontalSampleSimulationPhase=2,
-                      maxSimulationDepth=5, maxiterations=2, 
+                      maxSimulationDepth=10, 
+                      maxiterations=1, 
                       explorationweight=1.0,
                       earlystop=earlystop,
                       saveSimulatedNode=true)