update

src/interface.jl

@@ -250,8 +250,7 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
           error("DecisionMaker has more than one key per categories")
         end
       end
-      println("--> SQLLLM decisionMaker() ", @__FILE__, " ", @__LINE__)
-      pprintln(Dict(responsedict))
+
       return responsedict
     catch e
       io = IOBuffer()
@@ -499,7 +498,7 @@ julia>
 function evaluator(state::T1, text2textInstructLLM::Function;
   addSQLVectorDB::Union{Function, Nothing}=nothing
   ) where {T1<:AbstractDict}
-  println("Evaluating state", @__FILE__, " ", @__LINE__)
+  
   # systemmsg = 
   # """
   # You are a helpful assistant that analyzes agent's trajectories to find solutions and observations (i.e., the results of actions) to answer the user's questions.
@@ -737,14 +736,16 @@ function evaluator(state::T1, text2textInstructLLM::Function;
       # mark as terminal state when the answer is achieved
       if accepted_as_answer == "Yes"
         state[:isterminal] = true
-        state[:reward] = 1
+
+        # user score as reward because different answers hold different value for the user.
+        state[:reward] = responsedict[:score] 
 
         #add to vectorDB
         if addSQLVectorDB !== nothing
           addSQLVectorDB(state)
         end
       end
-      println("--> 5 Evaluator ", @__FILE__, " ", @__LINE__)
+      println("~~~ 5 Evaluator() ", @__FILE__, " ", @__LINE__)
       pprintln(Dict(responsedict))
 
       return responsedict[:score]
@@ -953,7 +954,7 @@ julia> state = Dict(
 
 # TODO
   - [] add embedding of newstate and store in newstate[:embedding]
-
+  - [WORKING] should getdata() return isterminal?
 # Signature
 """
 function transition(state::T, args::NamedTuple
@@ -992,17 +993,13 @@ function transition(state::T, args::NamedTuple
     else
       error("undefined LLM function. Requesting $actionname")
     end
-  
   # this section allow LLM functions above to have different return values.
-  result = haskey(response, :result) ? response[:result] : nothing
+  success::Bool = haskey(response, :success) ? response[:success] : false
+  result = success ? response[:result] : response[:errormsg]
   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
-  
   newNodeKey, newstate = makeNewState(state, thoughtDict, JSON3.write(result), select, reward, isterminal)
-  println("SQLLLM transition() 1 ", @__FILE__, " ", @__LINE__)
   progressvalue::Integer = evaluatorF(newstate, text2textInstructLLM; 
                                       addSQLVectorDB=addSQLVectorDBF)
 
@@ -1090,14 +1087,14 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
   addSQLVectorDB::Union{Function, Nothing}=nothing, 
   querySQLVectorDB::Union{Function, Nothing}=nothing
   )::String where {T<:AbstractString}
-  #[WORKING] add extra context for Evaluator so that it knows the observation is from seaching a database
+  # add extra context for Evaluator so that it knows the observation is from seaching a database
   query = "Search the database for {$query}"
   initialstate = Dict{Symbol, Any}(
     :reward=> 0,
     :isterminal=> false,
     :evaluation=> "None",
-    :suggestion=> "None",
     :evaluationscore=> 0,
+    :suggestion=> "None",
     :accepted_as_answer=> "No",
     :lesson=> nothing,
 
@@ -1121,10 +1118,13 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
                     addSQLVectorDB=addSQLVectorDB,
                   )
 
-  _, result = LLMMCTS.runMCTS(initialstate, transition, transitionargs;
-                          totalsample=1, maxdepth=3, maxiterations=1, explorationweight=1.0)
-  latestKey, _ = GeneralUtils.findHighestIndexKey(result[:thoughtHistory], "observation")
-  resulttext = result[:thoughtHistory][latestKey]
+  earlystop(state) = state[:reward] >= 8 ? true : false
+
+  _, resultState = LLMMCTS.runMCTS(initialstate, transition, transitionargs;
+                          totalsample=1, maxdepth=3, maxiterations=3, explorationweight=1.0,
+                          earlystop=earlystop)
+  latestKey, _ = GeneralUtils.findHighestIndexKey(resultState[:thoughtHistory], "observation")
+  resulttext = resultState[:thoughtHistory][latestKey]
 
   return resulttext
 end