update

src/interface.jl

@@ -91,10 +91,17 @@ function decisionMaker(a::T1, state::T2)::Dict{Symbol, Any} where {T1<:agent, T2
   $(JSON3.write(state[:storeinfo]))
   """
 
+  lessonDict = copy(JSON3.read("lesson.json"))
+
   lesson =
-  if isempty(a.lesson)
+  if isempty(lessonDict)
     ""
   else
+    lessons = Dict{Symbol, 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. 
@@ -102,7 +109,7 @@ function decisionMaker(a::T1, state::T2)::Dict{Symbol, Any} where {T1<:agent, T2
     did previously. Use them to improve your strategy to help the user.
 
     Here are some lessons:
-    $(JSON3.write(a.lesson[:lesson_1][:lesson]))
+    $(JSON3.write(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. 
@@ -211,8 +218,14 @@ function decisionMaker(a::T1, state::T2)::Dict{Symbol, Any} where {T1<:agent, T2
 
         # check if dict has all required value
         dummya::AbstractString = thoughtDict[:thought]
-        dummyb::AbstractString = thoughtDict[:action][:name]
-        dummyc::AbstractString = thoughtDict[:action][:input]
+        actionname::AbstractString = thoughtDict[:action][:name]
+        actioninput::AbstractString = thoughtDict[:action][:input]
+
+        if actionname ∈ ["winestock", "chatbox", "recommendbox"]
+          # LLM use available function
+        else
+          error("DecisionMaker use wrong function")
+        end
 
         return thoughtDict
       catch e

src/mcts.jl

@@ -136,31 +136,84 @@ function expand(a::T1, node::MCTSNode, decisionMaker::Function,
                 progressValueEstimator::Function, reflector::Function; n::Integer=3) where {T1<:agent}
 
   nthSample = 0
-  while nthSample < n
-
-    thoughtDict = decisionMaker(a, node.state)
-
-    newNodeKey, newstate, reward, isterminalstate = 
-                                    MCTStransition(a, node.state, thoughtDict)
-    
-    # add progressValueEstimator
-    stateevaluation, statevalue = progressValueEstimator(a, newstate)
-
-    if reward < 0
-      pprint(newstate[:thoughtHistory])
-      newstate[:evaluation] = stateevaluation
-      newstate[:lesson] = reflector(a, newstate)
-      a.lesson[:lesson_1] = deepcopy(newstate)
-      print("---> reflector()")
-    end
-
-    if newNodeKey ∉ keys(node.children)
-      node.children[newNodeKey] = MCTSNode(newNodeKey, newstate, 0, statevalue, 
-                          reward, isterminalstate, node, Dict{String, MCTSNode}())
-    end
+  while true
     nthSample += 1
+    if nthSample <= n
+      println("---> expand() sample $nthSample")
+      thoughtDict = decisionMaker(a, node.state)
+
+      newNodeKey, newstate, reward, isterminalstate = 
+                                      MCTStransition(a, node.state, thoughtDict)
+      
+      # add progressValueEstimator
+      stateevaluation, statevalue = progressValueEstimator(a, newstate)
+
+      if reward < 0
+        pprint(newstate[:thoughtHistory])
+        newstate[:evaluation] = stateevaluation
+        newstate[:lesson] = reflector(a, newstate)
+
+        # store new lesson for later use
+        lessonDict = copy(JSON3.read("lesson.json"))
+        latestLessonKey, latestLessonIndice = 
+          GeneralUtils.findHighestIndexKey(state[:thoughtHistory], "lesson")
+        nextIndice = latestLessonKey == :NA ? 1 : latestLessonIndice + 1
+        newLessonKey = Symbol("lesson_$(nextIndice)")
+        lessonDict[newLessonKey] = newstate
+        open("lesson.json", "w") do io
+          JSON3.pretty(io, lessonDict)
+        end
+        print("---> reflector()")
+      end
+
+      if newNodeKey ∉ keys(node.children)
+        node.children[newNodeKey] = MCTSNode(newNodeKey, newstate, 0, statevalue, 
+                            reward, isterminalstate, node, Dict{String, MCTSNode}())
+      end
+    else
+      break
+    end
   end
 end
+# function expand(a::T1, node::MCTSNode, decisionMaker::Function, 
+#                 progressValueEstimator::Function, reflector::Function; n::Integer=3) where {T1<:agent}
+
+#   nthSample = 0
+#   while nthSample <= n
+#     nthSample += 1
+#     println("---> expand() sample $nthSample")
+#     thoughtDict = decisionMaker(a, node.state)
+
+#     newNodeKey, newstate, reward, isterminalstate = 
+#                                     MCTStransition(a, node.state, thoughtDict)
+    
+#     # add progressValueEstimator
+#     stateevaluation, statevalue = progressValueEstimator(a, newstate)
+
+#     if reward < 0
+#       pprint(newstate[:thoughtHistory])
+#       newstate[:evaluation] = stateevaluation
+#       newstate[:lesson] = reflector(a, newstate)
+
+#       # store new lesson for later use
+#       lessonDict = copy(JSON3.read("lesson.json"))
+#       latestLessonKey, latestLessonIndice = 
+#         GeneralUtils.findHighestIndexKey(state[:thoughtHistory], "lesson")
+#       nextIndice = latestLessonKey == :NA ? 1 : latestLessonIndice + 1
+#       newLessonKey = Symbol("lesson_$(nextIndice)")
+#       lessonDict[newLessonKey] = newstate
+#       open("lesson.json", "w") do io
+#         JSON3.pretty(io, lessonDict)
+#       end
+#       print("---> reflector()")
+#     end
+
+#     if newNodeKey ∉ keys(node.children)
+#       node.children[newNodeKey] = MCTSNode(newNodeKey, newstate, 0, statevalue, 
+#                           reward, isterminalstate, node, Dict{String, MCTSNode}())
+#     end
+#   end
+# end
 
 
 
@@ -299,8 +352,8 @@ function MCTStransition(a::T1, state::T2, thoughtDict::T3
   newstate = deepcopy(state)
   newstate[:thoughtHistory][latestThoughtKey] = thoughtDict[:thought]
   newstate[:thoughtHistory][latestActionKey] = thoughtDict[:action]
-  latestObservationKey = Symbol("observation_$(nextIndice)")
-  newstate[:thoughtHistory][latestObservationKey] = response
+  newObservationKey = Symbol("observation_$(nextIndice)")
+  newstate[:thoughtHistory][newObservationKey] = response
   newstate[:reward] = reward
   newstate[:select] = select
   newstate[:isterminal] = isterminal

src/type.jl

@@ -101,7 +101,6 @@ julia> agent = YiemAgent.bsommelier(
     :customerinfo => Dict{Symbol, Any}(),
     :storeinfo => Dict{Symbol, Any}(),
     )
-  lesson::Dict{Symbol, Any} = Dict{Symbol, Any}()
   mctstree::Dict{Symbol, Any} = Dict{Symbol, Any}()
   
   # 1-historyPoint is in Dict{Symbol, Any} and compose of: 

test/test_1.jl

@@ -116,6 +116,7 @@ result = GeneralUtils.sendMqttMsg(outgoingMsg)
 
 
 
+
 outgoingMsg = Dict(
   :msgMeta=> msgMeta,
   :payload=> Dict(
@@ -144,20 +145,6 @@ result = GeneralUtils.sendMqttMsg(outgoingMsg)
 
 
 
-outgoingMsg = Dict(
-  :msgMeta=> msgMeta,
-  :payload=> Dict(
-    :text=> "I already told you I like Red wine. Why did you ask me about other wine type?",
-    :select=> nothing,
-    :reward=> -1,
-    :isterminal=> false,
-  )
-)
-result = GeneralUtils.sendMqttMsg(outgoingMsg)
-
-
-
-
 outgoingMsg = Dict(
   :msgMeta=> msgMeta,
   :payload=> Dict(
@@ -175,7 +162,7 @@ result = GeneralUtils.sendMqttMsg(outgoingMsg)
 outgoingMsg = Dict(
   :msgMeta=> msgMeta,
   :payload=> Dict(
-    :text=> "I didn't like the one you recommend. You like dry wine.",
+    :text=> "What are you saying. I don't understand.",
     :select=> nothing,
     :reward=> -1,
     :isterminal=> false,
@@ -187,4 +174,15 @@ result = GeneralUtils.sendMqttMsg(outgoingMsg)
 
 
 
+outgoingMsg = Dict(
+  :msgMeta=> msgMeta,
+  :payload=> Dict(
+    :text=> "I like dry wine with medium acidity.",
+    :select=> nothing,
+    :reward=> 0,
+    :isterminal=> false,
+  )
+)
+result = GeneralUtils.sendMqttMsg(outgoingMsg)
+