update

src/interface.jl

@@ -114,10 +114,10 @@ function decisionMaker(a::T1, state::T2)::String where {T1<:agent, T2<:AbstractD
 
   You should only respond in JSON format as describe below:
   {
-    "Thought_1": "reasoning",
-    "Thought_2": "reasoning",
+    "Thought_1": "reasoning 1",
+    "Thought_2": "reasoning 2",
     ...
-    "Thought_n": "reasoning",
+    "Thought_n": "reasoning n",
     "Action_1": {"name": "action to take", "input": "Action input"},
     "Observation_1": "result of the action"
   }
@@ -131,10 +131,10 @@ function decisionMaker(a::T1, state::T2)::String where {T1<:agent, T2<:AbstractD
     "Action_1": {"name": "chatbox", "input": "What will you use it for?"}
   }
   {
-    "Question": "I'm looking for a sedan.",
+    "Question": "I'm looking for a sedan with an automatic driving feature.",
     "Thought_1": "I have many types of sedans in my inventory, each with diverse features.",
-    "Thought_2": "It would be easier to make a recommendation if I knew what feature the user is looking for. I should ask the user.",
-    "Action_1": {"name": "chatbox", "input": "Do you have any specific feature in mind?"}
+    "Thought_2": "But there is only 1 car that has the feature customer wanted.",
+    "Action_1": {"name": "finish", "input": "I recommend a Tesla model Y. It has your requested feature and much more."}
   }
 
   $reflect
@@ -304,18 +304,18 @@ function conversation(a::T, userinput::Dict) where {T<:agent}
   
     
     else    #[PENDING] new thinking 
-      initialState = Dict(
+      initialState = Dict{Symbol, Any}(
 
         # deepcopy the info to prevent modifying the info unintentionally during MCTS planning
         :customerinfo=> deepcopy(a.keywordinfo[:customerinfo]),
         :storeinfo=> deepcopy(a.keywordinfo[:storeinfo]),  
 
-        :thoughtHistory=> Dict{Symbol, Any}( # contain question, thought_1, action_1, observation_1, thought_2, ...
+        :thoughtHistory=> OrderedDict{Symbol, Any}( # contain question, thought_1, action_1, observation_1, thought_2, ...
           :Question=> userinput[:text],
           )  
         )
       bestplan = runMCTS(a, initialState, decisionMaker, stateValueEstimator, reflector,
-                        isterminal, 3, 10, 1000, 1.0)
+                        isterminal, 2, 10, 1000, 1.0)
       error("---> bestplan")
 
       # actor loop(bestplan)

src/llmfunction.jl

@@ -1,6 +1,6 @@
 module llmfunction
 
-# export wikisearch, winestock, askbox
+export virtualWineCustomerChatbox
 
 using HTTP, JSON3, URIs, Random
 using GeneralUtils
@@ -63,10 +63,6 @@ end
 julia> 
 ```
 
-# TODO
-  - [x] update docstring
-  - [TESTING] implement the function
-
 # Signature
 """
 function virtualWineCustomerChatbox(a::T1, input::T2)::String where {T1<:agent, T2<:AbstractString}
@@ -89,6 +85,7 @@ function virtualWineCustomerChatbox(a::T1, input::T2)::String where {T1<:agent,
     receiverName= "virtualWineCustomer",
     mqttBroker= a.config[:mqttServerInfo][:broker],
     mqttBrokerPort= a.config[:mqttServerInfo][:port],
+    msgId = "dummyid" #CHANGE remove after testing finished
   )
 
   outgoingMsg = Dict(

src/mcts.jl

@@ -5,7 +5,7 @@
 
 module mcts
   
-export MCTSNode, runMCTS
+export MCTSNode, runMCTS, isleaf
 
 using Dates, UUIDs, DataStructures, JSON3, Random
 using GeneralUtils
@@ -18,8 +18,18 @@ using ..type, ..llmfunction
 # Arguments
   - `state::T`
     a state of a game. Can be a Dict or something else. 
-    For example:
-    state = Dict(
+  - `visits::Integer `
+    number of time the game visits this state
+  - `stateValue::Float64`
+    state value
+  - `children::Dict{T, MCTSNode}`
+    children node
+
+# Return
+  - `nothing`
+# Example
+```jldoctest
+julia> state = Dict(
         :info=> Dict(),           # keyword info
         :thoughtHistory=> Dict(
           :question=> _,
@@ -30,22 +40,8 @@ using ..type, ..llmfunction
           ...
           )  
         )
-  - `visits::Integer `
-    number of time the game visits this state
-  - `stateValue::Float64`
-    state value
-
-# Return
-
-# Example
-```jldoctest
-julia> 
 ```
 
-# TODO
-  [] update docstring
-  [x] implement the function
-
 # Signature
 """
 struct MCTSNode{T<:AbstractDict}
@@ -131,14 +127,15 @@ function expand(a::T1, node::MCTSNode, state::T2, decisionMaker::Function, state
                           )
       :Observation_1 => ""
     """
-
+    @show state
+    @show thoughtDict
     newstate = MCTStransition(a, node.state, thoughtDict)  #[] Implement your transition function
     
     if newstate ∉ keys(node.children)
-        node.children[newstate] = MCTSNode(newstate, 0, 0.0, Dict{T, MCTSNode}())
+      statetype = typeof(state)
+      node.children[newstate] = MCTSNode(newstate, 0, 0.0, Dict{statetype, MCTSNode}())
     end
   end 
-  error("--> expand")  
 end
 
 """
@@ -208,7 +205,7 @@ end
     one of YiemAgent's agent
   - `state::T2`
     current game state
-  - `thoughtDict::T2`
+  - `thoughtDict::T3`
     contain Thought, Action, Observation
 
 # Return
@@ -217,26 +214,32 @@ end
 
 # Example
 ```jldoctest
+julia> state = Dict{Symbol, Dict{Symbol, Any}}(
+          :thoughtHistory => Dict(:Question => "Hello, I want to buy a bottle of wine."), 
+          :storeinfo => Dict(), 
+          :customerinfo => Dict()
+          )
 julia>  thoughtDict = Dict(
-  :Question=> "I want to buy a bottle of wine."
-  :Thought_1=>  "The customer wants to buy a bottle of wine. This is a good start!",
+          :Question=> "I want to buy a bottle of wine.",
+          :Thought_1=>  "The customer wants to buy a bottle of wine.",
           :Action_1=>   Dict{Symbol, Any}(
                           :name=>"Chatbox", 
-                  :input=>"What occasion are you buying the wine for?"
+                          :input=>"What occasion are you buying the wine for?",
                           ),
           :Observation_1 => ""
           )
 ```
 
 # TODO
-  - [x] update docstring
-  - [TESTING] implement the function
+  - [] update docstring
+  - [PENDING] add other actions
 
 # Signature
 """
-function MCTStransition(a::T1, state::T2, thoughtDict::T2)::AbstractDict where {T1<:agent, T2<:AbstractDict}
-  latestThoughtKey, latestindice = GeneralUtils.findHighestIndexKey(thoughtDict, "Thought")
-  latestActionKey = GeneralUtils.findHighestIndexKey(thoughtDict, "Action")
+function MCTStransition(a::T1, state::T2, 
+            thoughtDict::T3)::AbstractDict where {T1<:agent, T2<:AbstractDict, T3<:AbstractDict}
+  latestThoughtKey, _ = GeneralUtils.findHighestIndexKey(thoughtDict, "Thought")
+  latestActionKey, latestActionIndice = GeneralUtils.findHighestIndexKey(thoughtDict, "Action")
   _action = thoughtDict[latestActionKey]
   actionname = _action[:name]
   actioninput = _action[:input]
@@ -244,7 +247,7 @@ function MCTStransition(a::T1, state::T2, thoughtDict::T2)::AbstractDict where {
   # map action and input() to llm function
   response =
   if actionname == "chatbox"
-    virtualWineCustomerChatbox(a, actioninput)  # user virtu
+    virtualWineCustomerChatbox(a, actioninput)  # virtual customer
   elseif actionname == "winestock"
 
   elseif actionname == "finish"
@@ -257,28 +260,38 @@ function MCTStransition(a::T1, state::T2, thoughtDict::T2)::AbstractDict where {
   newstate = deepcopy(state)
   newstate[:thoughtHistory][latestThoughtKey] = thoughtDict[latestThoughtKey]
   newstate[:thoughtHistory][latestActionKey] = thoughtDict[latestActionKey]
-  latestObservationKey = Symbol("Observation_$(latestindice)")
+  latestObservationKey = Symbol("Observation_$(latestActionIndice)")
   newstate[:thoughtHistory][latestObservationKey] = response
 
-
-  error("--> transition")
   return newstate
 end
 
-"""
+
+""" Determine whether a node is a leaf node of a search tree.
 
 # Arguments
-  
+  - `node::MCTSNode`
+    a search tree node
 # Return
-
+  - `result::Bool`
+    true if it is a leaf node, false otherwise.
 # Example
 ```jldoctest
-julia> 
-```
+julia> using Revise
+julia> using YiemAgent, DataStructures
+julia> initialState = Dict{Symbol, Any}(
+          :customerinfo=> Dict{Symbol, Any}(),
+          :storeinfo=> Dict{Symbol, Any}(),  
 
-# TODO
-  - [] update docstring
-  - [x] implement the function
+          :thoughtHistory=> OrderedDict{Symbol, Any}(
+            :Question=> "How are you?",
+            )  
+          )
+julia> statetype = typeof(initialState)
+julia> root = YiemAgent.MCTSNode(initialState, 0, 0.0, Dict{statetype, YiemAgent.MCTSNode}())
+julia> YiemAgent.isleaf(root)
+true
+```
 
 # Signature
 """
@@ -366,13 +379,14 @@ function runMCTS(
     end
 
     expand(a, node, node.state, decisionMaker, stateValueEstimator, n=n)
-    error("---> runMCTS")
+    
     leaf_node = node.children[node.state] # mark leaf node
     reward = simulate(leaf_node.state, maxDepth)
     backpropagate(leaf_node, reward)
   end
 
   best_child_state = argmax([child.total_reward / child.visits for child in values(root.children)])
+  error("---> runMCTS")
   return best_child_state
 end
 

test/runtest.jl

@@ -55,7 +55,7 @@ tools=Dict( # update input format
     receiveInternalMsgChannel,
     agentConfig, 
     name= "assistant",
-    id= "randomSessionID",  # agent instance id
+    id= "testingSessionID",  # agent instance id
     tools=tools,
   )