update

src/interface.jl

@@ -1,6 +1,6 @@
 module interface
     
-export addNewMessage, conversation, decisionMaker
+export addNewMessage, conversation, decisionMaker, isterminal
 
 using JSON3, DataStructures, Dates, UUIDs, HTTP, Random, MQTTClient
 using GeneralUtils
@@ -63,25 +63,70 @@ using ..type, ..util, ..llmfunction, ..mcts
 function decisionMaker(a::T1, state::T2) where {T1<:agent, T2<:AbstractDict}
   customerinfo = 
   """
-    I will give you the following information about customer:
-    $(JSON3.write(state[:customerinfo]))
+  I will give you the following information about customer:
+  $(JSON3.write(state[:customerinfo]))
   """
 
   storeinfo =
   """
-    I will give you the following information about your store:
-    $(JSON3.write(state[:storeinfo]))
+  I will give you the following information about your store:
+  $(JSON3.write(state[:storeinfo]))
   """
 
+  reflect = ""
+  # """
+  # You have attempted to answer the following question before and failed. The following 
+  # reflection(s) give a plan to avoid failing to answer the question in the same way you did 
+  # previously. Use them to improve your strategy of correctly answering the given question.
+  # (trajectories)
+  # """
+
+
   #[WORKING] 
   _prompt = 
   """
-    You are a helpful sommelier working for a wine store. 
-    You helps users by searching wine that match the user preferences from your inventory.
+  You are a helpful sommelier working for a wine store. 
+  Your goal is to reccommend the best wine from your inventory that match the user preferences.
 
-    $customerinfo
+  $customerinfo
 
-    You must follow the 
+  You must follow the following criteria:
+  1) Get to know what occasion the user is buying wine for
+  2) Get to know what food the user will have with wine
+  3) Get to know how much the user willing to spend
+  4) Get to know type of wine the user is looking for 
+    e.g. Red, White, Sparkling, Rose, Dessert, Fortified
+  5) Get to know what wine characteristics the user is looking for 
+    e.g. tannin, sweetness, intensity, acidity
+  6) Check your inventory for the best wine that match the user preference
+
+  You should only respond with interleaving step-by-step Thought, Action, Observation steps. 
+  Thought can reason about the current situation, and Action can be three types:
+  1) Chatbox[text], which you can use to interact with the user.
+  2) Winestock[query], which you can use to find wine in your inventory.
+  3) Finish[answer], which returns your wine reccommendation to the user.
+
+  You should only respond in JSON format as describe below:
+  {
+    "Thought_1": "reasoning",
+    "Thought_2": "reasoning",
+    ...
+    "Thought_n": "reasoning",
+    "Action_1": "action to take",
+    "Observation_1": "result of the action"
+  }
+
+  Here are some examples:
+  {
+    "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": "
+  }
+
+  $reflect
+
+  $(JSON3.write(state[:thoughtHistory]))
   """
 
   prompt =  formatLLMtext_llama3instruct("system", _prompt)
@@ -89,6 +134,7 @@ function decisionMaker(a::T1, state::T2) where {T1<:agent, T2<:AbstractDict}
   thought = iterativeprompting(a, prompt, syntaxcheck_json)
 
 
+
   error("--> decisionMaker")
   return thought
 end
@@ -148,6 +194,35 @@ function reflector()
 end
 
 
+"""
+
+  Arguments\n
+  -----
+    
+  Return\n
+  -----
+
+  Example\n
+  -----
+  ```jldoctest
+  julia> 
+  ```
+
+  TODO\n
+  -----
+    [] update docstring
+    [] implement the function
+    [] implement RAG to pull similar experience
+
+  Signature\n
+  -----
+"""
+function isterminal()
+
+end
+
+
+
 
 """ Chat with llm.