diff --git a/Manifest.toml b/Manifest.toml
index 1a41a0e..3b7c046 100644
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -2,7 +2,7 @@
 
 julia_version = "1.12.6"
 manifest_format = "2.0"
-project_hash = "ec4f3941a75715b7ba32ddf816f49fe57098c82d"
+project_hash = "7f67bfadc362a449809bc58aae732499b3d18e7f"
 
 [[deps.Accessors]]
 deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "MacroTools"]
@@ -269,18 +269,18 @@ uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
 version = "1.11.0"
 
 [[deps.GeneralUtils]]
-deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "JSON", "NATS", "PrettyPrinting", "Random", "Revise", "SHA", "UUIDs"]
-git-tree-sha1 = "b172f75aa622507027cd269af2cc5f335e9821eb"
+deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "HTTP", "JSON", "NATS", "PrettyPrinting", "Random", "Revise", "SHA", "UUIDs"]
+git-tree-sha1 = "8e0ff2ce28b38f779883fddd2eb6fee3551194ac"
 repo-rev = "main"
 repo-url = "https://git.yiem.cc/ton/GeneralUtils"
 uuid = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
-version = "0.4.4"
+version = "0.4.8"
 
 [[deps.HTTP]]
 deps = ["Base64", "CodecZlib", "Dates", "EnumX", "PrecompileTools", "Random", "Reseau", "SHA", "URIs", "UUIDs", "Zlib_jll"]
-git-tree-sha1 = "e718a35dd7386ccd6bed64a1d84d661972404b99"
+git-tree-sha1 = "69343dd8afb1671b84c3aa2dda511238d0919a55"
 uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3"
-version = "2.5.1"
+version = "2.5.0"
 
 [[deps.HashArrayMappedTries]]
 git-tree-sha1 = "2eaa69a7cab70a52b9687c8bf950a5a93ec895ae"
diff --git a/Project.toml b/Project.toml
index a8742ca..a5194f4 100644
--- a/Project.toml
+++ b/Project.toml
@@ -24,5 +24,5 @@ UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 
 [compat]
 Dates = "1.11.0"
-GeneralUtils = "0.4.4"
+GeneralUtils = "0.4.8"
 JSON = "1.6.1"
diff --git a/src/interface.jl b/src/interface.jl
index 2891bf5..0803f2f 100644
--- a/src/interface.jl
+++ b/src/interface.jl
@@ -152,9 +152,9 @@ function decisionMaker(state::T1, text2textInstructLLM::Function, llmFormatName:
     responsedict = nothing
      try
       _responsedict = JSON.parse(response)
-      responsedict = GeneralUtils.dictify(_responsedict, keytype=String)
+      responsedict = GeneralUtils.dictify(_responsedict, keytype=String, sort_order=requiredKeys)
     catch
-      println("\nERROR decisionMaker() failed to parse response: $response", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      println("\nERROR decisionMaker() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
       continue
     end
 
@@ -162,7 +162,7 @@ function decisionMaker(state::T1, text2textInstructLLM::Function, llmFormatName:
     ispass, errormsg = GeneralUtils.checkAgentResponse_JSON(responsedict, requiredKeys)
     if !ispass
       errornote = errormsg
-      println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
       continue
     end
 
@@ -324,9 +324,9 @@ function evaluator(state::T1, text2textInstructLLM::Function, llmFormatName::Str
     responsedict = nothing
     try
       _responsedict = JSON.parse(response)
-      responsedict = GeneralUtils.dictify(_responsedict, keytype=String)
+      responsedict = GeneralUtils.dictify(_responsedict, keytype=String, sort_order=requiredKeys)
     catch
-      println("\nERROR SQLLLM evaluator() failed to parse response: $response", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      println("\nERROR SQLLLM evaluator() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
       continue
     end
 
@@ -334,7 +334,7 @@ function evaluator(state::T1, text2textInstructLLM::Function, llmFormatName::Str
     ispass, errormsg = GeneralUtils.checkAgentResponse_JSON(responsedict, requiredKeys)
     if !ispass
       errornote = errormsg
-      println("\nERROR SQLLLM evaluator() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      println("\nERROR SQLLLM evaluator() $errornote --(not qualify response)> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
       continue
     end
     
@@ -379,197 +379,7 @@ function evaluator(state::T1, text2textInstructLLM::Function, llmFormatName::Str
   end
   error("Evaluator failed to generate an evaluation, Response: \n$response\n<|End of error|>")
 end
-# function evaluator(state::T1, thoughtDict, text2textInstructLLM::Function, llmFormatName::String; 
-#   maxattempt=10
-#   ) where {T1<:AbstractDict}
 
-#   systemmsg = 
-#   """
-#   You are a helpful assistant that analyzes agent's trajectory to find solutions and observations (i.e., the results of actions) to answer the user's questions.
-
-#   Definitions:
-#   "question" is the user's question
-#   "plan" is agent's plan to complete the task from the current situation
-#   "action_name" is the name of the action taken, which can be one of the following functions:
-#       - RUNSQL, which you can use to execute SQL against the database. Action_input for this function must be a single SQL query to be executed against the database.
-#         For more effective text search, it's necessary to use case-insensitivity and the ILIKE operator.
-#         Do not wrap the SQL as it will be executed against the database directly and SQL must be ended with ';'.
-#   "action_input" is the input to the action
-#   "observation" is result of the preceding immediate action
-
-#   At each round of conversation, you will be given the following information:
-#   trajectory: A history of how you worked on the question chronologically
-#   evaluatee_context: The context that evaluatee use to make a decision
-
-#   You must follow the following guidelines:
-#   - When the search returns no result, validate whether the SQL query makes sense before accepting it as a valid answer.
-
-#   You should then respond to the user with:
-#   1) Trajectory_evaluation: Analyze the trajectory of a solution to answer the user's original question.
-#     - Evaluate the correctness of each section and the overall trajectory based on the given question.
-#     - Provide detailed reasoning and analysis, focusing on the latest thought, action, and observation.
-#     - Incomplete trajectory are acceptable if the thoughts and actions up to that point are correct, even if the final answer isn't reached.
-#     - Do not generate additional thoughts or actions.
-#   2) Answer_evaluation: 
-#     - Focus only on the matter mentioned in the question and comprehensively analyze how the latest observation's details addresses the question
-#   3) Accepted_as_answer: Decide whether the latest observation's content answers the question. Can be "yes" or "no"
-#     Bad example (The observation didn't answers the question):
-#       question: Find cars with 4 wheels.
-#       observation: There are an apple in the table.
-#     Good example (The observation answers the question): 
-#       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. 
-#       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 no results are returned.
-#       - 5 indicates that the trajectory are correct but the observation is incorrect or failed
-#       - 6 indicates that the trajectory are correct, but the observation's content doesn't directly answer the question
-#       - 8 indicates that both the trajectory are correct, and the observation's content directly answers the question.
-#       - 9 indicates a perfect perfomance. Both the trajectory are correct, and the observation's content directly answers the question, surpassing your expectations.
-#   5) Suggestion: what are the possible reason of this outcome, what can one learn from it and what suggestion can made?
-
-#   You should only respond in format as described below:
-#   Trajectory_evaluation: ...
-#   Answer_evaluation: ...
-#   Accepted_as_answer: ...
-#   Score: ...
-#   Suggestion: ...
-
-#   Let's begin!
-#   """
-#   #[WORKING] add what I should think --> this will be the think for decisionMaker()
-#   header = ["Trajectory_evaluation:", "Answer_evaluation:", "Accepted_as_answer:", "Score:", "Suggestion:"]
-#   dictkey = ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"]
-
-#   action_history = ""
-#   for (k, v) in state["action_history"]
-#     action_history *= "$k: $v\n" 
-#   end
-
-#   errornote = "N/A"
-#   for attempt in 1:maxattempt
-#       usermsg =
-#       """
-#       <trajectory>
-#       $action_history
-#       </trajectory>
-#       """
-#     context =
-#       """
-#       <context>
-#         <evaluatee_context>
-#         thoughtDict[:context]
-#         </evaluatee_context>
-#         P.S. $errornote
-#       </context>
-#     """
-
-#     unformatPrompt =
-#       [
-#         Dict(:name => "system", :text => systemmsg),
-#         Dict(:name => "user", :text => usermsg)
-#       ]
-#     # put in model format
-#     prompt = GeneralUtils.formatLLMtext(unformatPrompt, llmFormatName)
-#     # add info
-#     prompt = prompt * context
-
-#     response = text2textInstructLLM(prompt, modelsize="medium")
-#     response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
-#     think, response = GeneralUtils.extractthink(response)
-
-#     # sometime LLM output something like **Comprehension**: which is not expected
-#     response = replace(response, "**"=>"")
-#     response = replace(response, "***"=>"")
-
-#     # check whether response has all header
-#     detected_kw = GeneralUtils.detectKeywordVariation(header, response)
-#     missingkeys = [k for (k, v) in detected_kw if v === nothing]
-#     if !isempty(missingkeys)
-#       errornote = "$missingkeys are missing from your previous response"
-#       println("\nERROR SQLLLM extractContent_dataframe() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue
-#     elseif sum([length(i) for i in values(detected_kw)]) > length(header)
-#       errornote = "\nYour previous attempt has duplicated points according to the required response format"
-#       println("\nERROR SQLLLM extractContent_dataframe() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue
-#     end
-
-#     responsedict = GeneralUtils.textToDict(response, header; 
-#                                             dictKey=dictkey, symbolkey=false)
-    
-#     responsedict["score"] = responsedict["score"][1]  # some time "6\nThe trajectories are incomplete" is generated but I only need the number.
-#     try
-#       responsedict["score"] = parse(Int, responsedict["score"]) # convert string "5" into integer 5
-#     catch
-#       errornote = "Your previous attempt's score has wrong format"
-#       println("\nERROR SQLLLM evaluator() Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue
-#     end
-
-#     accepted_as_answer::AbstractString = responsedict["accepted_as_answer"]
-
-#     if accepted_as_answer ∉ ["yes", "no"]
-#       errornote = "Your previous attempt's accepted_as_answer has wrong format"
-#       println("\nERROR SQLLLM evaluator() Attempt $attempt/$maxattempt. $errornote --(not qualify response)--> $(responsedict["accepted_as_answer"]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue
-#     end
-
-#     # add to state here instead to in transition() because the latter causes julia extension crash (a bug in julia extension)
-#     state["evaluation"] = "$(responsedict["trajectory_evaluation"]) $(responsedict["answer_evaluation"])" 
-#     state["evaluationscore"] = responsedict["score"] 
-#     state["accepted_as_answer"] = responsedict["accepted_as_answer"]
-#     state["suggestion"] = responsedict["suggestion"]
-
-#     # mark as terminal state when the answer is achieved
-#     if accepted_as_answer ∈ ["Yes", "yes"] 
-
-#       # mark the state as terminal state because the evaluation say so.
-#       state["isterminal"] = true
-
-#       # evaluation score as reward because different answers hold different value for the user.
-#       state["reward"] = responsedict["score"]
-#     end
-
-#     println("\nSQLLLM evaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#     pprintln(Dict(responsedict))
-
-#     # # store for later training
-#     # responsedict[:action_history] = state[:action_history]
-#     # responsedict[:system] = systemmsg
-#     # responsedict[:usermsg] = usermsg
-#     # responsedict[:prompt] = prompt
-#     # responsedict[:context] = context
-#     # responsedict[:think] = think
-
-#     # # read sessionId
-#     # sessionid = JSON.parse("/appfolder/app/sessionid.json")
-#     # # save to filename ./log/decisionlog.txt
-#     # println("saving SQLLLM evaluator() to disk")
-#     # filename = "agent_evaluator_log_$(sessionid[:id]).json"
-#     # filepath = "/appfolder/app/log/$filename"
-#     # # check whether there is a file path exists before writing to it
-#     # if !isfile(filepath)
-#     #   decisionlist = [responsedict]
-#     #   println("Creating file $filepath")
-#     #   open(filepath, "w") do io
-#     #     JSON3.pretty(io, decisionlist)
-#     #   end
-#     # else
-#     #   # read the file and append new data
-#     #   decisionlist = copy(JSON.parse(filepath))
-#     #   push!(decisionlist, responsedict)
-#     #   println("Appending new data to file $filepath")
-#     #   open(filepath, "w") do io
-#     #     JSON3.pretty(io, decisionlist)
-#     #   end
-#     # end
-
-#     return responsedict["score"]
-#   end
-#   error("Evaluator failed to generate an evaluation, Response: \n$response\n<|End of error|>")
-# end
 
 """
 
@@ -777,6 +587,10 @@ function transition(state::T, args::NamedTuple
   response = nothing
   if thoughtDict["action_name"] == "RUNSQL"
     response = SQLexecution(executeSQL, thoughtDict["action_input"])
+    # println("\n--- SQLLLM transition() response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    # println(response)
+    # println("---")
+
   else
     error("undefined LLM function. Requesting $(thoughtDict["action_name"])")
   end
@@ -788,7 +602,11 @@ function transition(state::T, args::NamedTuple
     else
       evaluatorF(newstate, text2textInstructLLM, llmFormatName)
     end
-
+  
+  println("\n--- SQLLLM transition() thoughtDict ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+  pprintln(thoughtDict)
+  println("---")
+  # error("SQLLLM transition() end")
   return (newNodeKey=newNodeKey, newstate=newstate, progressvalue=progressvalue)
 end
 
@@ -1102,6 +920,10 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
     );
     """
 
+  # println("\n--- SQLLLM query() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+  # println("---")
+  # error("SQLLLM query() end")
+
   initialstate["context"]["table_schema"] = table_schema
 
   transitionargs = (
@@ -1128,6 +950,8 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
                   saveSimulatedNode=true,
                   multithread=false)
   
+  # error("SQLLLM query() end")
+  
   # compare all high value state answer then select the best one
   if length(highValueState) > 1
     selected = compareState(query, highValueState, text2textInstructLLM, llmFormatName)
@@ -1144,17 +968,16 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
   latest_action = resultState["action_history"]["$max_ind"]
   
   #CHANGE add to vectorDB only if the answer is achieved and the state is terminal 
-  # sql = latest_action["action_input"]
-  # if insertSQLVectorDB !== nothing && resultState["isterminal"] == true &&
-  #   resultState["accepted_as_answer"] == "yes"
-  #   insertSQLVectorDB(resultState["question"], sql)
-  # end
-
-  # println("\n--- SQLLLM query() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  # println(latest_action)
-  # println("---")
-  # error("SQLLLM query() end")
+  sql = latest_action["action_input"]
+  if insertSQLVectorDB !== nothing && resultState["isterminal"] == true &&
+    resultState["accepted_as_answer"] == "yes"
+    insertSQLVectorDB(resultState["question"], sql)
+  end
 
+  println("\n--- SQLLLM query() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+  println(resultState["result_raw"])
+  println("---\n")
+  
   return (result_str=latest_action["action_result"], result_raw=resultState["result_raw"])
 end
 
diff --git a/src/llmfunction.jl b/src/llmfunction.jl
index 33fa2fb..0cf8f60 100644
--- a/src/llmfunction.jl
+++ b/src/llmfunction.jl
@@ -514,10 +514,10 @@ function SQLexecution(executeSQL::Function, sql::T
           df
         end
       result = GeneralUtils.dfToString(df1)
-      println("\n~~~ SQLexecution() result: ", @__FILE__, " ", @__LINE__)
-      println(sql)
-      println(df1)
-      println("\n")
+      # println("\n~~~ SQLexecution() result: ", @__FILE__, " ", @__LINE__)
+      # println(sql)
+      # println(df1)
+      # println("\n")
       return (result_str=result, result_raw=df1, success=true, errormsg=nothing)
     end
   catch e