update

src/interface.jl

@@ -159,11 +159,11 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
     - If you are unable to find the requested information, kindly inform the user, "The current data in our database does not provide the specific answer to your query".
     - Text information in the database usually stored in lower case. If your search returns empty, try using lower case to search.
 
-    You should then respond to the user with interleaving Understanding, Reasoning, Plan, Action:
+    You should then respond to the user with interleaving Comprehension, Plan, Action_name, Action_input:
-    1) Comprehension: 
+      Comprehension: state your comprehension about the current situation.
-      - State your comprehension about the current situation.
+      Plan: Given the current circumstances, outline a detailed, step-by-step plan to accomplish the task. Be specific.
-    2) Plan: Given the current circumstances, outline a detailed, step-by-step plan to accomplish the task. Be specific.
+      Action_name: (Typically corresponds to the execution of the first step in your plan)
-    3) Action_name (Must be aligned with your plan): Can be one of the following functions: 
+        Can be one of the following function names: 
         - 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 ';'.
@@ -195,6 +195,8 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
     similarSQL_ = sql !== nothing ? sql : "None"
   end
 
+  header = ["Comprehension:", "Plan:", "Action_name:", "Action_input:"]
+  dictkey = ["comprehension", "plan", "action_name", "action_input"]
 
   for attempt in 1:10
     QandA = generatequestion(state, context, text2textInstructLLM; similarSQL=similarSQL_)
@@ -252,37 +254,31 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
       continue
     end
 
-    header = ["Comprehension:", "Plan:", "Action_name:", "Action_input:"]
+    # # detect if there are more than 1 key per categories
-    dictkey = ["comprehension", "plan", "action_name", "action_input"]
+    # wordcount = GeneralUtils.countGivenWords(response, header)
-
+    # duplicateKeywordFlag = false
-    # detect if there are more than 1 key per categories
+    # for (i, v) in enumerate(wordcount)
-    wordcount = GeneralUtils.countGivenWords(response, header)
+    #   keyword = header[i]
-    duplicateKeywordFlag = false
+    #   keywordNumber = v
-    for (i, v) in enumerate(wordcount)
+    #   if keywordNumber > 1
-      keyword = header[i]
+    #     errornote = "\nSQL query has duplicated keyword, $keyword"
-      keywordNumber = v
+    #     println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      if keywordNumber > 1
+    #     duplicateKeywordFlag = true
-        errornote = "\nSQL query has duplicated keyword, $keyword"
+    #     break
-        println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    #   end
-        duplicateKeywordFlag = true
+    # end
-        break
+    # duplicateKeywordFlag == true ? continue : nothing
-      end
-    end
-    duplicateKeywordFlag == true ? continue : nothing
 
     # check whether response has all header
-    kw = []
+    detected_kw = GeneralUtils.detect_keyword(header, response)
-    # use for loop and detect_keyword function to get the exact variation of each keyword in the text then push to kw list
+    if sum(values(detected_kw)) < length(header)
-    for keyword in header
+      errornote = "\nSQL decisionMaker() response does not have all header"
-      detected = GeneralUtils.detect_keyword(keyword, response)
+      continue
-      push!(kw, detected)
+    elseif sum(values(detected_kw)) > length(header)
-    end
+      errornote = "\nSQL decisionMaker() response has duplicated header"
-    if nothing ∈ kw
+      continue
-      println("Some keywords are missing, Required keywords=$header, Response keywords=$kw ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      continue  # try again next loop
     end
 
-    # textToDict() search for action_input
     responsedict = GeneralUtils.textToDict(response, header; 
                                             dictKey=dictkey, symbolkey=true)
 
@@ -315,7 +311,7 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
       end
     end
 
-    for i ∈ [:comprehension, :plan, :action_name, :action_input]
+    for i ∈ Symbol.(dictkey)
       if length(JSON3.write(responsedict[i])) == 0
         errornote = "\n $i is empty"
         println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
@@ -323,14 +319,14 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
       end
     end
 
-    # check if there are more than 1 key per categories
+    # check whether response has all header
-    for i ∈ [:comprehension, :plan, :action_name, :action_input]
+    detected_kw = GeneralUtils.detect_keyword(header, response)
-      matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
+    if sum(values(detected_kw)) < length(header)
-      if length(matchkeys) > 1
+      errornote = "\nSQL decisionMaker() response does not have all header"
-        errornote = "\n $i has more than one key"
+      continue
-        println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    elseif sum(values(detected_kw)) > length(header)
-        continue
+      errornote = "\nSQL decisionMaker() response has duplicated header"
-      end
+      continue
     end
 
     state[:decisionMaker] = responsedict
@@ -340,244 +336,7 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
   end
   error("DecisionMaker failed to generate a thought \n", response)
 end
-# function decisionMaker(state::T1, context, text2textInstructLLM::Function,
-#   ; querySQLVectorDBF::Union{T2, Nothing}=nothing
-#   )::Dict{Symbol, Any} where {T1<:AbstractDict, T2<:Function}
 
-#   # lessonDict =
-#   # if isfile("lesson.json")
-#   #   lessonDict = copy(JSON3.read("lesson.json"))
-#   # else
-#   #   lessonDict = nothing
-#   # end
-
-#   # lessonDict = nothing
-
-#   # lesson =
-#   # if lessonDict === nothing
-#   #   ""
-#   # else
-#   #   """
-#   #   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. 
-#   #   The following lesson(s) give a plan to avoid failing to help the user in the same way you 
-#   #   did previously. Use them to improve your strategy to help the user.
-
-#   #   Here are some lessons in JSON format:
-#   #   $(JSON3.write(lessonDict))
-
-#   #   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. 
-#   #   """
-#   # end
-
-#   systemmsg = 
-#   """
-#     You are a helpful assistant that find the data from a database to satisfy the user's query. 
-#     You are also eager to improve your helpfulness.
-
-#     For your information:
-#     - Observation: Result of the immediately preceding action
-
-#     At each round of conversation, the user will give you the current situation:
-#     User Query: ...
-#     Example: ...
-#     Your Q&A: ...
-#     Your work progress: ...
-#     Evaluation: Evaluation of the immediately preceding action and observation
-#     Suggestion: Suggestion for the immediately preceding action and observation
-    
-#     You must follow the following guidelines:
-#     - Keep SQL queries focused only on the provided information.
-
-#     You should follow the following guidelines:
-#     - Do not create any table in the database
-#     - A junction table can be used to link tables together. Another use case is for filtering data.
-#     - If you can't find a single table that can be used to answer the user's query, try joining multiple tables to see if you can obtain the answer.
-#     - If you are unable to find the requested information, kindly inform the user, "The current data in our database does not provide the specific answer to your query".
-#     - Text information in the database usually stored in lower case. If your search returns empty, try using lower case to search.
-
-#     You should then respond to the user with interleaving Understanding, Reasoning, Plan, Action:
-#     1) Comprehension: 
-#       - State your comprehension about the current situation.
-#     2) Plan: Given the current circumstances, outline a detailed, step-by-step plan to accomplish the task. Be specific.
-#     3) Action_name (Must be aligned with your plan): Can be one of the following functions: 
-#         - GETDATA, which you can use to get the data from 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 ';'.
-#     4) Action_input: Input to the action
-
-#     You should only respond in format as described below:
-#     Comprehension: ...
-#     Plan: ...
-#     Action_name: ...
-#     Action_input: ...
-
-#     Let's begin!
-#   """
-  
-#   workprogress = ""
-#   for (k, v) in state[:thoughtHistory]
-#     if k ∉ [:question]
-#       workprogress *= "$k: $v\n"
-#     end
-#   end
-  
-#   response = nothing # store for show when error msg show up
-#   errornote = ""
-
-#   # provide similar sql only for the first attempt
-#   similarSQL_ = "None"
-#   if length(state[:thoughtHistory]) == 1
-#     sql, distance = querySQLVectorDBF(state[:thoughtHistory][:question])
-#     similarSQL_ = sql !== nothing ? sql : "None"
-#   end
-
-
-#   for attempt in 1:10
-#     QandA = generatequestion(state, context, text2textInstructLLM; similarSQL=similarSQL_)
-
-#     usermsg =
-#     """
-#     $(context[:tablelist])
-#     User query: $(state[:thoughtHistory][:question])
-#     Example: $similarSQL_
-#     Your Q&A: $QandA
-#     Your work progress: $workprogress
-#     Evaluation: $(state[:evaluation])
-#     Suggestion: $(state[:suggestion])
-#     $errornote
-#     """
-
-#     _prompt = 
-#     [
-#       Dict(:name=> "system", :text=> systemmsg),
-#       Dict(:name=> "user", :text=> usermsg)
-#     ]
-
-#     # put in model format
-#     prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
-#     response = text2textInstructLLM(prompt)
-
-#     # LLM tends to generate observation given that it is in the input
-#     response = 
-#       if occursin("observation:", response)
-#         string(split(response, "observation:")[1])
-#       elseif occursin("Observation:", response)
-#         string(split(response, "Observation:")[1])
-#       elseif occursin("observation_", response)
-#         string(split(response, "observation_")[1])
-#       elseif occursin("Observation_", response)
-#         string(split(response, "Observation_")[1])
-#       else
-#         response
-#       end
-
-#     # sometime LLM output something like **Comprehension**: which is not expected
-#     response = replace(response, "**"=>"")
-#     response = replace(response, "***"=>"")
-
-#     # some time LLM output Plan_1: so we need to detect and replace topic numbering
-#     regex = r"_[0-1000]+:"
-#     matches = collect(eachmatch(regex, response))
-#     for m in matches
-#       response = replace(response, string(m.match)=>":") 
-#     end
-
-#     if occursin("NULL", response)
-#       errornote = "\nSQL decisionMaker() NULL response is not allowed"
-#       println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue
-#     end
-
-#     header = ["Comprehension:", "Plan:", "Action_name:", "Action_input:"]
-#     dictkey = ["comprehension", "plan", "action_name", "action_input"]
-
-#     # detect if there are more than 1 key per categories
-#     wordcount = GeneralUtils.countGivenWords(response, header)
-#     duplicateKeywordFlag = false
-#     for (i, v) in enumerate(wordcount)
-#       keyword = header[i]
-#       keywordNumber = v
-#       if keywordNumber > 1
-#         errornote = "\nSQL query has duplicated keyword, $keyword"
-#         println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#         duplicateKeywordFlag = true
-#         break
-#       end
-#     end
-#     duplicateKeywordFlag == true ? continue : nothing
-
-#     # check whether response has all header
-#     kw = []
-#     # use for loop and detect_keyword function to get the exact variation of each keyword in the text then push to kw list
-#     for keyword in header
-#       detected = GeneralUtils.detect_keyword(keyword, response)
-#       push!(kw, detected)
-#     end
-#     if nothing ∈ kw
-#       println("Some keywords are missing, Required keywords=$header, Response keywords=$kw ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue  # try again next loop
-#     end
-
-#     # textToDict() search for action_input
-#     responsedict = GeneralUtils.textToDict(response, header; 
-#                                             dictKey=dictkey, symbolkey=true)
-
-#     delete!(responsedict, :observation)
-
-#     # remove backticks Error occurred: MethodError: no method matching occursin(::String, ::Vector{String})
-#     if occursin("```", responsedict[:action_input])
-#       sql = GeneralUtils.extract_triple_backtick_text(responsedict[:action_input])[1]
-#       if sql[1:4] == "sql\n"
-#         sql = sql[5:end]
-#       end
-#       sql = split(sql, ';')   # some time there are comments in the sql
-#       sql = sql[1] * ';'
-
-#       responsedict[:action_input] = sql
-#     end
-
-#     toollist = ["TABLEINFO", "GETDATA"]
-#     if responsedict[:action_name] ∉ toollist
-#       errornote = "\nYou must only use the given functions"
-#       println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue
-#     end
-
-#     for i in toollist
-#       if occursin(i, responsedict[:action_input])
-#         errornote = "\n action_name is in action_input which is not allowed."
-#         println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#         continue
-#       end
-#     end
-
-#     for i ∈ [:comprehension, :plan, :action_name, :action_input]
-#       if length(JSON3.write(responsedict[i])) == 0
-#         errornote = "\n $i is empty"
-#         println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#         continue
-#       end
-#     end
-
-#     # check if there are more than 1 key per categories
-#     for i ∈ [:comprehension, :plan, :action_name, :action_input]
-#       matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
-#       if length(matchkeys) > 1
-#         errornote = "\n $i has more than one key"
-#         println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#         continue
-#       end
-#     end
-
-#     state[:decisionMaker] = responsedict
-
-#     return responsedict
-
-#   end
-#   error("DecisionMaker failed to generate a thought \n", response)
-# end
 
 """ Assigns a scalar value to each new child node to be used for selec-
 tion and backpropagation. This value effectively quantifies the agent's progress in task completion,
@@ -1429,7 +1188,6 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
 
       header = ["Understanding:", "Q1:"]
       dictkey = ["understanding", "q1"]
-      
       responsedict = GeneralUtils.textToDict(response, header; 
                                             dictKey=dictkey, symbolkey=true)
       response = "Q1: " * responsedict[:q1]

test/Manifest.toml

@@ -0,0 +1,41 @@
+# This file is machine-generated - editing it directly is not advised
+
+julia_version = "1.11.4"
+manifest_format = "2.0"
+project_hash = "71d91126b5a1fb1020e1098d9d492de2a4438fd2"
+
+[[deps.Base64]]
+uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+version = "1.11.0"
+
+[[deps.InteractiveUtils]]
+deps = ["Markdown"]
+uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
+version = "1.11.0"
+
+[[deps.Logging]]
+uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
+version = "1.11.0"
+
+[[deps.Markdown]]
+deps = ["Base64"]
+uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
+version = "1.11.0"
+
+[[deps.Random]]
+deps = ["SHA"]
+uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+version = "1.11.0"
+
+[[deps.SHA]]
+uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
+version = "0.7.0"
+
+[[deps.Serialization]]
+uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
+version = "1.11.0"
+
+[[deps.Test]]
+deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
+uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+version = "1.11.0"

test/Project.toml

@@ -0,0 +1,2 @@
+[deps]
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"