update

Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.12.6"
 manifest_format = "2.0"
-project_hash = "09bd5c43d6ad954d8be233d27fc343ea1149c0b0"
+project_hash = "d8b83d09e35f3ba09b54977607be02d80ec68613"
 
 [[deps.Accessors]]
 deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "MacroTools"]
@@ -760,9 +760,7 @@ version = "0.7.0"
 
 [[deps.SQLLLM]]
 deps = ["CSV", "DataFrames", "DataStructures", "Dates", "FileIO", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "PrettyPrinting", "Random", "Revise", "StatsBase", "Tables", "URIs", "UUIDs"]
-git-tree-sha1 = "93cc1ae6202279a2eb4e1dbfff706c5bc158609d"
+path = "../SQLLLM"
-repo-rev = "main"
-repo-url = "https://git.yiem.cc/ton/SQLLLM"
 uuid = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
 version = "0.2.5"
 
@@ -983,10 +981,10 @@ uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
 version = "1.6.1"
 
 [[deps.YiemAgent]]
-deps = ["CSV", "DataFrames", "DataStructures", "Dates", "GeneralUtils", "HTTP", "JSON", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SQLLLM", "Serialization", "URIs", "UUIDs"]
+deps = ["CSV", "DataFrames", "DataStructures", "Dates", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "Serialization", "URIs", "UUIDs"]
 path = "."
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.4.0"
+version = "0.4.1"
 
 [[deps.Zlib_jll]]
 deps = ["Libdl"]

Project.toml

@@ -30,4 +30,3 @@ HTTP = "2.4.0"
 JSON = "1.6.1"
 LLMMCTS = "0.1.5"
 NATS = "0.1.0"
-SQLLLM = "0.2.5"

src/interface.jl

@@ -75,7 +75,7 @@ OrderedDict{String, Any} with 4 entries:
 """
 function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
   ) where {T<:agent}
-  println("\nExecuting YiemAgent decisionMaker()")
+
   # lessonDict = copy(JSON.parsefile("lesson.json"))
 
   # lesson =
@@ -108,9 +108,9 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
   context = 
     """
     <internal_context_for_assistant>
-      <thought_history>
+      <assistant_action_history>
         $(GeneralUtils.dict_to_string_html(a.memory["shortmem"]))
-      </thought_history>
+      </assistant_action_history>
     </internal_context_for_assistant>
     """
 
@@ -269,9 +269,9 @@ function evaluator(a::T1, timeline, decisiondict, evaluateecontext
     context =
     """
     <context>
-      <trajectory>
+      <assistant_trajectories>
       $timeline
-      </trajectory>
+      </assistant_trajectories>
       <evaluatee_context>
       $evaluateecontext
       </evaluatee_context>
@@ -320,177 +320,10 @@ function evaluator(a::T1, timeline, decisiondict, evaluateecontext
 
     println("\nEvaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
     pprintln(Dict(responsedict))
-
+    return responsedict
-    # # read sessionId
-    # sessionid = a.id
-    # # save to filename ./log/decisionlog.txt
-    # println("saving SQLLLM evaluator() to disk")
-    # filename = "agent_evaluator_log_$(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
-    #     JSON.pretty(io, decisionlist)
-    #   end
-    # else
-    #   # read the file and append new data
-    #   decisionlist = copy(JSON.parsefile(filepath))
-    #   push!(decisionlist, responsedict)
-    #   println("Appending new data to file $filepath")
-    #   open(filepath, "w") do io
-    #     JSON.pretty(io, decisionlist)
-    #   end
-    # end
-
-      return responsedict
   end
   error("Evaluator failed to generate an evaluation, Response: \n$response\n<|End of error|>")
 end
-# function evaluator(a::T1, timeline, decisiondict, evaluateecontext
-#   ) where {T1<:agent}
-
-#   systemmsg = 
-#   """
-#   <Your role>
-#     - You are a master sommelier of an online wine store.
-#   </Your role>
-#   <Situation>
-#     - Under your supervision, a trainee sommelier is engaging with a store customer. Each time the customer speaks, the trainee will assess the situation, determine the next course of action, and pause to await your guidance before proceeding.
-#   </Situation>
-#   <Your mission>
-#     - Improve a trainee sommelier decision based on the store policy and guidelines while ensuring seamless interactions between the trainee and customers.
-#   </Your mission>
-#   <At each round of conversation, you will be given the following information> 
-#     - trajectory: A conversation between your trainee and the customer that have occurred up until now
-#     - evaluatee_context: The context that evaluatee use to make a decision
-#     - evaluatee_decision: The decision made by the evaluatee, consists of the following elements:
-#       "plan" is the trainee's plan
-#       "action_name" is the name of the action taken, which can be one of the available tool name.
-#       "action_input" is the input to the action.
-#   </At each round of conversation, you will be given the following information>
-#   <You must follow the following policy>
-#     - Use only infomation provided by the store policy and guidelines as a bedrocks for your response.
-#   </You must follow the following policy>
-#   <You should follow the following guidelines>
-#     - The trainee's plan, action_name, and action_input must be logically consistent
-#     - The trainee's action_input should be in a proper format as specified by the tools.
-#     - The trainee's action name and action input should make sense. For example, if the trainee isn't finished talking, he shouldn't use the END_CONVER_GUIDELINE tool.
-#   </You should follow the following guidelines>
-#   <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) decision_evaluation: 
-#       - Examine how the trainee's decisions align with the store's policies and guidelines before proceeding.
-#     3) suggestion: Based store policy and guidelines, provide a suggestion for the immediate decision step only.
-#     4) approval: Can be "yes" or "no". "no" if the suggestion contradict the trainee's decision; otherwise, it is "yes".
-
-#   </You should then respond to the user with>
-#   <You should only respond in JSON format as described below>
-#   {
-#     "trajectory_evaluation": "...",
-#     "decision_evaluation": "...",
-#     "suggestion": "...",
-#     "approval": "...",
-#   }
-#   </You should only respond in format as described below>
-
-#   Let's begin!
-#   """
-#   requiredKeys = [:trajectory_evaluation, :decision_evaluation, :approval, :suggestion]
-#   errornote = "N/A"
-
-#   for attempt in 1:10
-#     evaluateecontext = replace(evaluateecontext, "<context>" => "")
-#     evaluateecontext = replace(evaluateecontext, "</context>" => "")
-
-#     context =
-#     """
-#     <context>
-#       <trajectory>
-#       $timeline
-#       </trajectory>
-#       <evaluatee_context>
-#       $evaluateecontext
-#       </evaluatee_context>
-#       <evaluatee_decision>
-#       {plan: $(decisiondict["plan"]), action_name: $(decisiondict["action_name"]), action_input: $(decisiondict["action_input"])}
-#       </evaluatee_decision>
-#       P.S. $errornote
-#     </context>
-#     """
-
-#     unformatPrompt =
-#       [
-#         Dict("name" => "system", "text" => systemmsg),
-#       ]
-
-#     # put in model format
-#     prompt = GeneralUtils.formatLLMtext(unformatPrompt, a.llmFormatName)
-#     # add info
-#     prompt = prompt * context
-
-#     response = a.context.text2textInstructLLM(prompt; senderId=a.id)
-#     response = GeneralUtils.deFormatLLMtext(response, a.llmFormatName)
-#     response = GeneralUtils.remove_french_accents(response)
-#     # response = replace(response, '$'=>"USD")
-#     think, response = GeneralUtils.extractthink(response)
-    
-#     responsedict = nothing
-#     try
-#       responsedict = copy(JSON.parsefile(response))  
-#     catch
-#       println("\nERROR YiemAgent generatechat() failed to parse response: $response", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue
-#     end
-
-#     # check whether all answer's key points are in responsedict
-#     ispass, errormsg = checkAgentResponse_JSON(responsedict, requiredKeys)
-#     if !ispass
-#       errornote = errormsg
-#       println("\nERROR YiemAgent evaluator() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-#       continue
-#     end
-
-#     # if accepted_as_answer ∉ ["yes", "no"] # [PENDING] add errornote into the prompt
-#     #   error("generated accepted_as_answer has wrong format")
-#     # end
-
-#     println("\nEvaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#     pprintln(Dict(responsedict))
-
-#     # # read sessionId
-#     # sessionid = a.id
-#     # # save to filename ./log/decisionlog.txt
-#     # println("saving SQLLLM evaluator() to disk")
-#     # filename = "agent_evaluator_log_$(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
-#     #     JSON.pretty(io, decisionlist)
-#     #   end
-#     # else
-#     #   # read the file and append new data
-#     #   decisionlist = copy(JSON.parsefile(filepath))
-#     #   push!(decisionlist, responsedict)
-#     #   println("Appending new data to file $filepath")
-#     #   open(filepath, "w") do io
-#     #     JSON.pretty(io, decisionlist)
-#     #   end
-#     # end
-
-#       return responsedict
-#   end
-#   error("Evaluator failed to generate an evaluation, Response: \n$response\n<|End of error|>")
-# end
-
 
 """ Chat with llm.
 
@@ -543,8 +376,8 @@ function conversation(a::sommelier; userinput::Union{Dict{String, Any}, JSON.Obj
     # thinking loop until AI wants to communicate with the user
     loopcount = 0
     while true
-      @info "YiemAgent conversation() 2-0 count $loopcount" @__LINE__
       loopcount += 1
+      @info "YiemAgent conversation() 2-0 count $loopcount" @__LINE__
       thoughtdict, _ = think(a)
       if thoughtdict["action_name"] ∈ ["CHAT_BOX"]
         @info "YiemAgent conversation() 2-1" @__LINE__
@@ -566,45 +399,6 @@ function conversation(a::sommelier; userinput::Union{Dict{String, Any}, JSON.Obj
   end
 end
 
-# function conversation(a::Union{companion, virtualcustomer}, userinput::Dict; 
-#                       converPartnerName::Union{String, Nothing}=nothing,
-#                       maximumMsg=50)
-
-#   chatresponse = nothing
-
-#   if userinput["text"] == "newtopic"
-#     clearhistory(a)
-#     return "Okay. What shall we talk about?"
-#   else
-#     # add usermsg to a.chathistory
-#     addNewMessage(a, "user", userinput["text"]; maximumMsg=maximumMsg)
-
-#     # add user activity to events memory
-#     push!(a.memory["events"],
-#       eventdict(;
-#         event_description="the user talks to the assistant.",
-#         timestamp=Dates.now(),
-#         subject="user",
-#         action_name="CHAT_BOX",
-#         action_input=userinput["text"],
-#       )
-#     )
-#     chatresponse = generatechat(a; converPartnerName=converPartnerName, recentEventNum=20)
-
-#     addNewMessage(a, "assistant", chatresponse; maximumMsg=maximumMsg)
-
-#     push!(a.memory["events"],
-#       eventdict(;
-#         event_description="the assistant talks to the user.",
-#         timestamp=Dates.now(),
-#         subject="assistant",
-#         action_name="CHAT_BOX",
-#         action_input=chatresponse,
-#       )
-#     )
-#     return chatresponse
-#   end
-# end
 
 """
 # Arguments
@@ -831,9 +625,9 @@ function generatechat(a::T; recentevents::Integer=20, maxattempt=10
   context = 
     """
     <internal_context_for_assistant>
-      <thought_history>
+      <assistant_action_history>
         $(GeneralUtils.dict_to_string_html(a.memory["shortmem"]))
-      </thought_history>
+      </assistant_action_history>
     </internal_context_for_assistant>
     """