update

src/llmfunction.jl

@@ -394,8 +394,7 @@ function extractWineAttributes_1(a::T1, input::T2
       end
 
       #[TESTING] remove tasting_notes because the database didn't prepare to be search using it
-      responsedict[:tasting_notes] = "NA"
-      responsedict[:flavor] = "NA"
+      responsedict[:tasting_notes] = responsedict[:flavor]
 
       result = ""
       for (k, v) in responsedict
@@ -658,6 +657,138 @@ function jsoncorrection(config::T1, input::T2, correctJsonExample::T3;
   end
 end
 
+# [WORKING] check whether  
+# function isrecommend(state::T1, text2textInstructLLM::Function
+#   ) where {T1<:AbstractDict}
+
+#   systemmsg = 
+#   """
+#   You are a helpful assistant that analyzes agent's trajectories to find solutions and observations (i.e., the results of actions) to answer the user's questions.
+
+#   Definitions:
+#   "question" is the user's question.
+#   "thought" is step-by-step reasoning about the current situation.
+#   "plan" is what to do 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:
+#       1) CHATBOX[text], which you can use to talk with the user. "text" is in verbal English. 
+#       2) WINESTOCK[query], which you can use to find info about wine in your inventory. "query" is a search term in verbal English. The best query must includes "budget", "type of wine", "characteristics of wine" and "food pairing".
+#   "action_input" is the input to the action
+#   "observation" is result of the preceding immediate action.
+
+#   At each round of conversation, the user will give you:
+#   Context: ...
+#   Trajectories: ...
+    
+#   You should then respond to the user with:
+#   1) trajectory_evaluation:
+#     - Analyze the trajectories of a solution to answer the user's original question. 
+#       Then given a question and a trajectory, evaluate its correctness and provide your reasoning and
+#       analysis in detail. Focus on the latest thought, action, and observation. 
+#       Incomplete trajectories can be correct if the thoughts and actions so far are correct, 
+#       even if the answer is not found yet. Do not generate additional thoughts or actions.
+#   2) answer_evaluation: Focus only on the matter mentioned in the question and analyze how the latest observation addresses the question.
+#   3) accepted_as_answer: Decide whether the latest observation's content answers the question. The possible responses are either 'Yes' or 'No.'
+#     Bad example (The observation didn't answers the question):
+#       question: Find cars with 4 wheels.
+#       observation: There are 2 cars 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. 
+#       - 0 means the trajectories are incorrect.
+#       - 9 means the trajectories are correct, and the observation's content directly answers the question.
+#   5) suggestion: if accepted_as_answer is "No", provide suggestion.
+
+#   You should only respond in format as described below:
+#   trajectory_evaluation: ...
+#   answer_evaluation: ...
+#   accepted_as_answer: ...
+#   score: ...
+#   suggestion: ...
+
+#   Let's begin!
+#   """
+
+#   thoughthistory = ""
+#   for (k, v) in state[:thoughtHistory]
+#     thoughthistory *= "$k: $v\n" 
+#   end
+
+#   usermsg =
+#   """
+#   Context: None
+#   Trajectories: $thoughthistory
+#   """
+
+#   _prompt = 
+#   [
+#     Dict(:name=> "system", :text=> systemmsg),
+#     Dict(:name=> "user", :text=> usermsg)
+#   ]
+
+#   # put in model format
+#   prompt = GeneralUtils.formatLLMtext(_prompt, "llama3instruct")
+#   prompt *=
+#     """
+#     <|start_header_id|>assistant<|end_header_id|>
+#     """
+
+#   for attempt in 1:5
+#     try
+#       response = text2textInstructLLM(prompt)
+#       responsedict = GeneralUtils.textToDict(response,
+#             ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"], 
+#             rightmarker=":", symbolkey=true)
+
+#       # check if dict has all required value
+#       trajectoryevaluation_text::AbstractString = responsedict[:trajectory_evaluation]
+#       answerevaluation_text::AbstractString = responsedict[:answer_evaluation]
+#       responsedict[:score] = parse(Int, responsedict[:score]) # convert string "5" into integer 5
+#       score::Integer = responsedict[:score]
+#       accepted_as_answer::AbstractString = responsedict[:accepted_as_answer]
+#       suggestion::AbstractString = responsedict[:suggestion]
+
+#       # add to state here instead to in transition() because the latter causes julia extension crash (a bug in julia extension)
+#       state[:evaluation] = "$(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"
+#         state[:isterminal] = true
+#         state[:reward] = 1
+#       end
+#       println("--> 5 Evaluator ", @__FILE__, " ", @__LINE__)
+#       pprintln(Dict(responsedict))
+#       return responsedict[:score]
+#     catch e
+#       io = IOBuffer()
+#       showerror(io, e)
+#       errorMsg = String(take!(io))
+#       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
+#       println("")
+#       println("Attempt $attempt. Error occurred: $errorMsg\n$st")
+#       println("")
+#     end
+#   end
+#   error("evaluator failed to generate an evaluation")
+# end
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+