update

src/interface.jl

@@ -339,6 +339,286 @@ julia>
 
 # Signature
 """
+# function evaluator(state::T1, text2textInstructLLM::Function;
+#   insertSQLVectorDB::Union{Function, Nothing}=nothing
+#   ) 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" is the taken action which can be one of the following functions:
+#   #   1) TABLEINFO[list_of_table_name], which you can use to get the data type of a table column.
+#   #   2) GETDATA[instruction], which you can use to get the data from the database.
+#   #   3) ANSWERBOX[answer], which returns your answer to the user. "answer" is your answer to the user question.
+#   # "observation" is result of the action in JSON format.
+
+#   # At each round of conversation, the user will give you:
+#   # Context: ...
+#   # Trajectories: ...
+    
+#   # You should then respond to the user with:
+#   # - Original_question: Repeat the original question.
+#   # - Evaluation (you must evaluate all of the following points):
+#   #     1) Analyze the trajectories of a solution to answer the user's original question. 
+#   #       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) How the observation addresses the original question?
+#   #     3) Provide suggestion (if applicable).
+#   # - Score: Correctness score s where s is an integer from 0 to 10.
+#   # - Accepted_as_answer: Decide whether to accept the observation as the answer to the original question.
+#   #     1) The accepted observation should directly answer the question.
+#   #     2) The possible responses are either 'Yes' or 'No.' 
+
+#   # You should only respond in JSON format as described below:
+#   #   {"original_question": ..., "evaluation": ..., "score": ..., "accepted_as_answer": ...}
+
+#   # Here are correct trajectory examples:
+#   # user:
+#   # {
+#   #   "question": "I'm looking for a sedan with an automatic driving feature.",
+#   #   "thought_1": "I have many types of sedans in my inventory, each with diverse features.",
+#   #   "thought_2": "I should check our inventory first to see if we have the one our customer wants.", 
+#   #   "action_1": {"name": "inventory", "input": "a sedan with an automatic driving feature"},
+#   #   "observation_1": "Yiem Model A, Conez Model B"
+#   # }
+#   # assistant:
+#   # {
+#   #   "original_question": "the user is looking for a sedan with an automatic driving feature.",
+#   #   "evaluation": "This trajectory is correct because it is logical to use the INVENTORY function to search for inventory based on the details provided in the question, which could lead to a potential answer. The user is asking whether do you have a sedan with an automatic driving feature and the observation provides a list of sedan models that you have. Thus, it is accepted as the answer.",
+#   #   "score": 10,
+#   #   "accepted_as_answer": "Yes"
+#   # }
+
+#   # user:
+#   # {
+#   #   "question": "How many cars that fitted with a stereo we have?",
+#   #   "thought_1": "I have many types of car in my inventory, each with diverse features.",
+#   #   "thought_3": "I should check our inventory.", 
+#   #   "action_1": {"name": "inventory", "input": "vehicle with a stereo"},
+#   #   "observation_1": "2015 Conez truck."
+#   # }
+#   # assistant:
+#   # {
+#   #   "evaluation": “This approach is correct. It's reasonable to use the INVENTORY function to search for inventory. However, the query asked for a car but the observation was a truck. Thus it is not accepted as the answer. To improve, make sure to input the correct terms and match the requested criteria accurately.”,
+#   #   "score": 5,
+#   #   "accepted_as_answer": "No"
+#   # }
+
+#   # Here are incorrect trajectory examples:
+#   # user:
+#   # {
+#   #   "question": "I'm looking for a sedan with an automatic driving feature. Do you have it in stock?",
+#   #   "thought_1": "I have many types of sedans in my inventory, each with diverse features.",
+#   #   "thought_2": "I will use SEARCHINTERNET function to search for the car.", 
+#   #   "action_1": {"name": "SEARCHINTERNET", "input": "a sedan with an automatic driving feature.},
+#   #   "observation_1": "Teza Model A, Teza Model B"
+#   # }
+#   # assistant:
+#   # {
+#   #   "evaluation": "This trajectory is incorrect. Using the SEARCHINTERNET function to search for a sedan in the Internet is illogical because the question asked for the cars available for sale at your dealership. To improve, ensure that you read the question clearly.",
+#   #   "score": 0,
+#   #   "accepted_as_answer": "No"
+#   # }
+
+#   # Let's begin!
+#   # """
+  
+#   # 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 action.
+
+#   # At each round of conversation, the user will give you:
+#   # Context: ...
+#   # Trajectories: ...
+    
+#   # You should then respond to the user with:
+#   # - original_question: Repeat the original question.
+#   # - evaluation (you must evaluate all of the following points in a single paragraph):
+#   #     1) Analyze the trajectories of a solution to answer the user's original question. 
+#   #       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) How the observation addresses the question exactly?
+#   # - accepted_as_answer: Decide whether to accept the observation as the answer to the original question.
+#   #     1) if the observation's content directly answers the question then just accept it as the answer. Oherwise, it is not. The possible responses are either 'Yes' or 'No.'
+#   # - score: Correctness score s where s is a single integer between 0 to 9. 
+#   #     1) 0 means the trajectories are incorrect.
+#   #     2) 9 means the trajectories are correct, and the observation's content directly answers the question.
+#   # - suggestion: if accepted_as_answer is "No", provide suggestion.
+
+#   # You should only respond in format as described below:
+#   # original_question: ...
+#   # evaluation: ...
+#   # accepted_as_answer: ...
+#   # score: ...
+#   # suggestion: ...
+
+#   # Let's begin!
+#   # """
+
+#   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
+#   "understanding" is agent's understanding about the current situation
+#   "reasoning" is agent's step-by-step reasoning about the current situation
+#   "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:
+#       - 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 ';'.
+#   "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:
+#   Trajectory: ...
+#   Error note: error note from your previous attempt
+
+#   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
+#     - State your rationale
+#   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. 
+#       Score guideline:
+#       - 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 the observation is incorrect or failed
+#       - 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: 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
+
+#   errornote = ""
+
+#   for attempt in 1:5
+#     usermsg =
+#     """
+#     Trajectory: $thoughthistory
+#     Error note: $errornote
+#     """
+
+#     _prompt = 
+#     [
+#       Dict(:name=> "system", :text=> systemmsg),
+#       Dict(:name=> "user", :text=> usermsg)
+#     ]
+
+#     # put in model format
+#     prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+#     prompt *=
+#       """
+#       <|start_header_id|>assistant<|end_header_id|>
+#       """
+
+#     header = ["Trajectory_evaluation", "Answer_evaluation", "Accepted_as_answer", "Score", "Suggestion"]
+
+#     try
+#       response = text2textInstructLLM(prompt)
+#       # make sure every header is in the response
+#       for i in header
+#         detected = GeneralUtils.detect_keyword(i, response)
+#         if detected === nothing
+#           error("Keyword $i not found in response")
+#         end
+#       end
+
+#       responsedict = GeneralUtils.textToDict(response,
+#         header; 
+#         rightmarker=":", symbolkey=true, lowercasekey=true)
+
+#       # check if dict has all required value
+#       trajectoryevaluation_text::AbstractString = responsedict[:trajectory_evaluation]
+#       answerevaluation_text::AbstractString = responsedict[:answer_evaluation]
+#       # responsedict[:score] = replace(responsedict[:score], r"\(.*?\)" => "")  # remove (...) if there is any.
+#       responsedict[:score] = responsedict[:score][1]  # some time "6\nThe trajectories are incomplete" is generated but I only need the number.
+#       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]
+
+#       if accepted_as_answer ∉ ["Yes", "No"] # [PENDING] add errornote into the prompt
+#         error("generated accepted_as_answer has wrong format")
+#       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"
+
+#         # 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("\n~~~ 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
 function evaluator(state::T1, text2textInstructLLM::Function;
   insertSQLVectorDB::Union{Function, Nothing}=nothing
   ) where {T1<:AbstractDict}
@@ -490,14 +770,16 @@ function evaluator(state::T1, text2textInstructLLM::Function;
   "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:
+  <At each round of conversation, the user will give you>
-  Context: ...
   Trajectory: ...
+  Error note: error note from your previous attempt
+  </At each round of conversation, the user will give you>
 
-  You must follow the following guidelines:
+  <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 must follow the following guidelines>  
 
-  You should then respond to the user with:
+  <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.
@@ -520,13 +802,15 @@ function evaluator(state::T1, text2textInstructLLM::Function;
       - 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: if accepted_as_answer is "No", provide suggestion.
+  </You should then respond to the user with>
 
-  You should only respond in format as described below:
+  <You should only respond in format as described below>
   Trajectory_evaluation: ...
   Answer_evaluation: ...
   Accepted_as_answer: ...
   Score: ...
   Suggestion: ...
+  </You should only respond in format as described below>
 
   Let's begin!
   """
@@ -536,10 +820,15 @@ function evaluator(state::T1, text2textInstructLLM::Function;
     thoughthistory *= "$k: $v\n" 
   end
 
-  for attempt in 1:5
+  errornote = ""
+
+  for attempt in 1:10
+    errorFlag = false
+
     usermsg =
     """
     Trajectory: $thoughthistory
+    Error note: $errornote
     """
 
     _prompt = 
@@ -555,10 +844,23 @@ function evaluator(state::T1, text2textInstructLLM::Function;
       <|start_header_id|>assistant<|end_header_id|>
       """
 
-    try
+    header = ["Trajectory_evaluation", "Answer_evaluation", "Accepted_as_answer", "Score", "Suggestion"]
+
     response = text2textInstructLLM(prompt)
+    # make sure every header is in the response
+    for i in header
+      detected = GeneralUtils.detect_keyword(i, response)
+      if detected === nothing
+        errornote = "Keyword $i not found in response"
+        errorFlag = true
+      end
+    end
+    if errorFlag
+      continue  # skip to the next iteration
+    end
+
     responsedict = GeneralUtils.textToDict(response,
-        ["Trajectory_evaluation", "Answer_evaluation", "Accepted_as_answer", "Score", "Suggestion"]; 
+      header; 
       rightmarker=":", symbolkey=true, lowercasekey=true)
 
     # check if dict has all required value
@@ -594,20 +896,10 @@ function evaluator(state::T1, text2textInstructLLM::Function;
     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
 
-
 """
 
 # Arguments
@@ -979,9 +1271,9 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
   earlystop(state) = state[:reward] >= 8 ? true : false
 
   _, _, resultState = LLMMCTS.runMCTS(initialstate, transition, transitionargs;
-                      horizontalSampleExpansionPhase=2, 
+                      horizontalSampleExpansionPhase=5, 
-                      horizontalSampleSimulationPhase=1,
+                      horizontalSampleSimulationPhase=2,
-                      maxSimulationDepth=3, maxiterations=2, 
+                      maxSimulationDepth=10, maxiterations=2, 
                       explorationweight=1.0,
                       earlystop=earlystop,
                       saveSimulatedNode=true)

test/runtest.jl

@@ -35,7 +35,7 @@ function text2textInstructLLM(prompt::String)
     msgPurpose="inference",
     senderName="yiemagent",
     senderId=sessionId,
-    receiverName="text2textinstruct_medium",
+    receiverName="text2textinstruct_small",
     mqttBrokerAddress=config[:mqttServerInfo][:broker],
     mqttBrokerPort=config[:mqttServerInfo][:port],
   )
@@ -64,7 +64,7 @@ function getEmbedding(text::T) where {T<:AbstractString}
     msgPurpose="embedding",
     senderName="yiemagent",
     senderId=sessionId,
-    receiverName="text2textinstruct_medium",
+    receiverName="text2textinstruct_small",
     mqttBrokerAddress=config[:mqttServerInfo][:broker],
     mqttBrokerPort=config[:mqttServerInfo][:port],
   )