update

src/interface.jl

@@ -230,8 +230,9 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
     ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
+    prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
     response = text2textInstructLLM(prompt; llmkwargs=llmkwargs)
+    response = GeneralUtils.deFormatLLMtext(response, "granite3")
 
     # LLM tends to generate observation given that it is in the input
     response = 
@@ -448,12 +449,13 @@ function evaluator(state::T1, text2textInstructLLM::Function
     ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
+    prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
 
     header = ["Trajectory_evaluation:", "Answer_evaluation:", "Accepted_as_answer:", "Score:", "Suggestion:"]
     dictkey = ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"]
 
     response = text2textInstructLLM(prompt, modelsize="medium")
+    response = GeneralUtils.deFormatLLMtext(response, "granite3")
 
     # sometime LLM output something like **Comprehension**: which is not expected
     response = replace(response, "**"=>"")
@@ -603,7 +605,7 @@ function reflector(config::T1, state::T2)::String where {T1<:AbstractDict, T2<:A
   ]
 
   # put in model format
-  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
+  prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
   externalService = config[:externalservice][:text2textinstruct]
 
   # apply LLM specific instruct format
@@ -1176,10 +1178,11 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
     ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
+    prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
 
     try
       response = text2textInstructLLM(prompt, modelsize="medium")
+      response = GeneralUtils.deFormatLLMtext(response, "granite3")
       
       # check if response is valid
       q_number = count("Q", response)

src/llmfunction.jl

@@ -406,9 +406,10 @@ function getdata_decisionMaker(state::Dict, context::Dict, text2textInstructLLM:
       ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+    prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
     try
       response = text2textInstructLLM(prompt, modelsize="medium")
+      response = GeneralUtils.deFormatLLMtext(response, "granite3")
 
       header = ["Comprehension:", "Plan:", "Code:"]
       dictkey = ["comprehension", "plan", "code"]
@@ -627,12 +628,13 @@ function extractContent_dataframe(df::DataFrame, text2textInstructLLM::Function,
     ]
 
   # put in model format
-  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+  prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
   header = ["About_resulting_table:", "Search_summary:"]
   dictkey = ["about_resulting_table", "search_summary"]
 
   for i in 1:5
     response = text2textInstructLLM(prompt, modelsize="medium")
+    response = GeneralUtils.deFormatLLMtext(response, "granite3")
 
     kw = []
     # use for loop and detect_keyword function to get the exact variation of each keyword in the text then push to kw list
@@ -762,13 +764,14 @@ function getTableNameFromSQL(sql::T, text2textInstructLLM::Function)::Vector{Str
     ]
 
   # put in model format
-  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+  prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
   header = ["Table_name:"]
   dictkey = ["table_name"]
 
   for attempt in 1:5
     try
       response = text2textInstructLLM(prompt, modelsize="medium")
+      response = GeneralUtils.deFormatLLMtext(response, "granite3")
       responsedict = GeneralUtils.textToDict(response, header; 
                                             dictKey=dictkey, symbolkey=true)
       response = copy(JSON3.read(responsedict[:table_name]))
@@ -914,7 +917,7 @@ function compareState(question::String, highValueStateList::Vector{T},
     ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+    prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
 
     header = ["Comparison:", "Rationale:", "Selected_response_number:"]
     dictkey = ["comparison", "rationale", "selected_response_number"]
@@ -924,6 +927,7 @@ function compareState(question::String, highValueStateList::Vector{T},
     # sometime LLM output something like **Comprehension**: which is not expected
     response = replace(response, "**"=>"")
     response = replace(response, "***"=>"")
+    response = GeneralUtils.deFormatLLMtext(response, "granite3")
 
     # make sure every header is in the response
     for i in header