update

src/llmfunction.jl

@@ -518,8 +518,8 @@ function SQLexecution(executeSQL::Function, sql::T
     row, column = tablesize
     if row == 0
       error("\nThe resulting table has 0 row. Please try again.")
-    elseif column > 30
-      error("\nSQL execution failed. An unexpected error occurred. Please try again.")
+    elseif column > 50
+      error("\nSQL execution success but there are more than 50 rows Please be more specific.")
     end
 
     df1 =
@@ -821,7 +821,7 @@ julia>
 
 # Notes
 - The function makes up to 10 attempts to get a valid response from the LLM
-- Each state in highValueStateList should contain a thoughtHistory with action_input and observation
+- Each state in highValueStateList should contain a action_history with action_input and observation
 - The LLM evaluates attempts based on accuracy and relevance to the original question
 """
 function compareState(question::String, highValueStateList::Vector{T},
@@ -866,14 +866,14 @@ potentialSolution = []
    keys = ["action_input", "observation"]
    # extract the last action_name, action_input, observation of each state in highValueStateList and store them in a dictionary then push into potentialSolution
    for state in highValueStateList
-     thoughtHistory = state["thoughtHistory"]
+     action_history = state["action_history"]
      _, currentstate_latestIndice = 
-       GeneralUtils.findHighestIndexKey(thoughtHistory, keys[1])
+       GeneralUtils.findHighestIndexKey(action_history, keys[1])
      latestKeys = makekey.(keys, currentstate_latestIndice)
      d = Dict()
      # get the last action_name, action_input, observation of currentstate
      for (i,v) in enumerate(keys)
-       d[v] = thoughtHistory[latestKeys[i]]
+       d[v] = action_history[latestKeys[i]]
      end
      push!(potentialSolution, d)
    end