Merge pull request 'update' (#18) from v0.6.0-output_text_image into main

Reviewed-on: #18

Project.toml

@@ -1,6 +1,6 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.6.0"
+version = "0.6.3"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
 
 [deps]

src/interface.jl

@@ -474,7 +474,7 @@ function think(a::T)::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict,
 
   elseif thoughtdict["action_name"] == "SEARCH_WINE_DATABASE" 
     @info "YiemAgent think() 5 " @__LINE__
-    thoughtdict, result_raw = search_wine_database!(a, thoughtdict; useSQLLLM=true)
+    thoughtdict, result_raw = search_wine_database!(a, thoughtdict; useSQLLLM=false)
     #WORKING result_raw will be a df. i need to get images so i can send to frontend
   else
     @info "YiemAgent think() 6 " @__LINE__

src/llmfunction.jl

@@ -517,13 +517,13 @@ function generatesql(a::T, searchterm::String,
   requiredKeys = ["plan", "action_name", "action_input"]
   errornote = ""
   # provide similar sql only for the first attempt
-  sql, distance = a.context.similarSQLVectorDB(searchterm)
+  # sql, distance = a.context.similarSQLVectorDB(searchterm)
 
-  similarSQL_ = sql !== nothing ? sql : "None"
+  # similarSQL_ = sql !== nothing ? sql : "None"
-  # if sql is really close, just use it
+  # # if sql is really close, just use it
-  if similarSQL_ != "None" && distance <= 0.1
+  # if similarSQL_ != "None" && distance <= 0.1
-    return similarSQL_
+  #   return similarSQL_
-  end
+  # end
 
   #CHANGE use find_related_tables_for_user_question and inject only related table schema instead
   # of hard code table schema. CPU embedding is too slow. use embedding service on GPU. 
@@ -541,12 +541,6 @@ function generatesql(a::T, searchterm::String,
       <database_table_schema>
         $table_schema
       </database_table_schema>
-      <possible SQL for user's search term>
-        $similarSQL_
-      </possible SQL for user's search term>
-      <error_note>
-        $errornote
-      <error_note>
     </internal_context_for_assistant>
     """
   input = context * searchterm