From e5b19dd268ae659d92b273271806cd5bffa0cbb4 Mon Sep 17 00:00:00 2001
From: narawat <narawat@gmail.com>
Date: Wed, 15 Jul 2026 12:14:32 +0700
Subject: [PATCH] update

---
 Project.toml       |  2 +-
 src/llmfunction.jl | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/Project.toml b/Project.toml
index 855138b..06bd2bc 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.6.0"
+version = "0.6.1"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
 
 [deps]
diff --git a/src/llmfunction.jl b/src/llmfunction.jl
index 18840d7..3e2140e 100644
--- a/src/llmfunction.jl
+++ b/src/llmfunction.jl
@@ -519,11 +519,11 @@ function generatesql(a::T, searchterm::String,
   # provide similar sql only for the first attempt
   sql, distance = a.context.similarSQLVectorDB(searchterm)
 
-  similarSQL_ = sql !== nothing ? sql : "None"
-  # if sql is really close, just use it
-  if similarSQL_ != "None" && distance <= 0.1
-    return similarSQL_
-  end
+  # similarSQL_ = sql !== nothing ? sql : "None"
+  # # if sql is really close, just use it
+  # if similarSQL_ != "None" && distance <= 0.1
+  #   return similarSQL_
+  # 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.