diff --git a/src/llmUtil.jl b/src/llmUtil.jl
index 44516f5..e0cbef0 100644
--- a/src/llmUtil.jl
+++ b/src/llmUtil.jl
@@ -1,7 +1,7 @@
 module llmUtil
 
 export formatLLMtext, extractthink, checkAgentResponse_JSON, clean_json_response,
-        extract_vector_metadata, generate_embedding_payloads, resolve_semantic_cluster,
+        extract_column_metadata, generate_embedding_payloads, resolve_semantic_cluster,
         harvest_entity_catalog, resolve_entity
 
 using UUIDs, JSON, Dates, DataFrames, StringDistances, Graphs, LibPQ
@@ -246,7 +246,7 @@ Returns a DataFrame designed for vector embedding generation.
 ```julia
 julia> using GeneralUtils
 julia> pg_conn = "host=localhost port=5432 dbname=winedb user=admin password=secret"
-julia> df = GeneralUtils.extract_vector_metadata(pg_conn)
+julia> df = GeneralUtils.extract_column_metadata(pg_conn)
 DataFrame
 6 rows × 7 columns
 table_name  column_name  data_type  column_description  is_primary_key  is_foreign_key  vector_id
@@ -259,7 +259,7 @@ products    price        numeric    Product price       false           false
 products    user_id      integer    Reference to user   false           true          col_products_user_id
 ```
 """
-function extract_vector_metadata(pg_conn_str::String)::DataFrame
+function extract_column_metadata(pg_conn_str::String)::DataFrame
   conn = LibPQ.Connection(pg_conn_str)
   
   # This direct SQL query pulls the column specifications along with column-level descriptions
@@ -309,12 +309,12 @@ end
 
 """ Generate embedding payloads from vector metadata DataFrame.
 
-Transforms the metadata DataFrame from `extract_vector_metadata` into a vector of
+Transforms the metadata DataFrame from `extract_column_metadata` into a vector of
 dictionaries structured for vector embedding storage and retrieval.
 
 # Arguments
 - `df::DataFrame`
-  A DataFrame with columns from `extract_vector_metadata`: `table_name`, `column_name`,
+  A DataFrame with columns from `extract_column_metadata`: `table_name`, `column_name`,
   `data_type`, `column_description`, `is_primary_key`, `is_foreign_key`, `vector_id`.
 
 # Return
@@ -335,7 +335,7 @@ The function constructs rich text payloads by:
 ```julia
 julia> using GeneralUtils
 julia> pg_conn = "host=localhost port=5432 dbname=winedb user=admin password=secret"
-julia> df = GeneralUtils.extract_vector_metadata(pg_conn)
+julia> df = GeneralUtils.extract_column_metadata(pg_conn)
 julia> payloads = GeneralUtils.generate_embedding_payloads(df)
 3-element Vector{Dict}:
  Dict("id" => "col_users_id", "text_content" => "Table: users | Column: id | Type: integer [PRIMARY KEY] | Description: User ID", "metadata" => Dict("table" => "users", "column" => "id", "type" => "integer"))