lülülülü

lib.py

@@ -55,7 +55,7 @@ def prompt_embedding(prompt):
     )
     cur = conn.cursor()
 
-    cur.execute(f"select id, doc_id, filepath, embedding <-> %s::vector as distance from dbtable order by distance limit 3;", (embedding["embedding"],))
+    cur.execute(f"select id, filepath, embedding <-> %s::vector as distance from dbtable order by distance limit 3;", (embedding["embedding"],))
 
     result = cur.fetchall()
 

main.py

@@ -49,7 +49,6 @@ def create_embeddings(pContent):
     create_table_query = '''
         create table if not exists dbtable (
         id SERIAL PRIMARY KEY,
-        doc_id Serial NOT NULL,
         filepath TEXT NOT NULL,
         embedding VECTOR NOT NULL
         );
@@ -67,7 +66,7 @@ def create_embeddings(pContent):
             # print(merged_info)
             response = ollama.embeddings(model="mxbai-embed-large", prompt=merged_info)
             #embedding_list.append(response["embedding"])
-            insert_data = f"insert into dbtable (filepath, embedding) Values ('{content['path']}', %s) Returning doc_id;"
+            insert_data = f"insert into dbtable (filepath, embedding) Values ('{content['path']}', %s) Returning id;"
             cur.execute(insert_data, (response["embedding"],))
             doc_id = cur.fetchone()[0]
             insert_data_mongo(doc_id, chunk)