removed table name and fixed new table names

backend/semantic_search.py

@@ -3,18 +3,32 @@ import os
 import gradio as gr
 from sentence_transformers import SentenceTransformer
 
-
 db = lancedb.connect(".lancedb")
 
-TABLE = db.open_table(os.getenv("TABLE_NAME"))
+MODEL1_STRATEGY1 = "model1_fixed.lance"
+MODEL2_STRATEGY1 = "model2_fixed.lance"
+MODEL3_STRATEGY1 = "model3_fixed.lance"
+
 VECTOR_COLUMN = os.getenv("VECTOR_COLUMN", "vector")
 TEXT_COLUMN = os.getenv("TEXT_COLUMN", "text")
 BATCH_SIZE = int(os.getenv("BATCH_SIZE", 32))
 
 retriever = SentenceTransformer(os.getenv("EMB_MODEL"))
 
+def get_table_name():
+    emb_model = os.getenv("EMB_MODEL")
+    if emb_model == "sentence-transformers/all-MiniLM-L6-v2":
+        return MODEL1_STRATEGY1
+    elif emb_model == "BAAI/bge-large-en-v1.5":
+        return MODEL2_STRATEGY1
+    elif emb_model == "openai/text-embedding-ada-002":
+        return MODEL3_STRATEGY1
+    else:
+        raise ValueError(f"Unsupported embedding model: {emb_model}")
 
 def retrieve(query, k):
+    table_name = get_table_name()
+    TABLE = db.open_table(table_name)
     query_vec = retriever.encode(query)
     try:
         documents = TABLE.search(query_vec, vector_column_name=VECTOR_COLUMN).limit(k).to_list()
@@ -23,4 +37,4 @@ def retrieve(query, k):
         return documents
 
     except Exception as e:
-        raise gr.Error(str(e))
+        raise gr.Error(str(e))