Update app.py

app.py

@@ -59,15 +59,25 @@ def get_llm(selected_model):
     )
 
 # Cache the vector store loading
+# @st.cache_resource
+# def load_vector_store(selected_embedding_model):
+#     if selected_embedding_model == "OpenAI":
+#         embeddings = OpenAIEmbeddings(model="text-embedding-3-large", api_key=os.getenv("OPENAI_API_KEY"))
+#         return FAISS.load_local("faiss_index_medical_OpenAI", embeddings, allow_dangerous_deserialization=True)
+#     else:
+#         embeddings = HuggingFaceEmbeddings(model_name="abhinand/MedEmbed-large-v0.1")
+#         return FAISS.load_local("faiss_index_medical_MedEmbed", embeddings, allow_dangerous_deserialization=True)
+
+
 @st.cache_resource
 def load_vector_store(selected_embedding_model):
     if selected_embedding_model == "OpenAI":
         embeddings = OpenAIEmbeddings(model="text-embedding-3-large", api_key=os.getenv("OPENAI_API_KEY"))
-        return FAISS.load_local("faiss_index_medical_OpenAI", embeddings, allow_dangerous_deserialization=True)
+        return FAISS.load_local("faiss_table", embeddings, allow_dangerous_deserialization=True)
     else:
         embeddings = HuggingFaceEmbeddings(model_name="abhinand/MedEmbed-large-v0.1")
         return FAISS.load_local("faiss_index_medical_MedEmbed", embeddings, allow_dangerous_deserialization=True)
-
+        
 # Load the selected vector store
 vector_store = load_vector_store(selected_embedding_model)
 llm = get_llm(selected_model)