Update app.py

app.py

@@ -28,17 +28,6 @@ options = {
                 'RAG': True}
 }
 
-#option: 
-#  systemRole, systemStyle, instruction
-
-#persist_directory1 = './DB_Decreti'
-#embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
-#db = Chroma(persist_directory=persist_directory1, embedding_function=embedding)
-#NumeroDocumenti = 10  
-#query = 'Come funziona la generazione delle PDA'      
-#result = db.similarity_search(query, k=NumeroDocumenti)
-
-
 st.set_page_config(page_title="BonsiAI", page_icon="🤖")
 
 def gen_augmented_prompt(prompt, top_k) :
@@ -109,9 +98,6 @@ def chat_box() :
             st.markdown(message["content"])
 
 def formattaPrompt(prompt, systemRole, systemStyle, instruction):
-    
-    lunghezza = 'Attenzione! Il testo generato deve essere lungo ' + st.session_state.max_tokens*2 + ' CARATTERI'
-    
     if instruction.startswith("http"):
         try:
             with st.spinner("Ricerca in Drive...") :
@@ -170,10 +156,20 @@ def show_source(links) :
         for i, link in enumerate(links) :
             st.info(f"{link}")
 
+def init_vector_db(){
+    persist_directory1 = './DB_Decreti'
+    embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
+    db = Chroma(persist_directory=persist_directory1, embedding_function=embedding)
+    #NumeroDocumenti = 10  
+    #query = 'Come funziona la generazione delle PDA'      
+    #result = db.similarity_search(query, k=NumeroDocumenti)
+}
+
 init_state()
 sidebar()
 header()
 chat_box()
+init_vector_db()
 
 if prompt := st.chat_input("Chatta con BonsiAI..."):
     st.chat_message("user").markdown(prompt)