Update app.py

app.py

@@ -43,6 +43,8 @@ load_dotenv()  #
 groq_api_key = "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" # os.environ['GROQ_API_KEY']
 print("groq_api_key: ", groq_api_key)
 
+# st.title("Chat with Docs - Groq Edition :) ")
+st.title("Literature Based Research (LBR) - A. Unzicker and J. Bours - Chat with Docs - Groq Edition (Very Fast!) - VERSION 3 - March 8 2024")
 
 if "vector" not in st.session_state:
 
@@ -104,9 +106,20 @@ if "vector" not in st.session_state:
     
     st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
     st.session_state.documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
-    # 
+    # https://python.langchain.com/docs/integrations/vectorstores/faiss
+    # docs_and_scores = db.similarity_search_with_score(query)
+    # Saving and loading
+    # You can also save and load a FAISS index. 
+    # This is useful so you don’t have to recreate it everytime you use it.
+    # db.save_local("faiss_index")
+    # new_db = FAISS.load_local("faiss_index", embeddings)
+    # docs = new_db.similarity_search(query)
+    # docs[0]
+    # Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})
+    #
     # st.session_state.vector = FAISS.from_documents(st.session_state.documents, st.session_state.embeddings) # ORIGINAL
     st.session_state.vector = FAISS.from_documents(st.session_state.documents, st.session_state.embeddings) # ORIGINAL
+    st.session_state.vector.save_local("faiss_index")
     # ZIE: 
     # ZIE VOOR EEN APP MET CHROMADB:
     # https://github.com/vndee/local-rag-example/blob/main/rag.py
@@ -117,7 +130,7 @@ if "vector" not in st.session_state:
 
     
 # st.title("Chat with Docs - Groq Edition :) ")
-st.title("Literature Based Research (LBR) - A. Unzicker and J. Bours - Chat with Docs - Groq Edition (Very Fast!) - VERSION 3 - March 8 2024")
+# st.title("Literature Based Research (LBR) - A. Unzicker and J. Bours - Chat with Docs - Groq Edition (Very Fast!) - VERSION 3 - March 8 2024")
 
 llm = ChatGroq(
             temperature=0.2,