Update app.py

app.py

@@ -15,9 +15,9 @@ from langchain_community.embeddings import OllamaEmbeddings
 # JB:
 from langchain.embeddings import FastEmbedEmbeddings
 
-# from langchain_community.vectorstores import FAISS
+from langchain_community.vectorstores import FAISS
 # from langchain.vectorstores import Chroma
-from langchain_community.vectorstores import Chroma
+# from langchain_community.vectorstores import Chroma
 
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chains.combine_documents import create_stuff_documents_chain
@@ -45,17 +45,17 @@ 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)
     # 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
     # ZIE: 
     # ZIE VOOR EEN APP MET CHROMADB:
     # https://github.com/vndee/local-rag-example/blob/main/rag.py
     # https://raw.githubusercontent.com/vndee/local-rag-example/main/rag.py
     # Chroma.from_documents(documents=chunks, embedding=FastEmbedEmbeddings())
-    st.session_state.vector = Chroma.from_documents(st.session_state.documents, st.session_state.embeddings) # JB
+    # st.session_state.vector = Chroma.from_documents(st.session_state.documents, st.session_state.embeddings) # JB
 
 
 # st.title("Chat with Docs - Groq Edition :) ")
-st.title("Literature Based Research (LBR) - Alexander Unzicker and Jan Bours - Chat with Docs - Groq Edition (Very Fast!) ")
-
+st.title("Literature Based Research (LBR) - Alexander Unzicker and Jan Bours - Chat with Docs - Groq Edition (Very Fast!) - 2")
 
 llm = ChatGroq(
             groq_api_key=groq_api_key,