use qdrant instead

app.py

@@ -6,6 +6,8 @@ from langchain.document_loaders import TextLoader
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.vectorstores import FAISS
+from langchain_community.vectorstores import Qdrant
+
 from langchain.memory import ConversationBufferMemory
 from langchain.document_loaders import TextLoader
 from tempfile import NamedTemporaryFile
@@ -15,11 +17,13 @@ def main():
 # Initialize the Streamlit app
     st.title('Document-Based Q&A System')
 
-# API Key input securely
-    api_key = st.text_input("Enter your OpenAI API key:", type="password")
-    if api_key:
-        os.environ["OPENAI_API_KEY"] = api_key
-        st.success("API Key has been set!")
+# API Key input securely, API KEY defined in settings
+#   api_key = st.text_input("Enter your OpenAI API key:", type="password")
+#    if api_key:
+#        os.environ["OPENAI_API_KEY"] = api_key
+#        st.success("API Key has been set!")
+
+    
 
 # File uploader
     uploaded_file = st.file_uploader("Upload your document", type=['txt'])
@@ -35,9 +39,15 @@ def main():
         data = text_splitter.split_documents(data)
 
     # Create vector store
+        
         embeddings = OpenAIEmbeddings()
-        vectorstore = FAISS.from_documents(data, embedding=embeddings)
-
+        #vectorstore = FAISS.from_documents(data, embedding=embeddings)
+        vectorstore = Qdrant.from_documents(
+            data,
+            embeddings,
+            location=":memory:",  # Local mode with in-memory storage only
+            collection_name="my_documents",
+    )
     # Create conversation chain
         llm = ChatOpenAI(temperature=0.3, model_name="gpt-4-turbo")
         memory = ConversationBufferMemory(