Ready for production

Author: michelatdatastax

.streamlit/secrets.toml.example

@@ -21,4 +21,9 @@ datastax = "password_abc"
 # Follow the rule: username = "localization_code"
 # Where localization_code defines the language from localization.csv
 postnl = "nl_NL"
-datastax = "en_US"
+datastax = "en_US"
+
+[delete_option]
+# Follow the rule: username = "delete_option"
+# Where delete_option = True allows the 'Delete Context' button to be shown for that username
+michel = "True"

app.py

@@ -73,20 +73,16 @@ def check_password():
 
     def login_form():
         """Form with widgets to collect user information"""
-        with st.form("Credentials"):
+        with st.form("credentials"):
             st.text_input('Username', key='username')
             st.text_input('Password', type='password', key='password')
             st.form_submit_button('Login', on_click=password_entered)
 
     def password_entered():
         """Checks whether a password entered by the user is correct."""
-        if st.session_state['username'] in st.secrets[
-            "passwords"
-        ] and hmac.compare_digest(
-            st.session_state['password'],
-            st.secrets.passwords[st.session_state['username']],
-        ):
+        if st.session_state['username'] in st.secrets['passwords'] and hmac.compare_digest(st.session_state['password'], st.secrets.passwords[st.session_state['username']]):
             st.session_state['password_correct'] = True
+            st.session_state.user = st.session_state['username']
             del st.session_state['password']  # Don't store the password.
         else:
             st.session_state['password_correct'] = False
@@ -102,8 +98,8 @@ def password_entered():
     return False
 
 def logout():
-    del st.session_state['password_correct']
-    del st.session_state['username']
+    del st.session_state.password_correct
+    del st.session_state.user
 
 # Function for Vectorizing uploaded data into Astra DB
 def vectorize_text(uploaded_files):
@@ -174,7 +170,7 @@ def load_rails(username):
 if not check_password():
     st.stop()  # Do not continue if check_password is not True.
 
-username = st.session_state['username']
+username = st.session_state.user
 language = st.secrets.languages[username]
 lang_dict = load_localization(language)
 
@@ -335,24 +331,23 @@ def load_prompt():
                 memory.clear()
 
 # Drop the vector data and start from scratch
-with st.sidebar:
-    with st.form('delete_context'):
-        st.caption(lang_dict['delete_context'])
-        submitted = st.form_submit_button(lang_dict['delete_context_button'])
-        if submitted:
-            with st.spinner(lang_dict['deleting_context']):
-                vectorstore.clear()
-                memory.clear()
-                st.session_state.clear()
-                st.session_state.messages = [AIMessage(content=lang_dict['assistant_welcome'])]
+if (username in st.secrets['delete_option'] and st.secrets.delete_option[username] == 'True'):
+    with st.sidebar:
+        with st.form('delete_context'):
+            st.caption(lang_dict['delete_context'])
+            submitted = st.form_submit_button(lang_dict['delete_context_button'])
+            if submitted:
+                with st.spinner(lang_dict['deleting_context']):
+                    vectorstore.clear()
+                    memory.clear()
+                    st.session_state.messages = [AIMessage(content=lang_dict['assistant_welcome'])]
 
 # Draw rails
 with st.sidebar:
         st.subheader(lang_dict['rails_1'])
         st.caption(lang_dict['rails_2'])
         for i in rails_dict:
-            if i>1:
-                st.markdown(f"{i-1}. {rails_dict[i]}")
+            st.markdown(f"{i}. {rails_dict[i]}")
 
 # Draw all messages, both user and agent so far (every time the app reruns)
 for message in st.session_state.messages:
@@ -390,16 +385,15 @@ def load_prompt():
         print(f"Using chain: {chain}")
 
         # Call the chain and stream the results into the UI
-        callback = StreamHandler(response_placeholder)
-        response = chain.invoke({'question': question, 'chat_history': history}, config={'callbacks': [callback]})
+        response = chain.invoke({'question': question, 'chat_history': history}, config={'callbacks': [StreamHandler(response_placeholder)]})
         print(f"Response: {response}")
         content = response.content
 
         # Write the sources used
         relevant_documents = retriever.get_relevant_documents(question)
-        content += """
+        content += f"""
         
-*The following context was used for this answer:*  
+*{lang_dict['sources_used']}:*  
 """
         sources = []
         for doc in relevant_documents:
@@ -421,4 +415,4 @@ def load_prompt():
         st.session_state.messages.append(AIMessage(content=content))
 
 with st.sidebar:
-            st.caption("v11.07.01")
+            st.caption("v11.20.01")

datastax.md

@@ -0,0 +1,26 @@
+## Your personal effectivity booster
+Generative AI is considered to bring the next Industrial Revolution.
+
+Why? Studies show a **37% efficiency boost** in day to day work activities!
+
+#### What is this app?
+This app is a Chat Agent which takes into account Enterprise Context to provide meaningfull and contextual responses.
+Why is this a big thing? It is because the underlying Foundational Large Language Models are not trained on Enterprise Data. They have no way of knowing anything about your organization.
+Also they are trained upon a moment in time, so typically miss out on relevant and recent information.
+
+#### What does it know?
+De app has been pre-loaded with the follpowing information:
+- [DataStax homepage](https://www.datastax.com)
+- [Vector search for Genrative AI Apps](https://www.datastax.com/resources/whitepaper/vector-search-for-generative-ai-apps)
+- [Astra DB Datasheet](https://cdn.sanity.io/files/bbnkhnhl/production/325d36f40acc590b146ea9d77442cf4ed1da9eac.pdf)
+- [Digital Champion: Priceline](https://www.datastax.com/digital-champions/martin-brodbeck-delivers-the-best-travel-products-to-priceline-customers-by-leveraging-real-time-ai)
+- [Luna for Apache Cassandra](https://www.postnl.nl/zakelijke-oplossingen/duurzaamheid/)
+
+This means you can start interacting with your personal assistant based on the above information!
+
+#### Add additonal content
+Just drop a PDF or Text file into the upload box in the sidebar and hit `Save`.
+
+By the way... Be careful with the `Delete context` button. As this will do exactly that. It deletes the preloaded content mentioned above rendering the personal assistant non-contextual 😉
+
+---

documents/DataStax Astra DB Datasheet April 2023.pdf

@@ -0,0 +1,129 @@
+Datastax is an AI company focussing on Generative AI with their Vector Database Astra DB.
+
+DATASTAX ASTRA DB
+Vector and RAG Made Easier
+Astra DB gives developers the APIs, real-time data and complete ecosystem integrations to put accurate RAG and Gen AI apps in production - FAST.
+
+START FOR FREE
+
+ASTRA DB VECTOR
+Register now to try the new Astra DB vector experience in public preview.
+
+
+ASTRA ASSISTANTS API
+Unify data and LLMs within a single API within our Astra DB vector database.
+
+
+RAG IN PRODUCTION
+See Skypoint Cloud’s LangChain RAG app in production.
+
+
+VERCEL & ASTRA DB
+Transform Product Docs into a RAG-Powered App with Vercel and Astra DB.
+
+Real-time Vector data for RAG
+Accurate Gen AI
+Accurate Gen AI
+Vector updates available with ZERO latency delays.
+
+Production-level
+Production-level
+High performance and serverless for distributed production workloads.
+
+Proven and Trusted
+Proven and Trusted
+Built on Cassandra, choice of AI leaders, with enterprise security and compliance.
+
+DEVELOPERS
+RAG Made Easier
+Elegant APIs and powerful integrations for production-level RAG and FLARE.
+
+TRY FOR FREE
+EXPLORE EXAMPLES
+Create
+Create a table using the VECTOR type
+
+Load
+Easily load vectors like any other data type
+
+Index
+Create a Storage Attached Index which enables high performance ANN (Approximate Nearest Neighbor) search
+
+Query
+Query to perform similarity search
+
+CREATE TABLE my_vectors (my_id text PRIMARY KEY, my_vector vector<float, 3>);
+Generative AI Leaders Shaping their Industries
+Bolstering Bookings with Real-time AI and Vector Search
+Transforming Banking Through Generative AI
+Improving Operational Efficiency with GenAI
+Empowering Financial Management with Gen AI
+Banking on Astra DB to Provide Real-Time Financial Transaction Data Enrichment with AI
+Bolstering Bookings with Real-time AI and Vector Search
+Priceline leverages real-time data infrastructure and Generative AI to build highly personalized experiences for customers, combining AI with real-time vector search.
+
+LEARN MORE
+“Priceline has been at the forefront of using machine learning for many years. Vector search gives us the ability to semantically query the billions of real-time signals we receive as part of our checkout experience that flow back to Astra DB. We plan to use Google Cloud’s Generative AI capabilities alongside Astra DB’s vector search to power our real time data infrastructure and Generative AI experiences."
+
+Martin Brodbeck
+CTO, Priceline
+Transforming Banking Through Generative AI
+Capital One has consistently invested in technologies that reinvent the banking and financial services industry. Capital One leverages Generative AI  for more engaging and human-centered digital banking experiences, all facilitated by instant access to real-time data.
+
+LEARN MORE
+ “Vector search is a really important aspect of Generative AI. It has multiple applications for example, holding in personalized or contextually relevant information to those large language model interactions to make them more contextually relevant.”
+
+Kel Vanee
+VP of Machine Learning Engineering
+Improving Operational Efficiency with GenAI
+SkyPoint AI is revolutionizing senior care through its use of Generative AI healthcare solutions. The company builds real-time AI applications with vector search, empowered by advanced LLMs and AI agents.
+
+LEARN MORE
+“Finding specific policies and operating procedures is time-consuming and challenging due to scattered storage across various platforms. Using Astra DB's vector search capabilities, SkyPoint can leverage these benefits to enhance its services, offering users real-time, contextual, and personalized solutions. This leads to accurate predictions and personalized healthcare outcomes at a lower cost.”
+
+Tisson Mathew
+Founder & CEO, SkyPoint AI
+Empowering Financial Management with Gen AI
+Macquarie Bank utilizes DataStax and Bard to facilitate a customized search experience, allowing users to search for and categorize their payments, enhancing their financial management capabilities.
+
+LEARN MORE
+"In 2017, we started to use DataStax. For the first time in the bank's history, we can provide a real 360-degree view of customers—not only to internal people but also to the customers: all the products in a nice, simple view. Personalization will be more and more important. In our opinion, that will be the key differentiation.”
+
+Luis Uguina
+Chief Digital Officer, Macquarie
+Banking on Astra DB to Provide Real-Time Financial Transaction Data Enrichment with AI
+The world's leading financial companies build with Bud's simple APIs to turn messy financial data into powerful customer insights. With Astra DB, Bud has a reliable platform for its data that lets its internal team concentrate on new services and using financial data to create value for customers, rather than supporting our database infrastructure.
+
+"Financial services enterprises are unlocking the value of enriched transactional data with the Bud platform. We depend on DataStax Astra DB for the dynamic scalability and high performance we need to apply machine learning (ML) to real-time data. This means we can provide highly accurate, actionable insights for our clients and their customers."
+
+Michael Cullum
+VP of Engineering, Bud
+REVIEWS
+Hear from our customers
+Trust Radius icon
+Incentivized Review
+Astra DB has been a phenomenal tool, we have gained an organized workplace due to it. The data structure is so well maintained, and our employees rely working on Astra DB on daily basis. Its simple to use nature and cost effectiveness is an eye catcher. 
+Kartik Pujari
+Full Stack Engineer
+EDII Software Solutions
+Information Technology & Services | 51-200 employees
+Read Full Review
+Incentivized Review
+It has helped us keeping our data secured and organized. The working environment has become more efficient due to this. 
+Avinash Bhatt
+Senior Web Administrator
+EDII Software and Solutions
+Information Technology & Services | 51-200 employees
+Read Full Review
+READ ALL REVIEWS
+Ready to get started with Vector Search? 
+Deliver Generative AI apps on the world’s most powerful, most scalable vector database.
+
+TRY FOR FREE
+Trusted by leaders in every industry
+Capital One
+Macquarie
+Priceline
+Audi
+T-Mobile
+The Home Depot

documents/Datastax homepage.txt

@@ -0,0 +1,79 @@
+LUNA FOR APACHE CASSANDRA®
+Support mission-critical Cassandra deployments
+Deliver apps at scale with unparalleled expertise and support from the team behind the open-source Apache Cassandra®.
+
+CONTACT US
+BENEFITS
+Get more from Apache Cassandra
+Enterprise support from the experts
+Best practices, advice, and SLA-based support to keep your Cassandra deployment in top shape.
+
+Enterprise support from the experts
+Data management Expertise
+Develop Cassandra apps with your choice of API: . Build real-time data pipelines between Cassandra and upstream & downstream systems using .
+
+Data management Expertise
+Cloud-native support
+Expert support for K8ssandra which is a production-ready platform for running Cassandra on Kubernetes, with necessary metrics and tooling.
+
+Cloud-native support
+Zero downtime
+Experience 24x7x365 availability with Apache Cassandra’s active-everywhere architecture.
+
+Zero downtime
+Linear scalability
+Scale without compromising on performance or latency to seamlessly manage the most demanding real-time workloads.
+
+Linear scalability
+High performance
+Create real-time and highly-interactive customer experiences with blisteringly fast reads and writes.
+
+High performance
+CONTACT US
+Real-time data at work
+Wood Hole Oceanographic Institution
+Logate
+Delivering 90TB of oceanographic data with performance and reliability
+Woods Hole uses Luna to optimize their Apache Cassandra architecture, improve performance, and stabilize access to oceanographic observation data with increased reliability.
+
+LEARN MORE
+“I recommend anybody using Cassandra who is unsure if they are getting the most out of it to try Luna. It's been great.”
+
+Jeff Glatstein
+Data Delivery Manager at WHOI
+Expert support with Luna delivers a richer Cassandra experience.
+Logate relies on Apache Cassandra to deliver cloud-ready apps that scale without service outages and automatically balance loads across nodes. Apache Cassandra, combined with expert support from DataStax Luna, delivers more availability, better redundancy, and easier scaling for Logate customers.
+
+LEARN MORE
+“We found the educational material around Cassandra from DataStax incredibly helpful to our team as we scaled up our deployments, and we also used DataStax Luna so we could have consulting support available to us. Luna makes it easy for us to get the help we need whenever we need it.”
+
+Danilo Mišović
+Technical Lead at Logate
+Packages
+Luna Standard for Apache Cassandra
+For small production environments
+
+CONTACT US
+Node-based pricing (20-150 nodes)
+Unlimited tickets
+24x7 support coverage
+1 hour urgent response time
+Luna Plus for Apache Cassandra
+For large production environments
+
+CONTACT US
+Node-based pricing (150 nodes +)
+Unlimited tickets
+24x7 support coverage
+1 hour urgent response time
+Support for additional open source projects including K8ssandra (deployments of 150+ nodes)
+Access to Solution Architects
+Premium support add-on
+An enterprise support partnership
+
+CONTACT US
+Access to Named Engineer/Technical Account Manager
+15 minute urgent response time
+Engineering escalation
+Community approved patch support
+Long term support for Apache Cassandra versions beyond end of support from the project