Presentation at the Pulsar Meetup in Bangalore on the use of vector search and generative AI when integrated with streaming. Explores mechanics of how vector embeddings work, the backdrop of retrieval augmented generation, and how vector databases like AstraDB enable a rich generative experience for AI applications.

1. ©2023 DataStax. – All rights reserved
Pulsar Meetup Bangalore
Aug 2023
Devin Bost | Jauneet Singh | Chirag Shah

2. ©2023 DataStax. – All rights reserved
2
Unifying Power: DataStax Marrying Cassandra
and Pulsar for unified real-time AI
● Scalability and Performance
● Multi-Tenancy
● Durability and Reliability
● Ecosystem and Community
● Security

3. ©2023 DataStax. – All rights reserved
3
Multi-Protocol Support for Apache Pulsar and Luna
Streaming
Enables Luna Streaming to be the drop in replacement for
existing messaging/streaming deployments
• Protect your investment in legacy technologies like JMS
and AMQP
○ TIBCO EMS
○ IBM MQ
○ RabbitMQ
• Upgrade existing deployments of Kafka with newer
infrastructure built with enterprise functionality
• Maintain existing expertise and skill sets for legacy
technologies
• Remove interoperability challenges between various
messaging/streaming platforms
• Full compatibility at the specification and feature set level
• Deploy a common infrastructure to meet all your
messaging and streaming requirements
Adding Starlight Protocol
Compatibility
**MQTT, ActiveMQ, RocketMQ coming soon

4. ©2023 DataStax. – All rights reserved
Real-Time Generative
AI

5. ©2023 DataStax. – All rights reserved
● How does GPT work (high level)?
● How do we architect streaming
applications with this technology?
● What about when we want to enrich
it with our own data?

6. ©2023 DataStax. – All rights reserved

7. ©2023 DataStax. – All rights reserved

8. ©2023 DataStax. – All rights reserved

9. ©2023 DataStax. – All rights reserved

10. ©2023 DataStax. – All rights reserved
Document Representations

11. ©2023 DataStax. – All rights reserved

12. ©2023 DataStax. – All rights reserved
Generative Pretrained Transformer
(GPT) is a type of deep learning model
designed for Sequence to Sequence
translation

13. ©2023 DataStax. – All rights reserved
Process:
1. Compute output
2. Measure loss (error)
3. Calculate gradient
4. Update neurons from gradient
5. Repeat (many times)
Transformer Architecture

14. ©2023 DataStax. – All rights reserved
pre-2021

15. ©2023 DataStax. – All rights reserved

16. ©2023 DataStax. – All rights reserved
(prompt engineering)

17. ©2023 DataStax. – All rights reserved

18. ©2023 DataStax. – All rights reserved

19. ©2023 DataStax. – All rights reserved

20. ©2023 DataStax. – All rights reserved

21. ©2023 DataStax. – All rights reserved

22. ©2023 DataStax. – All rights reserved

23. ©2023 DataStax. – All rights reserved
Code for this demo: https://github.com/devinbost/pulsar_workshop/blob/openai-
embed2/scenarios/native-pulsar/standalone-functions/openai-chat-inquiry-
function/src/main/java/com/example/pulsarworkshop/InquiryTransformFunction.java

24. ©2023 DataStax. – All rights reserved
Generative AI library: https://github.com/langstream/langstream
Step Functions: https://github.com/datastax/pulsar-transformations
Prompt Engineering: https://www.promptingguide.ai/
https://www.datastax.com/iloveai
Watch for the recordings from our
I Love AI conference.

25. ©2023 DataStax. – All rights reserved
Datastax is THE real-time AI
company
Aug 2023
Devin Bost | Jauneet Singh | Chirag Shah