☕️ Welcome to this workshop to build your own Java AI Agent using Retrieval Augmented Generation.

🏆 It leverages the best of open-source for fast implementation of the RAG pattern for production quality applications.

🤩 The completed RAG ChatBot will demonstrate how your AI Agent can do

• LLM and Prompt Engineering
• Conversational Memory
• Vector Similarity Searching and Dense Passage Retrieval
• Transform, chunk, and vectorise unstructured files like PDFs
• Caching of LLM responses for latency and cost performance
• Reranking of search results
• Vector calculations using JVector
• Online searching using the Tavily service
• Hybrid Searching
• Closed Loop Feedback System
• LLM Function Calling
• Time Series Vector Similarity Searching

♻️ This workshop uses Java 21, Spring AI and Vaadin for the UI. The use of Spring and Vaadin is minimal, the code is intended to be re-used in other frameworks.

👩‍ It is CQL compatbile with Apache Cassandra® 5.0 and AstraDB Vector. Database schemas and data models are intentionally flexible so the concepts in the workshop can be retrofitted to your needs and your production.

🙇‍ The workshop will use the services: OpenAI, Tavily, and AstraDB. You will need accounts and api keys for each of these.

🌴 Each step in the workshop is a separate branch in this repository, you will need to be familiar with git switching between branches.

💪🏽 To move on to read the requirements setup step, do the following:

git switch workshop-intro-requirements

• Java 21 (or beyond)
• Command line git and git knowledge
• DataStax Astra DB (you can sign up through your Github account)
• OpenAI account (you can sign up through your Github account)
• Tavily account (you can sign up through your Github account)
• An editor of your choice

All work is copyrighted to DataStax, Inc