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Build your own Java RAG AI Agent

☕️ 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 vaadin spring tika openai cassandra tavily


All work is copyrighted to DataStax, Inc   

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