Antonio Goncalves
Microsoft
Antonio is a Principal Software Engineer at Microsoft living in Paris. He evolves in the Jakarta EE, Spring, Micronaut, Quarkus and now AI landscapes. From distributed systems to microservices and the cloud, today he helps his customers to develop the architecture that suits them the best.Aside from working for his customers, Antonio writes books (Java EE and Quarkus), creates online courses, talks at international conferences (Devoxx, JavaOne, GeeCon…), writes technical papers, articles and co-presents on the Technical French pod cast called Les Cast Codeurs. He has also co-created the Paris JUG, Voxxed Microservices and Devoxx France. For all his work for the community Antonio has been made Java Champion.
AI technologies, and particularly large language models (LLMs), have been popping up like mushrooms lately. But how can you use them in your applications?
In this workshop, we will use a chatbot to interact with GPT-4 and implement the Retrieval Augmented Generation (RAG) pattern. Using a vector database, the model will be able to answer questions in natural language and generate complete, sourced responses from your own documents. To do this, we will create a Quarkus service based on the open-source LangChain4J and ChatBootAI frameworks to test our chatbot. Finally, we will deploy everything to the Cloud.
After a short introduction to language models (operation and limitations), and prompt engineering, you will:
At the end of the workshop, you will have a clearer understanding of large language models and how they work, as well as ideas for using them in your applications. You will also know how to create a functional knowledge base and chatbot, and how to deploy them in the cloud.
More In this workshop, we will use a chatbot to interact with GPT-4 and implement the Retrieval Augmented Generation (RAG) pattern. Using a vector database, the model will be able to answer questions in natural language and generate complete, sourced responses from your own documents. To do this, we will create a Quarkus service based on the open-source LangChain4J and ChatBootAI frameworks to test our chatbot. Finally, we will deploy everything to the Cloud.
After a short introduction to language models (operation and limitations), and prompt engineering, you will:
- Create a knowledge base: local HuggingFace LLMs, embeddings, a vector database, and semantic search
- Use LangChain4J to implement the RAG (Retrieval Augmented Generation) pattern
- Create a Quarkus API to interact with the LLM: OpenAI / AzureOpenAI
- Use ChatBootAI to interact with the Quarkus API
- Improve performance thanks to prompt engineering
- Containerize the application
- Deploy the containerized application to the Cloud
At the end of the workshop, you will have a clearer understanding of large language models and how they work, as well as ideas for using them in your applications. You will also know how to create a functional knowledge base and chatbot, and how to deploy them in the cloud.
