Antonio Goncalves is a senior developer living in Paris. He evolved in the Java EE landscape for a while and then moved on to Spring, Micronaut, Quarkus and now Intelligent Applications. From distributed systems to microservices and cloud, today he helps his customers to develop the architecture that suits them the best.
Aside from working with customers, Antonio wrote a few books (Java EE and Quarkus), talks at international conferences (Devoxx, JavaOne, GeeCon…), writes technical papers and articles, gives on-line courses (PluralSight, Udemy) and co-presents the Technical French pod cast Les Cast Codeurs. He has co-created the Paris JUG, Voxxed Microservices and Devoxx France.
For all his work for the community he has been made Java Champion a few years ago.
Come to our BOF to discuss with members of the LangChain4j community the present and future of the project!
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 (operations 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
- Tweak your RAG integration
- Optimize for quality, cost or size
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.
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