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World Congress 2025 • Aug 20, 2025 • Session details

Create AI-Infused Java Apps with LangChain4j

Daniel Oh , Kevin Dubois

Think you must learn Python to build robust AI applications? Discover how LangChain4j empowers enterprise Java developers to natively integrate LLMs, RAG, and autonomous agents.

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#1 about 3 min

Navigating the AI landscape as a Java developer

Shifting focus from experimental training to enterprise integration reveals opportunities for Java applications.

#2 about 3 min

Bridging Java applications and AI models with LangChain4j

Integrating language models into enterprise frameworks provides a native experience for application developers.

#3 about 2 min

Bootstrapping Java projects for AI model providers

Configuring the necessary dependencies connects local projects to various open model specifications.

#4 about 4 min

Structuring model responses using prompt engineering annotations

Defining system messages and expected return types facilitates extracting structured data from conversational interfaces.

#5 about 2 min

Enabling continuous interactions using chat memory providers

Implementing chat memory provides stateless models with the necessary context for sequential and parallel requests.

#6 about 4 min

Connecting models to legacy systems via function calling

Annotating internal services empowers models to retrieve proprietary data or execute programmatic actions securely.

#7 about 4 min

Orchestrating tools autonomously using agent AI and MCP

Establishing a model context protocol server enables external agents to evaluate and trigger appropriate functions autonomously.

#8 about 4 min

Sanitizing AI interactions using input and output guardrails

Sanitizing data bindings prevents prompt injections and restricts models from generating harmful or inaccurate responses.

#9 about 2 min

Enhancing model accuracy with retrieval-augmented generation capabilities

Ingesting local documents into a vector store provides proprietary context to foundational models.

#10 about 5 min

Building a customer support agent with RAG and MCP

Combining database queries, document retrieval, and remote protocol execution orchestrates a comprehensive conversational support interface.

Matching moments

2:54 min

Utilizing Java frameworks to interface with AI models

Mary Grygleski Mary Grygleski · LIVE

2:55 min

Building AI interactions in Java with LangChain4j

Alex Soto Alex Soto · World Congress 2025

2:56 min

Integrating local AI models into Java Quarkus applications

Cedric Clyburn Cedric Clyburn · World Congress 2024

2:31 min

Accelerating AI delivery with Quarkus and LangChain4j

Daniel Oh Daniel Oh · World Congress 2026 Europe

3:36 min

Overview of generative AI and the presentation agenda

juarezjunior juarezjunior · World Congress 2024

1:35 min

Addressing the Python bias in AI frameworks for Java

Daniel Oh Daniel Oh · World Congress 2025

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