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Quarkus and LangChain4J - A Match Made in Heaven

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Manage episode 445997956 series 2469611
Content provided by Adam Bien. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Adam Bien or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
An airhacks.fm conversation with Georgios Andrianakis (@geoand86) about:
discussion on integrating langchain4j with quarkus for enterprise AI applications, similarities between LLM integration and microservice architecture, benefits of using Java and MicroProfile for AI development, explanation of AI services, chat memory, and tools in LangChain4J, importance of session management and fault tolerance in LLM applications, vector databases and embeddings for efficient information retrieval, RAG (Retrieve Augmented Generation) implementation in enterprise settings, Quarkus dev mode features for LLM experimentation, native image support with GraalVM, local inference possibilities with Java 21's Vector API and quantized models, challenges in prompt engineering and model selection, upcoming features in LangChain4J including Ollama tool support and improved result streaming, future developments in Java for AI and GPU support with Project Babylon, importance of enterprise-grade features like CI/CD, testing, and cloud deployment for LLM applications

Georgios Andrianakis on twitter: @geoand86

  continue reading

344 episodes

Artwork
iconShare
 
Manage episode 445997956 series 2469611
Content provided by Adam Bien. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Adam Bien or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
An airhacks.fm conversation with Georgios Andrianakis (@geoand86) about:
discussion on integrating langchain4j with quarkus for enterprise AI applications, similarities between LLM integration and microservice architecture, benefits of using Java and MicroProfile for AI development, explanation of AI services, chat memory, and tools in LangChain4J, importance of session management and fault tolerance in LLM applications, vector databases and embeddings for efficient information retrieval, RAG (Retrieve Augmented Generation) implementation in enterprise settings, Quarkus dev mode features for LLM experimentation, native image support with GraalVM, local inference possibilities with Java 21's Vector API and quantized models, challenges in prompt engineering and model selection, upcoming features in LangChain4J including Ollama tool support and improved result streaming, future developments in Java for AI and GPU support with Project Babylon, importance of enterprise-grade features like CI/CD, testing, and cloud deployment for LLM applications

Georgios Andrianakis on twitter: @geoand86

  continue reading

344 episodes

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