
Learn to build applications with LLMs using LangChain, covering prompt templates, LLM setup, and chain creation. Gain practical insights through code examples and explore advanced techniques like few-shot prompting.

Introduction to LangChain framework for building apps with large language models, comparing GPT-3 and open-source alternatives, and exploring key components for advanced NLP applications.

Explore the future of AI with LLMs, LangChain, and Pinecone. Learn to build powerful applications using cutting-edge tools and techniques in this comprehensive workshop led by industry experts.

Learn to create a chatbot and conversational agent using Falcon 40B, the top open-source LLM, with Hugging Face Transformers and LangChain. Explore code generation, refactoring, and agent capabilities.

Explore chatbot memory implementation in LangChain, including Conversation Chain, Summary Memory, and Buffer Window Memory. Learn to enhance AI conversations with context retention.

Learn to enhance Llama 2 using Retrieval Augmented Generation (RAG). Build a pipeline with Pinecone, Llama 2 13B, and integrate it using Hugging Face and LangChain for improved, up-to-date language model performance.

Explore the cutting-edge world of Agentic Retrieval-Augmented Generation (RAG) systems in this 31-minute conference talk from Devoxx. Discover how to construct an advanced Agentic RAG system using open-source technologies like Langchain, Ollama, and Milv…

Explore the RAG (retrieval-augmented generation) pattern in this 31-minute conference talk from Devoxx. Learn the fundamental concepts behind RAG and gain hands-on experience setting up a practical RAG infrastructure using Docker. Discover how to configu…

Explore the integration of Java with AI and large language models in this comprehensive conference talk. Learn how to consume, train/retrain, and infer various AI models, including LLMs like OpenAI, Llama, Gemma, and Mistral. Discover practical applicati…

Embark on a comprehensive deep-dive into Retrieval Augmented Generation (RAG) techniques, from basic implementations to advanced strategies. Learn how to overcome common challenges such as inaccurate or incomplete answers, outdated information, and ineff…

Explore the evolution and capabilities of Quarkus/LangChain4j integration in this conference talk. Discover how Quarkus provides an ideal runtime for developing intelligent Java applications, leveraging LangChain4j's potential. Learn about seamless CDI i…

Develop AI-infused GitHub bots using Quarkus GitHub App Framework and LangChain4j to enhance workflows, automate tasks, and boost productivity in your development process.

Explore LLM limits through outrageous applications using LangChain4j and Quarkus. Witness complex tasks, potential havoc, and self-evaluating models in this thrilling journey of AI possibilities.

Implement efficient local LLM inference in Java, leveraging modern features for CPU-based performance without specialized hardware. Explore flexible frameworks adaptable to multiple architectures.

Explore Java development with AI using LangChain4J and Spring AI. Learn to extract information from images for automatic form-filling using Azure OpenAI and Container Apps in a microservice architecture.

Elevate RAG implementations using LangChain4j. Master advanced chunking, query refinement, metadata filtering, document reranking, data lifecycle management, and evaluation techniques for accurate, relevant results.

Explore GraphRAG: enhancing LLMs with knowledge graphs for improved context and accuracy in generative tasks. Learn to extract, correlate, and navigate data using Neo4j and LangChain.

Enhance Java apps with Python using GraalPy. Embed Python code, install packages, and explore use cases like AI integration, data visualization, and multilingual scripting in Java projects.

Explore LangChain's core concepts and build LLM-based applications using Retrieval Augmented Generation for document-based question answering.

Explore Retrieval Augmented Generation using LangChain. Learn to build and optimize question-answering systems with vector stores, embedding models, and LLMs for private data applications.

Explore Retrieval Augmented Generation using LangChain to build a question-answering system with private data, leveraging vector stores, embedding models, and LLMs.