Guillaume Laforge is a Developer Advocate for Google Cloud, where he specializes in Generative AI, service orchestration, and serverless compute solutions. He is also a Java Champion and the co-creator of the Apache Groovy programming language.
LangChain4j was presented for the first time at Devoxx.be 2023 and since then it has met a lot of interest from Java Developers.
Come to this panel discussion where member of the LangChain4j community discuss the present and future of the project.
You can submit your questions to the panelists at any time using this form: https://forms.gle/VDR5ghpY2sfrCbKs7
See you there!
Come to our BOF to discuss with members of the LangChain4j community the present and future of the project!
It’s easy to get started with Retrieval Augmented Generation, but you’ll quickly be disappointed with the generated answers: inaccurate or incomplete, missing context or outdated information, bad text chunking strategy, not the best documents returned by your vector database, and the list goes on.
After meeting thousands of developers across Europe, we’ve explored those pain points, and will share with you how to overcome them. As part of the team building a vector database we are aware of the different flavors of searches (semantic, meta-data, full text, multimodal) and embedding model choices. We have been implementing RAG pipelines across different projects and frameworks and are contributing to LangChain4j.
In this deep-dive, we will examine various techniques using LangChain4j to bring your RAG to the next level: with semantic chunking, query expansion & compression, metadata filtering, document reranking, data lifecycle processes, and how to best evaluate and present the results to your users.
Embarking on your RAG journey may seem effortless, but achieving satisfying results often proves challenging. Inaccurate, incomplete, or outdated answers, suboptimal document retrieval, and poor text chunking can quickly dampen your initial enthusiasm.
In this session, we'll leverage LangChain4j to elevate your RAG implementations. We'll explore:
- Advanced Chunking Strategies: Optimize document segmentation for improved context and relevance.
- Query Refinement Techniques: Expand and compress queries to enhance retrieval accuracy.
- Metadata Filtering: Leverage metadata to pinpoint the most relevant documents.
- Document Reranking: Reorder retrieved documents for optimal result presentation.
- Data Lifecycle Management: Implement processes to maintain data freshness and relevance.
- Evaluation and Presentation: Assess the effectiveness of your RAG pipeline and deliver results that meet user expectations.
Join us as we transform your simplistic RAG experience from one of frustration to delight your users with meaningful and accurate answers.
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