Exploring ONNX, Embedding Models, and Retrieval Augmented Generation (RAG) with Langchain4j

An airhacks.fm conversation with Dmytro Liubarskyi (@langchain4j)
about: Dmytro previously on "#285 How LangChain4j Happened",
discussion about ONNX format and runtime for running neural network
models in Java, using langchain4j library for seamless integration
and data handling, embedding models for converting text into vector
representations, strategies for handling longer text inputs by
splitting and averaging embeddings, overview of the retrieval
augmented generation (RAG) pipeline and its components, using
embeddings for query transformation, routing, and data source
selection in RAG, integrating Langchain4j with quarkus and CDI for
building AI-powered applications, Langchain4j provides pre-packaged
ONNX models as Maven dependencies, embedding models are faster and
smaller compared to full language models, possibilities of using
embeddings for query expansion, summarization, and data source
selection, cross-checking model outputs using embeddings or another
language model, decomposing complex AI services into smaller,
specialized sub-modules, injecting the right tools and data based
on query classification

Dmytro Liubarskyi on twitter: @langchain4j