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

  • Jun 1 2024
  • Duração: 1 hora e 9 minutos
  • Podcast

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

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

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  • Sumário

  • 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
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Sinopse

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
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