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Exploring ONNX, Embedding Models, and Retrieval Augmented Generation (RAG) with Langchain4j
MP3•Episoder hjem
Manage episode 421443440 series 2469611
Innhold levert av Adam Bien. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Adam Bien eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
An airhacks.fm conversation with Dmytro Liubarskyi (@langchain4j) about:
…
continue reading
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
319 episoder
MP3•Episoder hjem
Manage episode 421443440 series 2469611
Innhold levert av Adam Bien. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Adam Bien eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
An airhacks.fm conversation with Dmytro Liubarskyi (@langchain4j) about:
…
continue reading
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
319 episoder
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