Learn how layout-aware annotators in Spark NLP enable efficient multimodal document ingestion by aligning images with text, selectively applying VLM captioning, and reconstructing semantically rich content for high-quality RAG indexing and retrieval.
The RAG ingestion problem In real-world RAG systems, the quality of the final answer is constrained by the quality of the indexed representation. If the ingestion layer fails to capture...
TL;DR: Spark NLP’s upgraded Llama.cpp backend now supports a wider range of modern LLM families, including quantized and multimodal models. The integration delivers faster, memory-efficient inference and seamless Spark pipeline...
John Snow Labs is thrilled to introduce a powerful set of new ONNX based clinical Named Entity Recognition (NER) models for English, Italian, and Spanish, in its’ most recent release...
Hear Me Out: How to Convert Your Voice to Text with Spark NLP and Python Automatic Speech Recognition — ASR (or Speech to Text) is an essential task in Natural...
Vector representations of texts longer than a word in natural language processing (NLP) refer to representing a sequence of words, chunks, or sentences as a vector in a high-dimensional space....
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