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...
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...
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