RAG
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Scale Up Your RAG: A Rust-Powered Indexing Pipeline with LanceDB and Candle | by Alon Agmon | Jul, 2024
Building a high-performance embedding and indexing system for large-scale document processing Photo by Marc Sendra Martorell on Unsplash Recently, Retrieval-Augmented…
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Knowledge Bases for Amazon Bedrock now supports advanced parsing, chunking, and query reformulation giving greater control of accuracy in RAG based applications
Knowledge Bases for Amazon Bedrock is a fully managed service that helps you implement the entire Retrieval Augmented Generation (RAG)…
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Enhancing Language Models with RAG: Best Practices and Benchmarks
Retrieval-Augmented Generation (RAG) techniques face significant challenges in integrating up-to-date information, reducing hallucinations, and improving response quality in large language…
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Transformers 4.42 by Hugging Face: Unleashing Gemma 2, RT-DETR, InstructBlip, LLaVa-NeXT-Video, Enhanced Tool Usage, RAG Support, GGUF Fine-Tuning, and Quantized KV Cache
Hugging Face has announced the release of Transformers version 4.42, which brings many new features and enhancements to the popular…
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LongRAG: A New Artificial Intelligence AI Framework that Combines RAG with Long-Context LLMs to Enhance Performance
Retrieval-Augmented Generation (RAG) methods enhance the capabilities of large language models (LLMs) by incorporating external knowledge retrieved from vast corpora.…
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The R in “RAG” Stands for “Royalties” – O’Reilly
The latest release of O’Reilly Answers is the first example of generative royalties in the AI era, created in partnership…
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Exploring RAG Applications Across Languages: Conversing with the Mishnah | by Shlomo Tannor | May, 2024
Building a cross-lingual RAG system for Rabbinic texts 15 min read · May 23, 2024 Robot studying The Mishnah. Credit:…
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