AI Fundamentals

 

  • Machine Learning
  • Deep Learning
  • Neural Networks
  • Natural Language Processing (NLP)
  • Computer Vision
  • Reinforcement Learning

Live Broadcast: Large Model + Knowledge Base (RAG) for Industry Digitalization

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Live Broadcast: Large Model + Knowledge Base (RAG) for Industry Digitalization

In the blink of an eye, 2024 is nearing its end. This year, the “Huawei Expert Live Room” has successfully held 7 live broadcasts, sharing Huawei’s experience in industry digital transformation, covering construction, steel, non-ferrous metals, smelting, transportation, oil and gas, and continuously shaping the brand image of “digital transformation partners”. Recently, some friends commented … Read more

Expert Interpretation of Nat Rev Immunol

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Expert Interpretation of Nat Rev Immunol

Interpretation | Xu Anlong (School of Life Sciences, Beijing University of Chinese Medicine) The key mechanism that enables the human adaptive immune system to recognize diverse antigens and produce specific antibodies is the recombination activating genes RAG1 and RAG2 (collectively known as RAG), which encode the recombinase RAG that catalyzes V(D)J recombination to form diverse … Read more

Enhancing RAG: Choosing the Best Embedding and Reranker Models

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Detailed steps and code are provided for how to choose the best embedding model and reranker model. When building a Retrieval-Augmented Generation (RAG) pipeline, one of the key components is the retriever. We have various embedding models to choose from, including OpenAI, CohereAI, and open-source sentence transformers. Additionally, there are several rerankers available from CohereAI … Read more

Integrating Text and Knowledge Graph Embeddings to Enhance RAG Performance

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Integrating Text and Knowledge Graph Embeddings to Enhance RAG Performance

Source: DeepHub IMBA This article is approximately 4600 words long and is recommended to be read in 10 minutes. In this article, we will combine text and knowledge graphs to enhance the performance of our RAG. In our previous articles, we introduced examples of combining knowledge graphs with RAG. In this article, we will combine … Read more

Three Advanced Retrieval Techniques in RAG

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Three Advanced Retrieval Techniques in RAG

Source: DeepHub IMBA This article is about 3000 words long and is recommended to be read in 5 minutes. This article will explore three effective techniques to enhance document retrieval in applications based on <strong>RAG</strong>. By combining these techniques, it is possible to retrieve documents that closely match user queries, thus generating better answers. The … Read more

RAG Meets LLMs: Advancing Retrieval-Augmented Large Language Models

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RAG Meets LLMs: Advancing Retrieval-Augmented Large Language Models

Source: ZHUAN ZHI This article is approximately 1000 words long and is recommended for a 5-minute read. In this tutorial, we provide a comprehensive review of the existing research on Retrieval-Augmented Large Language Models (RA-LLMs). As one of the most advanced technologies in the field of artificial intelligence, Retrieval-Augmented Generation (RAG) technology can provide reliable … Read more

3 Common Query Expansion Methods to Improve RAG Capabilities in Langchain

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3 Common Query Expansion Methods to Improve RAG Capabilities in Langchain

Source: DeepHub IMBA This article is about 2700 words long, and it is recommended to read it in 5 minutes. This article mainly introduces 3 commonly used methods in Langchain. There are various methods to enhance the capabilities of Retrieval-Augmented Generation (RAG), one of which is called query expansion. Here we mainly introduce 3 commonly … Read more

What Is LightRAG, Better Than GraphRAG?

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What Is LightRAG, Better Than GraphRAG?

1. Why Introduce LightRAG? Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, allowing LLMs to generate more accurate and contextually relevant responses, significantly improving utility in practical applications. • By adapting to domain-specific knowledge, RAG systems ensure that the information provided is not only relevant but also meets user needs. … Read more

Design Patterns for Compound AI Systems (Conversational AI, CoPilots & RAG)

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Design Patterns for Compound AI Systems (Conversational AI, CoPilots & RAG)

Author: Raunak Jain March 18, 2024 Translator: Chen Zhiyan Proofreader: zrx This article is approximately 3700 words long and is suggested to be read in 9 minutes. How to build a process-configurable compound AI system using open-source tools. Original Title: Design Patterns for Compound AI Systems (Conversational AI, CoPilots & RAG) Original Link: https://medium.com/@raunak-jain/design-patterns-for-compound-ai-systems-copilot-rag-fa911c7a62e0 What … Read more

12 Common Pain Points and Solutions for RAG Development

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12 Common Pain Points and Solutions for RAG Development

Source: DeepHub IMBA This article is approximately 5400 words long and is recommended for a reading time of over 10 minutes. This article discusses 12 pain points encountered during the development of RAG pipelines (7 of which are sourced from papers, and 5 from our own summary), and proposes corresponding solutions to these pain points. … Read more