Anything LLM

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

Can Long Context Replace RAG?

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MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, targeting NLP master’s and doctoral students, university professors, and researchers from enterprises. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. … 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 3500 words long and is suggested to be read in 10 minutes. How to build a flow-configurable compound artificial intelligence system using open-source tools. In the previous section, we introduced what a compound artificial intelligence system is, its system components, … Read more

Open Source End-to-End RAG Solution RAGFlow

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Open Source End-to-End RAG Solution RAGFlow

Introduction RAG has developed to become a consensus for LLM’s service to B-end, however, questions regarding it have never ceased to exist. Simply put: for many Q&A systems represented by individuals and small to medium enterprises, there is indeed no need to use RAG. However, these long-context LLMs have either already addressed or are in … Read more

New Paradigm of Large Language Models: RAG for Cost Reduction and Efficiency

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New Paradigm of Large Language Models: RAG for Cost Reduction and Efficiency

1 Algorithm Introduction Retrieval Augmented Generation (RAG) has become one of the hottest applications of large language models (LLM). After the recent boom in large models, everyone must have a certain understanding of their capabilities. However, when we apply large models to practical business scenarios, we find that generic foundational models generally cannot meet our … Read more

Guidelines for RAG Design Choices

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Guidelines for RAG Design Choices

Author: Kuang Ji Reviewed by: Los Introduction: The author provides the most important design guidelines for RAG system design at various stages. This analysis covers five stages: indexing, storage, retrieval, synthesis, and evaluation, detailing the important design rules for each stage of the RAG system. The author combines practical experience in building RAG systems with … Read more

Balancing Internal and External Knowledge in LLMs

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Balancing Internal and External Knowledge in LLMs

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university professors, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for the advancement … Read more

Can RAG Improve LLM Inference Ability? Recent Research from Renmin University

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Can RAG Improve LLM Inference Ability? Recent Research from Renmin University

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, with an audience covering NLP graduate students, university teachers, and industry researchers. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for the … Read more

Advanced RAG: Enhancing RAG Performance

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Advanced RAG: Enhancing RAG Performance

Author: Luv Bansal Translation: wwl Proofreading: Zhang Yiran This article is approximately 4400 words long and is recommended for a reading time of over 10 minutes. This article discusses various techniques for optimizing different parts of the RAG pipeline and enhancing the overall RAG workflow. Image generated by the author using Dalle-3 provided by Bing … Read more