freeCodeCamp·2yMastering RAG from Scratch
Learn how to implement Retrieval-Augmented Generation (RAG) from scratch with an in-depth course on the freeCodeCamp.org YouTube channel. RAG combines retrieval systems with advanced natural language generation and is valuable in chatbot development and other fields.
Hacker News·2yinfiniflow/ragflow: RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
RAGFlow is an open-source RAG engine based on deep document understanding. It offers a streamlined workflow for businesses, supports various data formats, and provides truthful question-answering capabilities.
Hacker News·2ytruefoundry/cognita: RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry
Cognita is an open-source framework for building modular, open source applications for production. It provides a simple way to organize your codebase and offers a production-ready environment. The key issues in productionizing a RAG system from a Jupyter Notebook include chunking and embedding job, query service, LLM/embedding model deployment, and vector DB deployment. Cognita allows for customization and experimentation of a RAG system and comes with a UI for easy configuration.
Data Science Central·2y2 addressing the limitations of RAG
The post explores the limitations of RAG and introduces the idea of a GRAPHRAG to overcome these limitations by combining a knowledge graph with RAG. Graph RAG enriches the standard LLM approach with structured information from a knowledge graph.
Irrational Exuberance·2yMy advice for how to use LLMs in your product.
Advice on using LLMs in products, mental models, revamping workflows, retrieval augmented generation (RAG), rate of innovation, human-in-the-loop (HITL), hallucinations and legal liability, zero to one versus one to N, copyright law, data processing agreements, and provider availability.
Community Picks·2yFour Data Cleaning Techniques to Improve Large Language Model (LLM) Performance
This post explores four common natural language processing techniques to clean text before ingestion in large language models. It highlights the importance of data cleaning to ensure accuracy, improve quality, and facilitate analysis. The post also discusses the use of retrieval-augmented generation (RAG) in enhancing the performance of large language models.