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RAG (Retrieval-Augmented Generation)

*HUM, meaning of "RAG" seems to have slightly evolved from the initial paper from Meta "[Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](doc:2022/01/2005_11401_retrieval_augmente)" to its use now (2023-08).

In the original paper, the researchers described a method that combines a retrieval component with a generative language model to improve performance on knowledge-intensive tasks. The original concept, as described in the paper, focused on the training of such a model. The training process involves learning to retrieve relevant documents and use them effectively during the generation of text.

The term RAG  is now commonly used to describe systems that employ a two-step process:
1. Retrieval Step: The system retrieves relevant information or documents from a large corpus or database that may be useful in generating a response.
2. Generation Step: A generative language model, like a transformer-based model, uses the retrieved information to inform its response, ensuring that the output is contextually relevant and factually accurate.

In summary, while the term RAG originated from the training methodology of such systems, it is now also used to describe the architecture of systems that incorporate a retrieval step before the generative step in their operational use.

> RAG acts just like any other [seq2seq](tag:sequence_to_sequence_learning.html) model. However, RAG has an intermediate component that retrieves contextual documents from an external knowledge base (like a Wikipedia text corpus). These documents are then used in conjunction with the input sequence and passed into the underlying seq2seq generator. [src](doc:2021/02/retrieval_augmented_generation_)

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58 Documents (Long List)

Akshay sur X : "Auto-Document Retrieval..."

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2024-09-07 About

Rohan Paul sur X : "RAG or Long Context ?? ..."

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2024-08-24 About

The GraphRAG Manifesto: Adding Knowledge to GenAI

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2024-07-13 About

[2405.20139] GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning

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2024-06-02 About

LlamaIndex sur X : "Save Memory (and Money) in RAG pipeline with @Cohere 's Int8 and Binary Embeddings..."

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2024-03-30 About

LlamaIndex 🦙 sur X : "An emerging technique to better represent your data for RAG/LLM applications is to only chunk the data, but also hierarchically cluster and index it..."

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2024-03-30 About

LlamaIndex 🦙 sur X : "RAFT - Retrieval Augmented Fine Tuning..."

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2024-03-28 About

Command-R: RAG at Production Scale

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2024-03-13 About

RAG makes LLMs better and equal | Pinecone

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2024-03-13 About

What you should know about RAG (from beginner to advanced) | by Jonathan Nguyen | Medium

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RAG (Retrieval-Augmented Generation)

2024-03-11 About

RAG CLI - LlamaIndex

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2024-03-10 About

Akshay 🚀 sur X : "Let's build a "Chat with your code" RAG application, step-by-step"

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2024-03-09 About

How to Build a RAG System With LlamaIndex, OpenAI, and MongoDB Vector Database | MongoDB

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2024-03-03 About

Ravi Theja sur X : "𝐀𝐜𝐭𝐢𝐯𝐞𝐑𝐀𝐆: 𝐑𝐞𝐯𝐞𝐚𝐥𝐢𝐧𝐠 𝐭𝐡𝐞 𝐓𝐫𝐞𝐚𝐬𝐮𝐫𝐞𝐬 𝐨𝐟 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐯𝐢𝐚 𝐀𝐜𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠..."

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2024-02-25 About

Jerry Liu sur X : "a big step towards better RAG... is to just have a really nice PDF parser. It’s so important because a good parser unlocks way more interesting indexing/retrieval strategies…"

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2024-02-23 About

Jerry Liu sur X : "Build a RAG backend over any website in a single CLI command ..."

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2024-02-03 About

bclavie/RAGatouille

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2024-01-26 About

LlamaIndex 🦙 sur X : "Use RAG to build advanced text-to-SQL..."

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2024-01-24 About

Jerry Liu sur X : "4 Levels of Agents for RAG..."

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2024-01-23 About

Ravi Theja sur X : "Propositions as Chunks to Improve RAG Performance with @llama_index..."

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2024-01-02 About

Full-stack RAG with Jina Embeddings v2 and LlamaIndex

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2023-12-23 About

[2312.10997] Retrieval-Augmented Generation for Large Language Models: A Survey

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2023-12-23 About

Jerry Liu sur X : "There's different ways you can parse embedded tables for RAG..."

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2023-12-02 About

LlamaIndex 🦙 sur X : "A big issue with RAG is that stuffing more context ≠ better...

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2023-11-07 About

Jerry Liu sur X : "Advanced RAG architectures need to implement dynamic retrieval..."

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2023-10-22 About

Self-RAG: Learning to Retrieve, Generate and Critique through Self-Reflection

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2023-10-20 About

L. Dietz @deeds@mastodon.social @dietz@idf.social sur X : "How to evaluate RAG / RACAS (Retrieval-Augmented-Clustering-Augmented-Summarization) systems for Article Generation..."

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2023-10-20 About

Jerry Liu sur X : "Using LLMs for automated decision making (routing) is a huge part of an advanced RAG/agent app But LLMs are expensive and slow... Fine-tune embeddings instead"

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2023-10-19 About

[2310.03025] Retrieval meets Long Context Large Language Models

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2023-10-07 About

Jerry Liu sur X : "The idea of “small-to-big” retrieval: - retrieve on smaller pieces, expand into more context for LLM synthesis..."

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2023-10-02 About

Rohan sur X : "Multi Document Agent architecture (v0) in @llama_index... allows answering broader set of questions over multiple documents..."

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2023-10-02 About

Fine-Tuning Your Embedding Model to Maximize Relevance Retrieval in RAG Pipeline | by Wenqi Glantz | Sep, 2023 | Better Programming

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2023-09-13 About

How to Optimize Retrieval-Augmented Generation

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RAG (Retrieval-Augmented Generation)

2023-09-08 About

Jerry Liu sur X : "A simple trick to improve retrieval for RAG 💡: Embed “references” to each text chunk instead of the chunk itself (e.g. smaller chunks, summaries)..."

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2023-09-06 About

Building Performant RAG Applications for Production - LlamaIndex

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2023-08-30 About

Jerry Liu sur X : "how do you get a good retrieval dataset w/o spending a ton of money on human labels? Answer: use LLMs"

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2023-08-30 About

Jerry Liu sur X : "This might be the first time ChatGPT (+@jxnlco) helped us come up with a better retrieval algorithm for RAG..."

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2023-08-28 About

Jerry Liu sur X : "One major way to improve your RAG system is to fine-tune your embedding model"

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2023-08-25 About

Yi Ding -- prod/acc sur X : "First is best for retrieval when using ChatGPT models."

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2023-08-23 About

Comparing Methods for Structured Retrieval (Auto-Retrieval vs. Recursive Retrieval) - LlamaIndex 🦙 0.8.6

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2023-08-23 About

Jerry Liu sur X : "8 key considerations for building *production-grade* LLM apps over your data (RAG)..."

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2023-08-20 About

Knowledge Graph RAG Query Engine - LlamaIndex

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2023-08-17 About

Jerry Liu on X: “Graph RAG” in @llama_index: a new method of augmenting LLMs with context from a graph database

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2023-08-17 About

Knowledge Graphs & LLMs: Fine-Tuning Vs. Retrieval-Augmented Generation | by Tomaz Bratanic | Neo4j Developer Blog (June 6 2023)

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2023-08-13 About

LlamaIndex: Harnessing the Power of Text2SQL and RAG to Analyze Product Reviews | by Ravi Theja | LlamaIndex Blog | Aug, 2023 | Medium

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2023-08-13 About

Jerry Liu sur Twitter : "Some critical data considerations that you must take into account to make your LLM application production-ready"

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2023-07-23 About

Jerry Liu sur Twitter : "Using cross-encoding as a reranking step can dramatically speed up LLM inference time AND improve accuracy!"

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2023-07-20 About

Jerry Liu sur Twitter : Hot take: if you want to... deliver technical differentiation, you will need to learn LLM development in a “bottoms-up” fashion

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2023-07-16 About

Jerry Liu sur Twitter : "If you’re building “chat over your PDFs” with LLMs, you need to deal with the pesky issue of how to parse embedded tables/diagrams..."

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2023-07-07 About

Jerry Liu sur Twitter : "The DSP project carries a lot of insights for improving RAG..."

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2023-06-18 About

Build Industry-Specific LLMs Using Retrieval Augmented Generation | by Skanda Vivek | May, 2023 | Towards Data Science

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2023-06-16 About

Retrieval-augmented LMs, and prompting

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2023-06-16 About

Retrieval Augmented Generation (RAG) | Prompt Engineering Guide

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2023-06-16 About

[2207.06300] Re2G: Retrieve, Rerank, Generate

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2022-07-14 About

[2108.13934] Robust Retrieval Augmented Generation for Zero-shot Slot Filling

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2022-01-19 About

[2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

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2022-01-19 About

Hugging Face sur Twitter : "Transformers release of the Retrieval-Augmented Generation model in collaboration with @facebookai!"

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2021-02-23 About

Retrieval Augmented Generation with Huggingface Transformers and Ray | Distributed Computing with Ray

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2021-02-11 About

Properties

sl:creationDate : 2022-01-19
sl:creationTime : 2022-01-19T17:11:58Z
rdf:type : sl:Tag
skos:altLabel :
- RAG@en
- Retrieval-Augmented Generation@en
skos:prefLabel : RAG (Retrieval-Augmented Generation)@en