Rost Glukhov3d ago

Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

#AI #LLM #RAG #Embeddings #Reranking #Vector Database #Fine-Tuning

https://www.glukhov.org/rag/

Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide

Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

Rost Glukhov | Personal site and technical blog
Rost GlukhovFeb 23
Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide:
https://www.glukhov.org/rag/
#AI #LLM #RAG #Embeddings #Reranking #VectorDatabase
Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide

Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

Rost Glukhov | Personal site and technical blog
Reddit Tech VN BotNov 24

Tìm kiếm thuật toán tương tự chuỗi tốt nhất cho RAG mà không cần mô hình. Các lựa chọn gồm Levenshtein, Jaccard, Soundex... #RAG #ThuậtToánTươngTự #NonModelBased #TìmKiếm #StringSimilarity # Algorithm #TươngTựChuỗi #Reranking

https://www.reddit.com/r/LocalLLaMA/comments/1p5ua3s/what_are_the_best_options_for_nonmodel_based/

AI SparkupOct 23

500만 문서 RAG 구축 실전 기록: ROI 높은 5가지 핵심 전략

500만 개 이상의 문서를 처리한 8개월간의 RAG 구축 실전 경험. 프로토타입과 프로덕션의 간극을 메우는 ROI 높은 5가지 핵심 전략과 검증된 기술 스택을 소개합니다.

https://aisparkup.com/posts/5752

Rost GlukhovJun 25, 2025
Reranking text documents with Ollama and Qwen3 Reranker model - in Go:
https://www.glukhov.org/post/2025/06/reranking-with-ollama-qwen3-reranker-golang/
#go #ollama #rag #reranking #qwen3 #llm #ai
Reranking documents with Ollama and Qwen3 Reranker model - in Go - Rost Glukhov | Personal site and technical blog

Reranking text documents with Ollama and Qwen3 Reranker model - in Golang

Rost GlukhovJun 19, 2025
Reranking text documents with Ollama and Qwen3 Embedding model - in Golang:
https://www.glukhov.org/post/2025/06/reranking-with-ollama-qwen3-embedding-golang/
#ollama #embedding #reranking #golang #ai #llm
Reranking text documents with Ollama and Qwen3 Embedding model - in Go - Rost Glukhov | Personal site and technical blog

Reranking text documents with Ollama and Qwen3 Embedding model - in Golang

Hacker NewsMay 23, 2025

You Don't Need Re-Ranking: Understanding the Superlinked Vector Layer

https://superlinked.com/vectorhub/articles/why-do-not-need-re-ranking

#HackerNews #You #Need #Re-Ranking #Understanding #the #Superlinked #Vector #Layer #Superlinked #Vector #Layer #ReRanking #AI #Technology #Vector #Hub

Why You Don't Need Re-Ranking: Understanding the Superlinked Vector Layer | VectorHub by Superlinked

Discover how Superlinked eliminates the need for re-ranking in vector search systems. Learn about its unified multimodal vectors, dynamic intent capture that improve search relevance, speed, and scalability.

michabbbAug 31, 2024

Jina Al just released Jina ColBERT v2, a Multilingual Late Interaction Retriever for #Embedding and #Reranking. The new model supports 89 languages with superior retrieval performance, user-controlled output dimensions, and 8192 token-length.

https://jina.ai/news/jina-colbert-v2-multilingual-late-interaction-retriever-for-embedding-and-reranking/

#ai #llm

Jina ColBERT v2: Multilingual Late Interaction Retriever for Embedding and Reranking

Jina ColBERT v2 supports 89 languages with superior retrieval performance, user-controlled output dimensions, and 8192 token-length.

Published papers at TMLRJan 28, 2023

Boosting Search Engines with Interactive Agents

Leonard Adolphs, Benjamin Börschinger, Christian Buck et al.

https://openreview.net/forum?id=0ZbPmmB61g

#retrieval #search #reranking

Boosting Search Engines with Interactive Agents

This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks. Our approach uses machine reading to...

OpenReview