Tagged articles

Multi‑Path Retrieval

3 articles · Page 1 of 1

Jul 22, 2026 · Artificial Intelligence

Understanding RAG: Chunking, Embedding, Similarity, HNSW and Multi‑Path Retrieval

This article explains why Retrieval‑Augmented Generation is needed, walks through the offline and online pipeline, details knowledge sources, embedding models, chunking strategies, similarity metrics, the HNSW index, multi‑path recall, RRF fusion and cross‑encoder reranking, and summarizes practical takeaways for building effective RAG systems.

ChunkingCross‑EncoderEmbedding

0 likes · 36 min read

Understanding RAG: Chunking, Embedding, Similarity, HNSW and Multi‑Path Retrieval

Tech Freedom Circle

Sep 25, 2025 · Artificial Intelligence

RAGFlow Search Engine Deep Dive: Multi‑Path Retrieval, Fusion, and Reranking

The article provides a detailed technical analysis of RAGFlow's search engine, covering the Searcher class coordination, adaptive multi‑path retrieval (vector, keyword, and knowledge‑graph), intelligent fusion with weighted scoring, caching, performance monitoring, and both built‑in and model‑driven reranking to achieve high‑precision results.

Multi‑Path RetrievalPerformance OptimizationReranking

0 likes · 32 min read

RAGFlow Search Engine Deep Dive: Multi‑Path Retrieval, Fusion, and Reranking

Tencent Cloud Developer

Apr 11, 2022 · Artificial Intelligence

Recall Module in Recommendation Systems: Multi-Path Retrieval and Optimization

The recall module in recommendation systems retrieves thousands of items from massive pools using parallel non-personalized and personalized paths—such as hot-item, content-based, behavior-based, and deep-model recall—prioritizing coverage and low latency while addressing challenges like hard-negative sampling, selection bias, objective alignment, and channel competition to feed downstream ranking.

AIMachine LearningMulti‑Path Retrieval

0 likes · 15 min read

Recall Module in Recommendation Systems: Multi-Path Retrieval and Optimization