Tagged articles

multi-path recall

3 articles · Page 1 of 1
Fei's Miscellaneous Talks
Fei's Miscellaneous Talks
Aug 31, 2026 · Artificial Intelligence

Multi-Path Recall Architecture: From Tag & Vector Search to Recall Manager Engineering

This article details the multi-path recall architecture in recommender systems, covering five recall sources (tag, vector, popularity, collaborative filtering, exploration), the Recall Manager's parallel scheduling and circuit breaking, engineering deployment pipelines with offline/online evaluation, and future trends like generative recall and joint optimization.

A/B testingANN vector searchRecall Manager
0 likes · 38 min read
Multi-Path Recall Architecture: From Tag & Vector Search to Recall Manager Engineering
Tech Freedom Circle
Tech Freedom Circle
Mar 19, 2026 · Artificial Intelligence

Failed Alibaba Interview: The 4 RAG Modules and 6 Design Principles You Need

The article dissects a failed Alibaba second‑round interview where the candidate answered only “vector‑search‑enhanced” for a RAG design, and then presents a systematic, four‑module RAG architecture together with six design principles, detailed indexing, query understanding, multi‑path recall, and context generation techniques to help candidates demonstrate comprehensive technical depth.

AI architectureRAGRetrieval-Augmented Generation
0 likes · 22 min read
Failed Alibaba Interview: The 4 RAG Modules and 6 Design Principles You Need
Volcano Engine Developer Services
Volcano Engine Developer Services
Nov 18, 2025 · Artificial Intelligence

How a Single SQL Query Powers Multi‑Modal AI Search with veDB‑Search

veDB‑Search extends MySQL compatibility to enable AI‑driven hybrid retrieval—combining vector, full‑text, and scalar searches—through a single SQL statement, simplifying architecture, boosting performance, and supporting in‑database embedding for seamless "search everything" capabilities.

In-DB EmbeddingSQL Vector Searchhybrid retrieval
0 likes · 16 min read
How a Single SQL Query Powers Multi‑Modal AI Search with veDB‑Search