ANN | BestHub

Xiaohongshu Tech REDtech

May 22, 2025 · Artificial Intelligence

Scalable Overload-Aware Graph-Based Index Construction for 10‑Billion‑Scale Vector Similarity Search (SOGAIC)

The paper introduces SOGAIC, a scalable overload‑aware graph‑based index construction system for billion‑scale vector similarity search that uses adaptive overlapping partitioning and load‑balanced distributed scheduling to cut construction time by 47.3% while maintaining high recall.

ANNLarge ScaleVector Search

0 likes · 13 min read

Scalable Overload-Aware Graph-Based Index Construction for 10‑Billion‑Scale Vector Similarity Search (SOGAIC)

JD Tech

May 17, 2024 · Artificial Intelligence

Optimizing JD Advertising Retrieval Platform: Balancing Compute, Data Scale, and Iterative Efficiency

The article details how JD's advertising retrieval platform tackles the core challenge of balancing limited compute resources with massive data by optimizing compute allocation, improving model scoring efficiency, and enhancing iteration speed through distributed execution graphs, adaptive algorithms, and platform‑level infrastructure improvements.

ANNSearchadvertising

0 likes · 24 min read

Optimizing JD Advertising Retrieval Platform: Balancing Compute, Data Scale, and Iterative Efficiency

Rare Earth Juejin Tech Community

Jan 12, 2024 · Artificial Intelligence

Understanding Vector Databases, ANN Algorithms, and Their Integration with Large Language Models

This article explains the fundamentals of vector databases, how high‑dimensional vector data is generated and stored, reviews common ANN search algorithms such as Flat, k‑means and LSH, discusses benchmarking and product selection, and demonstrates practical integration of vector stores with LLMs using LangChain and Python code.

ANNEmbeddingsLLM integration

0 likes · 17 min read

Understanding Vector Databases, ANN Algorithms, and Their Integration with Large Language Models

Sohu Tech Products

Nov 1, 2023 · Databases

Engineering Practices of Douyin's Vector Database: From Retrieval Challenges to Cloud‑Native Solutions

Douyin tackled vector‑retrieval challenges by optimizing HNSW and creating a high‑performance IVF algorithm, implementing custom scalar quantization, SIMD acceleration, and a DSL‑driven engine that merges filtering with search, then built a cloud‑native, storage‑compute‑separated vector database (VikingDB) delivering sub‑10 ms latency, real‑time updates, multi‑tenant support, and secure, scalable retrieval for LLM‑driven applications.

ANNLLM integrationcloud native

0 likes · 18 min read

Engineering Practices of Douyin's Vector Database: From Retrieval Challenges to Cloud‑Native Solutions

Baidu Geek Talk

Oct 31, 2023 · Artificial Intelligence

Interview on Baidu's Open‑Source Large‑Scale Vector Search Engine Puck

Baidu has open‑sourced its high‑performance, trillion‑scale vector search engine Puck—originally built for ultra‑large image‑search workloads, winner of multiple BIGANN categories, now supporting diverse embeddings alongside the medium‑size Tinker algorithm—to accelerate community innovation, improve code quality, and broaden AI retrieval applications across search, recommendation and cloud services.

AIANNBaidu

0 likes · 12 min read

Interview on Baidu's Open‑Source Large‑Scale Vector Search Engine Puck

DataFunTalk

Oct 30, 2023 · Databases

Engineering Practices and Evolution of Douyin’s Cloud‑Native Vector Database

This article outlines Douyin’s step‑by‑step engineering evolution of its cloud‑native vector database, covering the background of vector search, core concepts, algorithmic optimizations, storage‑compute separation, streaming updates, multi‑tenant orchestration, and future applications such as large language model integration.

ANNDouyinSearch

0 likes · 17 min read

Engineering Practices and Evolution of Douyin’s Cloud‑Native Vector Database

Baidu Geek Talk

Sep 4, 2023 · Artificial Intelligence

Puck: Baidu’s Open‑Source High‑Performance ANN Retrieval Engine

Puck, Baidu’s open‑source Approximate Nearest Neighbor engine built on the proprietary Puck and Tinker algorithms, delivers high recall, accuracy and throughput across tiny to trillion‑scale datasets, outperforms rivals in benchmarks—including first‑place BIGANN 2021—while offering a simple, extensible API, proven reliability in dozens of Baidu services, and an Apache 2.0 license encouraging community contributions.

ANNBaiduOpen-source

0 likes · 7 min read

Puck: Baidu’s Open‑Source High‑Performance ANN Retrieval Engine

Zhuanzhuan Tech

Sep 21, 2022 · Artificial Intelligence

Vector Retrieval and Product Quantization with Faiss

This article explains the challenges of large‑scale vector retrieval, compares Faiss index types such as brute‑force, graph‑based and product quantization, and details how product quantization works, its memory‑speed trade‑offs, hierarchical quantization, and practical hyper‑parameter tuning.

ANNVector Searchembedding

0 likes · 9 min read

Vector Retrieval and Product Quantization with Faiss

Kuaishou Tech

Dec 10, 2021 · Artificial Intelligence

Kuaishou and Tsinghua University Win NeurIPS'21 Billion-Scale ANN Challenge with FAISS‑Optimized KST_ANN Solution

On December 6, Kuaishou and Tsinghua University’s joint team secured first place in the NeurIPS'21 Billion‑Scale Approximate Nearest Neighbor Search Challenge by leveraging a FAISS‑optimized, memory‑efficient KST_ANN algorithm that achieved over 6% higher recall on multiple billion‑scale datasets, showcasing the practical impact of large‑scale vector retrieval in AI‑driven services.

AIANNKST_ANN

0 likes · 5 min read

Kuaishou and Tsinghua University Win NeurIPS'21 Billion-Scale ANN Challenge with FAISS‑Optimized KST_ANN Solution

Kuaishou Tech

Nov 29, 2021 · Artificial Intelligence

Starry Vector Retrieval Platform: Architecture, Features, and Performance

The article describes the design, challenges, architecture, key features, algorithm optimizations, and future roadmap of Kuaishou's Starry vector retrieval platform, which delivers high‑performance, high‑reliability, and easy‑to‑use large‑scale ANN search for diverse business scenarios.

AI PlatformANNVector Search

0 likes · 14 min read

Starry Vector Retrieval Platform: Architecture, Features, and Performance

DataFunTalk

Apr 6, 2020 · Artificial Intelligence

Introducing DeepMatch: An Open‑Source Library for Deep Retrieval Matching Algorithms

DeepMatch is an open‑source Python library that implements several mainstream deep‑learning based recall‑matching algorithms, provides easy installation via pip, detailed usage examples with code, and supports exporting user and item vectors for ANN search, making it ideal for rapid experimentation and learning in recommendation systems.

ANNOpen-sourcePython

0 likes · 10 min read

Introducing DeepMatch: An Open‑Source Library for Deep Retrieval Matching Algorithms