Milvus | BestHub

Spring Full-Stack Practical Cases

Apr 10, 2025 · Artificial Intelligence

Build a RAG-Powered Knowledge Base with Spring Boot, Milvus, and Ollama

This guide walks through creating a Retrieval‑Augmented Generation (RAG) system using Spring Boot 3.4.2, Milvus vector database, and the bge‑m3 embedding model via Ollama, covering environment setup, dependency configuration, vector store operations, and integration with a large language model to deliver refined, similarity‑based answers.

LLMMilvusRAG

0 likes · 11 min read

Build a RAG-Powered Knowledge Base with Spring Boot, Milvus, and Ollama

macrozheng

Feb 17, 2025 · Artificial Intelligence

Unlock DeepSeek4j 1.4: Build a Private AI Knowledge Base with Spring Boot

This guide explains why DeepSeek4j is needed, its core features, and provides step‑by‑step instructions—including dependency setup, configuration, code examples, and a complete RAG pipeline using Milvus—to help developers quickly create a private AI knowledge base with Spring Boot.

AIDeepSeek4jJava

0 likes · 12 min read

Unlock DeepSeek4j 1.4: Build a Private AI Knowledge Base with Spring Boot

Java Architecture Diary

Feb 13, 2025 · Artificial Intelligence

Create a Java RAG System Using DeepSeek R1, Milvus, and Spring

This guide walks through building a Java RAG system with DeepSeek R1, Milvus, and Spring, covering environment setup, vector model integration via OpenAI protocol, Maven dependencies, data embedding, and a chat endpoint that combines semantic retrieval with LLM generation.

AI integrationDeepSeekJava

0 likes · 11 min read

Create a Java RAG System Using DeepSeek R1, Milvus, and Spring

DataFunSummit

Oct 30, 2024 · Databases

Design and Implementation of Vector Databases: Architecture, Indexing, and AI Optimizations

This article introduces vector databases as the foundation for efficient high‑dimensional data retrieval in generative AI, covering their background, Milvus’s cloud‑native architecture, key indexing techniques, performance‑trade‑offs, AI‑driven optimizations, and a Q&A session.

AIIndexingMilvus

0 likes · 15 min read

Design and Implementation of Vector Databases: Architecture, Indexing, and AI Optimizations

DaTaobao Tech

Sep 20, 2024 · Databases

Database Technology Evolution: From Hierarchical to Vector Databases

The article chronicles the evolution of database technology from early hierarchical and network models through relational, column‑store, document, key‑value, graph, time‑series, HTAP, and finally vector databases, detailing each system’s architecture, strengths, limitations, typical uses, and future trends toward specialization, distributed cloud‑native designs, and AI‑driven applications.

HBaseHTAPInfluxDB

0 likes · 52 min read

Database Technology Evolution: From Hierarchical to Vector Databases

DataFunSummit

Aug 24, 2024 · Databases

Cloud‑Native Storage Solutions for Large‑Scale Vector Data with Milvus and Zilliz

This article presents a comprehensive overview of Zilliz’s cloud‑native vector database ecosystem, detailing Milvus’s distributed architecture, indexing and query capabilities, related tools such as Towhee and GPTCache, storage challenges, tiered storage designs, performance metrics, and real‑world AI use cases like code‑assist and RAG‑based Q&A systems.

AI infrastructureANN SearchLarge Scale Storage

0 likes · 21 min read

Cloud‑Native Storage Solutions for Large‑Scale Vector Data with Milvus and Zilliz

Rare Earth Juejin Tech Community

Jul 18, 2024 · Backend Development

Implementing Full‑Text Document Search with Elasticsearch and Milvus

This article describes how to combine Elasticsearch’s keyword matching with Milvus’s vector‑based semantic search to build a scalable document search service, covering data preprocessing, architecture, query handling, custom scoring, DSL configuration, and result merging.

Full-Text SearchMilvusVector Search

0 likes · 12 min read

Implementing Full‑Text Document Search with Elasticsearch and Milvus

JD Tech

Jul 15, 2024 · Databases

A Comprehensive Overview of Nine Database Types and Polyglot Persistence Practices

This article provides an in‑depth survey of nine database categories—including relational, key‑value, columnar, document, graph, time‑series, and vector databases—detailing their architectures, advantages, disadvantages, best‑practice recommendations, typical use cases, and how they can be combined in polyglot persistence solutions.

ClickHouseDatabase TypesHBase

0 likes · 41 min read

A Comprehensive Overview of Nine Database Types and Polyglot Persistence Practices

Rare Earth Juejin Tech Community

May 9, 2024 · Artificial Intelligence

Exploring Sentence and Paragraph Search with Milvus Vector Search vs Elasticsearch

This article examines how vector search using Milvus handles sentence and paragraph queries compared to traditional Elasticsearch, demonstrating the advantages of embedding‑based semantic matching for document search scenarios through practical experiments and visual results.

MilvusSemantic SearchVector Search

0 likes · 11 min read

Exploring Sentence and Paragraph Search with Milvus Vector Search vs Elasticsearch

Rare Earth Juejin Tech Community

Mar 22, 2024 · Artificial Intelligence

Improving Document Search with Vector Search: From Elasticsearch Limitations to Milvus Integration

This article explains how traditional keyword search with Elasticsearch often yields inaccurate or incomplete results for document retrieval, introduces vectorization and semantic search using NLP embeddings, and demonstrates a practical workflow that combines these techniques with the Milvus vector database to achieve more accurate and efficient document search.

AIMilvusNLP

0 likes · 13 min read

Improving Document Search with Vector Search: From Elasticsearch Limitations to Milvus Integration

Zhuanzhuan Tech

Nov 29, 2023 · Artificial Intelligence

Applying CLIP and Milvus for Image Similarity Search in E‑commerce Risk Control

The article explains how an e‑commerce risk‑control team leverages OpenAI's CLIP model to generate image and text embeddings and stores them in the Milvus cloud‑native vector database to enable fast, scalable similarity searches for compliance verification and risk detection.

AIClipMilvus

0 likes · 11 min read

Applying CLIP and Milvus for Image Similarity Search in E‑commerce Risk Control

DataFunSummit

Aug 30, 2023 · Databases

Milvus: An AI‑Native Vector Database for Large Language Model Applications

This article introduces Milvus, an open‑source, cloud‑native vector database designed for AI workloads, explains how it helps mitigate large‑model hallucinations, outlines its CVP architecture, showcases performance benchmarks, and explores diverse application scenarios and future directions for LLM‑vector database integration.

AILLMMilvus

0 likes · 13 min read

Milvus: An AI‑Native Vector Database for Large Language Model Applications

Rare Earth Juejin Tech Community

Jul 27, 2023 · Artificial Intelligence

Implementing Text‑Based Image Search Using OCR, Transformers, and Vector Databases

This article explains how to build a text‑to‑image search system by first extracting text with OCR, then storing image paths and textual embeddings in a SQLite or Milvus vector database, and finally improving retrieval with Transformer‑based sentence embeddings and image‑captioning models.

Image SearchMilvusOCR

0 likes · 16 min read

Implementing Text‑Based Image Search Using OCR, Transformers, and Vector Databases

Rare Earth Juejin Tech Community

Jul 25, 2023 · Artificial Intelligence

Building a Reverse Image Search Engine with Geometric Distance, ResNet Feature Embeddings, Clustering, and Milvus Vector Database

This article walks through implementing a reverse image search system, starting with simple pixel‑based geometric distance, then improving accuracy using ResNet‑derived feature embeddings, accelerating queries with K‑means clustering, and finally deploying a Milvus vector database for fast, scalable similarity retrieval.

ClusteringComputer VisionFeature Extraction

0 likes · 17 min read

Building a Reverse Image Search Engine with Geometric Distance, ResNet Feature Embeddings, Clustering, and Milvus Vector Database

HomeTech

Dec 16, 2022 · Artificial Intelligence

Building and Optimizing a Milvus‑Based Vector Search Platform

This article describes the background, technical selection, architecture, deployment, performance tuning, and operational practices of a Milvus‑driven vector retrieval platform, including cloud‑native deployment, index choices, capacity planning, and real‑world application cases that improve recall latency and resource efficiency.

AIIndexingMilvus

0 likes · 12 min read

Building and Optimizing a Milvus‑Based Vector Search Platform

DeWu Technology

Nov 25, 2022 · Databases

Milvus Vector Database Performance Testing and Architecture Analysis

The author stress‑tested Milvus 2.1.4’s cloud‑native, micro‑service architecture—detailing its write and search paths, evaluating FLAT index performance across 100 K to 10 M 512‑dim vectors, uncovering scaling, scheduler, segment‑rebalance, and upgrade issues, and concluding the system is robust but benefits from graph‑based indexes and Helm‑driven scaling.

Database ArchitectureMilvusVector Search

0 likes · 10 min read

Milvus Vector Database Performance Testing and Architecture Analysis

Youzan Coder

Oct 24, 2022 · Artificial Intelligence

Knowledge Base Retrieval Matching: Algorithm and Engineering Service Practice

The article outlines a comprehensive knowledge‑base retrieval matching solution—combining PageRank‑enhanced DSL rewriting, keyword and dual‑tower vector recall, contrastive fine‑ranking, and optimized vector‑based ranking—implemented via offline DP training and Sunfish online inference on Milvus, with applications in enterprise search and recommendations and future plans for graph‑neural embeddings.

InfoNCEMilvusNLP

0 likes · 12 min read

Knowledge Base Retrieval Matching: Algorithm and Engineering Service Practice

360 Quality & Efficiency

Jul 1, 2022 · Artificial Intelligence

Building an End-to-End Image Search System with Milvus and VGG

This article presents a complete image‑search solution that extracts visual features with the VGG16 model, stores them in the Milvus vector database, and provides a set of web APIs for training, querying, counting, searching, and deleting image vectors, all deployed via Docker containers.

AIMilvusVGG

0 likes · 7 min read

Building an End-to-End Image Search System with Milvus and VGG

DataFunSummit

Mar 29, 2022 · Databases

AI-Driven Unstructured Data Analysis and Retrieval with Milvus and Towhee

This article explains how the Milvus vector database and the Towhee embedding framework together enable large‑scale, high‑throughput semantic analysis and retrieval of unstructured data such as images, video, and audio by leveraging AI‑powered vectorization and search pipelines.

AIMilvusSemantic Search

0 likes · 13 min read

AI-Driven Unstructured Data Analysis and Retrieval with Milvus and Towhee

iQIYI Technical Product Team

Aug 20, 2021 · Artificial Intelligence

Engineering Practice of Online Vector Recall Service at iQIYI

iQIYI’s engineering team built an online vector‑recall service on Milvus, wrapping it with a Dubbo‑gRPC interface to serve 6 M 64‑dimensional embeddings at roughly 3 k QPS and 20 ms p99 latency, integrating query‑embedding generation, simplifying recommendation pipelines, and demonstrating the performance and operational advantages of a platformized ANN‑based recall layer.

AIMilvusVector Search

0 likes · 14 min read

Engineering Practice of Online Vector Recall Service at iQIYI