Hands-on tutorial on how to create a streamlined, powerful RAG pipeline that balances efficiency, accuracy, and scalability using the QwQ-32B model and Milvus.

Lumina Wang

How to Build RAG with Milvus, QwQ-32B and Ollama

Explore CoRAG, a novel retrieval-augmented generation method that refines queries iteratively to improve multi-hop reasoning and factual answers.


 Haziqa Sajid, Freelance Technical Writer

 Haziqa Sajid

Chain-of-Retrieval Augmented Generation

Use a vector database for similarity search and AI-powered applications; use a document database for flexible schema and JSON-like data storage.

Chloe Williams, Technical Writer at Zilliz. 

Chloe Williams

Vector Databases vs. Document Databases

Discover how Model Context Protocol (MCP) pairs with Milvus to eliminate AI integration hassles, enabling smarter agents with seamless data access and flexibility.

Stephen Batifol is a Developer Advocate at Zilliz.

Stephen Batifol

How to Use Anthropic MCP Server with Milvus

Discover how Topological Data Analysis (TDA) reveals hidden embedding model weaknesses and helps optimize vector database performance.


Wania Shafqat

Optimizing Embedding Model Selection with TDA Clustering: A Strategic Guide for Vector Databases

Use a vector database for similarity search and semantic relationships; use a time series database for tracking value changes over time.

Vector Databases vs. Time Series Databases

Discover the best RAG evaluation tools to improve AI app reliability, prevent hallucinations, and boost performance across different frameworks.


Yesha Shastri, Freelance Technical Writer

Yesha Shastri

Top 10 RAG & LLM Evaluation Tools You Don't Want To Miss

Discover how vector databases enhance AI reliability in legal tech, ensuring accurate, compliant, and trustworthy AI-powered legal solutions.


Chris Churilo

Bringing AI to Legal Tech: The Role of Vector Databases in Enhancing LLM Guardrails

Use a vector database for AI-powered similarity search; use a key-value database for high-throughput, low-latency simple data lookups.

Vector Databases vs. Key-Value Databases

KNN is a supervised machine learning technique and algorithm for classification and regression. This post is the ultimate guide to KNN. 

Zilliz

What is the K-Nearest Neighbors (KNN) Algorithm in Machine Learning?

Milvus WebUI is a built-in GUI introduced in Milvus v2.5  for system observability. WebUI comes pre-installed with your Milvus instance and offers immediate access to critical system metrics and management features.

Milvus WebUI: A Visual Management Tool for Your Vector Database

A vector database stores, indexes, and searches vector embeddings generated by machine learning models for fast information retrieval and similarity search. 

Frank Liu

What is a Vector Database and how does it work: Implementation, Optimization & Scaling for Production Applications

Zilliz Cloud transforms legal document analysis with AI-driven Semantic Search and Retrieval-Augmented Generation (RAG). By combining keyword and vector search, it enables faster, more accurate contract analysis, case law research, and regulatory tracking.










Legal Document Analysis: Harnessing Zilliz Cloud's Semantic Search and RAG for Legal Insights

Use a vector database for AI-powered similarity search; use a graph database for complex relationship-based queries and network analysis.

Vector Databases vs. Graph Databases

Learn the right ETL tools for unstructured data to power AI. Explore key challenges, tool comparisons, and integrations with Milvus for vector search.


Selecting the Right ETL Tools for Unstructured Data to Prepare for AI

In this article, we will talk about the o1 series from a developer's perspective, exploring how these models can be implemented for sophisticated use cases. 

Ruben Winastwan, Freelance Technical Writer

Ruben Winastwan

OpenAI o1: What Developers Need to Know

In this article, we will explore how multimodal AI enhances AI systems by bridging audio, text, and vector search. 

Denis Kuria is a freelance technical writer at Zilliz. 

Denis Kuria

Enhancing Multimodal AI: Bridging Audio, Text, and Vector Search

Learn how to set up DeepSeek-R1 on your local machine using Ollama, AnythingLLM, and Milvus in just 10 minutes. Bypass busy servers and enhance AI responses with custom data.


DeepSeek Always Busy? Deploy It Locally with Milvus in Just 10 Minutes—No More Waiting!

Explore the evolution of Text-to-Speech technology from mechanical devices to neural networks. Learn how TTS works, compare popular models, and implement it using Google Cloud Platform.


Benito Martin

From Text to Speech: A Deep Dive into TTS Technologies

explore how Cloudera can be integrated with Milvus to effectively implement some of the key functionalities of RAG pipelines. 

Building RAG Pipelines for Real-Time Data with Cloudera and Milvus

Discover how audio transformers enhance sound processing. Explore their principles, selection criteria, popular models, applications, and key challenges.


Choosing the Right Audio Transformer: An In-depth Comparison

In contrast to OpenAI’s Deep Research, this example ran locally, using only open-source models and tools like Milvus and LangChain.

Stefan Webb

Introducing DeepSearcher: A Local Open Source Deep Research

we're excited to announce Zilliz Cloud's integration with Datadog, enabling comprehensive monitoring and observability for your vector database deployments with your favorite monitoring tool.

Steffi Li

Proactive Monitoring for Vector Database: Zilliz Cloud Integrates with Datadog

Discover how AI and vector databases are revolutionizing video surveillance with real-time analysis, faster threat detection, and intelligent search capabilities for enhanced security.

AI Integration in Video Surveillance Tools: Transforming the Industry with Vector Databases

Discover audio data, its characteristics, processing techniques, and key challenges. Learn how to tackle them effectively in AI applications.


Getting Started with Audio Data: Processing Techniques and Key Challenges

In this blog, we'll examine LLM guardrails, technical systems, and processes designed to ensure LLMs' safe and reliable operation.

Simon Mwaniki 

Everything You Need to Know About LLM Guardrails

Discover how DeepSeek R1's open-source AI model with superior reasoning capabilities and lower costs is disrupting the AI landscape and challenging tech giants like OpenAI.


Why Deepseek is Waking up AI Giants Like OpenAI And Why You Should Care

Explore DeepSeek-VL2, the open-source MoE vision-language model. Discover its architecture, efficient training pipeline, and top-tier performance.


DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

In this article, we’ll explore how DeepRAG works, unpack its key components, and show how vector databases like Milvus and Zilliz Cloud can further enhance its retrieval capabilities.


DeepRAG: Thinking to Retrieval Step by Step for Large Language Models

Discover how Zilliz Cloud BYOC brings enterprise-grade security, networking isolation, and infrastructure automation to vector database deployments in AWS

Zilliz Cloud BYOC Upgrades: Bring Enterprise-Grade Security, Networking Isolation, and More

Discover the top AI-powered search engines of 2025, including OpenAI, Google AI, Bing, Perplexity, and Arc Search. Compare features, strengths, and limitations.

ShriVarsheni R

Top 5 AI Search Engines to Know in 2025

Learn how to implement pre-trained models for audio AI applications. Explore speech recognition, audio classification, and TTS with practical code examples.

Unlocking Pre-trained Models: A Developer’s Guide to Audio AI Tasks

Compare OpenAI's o1 and o3-mini with DeepSeek R1's open-source alternative. Discover which AI model offers the best balance of reasoning capabilities and cost efficiency.


DeepSeek vs. OpenAI: A Battle of Innovation in Modern AI

Explore the 10 most popular audio embedding models including Wav2Vec 2.0, VGGish, and OpenL3. Learn how they transform sound into vectors for AI applications

Top 10 Most Used Embedding Models for Audio Data 

Learn how to build Retrieval Augmented Generation (RAG) applications using Dify for orchestration and Milvus for vector storage in this step-by-step guide.

Building RAG with Dify and Milvus

Explore knowledge injection techniques like fine-tuning and RAG. Compare their effectiveness in improving accuracy, knowledge retention, and task performance.


Knowledge Injection in LLMs: Fine-Tuning and RAG

Explore how DeepSeek V3 achieves GPT-4 level performance at fraction of the cost. Learn about MLA, MoE, and MTP innovations driving this open-source breakthrough.

Why DeepSeek V3 is Taking the AI World by Storm: A Developer’s Perspective

Discover how DeepSeek's affordable AI ecosystem challenges Silicon Valley giants with powerful integrations for developers and businesses—from RAG systems to productivity tools, all at 90% lower cost.


Long List of Awesome DeepSeek Integrations You Should Know

Discover how vector databases like Milvus and Zilliz Cloud enable efficient audio similarity search at scale, transforming music recommendations and audio retrieval applications.


Scaling Audio Similarity Search with Vector Databases

Over the course of the day, teams built working RAG-powered applications using the Milvus vector database—many of them solving real-world problems in healthcare, legal access, sustainability, and more—all within just a few hours.


Emily Kurze

Empowering Innovation: Highlights from the Women in AI RAG Hackathon

Learn how to build scalable multimodal AI pipelines using Datavolo and Milvus. Discover best practices for handling unstructured data and implementing RAG systems.

Multimodal Pipelines for AI Applications

In this blog, we will explore the nature of hallucinations, the taxonomy that provides a framework for categorizing them, the FAVABENCH dataset designed for evaluation, and how FAVA detects and corrects errors. 


Enhancing AI Reliability Through Fine-Grained Hallucination Detection and Correction with FAVA

In this guide, we’ll break down the main components of RAG costs, show you how to calculate these expenses using the Zilliz RAG Cost Calculator, and explore strategies to manage spending efficiently.


How to Calculate the Total Cost of Your RAG-Based Solutions

AI and vector databases are transforming retail, enhancing personalization, search, customer service, and operations. Discover how Zilliz Cloud helps drive growth and innovation.










How AI and Vector Databases Are Transforming the Consumer and Retail Sector

This blog will summarize the monumental changes AI brought to Information Retrieval (IR) in 2024.


Jiang Chen

How AI Is Transforming Information Retrieval and What’s Next for You

These multimodal RAG patterns include grounding all modalities into a primary modality, embedding them into a unified vector space, or employing hybrid retrieval with raw data access. 


3 Key Patterns to Building Multimodal RAG: A Comprehensive Guide 

Explore how vector databases and AI are transforming marketing platforms, enabling real-time personalization and predictive analytics while balancing automation with creativity.



The AI Revolution in Marketing: How Vector Databases Are Unlocking True Personalization

Discuss the role of semantic search in processing unstructured data, how finite state machines enable reliable generation, and practical implementations using modern tools for structured outputs from LLMs. 


Producing Structured Outputs from LLMs with Constrained Sampling

We will discuss how the Gandalf project revealed LLMs' vulnerabilities to adversarial attacks. Additionally, we will address the role of vector databases in AI security.


Insights into LLM Security from the World’s Largest Red Team

We’ll explore the limitations of binary relevance labels, how fine-grained relevance scoring works, and why it’s a game-changer for zero-shot text rankers


Teaching LLMs to Rank Better: The Power of Fine-Grained Relevance Scoring

Empower and celebrate women in AI at the Women in AI RAG Hackathon at Stanford. Engage with experts, build innovative AI projects, and compete for prizes. 


Empowering Women in AI: RAG Hackathon at Stanford

RocketQA is a highly optimized dense passage retrieval framework designed to enhance open-domain question-answering (QA) systems. 

RocketQA: Optimized Dense Passage Retrieval for Open-Domain Question Answering

This blog explores why Postgres, with its vector search add-on, pgvector, works well for smaller projects and simpler use cases but reaches its limits for large-scale vector search. 

Beyond PGVector: When Your Vector Database Needs a Formula 1 Upgrade

Lexical search offers exact term matching; full-text search allows for fuzzy matching; semantic search understands context and intent.

Semantic Search vs. Lexical Search vs. Full-text Search

GPL is an unsupervised domain adaptation technique for dense retrieval models that combines a query generator with pseudo-labeling. 


GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

This post explores the key components of RAG, its evolution, technical implementation, evaluation methods, and potential for real-world applications. 


Advancing LLMs: Exploring Native, Advanced, and Modular RAG Approaches

Rob Quiros shared how integrating permissions and authorization into partitions can secure data at the chunk level, addressing privacy concerns.


Building Secure RAG Workflows with Chunk-Level Data Partitioning

Annoy is a lightweight, open-source library designed for fast, approximate nearest-neighbor searches in high-dimensional vector spaces.


Fendy Feng

What is Annoy?

This blog will discuss the growing need to detect machine-generated text, past detection methods, and a new approach: Binoculars. 

Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text

Discover how Augmented SBERT uses data augmentation to enhance the bi-encoder for pairwise sentence scoring. 

Augmented SBERT: A Data Augmentation Method to Enhance Bi-Encoders for Pairwise Sentence Scoring

Discover how AI and vector databases are transforming sales platforms with intelligent lead matching, automated workflows, and real-time insights. Learn why 43% of sales teams use AI in 2024.

Beyond the Pitch: Vector Databases and AI are Rewriting the Sales Playbook

Learn how to build AI-powered video editing tools using CLIP, ResNet, and vector databases. Discover implementation steps for intelligent search, automated tagging, and scalable video processing.

AI Video Editing Software: Revolutionizing Video Tech Through Intelligent Search and Automation

AI agents are like having a supercharged assistant by your side—analyzing data, making decisions, and seamlessly integrating with tools and environments. 

Top 10 AI Agents to Watch in 2025 🚀

LLM frameworks simplify workflows, enhance performance, and integrate seamlessly with existing systems, helping developers unlock the full potential of LLMs with less effort.

10 Open-Source LLM Frameworks Developers Can’t Ignore in 2025

Discover how AI and vector databases are revolutionizing legal work through advanced document processing, semantic search, and contract analysis capabilities.

AI Integration in the Legal Industry: Revolutionizing Legal Practice with Data-Driven Solutions

RA-DIT, or Retrieval-Augmented Dual Instruction Tuning, is a method for fine-tuning both the LLM and the retriever in a RAG setup to enhance overall response quality.

Enhancing RAG with RA-DIT: A Fine-Tuning Approach to Minimize LLM Hallucinations

 In this blog, we will explore Qwen and vLLM and how combining both with the Milvus vector database can be used to build a robust RAG system.

Building RAG Applications with Milvus, Qwen, and vLLM

Voyager is an Approximate Nearest Neighbor (ANN) search library optimized for high-dimensional vector data.

What is Voyager?

This tutorial has demonstrated how to leverage Milvus and Friendli Serverless Endpoints to implement advanced RAG and multi-modal queries. 

Wonook is currently doing research and software engineering at FriendliAI. 

Wonook Song

Leveraging Milvus and Friendli Serverless Endpoints for Advanced RAG and Multi-Modal Queries

We're thrilled to announce the release of Milvus 2.5, a significant step in our journey to build the world's most complete solution for all search workloads. 

Introducing Milvus 2.5: Built-in Full-Text Search, Advanced Query Optimization, and More 🚀

Many-Shot In-Context Learning is an NLP technique where a model generates predictions by observing multiple examples within the input context. 

Unlocking the Power of Many-Shot In-Context Learning in LLMs

A step-by-step guide to building an AI-powered FAQ system using Milvus as the vector database, LangChain.js for workflow coordination, and Strapi for content management

Build RAG with LangChainJS, Milvus, and Strapi

Matryoshka Representation Learning (MRL) is a method for generating hierarchical, nested embeddings that capture information at multiple levels of abstraction. 

Matryoshka Representation Learning Explained: The Method Behind OpenAI’s Efficient Text Embeddings

The Data Prep Kit (DPK) is an open-source toolkit by IBM Research designed to streamline unstructured data preparation for building AI applications. 

Introducing IBM Data Prep Kit for Streamlined LLM Workflows

In this tutorial, we will explore how to build a robust RAG application by combining the capabilities of Milvus, a scalable vector database optimized for similarity search, and DBRX. 

Building a RAG Application with Milvus and Databricks DBRX

The Goldfish Loss technique prevents the verbatim reproduction of training data in LLM output by modifying the standard next-token prediction training objective.

Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs

We discussed the checkability training approach to help LLMs generate accurate answers that humans can easily understand and verify. 


Prover-Verifier Games Improve Legibility of LLM Outputs

Zilliz Cloud delivers reliable vector storage and search, while Vectorize automates your RAG pipelines and keeps your embeddings up-to-date. 

Jamie Ferguson focuses on shaping the product strategy and driving growth at Vectorize, drawing on her technical background in AI and engineering.

Jamie Ferguson

Deliver RAG Applications 10x Faster with Zilliz and Vectorize

AXN (Adaptive Cross-Encoder Nearest Neighbor Search) uses a sparse matrix of CE scores to approximate k-NN results, reducing computation while maintaining high accuracy.

Next-Gen Retrieval: How Cross-Encoders and Sparse Matrix Factorization Redefine k-NN Search

We’ve explored how multi-tenancy frameworks play a critical role in the scalability, security, and performance of RAG-powered knowledge bases. 

Robert Guo

Designing Multi-Tenancy RAG with Milvus: Best Practices for Scalable Enterprise Knowledge Bases

An overview of various RAG pipeline architectures, retrieval and evaluation frameworks, and examples of biases and failures in LLMs.


Evaluating Retrieval-Augmented Generation (RAG): Everything You Should Know

Purpose-built vector databases outperform dual-system setups by unifying Sparse-BM25 and semantic search in a single, efficient implementation.

Elasticsearch Was Great, But Vector Databases Are the Future

GLiNER is an open-source NER model using a bidirectional transformer encoder. 


GLiNER: Generalist Model for Named Entity Recognition Using Bidirectional Transformer

Mixture-of-Agents (MoA) is a framework where multiple specialized LLMs, or "agents," collaborate to solve tasks by leveraging their unique strengths. 


Mixture-of-Agents (MoA): How Collective Intelligence Elevates LLM Performance

RouteLLM is an open-source framework that enables developers to efficiently route tasks to the most suitable LLMs based on cost, latency, and accuracy.

RouteLLM: An Open-Source Framework for Navigating Cost-Quality Trade-Offs in LLM Deployment

Compare top vector database vendors, run benchmarks, and choose the right solution for your AI-driven applications. Download the guide now!


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