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Milvus Community

Zilliz hosts Unstructured Data Meetups in various locations around the world with the goal of bringing together developers who are interested in building generative AI applications with unstructured data. As new AI models and platforms are growing at a rapid pace, meetups have become a great forum for developers and engineers curious about or working on GenAI applications to learn and network.

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Upcoming Zilliz Unstructured Data Meetups

Why Zilliz Cloud is the Top Chioce for Enterprise-Level Vector Search.

Related Resources

Success Stories

Popular Vector Database Use Cases<0></0>Zilliz Cloud Can Make a Difference 

Saving time and costs when combining Zilliz Cloud with GPTCache

How Zilliz Cloud Augments LLM Applications

RAG for LLMs: Updating and expanding LLMs’ knowledge base for more accurate answers

<0>The CVP stack (ChatGPT/LLMs + a vector database + prompt-as-code)</0> is an increasingly popular AI stack that shows the value of vector databases for LLM enhancement. We can use <1>OSS Chat</1> as an example to demonstrate how the CVP stack works.

OSS Chat is a chatbot that can answer questions about GitHub projects. It collects and stores information from various GitHub repositories and their documentation pages in Zilliz Cloud in the form of embeddings. When a user asks OSS Chat about any open-source project, Zilliz Cloud conducts a similarity search to find the topk most relevant outcomes. Then these results are combined with the original question to create a prompt that gives ChatGPT a broader context, resulting in more accurate answers.

We can also incorporate the GPTCache into the CVP stack to reduce costs and speed up responses.

ChatGPT/LLMs + a vector database + prompt-as-code

The CVP Stack

Retrieval Augmented Generation (RAG)

Milvus Integrations with Popular AI Projects

Lacking domain-specific information

Prone to hallucination 

Costly and slow

Failure to access up-to-date information

Token Limit

Immutable pre-training data

LLM Limitations

LLM Projects Utilizing Milvus & Zilliz Cloud

Vector Database Use Cases: Retrieval Augmented Generation - Zilliz

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Vector Database Use Cases

원활한 AI 워크플로

콘텐츠

무료로 시작하고 쉽게 확장하세요

Zilliz Cloud 서버리스로 오늘 GenAI 앱 구축을 시작하세요

AI 어시스턴트

밀버스 앰배서더 프로그램

이벤트

디스코드 포럼

비정형 데이터 밋업

Milvus 기본 사항

커뮤니티에 오신 것을 환영합니다!

Milvus 커뮤니티

Zilliz는 비정형 데이터를 사용하여 생성형 AI 애플리케이션을 구축하는 데 관심 있는 개발자들을 한자리에 모으는 것을 목표로 전 세계 여러 지역에서 비정형 데이터 밋업을 개최합니다. 새로운 AI 모델과 플랫폼이 빠르게 성장함에 따라 밋업은 GenAI 애플리케이션에 대해 궁금하거나 작업하는 개발자와 엔지니어가 배우고 네트워크를 형성할 수 있는 훌륭한 포럼이 되었습니다.

지난 밋업

기다리고 싶지 않으세요?

이벤트의 발표자 또는 후원자가 되기 위해 신청하세요!

발표자 또는 후원자가 되고 싶으신가요?

다가오는 이벤트

다가오는 Zilliz 비정형 데이터 밋업

Zilliz Cloud가 엔터프라이즈 수준의 벡터 검색에 적합한 이유

성공 사례

GPTCache와 함께 시간과 비용 절감

Zilliz Cloud가 LLM 애플리케이션을 향상하는 방법

RAG로 LLM의 지식 기반 업데이트 및 확장

<0>CVP 스택(ChatGPT/LLM + 벡터 데이터베이스 + 코드 기반 프롬프트)</0>은 AI의 성능을 극대화하는 인기 있는 기술 스택입니다. <1>OSS Chat</1>을 예로 들어 CVP 스택이 어떻게 작동하는지 설명합니다.

OSS Chat은 GitHub 프로젝트에 대한 질문에 답변하는 챗봇입니다. 다양한 GitHub 저장소와 문서에서 정보를 수집하고 이를 Zilliz Cloud에 벡터 임베딩 형태로 저장합니다. 사용자가 프로젝트에 대한 질문을 하면 Zilliz Cloud는 유사성 검색을 수행해 가장 관련성이 높은 결과를 반환하고, 이 결과는 질문과 결합되어 ChatGPT가 더 정확한 답변을 제공합니다.

CVP 스택에 GPTCache를 통합하여 비용 절감 및 응답 속도를 더욱 개선할 수 있습니다.

ChatGPT/LLM + 벡터 데이터베이스 + 코드 기반 프롬프트

CVP 스택

RAG (Retrieval Augmented Generation)

도메인별 정보 부족

환각 현상 발생 가능성

비용이 많이 들고 속도가 느림

토큰 제한

변경 불가능한 사전 학습 데이터

LLM의 한계

Milvus & Zilliz Cloud를 활용한 LLM 프로젝트

벡터 데이터베이스 사용 사례: RAG (Retrieval Augmented Generation) - Zilliz

당신의 스토리를 공유하세요!

벡터 데이터베이스 사용 사례

Hands-on Tutorial: Building an Agent to Reason about Private Data with Milvus

Combining Lexical and Semantic Search with Milvus 2.5

Bedrock Data Automation (Preview): Simplifying Unstructured Data Processing

Voice-to-Value: LLM-Powered Customer Interaction Analysis

Building the Next-Gen Apps with Multimodal Retrieval using Twelve Labs & Milvus

Bridging Data Pipelines and AI: Powering Insights with Airbyte and Milvus

Building an Accuracy Flywheel for your LLM RAG Apps

Efficient Inference and Information Retrieval for Agents: SambaNova + Milvus

Evaluating RAG pipelines built on unstructured data

Gandalf: Insights from the World's Largest Red Team

Structuring Unstructured Text using generative AI: The key to information extraction

LLM Agent Observability: Lessons Learned from Real-World Applications

Dense Embeddings != Complete Search - a sneak peak of Milvus 2.5

Beyond RAG Partitions: Per-User, Per-Chunk Access Policy

Challenges in Structured Document Data Extraction at Scale with LLMs

Multimodal Search with Open-Source Tools


Timeplus: One single binary to tackle streaming and historical analytics


Enhancing Context Provision for LLMs: Beyond RAG and Prompts


Large Language Models ❤️ Knowledge Graphs

RGB-X Model Development: Exploring Four Channel ML Workflows

How Inkeep and Zilliz built an AI Assistant 

Improving RAG with Knowledge Graph + Multi-modality + Milvus

Transformers4rec: Harnessing NLP Advancements for Cutting-Edge Recommender Systems

Small to Slide: RAG using Multimodal LLMs.

From CLIP to JinaCLIP: General Text-Image Representation Learning for Search and Multimodal RAG

The Journey of Large Language Models at GetYourGuide

Multi-Agent Systems with Mistral AI, Milvus and llama-agents

Advanced Video Search - Leveraging Twelve Labs and Milvus for Semantic Retrieval

Implement Agentic RAG Using Claude 3.5 Sonnet, LlamaIndex, and Milvus

Modern Analytics & Reporting with Milvus Vector DB and GenAI

Combining Hugging Face Transformer Models and Visual Data with FiftyOne

A Different Angle: Retrieval Optimized Embedding Models

Building the Future of Neural Search: How to Train State-of-the-Art Embeddings

From Dev to Prod: Vector Database Made Easy

Ensuring Secure and Permission-Aware RAG Deployments

How CXAI Toolkit uses RAG for Intelligent Q&A

Docker tools & Open Source libraries for productivity

Scaling Vector Search: How Milvus Handles Billions+

How to Load Test an LLM API using Gatling

Improving Analytics with Time Series and Vector Databases

Garbage In, Garbage Out: Why poor data curation is killing your AI models (and how to fix it)

It's your unstructured data: How to get your GenAI app to production (and speed up your delivery)

How Vector Databases are Revolutionizing Unstructured Data Search in AI Applications


Harnessing the Power of Generative AI with Large Language Models in Human Capital Management

Building an Agentic RAG locally with Ollama and Milvus

Specializing Small Language Models With Less Data

Occiglot - Open Language Models by and for Europe

Building Production Ready Search Pipelines with Spark and Milvus


Challenges associated with using LLM-as-a-judge

Generating privacy-protected synthetic data using Secludy and Milvus

NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI


Building RAG with self-deployed Milvus vector database and Snowpark Container Services

Infrastructure Challenges in Scaling RAG with Custom AI models

Full RAG: A Modern Architecture for Hyperpersonalization

Introduction to Open Source RAG and RAG Evaluation

Copilot Workspace: What it is, how it works, why it matters

Multilingual narrative tracking in the news - real-time experiments

Exploring Multimodal Embeddings with Milvus

"I see eyes in my soup": How Delivery Hero implemented the safety system for AI-generated images

Processing streaming data in Kafka with Timeplus Proton and Zilliz

Visualizing the Evolution of ML Models: Insights and Tools for Enhanced Understanding

Vector Databases 101 - An introduction to the world of Vector Databases

Fact vs. Fiction: Autodetecting Hallucinations in LLMs

Training state-of-the-art general text embedding

Advanced Retrieval-Augmented Generation apps with LlamaIndex

Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost

Voyage AI: cutting-edge embeddings and rerankers for search and RAG

Chat with your data, privately and locally

Sometimes people worry that if they ask ChatGPT a question, their queries may be used to train the AI model. However, if you use a local application or RAG to search for proprietary documents stored locally on your machine, like patents or personal diaries, the data will be safe. Although this is a good solution, it may slow down the process of running LLMs locally if models with original weights are used. In this talk, Jay proposes a local and optimized RAG pipeline for systems using consumer desktop/laptop NVIDIA GPUs.  We will use Milvus as our vector database for faster vector search, and TensorRT-LLM optimized open-source LLM leveraging local GPU compute.

Introducing Milvus and new features in 2.4 release

The latest version of Milvus introduces sparse vectors to use in your semantic searches. Learn from Yi, Software Engineer at Zilliz, how you can use sparse and dense embeddings to power your RAG apps.


Voyage AI Embedding for your RAG apps 

Voyage AI is a team of leading AI researchers, dedicated to enabling teams to build better RAG applications via quality retrieval. This talk will present the Voyage AI state-of-the-art embedding models and APIs, which outperforms alternatives such as OpenAI v3 text embeddings. These models are a part of the Zilliz Cloud Pipelines.

VectorDB Schema Design 101 - Considerations for Building a Scalable and Performant Vector Search

Approximate Nearest Neighbor Search in Recommender Systems

Path to Production: LLM System Evaluations and Observability

Constructing a Multidimensional Vector Database of Images for Behavioral Science 

Milvus 커뮤니티
비정형 데이터 밋업

비정형 데이터 밋업

다가오는 이벤트

Seattle

Berlin

New York

San Francisco

South Bay

Tokyo

기다리고 싶지 않으세요?

지난 밋업

Hands-on Tutorial: Building an Agent to Reason about Private Data with Milvus

Combining Lexical and Semantic Search with Milvus 2.5

Bedrock Data Automation (Preview): Simplifying Unstructured Data Processing

Voice-to-Value: LLM-Powered Customer Interaction Analysis

Full Text Search with Milvus 2.5

Building the Next-Gen Apps with Multimodal Retrieval using Twelve Labs & Milvus

Bridging Data Pipelines and AI: Powering Insights with Airbyte and Milvus

Building an Accuracy Flywheel for your LLM RAG Apps

Efficient Inference and Information Retrieval for Agents: SambaNova + Milvus

Milvus 커뮤니티비정형 데이터 밋업

비정형 데이터 밋업

다가오는 이벤트

Seattle

Berlin

New York

San Francisco

South Bay

Tokyo

기다리고 싶지 않으세요?

지난 밋업

Hands-on Tutorial: Building an Agent to Reason about Private Data with Milvus

Combining Lexical and Semantic Search with Milvus 2.5

Bedrock Data Automation (Preview): Simplifying Unstructured Data Processing

Voice-to-Value: LLM-Powered Customer Interaction Analysis

Full Text Search with Milvus 2.5

Building the Next-Gen Apps with Multimodal Retrieval using Twelve Labs & Milvus

Bridging Data Pipelines and AI: Powering Insights with Airbyte and Milvus

Building an Accuracy Flywheel for your LLM RAG Apps

Efficient Inference and Information Retrieval for Agents: SambaNova + Milvus

Milvus 커뮤니티
비정형 데이터 밋업