Seamless AI Workflows

Content

Start Free, Scale Easily

Start to build your GenAl apps today with Zilliz Cloud Serverless

AI Assistant

Milvus Ambassador Program

Events

Discord Forum

GitHub

Unstructured Data Meetups

Milvus Basics

Slack

Welcome to the community!

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.

Past Meetups

Don't want to wait? 

Apply to be a speaker or our sponsor in our events!

Want to be a speaker or sponsor?

Upcoming Events

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

Share Your Story with Us!

Vector Database Use Cases

シームレス AI ワークフロー

コンテンツ

無料で始めて、簡単にスケール

Zilliz Cloud Serverlessで今日からGenAIアプリを構築し始めましょう

AIアシスタント

ミルバス・アンバサダー・プログラム

イベント

Discordフォーラム

非構造化データミートアップ

Milvusの基本

スラック

コミュニティへようこそ！

Milvusコミュニティ

過去のミートアップ

イベントでのスピーカーまたはスポンサーを申し込む！

スピーカーまたはスポンサーになりたいですか？

今後のイベント

なぜZilliz Cloudが企業レベルにおいてベクトル検索の最適な選択肢なのか。

成功事例

Zilliz CloudとGPTCacheを組み合わせて時間とコストを節約

Zilliz CloudがLLMアプリケーションを拡張する方法

LLMのためのRAG: LLMの知識ベースを更新・拡張し、より正確な回答を提供

<0>CVPスタック (ChatGPT/LLMs + a vector database + prompt-as-code)</0> は、ベクトルデータベースが備えたLLMを強化する価値を示す人気のAIスタックです。<1>OSS Chat</1>を例として、CVPスタックの仕組みを説明できます。

OSS ChatはGitHubプロジェクトに関する質問に答えるチャットボットです。さまざまなGitHubリポジトリやそのドキュメントページから情報を収集し、それを埋め込みの形式でZilliz Cloudに保存します。 ユーザーがOSS Chatにオープンソースプロジェクトについて質問すると、Zilliz Cloudが類似性検索を実行して最も関連性の高いTop-kの結果を探します。 そして、 これらの結果は元の質問と組み合わせられてプロンプトを生成し、ChatGptにより正確な回答を生成するためのより広範なコンテキストを提供します。

また、GPTCacheをCVPスタックに組み込むことで、コストを削減し、応答を高速化できます。

ChatGPT/LLMs + ベクトルデータベース + Prompt-as-Code

CVPスタック

検索拡張生成 (RAG)

領域特定の知識の欠如

ハルシネーションの発生傾向 

高くて遅い

トークンの制限

不変の事前トレーニングデータ

LLMの局限

MilvusとZilliz Cloudを活用したLLMプロジェクト

ベクトルデータベースのユースケース：検索拡張生成 (RAG) - Zilliz

自分の物語を語りましょう！

ベクトルデータベースのユースケース

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コミュニティ
非構造化データミートアップ

非構造化データミートアップ

今後のイベント

Seattle

Berlin

New York

San Francisco

South Bay

Tokyo

Don't want to wait?

過去のミートアップ

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コミュニティ非構造化データミートアップ

非構造化データミートアップ

今後のイベント

Seattle

Berlin

New York

San Francisco

South Bay

Tokyo

Don't want to wait?

過去のミートアップ

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コミュニティ
非構造化データミートアップ