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A vector database is optimized for vector embeddings. Learn what vector databases are, how they work, why they’re useful & more.

Why Vector Databases Matter for Unstructured Data

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Read how Milvus 2.3 continues to make significant performance gains for vector collection workloads, and support billion scale collection. 

Read how Milvus 2.2.3 continues to make significant performance gains for vector collection workloads, and support billion scale collection. 

How the best vector database continues to get faster

Milvus Performance Evaluation 2023

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Zilliz built a cloud-native vector database that supports the scalability requirements of managing tens of billions of vectors with high elasticity performance.

How Zilliz built a Cloud Native Vector SaaS

Manu: A Cloud Native Vector Data Management System

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Learn how Zillz built Milvus, an open source data management system, to efficiently manage large-scale vector data.

Purpose-built for high for large scale vectors 

Milvus: A Purpose-Built Vector Data Management System

In this hands-on tutorial, we’ll introduce LLMs and two main problems they face when it comes to production. First, high cost. Second, lack of domain knowledge. We then introduce vector databases as a solution to this problem. We cover how a vector database can facilitate data injection and caching through the use of vector embeddings.

Then we’ll use this knowledge to build an LLM application using LlamaIndex and Milvus, the world’s most popular vector database.

What you’ll need:
- Python 3.9 or above
- A basic understanding of vectors and databases

What you’ll learn:
- What is a vector database
- Why do LLMs face data issues?
- How to deal with data issues in an LLM

# What will you learn
In this hands-on tutorial, we’ll introduce LLMs and two main problems they face when it comes to production. First, high cost. Second, lack of domain knowledge. We then introduce vector databases as a solution to this problem. We cover how a vector database can facilitate data injection and caching through the use of vector embeddings.

Then we’ll use this knowledge to build an LLM application using LlamaIndex and Milvus, the world’s most popular vector database.

What you’ll need:
- Python 3.9 or above
- A basic understanding of vectors and databases

## Topics covered
- What is a vector database
- Why do LLMs face data issues?
- How to deal with data issues in an LLM

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Training

Join us for a hands-on tutorial building LLM applications that scale using Milvus and LlamaIndex.

Tutorial: Working with LLMs at Scale | Zilliz Webinar

# What to Except
You’ll receive an email from Zoom with the details to login for the session. We look forward to seeing you at the upcoming webinar!

- Thursday, June 15, 2023
- 9:00 AM Pacific | 12:00 PM Eastern
- Hands-on tutorial

## What you'll need
- Python 3.9 or above
- Code editor like VS Code
- A basic understanding of vectors and databases

Yujian Tang

Tutorial: Working with LLMs at Scale

In this hands-on tutorial, we’ll introduce embeddings and vector search from both an ML- and application-level perspective. We’ll start with a high-level overview of embeddings and discuss best practices around embedding generation and usage. Then we’ll use this knowledge to build a semantic text search application. Finally, we’ll see how we can put our application into production using Milvus, the world’s most popular open-source vector database.

What you’ll need:
- Python 3.9 or above
- A basic understanding of vectors and databases

What you’ll learn:
- What is vector database
- What is semantic similarity
- How to use a vector database to find similar texts

# What will you learn?
In this hands-on tutorial, we’ll introduce embeddings and vector search from both an ML- and application-level perspective. We’ll start with a high-level overview of embeddings and discuss best practices around embedding generation and usage. 

Then we’ll use this knowledge to build a semantic text search application. Finally, we’ll see how we can put our application into production using Milvus, the world’s most popular open-source vector database.

## What you’ll need:
- Python 3.9 or above
- A basic understanding of vectors and databases

## What you’ll learn:
- What is vector database
- What is semantic similarity
- How to use a vector database to find similar texts


Get hands-on in this live tutorial where you'll learn how to use a vector database to build a semantic text search application.

Tutorial: Building a Semantic Text Search Application | Zilliz Webinar

Tutorial: Building a Semantic Text Search Application

Connecting external data sources to LLMs to give them memory is a crucial part of many LLM applications. This can take the form of connecting to pre-existing large corpora of data, remembering user conversations, or even creating “new memories” by reflection. Underpinning all of all this is retrieval - the process of pulling relevant pieces of information into context.

Join LangChain’s Harrison Chase for a deep dive into retrieval where we dissect the challenges of finding relevant information from a large corpus of data. 

You’ll learn:
- What is memory and why is it important
- Types of memory
- Basics of semantic search
- Edge cases of semantic search
- Generative Agent examples

# About the session
Connecting external data sources to LLMs to give them memory is a crucial part of many LLM applications. This can take the form of connecting to pre-existing large corpora of data, remembering user conversations, or even creating “new memories” by reflection. Underpinning all of all this is retrieval - the process of pulling relevant pieces of information into context.

Join LangChain’s Harrison Chase for a deep dive into retrieval where we dissect the challenges of finding relevant information from a large corpus of data. 

## You'll learn
- What is memory and why is it important
- Types of memory
- Basics of semantic search
- Edge cases of semantic search
- Generative Agent examples

Webinar

Join LangChain's Harrison Chase for a live session exploring different retrieval techniques for getting the most relevant context from your LLM application.

Memory for LLM Applications with LangChain's Harrison Chase | Zilliz Webinar

# What to Except
You’ll receive an email from Zoom with the details to login for the session. We look forward to seeing you at the upcoming webinar!

- Thursday, June 8, 2023
- 9:00 AM Pacific | 12:00PM Eastern
- 45-minute presentation followed by audience questions

Harrison Chase

Memory for LLM applications: Different retrieval techniques for getting the most relevant context

The Hierarchical Navigable Small World (HNSW) is one of the most popular graph-based indices for nearest neighbor search. Join us for a fireside chat with Yury Malkov, as we discuss how he ended up developing this critical method of high-dimensional data analysis. We’ll explore how he got started in computer science and where he sees the future of vector search and LLMs. You’ll even get a chance to ask Yury questions at the end of the chat, you won’t want to miss it!

Topics covered:
- Why HNSW and other graph indexes are great tools for vector search
- What is the future of vector search?
- Thoughts on the proliferation and increasing importance of LLMs as it relates to vector data

## About the Session
The Hierarchical Navigable Small World (HNSW) is one of the most popular graph-based indices for nearest neighbor search. Join us for a fireside chat with [Yury Malkov](https://twitter.com/malkovyury), as we discuss how he ended up developing this critical method of high-dimensional data analysis. We’ll explore how he got started in computer science and where he sees the future of vector search and LLMs. You’ll even get a chance to ask Yury questions at the end of the chat, you won’t want to miss it!

# Topics covered:
- Why HNSW and other graph indexes are great tools for vector search
- What is the future of vector search?
- Thoughts on the proliferation and increasing importance of LLMs as it relates to vector data

Register for a free webinar with Yury Malkov, author of HNSW and hear his predictions on the future of vector search.

Fireside chat with HNSW author Yury Malkov | Zilliz Webinar

Yury Malkov

What is the future of vector search? A fireside chat with HNSW author Yury Malkov

Zilliz is excited to announce the general availability of Zilliz Cloud, a fully-managed Milvus service made by the creators of Milvus. This new multi-cloud service (AWS and GCP) offers consistent management, security, and governance experience across all clouds. It provides reliability & resiliency built-in, 99.99% uptime SLA, and zero data corruption. Frank Liu, Director of Operations & ML Architect at Zilliz, will demonstrate key product features in this webinar. 

He will dive into:
- New cluster sizes to support over a billion multi-dimensional vectors
- Embedding model integrations (OpenAI, Hugging Face, Cohere)
- Powerful Backup and restore
- Simple Migration from Milvus to Zilliz
- All the latest Milvus features
- Performance improvements and more!

# What will you learn?
Zilliz is excited to announce the general availability of Zilliz Cloud, a fully-managed Milvus service made by the creators of Milvus. This new multi-cloud service (AWS and GCP) offers consistent management, security, and governance experience across all clouds. It provides reliability & resiliency built-in, 99.99% uptime SLA, and zero data corruption. Frank Liu, Director of Operations & ML Architect at Zilliz, will demonstrate key product features in this webinar. 

## Topics covered:
- New cluster sizes to support over a billion multi-dimensional vectors
- Embedding model integrations (OpenAI, Hugging Face, Cohere)
- Powerful Backup and restore
- Simple Migration from Milvus to Zilliz
- All the latest Milvus features
- Performance improvements and more!

Learn the differences between Milvus and Zilliz Cloud and understand how to start building vector search applications.

Unlocking the power of vector search with Zilliz Cloud | Zilliz Webinar

Frank Liu

Unlocking the power of vector search in Zilliz Cloud

In-context learning has emerged as a powerful method to augment LLMs with our own data. By inserting relevant context into the input prompt, we can leverage the LLM's sophisticated reasoning abilities to generate accurate and meaningful responses that reflect our unique data inputs. 

LlamaIndex is a simple, flexible interface to connect your LLM's with external data. Join this session with LlamaIndex co-founder and CEO, Jerry Liu. He’ll share details on how he built LlamaIndex, discuss the various tools it contains and how you can get started enriching your LLM with private data.

You'll learn:
- How LlamaIndex can help augment your LLM with private data
- What LlamaIndex tools exist for data connection, indices and queries 
- Best practices for integrating LlamaIndex with Milvus

# About the Session
In-context learning has emerged as a powerful method to augment LLMs with our own data. By inserting relevant context into the input prompt, we can leverage the LLM's sophisticated reasoning abilities to generate accurate and meaningful responses that reflect our unique data inputs. 

LlamaIndex is a simple, flexible interface to connect your LLM's with external data. Join this session with LlamaIndex co-founder and CEO, [Jerry Liu](https://twitter.com/jerryjliu0). He’ll share details on how he built LlamaIndex, discuss the various tools it contains and how you can get started enriching your LLM with private data.

## What you'll learn:
- How LlamaIndex can help augment your LLM with private data
- What LlamaIndex tools exist for data connection, indices and queries 
- Best practices for integrating LlamaIndex with Milvus

Zilliz webinar covering how to boost your LLM with private data with LlamaIndex to generate accurate and meaningful responses that reflect unique data inputs.

Boost your LLM with Private Data Using LlamaIndex | Zilliz Webinar

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