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Discover why Forrester named Zilliz a Leader in Vector Databases for AI. Accelerate your AI initiatives with Zilliz's innovative vector processing solutions.

The Forrester Wave™: Vector Database Providers, Q3 2024

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Confused by the growing number of vector database solutions? Download this free guide to compare features, understand use cases, and find the best database for managing unstructured data at scale.

The Definitive Guide to Choosing a Vector Database | Free Download

The Definitive Guide to Choosing a Vector Database

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This paper introduces FARGO, a novel framework that revolutionizes how we approach the Maximum Inner Product Search (MIPS) problem

Understanding Fast Maximum Inner Product Search 

FARGO: Fast Maximum Inner Product Search via Global Multi-Probing

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This paper introduces Starling, a framework that revolutionizes vector database segment handling through a novel dual-component architecture

Starling: I/O-Efficient Disk-Resident Graph Index Framework

Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment

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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. 

Download the benchmark report to learn about our evaluation of Milvus performance in 2024. See how Milvus 2.2.3 is now faster than Milvus 2.0.

Milvus Performance Report | Zilliz

Milvus Performance Report

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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 talk, you will learn how to build an Agentic RAG System using Llama Agents and Milvus. 


## About the session
With the recent release of Llama Agents, we can now build agents that are async first and run as their own service. During this webinar, Stephen will show you how to build an Agentic RAG System using Llama Agents and Milvus. 


## Topics Covered:
- How to make agents leverage planning, memory, and tools to accomplish a variety of tasks.
- How to empower your RAG system by using a multi-agent framework and by being able to call custom user-defined functions.
- Common issues like hallucinations and how to add fallback and self-correction mechanisms so your agent can try to fix itself.




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Training

Join us for a webinar to learn how to build an Agentic RAG System using Llama Agents and Milvus. 

Building an Agentic RAG locally with Milvus, Ollama and Llama Agents

Stephen Batifol is a Developer Advocate at Zilliz.

Stephen Batifol

Hands-on demo: Build GenAI apps with Zilliz Cloud. Learn vector search, create a chatbot, and explore advanced techniques.

## About the session
Join our hands-on demo of Zilliz Cloud, a fully managed vector database service built on the popular open-source Milvus. In this interactive session, our AI/ML lead Frank Liu will walk you through building and scaling vector search applications with live examples.

This webinar is designed for developers keen to work with vector databases. It will teach you how to implement efficient, scalable search solutions for your GenAI apps. Frank will walk through and execute code in notebooks, allowing you to follow along and understand each step.

## Topics Covered
- Live Zilliz Cloud setup and configuration
- Building a simple chatbot step-by-step
- Advanced search techniques with example code

By the end, you'll have a clear understanding of how to leverage Zilliz Cloud in your projects and hands-on experience with its capabilities. Come prepared with questions as we explore this powerful vector database through practical demos.


Join us for a webinar to demo Zilliz Cloud, a fully managed vector database service built on the popular open-source Milvus

Hands-On Demo: Building and Scaling Vector Search Apps with Zilliz Cloud

Frank Liu

This webinar will demo evaluating a RAG pipeline using Milvus and RAG metrics like context F1-score and answer correctness.

## About the session
Retrieval Augmented Generation (RAG) enhances chatbots by incorporating custom data in the prompt. Using large language models (LLMs) as judge has gained prominence in modern RAG systems. This talk will demo Ragas, an open-source automation tool for RAG evaluations. Christy will talk about and demo evaluating a RAG pipeline using Milvus and RAG metrics like context F1-score and answer correctness.

## Topics Covered
- Foundation Model Evaluation vs RAG Evaluation
- Do you need human-labeled ground truths?
- Human Evaluation vs LLM-as-a-judge Evaluations
- Overall RAG vs RAG component Evaluations
- Example of different Retrieval methods with Evaluation
- Example of different Generation methods with Evaluation





Join us for a webinar to talk about and demo evaluating a RAG pipeline using Milvus and RAG metrics like context F1-score and answer correctness.

RAG Evaluation with Ragas

Christy Bergman is a passionate Developer Advocate at Zilliz. 

Christy Bergman

In this talk, Stephen will show you how you can build an Agentic RAG System using Langchain and Milvus. 


## About the session
RAG systems are talked about in detail, but usually stick to the basics. In this talk, Stephen will show you how to build an Agentic RAG System using Langchain and Milvus. 


## Topics Covered:

- How to make agents leverage planning, memory, and tools to accomplish a variety of tasks.
- How to empower an LLM to perform web searches and call custom user-defined functions.
- Common issues like hallucinations and how to add fallback and self-correction mechanisms so your agent can try to fix itself.



Join us for a webinar to learn how to build an Agentic RAG System using Langchain and Milvus. 

Building an Agentic RAG locally with Milvus, Ollama and LangGraph

Join us for a webinar with Haystack about building full-stack open source agents with Haystack and Milvus.

## What will you learn?
Have you ever wanted a personal chef? You've probably heard the joke "being in a relationship is just asking each other 'what do you want to eat for dinner' until you die." Sure, you can just browse recipes online but who knows if they are any good? LLMs to the rescue!

In this session, I'll demonstrate taking a dataset on Kaggle of my favorite cookbook recipes, pulling data into a Milvus vector database instance, and building an agentic Haystack RAG pipeline so I can search for tasty recipes with natural language. I'll even take it one step further with a function call to make an Amazon shopping list with the ingredients. Join us for this session to see how you can solve real-world problems with RAG and answer the age old question "what's for dinner?"


## Topics Covered 

- How to build a real-world RAG app
- Getting started with Haystack
- Ingesting data into Milvus


In this session, I'll demonstrate building an agentic Haystack and Milvus RAG pipeline so I can use natural language to search for tasty recipes.

Having a Personal Chef with Vector Search and Natural Language using Haystack and Milvus

Tilde (they/them) is a San Francisco-based artist and engineer and the Senior Developer Advocate at deepset. 

Tilde Thurium

Answer 'What's for Dinner?' with Vector Search and Natural Language using Haystack and Milvus

Join us for a technical training webinar where we will showcase how you can build a RAG using Milvus.

## What will you learn?
Join us for a technical webinar where we will showcase how you can build a RAG using Milvus. Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.

This session is designed for developers eager to learn more about RAG and Vector Databases. Through live demos, we will show you how you can effectively build a RAG application to be able to incorporate external knowledge from pre-existing sources.

## Topics Covered 

- Introduction to RAG: What is RAG? When do you need it and how to build it? 
- What is the RAG tech stack: RAG uses different components, we will go through them.  
- Building a RAG app live: You will see how one can build a RAG app using Milvus. 

This interactive session will equip you with the knowledge to efficiently build and use RAG using unstructured data.


This session is designed for developers eager to learn more about RAG and Vector Databases through a live demo. 

A Beginners Guide to Building a RAG App Using Open Source Milvus | Zilliz Webinar

A Beginners Guide to Building a RAG App Using Open Source Milvus

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

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

Tutorial: Working with LLMs at Scale | Zilliz Webinar

Yujian Tang, Developer Advocate at Zilliz. 

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

​Sneak Peak of Milvus 2.5 - Quick look at our BM25 implementation and sparse vector optimizations


## What will you learn?

Dense embeddings miss exact matches. Keyword search misses semantic meaning. Running two separate systems is a maintenance nightmare. We'll show how Milvus 2.5's hybrid search tackles this with a unified solution, preview its sparse-based BM25 implementation, and share performance numbers against current Elasticsearch-based architectures.


### Topics covered:
- Where dense embeddings fall short and how a unified system architectures address the search needs
- ​Sneak Peak of Milvus 2.5 - Quick look at our BM25 implementation and sparse vector optimizations
- ​Benchmark results comparing hybrid search latency and throughput vs ElasticSsearch
- ​What's Next - Brief overview of upcoming features in our technical roadmap

Webinar

Get a Sneak Peak of Milvus 2.5 - Quick look at our BM25 implementation and sparse vector optimizations

Dense Embeddings != Complete Search - New Milvus 2.5

## What to Expect
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!

- Thurs, Dec 5
- 9:00 AM Pacific | 12:00 PM Eastern
- 45-minute presentation | 15-minute Q&A

James Luan

Dense Embeddings != Complete Search - A Sneak Peak of Milvus 2.5

Learn how to manage and control access to data in vector databases, especially when dealing with sensitive information in enterprise settings


## What will you learn?

Partitioning vector databases has proven to be a useful tool for privacy and per-tenant isolation. Recent releases of vector db software, including Milvus, have continued to improve partitioning capabilities such as pushing the number of partitions into the millions and providing improved selection of partitions per tenant.

Despite these advances, management overhead increases with the number of partitions. Relative to the capabilities enterprises require and have come to expect from their existing storage systems and databases, there is still a shortfall. New capabilities specific to how vector databases store data and how they are used in RAG applications are needed.


### Topics covered:
- Origins of enterprise requirements for granular access control and policy in storage systems.
- Sensitive data identification: data classification versus access control.
- The problem data-duplication in enterprise datasets presents when copying permissions from documents to chunks.
- How enterprise access requirements can be met with per-user, per-chunk access control
- Case study and example implementation.

Learn how to manage and control access to data in vector databases, especially when dealing with sensitive information in enterprise settings

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

## What to Expect
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!

- Thurs, Nov 21
- 9:00 AM Pacific | 12:00 PM Eastern
- 45-minute presentation | 15-minute Q&A

Rob Quiros

Learn advanced techniques to optimize your RAG system for production, including query transformation, Colbert rerankers etc.

## About this webinar
We explore effective strategies for optimizing your RAG setup to make it production-ready. We will cover practical techniques such as data pre-processing, query expansion & reformulation, adaptive chunk sizing, cross-encoder reranking, Colbertv2 rerankers, ensemble retrieval etc. to enhance the accuracy of information retrieval in RAG systems. We will also dive into evaluating RAG performance using relevant tools and metrics, providing you with a comprehensive understanding of how to optimize and assess your RAG pipeline.

### Topics covered
- How to enhance the accuracy of information retrieval in RAG systems
- Advanced techniques to optimize your RAG system for production, including query transformation, Colbert rerankers etc.
- Evaluating RAG performance using relevant tools and metrics


Advanced RAG Optimization To Make it Production-ready

Aravind Parameswaran

Master practical approaches to evaluate and optimize RAG systems, equipped with the knowledge to implement these tools effectively in your own applications.


## What will you learn?
Retrieval-Augmented Generation (RAG) has emerged as a cornerstone technology powering the latest wave of Generative AI applications, from sophisticated question-answering systems to advanced semantic search engines. As RAG's popularity has grown, we've witnessed a proliferation of methods promising to enhance the traditional RAG pipeline. These innovations include query rewriting, intelligent routing, and result reranking—but how do we measure their real impact on application performance?

Join us for an informational webinar where we'll explore robust evaluation frameworks, including LLM-as-a-Judge methodologies, industry-standard benchmarking datasets, and innovative synthetic data generation techniques. By the end of this session, you'll master practical approaches to evaluate and optimize RAG systems, equipped with the knowledge to implement these tools effectively in your own applications.

### Topics covered:
- LLM-as-a-Judge
- MT-Bench
- LM Eval Harness
- Synthetic data generation

Join us in a webinar to explore practical approaches to evaluate and optimize RAG systems

Evaluating Retrieval-Augmented Generation

Stefan Webb

Learn why you should use a RAG pipeline for LLM apps and follow a demo by Cloudera. 

## What will you learn?
We will discuss the value of RAG architecture at a high level and dive into an example that we can demo.  The idea is to first demo without context, giving horrible results.  Then we will walk through the steps of building a RAG pipeline that we can use to generate better data. 

### Topics covered:

- Why you should use RAG Pipeline for LLM Apps
- How RAG reduces hallucinations
- What Milvus and Cloudera offer to support RAG
- Building a RAG Application with Cloudera and Milvus
- Deep Dive on Example (Chat bot use case)
- Demo- building a RAG pipeline to capture contextual data and update in near-real time
- Review



Join our webinar to discuss the value of RAG architecture at a high level and delve into an example that we can demo.

RAG Pipelines with Real-Time Data

Christopher Burns

Chris Joynt

Join our upcoming webinar with InfluxDB to learn the powerful combination of time series data and vector similarity search to revolutionize urban traffic management.

## What will you learn? 
In this webinar, we’ll explain the powerful combination of time series data and vector similarity search to revolutionize urban traffic management. Learn how to transform raw sensor data from InfluxDB into meaningful vectors, enabling advanced pattern recognition and anomaly detection using Milvus, a high-performance vector database.

Through a practical use case of real-time traffic monitoring, we'll demonstrate how this innovative approach can swiftly identify and categorize traffic anomalies, from accidents to construction zones. This webinar is essential for data scientists, traffic engineers, and urban planners looking to harness the full potential of their time series data for complex, real-world applications.


### Topics covered:
- Fundamentals of time series vectorization: Converting InfluxDB data for vector database use
- Integrating InfluxDB and Milvus for a comprehensive traffic monitoring solution
- Implementing similarity search in Milvus to classify traffic anomalies
- Best practices for real-time data processing and anomaly detection



Join our upcoming webinar with InfluxDB to learn how to take your data analysis to the next level by building predictive analytics using tools and techniques.

Power Your Predictive Analytics with InfluxDB

Anais Dotis-Georgiou

Time Series to Vectors: Leveraging InfluxDB and Milvus for Similarity Search

Learn how data engineering can resolve common challenges associated with deploying and scaling effective AI usage.

Data engineering is the backbone of AI systems. After all, the success of AI models heavily depends on the volume, structure, and quality of the data that they rely upon to produce results. With proper tools and practices in place, data engineering can address a number of common challenges that organizations face in deploying and scaling effective AI usage.

Learn how to:
- Quickly integrate data from multiple sources across different environments
- Build scalable and efficient data pipelines that can handle large, complex workloads
- Ensure that high-quality, relevant data is fed into AI systems
- Enhance the performance of AI models with optimized and meaningful input data
- Maintain robust data governance, compliance, and security measures
- Support real-time AI applications


Learn how to enhance the performance of AI models with optimized and meaningful input data.

The Importance of Data Engineering for Successful AI

Tim Spann is the Principal Developer Advocate at Zilliz. 

Tim Spann

Data Engineering Best Practices for AI

Live demo of Datavolo, powered by Apache NiFi, which highlights how data engineers can implement production-grade multimodal pipelines for AI.


## What will you learn?
After organizations identify and prototype high-value use cases for GenAI, the journey to production begins. Key operational concerns emerge on this journey to Day 2, including scalability, governance, automation, error handling, and more.

Data pipelines that support AI apps will need to provide features for the continuous and automated ingestion of enterprise documents and data (similar to CDC but for the multimodal world).

## Topics covered:
- Live demo of Datavalo, powered by Apache NiFi
- How data engineers can implement production-grade multimodal pipelines for AI



In this talk, we’ll provide a live demo of Datavolo, powered by Apache NiFi, which highlights how data engineers can implement production-grade multimodal pipelines for AI.

Multimodal Pipelines for AI Apps: Journey To Day 2

Sam Lachterman

This webinar introduces Zilliz Cloud's latest features designed to address challenges head-on, enabling you to build and maintain robust, efficient AI infrastructure.


## About this webinar
As AI applications move from experimentation to production, developers and technical leaders face new challenges in managing vector databases at scale. This webinar introduces Zilliz Cloud's latest features designed to address these challenges head-on, enabling you to build and maintain robust, efficient AI infrastructure.
Join us to explore how these new capabilities can solve common problems in AI infrastructure management.

### Topics covered
- Implement seamless data migration and recovery strategies, ensuring data portability and eliminating vendor lock-in
- Streamline integration of unstructured data from diverse sources, enhancing the breadth and depth of your AI models
- Optimize query performance and system availability for high-traffic AI applications
- Automate resource allocation to handle variable workloads efficiently
- Achieve enterprise-grade reliability and monitoring for mission-critical AI systems


Through technical demonstrations and real-world use cases, you'll gain practical insights into leveraging these features to overcome bottlenecks, enhance system resilience, and accelerate your AI initiatives.



Join this webinar to address the challenges in managing vector databases at scale through Zilliz Cloud's latest features.

Building Resilient AI Infrastructure: Deep Dive Zilliz Cloud's New Production-Ready Features 

Jiang Chen

This session will dive into the architecture decisions that enable the Milvus vector database to seamlessly scale horizontally, provide users with tunable consistency, orchestrate in-memory and on-disk indexing, and scalable search strategies.


## About this session
80% of the data in the world is unstructured data and vector databases can help you get valuable insights from unstructured data. The team at Zilliz built Milvus, one of the most popular open source vector databases, as a distributed system to support vector collections that exceed billion-scale. This session will dive into the architecture decisions that enable the Milvus vector database to seamlessly scale horizontally, provide users with tunable consistency, orchestrate in-memory and on-disk indexing, and scalable search strategies.


Dive into the architecture decisions that enable the Milvus vector database to seamlessly scale horizontally and more

Powering GenAI with Scalable Vector Solutions

The newly introduced Fivetran’s Milvus Connector further extends the data landscape to make every data source semantic-searchable

## About this Webinar
Fivetran now supports Milvus vector database as a destination, making it easier to onboard every data source for RAG and AI-powered search. Fivetran is a cloud-based data movement platform that makes it easy to extract, load, and transform data between a variety of sources and destinations. Fivetran connects over 500 sources, such as databases/warehouses, SaaS applications, ERPs, and files. The newly introduced [Fivetran's Milvus Destination](https://fivetran.com/docs/destinations/milvus) further extends the data landscape to make every data source semantic-searchable. By ingesting the source data from a diverse set of database/warehouses and business apps to Milvus vector database, this integration makes the development of AI workflows easier.

![](https://assets.zilliz.com/Fivetran_Milvus_Architecture_Diagram_12_8c6be1b0dc.png)

In this webinar, we will introduce Fivetran's Milvus Destination and give a live demo on how to use the connector to build a Retrieval-Augmented Generation (RAG) chatbot for GitHub issues with simple configurations. 

### Topics covered:
- Announcing the launch of Fivetran's Milvus Destination connector
- Demo how to make data from various business apps searchable
- Utilize the information in AI applications such as RAG

Join us for a webinar with Fivetran to demo how to make data from various business apps searchable 

AI-powered Search for Every Data Source with Fivetran’s Milvus Connector

Abhijeeth Padarthi

Build AI-powered Search for Every Data Source with Fivetran and Milvus

Afraid of losing control over your data with AI? Meet PrivateGPT, the framework for GenAI applications with privacy at core

## About the session
When dealing with AI tools, compliance might be a risk due to platforms storing your prompts and documents, leading to data leaks. You are responsible for ensuring your organization’s use of AI complies with data privacy regulations.

PrivateGPT is an open-source project boasting 53K Github stars and a Discord community of over 5K people. It provides a versatile framework for creating context-aware GenAI applications with privacy at core, offering a default API for common tasks and the flexibility to customize for specific applications attending to their privacy needs.

### Topics covered:
- The different levels of Privacy you can find in B2B AI products.
- The functionalities PrivateGPT provides from a developer point of view.
- How to leverage PrivateGPT to ensure your organization’s use of AI complies with data privacy regulations depending on the privacy level required by covering different setups.


Join our webinar with PrivateGPT to ensure your organization’s use of AI complies with data privacy regulations depending on the privacy level required by covering different setups. 

AI breaks Privacy: How PrivateGPT Fixes It

Daniel Gallego Vico

In this webinar, we will look at how Unstract, an open-source platform purpose-built for structured document data extraction, solves challenges with scaling structured document data.

## About this Webinar
All businesses have to deal with unstructured documents at some level. Some have to deal with them at scale. While an LLM-powered approach to this problem is most certainly head and shoulders above traditional machine learning-based approaches, it is not without its challenges. Top concerns being accuracy and cost, which can really begin to hurt at scale.

In this webinar, we will look at how Unstract, an open source platform purpose-built for structured document data extraction, solves these challenges. Dealing with 5M+ pages of structured content extraction per month, Unstract uses various techniques to attain accuracy and cost efficiency, chief among them—the use of vector databases.

### Topics Covered
- Introduction to Unstructured Data Processing
- Processing Document Data
- Extraction Difficulties
- Utilizing Milvus
- Demo


Join this webinar to learn about unstructured document processing. 

Challenges in Structured Document Data Extraction at Scale with LLMs

Shuveb Hussain

We'll vectorize a dataset of text and images into the same embedding space, store them in Milvus, retrieve all relevant data given an LLM query, and input multimodal data as context into GPT-4o.


## About the session
We've seen an influx of powerful multimodal capabilities in many LLMs. In this tutorial, we'll vectorize a dataset of text and images into the same embedding space, store them in Milvus, retrieve all relevant data given an LLM query, and input multimodal data as context into GPT-4o.

### Topics Covered
- Multimodal embedding models
- Milvus multi-vector hybrid search
- Multimodal generative models
- Demo of multimodal using Milvus and Zilliz



Join us for a webinar for a demo of multimodal RAG with Milvus and GPT-4o

Multimodal RAG with Milvus and GPT-4o

Tim will do a presentation on why you should add a Cloud Native Vector Database to your Data and AI platform.

## About the session
In this talk, Tim will do a presentation on why you should add a Cloud Native Vector Database to your Data and AI platform. He will also cover a quick introduction to Milvus, Vector Database, and unstructured data processing. By adding Milvus to your architecture, you can scale out and improve your AI use cases through RAG, real-time Search, multimodal search, recommendation engines, fraud detection, and many more emerging use cases.

Edge devices even as small and inexpensive as a Raspberry Pi 5, can work in machine learning, deep learning, and AI use cases and be enhanced with a vector database.

## Topics Covered
- Introduction to Unstructured Data Processing
- Introduction to Milvus
- Adding Milvus to Your Infrastructure
- AI Use Case Improvements
- Edge devices working with AI and vector databases


Join us for a webinar on why you should add a Cloud Native Vector Database to your Data and AI platform

Unstructured Data Processing from Cloud to Edge

The presentation will delve into the ASIMOV project, a novel initiative that leverages Retrieval-Augmented Generation (RAG) to provide precise,  domain-specific assistance to telecommunications engineers and technicians. The session will focus on the unique capabilities of Milvus DB, the chosen vector database for the project, and its advantages over other vector databases.


## About the session
The presentation will delve into the ASIMOV project, a novel initiative that leverages Retrieval-Augmented Generation (RAG) to provide precise, domain-specific assistance to telecommunications engineers and technicians. The session will focus on the unique capabilities of Milvus DB, the chosen vector database for the project, and its advantages over other vector databases.


### Topics Covered:

- Introduction to the ASIMOV Project: An overview of the project, its objectives, and the role of RAG and Milvus DB in achieving these goals.
- Why RAG?: A discussion on why RAG was chosen for the project, its advantages over traditional LLMs, and how it improves the efficiency of retrieving domain-specific knowledge.
- Milvus DB vs. Chroma DB and Pinecone: A comparative analysis highlighting the advantages of Milvus DB over Chroma DB and Pinecone, focusing on aspects like performance, developer-friendly features, and on-premises storage.
- Optimization of Similarity Search: Insights into how the reorganization of metadata attributes into distinct columns in Milvus DB enhances the similarity search functionality of the RAG system.
- Implications for Telecommunications Engineering: A discussion on how the features of ASIMOV can solve common challenges in telecommunications engineering, streamline configurations and troubleshooting processes, and serve as a training resource for budding engineers.

Attending this session will give you a deeper understanding of the potential of RAG and Milvus DB in telecommunications engineering. You will learn how to address common challenges in the field and enhance the efficiency of their operations. The session will equip you with the knowledge to make informed decisions about the choice of vector databases, and how best to use them for your use-cases



Join us for a webinar to delve into the ASIMOV project, a novel initiative that leverages Retrieval-Augmented Generation (RAG) to provide precise, domain-specific assistance to telecommunications engineers and technicians.

ASIMOV: Enterprise RAG at Dialog Axiata PLC

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