A comprehensive introduction to vector embeddings and how to generate them with popular open-source models. 


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Vector Embeddings Done Right

How to Get the Right Vector Embeddings

A comprehensive guide on migrating your data from Elasticsearch, FAISS, and older Milvus 1.x to Milvus 2.x versions.

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How to Migrate Your Elasticsearch and FAISS Data to Milvus Seamlessly

How to Migrate Your Data to Milvus Seamlessly: A Comprehensive Guide

A practical tutorial to assist you in quickly getting started with the GPU version of Milvus and significantly boosting your vector search performance by ten times.

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Getting Started with GPU-Powered Milvus Vector Database: Unlocking 10x Higher Performance

Getting Started with GPU-Powered Milvus: Unlocking 10x Higher Performance

Experiment with LangChain’s self-query feature to build a simple RAG app that combines the LLM and a vector database like Milvus.

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How to Use LangChain to Query a Vector Database Like Milvus 

Using LangChain to Self-Query a Vector Database

Using vector embeddings to find data errors, find samples not present in your training data, find hallucinations, and fix errors in retrieval augmented generation.

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 Zilliz x Galileo — The Power of Vector Embeddings

Zilliz x Galileo: The Power of Vector Embeddings

The Zilliz and Confluent integration opens new avenues for leveraging Generative Artificial Intelligence (GenAI) in real-time scenarios.

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Explore Zilliz's new integration with Confluent Cloud

Zilliz Makes Real-Time AI a Reality with Confluent

Exploring Knowhere 2.0: GPU acceleration, Cosine similarity, ARM support, and more advanced features for supercharging Milvus.

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Your guide to the latest updates of Milvus vector search engine

Exploring the Marvels of Knowhere 2.0

Compare Weaviate vs. Zilliz Cloud and Milvus in this in-depth benchmark, cost, and features comparison.

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How to Choose A Vector Database: Weaviate Cloud vs. Zilliz Cloud

Building a chatbot for the Towards Data Science publication using the Zilliz vector database

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Chat Towards Data Science: Building a Chatbot with Zilliz Vector Database

Chat Towards Data Science: Building a Chatbot with Zilliz Cloud 

Equipping you with the knowledge you need to get started with Pgvector and explore other vector databases as an alternative.

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Exploring Pgvector, Its Limitations, and Other Vector Databases

Getting Started with Pgvector: A Guide for Developers Exploring Vector Databases

What is Llama 2, and how does it perform in question answering compared to ChatGPT?

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Meta AI’s Llama 2 vs. OpenAI’s ChatGPT

Comparing Llama 2 Chat and ChatGPT: How They Perform in Question Answering

Explore Milvus 2.3 from three crucial angles: database selection, development experience, and system reliability.

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Why Milvus Is a Compelling Option for Your Apps?

An Engineering Perspective: Why Milvus is a Compelling Option for Your Apps?

Compare Elastic Cloud vs. Zilliz Cloud in this in-depth benchmark, cost and features comparison. 

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How to Choose A Vector Database: Elastic Cloud vs. Zilliz Cloud

The Milvus 2.3.0 release contains many exciting new features and improvements. This blog post will highlight some of the more prominent features. 

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The exciting new features and improvements in Milvus 2.3

What’s New in Milvus 2.3

When building LLM applications, you’ll face two key challenges: the high cost of API calls and slow response times. GPTCache is a perfect open-source solution that addresses these challenges and offers many benefits, including reduced network latency, improved availability, and better scalability.

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Building LLM Apps with 100x Faster Responses and Drastic Cost Reduction Using GPTCache 

Learn about the difference in vector embeddings between models and how to use multiple collections of vector data in one Jupyter Notebook.

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How vector embeddings generated by different neural networks differ

Comparing Different Vector Embeddings

Learn how to build a simple chatbot with Python using Milvus and Towhee.

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How to Build an AI Chatbot with Milvus and Towhee

The synergy of Zilliz Cloud and GPTCache creates an environment where AI applications can generate more accurate, timely, cost-efficient, and performant responses, significantly improving the user experience. 

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Building LLM Augmented Apps with Zilliz Cloud 

Using Hugging Face models and a powerful vector database like Milvus to perform image similarity searches and find celebrity fashion styles that match your own. 

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Using AI and Milvus to Find Your Celebrity Stylist

Using AI to Find Your Celebrity Stylist (Part II)

How to generate image segmentation for fashion items, add your image data to Milvus, and which celebrity your dress is most like. 

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Using AI to Find Your Celebrity Stylist

Using AI to Find Your Celebrity Stylist (Part I)

The expansion of Zilliz Cloud to AWS and GCP Singapore regions provides our customers greater flexibility to suit their needs and unprecedented access. 


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Zilliz Cloud Expands to AWS and GCP Singapore

A look at why it’s important to include citations and how you can get citations from a high level.

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Retrieval Augmented Generation with Citations 

What sets vector databases apart, and how to select the right one for your project

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What is a real vector database?

What Is a Real Vector Database?

Zilliz Cloud makes Zilliz Cloud accessible to everyone by minimizing the overall usage costs for building AI apps

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Zilliz Cloud: a Fully-Managed Vector Database That Minimizes Users’ Costs for Building AI Apps

A hands-on tutorial with all you need to add vector search capabilities to your web apps using Milvus JavaScript Client. 

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Getting Started with the Milvus JavaScript Client

Getting Started With the Milvus JavaScript Client

A look at the benefits of this critical infrastructure for AI, why democratizing them is crucial, and how to make them accessible to everyone.

Vector Database Stories

From company news to technical tutorials – explore the most popular content on the Zilliz blog.