Christy Bergman

Developer Advocate

Christy Bergman is a passionate Developer Advocate at Zilliz. She previously worked in distributed computing at Anyscale and as a Specialist AI/ML Solutions Architect at AWS. Christy studied applied math, is a self-taught coder, and has published papers, including one with ACM Recsys. She enjoys hiking and bird watching.

Engineering

Setting up Milvus on Amazon EKS

This blog provides step-by-step guidance on deploying a Milvus cluster using EKS and other services.

Jul 16, 202415 min read

Engineering

Milvus Server Docker Installation and Packaging Dependencies

This post introduces key dependencies and release update frequency for deploying the Milvus server on standalone Docker.

Apr 17, 20243 min read

Engineering

Building an Open Source Chatbot Using LangChain and Milvus in Under 5 Minutes

A start-to-finish tutorial for RAG retrieval and question-answering chatbot on custom documents using Milvus, LangChain, and an open-source LLM.

Nov 29, 20239 min read

Product

Demystifying the Milvus Sizing Tool

Explore how to use the Sizing Tool to select the optimal configuration for your Milvus deployment.

Apr 26, 20243 min read

Engineering

Choosing the Right Embedding Model for Your Data

This blog touched on some popular embedding models used in RAG applications.

May 22, 20246 min read

Company

Zilliz at CalHacks 2023

If you didn't catch CalHack 2023, read this recap complete with attendee and prize stats as well as overviews of winning projects.

Nov 03, 20235 min read

Product

How to Connect to Milvus Lite Using LangChain and LlamaIndex

Milvus Lite is now the default method for third-party connectors like LangChain and LlamaIndex to connect to Milvus, the popular open-source vector database.

Jun 07, 20244 min read

Engineering

Embedding Inference at Scale for RAG Applications with Ray Data and Milvus

This blog showed how to use Ray Data and Milvus Bulk Import features to significantly speed up the vector generation and efficiently batch load them into a vector database.

Apr 12, 20248 min read

Engineering

Exploring Three Key Strategies for Building Efficient Retrieval Augmented Generation (RAG)

Three key strategies to get the most out of RAG: smart text chunking, iterating on different embedding models, and experimenting with different LLMs

Jul 03, 20245 min read