Generative AI Resource Hub
Tutorials, Code Examples, and Best Practices for Developing and Deploying GenAI Applications.
Learn
Build
Explore
Learn
Basic concepts developers need to understand to create RAG/GenAl applications
- Start Reading
Embedding 101
11 articles ·121 min
A beginner's guide to understanding vector embeddings and implementing techniques in data science and machine learning.
- Start Reading
Vector Database 101: Everything You Need to Know
27 articles ·228 min
Learn all about Vector Databases, how they work, and what technical details you should have a solid grasp on to make smart technical decisions.
- Start Reading
Information Retrieval 101
9 articles ·108 min
Learn how to master the art of retrieving the right information at the right time.
- Start Reading
Large Language Models (LLMs) 101
22 articles ·247 min
Understand the basics of large language models (LLMs) and how they can be used to power real-world AI applications like chatbots, question answering, and more.
- Start Reading
GenAI Ecosystem
6 articles ·56 min
Explore the landscape of Generative AI, its tools, applications, and impact on various industries.
Build
Practical resources to help you build sample RAG/GenAl applications
Notebooks
Build RAG with Milvus
Semantic Search with Milvus and OpenAI
Question Answering Using Milvus and Hugging Face
Retrieval-Augmented Generation (RAG) with Milvus and LlamaIndex
Retrieval-Augmented Generation (RAG) with Milvus and LangChain
Retrieval-Augmented Generation (RAG) with Milvus and Haystack
Semantic Search with Milvus and VoyageAI
Retrieval-Augmented Generation (RAG) with Milvus and BentoML
Retrieval-Augmented Generation (RAG) with Milvus and DSPy
Demo Videos
17 min watch
Using LLM Agents with Llama 3, LangGraph and Milvus
15 min watch
Building Intelligent RAG Applications with LangServe, LangGraph, and Milvus
Explore
After building your GenAl/RAG demo, learn what it takes to deploy it into production effectively
