Rizki Sasri Dwitama
Datascience

Milvus: Syllabus

Mastering Milvus: Scalable Vector Search and AI-Powered Retrieval with Python

Introduction

Milvus is an open-source, highly scalable vector database designed for AI-powered similarity search and large-scale vector retrieval. This book provides a hands-on approach to building, managing, and optimizing vector search applications using Milvus with Python. Covering 90% practical implementation, this book ensures readers master Milvus for real-world AI-driven applications.

Module 1: Introduction to Vector Databases and Milvus

  • Understanding vector databases and their use cases
  • Why Milvus? Key features and architecture
  • Installing and setting up Milvus with Docker and Kubernetes
  • Running Milvus in standalone and distributed modes

Module 2: Understanding Vector Embeddings and Indexing

  • Basics of vector embeddings in AI applications
  • Generating embeddings using OpenAI, Hugging Face, and TensorFlow
  • Storing, updating, and deleting embeddings in Milvus
  • Choosing the right index type (IVF, HNSW, ANNOY, PQ)

Module 3: Data Storage and Querying in Milvus

  • Creating and managing collections in Milvus
  • Performing CRUD operations on vector data
  • Querying vectors using similarity search (L2, IP, Cosine)
  • Filtering queries using metadata and hybrid search

Module 4: Indexing and Performance Optimization

  • Understanding nearest neighbor search (ANN) techniques
  • Configuring Milvus for large-scale indexing
  • Optimizing query performance with hybrid search
  • Memory management and resource allocation

Module 5: AI-Powered Search and Recommendations with Milvus

  • Implementing semantic search with Milvus
  • Building a recommendation engine using vector similarity
  • Enhancing search relevance with multi-modal embeddings
  • Personalized AI-driven retrieval techniques

Module 6: Integrating Milvus with Machine Learning Models

  • Using Milvus for NLP and computer vision applications
  • Implementing Retrieval-Augmented Generation (RAG) with LLMs
  • Integrating Milvus with TensorFlow, PyTorch, and LangChain
  • Enhancing chatbot responses using vector search

Module 7: Deploying Milvus in Production

  • Running Milvus as a microservice with RESTful APIs
  • Deploying Milvus on AWS, GCP, and Azure
  • Containerizing Milvus with Docker and Kubernetes
  • Implementing monitoring and logging for production workloads

Module 8: Security and Access Control in Milvus

  • Implementing authentication and authorization
  • Encrypting vector data at rest and in transit
  • Securing API endpoints for AI-powered applications
  • Compliance and data privacy best practices

Hands-On Projects

Project 1: Building a Semantic Search Engine with Milvus

  • Generate text embeddings using OpenAI or Hugging Face models
  • Store and retrieve documents with semantic search
  • Implement filtering and ranking for relevance optimization
  • Extract image embeddings using CNN models
  • Implement a fast image search engine with Milvus
  • Optimize search with hybrid filtering techniques

Project 3: Implementing Retrieval-Augmented Generation (RAG) for LLMs

  • Store domain-specific knowledge in Milvus
  • Enhance LLM responses with contextual retrieval
  • Integrate Milvus with LangChain for real-time AI applications

Project 4: Deploying a Scalable Recommendation System

  • Store and index customer behavior vectors
  • Implement personalized recommendations with real-time vector search
  • Deploy the system in a cloud environment with Kubernetes

Project 5: Real-Time Search for IoT and Edge AI Applications

  • Deploy Milvus in an edge computing environment
  • Optimize query execution for low-latency retrieval
  • Secure and encrypt vector storage for privacy compliance

References

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