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ChromaDB: Syllabus
Mastering ChromaDB: Vector Databases and AI-Powered Search with Python
ChromaDB is a high-performance open-source vector database designed for AI-powered search, recommendation systems, and retrieval-augmented generation (RAG). This book provides a hands-on approach to building, managing, and optimizing vector search applications using ChromaDB with Python. Covering 90% practical implementation, this book ensures readers master ChromaDB for real-world AI-driven applications.
Module 1: Introduction to Vector Databases and ChromaDB
- What are vector databases?
- Use cases: AI search, recommendation engines, and embeddings
- Why ChromaDB? Key features and advantages
- Setting up ChromaDB with Python
Module 2: Understanding Vector Embeddings
- Basics of vector embeddings in AI applications
- Generating embeddings using OpenAI, Hugging Face, and TensorFlow
- Storing embeddings in ChromaDB
- Optimizing embeddings for different search tasks
Module 3: Storing and Querying Data in ChromaDB
- Creating and managing collections in ChromaDB
- Inserting, updating, and deleting vector data
- Querying ChromaDB using similarity search
- Implementing filtering and metadata-based retrieval
Module 4: Indexing and Performance Optimization
- Understanding nearest neighbor search techniques
- Indexing strategies for large-scale vector datasets
- Configuring ChromaDB for high-speed retrieval
- Optimizing storage and memory usage
Module 5: ChromaDB for AI-Powered Search and Recommendations
- Implementing semantic search with ChromaDB
- Building a recommendation engine using vector similarity
- Enhancing search relevance with hybrid filtering
- Personalizing user experiences with AI-driven retrieval
Module 6: Integrating ChromaDB with Machine Learning Models
- Using ChromaDB with natural language processing (NLP)
- Fine-tuning embedding models for domain-specific searches
- Implementing retrieval-augmented generation (RAG) with LLMs
- Combining ChromaDB with TensorFlow, PyTorch, and LangChain
Module 7: Deploying ChromaDB in Production
- Running ChromaDB as a standalone service
- Deploying ChromaDB on cloud environments (AWS, GCP, Azure)
- Containerizing ChromaDB with Docker and Kubernetes
- Implementing monitoring and logging for production systems
Module 8: Security and Access Control
- Implementing role-based access control (RBAC) in ChromaDB
- Encrypting vector data at rest and in transit
- Securing API endpoints for AI-powered applications
- Best practices for compliance and data privacy
Hands-On Projects
Project 1: Building a Semantic Search Engine with ChromaDB
- Generate text embeddings using OpenAI or Hugging Face models
- Store and retrieve documents with semantic search
- Implement metadata-based filtering for relevance ranking
Project 2: AI-Powered Product Recommendation System
- Use ChromaDB to store customer and product embeddings
- Implement vector similarity for personalized recommendations
- Optimize ranking with hybrid filtering techniques
Project 3: Implementing Retrieval-Augmented Generation (RAG) for LLMs
- Store domain-specific knowledge in ChromaDB
- Enhance LLM responses with context-aware retrieval
- Integrate ChromaDB with LangChain for intelligent query expansion
Project 4: Deploying a Large-Scale AI Search System
- Scale ChromaDB for real-time high-throughput queries
- Deploy ChromaDB in a cloud environment with Kubernetes
- Implement load balancing and caching for optimized performance
Project 5: Real-Time Vector Search in Edge AI Applications
- Deploy ChromaDB for real-time search in IoT and edge devices
- Implement fast query execution with optimized indexing
- Secure and encrypt vector storage for privacy compliance