Vector Databases: From Fundamentals to Production AI Systems
Course Curriculum
20 modules designed to master the subject.
Module 1: Introduction to Vector Databases
Why vector databases exist, semantic vs keyword search, and real-world use cases.
The Problem Vector Databases Solve: Why Semantic Search Changes Everything
Explore the fundamental limitations of traditional keyword search and discover how vector databases enable semantic understanding, high-dimensional search, and the backbone of modern AI systems.
Keyword Search vs Semantic Search: Bridging the Interaction Gap
Master the differences between lexical and vector-based search. Learn about inverted indexes, TF-IDF, embeddings, and why Hybrid Search is the production standard for AI applications.
Why Relational Databases are Not Enough for Vector Data
Understand the structural and algorithmic limitations of SQL and NoSQL databases when handling high-dimensional vectors. Learn about the Curse of Dimensionality and কেন specific vector databases are required for AI scale.
The Modern AI Stack: Where Vector Databases Live
Master the architecture of AI applications. Learn about the 'V-L-M' stack (Vector, LLM, Middleware) and how data flows from ingestion to real-time retrieval in production systems.
Vector Database Use Cases: Beyond the Chatbot
Discover the diverse applications of vector databases in production. From recommendation systems and visual search to anomaly detection and long-term agent memory.
Module 2: Embeddings Fundamentals
Learn how text and multimodal embeddings work and how to measure similarity.
What are Embeddings? Mapping Meaning to Coordinates
Deep dive into the core engine of vector search. Learn what embeddings are, how they represent conceptual space, and why 'Dimension' is the most important word in AI infrastructure.
How Text Embeddings Work: From Characters to Context
Understand the pipeline of text vectorization. Learn about tokenization, word embeddings vs. sentence embeddings, and the role of Transformers in creating context-aware vectors.
Image and Multimodal Embeddings: Seeing with Math
Learn how Vision Transformers and CLIP models bridge the gap between pixels and language. Explore how multimodal embeddings enable cross-modal search and the unified vector space.
Embedding Dimensionality: Balancing Nuance, Speed, and Cost
Master the most important parameter in vector database design. Learn why dimensionality matters, the impact of high vs. low dimensions, and how to use Matryoshka Embeddings for adaptive scaling.
Similarity Metrics: The Math of 'Closeness'
Master the mathematical foundations of vector search. Learn the differences between Cosine Similarity, Dot Product, and Euclidean Distance, and when to use each for optimal retrieval.
Module 3: Vector Search Concepts
Deep dive into indexing strategies like HNSW, IVF, and Approximate Nearest Neighbor search.
Nearest Neighbor Search: The Core of Vector Retrieval
Understand the fundamental logic of retrieval in vector databases. Learn about k-Nearest Neighbors (kNN), the difference between linear and indexed search, and how the 'k' parameter affects your AI application.
Approximate vs Exact Search: The Speed-Accuracy Trade-off
Understand the core trade-off in vector database engineering. Learn why 100% accuracy is often unnecessary and how Approximate Nearest Neighbor (ANN) enables sub-second retrieval across billions of vectors.
Indexing Strategies: HNSW, IVF, and the Art of Information Geometry
Master the core algorithms that power vector databases. Deep dive into Hierarchical Navigable Small Worlds (HNSW), Inverted File Indexes (IVF), and Product Quantization (PQ).
Recall vs Latency: Tuning Your Vector Database for Performance
Learn how to optimize vector search parameters. Understand the relationship between search speed and retrieval quality, and master tuning parameters like ef, M, and nprobe.
Filtering and Metadata: Combining Math with Logic
Learn how to master metadata in vector databases. Explore Pre-filtering vs Post-filtering, and how to build secure, multi-tenant AI applications using structured attributes.
Module 4: Vector Database Architecture
Understand the storage and query engines behind modern vector databases.
The Index Layer: The Brain of a Vector Database
Explore the internal architecture of vector databases. Learn how the index layer manages graph structures, cluster centroids, and the intersection between RAM and disk.
The Storage Layer: Persistence in Vector Databases
Learn how vector databases persist data to disk and cloud storage. Explore the difference between Row-based and Column-based storage for vectors, and the role of Object Storage like S3 in modern AI infra.
The Query Engine: Executing the Search Pipeline
Learn how a vector database processes a query. Explore the query lifecycle from embedding to result aggregation, re-ranking, and metadata filtering.
Metadata Storage: Managing Structured Data in a Vector World
Explore the internal key-value stores that power metadata filtering. Learn how vector databases index non-vector data and the performance implications of deep metadata schemas.
Scaling and Sharding: Building Billion-Scale Vector Systems
Master the art of distributed vector databases. Learn about Horizontal Scaling, Sharding strategies, and how to maintain high availability in production AI systems.
Module 5: Getting Started with Chroma
Learn the local-first open-source vector database for fast prototyping.
What is Chroma? The Open Source, Local-First Vector Database
Discover Chroma, the favorite tool for AI developers. Learn why local-first storage is perfect for prototyping and how Chroma simplifies the vector database lifecycle.
Local-First Vector Databases: Privacy, Speed, and Autonomy
Master the architecture of local-first AI. Learn why running your vector database alongside your application is the key to sub-millisecond latency and total data privacy.
Persistence and Storage Models: Navigating the Chroma Backend
Understand how Chroma saves data to disk. Learn about the interaction between SQLite, Parquet, and HNSW files, and how to manage database versioning.
Collection and Namespace Design: Organizing Your Vector Data
Learn how to structure your vector data for maximum efficiency. Explore the trade-offs between one big collection and many small collections, and how to use naming conventions as a management tool.
Project: Building a Local Semantic Search Index with Chroma
Put your knowledge into practice. Built a complete, persistent semantic search engine for local text files using Python and ChromaDB.
Module 6: Getting Started with Pinecone
Master the industry-leading managed serverless vector database.
Managed Vector Databases: Scaling Beyond the Hardware
Transition from local development to cloud infrastructure. Learn why managed vector databases like Pinecone are the backbone of production-grade AI systems.
Pinecone Architecture: Pods, Serverless, and the Distributed Brain
Understand how Pinecone manages vector data at scale. Explore the difference between Pod-based and Serverless architectures, and the role of Read Replicas.
Pinecone Index Configuration: Optimizing the Schema for Search
Learn how to tune your Pinecone index settings. Explore metadata indexing configurations, choosing the right distance metric, and the impact of pod types on performance.
Namespaces and Metadata Filtering in Pinecone: Precision at Scale
Learn how to partition and filter your data in Pinecone. Explore the powerful combination of Namespaces for logical isolation and Metadata for granular search.
Pinecone Costs and Performance: Managing the Bottom Line
Master the economics of managed vector databases. Learn how Pinecone pricing works, how to optimize your Read/Write Units, and the impact of vector dimensionality on your bill.
Module 7: Getting Started with OpenSearch
Building hybrid search systems with vector and keyword search in OpenSearch.
OpenSearch: The Power of Hybrid Search
Discover why OpenSearch is the enterprise favorite for AI. Learn how to combine the precision of Keyword Search with the intuition of Vector Search in a single engine.
OpenSearch Mapping: Defining Vector Fields and k-NN logic
Learn how to configure OpenSearch for vector search. Master the JSON mapping syntax for knn_vector fields and choosing your search engine (HNSW vs. IVF).
Hybrid Search in OpenSearch: The Best of Both Worlds
Master the art of combining Keyword and Vector search. Learn how to use OpenSearch's 'hybrid' query type and Reciprocal Rank Fusion (RRF) to deliver state-of-the-art results.
Choosing Your Engine: OpenSearch vs. Pure Vector Databases
Master the decision framework for AI infrastructure. Learn the trade-offs between the specialized speed of Pinecone/Chroma and the versatile power of OpenSearch.
Python Masterclass: Implementing Hybrid Search in OpenSearch
Go from theory to code. Build a production-ready Python client for OpenSearch Hybrid Search, including score normalization and pipeline management.
Module 8: CRUD Operations in Vector Databases
Handling data lifecycles: ingestion, updates, deletions, and re-indexing.
Creating Collections and Indexes: The Blueprint for Success
Master the first step of the data lifecycle. Learn how to design robust vector collections that won't break as your data grows or your models change.
Inserting Vectors: The Art of Bulk Upserts and Data Integrity
Master the ingestion phase of vector databases. Learn how to handle millions of records using batching, rate limiting, and the 'Upsert' pattern.
Updating and Deleting Vectors: Maintaining Data Freshness
Learn how to manage the 'D' in CRUD. Explore the differences between Vector updates and Metadata updates, and the performance impact of deletions on graph indexes.
Handling Versioned Data: Managing the Evolution of Knowledge
Solve the problem of duplicate facts. Learn how to manage multiple versions of documents in your vector database using 'active' flags and version metadata.
Re-indexing Strategies: The Infrastructure Migration
Learn how to migrate your vector database when your models or business needs change. Explore the 'No-Downtime' switch and the cost of rebuilding the brain.
Module 9: Querying Vector Databases
Master similarity search, top-k retrieval, and metadata filtering.
Multi-Modal Embeddings: Beyond the Written Word
Explore the next frontier of AI search. Learn how models like CLIP and ImageBind map images, text, and audio into a single, unified vector space.
Storing Image and Video Vectors: The Frame-by-Frame Pipeline
Master the ingestion of visual data. Learn how to convert images and long-form video into searchable vectors without overwhelming your infrastructure.
Text-to-Image and Image-to-Image: Querying the Visual Brain
Discover the two most powerful ways to search visual data. Learn how to transform a natural language query or a reference photo into a high-dimensional vector search.
Audio and Speech Embeddings: Searching the Soundscape
Enter the world of acoustic search. Learn how models like CLAP and AudioLDM transform speech, music, and ambient noise into multi-dimensional vectors.
Project: Building a Multi-Modal Visual Search Engine
Put your multi-modal skills to the test. Build a local AI application that can search through images using either text descriptions or reference photos.
Module 10: Advanced Query Techniques
Learn re-ranking, multi-query expansion, and cross-encoder refinement.
Introduction to RAG: Giving LLMs a Memory
Discover the most important application of vector databases. Learn how Retrieval-Augmented Generation (RAG) solves the problem of model hallucination and outdated knowledge.
The RAG Pipeline: Chunking, Embedding, and Prompting
Master the step-by-step workflow of RAG. Learn how to optimize document chunking, choose embedding models, and craft prompts that prevent LLM hallucinations.
Advanced RAG: Re-ranking, Context Pruning, and the Two-Stage Retrieval
Take your RAG system to production. Learn how to use Cross-Encoders for re-ranking and how to optimize context to save on LLM token costs.
RAG Frameworks: Building Faster with LangChain and LlamaIndex
Stop building from scratch. Learn how to use professional AI frameworks to orchestrate complex RAG pipelines, manage memory, and connect to hundreds of data sources.
Project: Building a Production-Ready RAG Document Bot
Combine everything you've learned. Build a professional RAG application that ingests PDFs, indexes them in a persistent store, and provides grounded answers with citations.
Module 11: Vector Databases in AI Systems
Integrating vector stores into RAG pipelines and context retrieval flows.
Why Evaluation is the Biggest Challenge in AI Search
Move beyond 'vibes-based' testing. Learn why evaluating RAG systems is difficult and the critical difference between Retrieval testing and Generation testing.
The RAGAS Framework: Measuring RAG with Math
Master the industry standard for RAG evaluation. Learn how to use RAGAS to calculate Faithfulness, Answer Relevancy, and Context Recall.
Building a Golden Set: Automated Test Data Generation
Learn how to build high-quality evaluation datasets. Explore synthetic data generation to create 'Golden Question-Answer' pairs from your raw documents.
Continuous Monitoring and Observability: Guarding the Live System
Master the operational side of AI search. Learn how to trace queries, identify latency bottlenecks, and monitor for 'Concept Drift' in your vector database.
Project: Evaluating Your AI Brain with RAGAS
Put your evaluation skills into practice. Build a test script that measures the quality of your RAG bot and produces a professional performance report.
Module 12: Vector Databases for Agents
Implementing long-term episodic and semantic memory for AI agents.
Long-Term Memory for Agents: The Persistent Brain
Learn how vector databases provide long-term memory for AI agents, allowing them to remember past interactions and user preferences across sessions.
Episodic vs. Semantic Memory: What to Remember
Master the distinction between episodic (event-based) and semantic (fact-based) memory in AI agents and how to store them in a vector database.
Agent Recall and Planning: The Retrieval Loop
Discover how agents use vector databases for planning and decision-making through recursive retrieval loops and past-action analysis.
Memory Expiration Strategies: Cleaning the Mind
Learn how to manage the 'infinite growth' problem in agent memory. Master TTL, importance-based pruning, and memory summarization.
Module 13: Multimodal Vector Search
Searching across text, images, audio, and video using multimodal embeddings.
Image Embeddings: Searching with Sight
Learn how to turn images into searchable vectors using CLIP and other vision-based models. Master the art of 'Searching by Example'.
Audio Embeddings: Finding the Sound
Learn how to convert audio clips into vectors. Discover how to build similarity search for music, speech, and environmental sounds.
Video and Document Embeddings: Temporal and Spatial Logic
Master the complexity of embedding video sequences and multi-page documents. Learn to handle temporal sequences and spatial layouts in vector search.
Cross-Modal Retrieval: The Universal Search
Experience the power of a shared vector space. Learn how to search for images with audio, video with text, and music with descriptions.
Storage and Indexing Challenges: Scaling Multimodal
Understand the technical hurdles of multimodal vector search. Learn about high-dimensional index overhead and managing large binary assets.
Module 14: Performance Optimization
Tuning indexes, batch ingestion, and caching for low-latency search.
Index Tuning: Balancing Speed and Accuracy
Learn how to tune your vector database indexes for production. Master the trade-offs between HNSW parameters and search recall.
Batch Ingestion: Data Loading at Scale
Learn how to ingest millions of vectors efficiently. Master batching, parallelization, and error handling for large-scale data loading.
Query Latency Optimization: The Need for Speed
Learn how to shave milliseconds off your vector queries. Master multi-threading, embedding latency, and network optimization.
Caching Strategies: Semantic and Exact Matches
Learn how to bypass your vector database entirely using caching. Master Exact Match, Semantic Cache, and Result Deduplication.
Cost-Performance Trade-offs: Finding the ROI
Master the economics of vector databases. Learn how to calculate ROI and choose between High-Accuracy and Low-Cost indexing.
Module 15: Scaling Vector Databases
Horizontal scaling, sharding, and high-availability strategies for enterprise search.
Horizontal Scaling: Growing with your Data
Learn how to scale your vector database across multiple machines. Master the principles of distributed search and load balancing.
Sharding Strategies: How to Split the Map
Master the logic of data partitioning. Learn about Random, Hash-based, and Metadata-based sharding for vector databases.
Replication and Data Consistency
Learn how to replicate your vector data for reliability. Master the trade-offs between 'Strong Consistency' and 'Eventual Consistency'.
High Availability: Designing for Zero Downtime
Master the art of building vector systems that never go down. Learn about Multi-AZ deployment and health-check orchestration.
Disaster Recovery: Backups and Regional Failover
Learn how to recover from a total system catastrophe. Master RPO, RTO, and cross-region backup strategies for vector data.
Module 16: Security and Access Control
Securing embeddings, tenant isolation, and audit logging.
Authentication and Authorization in Vector Systems
Learn how to protect your vector database from unauthorized access. Master API keys, IAM roles, and RBAC for vector data.
Securing Embeddings: Encryption and Data Hygiene
Understand the risks of 'Embedding Inversion' and learn how to encrypt your vector data at rest and in transit.
Sensitive Data Risks: The Hallucination of Privacy
Learn how vector databases can accidentally leak secrets through semantic similarity. Master the art of 'Redacting the RAG'.
Tenant Isolation: Preventing Cross-Pollination
Learn how to build multi-tenant AI applications safely. Master Namespaces, Collection-level isolation, and Shard-level security.
Audit Logging: Tracking the AI's Reading Habits
Learn how to record every interaction with your vector data. Master the art of 'Retrieval Auditing' for compliance and security.
Module 17: Vector Databases in Production
Monitoring, observability, and incident response for production vector stores.
Environment Separation: Dev, Staging, and Prod
Learn how to manage multiple vector environments. Master data migration between Dev, Staging, and Production indexes.
Monitoring and Observability: The Vector Vital Signs
Learn how to monitor your vector database performance. Master Recall metrics, throughput, and index health dashboards.
Index Versioning: Managing Change
Learn how to manage multiple versions of your vector index. Master the Blue-Green deployment strategy for vector data.
Rolling Updates: Zero-Downtime Re-indexing
Learn how to update your vector data without taking your search offline. Master the 'Dual-Write' and 'Background Rebuilding' patterns.
Incident Response: When the Vectors Go Rogue
Prepare for the worst. Learn how to handle index corruption, embedding drift, and retrieval storms in production.
Module 18: Real-World Projects
Build semantic search engines, RAG assistants, and recommendation systems.
Project 1: Building a Semantic Search Engine
Build a complete, end-to-end semantic search engine for technical documentation. Master ingestion, indexing, and retrieval UI.
Project 2: Building a RAG Knowledge Assistant
Build a production-grade RAG system. Learn to bridge the gap between Vector Retrieval and LLM Generation.
Project 3: Building a Recommendation System
Master user-to-item and item-to-item recommendations. Build a movie or product recommender using vector similarity.
Project 4: Building an Agent Memory Store
Implement long-term persistent memory for an AI Agent. Build a system that tracks user preferences and past events across multiple sessions.
Project 5: Building a Multimodal Search Platform
The final project of Module 18. Build a search engine that finds matching images based on text descriptions and vice-versa.
Module 19: Comparing Vector Databases
A decision framework for choosing between Pinecone, Chroma, and OpenSearch.
Feature Comparison: Pinecone vs. Chroma vs. OpenSearch
A side-by-side feature comparison of the top three vector databases. Learn their strengths, weaknesses, and unique capabilities.
Cost Models: Pricing the Search
Master the economics of vector databases. Compare Pinecone's serverless pricing with the infra costs of self-hosting.
Operational Complexity: The Maintenance Bill
Learn how much effort it takes to maintain your vector database. Master the trade-offs between 'Managed' and 'Self-Hosted'.
Local vs. Managed Trade-offs: Latency and Privacy
Discover why 'Local' isn't always faster and 'Managed' isn't always more expensive. Master the data privacy trade-offs.
Decision Framework: Which One Should You Pick?
The final lesson of Module 19. A step-by-step logic tree to help you choose the right vector database for your specific business case.
Module 20: Capstone Project
Build a production-grade scalable vector search platform.
Course Overview
Format
Self-paced reading
Duration
Approx 6-8 hours
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