A warm welcome to Vector Databases for RAG: FAISS, Pinecone, Chroma & Weaviate course by Uplatz.

What Are Vector Databases?
Vector databases are specialized data systems designed to store and search high-dimensional vectors — numerical representations of data such as text, images, audio, or code. These vectors (embeddings) capture semantic meaning, allowing machines to compare similarity between items using distance metrics like cosine similarity. Unlike traditional databases that search by exact matches or SQL filters, vector databases enable semantic retrieval, powering AI applications such as chatbots, recommendation engines, RAG pipelines, document search, and multimodal understanding.

How They Work
When data is converted into embeddings (vectors), these are stored in an index optimized for fast Approximate Nearest Neighbor (ANN) search. During a query, the user input is also transformed into a vector, and the database retrieves the most similar vectors based on distance calculations. Various indexing algorithms (e.g., HNSW, IVF, PQ) allow sub-second responses even with millions of vectors. Vector databases can also combine keyword filtering, metadata search, and semantic search for hybrid querying — making them ideal for production-grade AI systems.

Popular Vector Databases
This course dives deep into the four most widely used vector databases.

1. FAISS, developed by Facebook AI Research, is a high-performance local library ideal for fast similarity search and prototyping.

2. Chroma is a lightweight, open-source vector database built for LLM workflows and integrates smoothly with LangChain.

3. Pinecone is a fully managed cloud platform offering high scalability, enterprise-grade performance, and production-ready infrastructure.

4. Weaviate is an open-source vector database with both local and cloud deployment options, featuring GraphQL APIs, hybrid search, schema design, and strong multimodal capabilities. Together, these platforms cover everything from local experimentation to real-world AI deployment at scale.

Course Description

The rise of Generative AI and LLMs has made vector databases the new backbone of intelligent applications. Instead of searching by keywords, vector databases enable semantic search — retrieving results based on meaning and context. This course takes you from the mathematical foundations of embeddings all the way to building real-world AI apps using FAISS, Chroma, Pinecone, and Weaviate.

You’ll learn how embeddings work, how Approximate Nearest Neighbor (ANN) algorithms power high-speed search, and how to design production-ready Retrieval-Augmented Generation (RAG) pipelines with LLMs. By the end of the course, you’ll know exactly which vector database to use, when, and why — and how to deploy AI search systems at scale.

No outdated theory — this is hands-on, industry-grade content designed for modern AI engineers, ML/LLMOps teams, full-stack developers, and ambitious learners.

What You’ll Learn (Learning Objectives)

• Understand how vector databases work and why they are core to AI search and RAG systems

• Generate and evaluate embeddings using OpenAI, Hugging Face, & Python

• Implement ANN search and compare indexing strategies

• Build vector indexes using FAISS, Chroma, Pinecone, and Weaviate

• Create semantic and multimodal search engines from scratch

• Integrate vector DBs with LangChain and LLM APIs

• Design and deploy full RAG pipelines with real data

• Optimize query speed, memory usage, and scalability

• Understand trade-offs between open-source and cloud vector DBs

• Build production-grade AI applications for real clients

Who This Course Is For

• Data scientists and machine learning engineers working with embeddings or RAG pipelines

• Software/backend/full-stack engineers building chatbots or AI search systems

• Data engineers and MLOps professionals managing AI infrastructure

• NLP practitioners focused on similarity and context retrieval

• Researchers exploring high-dimensional search or ANN algorithms

• AI startup founders & product managers planning to integrate vector search

• Hackathon participants or builders prototyping AI tools

• Anyone aiming to master the data layer behind modern generative AI

Vector Databases for RAG: FAISS, Pinecone, Chroma & Weaviate - Course Curriculum

Module 1: Linear Algebra Foundations

• Lecture 1: Linear Algebra Basics
  (Vectors, matrices, dot product, cosine similarity, vector norms, and their role in embeddings)

Module 2: Probability & Statistics for Vector Search

• Lecture 2: Probability & Statistics for Vector Search
  (Distributions, similarity measures, distance metrics, and statistical intuition for high-dimensional search)

Module 3: Optimization & ANN Concepts

• Lecture 3: Optimization & Approximate Nearest Neighbor (ANN) Concepts
  (Gradient descent, loss functions, dimensionality reduction, and ANN algorithms such as HNSW, IVF, PQ)

Module 4: Hands-on Python Math Labs

• Lecture 4: Python Math Labs for Vector Search
  (NumPy-based linear algebra, similarity computations, and visualization of embedding spaces)

Module 5: Vector Database Foundations

• Lecture 5: Introduction to Vector Databases
  (Concepts, architecture, storage, and retrieval mechanisms)

Module 6: Working with Embeddings

• Lecture 6: Generating and Using Embeddings
  (Creating embeddings using OpenAI, Hugging Face, and sentence-transformers; storing and querying)

Module 7: FAISS (Facebook AI Similarity Search)

• Lecture 7: FAISS Overview and Setup

• Lecture 8: Indexing and Searching with FAISS

• Lecture 9: Building a Semantic Search Engine with FAISS

Module 8: Chroma — Open-Source Vector DB

• Lecture 10: Introduction to Chroma

• Lecture 11: Creating and Managing Collections

• Lecture 12: Using Chroma with LangChain and LLMs

Module 9: Pinecone — Managed Cloud Vector DB

• Lecture 13: Overview of Pinecone

• Lecture 14: Index Creation and Querying

• Lecture 15: Building a Semantic Search Pipeline in Pinecone

Module 10: Weaviate — Open-Source Vector DB with Cloud Option

• Lecture 16: Introduction to Weaviate

• Lecture 17: Schema Design, Data Ingestion, and Querying

• Lecture 18: Hybrid Search and GraphQL API

Module 11: Comparing Vector Databases

• Lecture 19: Comparing FAISS, Chroma, Pinecone, and Weaviate
  (Performance, scalability, pricing, and ecosystem trade-offs)

Module 12: Real-World Projects

• Lecture 20: Project 1 — Building a RAG Pipeline with LLMs and Vector DBs

• Lecture 21: Project 2 — Image Similarity Search

• Lecture 22: Project 3 — Knowledge Base Chatbot with Pinecone

Real-World Projects You’ll Build

• Semantic Search Engine with FAISS

• RAG Pipeline with LLMs & Pinecone

• Knowledge Base Chatbot Using LangChain

• Image Similarity Search System

• Performance Comparison Across Vector DBs

• Hands-on Deployment & Optimization

By the End of This Course…

You’ll be able to confidently design, choose, build, and deploy AI-native search and RAG systems using industry-leading vector databases — just like the systems powering ChatGPT, Midjourney, Notion AI, and Google Gemini.

Ready to master one of the most important skills in AI today?
Enroll now — and start building semantic search, multimodal AI, and intelligent applications with vector databases.