Vector Databases with Python: ChromaDB, Pinecone & RAG

Build AI Applications with Vector Databases, RAG, ChromaDB, Pinecone & LangChain

Learn how to build modern AI applications using Vector Databases, Retrieval-Augmented Generation (RAG), ChromaDB, Pinecone, LangChain, OpenAI, Semantic Search, and Python.

In this hands-on course, you'll build two complete real-world AI projects while learning the core technologies behind today's intelligent search systems and AI assistants. Instead of just learning theory, you'll write code from the very first lecture and build practical applications that you can use as portfolio projects or extend into your own products.

What You'll Build

1. A complete Semantic PDF Search Engine

2. A production-ready RAG (Retrieval-Augmented Generation) ChatbotThese projects will teach you how modern AI systems search documents, retrieve relevant information, and generate accurate, context-aware answers using Large Language Models (LLMs).

What You'll Learn

Throughout this course, you'll gain practical experience with:

• Python

• OpenAI API

• OpenAI Embeddings

• ChromaDB

• Pinecone

• LangChain

• Vector Databases

• Semantic Search

• Vector Search

• Retrieval-Augmented Generation (RAG)

• PDF Processing

• Document Chunking

• Cosine Similarity

• Metadata Filtering

• Top-K Retrieval

• Prompt Engineering

• Conversation History

• Source Citations

• Hybrid Search

• Production Best Practices

Course Journey

You'll begin by understanding how embeddings convert text into numerical vectors and why semantic search is far more powerful than traditional keyword search.

Next, you'll learn how to generate embeddings with the OpenAI API, compare vectors using cosine similarity, and build your own searchable knowledge base using ChromaDB.

You'll then build a complete Semantic PDF Search Engine capable of searching documents using natural language.

From there, you'll extend that project into a complete RAG chatbot using LangChain, OpenAI, and Pinecone, giving you hands-on experience building production-style AI applications.

Finally, you'll learn production best practices, including embedding strategies, chunking techniques, metadata filtering, hybrid search, and cloud vector databases.

Why Take This Course?

Unlike many AI courses that focus primarily on theory, this course emphasizes building real applications. Every major concept is reinforced through hands-on coding and practical projects that demonstrate how modern AI systems work in the real world.

By the end of this course, you'll understand how to design and build intelligent AI search systems from scratch and have portfolio-quality projects that showcase your skills.

Who Should Take This Course?

This course is ideal for:

• Python developers

• Software engineers

• AI developers

• Machine Learning engineers

• LangChain beginners

• Developers building RAG applications

• Anyone interested in Vector Databases, Semantic Search, and Generative AI

Whether you're expanding your AI skills, building intelligent document search systems, or creating production-ready RAG applications, this course provides practical knowledge you can apply immediately.

Enroll today and start building intelligent AI applications with Python, Vector Databases, ChromaDB, Pinecone, LangChain, OpenAI, Semantic Search, and Retrieval-Augmented Generation (RAG).