Vector Databases with Python: ChromaDB, Pinecone & RAG

About this course

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 BuildA complete Semantic PDF Search EngineA production-ready RAG (Retrieval-Augmented Generation) Chatbot

These 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:

PythonOpenAI APIOpenAI EmbeddingsChromaDBPineconeLangChainVector Databases

Semantic SearchVector SearchRetrieval-Augmented Generation (RAG)PDF Processing

Document Chunking

Cosine Similarity

Metadata FilteringTop-K Retrieval

Prompt Engineering

Conversation HistorySource Citations

Hybrid SearchProduction 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

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