开发者必备：向量数据库与RAG实战指南

本课程深入讲解向量数据库与RAG技术，通过项目实践构建生产级AI应用，涵盖ChromaDB、Pinecone及LangChain，助你掌握语义搜索与智能问答系统开发。

原始标题：Vector Databases for Developers: ChromaDB, Pinecone & RAG

本课程以项目为驱动，深入讲解如何利用向量数据库与 RAG 技术构建生产级 AI 应用，涵盖从文本分块、嵌入向量生成到基于 LangChain 的智能机器人开发。学员将掌握 ChromaDB 与 Pinecone 的应用，具备构建无幻觉、高性能智能问答系统的能力。您可告知您想深入了解的优化策略或具体业务场景以进行更有针对性的实践。

Published 8/2026
Created by Sudip Bhattacharyya
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 18 Lectures ( 9h 54m ) | Size: 3.6 GB

Learn Embeddings, Semantic Search, ChromaDB, Pinecone, LangChain & build production-ready RAG applications with Python.

What you’ll learn
⚡ Build AI-powered Semantic Search applications using Python, OpenAI Embeddings, ChromaDB, and Pinecone.
⚡ Understand Embeddings, Vector Databases, Cosine Similarity, Chunking, and Semantic Search from scratch.
⚡ Build production-ready Retrieval-Augmented Generation (RAG) applications using LangChain and modern AI workflows.
⚡ Create an AI-powered Semantic PDF Search Engine that searches documents using natural language.
⚡ Develop a complete RAG Chatbot with conversation history, source citations, and intelligent document retrieval.
⚡ Learn how to migrate from ChromaDB to Pinecone for scalable cloud-based vector search applications.
⚡ Optimize vector search systems using better chunking strategies, metadata filtering, Top-K retrieval, and hybrid search concepts.
⚡ Apply production best practices for building scalable AI applications with Vector Databases and Retrieval-Augmented Generation (RAG).

Requirements
❗ Basic Python programming knowledge.
❗ A Windows, macOS, or Linux computer.
❗ Visual Studio Code installed.
❗ An internet connection.
❗ An OpenAI API key (created during the course).
❗ No prior knowledge of AI, Vector Databases, Pinecone, ChromaDB, or LangChain is required.
❗ A willingness to learn by building real-world projects.

Description
Build Modern AI Applications with Vector Databases and Retrieval-Augmented Generation (RAG)

Have you ever wondered how ChatGPT, Claude, Gemini, and modern AI assistants search millions of documents and answer questions using your own data?

The answer isVector Databases andRetrieval-Augmented Generation (RAG).

In this hands-on course, you’ll learn how to build AI-powered Semantic Search applications and production-ready RAG systems completely from scratch using Python.

Instead of focusing only on theory, you’ll build real-world AI applications while learning the concepts behind embeddings, semantic search, vector databases, and modern AI architectures.

Throughout the course you’ll work with industry-standard technologies including

✨ Python

✨ OpenAI Embeddings

✨ ChromaDB

✨ Pinecone

✨ LangChain

✨ Semantic Search

✨ Vector Search

✨ RAG

✨ PDF Processing

You’ll begin by understanding how embeddings represent the meaning of text and why semantic search is far more powerful than traditional keyword search.

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

From there, you’ll build a complete Semantic PDF Search Engine capable of searching documents using natural language.

You’ll then extend that project into a complete Retrieval-Augmented Generation (RAG) Chatbot using LangChain and OpenAI.

The course also covers

✨ Document chunking

✨ Metadata filtering

✨ Top-K Retrieval

✨ Prompt Engineering

✨ Conversation History

✨ Source Citations

✨ ChromaDB

✨ Pinecone

✨ Hybrid Search

✨ Production Best Practices

By the end of the course, you’ll understand how modern AI search systems work and you’ll have built portfolio-quality projects that demonstrate practical AI development skills.

Unlike many AI courses that simply explain concepts, this course emphasizes building real applications that you can extend into your own products.

Whether you’re an AI developer, Python programmer, software engineer, or simply curious about Vector Databases and RAG, this course will give you practical skills that are immediately applicable in real-world projects.

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

Who this course is for
⭐ Python developers who want to build modern AI applications.
⭐ Software developers interested in Vector Databases and Retrieval-Augmented Generation (RAG).
⭐ Backend and Full Stack developers building AI-powered search applications.
⭐ Developers who want hands-on experience with ChromaDB, Pinecone, and LangChain.
⭐ Engineers interested in Semantic Search and Embedding models.
⭐ Anyone who wants to build production-ready AI applications using Python and OpenAI.
⭐ Students preparing for careers in AI Application Development and Generative AI.
⭐ Developers who prefer practical coding projects over theory.