| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Getting Started | |||
| 1. Lecture 01 – Welcome and Course Overview.pdf | 111.8 KB | ||
| 1. Welcome and Course Overview.mp4 | 68.4 MB | ||
| 2 - Working with Embeddings | |||
| 3 - Working with Embeddings | |||
| 4 - Project 1 AI Semantic PDF Search Engine | |||
| 10. Building the Semantic Search Engine.mp4 | 436.9 MB | ||
| 10. Lecture 10 - Building the Semantic Search Engine.docx.pdf | 240.4 KB | ||
| 5 - Building a Complete RAG Chatbot | |||
| 11. Lecture 11 - Understanding Retrieval-Augmented Generation (RAG).pdf | 136.3 KB | ||
| 11. Understanding Retrieval-Augmented Generation (RAG).mp4 | 196.6 MB | ||
| 12. Building the RAG Backend with LangChain.mp4 | 241.1 MB | ||
| 12. Lecture 12 - Building the RAG Backend with LangChain.pdf | 251.9 KB | ||
| 13. Building the Chat Interface.mp4 | 195.3 MB | ||
| 13. Lecture 13 - Building the Chat Interface.pdf | 202.4 KB | ||
| 14. Improving the RAG Chatbot.mp4 | 218.5 MB | ||
| 14. Lecture 14 - Improving the RAG Chatbot.pdf | 164.6 KB | ||
| 6 - Scaling with Pinecone | |||
| 15. Getting Started with Pinecone.mp4 | 240.2 MB | ||
| 15. Lecture 15 - Getting Started with Pinecone.pdf | 186.4 KB | ||
| 16. Lecture 16 - Migrating from ChromaDB to Pinecone.pdf | 201.6 KB | ||
| 16. Migrating from ChromaDB to Pinecone.mp4 | 213.7 MB | ||
| 7 - Production Best Practices | |||
| 17. Building Better Vector Search Systems.mp4 | 183.9 MB | ||
| 17. Lecture 17 - Building Better Vector Search Systems.pdf | 205.4 KB | ||
| 18. Course Summary and Next Steps.mp4 | 112.6 MB | ||
| 9. Lecture 09 - Loading and Indexing PDF Documents.docx.pdf | 207.6 KB | ||
| 9. Loading and Indexing PDF Documents.mp4 | 177.7 MB | ||
| 7. Getting Started with ChromaDB.mp4 | 249.5 MB | ||
| 7. Lecture 07 - Getting Started with ChromaDB.docx.pdf | 191.5 KB | ||
| 8. Lecture 08 - Searching with ChromaDB.docx.pdf | 214.9 KB | ||
| 8. Searching with ChromaDB.mp4 | 163.3 MB | ||
| 3. Lecture 03 - Setting Up the Development Environment.docx.pdf | 129.5 KB | ||
| 3. Setting Up the Development Environment.mp4 | 144.3 MB | ||
| 4. Creating Embeddings with the OpenAI API.mp4 | 260.8 MB | ||
| 4. Lecture 04 - Creating Embeddings with the OpenAI API.docx.pdf | 189 KB | ||
| 5. Lecture 05 - Measuring Semantic Similarity.docx.pdf | 192.8 KB | ||
| 5. Measuring Semantic Similarity.mp4 | 380.1 MB | ||
| 6. Chunking Documents for AI.mp4 | 103.9 MB | ||
| 6. Lecture 06 - Chunking Documents for AI.docx.pdf | 157.7 KB | ||
| 2. Downloading the Course Source Code from GitHub.pdf | 93.7 KB | ||
| 2. Lecture 02 – Understanding Embeddings and Vector Databases.pdf | 128.2 KB | ||
| 2. Understanding Embeddings and Vector Databases.mp4 | 86.5 MB |
Vector Databases for Developers: ChromaDB, Pinecone & RAG
https://WebToolTip.com
Published 8/2026
Created by Sudip Bhattacharyya
MP4 | Video: h264, 1920x1080 | 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.
| torrent name | size | uploader | age | seed | leech |
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| 507 MB | freecoursewb | 2 months | 26 | 2 | |
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