Load PDFs into Vector Database (PineCone DB) for RAG AI | Step-by-Step C...

Load PDFs into Vector Database (PineCone DB) for RAG AI | Step-by-Step Coding Tutorial | Part3

Welcome to Part 3 of our RAG AI Development Series!

In this video, we move beyond theory and dive into hands-on coding — learn how to load PDF documents into a vector database, a critical step for building a Retrieval-Augmented Generation (RAG) AI system.

In this session, we go hands-on:

📄 Load multiple PDF documents

🧠 Split content into chunks ️

📈 Embed using Google's Gemini Embedding Model

🗃️ Store vectors into Pinecone — preparing everything for your RAG (Retrieval-Augmented Generation) AI system!

👉 What You'll Learn:

1. How to load and process PDFs in bulk

2. How to split text for better semantic search

3. How to embed documents using Google AI

4. How to store embeddings into a vector database (Pinecone)

🛠️ Tech Stack:

1. Python

2. LangChain

3. Pinecone

4. Google Generative AI (Gemini Embeddings)

Part 1: RAG Theory & Concepts ➡️ https://www.youtube.com/watch?v=E2YsOkIsihQ

Part 2: Full Environment (Libraries + API Keys) Setup ➡️ https://www.youtube.com/watch?v=DO_crG2LdOo

Vector Database Complete Tutorial ➡️ https://www.youtube.com/playlist?list=PLoVvAgF6geYPFtTrec1x58GpwC3O6pMJl

📚 Full code and resources available here: https://gitlab.com/beyond_the_technology/rag_powered_ai_agent (Please drop me a email so I can grant you access for the code repo)

🔔 Make sure to subscribe and turn on notifications — in the next part, we'll build the RAG AI system using the loaded data!

https://www.youtube.com/watch?v=CJ9C00nj4X4