Vector Database | Pinecone Tutorial Part3 | Embeddings, Embed Model Sele...

Vector Database | Pinecone Tutorial Part3 | Embeddings, Embed Model Selection, Load vectors to index

In this Part 3 of our vector database tutorial series, we deep-dive into using Pinecone with real-world embedding models for semantic search and AI applications. 📚🧠

What you'll learn in this tutorial: 

✅ What is an embedding and how it works in vector space

✅ Understanding embedding models like multilingual-e5-large

✅ Choosing the right embedding model based on index dimensions

✅ How to create, configure, and connect to Pinecone index

✅ Setting vector dimension, metric, namespace, and other key attributes

✅ Using Pinecone Inference API to generate embeddings

✅ Upserting vectors into a namespace with metadata

✅ Running describe stats to validate your vector store

This is a must-watch if you're building AI-powered apps, RAG pipelines, or intelligent search systems with Python and Pinecone!

🔧 Tools & Tech Stack Covered:

    Pinecone Vector DB

    Embedding Model: multilingual-e5-large

    Cosine Similarity Metric

    Vector dimensions, upsert, and namespaces

💡 Code is fully explained and can be reused in your own projects!

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