FAISS Vector Database Explained | How to Use FAISS in LangChain | Agentic AI Bootcamp

Description

Welcome to Agentic AI Hands-On Bootcamp! 🚀 In this video, we’ll explore FAISS (Facebook AI Similarity Search) — a high-performance vector database built by Meta AI, designed for fast and efficient similarity search. You’ll learn: What is FAISS DB How FAISS stores & searches embeddings Step-by-step FAISS setup in LangChain Indexing & Searching concepts explained Difference between FAISS, Chroma, and Pinecone 📌 GitHub Repository (Code + Notes): 👉 https://github.com/dearnidhi/Agentic-AI-HandsOn-Bootcamp 📩 Connect with me: 🔗 LinkedIn: https://www.linkedin.com/in/nidhi-chouhan-544650b4/ 📸 Instagram: @dear_nidhi | @codenidhi ✉️ Email: nidhiyachouhan12@gmail.com ✨ Don’t forget to LIKE 👍, SHARE 📢 & SUBSCRIBE 🔔 for more tutorials on LangChain, Vector Databases & RAG systems! faiss db, faiss vector database, facebook ai similarity search, LangChain FAISS, Agentic AI Bootcamp, Nidhi Chouhan, CodeNidhi, vector database tutorial, embeddings in NLP, AI bootcamp, semantic search, similarity search, faiss indexing, RAG pipeline, LangChain vectorstore, faiss vs chroma vs pinecone #FAISS #VectorDatabase #LangChain #AgenticAI #NidhiChouhan #CodeNidhi #AI #MachineLearning #LLM #RAG #Python #Embeddings #LangChainBootcamp #MetaAI #SemanticSearch #DataScience