RAG From Scratch #3: Embeddings & ChromaDB Explained | LangChain + Python

🚀 RAG FROM SCRATCH — PART 3 How does AI understand the meaning of information inside a document? In Part 2, we broke our PDF into smaller chunks. Now we're taking the next step: TEXT → EMBEDDINGS → VECTORS → CHROMADB In this video, I explain and demonstrate: ✅ What are Embeddings? ✅ How text is converted into vectors ✅ Why vectors are useful for AI search ✅ Semantic similarity explained simply ✅ What is ChromaDB? ✅ How to store document chunks in ChromaDB ✅ Using Ollama with the nomic-embed-text model ✅ Performing a similarity search ✅ Finding relevant information from our real IT/VPN document 🔥 LIVE PROJECT RESULT: 2 PDF pages ↓ 7 text chunks ↓ Embeddings ↓ ChromaDB ↓ Question: "How do I connect to the VPN?" ↓ Relevant VPN information This is an important step in building our complete RAG application. 🧠 OUR RAG PIPELINE: PDF ↓ Document Loader ✅ ↓ Document Chunking ✅ ↓ Embeddings ✅ ↓ ChromaDB ✅ ↓ Retriever ⏳ ↓ LLM ⏳ ↓ Answer ⏳ 🎯 NEXT VIDEO: Retriever + Similarity Search We're building a complete RAG application from scratch, one layer at a time. If you're learning AI Engineering, Generative AI, RAG, LangChain, Python, LLMs, or Vector Databases, follow the complete series. #RAG #GenerativeAI #LangChain #Python #AIEngineering #LLM #ChromaDB #Embeddings #GenAI #artificialintelligencesingularity