Mar 21
Loading video player...
Curious about how vector databases function? This video builds one from scratch in Python, demonstrating how raw text is converted into vectors and then used for semantic search. We'll explore the core concepts of embedding models and how they power efficient data retrieval in modern ai models, highlighting the underlying deep learning principles. What you'll learn: → What embeddings are and how neural networks convert text into 768-dimensional vectors → Why cosine similarity is the default metric for text search (and when to use Euclidean distance or dot product instead) → How to build a working vector search engine in ~80 lines of Python → Why brute force search doesn't scale and what ANN algorithms (HNSW, IVF, Product Quantization) do differently → The critical difference between a vector index (FAISS) and a vector database (ChromaDB, Pinecone, Milvus, Qdrant) → How to evaluate vector databases for your RAG pipeline or AI application The progression: 00:00 The Vector Database Dilemma 01:30 Building Intuition: Custom Vector DB 03:02 What are Embeddings? 04:13 Cosine Similarity Explained 06:41 Version 1: Brute Force VectorDB 19:46 Version 2: Introducing FAISS 22:57 Version 3: FAISS VectorDB: Storage + Search 27:57 Version 4: ChromaDB 29:17 Benchmarking 33:41 Conclusion and Takeaways Repo: https://github.com/iRahulPandey/builld-vector-db-from-scratch.git Tools and resources: Python + NumPy (brute force implementation) FAISS by Meta AI: https://github.com/facebookresearch/faiss ChromaDB: https://www.trychroma.com Sentence-BERT paper (Reimers & Gurevych, 2019): https://arxiv.org/abs/1908.10084 HNSW paper (Malkov & Yashunin, 2016): https://arxiv.org/abs/1603.09320 ANN Benchmarks: https://ann-benchmarks.com 📬 No Noise. Just Build. Subscribe for deep dives into AI/ML engineering, MLOps, and building things from scratch. #VectorDatabase #FAISS #ChromaDB #RAG #SemanticSearch #Embeddings #MachineLearning #MLOps #ANN #HNSW #CosineSimilarity #BuildFromScratch #Python #AIEngineering