Build Vector Database From Scratch
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/buill...
Tools and resources:
Python + NumPy (brute force implementation)
FAISS by Meta AI: https://github.com/facebookresearch/f...
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.
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