Choosing Indexes for Similarity Search (Faiss in Python)
Facebook AI Similarity Search (Faiss) is a game-changer in the world of search. It allows us to efficiently search a huge range of media, from GIFs to articles - with incredible accuracy in sub-second timescales for billion+ size datasets. The success in Faiss is due to many reasons. One of those, in particular, is its flexibility. Faiss recognizes that there is no 'one-size-fits-all' in similarity search. Instead, Faiss comes with a wide range of search indexes - which we can mix and match to our choosing. However, this great flexibility produces a question - how do we know which size fits our use case? Which index do we choose? Should we use multiple indexes, or is one enough? This video will explore the pros and cons of some of the most important indexes - Flat, LSH, HNSW, and IVF. We will learn how we decide which to use and the impact of parameters in each index to build some of the best indexes for semantic search. 🌲 Pinecone Article: https://www.pinecone.io/learn/vector-... 🎉 Sign-up For New Articles Every Week on Medium! / membership Download script for Sift1M dataset: https://gist.github.com/jamescalam/a0... Similarity Search Series: • Vector Similarity Search and Faiss Course 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 👾 Discord / discord Mining Massive Datasets Book (Similarity Search): 📚 https://amzn.to/3CC0zrc (3rd ed) 📚 https://amzn.to/3AtHSnV (1st ed, cheaper) 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTu...
Locality Sensitive Hashing (LSH) for Search with Shingling + MinHashing (Python)
Faiss - Introduction to Similarity Search
349 - Understanding FAISS for efficient similarity search of dense vectors
Reinventing Entropy | Compression is Intelligence Part 1
3 Vector-based Methods for Similarity Search (TF-IDF, BM25, SBERT)
Product Quantization for Vector Similarity Search (+ Python)
Introduction to LlamaIndex with Python (2025)
Fine-tuning Large Language Models (LLMs) | w/ Example Code
Evaluation Measures for Search and Recommender Systems
MIT Just Revealed the AI Bubble's Fatal Flaw
HNSW for Vector Search Explained and Implemented with Faiss (Python)
Vector Search & Approximate Nearest Neighbors (ANN) | FAISS (HNSW & IVF)
What is Indexing? Indexing Methods for Vector Retrieval
Model Collapse Ends AI Hype
FAISS Vector Library with LangChain and OpenAI (Semantic Search)
Is RAG Still Needed? Choosing the Best Approach for LLMs
Vector Database Search - Hierarchical Navigable Small Worlds (HNSW) Explained
Using Vector Databases for Multimodal Embeddings and Search - Zain Hasan - NDC London 2024
Search Like You Mean It: Semantic Search with NLP and a Vector Database
