Dense Embeddings
How dense embeddings turn meaning into geometry: word2vec, GloVe, and contextual models, vector arithmetic, cosine similarity, and where the field is heading.
No direct links0 refs
IR
Embeddings & Retrieval
Dense and sparse embeddings, quantization, and vector search for semantic retrieval.
19 concepts
All Embeddings & Retrieval Concepts
Sparse vs Dense vs Hybrid Retrieval: BM25, BERT, and Reranking Compared
How sparse retrieval (BM25/TF-IDF), dense retrieval (BERT-style embeddings), and hybrid systems that combine both compare on recall, semantic understanding, computational cost, and operational complexity for modern search.
No direct links0 refs
BM25 Algorithm for Text Retrieval
Master the BM25 algorithm, the probabilistic ranking function powering Elasticsearch and Lucene for keyword-based document retrieval and search systems.
No direct links0 refs
Pooling Strategies
How a transformer’s per-token outputs become one embedding: CLS, mean, max, last-token, and attention pooling — what each does and when to use it.
No direct links0 refs
Contrastive Learning
Master contrastive learning for vector embeddings: how InfoNCE loss and self-supervised techniques train models to create high-quality semantic representations.
No direct links0 refs
Matryoshka Embeddings
Matryoshka embeddings: nested representations enabling dimension reduction by simple truncation without model retraining for flexible retrieval.
No direct links0 refs
Domain Adaptation for Embeddings
Domain adaptation for embeddings: transfer learning to fine-tune retrieval models across domains while preventing catastrophic forgetting.
No direct links0 refs
Cross-Lingual Alignment
Learn cross-lingual embedding alignment techniques like VecMap and MUSE for multilingual vector retrieval and zero-shot language transfer in search systems.
No direct links0 refs
Cross-Encoder vs Bi-Encoder
Understand the fundamental differences between independent and joint encoding architectures for neural retrieval systems.
No direct links0 refs
Multi-Vector Late Interaction
Explore ColBERT and other multi-vector retrieval models that use fine-grained token-level matching for superior search quality.
No direct links0 refs
Hybrid Retrieval Systems
Build hybrid retrieval systems combining BM25 sparse search with dense vector embeddings using reciprocal rank fusion for superior semantic search performance.
No direct links0 refs
Quantization Effects Simulator
Embedding quantization simulator: explore memory-accuracy trade-offs from float32 to int8 and binary representations for retrieval.
No direct links0 refs
Vector Quantization Techniques
Master vector compression techniques from scalar to product quantization. Learn how to reduce memory usage by 10-100× while preserving search quality.
No direct links0 refs
Binary Embeddings for Fast Search
Learn how binary embeddings use 1-bit quantization for ultra-compact vector representations, enabling billion-scale similarity search with 32x memory reduction.
No direct links0 refs
Vector Index Structures
Explore the fundamental data structures powering vector databases: trees, graphs, hash tables, and hybrid approaches for efficient similarity search.
No direct links0 refs
HNSW vs IVF-PQ vs LSH: Approximate Nearest Neighbor Algorithms Compared
How HNSW, IVF-PQ, and LSH compare for approximate nearest neighbor (ANN) search — recall, latency, memory, build cost, and update characteristics — with Annoy, ScaNN, and DiskANN included for completeness.
No direct links0 refs
LSH: Locality Sensitive Hashing
Explore how LSH uses probabilistic hash functions to find similar vectors in sub-linear time, perfect for streaming and high-dimensional data.
No direct links0 refs
IVF-PQ: Inverted File with Product Quantization
Learn how IVF-PQ combines clustering and compression to enable billion-scale vector search with minimal memory footprint.
No direct links0 refs
HNSW: Hierarchical Navigable Small World
How HNSW navigates a layered proximity graph to find nearest neighbors in logarithmic time — the default in-memory index of modern vector databases.
No direct links0 refs