Subject
13 entries
Embeddings
Bookmarks
Benchmarking Postgres vector search: pgvector vs Lantern
Tembo's benchmark comparing pgvector and Lantern for vector similarity search in PostgreSQL — tests query speed, indexing time, and recall across different dataset sizes. Practical data for choosing a Postgres vector extension.
txtai: All-in-One Embeddings Database
txtai is an all-in-one open-source embeddings database combining semantic search, LLM orchestration, and language model workflows. Positions itself as the engine underneath an AI application rather than a standalone vector database.
Contextually Splitting Documents — Neum AI
Neum AI introduces context-aware document splitting that improves RAG retrieval quality on structured documents like SEC filings and contracts — by splitting along semantic boundaries rather than fixed character counts. A practical improvement to the chunking step most RAG tutorials treat as an afterthought.
RAG Is More Than Just Embedding Search
Jason Liu's influential post arguing that RAG systems need more than vector similarity search — covering query understanding, routing, reranking, and structured extraction as the layers that separate working RAG from production-grade RAG. Written for the Instructor library blog.
BERTopic: BERT-Based Topic Modeling
BERTopic is the leading open-source topic modeling library using sentence embeddings and clustering rather than word co-occurrence statistics — produces coherent, human-readable topics that LDA-style models often can't. The changelog tracks its evolution as the library added new backends and features.
Semantic Search and Q&A with GPT-3 and Datasette
Simon Willison's tutorial on building Q&A over documentation using GPT-3, embeddings, and Datasette — an early practical guide to semantic search that Willison built for his own blog. One of the first clearly explained end-to-end RAG implementations from a respected practitioner.
Vectors Are Over? Hashes as the Future of AI Search
Algolia's provocative post arguing that hash-based retrieval outperforms vector search for many real-world search use cases — a counterargument to the vector database hype of 2022. Grounds the comparison in production search quality metrics.
The Vector Database Index
Gradient Flow's landscape map of vector databases — published in 2022 when the category was forming, covering Pinecone, Weaviate, Qdrant, Chroma, Milvus, and others. A useful historical snapshot of the vector DB market at the moment it became strategically important.
BLOOM Training Corpus: 2D Embedding Visualization
A 2D UMAP visualization of 10 million text chunks from the BLOOM training corpus, encoded with all-distilroberta-v1. A rare window into the geometry of a frontier model's pretraining data.
Text Embeddings Visually Explained
Cohere's visual primer on text embeddings explains how words and sentences become vectors in high-dimensional space, and what operations on those vectors mean semantically. A good conceptual foundation before diving into practical embedding-based applications like semantic search or classification.
Faiss: The Missing Manual
Pinecone's comprehensive tutorial on FAISS (Facebook AI Similarity Search) — the foundational open-source library for approximate nearest neighbor search over dense vectors. Essential reading before choosing or building any vector search system.
Sentence Transformers: Pretrained Models
The Sentence-BERT pretrained models documentation — a reference for choosing the right sentence embedding model for semantic similarity, semantic search, or paraphrase detection tasks. The go-to resource when you need to pick a model before training your own.
Building a Recommender System Using Embeddings
Drop Engineering's walkthrough of building a brand recommender using learned embeddings — training entity embeddings from user-brand interaction data to capture brand similarity in continuous vector space. A practical case study in embedding-based recommendations.