Pinecone Blog

Rust – A hard decision pays off

Edo Liberty, Pinecone CEO, provides a glimpse into the journey behind building a database company, including some insights into the latest innovations around the product and vector search.

What is a Vector Database? (2021)

Discover Vector Databases: How They Work, Examples, Use Cases, Pros & Cons, Selection and Implementation. They have combined capabilities of traditional databases and standalone vector indexes while specializing for vector embeddings.

Introduction to K-Means Clustering

Under unsupervised learning, all the objects in the same group (cluster) should be more similar to each other than to those in other clusters; data points from different clusters should be as different as possible. Clustering allows you to find and organize data into groups that have been formed organically, rather…

LangChain: The Missing Manual

The handbook to the LangChain library for building applications around generative AI and large language models (LLMs).

Fixing Hallucination with Knowledge Bases

Large Language Models (LLMs) have a data freshness problem. Even some of the most powerful models, like GPT-4, have no idea about recent events. Let's learn how to fix this.

Pinecone raises $100M Series B

Dear Pinecone Community, I am thrilled to share some exciting news with you all. We raised $100 million in Series B funding, led by Andreessen Horowitz, with participation from ICONIQ Growth, and our existing investors Menlo Ventures and Wing Venture Capital. This funding brings our valuation to $750 million, hitting…

Rewriting a high performance vector database in Rust

Nearest neighbor indexes for similarity search

Vector similarity search 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.

K-Nearest Neighbors

One Machine Learning algorithm that relies on the concepts of proximity and similarity is K-Nearest Neighbor (KNN). KNN is a supervised learning algorithm capable of performing both classification and regression tasks.

Vision Transformers (ViT) Explained

A deep dive into the unification of NLP and computer vision with the Vision Transformer (ViT).

BERTopic: The Future of Topic Modeling

BERTopic takes advantage of the superior language capabilities of (not yet sentient) transformer models and uses some other ML magic like UMAP and HDBSCAN to produce what is one of the most advanced techniques in language topic modeling today.

Product Quantization: Compressing high-dimensional vectors

Product quantization (PQ) is a popular method for dramatically compressing high-dimensional vectors to use 97% less memory, and for making nearest-neighbor search speeds 5.5x faster in our tests.

Introduction to Autoencoders