Here are some example applications that use similarity search and Pinecone.
Our Learn section explains the basics of vector databases and similarity search as a service.
How to create a simple semantic text search using Pinecone’s similarity search service.
How to build a question-answering application with similarity search and Pinecone.
We will walk you through how to index a set of questions and retrieve the most similar stored questions for a new (unseen) question. That way, you can link a new question to answers you might already have.
How to create a movie recommendation system on the Movielens dataset.
How to use Pinecone’s similarity search as a service to build an audio search application.
This enables you to:
- Find songs and metadata within a catalog, based on a sample
- Find similar sounds in an audio library
- Detect who's speaking in an audio file
- Take some new (unseen) audio recordings and search through the index to find the most similar matches, along with their YouTube links.
How to create a simple application for identifying duplicate documents.
How to label new texts automatically when there is an enormous number of potential labels.
This scenario is known as extreme classification. This is a supervised learning variant that deals with multi-class and multi-label problems involving many choices.