Examples
These examples demonstrate how you might build vector search into your
applications with Pinecone. You can view their source code to jumpstart your
own application.
Our Learn section explains the basics of vector search and vector databases.
Semantic search
How to create a simple semantic text search using Pinecone's similarity search service.
Basic hybrid searchHow to pair semantic search with a basic keyword filter.
Generative question answering (OpenAI)How to build retrieval enhanced generative QA systems with OpenAI.
Extractive question answeringHow to build an extractive QA application with similarity search.
Abstractive question answeringHow to build an abstractive (generative) QA application with similarity search.
Tabular question answeringHow to build a question-answering application for extracting answers from tables with similarity search.
NER searchHow to automatically extract entities from text and use them to improve search results.
Video transcription searchHow to create an app that searches video transcription data.
GIF description searchHow to create a GIF search app.
Image, audio, and video search
How to build advanced image search applications.
Facial similarity searchFind your celebrity doppelganger with facial similarity search.
Audio similarity searchHow to build advanced audio search applications.
Recommendation systems
How to use Pinecone to create a simple personalized article or content recommender.
Movie recommenderHow to create a movie recommendation system with the MovieLens dataset.
Other examples
How to create a simple application for identifying duplicate documents.
IT threat detectionHow to build an application for detecting rare events in IT threat detection.
Extreme classificationHow to label new texts automatically when there is an enormous number of potential labels.
Time series similarity searchHow to perform time-series "pattern" matching using a similarity search service.
Updated 10 days ago