Example applications

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.

Application Description Links Colab
Semantic text search How to create a simple semantic text search using Pinecone’s similarity search service. Semantic Textual Search
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Basic hybrid search Pinecone allows you to pair semantic search with a basic keyword filter.
If you know that the document you're looking for contains a specific word or set of words, you simply tell Pinecone to restrict the search to only include documents with those keywords.
Basic Hybrid Search
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Question-answering 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.
Question Answering
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Time series similarity search How to perform time-series "pattern" matching using a similarity search service.
In this example, we will explain what to do if you wanted to retrieve all the historical time series items that matched a particular pattern.
Time Series Similarity Search
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Video recommendations How to create a movie recommendation system on the Movielens dataset. Video Recommendation
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Audio similarity search 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.
Audio Similarity Search
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Document deduplication How to create a simple application for identifying duplicate documents. Document Deduplication
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Extreme classification with similarity search 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.
Extreme Classification
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Image similarity search How to create an image similarity search backend service.
You will learn how to:
- Use the pre-trained embedding model called squeezenet from torchvision to transform image data into vector embeddings
- Build an index with Pinecone to store these vector embeddings
- Send a new image as query, and retrieve similar images in the index
Image Similarity Search
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IT threat detection How to use Pinecone similarity search to build an application for detecting rare events.
Such application is common in cyber-security and fraud detection domains, wherein only a tiny fraction of the events are malicious.
IT Threat Detection
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Personalized article recommender How to use Pinecone's similarity search to create a simple personalized article or content recommender. Personalized Article Recommender
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