Pinecone is a cloud-native vector database that provides a simple and efficient way to store, search, and retrieve high-dimensional vector data. With Pinecone, you can focus on building your machine learning models instead of worrying about data storage and retrieval.
To get started with Pinecone, you'll need to sign up for an account on the Pinecone website. Once you've created an account, you can start using Pinecone by installing the Python client library:
pip install pinecone-client
This repository contains the official Python client library for Pinecone, along with examples and documentation.Start from basics to real-life-examples.
1. Nearest neighbor search with Pinecone: This example could demonstrate how to perform a nearest neighbor search using Pinecone. It could show how to upload vectors to Pinecone, perform a search, and retrieve the most similar vectors.
2.Text search with Pinecone: This example could demonstrate how to use Pinecone for text search. It could show how to convert text into high-dimensional vectors using techniques like word embeddings, and how to upload and search these vectors in Pinecone.
3.Image search with Pinecone: This example could demonstrate how to use Pinecone for image search. It could show how to extract features from images using techniques like convolutional neural networks, and how to upload and search these features in Pinecone.
4.Real-time recommendation engine with Pinecone: This example could demonstrate how to build a real-time recommendation engine using Pinecone. It could show how to upload user and item vectors, perform nearest neighbor searches to find similar items, and use these results to make recommendations in real-time.
For more detailed information on using Pinecone, please see the official documentation