Introduction to Pinecone

Pinecone is a cloud-native vector database designed for applications such as similarity search, recommendation systems, and natural language processing. Since its architecture is optimized for fast search and indexing at large scale (billions of vectors), it's suitable for real-time ML applications that require low-latency responses. Pinecone offers APIs, client SDKs for popular programming languages and multiple deployment options to choose from, including Serverless indexes.

Some of its important capabilities include:

• Support for multiple distance metrics – cosine, dot product, and Euclidean.

• REST API for programmatically interacting with Pinecone account using HTTP requests.

• Serverless indexes so users don't need to manage computing and storage resources.

• Rich set of data operations, including upsert, query, fetch, and more.

Let's cover some of these in detail.

Index types

In Pinecone, an index is the fundamental building block for organizing and working with vector data. Think of it as a specialized database specifically designed for high-dimensional vectors that perform three key functions:

1. Storage: It acts as the container for all the vector embeddings that are essentially compressed representations of images or text data.

2. Search: We can use an index to efficiently search for similar vectors based on the provided query. Imagine searching a library by the content of a ...