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  3. Vector Databases for Embeddings with Pinecone
  • 1

    Introduction to Pinecone

    Free

    Explore the mechanics behind Pinecone's vector database, from pods and indexes to comparing it with other databases. Learn to differentiate pod types, acquire API keys, and initialise Pinecone connection using python. Finally, you’ll learn how to create Pinecone indexes, exploring different parameters such as dimensionality, distance metrics, pod types, and others.

  • 2

    Pinecone Vector Manipulation in Python

    Get hands-on with Pinecone in Python, where we explore the practical side of using Pinecone for managing indexes, adding vectors with metadata, searching and retrieving vectors, and making updates or deletions. Gain a solid grasp of the key functions and ideas to smoothly handle data in the Pinecone vector database.

  • 3

    Performance Tuning and AI Applications

    In this chapter, learners delve into optimizing Pinecone index performance, leveraging multi-tenant namespaces for cost reduction, building semantic search engines, and creating retrieval-augmented question answering systems using Pinecone with the OpenAI API. Through these lessons, learners gain practical skills in performance tuning, semantic search, and retrieval-augmented question answering, empowering them to apply Pinecone effectively in real-world AI applications.

Initializing

Exercise

Your first Pinecone index

With your Pinecone client initialized, you're all set to begin creating an index! Indexes are used to store records, including the vectors and associated metadata, as well as serving queries and other manipulations. As you progress through the course, you'll see how these different steps build up to a modern AI system built on a vector database.

If you accidentally create a valid index that doesn't meet the specifications detailed in the instructions, you'll need to add the following code before your .create_index() code to delete it and re-create it:

pc.delete_index('my-first-index')

The Pinecone class has already been imported for you.

Instructions

100 XP
  • Import the ServerlessSpec class from pinecone.
  • Initialize the Pinecone connection using your API key.
  • Create a serverless index called "my-first-index" to hold vectors with 256 dimensions, and configure the index for the 'aws' cloud platform in the 'us-east-1' region.