Vector Databases for Embeddings with Pinecone

Discover how the Pinecone vector database is revolutionizing AI application development!

Discover how the Pinecone vector database is revolutionizing AI application development. Pinecone is a fully-managed, ultra-low query latency vector database solution that is cornering the market. In this course, you'll learn to ingest, manipulate, and query vectors from Pinecone indexes. You'll use these skills, along with other fundamental concepts such as Retrieval Augmented Generation (RAG) to enable AI applications such as semantic search engines and context-aware chatbots. Finally, you'll learn to optimize your production databases by looking at techniques for tuning performance, optimizing storage, and improving query latency.

• Introduction to Pinecone
• 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.
• 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.
• 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.