Vector Databases for Embeddings with Pinecone
Discover how the Pinecone vector database is revolutionizing AI application development!
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Course Description
Unlock the Power of Embeddings with Pinecone's Vector Database
In the introductory chapters, you'll delve into the fundamentals of Pinecone, understanding its core capabilities, benefits, and key concepts such as pods, indexes, and projects. Through hands-on lessons, you'll compare Pinecone with other vector databases, gaining insights into its unparalleled functionality and usability.Python Interaction with Pinecone
Equip yourself with the skills to interact seamlessly with Pinecone using Python. Learn to differentiate between pod types, set up your environment, and configure the Pinecone Python client. You will dive into the heart of Pinecone by learning to create vector databases programmatically, understand the parameters influencing Pinecone index creation, including dimensionality, distance metrics, pod types, and replicas, and master the art of ingesting vectors with metadata into Pinecone indexes. You will develop proficiency in querying and retrieving vectors using Python, and gain insights into updating and deleting vectors to handle concept drift effectively.Advanced Pinecone and AI Applications
Going beyond the fundamentals and explore advanced Pinecone concepts such as monitoring Pinecone performance, tuning for efficiency, and implementing multi-tenancy for access control. You will explore advanced applications, including semantic search engines built on Pinecone and integrating it with OpenAI API for projects like the RAG chatbot.For Business
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Introduction to Pinecone
FreeExplore 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.
What is Pinecone?50 xpFeatures of Pinecone50 xpThe Pinecone ecosytem100 xpWho's responsible for what?100 xpWorking with Pinecone in Python50 xpChoosing pod types100 xpInstall Pinecone Python client50 xpInitialize Pinecone connection100 xpCreating Pinecone vector databases50 xpCreate your first index100 xpSize, speed, and the distance metric100 xpAdvanced index creation100 xp - 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.
Ingest vectors with metadata50 xpPinecone operations100 xpVector ingestion with metadata100 xpQuerying and retrieving vectors50 xpQuerying vs. fetching100 xpQuerying Pinecone indexes100 xpFetching vectors100 xpUpdating and deleting vectors50 xpUpdating vectors with metadata100 xpDeleting vectors100 xp - 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.
Monitoring, performance tuning, and multitenancy50 xpMonitoring50 xpUpserts in parallel100 xpNamespaces100 xpSemantic search with Pinecone50 xpCreating and configuring a Pinecone index100 xpUpserting vectors into a Pinecone index100 xpQuerying vectors in a Pinecone index100 xpRAG chatbot with Pinecone and OpenAI API50 xpUpserting YouTube transcripts100 xpBuilding a retrieval function100 xpRAG questions answering function100 xpCongratulations!50 xp
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Prerequisites
Introduction to Embeddings with the OpenAI APIRyan Ong
See MoreLead Data Scientist
Ryan is a lead data scientist specialising in building AI applications using LLMs and vector databases. He is a PhD candidate in Natural Language Processing and Knowledge Graphs at Imperial College London, where he also completed his Master’s degree in Computer Science. Outside of data science, he writes a weekly newsletter, The Limitless Playbook, where he shares one actionable idea from the world's top thinkers and occasionally writes about core AI concepts.
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