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Vector Databases for Embeddings with Pinecone

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

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3 hours12 videos39 exercises

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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.
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In the following Tracks

Associate AI Engineer for Developers

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  1. 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.

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    Introduction to Pinecone indexes
    50 xp
    Creating a Pinecone client
    100 xp
    Your first Pinecone index
    100 xp
    Managing indexes
    50 xp
    Connecting to an index
    100 xp
    Deleting an index
    100 xp
    The Pinecone ecosystem
    100 xp
    Vector ingestion
    50 xp
    Checking dimensionality
    100 xp
    Ingesting vectors with metadata
    100 xp
  2. 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.

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  3. 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.

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GroupTraining 2 or more people?

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In the following Tracks

Associate AI Engineer for Developers

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datasets

YouTube TranscriptsStanford Question Answering Dataset (SQuAD)

collaborators

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Chris Harper

audio recorded by

James Chapman's avatar
James Chapman
James Chapman HeadshotJames Chapman

Curriculum Manager, DataCamp

James is a Curriculum Manager at DataCamp, where he collaborates with experts from industry and academia to create courses on AI, data science, and analytics. He has led nine DataCamp courses on diverse topics in Python, R, AI developer tooling, and Google Sheets. He has a Master's degree in Physics and Astronomy from Durham University, where he specialized in high-redshift quasar detection. In his spare time, he enjoys restoring retro toys and electronics.

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Ryan Ong HeadshotRyan Ong

Lead 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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