Guide To Vectors
  • Introduction
  • Guide to this Book
  • API documentation
  • Python SDK Documentation
  • Learn about vectors
    • 🌱Introduction to vectors
      • 🌱Applications of vectors
        • 🌱Vectors for classification
      • 🌱Limitations of vectors
    • 🌱What is vector search?
      • 🌱How to vector search
      • 🌱How to build image to text search using code
      • 🧍Try vector search with playground!
      • 💻Vector search with code
    • 🌱Terminology Guide
  • Unlock Vector AI
    • 🔍Inserting Into Vector AI
      • 🧍Inserting with playground
      • 💻Inserting with API
        • 💻Inserting with API - encoding while inserting (recommended)
        • 💻Inserting with API - encoding before inserting
        • 💻Inserting with API - encoding after inserting
      • 🧍How to check insertion succeeded
    • 🔍Searching with Vector AI
      • 🌱How to search with the playground
      • 🌱Combining with traditional search
        • 🧍How to combine exact text search with vector search
        • 💻How to add exact text search to vector search
      • 🌱Personalisation with vector search
        • 💻Personalised search/recommendations with vector search
      • 🌱Chunk search
        • 💻How To Chunk Search
        • 💻How To Do MultiVector Chunk Search
        • 💻How to do multi step chunk search
      • 🧍How to diversify search results
    • 🔍Clustering
      • 🌱Clustering Vectors From Deep Learning models
    • 🔍Aggregation
      • 💻Writing Your First Aggregation
      • 💻Publishing Your First Aggregation
    • 🔍Experimentation
      • 🌱Vector Evaluation
        • 🌱Evaluate Vector Bias
    • 🔍Jobs
      • 💻Tagging Jobs
      • 💻Chunking Jobs
      • 💻Encoding Jobs
      • 🧍List all jobs (active and inactive)
    • 🔍Encoding
    • 🔍Maintenance & Monitoring
      • 🧍How to view your collections
      • 💻How to share your collections
      • 💻How to back up your collections
      • 💻How to change name of a collection field
      • 💻How to change the schema of a collection
      • 💻How to remove a field in a collection
      • 💻How to request a read API key
  • Tutorials
    • 💻How to turn data into Vectors (code)
      • 💻How to turn text into Vectors
      • 💻How to turn images Into Vectors
      • 💻How to turn audio into Vectors
    • 💻Image Search For Developers
    • 💻How To Combine Different Vectors For Search
    • 💻How To Combine Different Vectors With Exact Matching Text
    • 💻Semantic NLP search with FAISS and VectorHub
  • ABOUT
    • Credits
    • Philosophy
    • Glossary
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Guide to this Book

How to use this Book

This book aims to introduce a range of audiences to vectors. From first learning about vectors, using vectors for building search experiences to more difficult topics such as optimizing vectors for personalized search engines and detecting biases in vectors.

This book is designed such that each piece of content exists in bite-sized chunks to allow for easier information consumption for the reader and intended to maximize utility and readability as much as possible.

Intended Audiences

Due to the varying levels at which different individuals can approach vectors, each article is written with pre-requisite knowledge and intended audiences. However, the intended audiences may not be exactly exhaustive and readers will often find that our articles are quite easy to digest despite the higher technical bar that has been set. As a result, even if the reader is not part of the intended audiences, they may still read on and continue to understand we encourage readers to also give feedback on where the reading can improve.

Codes and Content

The codes are designed in the following way:

Course Code

Level

🌱

Beginner (Usually suited for people new to a topic)

🧍

Intermediate (Usually suited for people looking to use the Vector AI playground)

🧮

Advanced (Mathematical) (Suited for people looking for a deeper mathematical understanding)

💻

Advanced (Code) (Suited for people looking to use the API or SDK)

Why write this book?

Vector AI's mission is to accelerate the development of artificial intelligence products using vectors. We see vectors as a technology that will change the future so we want to make using vectors as easy as possible. We discovered that the general data scientist/public does not know much about vectors so we set out to write this book to help others learn and understand them for their use. Along the way, we include tips on how to use vectors and how they can be used with the Vector AI API.

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Last updated 4 years ago

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