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

Encoding

Learn the different ways Vector AI can help you encode complex, unstructured data

Vector AI has deployed a few encoders that follow a very standard encoding scheme. This is to assist users with encoding as easily as possible.

These encoders are summarised in the table below.

Encoder Name

Description

text

Encode English text data into a vector.

text- multi

Encode multi-lingual text data into a vector. However, if you are looking to get good English vectors, we recommend the text encoder as it has better performance.

image

Encode an image.

image-text

Encode an image to make it searchable with text. However, if you are looking for good image similarity, we recommend the image encoder for better performance.

text-image

Encode text to make it searchable with images. However, If you are looking for text similarity, we recommend the text encoder for better performance.

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Last updated 4 years ago

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