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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  1. Tutorials

How to turn data into Vectors (code)

A guide to turning data into vectors with VectorHub.

Assumed Knowledge: Vectors Target Audience: Python developers, General developers Reading Time: 3 minutes

The process of turning structured/unstructured data (in the form of Excel Spreadsheets, videos, images, word documents, PDFs) into vectors can involve quite complicated pipelines.

To help transform data into vectors, we open-sourced a library called VectorHub (you can explore the hub at hub.vctr.ai). For this, you will need to use Python 3 (tested on Python 3.6/Python 3.7).

The library can be installed via pip:

$ pip install vectorhub

Once you install via pip, you can then use a model in Python. For example:

$ from vectorhub.encoders.text import ViText2Vec

You can easily instantiate the model using the below.

from vectorai import request_api_key
username = input("What is your username")
email = input("What is your email?")
api_key = request_api_key(username, email, description="Trying out VectorHub.")
vi = ViText2Vec(username, api_key)

Transforming your data into vectors is as simple as the following:

text = "My dog loves taking long walks on the beach!"
vector = vi.encode(text)
# Voila, you have your vector!

What is happening under the hood in VectorHub?

In this library, VectorHub abstracts away a few complexities to make the encoding smooth. There is, however, still a lot of room for customisation.

Quickly going over the diagram, as data is parsed through VectorHub, it is converted into a NumPy array. This array is fed through the model, pooled together and is then transformed into a native Python object.

For each of the different data types, we have sections on how each of them are converted into vectors so users can understand what is occurring in each of the processes.

These pages can be found below if you are interested:

PreviousHow to request a read API keyNextHow to turn text into Vectors

Last updated 4 years ago

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💻
💻How to turn images Into Vectors
💻How to turn audio into Vectors
💻How to turn text into Vectors
VectorHub ensures consistent input/output for models.
VectorHub ensures consistent input/output for models.