# How to turn text into Vectors

**Assumed Knowledge**: Vectors\
**Target Audience**: Python developers, general developers\
**Reading Time:** 3 minutes

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, and you can run all of the below on Colab.

The library can be installed via pip: 

```
$ pip install vectorhub[encoders-text-tfhub]
```

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

```
from vectorhub.encoders.text.tfhub import USE2Vec
enc = USE2Vec()
text= "How do you encode data?"
vector = enc.encode(image_url)
```

From this - you will have obtained a vector which can now be indexed and stored away for search. If you are interested in reading what is occurring under the hood or to write your own library for this - take a look below. 

**What is occurring under the hood?**

We vectorise a sentence by firstly tokenizing text into separate subwords (these are called tokens), each token is mapped to a separate vector which is then fed through the model. Note: models are not necessarily trained for the best vectors and representation space and specific models will need to be identified for different use cases. If there is a use case you would like, feel free to message us in our [Discord](https://discord.gg/CbwUxyD). 

![Pipeline for text2vec](/files/-MUCUuHNVKlMBs069dPc)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://learn.getvectorai.com/vector-ai-documentation/how-to-turn-data-into-vectors-code/how-to-turn-text-into-vectors.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.