Update README.md

Author: boba-and-beer

README.md

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 ![MonthlyDownloads](https://static.pepy.tech/personalized-badge/vectorai-nightly?period=month&units=none&left_color=black&right_color=red&left_text=Monthly%20Downloads)
 
 </div>
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+<hr>
 
 <h3 align="center">
 Vector AI is a framework designed to make the process of building production grade vector based applications as quickly and easily as possible. Create, store, manipulate, search and analyse vectors alongside json documents to power applications such as neural search, semantic search, personalised recommendations recommendations etc.
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 - Join our discord: https://discord.gg/CbwUxyD
 - For a more gentle introduction comparing our features, read https://getvectorai.com/production-ready-search-in-5-minutes/
 
-Features:
+<hr>
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+## Features
 - **Multimedia Data Vectorisation**: Image2Vec, Audio2Vec, etc (Any data can be turned into vectors through machine learning)
 - **Document Orientated Store**: Store your vectors alongside documents without having to do a db lookup for metadata about the vectors.
 - **Vector Similarity Search**: Enable searching of vectors and rich multimedia with vector similarity search. The backbone of many popular A.I use cases like reverse image search, recommendations, personalisation, etc.
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 - **Clustering**: Interpret your vectors and data by allocating them to buckets and get statistics about these different buckets based on data you provide.
 - **Vector Analytics**: Get better understanding of your vectors by using out-of-the-box practical vector analytics, giving you better understanding of the quality of your vectors.
 
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 ## Quick Terminologies
 
 - Models/Encoders (aka. Embedders) ~ Turns data into vectors e.g. Word2Vec turns words into vector
 - Vector Similarity Search (aka. Nearest Neighbor Search, Distance Search)
 - Collection (aka. Index, Table) ~ a collection is made up of multiple documents
 - Documents (aka. Json, Item, Dictionary, Row) ~ a document can contain vectors, text and links to videos/images/audio.
 
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+
 ## QuickStart
 
 Install via pip! Compatible with any OS.
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 vi_client.search_by_id('test-collection', '1', 'color_vector_', page_size=2)
 ```
 
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 ## Access Powerful Vector Analytics 
 
 Vector AI has powerful visualisations to allow you to analyse your vectors as easily as possible - in 1 line of code.
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 Compare vectors and their search performance on your documents easily!
 ![1D plot cosine simlarity](https://getvectorai.com/assets/gif/2d_cosine_similarity.gif)
 
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 ## Why Vector AI compared to other Nearest Neighbor implementations?
 
 - **Production Ready**: Our API is fully managed and can scale to power hundreds of millions of searches a day. Even at millions of searches it is blazing fast through edge caching, GPU utilisation and software optimisation so you never have to worry about scaling your infrastructure as your use case scales.
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 - **Real time access to data**: Vector AI data is accessible in real time, as soon as the data is inserted it is searchable straight away. No need to wait hours to build an index.
 - **Framework agnostic**: We are never going to force a specific framework on Vector AI. If you have a framework of choice, you can use it - as long as your documents are JSON-serializable! 
 
-## Bring your own Model or Vector
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+### Bring your own Model or Vector
 
 You can bring your own model or vectors and enjoy all the rich vector functionality that Vector AI provides.
 
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 }
 ```
 
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 ## Building Products with Vector AI
 Creating a multi-language AI fashion assistant: https://fashionfiesta.me | [Blog](https://getvectorai.com/how-we-built-a-vector-powered-outfit-recommender/)