# Python type annotations and why you should use them

In this post we’ll take closer look at the Python type annotations and some immediate benefits that come with using them. First part will give you some overview of the type annotation basics and second part will show you how to leverage them to speed up your code and create a basic web service to access the library in just a few lines of code.

# Fine-tuning locality-sensitive hashing

Locality-sensitive hashing (LSH) allows for fast retrieval of similar objects from an index - orders of magnitude faster than simple search at the cost of some additional computation and some false positives/negatives. In the last post I introduced LSH for angular distance. In this one I will tell you how you can fine-tune it to get the expected results.

# Locality-sensitive hashing for angular distance in Python

Locality-sensitive hashing (LSH) is an important group of techniques which can be used to speed up vastly the task of finding similar sets or vectors.

# Text indexing in python - mapping text to values using finite-state transducers

In the previous posts I wrote about the finite-state automata (FSA). Now we’ll cover finite-state transducers (FST), which allow to index text with values in libraries such as elasticsearch.

# Text indexing in Python - constructing FSA from unsorted input

In this post we’ll take closer look at the Python implementation of algorithm for constructing finite-state automata from unsorted set of words.

# Text indexing in Python with minimal finite-state automata

Have you ever wondered how Lucene/Elasticsearch does its job so well? This post will teach you about essential part of the Lucene index - minimal finite-state automaton (FSA).