Skip to main content

Fast Autocomplete 0.1.2

Project description


Fast autocomplete using Directed Acyclic Word Graph (DAWG) and
Levenshtein Edit Distance.

The results are cached via LFU (Least Frequently Used).


This library was written when we came to the conclusion that
Elasticsearch’s Autocomplete suggestor is not fast enough and doesn’t do
everything that we need:

1. Once we switched to Fast Autocomplete, our average latency went from
120ms to 30ms so an improvement of 3-4x in performance and errors
went down to zero.
2. Elasticsearch’s Autocomplete suggestor does not handle any sort of
combination of the words you have put in. For example Fast
Autocomplete can handle ``2018 Toyota Camry in Los Angeles`` when the
words ``2018``, ``Toyota Camry``, ``Los Angeles`` are seperately fed
into it. While Elasticsearch’s autocomplete needs that whole sentence
to be fed to it to show it in Autocomplete results.

You might say:

1. Regarding #1: Yes, but you are using caching. Answer: shhh Yes, keep
it quiet. We are also doing Levenshtein Edit distance using a C
library so it improves there too.
2. Regarding #2: I’m speechless. Answer: Ok, now we are talking.


In a nutshell, what the fast Autocomplete does is:

1. Populate the DAWG with your words.
2. Follow the graph nodes letter by letter until it finds nodes that
have words in them.
3. Continue after words are found on the graph until it reaches the leaf
4. Restart from the root node again until it reaches a letter that
doesn’t exist on the graph.
5. Depending on how much is left from the rest of the word, return all
the descendant words from where it got stuck
6. Or run Levenshtein edit distance to find closes words to what is left
and the continue from there.

By doing so, it can tokenize a text such as:

``2018 Toyota Camry in Los Angeles`` into [``2018``, ``toyota camry``,
``in``, ``los angeles``]

And return Autocomplete results as you type.


``pip install fast-autocomplete``

**Note: Fast Autocomplete only works with Python 3.6 and newer.**

Are you still on Python 2? TIME TO UPGRADE.




The data structure we use in this library is called Dawg.

DAWG stands for Directed Acyclic Word Graph. Here is an example DAWG
based on the “makes_models_short.csv” that is provided in the tests:

.. figure:: tests/animation/short.gif
:alt: dawg


.. figure:: tests/AutoCompleteWithSynonymsShort_Graph.svg
:alt: dawg



First of all lets start from your data. The library leaves it up to you
how to prepare your data. Imagine that we have a csv with the following
content from vehicles’ make and models:

.. code:: csv

alfa romeo,4c
alfa romeo,4c coupe
alfa romeo,giulia
bmw,1 series
bmw,2 series

What we want to do is to convert this to a dictionary of words and their

.. code:: py

import csv
from fast_autocomplete.misc import read_csv_gen

def get_words(path):

csv_gen = read_csv_gen(path, csv_func=csv.DictReader)

words = {}

for line in csv_gen:
make = line['make']
model = line['model']
if make != model:
local_words = [model, '{} {}'.format(make, model)]
while local_words:
word = local_words.pop()
if word not in words:
words[word] = {}
if make not in words:
words[make] = {}
return words

the ``read_csv_gen`` is just a helper function. You don’t really need
it. The whole point is that we are converting that csv to a dictionary
that looks like this:

.. code:: py

>>> words = get_words('path to the csv')
>>> words
{'acura zdx': {},
'zdx': {},
'acura': {},
'alfa romeo 4c': {},
'4c': {},
'alfa romeo': {},
'alfa romeo 4c coupe': {},
'4c coupe': {},
'alfa romeo giulia': {},
'giulia': {},
'bmw 1 series': {},
'1 series': {},
'bmw': {},
'bmw 2 series': {},
'2 series': {},
'2007': {},
'2017': {},
'2018': {}}

This is a dictionary of words to their context. We have decided that we
don’t want any context for the words in this example so all the contexts
are empty. However generally you will want some context around the words
for more complicated logics. The context is used to convert the words
“keys” into their context which is the value of the key in the words

In addition to words, we usually want a dictionary of synonyms.
Something like this:

.. code:: py

synonyms = {
"alfa romeo": ["alfa"],
"bmw": ["beemer", "bimmer"],
"mercedes-benz": ["mercedes", "benz"],
"volkswagen": ["vw"]

Note that synonyms are optional. Maybe in your use case you don’t need

Now we can use the above to initialize Autocomplete

.. code:: py

from fast_autocomplete import AutoComplete

autocomplete = AutoComplete(words=words, synonyms=synonyms)

At this point, AutoComplete has created a `dawg <#DAWG>`__ structure.

Now you can search!

- word: the word to return autocomplete results for
- max_cost: Maximum Levenshtein edit distance to be considered when
calculating results
- size: The max number of results to return

.. code:: py

>>>'2018 bmw 1', max_cost=3, size=3)
[['2018', 'bmw'], ['2018', 'bmw 1 series']]

Now what if we pressed a by mistake then? It still works. No problem.

.. code:: py

>>>'2018 bmw 1a', max_cost=3, size=3)
[['2018', 'bmw'], ['2018', 'bmw 1 series']]

Ok let’s search for Alfa now:

.. code:: py

>>>'alfa', max_cost=3, size=3)
[['alfa romeo'], ['alfa romeo 4c'], ['alfa romeo giulia']]

What if we don’t know how to pronounce alfa and we type ``alpha`` ?

.. code:: py

>>>'alpha', max_cost=3, size=3)
[['alfa romeo'], ['alfa romeo 4c'], ['alfa romeo giulia']]

It still works!

Fast-Autocomplete makes sure the results make sense!

Ok lets add the word ``Los Angeles`` there to the words:

.. code:: py

>>> words['los angeles'] = {}
>>> words['in'] = {}
>>>'2007 alfa in los', max_cost=3, size=3)
[['2007', 'alfa romeo', 'in'], ['2007', 'alfa romeo', 'in', 'los angeles']]

So far we have not used the context. And this library leaves it up to
you how to use the context. But basically if we giving a context to each
one of those words, then the above response could easly be translated to
the list of those contexts.


If our words dictionary was:

.. code:: py

words = {
'in': {},
'alfa romeo': {'type': 'make'},
'2007': {'type': 'year'},
'los angeles': {'type': 'location'},

Then the ``autocomplete.words`` can be used to map the results into
their context:


[['2007', 'alfa romeo', 'in'], ['2007', 'alfa romeo', 'in', 'los angeles']]

converted to contexts:

[[{'year': '2007'}, {'make': alfa romeo'}], [{'year': '2007'}, {'make': alfa romeo'}, {'location': 'los angeles'}]]


This package can actually draw the dawgs as it is populating them or
just once the dawg is populated for you! Here is the animation of
populating the dawg with words from “makes_models_short.csv”:

Draw animation of dawg populating

.. code:: py

from fast_autocomplete import AutoComplete, DrawGraphMixin

class AutoCompleteDraw(DrawGraphMixin, AutoComplete):
DRAW_POPULATION_ANIMATION_PATH = 'animation/short_.svg'

autocomplete = AutoCompleteDraw(words=words, synonyms=synonyms)

As soon as you initialize the above AutoCompleteDraw class, it will
populate the dawg and generate the animation! For an example of this
code properly setup, take a look at the tests. In fact the animation in
the `dawg <#dawg>`__ section is generated the same way via unit tests!

Note that if you have many words, the graph file will be big. Instead of
drawing all frames as the dawg is being populated, you can just draw the
final stage:

Draw the final graph

To draw just one graph that shows the final stage of the dawg, use the
draw mixin and run the draw_graph function:

.. code:: py

from fast_autocomplete import AutoComplete, DrawGraphMixin

class AutoCompleteDraw(DrawGraphMixin, AutoComplete):

autocomplete = AutoCompleteDraw(words=words, synonyms=synonyms)
autocomplete.draw_graph('path to file')


If you want to have a real-time interaction with Autocomplete results in
your terminal, you can use the demo module:

Just pass it an instance of the autocomplete and the search configs:

.. code:: py

from fast_autocomplete import demo

demo(autocomplete, max_cost=3, size=5)


1. Clone the repo
2. Make a virtualenv with Python 3.6 or newer
3. ``pip install -r requirements-dev.txt``

Run tests


We try to maintain high standard in code coverage. Currently the
``dawg`` module’s coverage is around 99%!


- Autocomplete by `Sep Dehpour <>`__ at `Fair
Financial Corp <>`__.
- LFU Cache by `Shane Wang <>`__

Other ways of doing AutoComplete

1. Elastic search. Yes, Elasticsearch generally is a *better*
Autocomplete solution than this library. I said generally. In our
specific use case, we wanted Autocomplete to be faster than
Elasticsearch and handle combination of words. Otherwise
Elasticsearch would have been perfect. Behind the scene Elasticsearch
uses Finite State Transducer (FST) in Lucene to achive AutoComplete.
FST is more complicated than what we have used in fast-autocomplete.

2. If your autocomplete is supposed to return results based on a big
blog of text (for example based on some book contents), then a better
solution is to go with Markov chains and conditional probability.
Yes, there is already a library out there for it! and it looks great.
Disclaimer: we have not actually used it since it doesn’t fit our
specific use-case.



DAWG stands for Directed Acyclic Word Graph. Originally we were using
Trie-Tree structure. But soon it was obvious that some branches needed
to merge back to other branches. Such as ``beemer`` and ``bmw`` branches
both need to end in the same node since they are synonyms. Thus we used

What are synonyms, clean synonyms and partial synonyms

Synonyms are words that should produce the same results.

- For example ``beemer`` and ``bmw`` should both give you ``bmw``.
- ``alfa`` and ``alfa romeo`` should both give you ``alfa romeo``

The synonyms get divided into 2 groups:

1. clean synonyms: The 2 words share little or no words. For example
``beemer`` vs. ``bmw``.
2. partial synonyms: One of the 2 words is a substring of the other one.
For example ``alfa`` and ``alfa romeo`` or ``gm`` vs. ``gmc``.

Internally these 2 types of synonyms are treated differently but as a
user of the library, you don’t need to really care about it. You just
provide the synonyms dictionary via defining the ``get_synonyms``

Why do you have a whole subtree for partial synonyms

Q: Partial synonym means the synonym is a part of the original word.
Such as ``alfa`` is a partial synonym for ``alfa romeo``. In that case
you are inserting both ``alfa`` and ``alfa romeo`` in the dawg. ``alfa``
will have ``alfa 4c`` and ``alpha romeo`` will have ``alfa romeo 4c``
branches. Why not just have ``alfa`` branches to be ``alfa romeo`` and
from there you will have automatically all the sub branches of
``alfa romeo``.

Answer: We use letters for edges. So ``alfa`` can have only one edge
coming out of it that is space (\`
``). And that edge is going to a node that has sub-branches to``\ alfa
romoe\ ``,``\ alfa 4c\ ``etc. It can't have a``
``going to that node and another`` ``going to``\ alfa
romeo\ ``'s immediate child. That way when we are traversing the dawg for the input of``\ alfa
4\` we get to the correct node.

I put Toyota in the Dawg but when I type ``toy``, it doesn’t show up.

Answer: If you put ``Toyota`` with capital T in the dawg, it expects the
search word to start with capital T too. We suggest that you lower case
everything before putting them in dawg. Fast-autocomplete does not
automatically do that for you since it assumes the ``words`` dictionary
is what you want to be put in the dawg. It is up to you to clean your
own data before putting it in the dawg.

Author: Sep Dehpour
License: UNKNOWN
Description: UNKNOWN
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Development Status :: 4 - Beta

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
fast_autocomplete-0.1.3-py3-none-any.whl (15.0 kB) Copy SHA256 hash SHA256 Wheel py3
fast-autocomplete-0.1.3.tar.gz (18.3 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page