Boolean text search using Eldar

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Installing

You can install the method by typing:

pip install eldar

Basic usage

from eldar import Query


# build list
documents = [
    "Gandalf is a fictional character in Tolkien's The Lord of the Rings",
    "Frodo is the main character in The Lord of the Rings",
    "Ian McKellen interpreted Gandalf in Peter Jackson's movies",
    "Elijah Wood was cast as Frodo Baggins in Jackson's adaptation",
    "The Lord of the Rings is an epic fantasy novel by J. R. R. Tolkien"]

eldar = Query('("gandalf" OR "frodo") AND NOT ("movie" OR "adaptation")')

# use `filter` to get a list of matches:
print(eldar.filter(documents))
# >>> ["Gandalf is a fictional character in Tolkien's The Lord of the Rings",
#     'Frodo is the main character in The Lord of the Rings']

# call to see if the text matches the query:
print(eldar(documents[0]))
# >>> True

# by default, words must match. Thus, "movie" != "movies":
print(eldar(documents[2]))
# >>> True

You can also use it to mask Pandas DataFrames:

from eldar import Query
import pandas as pd


# build dataframe
df = pd.DataFrame([
    "Gandalf is a fictional character in Tolkien's The Lord of the Rings",
    "Frodo is the main character in The Lord of the Rings",
    "Ian McKellen interpreted Gandalf in Peter Jackson's movies",
    "Elijah Wood was cast as Frodo Baggins in Jackson's adaptation",
    "The Lord of the Rings is an epic fantasy novel by J. R. R. Tolkien"],
    columns=['content'])

# build query object
eldar = Query('("gandalf" OR "frodo") AND NOT ("movie" OR "adaptation")')

# eldar's call returns True if the text matches the query.
# You can filter a dataframe using pandas mask syntax:
df = df[df.content.apply(eldar)]
print(df)

Parameters

There are three parameters that you can adjust in the query builder. By default:

Query(..., ignore_case=True, ignore_accent=True, match_word=True)

Let the query be query = '"movie"':

• If ignore_case is True, the documents "Movie" and "movie" will be matched. If False, only "movie" will be matched.
• If ignore_accent is True, the documents "mövie" will be matched.
• If match_word is True, the document will be tokenized and the query terms will have to match exactly. If set to False, the documents "movies" and "movie" will be matched. Setting this option to True may slow down the query.

Wildcards

Queries also support * as wildcard character. Wildcard matches any number (including none) of alphanumeric characters.

from eldar import Query


# sample document and query with multiple wildcards:
document = "Gandalf is a fictional character in Tolkien's The Lord of the Rings"
eldar = Query('"g*dal*"')

# call to see if the text matches the query:
print(eldar(document))
# >>> True

Building an index for faster queries

Searching in a large corpus using the Query object is slow, as each document has to be checked. For (much) faster queries, create an Index object, and build it using a list of documents.

from eldar import Index
from eldar.trie import Trie

documents = [
    "Gandalf is a fictional character in Tolkien's The Lord of the Rings",
    "Frodo is the main character in The Lord of the Rings",
    "Ian McKellen interpreted Gandalf in Peter Jackson's movies",
    "Elijah Wood was cast as Frodo Baggins in Jackson's adaptation",
    "The Lord of the Rings is an epic fantasy novel by J. R. R. Tolkien",
    "Frodo Baggins is a hobbit"
]

index = Index(ignore_case=True, ignore_accent=True)
index.build(documents)  # must only be done once

# persist and retrieve index from disk
index.save("index.p")  # but documents are copied to disk
index = Index.load("index.p")

print(index.search('"frodo b*" AND NOT hobbit'))  # support wildcards
print(index.count('"frodo b*" AND NOT hobbit'))  # shows only the count
# to only return document ids, set `return_ids` to True:
print(index.search('"frodo b*" AND NOT hobbit', return_ids=True))

It works like a usual search engine does: by keeping a dictionary that maps each word to its document ids. The boolean query is turned into an operation tree, where document ids are joined or intersected in order to return the desired matches.

License

This package is MIT licensed.