tinysearch 0.3.0

Tiny one-phase search engine

TinySearch

TinySearch is a tiny one-phase search engine. It is extremely easy to use and works well with simple lists where the query may not match the document text exactly.

This is a minimal search engine. You don't need to run separate, big instances of search engine when your use case is a few hundreds or thousands small documents.

Example

Input documents:

"Goldilocks and the Three Bears"
"Fuzzy Wuzzy"
"The Bear Went Over The Mountain"
"We're Going on a Bear Hunt"
"Brown Bear, Brown Bear, What Do You See?"

Search query:

bear

Results (ordered by best match):

"Brown Bear, Brown Bear, What Do You See?"
"Goldilocks and the Three Bears"
"The Bear Went Over The Mountain"
"We're Going on a Bear Hunt"

How to use

from tinysearch.search import Search

docs = [
    "Goldilocks and the Three Bears",
    "Fuzzy Wuzzy",
    "The Bear Went Over The Mountain",
    "We're Going on a Bear Hunt",
    "Brown Bear, Brown Bear, What Do You See?",
]
query = "bear"

s = Search(docs, query)

# How many results?
print(s.results.count)

# What is the top result?
print(s.results.matches[0].doc)

# Print all matches. Best results are at the top.
for m in s.results.matches:
    print(m.doc)

Pass your own analyzer

When tinysearch.analyzer.SimpleEnglishAnalyzer does not satisfy your needs, you can write your own analyzer and pass it to the Search object.

An analyzer inherits from tinysearch.analyzer.base.Analyzer. It only need to implement analyze method. The analyze method accepts a string representing the document on the input, and returns a list of strings representing tokens (terms). Everything that you need to make it happen can be implemented there. See the docstring of the Analyzer base class.

You can then pass your analyzer to Search:

my_analyzer = MyOwnAnalyzer()

s = Search(docs, query, analyzer=my_analyzer)
print(s.results.count)

Under the hood

When you pass documents to the Search object, each document is tokenized and transformed for easier search. The same process is applied to the query.

Then each document is scored using the TF-IDF algorithm to find the best match, and matches are returned sorted to the user. The best match is at the top.

License

See LICENSE.