learn xapian: searching part 2

04 Jul 2022

We currently have a database populated with some hackernews posts, it is really not helpful if we don’t implement the code to search the database and display results.

Note: keep in mind this is a continuation of my last article on indexing, you should check it out before continuing.

Queries in xapian

Queries are just a simple way by which documents are searched for in a database. Mostly use to search text-based terms, which can be combined using numbers of methods and operators to produce complex queries.

Logical operators

When making a query, you most likely want to search for multiple terms i.e "search engine" or ["search", "engine"]. You’d want results for both “search” and “engine”, xapian lets you do this using logical operators like:

• OP_OR - matches documents which match query A or B (or both)
• OP_AND - matches documents which match both query A and B
• OP_AND_NOT - matches documents which match query A but not B

just to name a few, you can see here for more details.

Query parser

To make searching databases simpler, Xapian provides a QueryParser class which converts a human readable query string into a Xapian Query object, for example: search AND engine NOT google

The QueryParser uses an internal process to convert the query string into terms. This is similar to the process used by the xapian.TermGenerator, which can be used at index time to convert a string into terms. It is often easiest to use xapian.QueryParser and xapian.TermGenerator on the same database.

create a queryparser in code would look like:

import xapian

def get_query_parser(default_op=xapian.Query.OP_AND):
    queryparser = xapian.QueryParser()
    queryparser.set_stemmer(xapian.Stem("en"))
    queryparser.set_stemming_strategy(queryparser.STEM_SOME)
    queryparser.set_default_op(default_op)
    return queryparser

Ranked matches

When you run a query using Xapian, what you get is a list of ranked matches. Every matched document returned when a query is run has a weight, the list of matches are sorted in descending order. The weight is an indicator of how good a document matched the query.

The actual weight is calculated by a weighting scheme; Xapian comes with a few different ones or you can write your own, although often the default is fine. (It uses a scheme called BM25, which takes into account things like how common a matching term is in a matching document compared to in the entire database, and the lengths of different matching documents.) reference here for more details

Code

Hers is what the implementation would look like

import xapian
import json
+ import pprint

with open("./hn_2014.json", "r") as fp:
    hn_posts = json.load(fp)

- # create a new database or open available database in the current directory
- database = xapian.WritableDatabase("./hn_index", xapian.DB_CREATE_OR_OPEN)
+ # Open the database we're going to search.
+ database = xapian.Database("./hn_index")

termgenerator = xapian.TermGenerator()
stemmer = xapian.Stem("english")
termgenerator.set_stemmer(stemmer)

searchables = ["title", "storyText", "author"]
id = "objectId"

# assign a unique slot for each field for consistency
slots = {x: i for i, x in enumerate(searchables, 1)}
slots[id] = max(slots.values()) + 1


def index():
    ...
    
def get_query_parser(default_op=xapian.Query.OP_AND):
    queryparser = xapian.QueryParser()
    queryparser.set_stemmer(xapian.Stem("en"))
    queryparser.set_stemming_strategy(queryparser.STEM_SOME)
    queryparser.set_default_op(default_op)
    return queryparser

+ def search(querystring, fields=[], offset=0, pagesize=20):

+    # Set up a QueryParser with a stemmer and suitable prefixes
+ 	 queryparser = get_query_parser()
    
+    # And parse the query
+    query = queryparser.parse_query(querystring)

+    for field in fields:
+        field_prefix = "X" + field.upper()
+        queryparser.add_prefix(field, field_prefix)
    
+    # Use an Enquire object on the database to run the query
+    enquire = xapian.Enquire(database)
+    enquire.set_query(query)

+    matches = []
	
+    # And print out something about each match
+    for match in enquire.get_mset(offset, pagesize):
+        data = json.loads(match.document.get_data())
+        matches.append(data)
    
+    return matches

pprint.pprint(search("python"))

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