Prospective search for python
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Welcome to PSearch


PSearch is a python library for prospective or persistent search. It finds matching queries for a document. This is different to traditional, or retrospective search, which finds matching documents for a query.

This is often used in 'alerts' applications, where users are alerted to new documents matching their queries. It can also be useful for generating matches in batch processing.

Typical use:
  • Create an index of queries
  • Repeatedly query that index with documents to find matches


PSearch requires python 2.5 or later and depends on numpy.

There is not yet an installation script, so put the psearch package somewhere in your PYTHONPATH.

Sample Usage

Create a store to hold the index
>>> store = MemoryStore()
Add some queries to the store:
>>> query1 = Query(1, [('information',), ('retrieval',)])
>>> query2 = Query(2, [('text', 'data'), ('mining',)], filters=[('price', 100, 200)])
>>> index([query1, query2], store)
Create a matcher:
>>> matcher = QueryMatcher(store)
Match queries to documents:
>>> doc = Document({'name': [['introduction', 'to', 'information', 'retrieval']]}, rangefilters={'price': [30.0]})
>>> list(matcher.matches(doc))

This example is explained in the sections that follow.



The method of storing indexed queries is configurable. PSearch comes with 3 built in options for storage:

Holds all data in memory. Data can optionally be read from or written to disk (uses pickle).
Stores data in a GDBM database. This is convenient as it is always included in the python distribution.
Stores data in a Tokyo Cabinet database. This is more efficient than GDBM, however it requires Tokyo Cabinet and the pytc bindings to be installed.


Terms are the indexed units in Information Retrieval. PSearch assumes documents and queries are already converted into terms, which are represented as a sequence of strings and makes no assumptions about the contents of these strings. In the example above, the document name "Introduction to Information Retrieval" is converted into the terms ['introduction', 'to', 'information', 'retrieval']. Although this could be achieved with str.lower().split(), it is common to:
  • use more sophisticated tokenization (splitting the text into words, phrases or symbols).
  • stem words (e.g. converting "fishing", "fished", "fish", and "fisher" to the root word, "fish")
  • remove stop words ('and', 'the', 'of', etc.)
  • add extra words to support synonyms. For example, adding "IR" as a synonym for "Information Retrieval".
  • add prefixes to terms based on the field in which they occur to support searching by field of the document.

Usually the converting of text to terms is application specific. There are many python libraries that are useful, for example nltk has several good algorithms for tokenization and stemming and contains lists of stop words.

The process used for converting the query to terms must be compatible with the process used for converting documents.


Queries contain an ID, terms, filters and optional user defined fields. The query terms are specified as a sequence of sequences of terms. The terms in each nested sequence are combined with OR and the sequences themselves are combined with AND.

Filters can be used to match numeric ranges. They consist of a starting value and an optional end value that specify the range.

In our example above, the first query searches for "information AND retrieval" and the second query searches for "(text OR data) AND mining" and filters the results so that all documents have a price between 100 and 200.


Documents consist of:

Text Search Terms
A mapping from fields to a sequence of sequences of terms. This allows for typical multi-valued document fields found in many applications
Range Filters
A mapping from fields to terms that can be used in range queries


Queries are currently limited to 31 terms, excluding terms combined with OR. This limit can be changed internally. This is a common upper bound, at the time of writing, google limits searches to 32 words.


Document size, term frequency, query complexity, use of filters, etc. all play a part in the performance of the system. There is a small benchmark program that can be used to run some quick tests:

$ python -m psearch.psearch_test  -p
indexed 10000 queries in 2.984095 seconds
100000 documents processed in 4.811013 seconds (20785.643345 docs/sec)