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cheshire3 1.1.0c3

Cheshire3 Search and Retrieval Engine and Information Framework

Latest Version: 1.1.1

21st November 2013 (2013-11-21)

Build Status

Description

Cheshire3 is a fast XML search engine, written in Python for extensability and using C libraries for speed. Cheshire3 is feature rich, including support for XML namespaces, unicode, a distributable object oriented model and all the features expected of a digital library system.

Standards are foremost, including SRU and CQL, as well as Z39.50 and OAI. It is highly modular and configurable, enabling very specific needs to be addressed with a minimum of effort. The API is stable and fully documented, allowing easy third party development of components.

Given a set of documents records, Cheshire3 can extract data into one or more indexes after processing with configurable workflows to add extra normalization and processing. Once the indexes have been constructed, it supports such operations as search, retrieve, browse and sort.

The abstract protocolHandler allows integration of Cheshire3 into any environment that will support Python. For example using Apache handlers or WSGI applications, any interface from standard APIs like SRU, Z39.50 and OAI (all included by default in the cheshire3.web sub-package), to an online shop front can be provided.

Authors

Cheshire3 Team at the University of Liverpool:

(Current maintainer in bold)

Latest Version

The latest stable version of Cheshire3 is available from PyPi - the Python Package Index:

http://pypi.python.org/pypi/cheshire3/

Bleeding edge source code is under version control and available from the Cheshire3 GitHub repository:

http://github.com/cheshire3/cheshire3

Previously, source code was available from our own Subversion server. The SVN repository is being kept alive for the time being as read-only, and best efforts will be made to keep it up-to-date with the master (i.e. stable/production) branch from the Cheshire3 Git repository. It is available at:

http://svn.cheshire3.org/repos/cheshire3

Previous versions, including code + dependency bundles, and auto-installation scripts are available from the Cheshire3 download site:

http://www.cheshire3.org/download/

Installation

The following guidelines assume that you administrative privileges on the machine you're installing on. If this is not the case, then you might need to use the option --user. For more details, see: http://docs.python.org/install/index.html#alternate-installation

Users (i.e. those not wanting to actually develop Cheshire3) have several choices:

Developers:

We recommend that you use virtualenv to isolate your development environment from system Python and any packages that may be installed there.

  1. In GitHub, fork the Cheshire3 GitHub repository

  2. Clone your fork of Cheshire3:

    git clone git@github.com:<username>/cheshire3.git

  3. Install dependencies [1]:

    pip install -r requirements.txt

  4. Install Cheshire3 in develop / editable mode:

    pip install -e .

  5. Read the Development section of this README

[1]While step 4 should theoretically resolve dependencies, we've found it more reliable to run this explicitly.

Requirements / Dependencies

Cheshire3 requires Python 2.6.0 or later. It has not yet been verified as Python 3 compliant.

As of the version 1.0 release Cheshire3's python dependencies should be resolved automatically by the standard Python package management mechanisms (e.g. pip, easy_install, distribute/setuptools).

However on some systems, for example if installing on a machine without network access, it may be necessary to manually install some 3rd party dependencies. In such cases we would encourage you to download the necessary Cheshire3 bundles from the Cheshire3 download site and install them using the automated build scripts included. If the automated scripts fail on your system, they should at least provide hints on how to resolve the situation.

If you experience problems with dependencies, please get in touch via the GitHub issue tracker or wiki, and we'll do our best to help.

Additional / Optional Features

Certain features within the Cheshire3 Information Framework will have additional dependencies (e.g. web APIs will require a web application server). We'll try to maintain an accurate list of these in the module docstring of the __init__.py file in each sub-package.

The bundles available from the Cheshire3 download site should continue to be a useful place to get hold of the source code for these pre-requisites.

Documentation

Documentation is available hosted by Read the Docs: http://docs.cheshire3.org

Some additional, but possibly redundant and outdated documentation is available on our website: http://cheshire3.org/docs/

If you downloaded the source code, either as a tarball, or by checking out the repository, you'll find a copy of the Sphinx based Documentation in the local docs directory.

There is additional documentation for the source code in the form of comments and docstrings. Documentation for most default object configurations can be found within the <docs> tag in the config XML for each object. We would encourage users to take advantage of this tag to provide documentation for their own custom object configurations.

Development

This section is intended for those who are intending to develop code to contribute back to Cheshire3.

The Cheshire3 code base, configurations and documentation are maintained in the Cheshire3 GitHub repository.

Development in the Cheshire3 GitHub repository will follow Vincent Driessen's branching model, and use git-flow to facilitate this.

So your workflow should be something like:

  1. Fork the GitHub repository
  2. Clone your forked repository onto you local development machine
  3. Fix bugs in the develop branch, or develop new features in your own feature branch and merge back into the develop branch.)
  4. Push your changes back to you github fork
  5. Issue a pull request

Developed code intended to be contributed back to Cheshire3 should follow the recommendations made by the standard Style Guide for Python Code (which includes the provision that guidelines may be ignored in situations where following them would make the code less readable.)

Particular attention should be paid to documentation and source code annotation (comments). All developed modules, functions, classes, and methods should be documented in the source code. Newly configured objects at the server level should be documented using the <docs> tag. Comments and Documentation should be accurate and up-to-date, and should never contradict the code itself.

Bugs, Feature requests etc.

Bug reports, feature requests etc. should be made using the GitHub issue tracker: https://github.com/cheshire3/cheshire3/issues

Licensing

Copyright © 2005-2013, the University of Liverpool. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  • Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
  • Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
  • Neither the name of the University of Liverpool nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

MARC Utilities

The following licensing conditions apply to the marc_utils module included in the Cheshire3 package. In the following statements, "This file" and "the Software" should be understood to mean marc_utils.py.

This file should be available from http://www.pobox.com/~asl2/software/PyZ3950/ and is licensed under the X Consortium license: Copyright (c) 2001, Aaron S. Lav, asl2@pobox.com All rights reserved.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, provided that the above copyright notice(s) and this permission notice appear in all copies of the Software and that both the above copyright notice(s) and this permission notice appear in supporting documentation.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR HOLDERS INCLUDED IN THIS NOTICE BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL INDIRECT OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

Except as contained in this notice, the name of a copyright holder shall not be used in advertising or otherwise to promote the sale, use or other dealings in this Software without prior written authorization of the copyright holder.

Examples

Command-line UI

Cheshire3 provides a number of command-line utilities to enable you to get started creating databases, indexing and searching your data quickly. All of these commands have full help available, including lists of available options which can be accessed using the --help option. e.g.:

``cheshire3 --help``

Creating a new Database

cheshire3-init [database-directory]
Initialize a database with some generic configurations in the given directory, or current directory if absent

Example 1: create database in a new sub-directory:

$ cheshire3-init mydb

Example 2: create database in an existing directory:

$ mkdir -p ~/dbs/mydb
$ cheshire3-init ~/dbs/mydb

Example 3: create database in current working directory:

$ mkdir -p ~/dbs/mydb
$ cd ~/dbs/mydb
$ cheshire3-init

Example 4: create database with descriptive information in a new sub-directory:

$ cheshire3-init --database=mydb --title="My Database" \
--description="A Database of Documents" mydb

Loading Data into the Database

cheshire3-load data
Load data into the current Cheshire3 database

Example 1: load data from a file:

$ cheshire3-load path/to/file.xml

Example 2: load data from a directory:

$ cheshire3-load path/to/directory

Example 3: load data from a URL:

$ cheshire3-load http://www.example.com/index.html

Searching the Database

cheshire3-search query
Search the current Cheshire3 database based on the parameters given in query

Example 1: search with a single keyword:

$ cheshire3-search food

Example 2: search with a complex CQL query:

$ cheshire3-search "cql.anywhere all/relevant food and \
rec.creationDate > 2012-01-01"

Exposing the Database via SRU

cheshire3-serve
Start a demo HTTP WSGI application server to serve configured databases via SRU

Please Note the HTTP server started is probably not sufficiently robust for production use. You should consider using something like mod_wsgi.

Example 1: start a demo HTTP WSGI server with default options:

$ cheshire3-serve

Example 2: start a demo HTTP WSGI server, specifying host name and port number:

$ cheshire3-serve --host myhost.example.com --port 8080

Python API

This section contains examples of using the Cheshire3 API from within Python, for embedding Cheshire3 services within a Python enabled web application framework, such as Django, CherryPy, mod_wsgi etc. or when the command-line interface is simply insufficient.

Initializing Cheshire3 Architecture

Initializing the Cheshire3 Architecture consists primarily of creating instances of the following types within the Cheshire3 Object Model:

Session
An object representing the user session. It will be passed around amongst the processing objects to maintain details of the current environment. It stores, for example, user and identifier for the database currently in use.
Server
A protocol neutral collection of databases, users and their dependent objects. It acts as an inital entry point for all requests and handles such things as user authentication, and global object configuration.

The first thing that we need to do is create a Session and build a Server.:

>>> from cheshire3.baseObjects import Session
>>> session = Session()

The Server looks after all of our objects, databases, indexes ... everything. Its constructor takes session and one argument, the filename of the top level configuration file. You could supply your own, or you can find the filename of the default server configuration dynamically as follows::

>>> import os
>>> from cheshire3.server import SimpleServer
>>> from cheshire3.internal import cheshire3Root
>>> serverConfig = os.path.join(cheshire3Root, 'configs', 'serverConfig.xml')
>>> server = SimpleServer(session, serverConfig)
>>> server
<cheshire3.server.SimpleServer object...

Most often you'll also want to work within a Database:

Database
A virtual collection of Records which may be interacted with. A Database includes Indexes, which contain data extracted from the Records as well as configuration details. The Database is responsible for handling queries which come to it, distributing the query amongst its component Indexes and returning a ResultSet. The Database is also responsible for maintaining summary metadata (e.g. number of items, total word count etc.) that may be need for relevance ranking etc.

To get a database.:

>>> db = server.get_object(session, 'db_test')
>>> db
<cheshire3.database.SimpleDatabase object...

After this you MUST set session.database to the identifier for your database, in this case 'db_test'::

>>> session.database = 'db_test'

This is primarily for efficiency in the workflow processing (objects are cached by their identifier, which might be duplicated for different objects in different databases).

Another useful path to know is the database's default path::

>>> dfp = db.get_path(session, 'defaultPath')

Note: You can often avoid having to type all of the above boiler-plate code, by Using the cheshire3 command

Using the cheshire3 command

One way to ensure that Cheshire3 architecture is initialized is to use the Cheshire3 interpreter, which wraps the main Python interpreter, to run your script or just drop you into the interactive console.

cheshire3 [script]
Run the commands in the script inside the current cheshire3 environment. If script is not provided it will drop you into an interactive console (very similar the the native Python interpreter.) You can also tell it to drop into interactive mode after executing your script using the --interactive option.

When initializing the architecture in this way, session and server variables will be created corresponding to instances of Session and Server respectively.

Additionally, if you ran the script from inside a Cheshire3 Database directory, or provided the Database identifier using the --database option, the Database will be available as db. The default RecordStore will also be available as recordStore if it was possible to discover from the Database.

Loading Data

In order to load data into your database you'll need a document factory to find your documents, a parser to parse the XML and a record store to put the parsed XML into. The most commonly used are defaultDocumentFactory and LxmlParser. Each database needs its own record store.:

>>> df = db.get_object(session, "defaultDocumentFactory")
>>> parser = db.get_object(session, "LxmlParser")
>>> recStore = db.get_object(session, "recordStore")

Before we get started, we need to make sure that the stores are all clear.:

>>> recStore.clear(session)
<cheshire3.recordStore.BdbRecordStore object...
>>> db.clear_indexes(session)

First you should call db.begin_indexing() in order to let the database initialise anything it needs to before indexing starts. Ditto for the record store.:

>>> db.begin_indexing(session)
>>> recStore.begin_storing(session)

Then you'll need to tell the document factory where it can find your data::

>>> df.load(session, 'data', cache=0, format='dir')
<cheshire3.documentFactory.SimpleDocumentFactory object...

DocumentFactory's load function takes session, plus:

data

this could be a filename, a directory name, the data as a string, a URL to the data and so forth.

If data ends in [(numA):(numB)], and the preceding string is a filename, then the data will be extracted from bytes numA through to numB (this is pretty advanced though - you'll probably never need it!)

cache

setting for how to cache documents in memory when reading them in. This will depend greatly on use case. e.g. if loading 3Gb of documents on a machine with 2Gb memory, full caching will obviously not work very well. On the other hand, if loading a reasonably small quantity of data over HTTP, full caching would read all of the data in one shot, closing the HTTP connection and avoiding potential timeouts. Possible values:

0
no document caching. Just locate the data and get ready to discover and yield documents when they're requested from the documentFactory. This is probably the option you're most likely to want.
1
Cache location of documents within the data stream by byte offset.
2
Cache full documents.
format

The format of the data parameter. Many options, the most common are:

xml:xml file. Can have multiple records in single file.
dir:a directory containing files to load
tar:a tar file containing files to load
zip:a zip file containing files to load
marc:a file with MARC records (library catalogue data)
http:a base HTTP URL to retrieve
tagName
the name of the tag which starts (and ends!) a record. This is useful for extracting sections of documents and ignoring the rest of the XML in the file.
codec
the name of the codec in which the data is encoded. Normally 'ascii' or 'utf-8'

You'll note above that the call to load returns itself. This is because the document factory acts as an iterator. The easiest way to get to your documents is to loop through the document factory::

>>> for doc in df:
...    rec = parser.process_document(session, doc)  # [1]
...    recStore.create_record(session, rec)         # [2]
...    db.add_record(session, rec)                  # [3]
...    db.index_record(session, rec)                # [4]
recordStore/...

In this loop, we:

  1. Use the Lxml Parser to create a record object.
  2. Store the record in the recordStore. This assigns an identifier to it, by default a sequential integer.
  3. Add the record to the database. This stores database level metadata such as how many words in total, how many records, average number of words per record, average number of bytes per record and so forth.
  4. Index the record against all indexes known to the database - typically all indexes in the indexStore in the database's 'indexStore' path setting.

Then we need to ensure this data is commited to disk::

>>> recStore.commit_storing(session)
>>> db.commit_metadata(session)

And, potentially taking longer, merge any temporary index files created::

>>> db.commit_indexing(session)

Pre-Processing (PreParsing)

As often than not, documents will require some sort of pre-processing step in order to ensure that they're valid XML in the schema that you want them in. To do this, there are PreParser objects which take a document and transform it into another document.

The simplest preParser takes raw text, escapes the entities and wraps it in a element::

>>> from cheshire3.document import StringDocument
>>> doc = StringDocument("This is some raw text with an & and a < and a >.")
>>> pp = db.get_object(session, 'TxtToXmlPreParser')
>>> doc2 = pp.process_document(session, doc)
>>> doc2.get_raw(session)
'<data>This is some raw text with an &amp; and a &lt; and a &gt;.</data>'

Searching

In order to allow for translation between query languages (if possible) we have a query factory, which defaults to CQL (SRU's query language, and our internal language).:

>>> qf = db.get_object(session, 'defaultQueryFactory')
>>> qf
<cheshire3.queryFactory.SimpleQueryFactory object ...

We can then use this factory to build queries for us::

>>> q = qf.get_query(session, 'c3.idx-text-kwd any "compute"')
>>> q
<cheshire3.cqlParser.SearchClause ...

And then use this parsed query to search the database::

>>> rs = db.search(session, q)
>>> rs
<cheshire3.resultSet.SimpleResultSet ...
>>> len(rs)
3

The 'rs' object here is a result set which acts much like a list. Each entry in the result set is a ResultSetItem, which is a pointer to a record.:

>>> rs[0]
Ptr:recordStore/1

Retrieving

Each result set item can fetch its record::

>>> rec = rs[0].fetch_record(session)
>>> rec.recordStore, rec.id
('recordStore', 1)

Records can expose their data as xml::

>>> rec.get_xml(session)
'<record>...

As SAX events::

>>> rec.get_sax(session)
["4 None, 'record', 'record', {}...

Or as DOM nodes, in this case using the Lxml Etree API::

>>> rec.get_dom(session)
<Element record at ...

You can also use XPath expressions on them::

>>> rec.process_xpath(session, '/record/header/identifier')
[<Element identifier at ...
>>> rec.process_xpath(session, '/record/header/identifier/text()')
['oai:CiteSeerPSU:2']

Transforming Records

Records can be processed back into documents, typically in a different form, using Transformers:

>>> dctxr = db.get_object(session, 'DublinCoreTxr')
>>> doc = dctxr.process_record(session, rec)

And you can get the data from the document with get_raw()::

>>> doc.get_raw(session)
'<?xml version="1.0"?>...

This transformer uses XSLT, which is common, but other transformers are equally possible.

Indexes

While Searching is the primary use of an Index, there are other API methods that can be used to get information from an Index in slightly different forms that can be useful when developing a user interface. This section describes those API methods and then shows how to really get your hands dirty by Looking Under the Hood and getting direct access to some of the object types that are used to process data within an Index.

Browsing

It is possible to browse through all terms in an index, just like reading the index in a book. This is usualy done through scan method of a Database object, so as to make use of the normal Index resolution machinery:

>>> qf = db.get_object(session, 'defaultQueryFactory')
>>> query = qf.get_query(session, 'dc.title = ""')
>>> terms = db.scan(session, query, nTerms=25, direction=">=")

terms will be a list of no more than 25 items representing the terms from the start of the Index that was resolved from the context dc.title (by convention the Dublin-Core definition of "title"; the title of a piece of work.) Each item in terms is a 2-item list:

  1. The unicode representation of the term
  2. A 3-item list: 0. internal numeric term id 1. number of records the term appears in 2. total number of occurrences of the term across the database

e.g.:

[u"zen and the art of motorcycle maintenance", [12345, 2, 3]]

It is also possible to use the scan method of an Index object directly:

>>> idx = db.get_object(session, 'idx-title')
>>> terms = idx.scan(session, query, nTerms=25, direction=">=")

The resulting terms will be the same as when obtained through the scan method of the Database object.

Facets and Filtering

Assuming that you have configured your Index with the setting vectors set to 1, it is possible to obtain search facets for the Index. That is to say that given a ResultSet obtained from a Searching, one can obtain a list of the terms that occur within the Records in that ResultSet. This list can be used to present a search user with options for refining their search.:

>>> qf = db.get_object(session, 'defaultQueryFactory')
>>> query = qf.get_query(session, 'c3.idx-text-kwd any "compute"')
>>> rs = db.search(session, query)
>>> idx = db.get_object(session, 'idx-author')
>>> facets = idx.facets(session, rs, nTerms=5)

The resulting facets will be a list representing the 5 terms that occur in the highest number of Records within the ResultSet. Setting nTerms to 0 (or omitting it) will return all terms within the Index for the Records within the ResultSet. Each item in terms is a 2-item list:

  1. The unicode representation of the term
  2. A 3-item list: 0. internal numeric term id 1. number of records the term appears in 2. total number of occurrences of the term across the database

e.g.:

[u"Crichton, Michael", [54321, 3, 24]]

Looking Under the Hood

Configuring Indexes, and the processing required to populate them requires some further object types, such as Selectors, Extractors, Tokenizers and TokenMergers. Of course, one would normally configure these for each index in the database and the code in the examples below would normally be executed automatically. However it can sometimes be useful to get at the objects and play around with them manually, particularly when starting out to find out what they do, or figure out why things didn't work as expected, and Cheshire3 makes this possible.

Selector objects are configured with one or more locations from which data should be selected from the Record. Most commonly (for XML data at least) these will use XPaths. A selector returns a list of lists, one for each configured location.:

>>> xp1 = db.get_object(session, 'identifierXPathSelector')
>>> rec = recStore.fetch_record(session, 1)
>>> elems = xp1.process_record(session, rec)
>>> elems
[[<Element identifier at ...

However we need the text from the matching elements rather than the XML elements themselves. This is achieved using an Extractor, which processes the list of lists returned by a Selector and returns a doctionary a.k.a an associative array or hash::

>>> extr = db.get_object(session, 'SimpleExtractor')
>>> hash = extr.process_xpathResult(session, elems)
>>> hash
{'oai:CiteSeerPSU:2 ': {'text': 'oai:CiteSeerPSU:2 ', ...

And then we'll want to normalize the results a bit. For example we can make everything lowercase::

>>> n = db.get_object(session, 'CaseNormalizer')
>>> h2 = n.process_hash(session, h)
>>> h2
{'oai:citeseerpsu:2 ': {'text': 'oai:citeseerpsu:2 ', ...

And note the extra space on the end of the identifier...:

>>> s = db.get_object(session, 'SpaceNormalizer')
>>> h3 = s.process_hash(session, h2)
>>> h3
{'oai:citeseerpsu:2': {'text': 'oai:citeseerpsu:2',...

Now the extracted and normalized data is ready to be stored in the index!

This is fine if you want to just store strings, but most searches will probably be at word or token level. Let's get the abstract text from the record::

>>> xp2 = db.get_object(session, 'textXPathSelector')
>>> elems = xp2.process_record(session, rec)
>>> elems
[[<Element {http://purl.org/dc/elements/1.1/}description ...

Note the {...} bit ... that's lxml's representation of a namespace, and needs to be included in the configuration for the xpath in the Selector.:

>>> extractor = db.get_object(session, 'ProxExtractor')
>>> hash = extractor.process_xpathResult(session, elems)
>>> hash
{'The Graham scan is a fundamental backtracking...

ProxExtractor records where in the record the text came from, but otherwise just extracts the text from the elements. We now need to split it up into words, a process called tokenization.:

>>> tokenizer = db.get_object(session, 'RegexpFindTokenizer')
>>> hash2 = tokenizer.process_hash(session, hash)
>>> h
{'The Graham scan is a fundamental backtracking...

Although the key at the beginning looks the same, the value is now a list of tokens from the key, in order. We then have to merge those tokens together, such that we have 'the' as the key, and the value has the locations of that type.:

>>> tokenMerger = db.get_object(session, 'ProxTokenMerger')
>>> hash3 = tokenMerger.process_hash(session, hash2)
>>> hash3
{'show': {'text': 'show', 'occurences': 1, 'positions': [12, 41]},...

After token merging, the multiple terms are ready to be stored in the index!

It is also possible to iterate through stores. This is useful for adding new indexes or otherwise processing all of the data without reloading it.

First find our index, and the indexStore::

>>> idx = db.get_object(session, 'idx-modificationDate')
>>> idxStore = idx.get_path(session, 'indexStore')

Then start indexing for just that index, step through each record, and then commit the terms extracted.:

>>> idxStore.begin_indexing(session, idx)
>>> for rec in recStore:
...     idx.index_record(session, rec)
recordStore/...
>>> idxStore.commit_indexing(session, idx)

This example will have the effect of 'touching' each Record, as if it had been updated. This might be useful if for example, you knew that your Database was being harvested periodically using OAI-PMH, and you wanted to indicate that all Records should be reharvested next time.

 
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