# Overview

This document provides an introduction to the native C++ Xapian API. This API provides programmers with the ability to index and search through (potentially very large) bodies of data using probabilistic and other ranking methods.

Note: The portion of the API currently documented here covers only the part of Xapian concerned with searching through existing databases, not that concerned with creating them.

This document assumes you already have Xapian installed, so if you haven't, it is a good idea to read Installing Xapian first.

You may also wish to read the Introduction to Information Retrieval for a background into the Information Retrieval theories behind Xapian.

This document does not detail the exact calling conventions (parameters passed, return value, exceptions thrown, etc...) for each method in the API. For such documentation, you should refer to the automatically extracted documentation, which is generated from detailed comments in the source code, and should thus remain up-to-date and accurate. This documentation is generated using the Doxygen application. To save you having to generate this documentation yourself, we include the built version in our distributions, and also keep the latest version on our website.

# Design Principles

API classes are either very lightweight or a wrapper around a reference counted pointer (this style of class design is sometimes known as PIMPL for "Private IMPLementation"). In either case copying is a cheap operation as classes are at most a few words of memory.

API objects keep a reference to other objects they rely on so the user doesn't need to worry about whether an object is still valid or not.

Where appropriate, API classes can be used as containers and iterators just like those in the C++ STL.

# Errors and exceptions

It is important to understand the errors which may be caused by the operations which you are trying to perform.

This becomes particularly relevant when using a large system, with such possibilities as databases which are being updated while you search through them, and distributed enquiry systems.

Errors in Xapian are all reported by means of exceptions. All exceptions thrown by Xapian will be subclasses of Xapian::Error. Note that `Xapian::Error` is an abstract class; thus you must catch exceptions by reference rather than by value.

There are two flavours of error, derived from `Xapian::Error`:

- Xapian::LogicError - for error conditions due to programming errors, such as a misuse of the API. A finished application should not receive these errors (though it would still be sensible to catch them).
- Xapian::RuntimeError - for error conditions due to run time problems, such as failure to open a database. You must always be ready to cope with such errors.

Each of these flavours is further subdivided, such that any particular error condition can be trapped by catching the appropriate exception. If desired, a human readable explanation of the error can be retrieved by calling Xapian::Error::get_msg().

In addition, standard system errors may occur: these will be reported by throwing appropriate exceptions. Most notably, if the system runs out of memory, a `std::bad_alloc` exception will be thrown.

# Terminology

## Databases

These may also occasionally be called Indexes. In Xapian (as opposed to a database package) a database consists of little more than indexed documents: this reflects the purpose of Xapian as an information retrieval system, rather than an information storage system.

The exact contents of a database depend on the type (see "Database Types" for more details of the database types currently provided).

## Queries

The information to be searched for is specified by a Query. In Xapian, queries are made up of a structured boolean tree, upon which relevance weightings are imposed: when the search is performed, the documents returned are filtered according to the boolean structure, and weighted (and sorted) according to the weighting scheme in use.

# Memory handling

The user of Xapian does not usually need to worry about how Xapian performs its memory allocation: Xapian objects can all be created and deleted as any other C++ objects. The convention is that whoever creates an object is ultimately responsible for deleting it. This becomes relevant when passing a pointer to data to Xapian: it will be the caller's responsibility to delete the object once it is finished with. Where an object is set by one API call and used by another, Xapian will assume that such pointers remain valid.

# The Xapian::Enquire class

The Xapian::Enquire class is central to all searching operations. It provides an interface for

- Specifying the database, or databases, to search across.
- Specifying a query to perform.
- Specifying a set of documents which a user considers relevant.
- Given the supplied information, returning a ranked set of documents for the user.
- Given the supplied information, suggesting a ranked set of terms to add to the query.
- Returning information about the documents which matched, such as their associated data, and which terms from the query were found within them.

A typical enquiry session will consist of most of these operations, in various orders. The Xapian::Enquire class presents as few restrictions as possible on the order in which operations should be performed. Although you must set the query before any operation which uses it, you can call any of the other methods in any order.

Many operations performed by the Xapian::Enquire class are performed lazily (ie, just before their results are needed). This need not concern the user except to note that, as a result, errors may not be reported as soon as would otherwise be expected.

# Specifying a database

When creating a Xapian::Enquire object, a database to search must be specified. Databases are specified by creating a Xapian::Database object. Generally, you can just construct the object, passing the pathname to the database. Xapian looks at the path and autodetects the database type.

With the Remote backend, you need to use the `Xapian::Remote::open()` factory function. The parameters you need to pass depend on whether you're using the TCP or prog variant, and whether you are creating a read-only or a writable database.

You can also create a "stub database" file which lists one or more databases. These files are recognised by the autodetection in the Database constructor (if the pathname is file rather than a directory, it's treated as a stub database file, or if the pathname is a directory containing a file called `XAPIANDB`) or you can open them explicitly using `Xapian::DB_BACKEND_STUB`. The stub database format specifies one database per line. For example:

    remote localhost:23876
    auto /var/spool/xapian/webindex

## Database types

The current types understood by Xapian are:

auto
  This isn't an actual database format, but rather auto-detection of one of the disk based backends (e.g. "glass") from a single specified path (which can be to a file or directory).
glass
  Glass is the default backend in Xapian 1.4.x. It supports incremental modifications, concurrent single-writer and multiple-reader access to a database. It's very efficient and highly scalable.
honey
  Honey will be the default backend in Xapian 3.0.x. It supports incremental modifications, concurrent single-writer and multiple-reader access to a database. It's very efficient and highly scalable.
inmemory
  This type is a database held entirely in memory. It was originally written for testing purposes only, but may prove useful for building up temporary small databases.
remote

  This can specify either a "program" or TCP remote backend, for example:

      remote :ssh xapian-prog.example.com xapian-progsrv /srv/xapian/db1

  or:

      remote xapian-tcp.example.com:12345

  If the first character of the second word is a colon (`:`), then this is skipped and the remainder of the line is used as the command to run xapian-progsrv and the "program" variant of the remote backend is used. Otherwise the TCP variant of the remote backend is used, and the rest of the line specifies the host and port to connect to.

These are no longer supported by Xapian 2:

chert
  Chert was the default backend in Xapian 1.2.x. It was deprecated in 1.4.0 and removed in 2.0.0. If you want to migrate an existing Chert database to Glass, the 'Admin Notes' describe how to do this.
brass
  Brass was the current "under development" database format in Xapian 1.2.x, 1.3.0 and 1.3.1. It was renamed to 'glass' in Xapian 1.3.2 because we decided to use backend names in ascending alphabetical order to make it easier to understand which backend is newest, and since 'flint' was used recently, we skipped over 'd', 'e' and 'f'.
flint
  Flint was the default backend in Xapian 1.0.x, and was deprecated in 1.2.x and removed in 1.3.0. If you want to migrate an existing Flint database to Chert, see the 'Admin Notes' for a way to do this.
quartz
  Quartz was the default backend prior to Xapian 1.0, and has been removed as of Xapian 1.1.0. If you want to migrate an existing Quartz database to Flint, see Admin Notes for a way to do this.

## Multiple databases

Xapian can search across several databases as easily as searching across a single one. Simply call Xapian::Database::add_database() for each database that you wish to search through.

You can also set up "pre-canned" listed of databases to search over using a "stub database" - see above for details.

# Specifying a query

Xapian implements both boolean and weighted searching. There are two obvious ways in which a pure boolean query could be combined with a weighted query:

- First perform the boolean search to create a subset of the whole document collection, and then do the weighted search on this subset, or
- Do the weighted search, and then filter out the resulting documents with a boolean query.

There is in fact a subtle difference in these two approaches. In the first, the collection statistics for the weighted query will be determined by the document subset which is obtained by running the boolean query. In the second, the collection statistics for the weighted query are determined by the whole document collection. These differences can affect the final result.

Suppose for example the boolean query is being used to retrieve documents in English in a database containing English and French documents. A word like "grand", exists in both languages (with similar meanings), but is more common in French than English. In the English subset it could therefore be expected to have a higher weight than it would get in the joint English and French databases.

Xapian takes the second approach simply because this can be implemented very efficiently. The first approach is more exact, but inefficient to implement.

Rather than implementing this approach as described above and first performing the weighted search and then filtering the results, Xapian actually performs both tasks simultaneously. This allows various optimisations to be performed, such as giving up on calculating a boolean AND operation when the weights that could result from further documents can have no effect on the result set. These optimisations have been found to often give a several-fold performance increase. The performance is particularly good for queries containing many terms.

## A query for a single term

A search query is represented by a Xapian::Query object. The simplest useful query is one which searches for a single term (and several of these can be combined to form more complex queries). A single term query can be created as follows (where `term` is a `std::string` holding the term to be searched for):

    Xapian::Query query(term);

A term in Xapian is represented simply by a string of bytes. Usually, when searching text, these bytes will represent the characters of the word which the term represents, but during the information retrieval process Xapian attaches no specific meaning to the term.

This constructor actually takes a couple of extra parameters, which may be used to specify positional and frequency information for terms in the query:

    Xapian::Query(const string & tname_,
            Xapian::termcount wqf_ = 1,
            Xapian::termpos term_pos_ = 0)

The `wqf` (Within Query Frequency) is a measure of how common a term is in the query. This isn't useful for a single term query unless it is going to form part of a more complex query. In that case, it's particularly useful when generating a query from an existing document, but may also be used to increase the "importance" of a term in a query. Another way to increase the "importance" of a term is to use `OP_SCALE_WEIGHT`. But if the intention is simply to ensure that a particular term is in the query results, you should use a boolean AND or AND_MAYBE rather than setting a high wqf.

The `term_pos` represents the position of the term in the query. Again, this isn't useful for a single term query by itself, but is used for phrase searching, passage retrieval, and other operations which require knowledge of the order of terms in the query (such as returning the set of matching terms in a given document in the same order as they occur in the query). If such operations are not required, the default value of 0 may be used.

Note that it may not make much sense to specify a wqf other than 1 when supplying a term position (unless you are trying to affect the weighting, as previously described).

Note also that the results of `Xapian::Query(tname, 2)` and `Xapian::Query(Xapian::Query::OP_OR, Xapian::Query(tname), Xapian::Query(tname))` are exactly equivalent.

## Compound queries

Compound queries can be built up from single term queries by combining them a connecting operator. Most operators can operate on either a single term query or a compound query. You can combine pair-wise using the following constructor:

    Xapian::Query(Xapian::Query::op op_,
            const Xapian::Query & left,
            const Xapian::Query & right)

The two most commonly used operators are `Xapian::Query::OP_AND` and `Xapian::Query::OP_OR`, which enable us to construct boolean queries made up from the usual AND and OR operations. But in addition to this, a "bag of words" query in its simplest form, where we have a list of terms which give rise to weights that need to be added together, is also made up from a set of terms joined together with `Xapian::Query::OP_OR`.

Some of the available `Xapian::Query::op` operators are:

Xapian::Query::OP_AND | Return documents returned by both subqueries.
Xapian::Query::OP_OR | Return documents returned by either subquery.
Xapian::Query::OP_AND_NOT | Return documents returned by the left subquery but not the right subquery.
Xapian::Query::OP_FILTER | As Xapian::Query::OP_AND, but use only weights from left subquery.
Xapian::Query::OP_AND_MAYBE | Return documents returned by the left subquery, but adding document weights from both subqueries.
Xapian::Query::OP_XOR | Return documents returned by one subquery only.
Xapian::Query::OP_NEAR | Return documents where the terms are with the specified distance of each other.
Xapian::Query::OP_PHRASE | Return documents where the terms are with the specified distance of each other and in the given order.
Xapian::Query::OP_ELITE_SET | Select an elite set of terms from the subqueries, and perform a query with all those terms combined as an OR query.

## Understanding queries

Each term in the query has a weight in each document. Each document may also have an additional weight not associated with any of the terms. By default the probabilistic weighting scheme BM25 is used to provide the formulae which give these weights.

A query can be thought of as a tree structure. At each node is an `Xapian::Query::op` operator, and on the left and right branch are two other queries. At each leaf node is a term, t, transmitting documents and scores, D and wD(t), up the tree.

A Xapian::Query::OP_OR node transmits documents from both branches up the tree, summing the scores when a document is found in both the left and right branch. For example,

                            docs       1    8    12    16    17    18
                            scores    7.3  4.1   3.2  7.6   3.8   4.7 ...
                              |
                              |
                    Xapian::Query::OP_OR
                          /       \
                         /         \
                        /           \
                       /             \
    docs     1   12   16   17         1   8   16   18
    scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...

A Xapian::Query::OP_AND node transmits only the documents found on both branches up the tree, again summing the scores,

                            docs       1   16
                            scores    7.3  7.6  ...
                              |
                              |
                    Xapian::Query::OP_AND
                          /       \
                         /         \
                        /           \
                       /             \
    docs     1   12   16   17         1   8   16   18
    scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...

A Xapian::Query::OP_AND_NOT node transmits up the tree the documents on the left branch which are not on the right branch. The scores are taken from the left branch. For example, again summing the scores,

                            docs       12   17
                            scores    3.2  3.8 ...
                              |
                              |
                  Xapian::Query::OP_AND_NOT
                          /       \
                         /         \
                        /           \
                       /             \
    docs     1   12   16   17         1   8   16   18
    scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...

A Xapian::Query::OP_AND_MAYBE node transmits the documents up the tree from the left branch only, but adds in the score from the right branch for documents which occur on both branches. For example,

                            docs       1    12   16   17
                            scores    7.3  3.2  7.6  3.8 ...
                              |
                              |
                 Xapian::Query::OP_AND_MAYBE
                          /       \
                         /         \
                        /           \
                       /             \
    docs     1   12   16   17         1   8   16   18
    scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...

Xapian::Query::OP_FILTER is like Xapian::Query::OP_AND, but weights are only transmitted from the left branch. For example,

                            docs       1   16
                            scores    3.1  3.1  ...
                              |
                              |
                   Xapian::Query::OP_FILTER
                          /       \
                         /         \
                        /           \
                       /             \
    docs     1   12   16   17         1   8   16   18
    scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...

Xapian::Query::OP_XOR is like Xapian::Query::OP_OR, but documents on both left and right branches are not transmitted up the tree. For example,

                            docs       8    12    17    18
                            scores    4.1   3.2  3.8   4.7 ...
                              |
                              |
                       Xapian::Query::OP_XOR
                          /       \
                         /         \
                        /           \
                       /             \
    docs     1   12   16   17         1   8   16   18
    scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...

A query can therefore be thought of as a process for generating an MSet from the terms at the leaf nodes of the query. Each leaf node gives rise to a posting list of documents with scores. Each higher level node gives rise to a similar list, and the root node of the tree contains the final set of documents with scores (or weights), which are candidates for going into the MSet. The MSet contains the documents which get the highest weights, and they are held in the MSet in weight order.

It is important to realise that within Xapian the structure of a query is optimised for best performance, and it undergoes various transformations as the query progresses. The precise way in which the query is built up is therefore of little importance to Xapian - for example, you can AND together terms pair-by-pair, or combine several using AND on a std::vector of terms, and Xapian will build the same structure internally.

## Using queries

### Weighted queries

A simple weighted query is created by connecting terms together with Xapian::Query::OP_OR operators. For example,

    Xapian::Query query("regulation");
    query = Xapian::Query(Xapian::Query::OP_OR, query, Xapian::Query("import"));
    query = Xapian::Query(Xapian::Query::OP_OR, query, Xapian::Query("export"));
    query = Xapian::Query(Xapian::Query::OP_OR, query, Xapian::Query("canned"));
    query = Xapian::Query(Xapian::Query::OP_OR, query, Xapian::Query("fish"));

This creates a weighted query with terms regulation, import, export, canned and fish.

In fact this style of creation is so common that there is the shortcut construction:

    vector<string> terms;
    terms.push_back("regulation");
    terms.push_back("import");
    terms.push_back("export");
    terms.push_back("canned");
    terms.push_back("fish");

    Xapian::Query query(Xapian::Query::OP_OR, terms.begin(), terms.end());

### Boolean queries

Suppose now we have this Boolean query,

    ('EEC' - 'France') and ('1989' or '1991' or '1992') and 'Corporate_Law'

This could be built up as bquery like this,

    Xapian::Query bquery1(Xapian::Query::OP_AND_NOT, "EEC", "France");

    Xapian::Query bquery2("1989");
    bquery2 = Xapian::Query(Xapian::Query::OP_OR, bquery2, "1991");
    bquery2 = Xapian::Query(Xapian::Query::OP_OR, bquery2, "1992");

    Xapian::Query bquery3("Corporate_Law");

    Xapian::Query bquery(Xapian::Query::OP_AND, bquery1, Xapian::Query(Xapian::Query::OP_AND(bquery2, bquery3)));

and this can be attached as a filter to `query` to run the weighted query with a Boolean filter,

    query = Xapian::Query(Xapian::Query::OP_FILTER, query, bquery);

If you want to run a pure boolean query, then set BoolWeight as the weighting scheme (by calling Enquire::set_weighting_scheme() with argument BoolWeight()).

### Plus and minus terms

A common requirement in search engine functionality is to run a weighted query where some terms are required to index all the retrieved documents (+ terms), and others are required to index none of the retrieved documents (- terms). For example,

    regulation import export +canned +fish -japan

the corresponding query can be set up by,

    vector<string> plus_terms;
    vector<string> minus_terms;
    vector<string> normal_terms;

    plus_terms.push_back("canned");
    plus_terms.push_back("fish");

    minus_terms.push_back("japan");

    normal_terms.push_back("regulation");
    normal_terms.push_back("import");
    normal_terms.push_back("export");

    Xapian::Query query(Xapian::Query::OP_AND_MAYBE,
                  Xapian::Query(Xapian::Query::OP_AND, plus_terms.begin(), plus_terms.end());
                  Xapian::Query(Xapian::Query::OP_OR, normal_terms.begin(), normal_terms.end()));

    query = Xapian::Query(Xapian::Query::OP_AND_NOT,
                    query,
                    Xapian::Query(Xapian::Query::OP_OR, minus_terms.begin(), minus_terms.end()));

## Undefined queries

Performing a match with an undefined query matches nothing, which is sometimes useful. Composing an undefined query with operators behaves just as it would for any subquery which matches nothing.

# Retrieving the results of a query

To get the results of the query, call the `Enquire::get_mset()` method:

    Xapian::MSet Xapian::Enquire::get_mset(Xapian::doccount first,
                               Xapian::doccount maxitems,
                               const Xapian::RSet * rset = 0,
                               const Xapian::MatchDecider * mdecider = 0) const

When asking for the results, you must specify (in `first`) the first item in the result set to return, where the numbering starts at zero (so a value of zero corresponds to the first item returned being that with the highest score, and a value of 10 corresponds to the first 10 items being ignored, and the returned items starting at the eleventh).

You must also specify (in `maxitems`) the maximum number of items to return. Unless there are not enough matching items, precisely this number of items will be returned. If `maxitems` is zero, no items will be returned, but the usual statistics (such as the maximum possible weight which a document could be assigned by the query) will be calculated. (See "The Xapian::MSet" below).

## The Xapian::MSet

Query results are returned in an Xapian::MSet object. The results can be accessed using a Xapian::MSetIterator which returns the matches in descending sorted order of relevance (so the most relevant document is first in the list). Each `Xapian::MSet` entry comprises a document id, and the weight calculated for that document.

An `Xapian::MSet` also contains various information about the search result:

firstitem
  The index of the first item in the result which was put into the MSet. (Corresponding to `first` in `Xapian::Enquire::get_mset()`)
max_attained
  The greatest weight which is attained in the full results of the search.
max_possible
  The maximum possible weight in the MSet.
docs_considered
  The number of documents matching the query considered for the MSet. This provides a lower bound on the number of documents in the database which have a weight greater than zero. Note that this value may change if the search is recalculated with different values for `first` or `max_items`.

See the automatically extracted documentation for more details of these fields.

The `Xapian::MSet` also provides methods for converting the score calculated for a given document into a percentage value, suitable for displaying to a user. This may be done using the convert_to_percent() methods:

    int Xapian::MSet::convert_to_percent(const Xapian::MSetIterator & item) const
    int Xapian::MSet::convert_to_percent(double wt) const

These methods return a value in the range 0 to 100, which will be 0 if and only if the item did not match the query at all.

## Accessing a document

A document in the database is accessed via a Xapian::Document object. This can be obtained by calling Xapian::Database::get_document(). The returned `Xapian::Document` is a reference counted handle so copying is cheap.

Each document can have the following types of information associated with it:

- document data - this is an arbitrary block of data accessed using Xapian::Document::get_data(). The contents of the document data can be whatever you want and in whatever format. Often it contains fields such as a URL or other external UID, a document title, and an excerpt from the document text. If you wish to interoperate with Omega, it should contain name=value pairs, one per line (recent versions of Omega also support one field value per line, and can assign names to line numbers in the query template).
- terms and positional information - terms index the document (like index entries in the back of a book); positional information records the word offset into the document of each occurrence of a particular term. This is used to implement phrase searching and the NEAR operator.
- document values - these are arbitrary pieces of data which are stored so they can be accessed rapidly during the match process (to allow sorting collapsing of duplicates, etc). Each value is stored in a numbered slot so you can have several for each document. There's currently no length limit, but you should keep them short for efficiency.

There's some overlap in what you can do with terms and with values. A simple boolean operator (e.g. document language) is definitely better done using a term and OP_FILTER.

Using a value allows you to do things you can't do with terms, such as "sort by price", or "show only the best match for each website". You can also perform filtering with a value which is more sophisticated than can easily be achieved with terms, for example: find matches with a price between $100 and $900. Omega uses boolean terms to perform date range filtering, but this might actually be better done using a value (the code in Omega was written before values were added to Xapian).

# Specifying a relevance set

Xapian supports the idea of relevance feedback: that is, of allowing the user to mark documents as being relevant to the search, and using this information to modify the search. This is supported by means of relevance sets, which are simply sets of document ids which are marked as relevant. These are held in Xapian::RSet objects, one of which may optionally be supplied to Xapian in the `rset` parameter when calling `Xapian::Enquire::get_mset()`.

## Match options

There are various additional options which may be specified when performing the query. These are specified by calling various methods of the Xapian::Enquire object. The options are as follows.

collapse key
  Each document in a database may have a set of numbered value slots. The contents of each value slot is a string of arbitrary length. The `set_collapse_key(Xapian::valueno collapse_key)` method specifies a value slot number upon which to remove duplicates. Only the most recently set duplicate removal key is active at any time, and the default is to perform no duplicate removal.
percentage cutoff
  It may occasionally be desirable to exclude any documents which have a weight less than a given percentage value. This may be done using `set_cutoff(Xapian::percent percent_cutoff)`.
sort direction

  Some weighting functions may frequently result in several documents being returned with the same weight. In this case, by default, the documents will be returned in ascending document id order. This can be changed by using `set_docid_order()` to set the sort direction.

  `set_docid_order(Xapian::Enquire::DESCENDING)` may be useful, for example, when it would be best to return the newest documents, and new documents are being added to the end of the database (which is what happens by default).

## Match decision functors

Sometimes it may be useful to return only documents matching criteria which can't be easily represented by queries. This can be done using a match decision functor. To set such a condition, derive a class from `Xapian::MatchDecider` and override the function operator, `operator()(const Xapian::Document &doc)`. The operator can make a decision based on the document values via `Xapian::Document::get_value(Xapian::valueno)`.

The functor will also have access to the document data stored in the database (via `Xapian::Document::get_data()`), but beware that for most database backends, this is an expensive operation to be calling for a lot of documents, so doing that is likely to slow down the search considerably.

# Expand - Suggesting new terms for the query

Xapian also supports the idea of calculating terms to add to the query, based on the relevant documents supplied. A set of such terms, together with their weights, may be returned by:

    Xapian::ESet Xapian::Enquire::get_eset(Xapian::termcount maxitems,
                               const Xapian::RSet & rset,
                   bool exclude_query_terms = true,
                   bool use_exact_termfreq = false,
                   double k = 1.0,
                   const Xapian::ExpandDecider * edecider = 0) const;
    Xapian::ESet Xapian::Enquire::get_eset(Xapian::termcount maxitems,
                               const Xapian::RSet & rset,
                               const Xapian::ExpandDecider * edecider) const

As for `get_mset`, up to `maxitems` expand terms will be returned, with fewer being returned if and only if no more terms could be found.

The expand terms are returned in sorted weight order in an Xapian::ESet item.

## exclude_query_terms

By default terms which are already in the query will never be returned by `get_eset()`. If `exclude_query_terms` is `false`) then query terms may be returned.

## use_exact_termfreq

By default, Xapian uses an approximation to the term frequency when `get_eset()` is called when searching over multiple databases. This approximation improves performance, and usually still returns good results. If you're willing to pay the performance penalty, you can get Xapian to calculate the exact term frequencies by passing `true` for `use_exact_termfreq`.

## Expand decision functors

It is often useful to allow only certain classes of term to be returned in the expand set. For example, there may be special terms in the database with various prefixes, which should be removed from the expand set. This is accomplished by providing a decision functor. To do this, derive a class from `Xapian::ExpandDecider` and override the function operator, `operator()(const string &)`. The functor is called with each term before it is added to the set, and it may accept (by returning `true`) or reject (by returning `false`) the term as appropriate.

# Thread safety

There's no pthread specific code in Xapian. If you want to use the same object concurrently from different threads, it's up to you to police access (with a mutex or in some other way) to ensure only one method is being executed at once. The reason for this is to avoid adding the overhead of locking and unlocking mutexes when they aren't required. It also makes the Xapian code easier to maintain, and simplifies building it.

For most applications, this is unlikely to be an issue - generally the calls to Xapian are likely to be from a single thread. And if they aren't, you can just create an entirely separate Xapian::Database object in each thread - this is no different to accessing the same database from two different processes.

# Examples

Examples of usage of Xapian are available in the examples subdirectory of xapian-core.