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# Customization of the Database
This section explains in detail how to configure a Nominatim import and
the various means to use external data.
## External postcode data
Nominatim creates a table of known postcode centroids during import. This table
is used for searches of postcodes and for adding postcodes to places where the
OSM data does not provide one. These postcode centroids are mainly computed
from the OSM data itself. In addition, Nominatim supports reading postcode
information from an external CSV file, to supplement the postcodes that are
missing in OSM.
To enable external postcode support, simply put one CSV file per country into
your project directory and name it `<CC>_postcodes.csv`. `<CC>` must be the
two-letter country code for which to apply the file. The file may also be
gzipped. Then it must be called `<CC>_postcodes.csv.gz`.
The CSV file must use commas as a delimiter and have a header line. Nominatim
expects three columns to be present: `postcode`, `lat` and `lon`. All other
columns are ignored. `lon` and `lat` must describe the x and y coordinates of the
postcode centroids in WGS84.
The postcode files are loaded only when there is data for the given country
in your database. For example, if there is a `us_postcodes.csv` file in your
project directory but you import only an excerpt of Italy, then the US postcodes
will simply be ignored.
As a rule, the external postcode data should be put into the project directory
**before** starting the initial import. Still, you can add, remove and update the
external postcode data at any time. Simply
run:
```
nominatim refresh --postcodes
```
to make the changes visible in your database. Be aware, however, that the changes
only have an immediate effect on searches for postcodes. Postcodes that were
added to places are only updated, when they are reindexed. That usually happens
only during replication updates.
## Installing Tiger housenumber data for the US
Nominatim is able to use the official [TIGER](https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html)
address set to complement the OSM house number data in the US. You can add
TIGER data to your own Nominatim instance by following these steps. The
entire US adds about 10GB to your database.
1. Get preprocessed TIGER 2021 data:
cd $PROJECT_DIR
wget https://nominatim.org/data/tiger2021-nominatim-preprocessed.csv.tar.gz
2. Import the data into your Nominatim database:
nominatim add-data --tiger-data tiger2021-nominatim-preprocessed.csv.tar.gz
3. Enable use of the Tiger data in your `.env` by adding:
echo NOMINATIM_USE_US_TIGER_DATA=yes >> .env
4. Apply the new settings:
nominatim refresh --functions
See the [developer's guide](../develop/data-sources.md#us-census-tiger) for more
information on how the data got preprocessed.
## Special phrases import
As described in the [Importation chapter](Import.md), it is possible to
import special phrases from the wiki with the following command:
```sh
nominatim special-phrases --import-from-wiki
```
But, it is also possible to import some phrases from a csv file.
To do so, you have access to the following command:
```sh
nominatim special-phrases --import-from-csv <csv file>
```
Note that the two previous import commands will update the phrases from your database.
This means that if you import some phrases from a csv file, only the phrases
present in the csv file will be kept into the database. All other phrases will
be removed.
If you want to only add new phrases and not update the other ones you can add
the argument `--no-replace` to the import command. For example:
```sh
nominatim special-phrases --import-from-csv <csv file> --no-replace
```
This will add the phrases present in the csv file into the database without
removing the other ones.

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docs/admin/Tokenizers.md
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# Tokenizers
The tokenizer module in Nominatim is responsible for analysing the names given
to OSM objects and the terms of an incoming query in order to make sure, they
can be matched appropriately.
Nominatim offers different tokenizer modules, which behave differently and have
different configuration options. This sections describes the tokenizers and how
they can be configured.
!!! important
The use of a tokenizer is tied to a database installation. You need to choose
and configure the tokenizer before starting the initial import. Once the import
is done, you cannot switch to another tokenizer anymore. Reconfiguring the
chosen tokenizer is very limited as well. See the comments in each tokenizer
section.
## Legacy tokenizer
The legacy tokenizer implements the analysis algorithms of older Nominatim
versions. It uses a special Postgresql module to normalize names and queries.
This tokenizer is currently the default.
To enable the tokenizer add the following line to your project configuration:
```
NOMINATIM_TOKENIZER=legacy
```
The Postgresql module for the tokenizer is available in the `module` directory
and also installed with the remainder of the software under
`lib/nominatim/module/nominatim.so`. You can specify a custom location for
the module with
```
NOMINATIM_DATABASE_MODULE_PATH=<path to directory where nominatim.so resides>
```
This is in particular useful when the database runs on a different server.
See [Advanced installations](Advanced-Installations.md#importing-nominatim-to-an-external-postgresql-database) for details.
There are no other configuration options for the legacy tokenizer. All
normalization functions are hard-coded.
## ICU tokenizer
The ICU tokenizer uses the [ICU library](http://site.icu-project.org/) to
normalize names and queries. It also offers configurable decomposition and
abbreviation handling.
To enable the tokenizer add the following line to your project configuration:
```
NOMINATIM_TOKENIZER=icu
```
### How it works
On import the tokenizer processes names in the following three stages:
1. During the **Sanitizer step** incoming names are cleaned up and converted to
**full names**. This step can be used to regularize spelling, split multi-name
tags into their parts and tag names with additional attributes. See the
[Sanitizers section](#sanitizers) below for available cleaning routines.
2. The **Normalization** part removes all information from the full names
that are not relevant for search.
3. The **Token analysis** step takes the normalized full names and creates
all transliterated variants under which the name should be searchable.
See the [Token analysis](#token-analysis) section below for more
information.
During query time, only normalization and transliteration are relevant.
An incoming query is first split into name chunks (this usually means splitting
the string at the commas) and the each part is normalised and transliterated.
The result is used to look up places in the search index.
### Configuration
The ICU tokenizer is configured using a YAML file which can be configured using
`NOMINATIM_TOKENIZER_CONFIG`. The configuration is read on import and then
saved as part of the internal database status. Later changes to the variable
have no effect.
Here is an example configuration file:
``` yaml
normalization:
- ":: lower ()"
- "ß > 'ss'" # German szet is unimbigiously equal to double ss
transliteration:
- !include /etc/nominatim/icu-rules/extended-unicode-to-asccii.yaml
- ":: Ascii ()"
sanitizers:
- step: split-name-list
token-analysis:
- analyzer: generic
variants:
- !include icu-rules/variants-ca.yaml
- words:
- road -> rd
- bridge -> bdge,br,brdg,bri,brg
```
The configuration file contains four sections:
`normalization`, `transliteration`, `sanitizers` and `token-analysis`.
#### Normalization and Transliteration
The normalization and transliteration sections each define a set of
ICU rules that are applied to the names.
The **normalisation** rules are applied after sanitation. They should remove
any information that is not relevant for search at all. Usual rules to be
applied here are: lower-casing, removing of special characters, cleanup of
spaces.
The **transliteration** rules are applied at the end of the tokenization
process to transfer the name into an ASCII representation. Transliteration can
be useful to allow for further fuzzy matching, especially between different
scripts.
Each section must contain a list of
[ICU transformation rules](https://unicode-org.github.io/icu/userguide/transforms/general/rules.html).
The rules are applied in the order in which they appear in the file.
You can also include additional rules from external yaml file using the
`!include` tag. The included file must contain a valid YAML list of ICU rules
and may again include other files.
!!! warning
The ICU rule syntax contains special characters that conflict with the
YAML syntax. You should therefore always enclose the ICU rules in
double-quotes.
#### Sanitizers
The sanitizers section defines an ordered list of functions that are applied
to the name and address tags before they are further processed by the tokenizer.
They allows to clean up the tagging and bring it to a standardized form more
suitable for building the search index.
!!! hint
Sanitizers only have an effect on how the search index is built. They
do not change the information about each place that is saved in the
database. In particular, they have no influence on how the results are
displayed. The returned results always show the original information as
stored in the OpenStreetMap database.
Each entry contains information of a sanitizer to be applied. It has a
mandatory parameter `step` which gives the name of the sanitizer. Depending
on the type, it may have additional parameters to configure its operation.
The order of the list matters. The sanitizers are applied exactly in the order
that is configured. Each sanitizer works on the results of the previous one.
The following is a list of sanitizers that are shipped with Nominatim.
##### split-name-list
::: nominatim.tokenizer.sanitizers.split_name_list
selection:
members: False
rendering:
heading_level: 6
##### strip-brace-terms
::: nominatim.tokenizer.sanitizers.strip_brace_terms
selection:
members: False
rendering:
heading_level: 6
##### tag-analyzer-by-language
::: nominatim.tokenizer.sanitizers.tag_analyzer_by_language
selection:
members: False
rendering:
heading_level: 6
#### Token Analysis
Token analyzers take a full name and transform it into one or more normalized
form that are then saved in the search index. In its simplest form, the
analyzer only applies the transliteration rules. More complex analyzers
create additional spelling variants of a name. This is useful to handle
decomposition and abbreviation.
The ICU tokenizer may use different analyzers for different names. To select
the analyzer to be used, the name must be tagged with the `analyzer` attribute
by a sanitizer (see for example the
[tag-analyzer-by-language sanitizer](#tag-analyzer-by-language)).
The token-analysis section contains the list of configured analyzers. Each
analyzer must have an `id` parameter that uniquely identifies the analyzer.
The only exception is the default analyzer that is used when no special
analyzer was selected.
Different analyzer implementations may exist. To select the implementation,
the `analyzer` parameter must be set. Currently there is only one implementation
`generic` which is described in the following.
##### Generic token analyzer
The generic analyzer is able to create variants from a list of given
abbreviation and decomposition replacements. It takes one optional parameter
`variants` which lists the replacements to apply. If the section is
omitted, then the generic analyzer becomes a simple analyzer that only
applies the transliteration.
The variants section defines lists of replacements which create alternative
spellings of a name. To create the variants, a name is scanned from left to
right and the longest matching replacement is applied until the end of the
string is reached.
The variants section must contain a list of replacement groups. Each group
defines a set of properties that describes where the replacements are
applicable. In addition, the word section defines the list of replacements
to be made. The basic replacement description is of the form:
```
<source>[,<source>[...]] => <target>[,<target>[...]]
```
The left side contains one or more `source` terms to be replaced. The right side
lists one or more replacements. Each source is replaced with each replacement
term.
!!! tip
The source and target terms are internally normalized using the
normalization rules given in the configuration. This ensures that the
strings match as expected. In fact, it is better to use unnormalized
words in the configuration because then it is possible to change the
rules for normalization later without having to adapt the variant rules.
###### Decomposition
In its standard form, only full words match against the source. There
is a special notation to match the prefix and suffix of a word:
``` yaml
- ~strasse => str # matches "strasse" as full word and in suffix position
- hinter~ => hntr # matches "hinter" as full word and in prefix position
```
There is no facility to match a string in the middle of the word. The suffix
and prefix notation automatically trigger the decomposition mode: two variants
are created for each replacement, one with the replacement attached to the word
and one separate. So in above example, the tokenization of "hauptstrasse" will
create the variants "hauptstr" and "haupt str". Similarly, the name "rote strasse"
triggers the variants "rote str" and "rotestr". By having decomposition work
both ways, it is sufficient to create the variants at index time. The variant
rules are not applied at query time.
To avoid automatic decomposition, use the '|' notation:
``` yaml
- ~strasse |=> str
```
simply changes "hauptstrasse" to "hauptstr" and "rote strasse" to "rote str".
###### Initial and final terms
It is also possible to restrict replacements to the beginning and end of a
name:
``` yaml
- ^south => s # matches only at the beginning of the name
- road$ => rd # matches only at the end of the name
```
So the first example would trigger a replacement for "south 45th street" but
not for "the south beach restaurant".
###### Replacements vs. variants
The replacement syntax `source => target` works as a pure replacement. It changes
the name instead of creating a variant. To create an additional version, you'd
have to write `source => source,target`. As this is a frequent case, there is
a shortcut notation for it:
```
<source>[,<source>[...]] -> <target>[,<target>[...]]
```
The simple arrow causes an additional variant to be added. Note that
decomposition has an effect here on the source as well. So a rule
``` yaml
- "~strasse -> str"
```
means that for a word like `hauptstrasse` four variants are created:
`hauptstrasse`, `haupt strasse`, `hauptstr` and `haupt str`.
### Reconfiguration
Changing the configuration after the import is currently not possible, although
this feature may be added at a later time.