| Name | berlin JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Identify locations and tag them with UN-LOCODEs and ISO-3166-2 subdivisions. |
| upload_time | 2024-04-17 11:52:48 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
geospatial
nlp
search
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# berlin-rs
A Python/Rust microservice to identify locations and tag them with UN-LOCODEs and
ISO-3166-2 subdivisions.
### Getting started
To test the Rust API locally:
```shell
make run
```
This will make an API available on port 3001. It serves simple requests of the
form:
```shell
curl 'http://localhost:3001/berlin/search?q=house+prices+in+londo&state=gb' | jq
```
replacing `localhost` with the local endpoint (`jq` used for formatting).
This will return results of the form:
```json
{
"time": "32.46ms",
"query": {
"raw": "house prices in londo",
"normalized": "house prices in londo",
"stop_words": [
"in"
],
"codes": [],
"exact_matches": [
"house"
],
"not_exact_matches": [
"house prices",
"prices in",
"prices",
"in londo",
"londo"
],
"state_filter": "gb",
"limit": 1,
"levenshtein_distance": 2
},
"results": [
{
"loc": {
"encoding": "UN-LOCODE",
"id": "gb:lon",
"key": "UN-LOCODE-gb:lon",
"names": [
"london"
],
"codes": [
"lon"
],
"state": [
"gb",
"united kingdom of great britain and northern ireland"
],
"subdiv": [
"lnd",
"london, city of"
]
},
"score": 1346,
"offset": {
"start": 16,
"end": 21
}
}
]
}
```
A Python wheel can also be built, using
```shell
make wheels
pip install build/wheels/berlin-0.1.0-xyz.whl
```
where `xyz` is your architecture.
Afterwards berlin should be functional inside a python shell/script. Example:
```python
import berlin
db = berlin.load('../data')
loc = db.query('manchester population', 'gb', 1)[0];
print("location:", loc.words)
```
### Description
Berlin is a location search engine which works on an in-memory collection of
all UN Locodes, subdivisions and states (countries). Here are the main
architectural highlights: On startup Berlin does a basic linguistic analysis of
the locations: split names into words, remove diacritics, transliterate
non-ASCII symbols to ASCII. For example, this allows us to find “Las Vegas”
when searching for “vegas”. It employs string interning in order to both
optimise memory usage and allow direct lookups for exact matches. If we can
resolve (parts of) the search term to an existing interned string, it means
that we have a location with this name in the database.
When the user submits the search term, Berlin first does a preliminary analysis
of the search term: 1) split into words and pairs of words 2) try to identify
the former as existing locations (can be resolved to existing interned strings)
and tag them as “exact matches”. This creates many search terms from the
original phrase. Pre-filtering step. Here we do three things 1) resolve exact
matches by direct lookup in the names and codes tables 2) do a prefix search
via a finite-state transducer 3) do a fuzzy search via a Levenshtein distance
enabled finite-state transducer. The pre-filtered results are passed through a
string-similarity evaluation algorithm and sorted by score. The results below a
threshold are truncated. A graph is built from the locations found during the
previous step in order to link them together hierarchically if possible. This
further boosts some locations. For example, if the user searches for “new york
UK” it will boost the location in Lincolnshire and it will show up higher than
New York city in the USA. It is also possible to request search only in a
specific country (which is enabled by default for the UK)
Berlin is able to find locations with a high degree of semantic accuracy. Speed
is roughly equal to 10-15 ms per every non-matching word (or typo) + 1 ms for
every exact match. A complex query of 8 words usually takes less than 100 ms
and all of the realistic queries in our test suite take less than 50 ms, while
the median is under 30 ms. Short queries containing an exact match (case
insensitive) are faster than 10 ms.
The architecture would allow to easily implement as-you-type search suggestions
in under 10 milliseconds if deemed desirable.
### License
Prepared by Flax & Teal Limited for ONS Alpha project.
Copyright © 2022, Office for National Statistics (https://www.ons.gov.uk)
Released under MIT license, see [LICENSE](LICENSE.md) for details.
### License
Prepared by Flax & Teal Limited for ONS Alpha project.
Copyright © 2022, Office for National Statistics (https://www.ons.gov.uk)
Released under MIT license, see [LICENSE](LICENSE.md) for details.
Raw data
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"description": "# berlin-rs\n\nA Python/Rust microservice to identify locations and tag them with UN-LOCODEs and\nISO-3166-2 subdivisions.\n\n\n### Getting started\n\nTo test the Rust API locally:\n\n```shell\n make run\n```\n\nThis will make an API available on port 3001. It serves simple requests of the\nform:\n\n```shell\ncurl 'http://localhost:3001/berlin/search?q=house+prices+in+londo&state=gb' | jq\n```\n\nreplacing `localhost` with the local endpoint (`jq` used for formatting).\n\nThis will return results of the form:\n\n```json\n{\n \"time\": \"32.46ms\",\n \"query\": {\n \"raw\": \"house prices in londo\",\n \"normalized\": \"house prices in londo\",\n \"stop_words\": [\n \"in\"\n ],\n \"codes\": [],\n \"exact_matches\": [\n \"house\"\n ],\n \"not_exact_matches\": [\n \"house prices\",\n \"prices in\",\n \"prices\",\n \"in londo\",\n \"londo\"\n ],\n \"state_filter\": \"gb\",\n \"limit\": 1,\n \"levenshtein_distance\": 2\n },\n \"results\": [\n {\n \"loc\": {\n \"encoding\": \"UN-LOCODE\",\n \"id\": \"gb:lon\",\n \"key\": \"UN-LOCODE-gb:lon\",\n \"names\": [\n \"london\"\n ],\n \"codes\": [\n \"lon\"\n ],\n \"state\": [\n \"gb\",\n \"united kingdom of great britain and northern ireland\"\n ],\n \"subdiv\": [\n \"lnd\",\n \"london, city of\"\n ]\n },\n \"score\": 1346,\n \"offset\": {\n \"start\": 16,\n \"end\": 21\n }\n }\n ]\n}\n```\n\nA Python wheel can also be built, using\n\n```shell\n make wheels\n pip install build/wheels/berlin-0.1.0-xyz.whl\n```\n\nwhere `xyz` is your architecture.\n\nAfterwards berlin should be functional inside a python shell/script. Example:\n\n```python\nimport berlin\n\ndb = berlin.load('../data')\nloc = db.query('manchester population', 'gb', 1)[0];\nprint(\"location:\", loc.words)\n```\n\n### Description\n\nBerlin is a location search engine which works on an in-memory collection of\nall UN Locodes, subdivisions and states (countries). Here are the main\narchitectural highlights: On startup Berlin does a basic linguistic analysis of\nthe locations: split names into words, remove diacritics, transliterate\nnon-ASCII symbols to ASCII. For example, this allows us to find \u201cLas Vegas\u201d\nwhen searching for \u201cvegas\u201d. It employs string interning in order to both\noptimise memory usage and allow direct lookups for exact matches. If we can\nresolve (parts of) the search term to an existing interned string, it means\nthat we have a location with this name in the database.\n\nWhen the user submits the search term, Berlin first does a preliminary analysis\nof the search term: 1) split into words and pairs of words 2) try to identify\nthe former as existing locations (can be resolved to existing interned strings)\nand tag them as \u201cexact matches\u201d. This creates many search terms from the\noriginal phrase. Pre-filtering step. Here we do three things 1) resolve exact\nmatches by direct lookup in the names and codes tables 2) do a prefix search\nvia a finite-state transducer 3) do a fuzzy search via a Levenshtein distance\nenabled finite-state transducer. The pre-filtered results are passed through a\nstring-similarity evaluation algorithm and sorted by score. The results below a\nthreshold are truncated. A graph is built from the locations found during the\nprevious step in order to link them together hierarchically if possible. This\nfurther boosts some locations. For example, if the user searches for \u201cnew york\nUK\u201d it will boost the location in Lincolnshire and it will show up higher than\nNew York city in the USA. It is also possible to request search only in a\nspecific country (which is enabled by default for the UK)\n\nBerlin is able to find locations with a high degree of semantic accuracy. Speed\nis roughly equal to 10-15 ms per every non-matching word (or typo) + 1 ms for\nevery exact match. A complex query of 8 words usually takes less than 100 ms\nand all of the realistic queries in our test suite take less than 50 ms, while\nthe median is under 30 ms. Short queries containing an exact match (case\ninsensitive) are faster than 10 ms.\n\nThe architecture would allow to easily implement as-you-type search suggestions\nin under 10 milliseconds if deemed desirable.\n\n\n### License\n\nPrepared by Flax & Teal Limited for ONS Alpha project.\nCopyright \u00a9 2022, Office for National Statistics (https://www.ons.gov.uk)\n\nReleased under MIT license, see [LICENSE](LICENSE.md) for details.\n\n### License\n\nPrepared by Flax & Teal Limited for ONS Alpha project.\nCopyright \u00a9 2022, Office for National Statistics (https://www.ons.gov.uk)\n\nReleased under MIT license, see [LICENSE](LICENSE.md) for details.\n\n",
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