Skip to main content

Rust-based Python wrapper for duckling library in Haskell.

Project description

PyDuckling

Project License - MIT pypi version Downloads PyPI status Linux Tests

Copyright © 2020– Treble.ai

ℹ️ This is a fork of the original pyduckling-native library. There are differences to the original:

  • Build against the latest version of Duckling
  • Supported Python versions range from 3.8 to 3.12
  • x86_64 Linux only, but contributions welcome if you dare to take on the challenge

Overview

This package provides native bindings for Facebook's Duckling in Python. This package supports all dimensions and languages available on the original library, and it does not require to spawn a Haskell server and does not use HTTP to call the Duckling API.

ℹ️ This package is completely Haskell-less

Installing

To install pyduckling, you can use both conda and pip package managers:

# Using pip
pip install pyduckling-native-phihos

ℹ️ Right now, we only provide package distributions for Linux (x86_64).

Version Matrix

The following table shows which PyDuckling version corresponds to which Duckling version

PyDuckling Duckling
0.2.0 v0.2.0.0 (commit 7520daa)

Package usage

PyDuckling provides access to the parsing capabilities of Duckling used to extract structured data from text.

# Core imports
from duckling import (load_time_zones, parse_ref_time,
                      parse_lang, default_locale_lang, parse_locale,
                      parse_dimensions, parse, Context)
# Install with pip install pendulum
import pendulum

# Load reference time for time parsing
time_zones = load_time_zones("/usr/share/zoneinfo")
bog_now = pendulum.now('America/Bogota').replace(microsecond=0)
ref_time = parse_ref_time(
    time_zones, 'America/Bogota', bog_now.int_timestamp)

# Load language/locale information
lang_es = parse_lang('ES')
default_locale = default_locale_lang(lang_es)
locale = parse_locale('ES_CO', default_locale)

# Create parsing context with time and language information
context = Context(ref_time, locale)

# Define dimensions to look-up for
valid_dimensions = ["amount-of-money", "credit-card-number", "distance",
                    "duration", "email", "number", "ordinal",
                    "phone-number", "quantity", "temperature",
                    "time", "time-grain", "url", "volume"]

# Parse dimensions to use
output_dims = parse_dimensions(valid_dimensions)

# Parse a phrase
result = parse('En dos semanas', context, output_dims, False)

This wrapper allows access to all the dimensions and languages available on Duckling:

Dimension Example input Example value output
amount-of-money "42€" {"value":42,"type":"value","unit":"EUR"}
credit-card-number "4111-1111-1111-1111" {"value":"4111111111111111","issuer":"visa"}
distance "6 miles" {"value":6,"type":"value","unit":"mile"}
duration "3 mins" {"value":3,"minute":3,"unit":"minute","normalized":{"value":180,"unit":"second"}}
email "duckling-team@fb.com" {"value":"duckling-team@fb.com"}
number "eighty eight" {"value":88,"type":"value"}
ordinal "33rd" {"value":33,"type":"value"}
phone-number "+1 (650) 123-4567" {"value":"(+1) 6501234567"}
quantity "3 cups of sugar" {"value":3,"type":"value","product":"sugar","unit":"cup"}
temperature "80F" {"value":80,"type":"value","unit":"fahrenheit"}
time "today at 9am" {"values":[{"value":"2016-12-14T09:00:00.000-08:00","grain":"hour","type":"value"}],"value":"2016-12-14T09:00:00.000-08:00","grain":"hour","type":"value"}
url "https://api.wit.ai/message?q=hi" {"value":"https://api.wit.ai/message?q=hi","domain":"api.wit.ai"}
volume "4 gallons" {"value":4,"type":"value","unit":"gallon"}

Dependencies

To compile pyduckling, you will require the latest nightly release of Rust, alongside Cargo. Also, it requires a Python distribution with its corresponding development headers. Finally, this project depends on the following Cargo crates:

  • PyO3: Library used to produce Python bindings from Rust code.
  • Maturin: Build system to build and publish Rust-based Python packages

Additionally, this package depends on Duckling-FFI, used to compile the native interface to Duckling on Haskell. In order to compile Duckling-FFI, you will require the Stack Haskell manager.

Installing locally

Via Docker

The only thing you need is a running Docker daemon and permission to run docker build and docker run. Then just run

./build.sh

All build dependencies are already installed in container images. The build script will get them and start containers for building the Haskell lib and the Python lib. The directories .cache, duckling-ffi/.stack-work and target will appear. The first two are for accelerating future builds and the last one will contain your final build result.

After the build.sh completed successfully, you can find wheel files (binary distributions of the library) inside target/wheels. The Python version (e.g. Python 3.11 = cp311), the libc variant (manylinux or musllinux, if you are unsure you probably need manylinux) and the CPU architecture (currently only x86_64) are encoded in the file name. Just pick the file matching to your system and install it with:

pip install -U target/wheels/<myfile>.whl

Manually

Besides Rust and Stack, you will require the latest version of maturin installed to compile this project locally:

pip install maturin toml

First, you will need to compile Duckling-FFI in order to produce the shared library libducklingffi, to do so, you can use the git submodule found at the root of this repository:

cd duckling-ffi
stack build

Then, you will need to move the resulting binary libducklingffi.a to the ext_lib folder:

cp duckling-ffi/libducklingffi.a ext_lib

After completing this procedure, it is possible to execute the following command to compile pyduckling:

maturin develop

In order to produce wheels, maturin build can be used instead. This project supports PEP517, thus pip can be used to install this package as well:

pip install -U .

Running tests

We use pytest to run tests as it follows (after calling maturin develop):

pytest -v duckling/tests

Changelog

Please see our CHANGELOG file to learn more about our new features and improvements.

Contribution guidelines

We follow PEP8 and PEP257 for pure python packages and Rust to compile extensions. We use MyPy type annotations for all functions and classes declared on this package. Feel free to send a PR or create an issue if you have any problem/question.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

pyduckling_native_phihos-0.2.0-cp312-cp312-musllinux_1_2_x86_64.whl (12.9 MB view details)

Uploaded CPython 3.12 musllinux: musl 1.2+ x86-64

pyduckling_native_phihos-0.2.0-cp312-cp312-manylinux_2_28_x86_64.whl (13.0 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.28+ x86-64

pyduckling_native_phihos-0.2.0-cp311-cp311-musllinux_1_2_x86_64.whl (12.9 MB view details)

Uploaded CPython 3.11 musllinux: musl 1.2+ x86-64

pyduckling_native_phihos-0.2.0-cp311-cp311-manylinux_2_28_x86_64.whl (13.0 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.28+ x86-64

pyduckling_native_phihos-0.2.0-cp310-cp310-musllinux_1_2_x86_64.whl (12.9 MB view details)

Uploaded CPython 3.10 musllinux: musl 1.2+ x86-64

pyduckling_native_phihos-0.2.0-cp310-cp310-manylinux_2_28_x86_64.whl (13.0 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.28+ x86-64

pyduckling_native_phihos-0.2.0-cp39-cp39-musllinux_1_2_x86_64.whl (12.9 MB view details)

Uploaded CPython 3.9 musllinux: musl 1.2+ x86-64

pyduckling_native_phihos-0.2.0-cp39-cp39-manylinux_2_28_x86_64.whl (13.0 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.28+ x86-64

pyduckling_native_phihos-0.2.0-cp38-cp38-musllinux_1_2_x86_64.whl (12.9 MB view details)

Uploaded CPython 3.8 musllinux: musl 1.2+ x86-64

pyduckling_native_phihos-0.2.0-cp38-cp38-manylinux_2_28_x86_64.whl (13.0 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.28+ x86-64

File details

Details for the file pyduckling_native_phihos-0.2.0-cp312-cp312-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pyduckling_native_phihos-0.2.0-cp312-cp312-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 8d59a4aac80b672756009521ec2a397a5cdf358feaa68e0d386ba30c41200f3d
MD5 e7b05e85523f3190a365383161a6461b
BLAKE2b-256 8e4ad213e7cfbffd33141d24a1fa4f60abe5f2063c655ec575ebad149a45c734

See more details on using hashes here.

Provenance

File details

Details for the file pyduckling_native_phihos-0.2.0-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pyduckling_native_phihos-0.2.0-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 7391852727cef9438c2e1d3aff5d3a54d0b3eea4ab9de3e8db7cd25a6663f384
MD5 b25c729acffa4d0a620efeba2b093170
BLAKE2b-256 319018e14542449923736a100ef95706b4c7a055aa55b304996f41b902f7bce6

See more details on using hashes here.

Provenance

File details

Details for the file pyduckling_native_phihos-0.2.0-cp311-cp311-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pyduckling_native_phihos-0.2.0-cp311-cp311-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 04f26b66a22f3209571d2bab0994989dc10aaa38b8f7527c3f0fd441e1c4143d
MD5 868f2575e3b3e686eaca8eb034660a37
BLAKE2b-256 33139a059cfc4e4e316d6f8fff65d76266d130e364e0db9d7a5201933d55d51d

See more details on using hashes here.

Provenance

File details

Details for the file pyduckling_native_phihos-0.2.0-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pyduckling_native_phihos-0.2.0-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c9de89de5f07af63987b45dbf33b70c19b9639cc150b74d68fca365f8a3ee26a
MD5 5b984266363ab0d788bc3d0621ef07fb
BLAKE2b-256 b7ad155b55b0b7738dab2e00909d2643d2dd1c7519a0fd0b33dee6e644ca72e1

See more details on using hashes here.

Provenance

File details

Details for the file pyduckling_native_phihos-0.2.0-cp310-cp310-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pyduckling_native_phihos-0.2.0-cp310-cp310-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 ea13d6ad4933d4ac27dc62ccea64bce68fa0aeddddea09a5be2640855cb3e00f
MD5 4e5057b916546bf6cd2f7a94096cd98e
BLAKE2b-256 41e7c4e628cf1e7313b9b4bf01b653e59592cbf381d2eb9b894eeb3cfd9095c0

See more details on using hashes here.

Provenance

File details

Details for the file pyduckling_native_phihos-0.2.0-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pyduckling_native_phihos-0.2.0-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 6ff3cfef4f9165b9a2dd5103fc0439a102bfc62ad8587c9bb6e12e282487b7f7
MD5 cd065b55f098b438f0c06e8abeae2c5c
BLAKE2b-256 101ebfabcb90bfcbb4aab1bb302221addbf1e607fddf232b1ab7dc429be36087

See more details on using hashes here.

Provenance

File details

Details for the file pyduckling_native_phihos-0.2.0-cp39-cp39-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pyduckling_native_phihos-0.2.0-cp39-cp39-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 e474a697847ff424bdd3bd1c675871661d757bc367d7aa840f7a7aa49dc3bb86
MD5 1e254f7c89f114056b5bcf99f310c3ee
BLAKE2b-256 8aedf9e5d053ff2cffa8e4d079cd03a6c1c1b1c2999e2a5a6d6f3edadeeafaf4

See more details on using hashes here.

Provenance

File details

Details for the file pyduckling_native_phihos-0.2.0-cp39-cp39-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pyduckling_native_phihos-0.2.0-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 75bf78efae316729e6a645db98094db7ed09de5eae2b48e8968e175cb6773f97
MD5 1554d8814d130661a652bdbea7279b25
BLAKE2b-256 157ed5987aae5f2743bb7b9e8497e08ddce3be294ece663895bd96ca5d977acf

See more details on using hashes here.

Provenance

File details

Details for the file pyduckling_native_phihos-0.2.0-cp38-cp38-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pyduckling_native_phihos-0.2.0-cp38-cp38-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 27091ab01e54afefd440a4c10d981bdba00f9f7da07008ea39df17cade874e44
MD5 2c576906083190584765fe075c369df8
BLAKE2b-256 9a9073c944e3c714fdf2a6527074fea3423558295e4dc1356673cc4b0ef7b267

See more details on using hashes here.

Provenance

File details

Details for the file pyduckling_native_phihos-0.2.0-cp38-cp38-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pyduckling_native_phihos-0.2.0-cp38-cp38-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 33bcefbe1999583a2abcb7dbbbd1008445db71bcd825d40e1b45f9e25dcfcec6
MD5 cd6035133e3e3c5cf7aab90b88a27937
BLAKE2b-256 f480dd7b5981af666128dc736a9455efce8aa60b3929aad804ff693fff59d40f

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page