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
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
To install pyduckling, you can use both conda and pip package managers:
# Using pip
pip install pyduckling-native-phihosℹ️ Right now, we only provide package distributions for Linux (x86_64).
The following table shows which PyDuckling version corresponds to which Duckling version
| PyDuckling | Duckling |
|---|---|
| 0.2.0 | v0.2.0.0 (commit 7520daa) |
| 0.2.1 (unreleased) | v0.2.0.0 (commit 7520daa) |
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 |
"[email protected]" | {"value":"[email protected]"} |
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"} |
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.
The only thing you need is a running Docker daemon
and permission to run docker build and docker run.
Then just run
./build.shAll 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>.whlBesides Rust and Stack, you will require the latest version of maturin installed to compile this project locally:
pip install maturin tomlFirst, 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 buildThen, you will need to move the resulting binary libducklingffi.a to the ext_lib folder:
cp duckling-ffi/libducklingffi.a ext_libAfter completing this procedure, it is possible to execute the following command to compile pyduckling:
maturin developIn 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 .We use pytest to run tests as it follows (after calling maturin develop):
pytest -v duckling/testsPlease see our CHANGELOG file to learn more about our new features and improvements.
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.