Lambda Feedback packages

Reusable packages for Lambda Feedback evaluation functions, originally extracted from the compareExpressions evaluation function. Each package is independently installable; together they share the lambdafeedback namespace. The app-specific core evaluation logic stays in compareExpressions.

The extraction refactor reshaped every package's API. Each package's CHANGELOG.md has the old → new migration table.

Install

The packages are distributed through git for now (not PyPI), from a release tag. They depend on each other by version, so install the ones you use and the packages they depend on from the same tag:

pip install "lambdafeedback-slr-parsing @ git+https://github.com/lambda-feedback/lambda-feedback-packages.git@v0.3.0-rc.2#subdirectory=slr_parsing"
pip install "lambdafeedback-criteria @ git+https://github.com/lambda-feedback/lambda-feedback-packages.git@v0.3.0-rc.2#subdirectory=criteria"
pip install "lambdafeedback-expression-parsing @ git+https://github.com/lambda-feedback/lambda-feedback-packages.git@v0.3.0-rc.2#subdirectory=expression_parsing"
pip install "lambdafeedback-units @ git+https://github.com/lambda-feedback/lambda-feedback-packages.git@v0.3.0-rc.2#subdirectory=units"

With Poetry: lambdafeedback-units = { git = "https://github.com/lambda-feedback/lambda-feedback-packages.git", tag = "v0.3.0-rc.2", subdirectory = "units" }, and likewise for each package.

Packages

Directory	Distribution	Import	Responsibility	Depends on
slr_parsing	lambdafeedback-slr-parsing	lambdafeedback.slr_parsing	Generic SLR(1) parser engine	—
criteria	lambdafeedback-criteria	lambdafeedback.criteria	Criteria DSL parser and evaluation graph	slr_parsing
expression_parsing	lambdafeedback-expression-parsing	lambdafeedback.expression_parsing	Safe SymPy parsing, preprocessing and LaTeX input/output	slr_parsing, sympy, latex2sympy2_extended
units	lambdafeedback-units	lambdafeedback.units	Unit-system data, physical-quantity parsing and dimensional analysis	slr_parsing, expression_parsing, sympy

from lambdafeedback.expression_parsing import (
    ExpressionParams,
    SympyParsingConfig,
    parse_expression,
    preprocess_expression,
)
from lambdafeedback.units import QuantityParams, parse_quantity

params = ExpressionParams.from_dict(evaluation_params)  # JSON parameters → typed
pre = preprocess_expression(response, params, name="response")  # aliases, brackets, |x|
expr = parse_expression(pre, SympyParsingConfig.from_params(params))

quantity = parse_quantity("9.81 m/s^2", QuantityParams.from_dict(evaluation_params))  # value, unit, dimension, SI forms

evaluation_result has been retired: use lf_toolkit.evaluation.Result from toolkit-python, with CriteriaGraph.export_feedback for criteria feedback.

Each package uses a src/ layout: units/src/lambdafeedback/units/. There is deliberately no lambdafeedback/__init__.py; the namespace is PEP 420, so the separately installed packages merge under one import root.

Python 3.12 and later is supported.

Development

The repo root is a non-package Poetry project that installs every package in develop mode, plus the tooling, into one venv:

poetry install                      # creates the venv with all packages + dev tools
poetry run scripts/test.sh -q       # every package's tests (one pytest process each)
poetry run pytest units/tests       # a single package
poetry run ruff check . && poetry run ruff format --check .
poetry run mypy
poetry run python tools/parity/check.py   # diff behaviour against the v0.1 baseline

Each package's tests run in their own pytest process, because every package has a tests package and one process can't import two packages with the same name.

CI runs all of these, on Python 3.12 to 3.14.

Parity check

tools/parity/baseline.json holds 1608 probe outputs captured from the v0.1 extraction (compareExpressions' code). tools/parity/check.py rebuilds the same probes through the current API. Any difference fails, unless it is listed in tools/parity/expected_changes.json with its reason and exact new value (bug fixes only).

Building and releasing

Each package builds on its own:

cd units && poetry build

During development, sibling dependencies resolve from the monorepo as path dependencies. The published metadata keeps plain version requirements. poetry run python scripts/check_dist.py builds all four, checks the wheels and sdists (namespace layout, py.typed, no local requirements, tests and changelog in the sdist), installs the wheels into a fresh venv and runs a smoke test; CI runs it with twine check.

The four packages share one version and are released together, through git (nothing is published to PyPI yet). Bump every pyproject.toml and the >=lower,<upper ranges between the packages, then push a tag:

• Release: vMAJOR.MINOR.PATCH (e.g. v0.4.0) runs release.yml.
• Pre-release: vMAJOR.MINOR.PATCH-alpha.N, -beta.N or -rc.N (e.g. v0.4.0-rc.1) runs prerelease.yml. Python spells it 0.4.0rc1 in pyproject.toml, and the ranges must admit it: >=0.4.0rc1,<0.5.0.

Both run build-release.yml: scripts/release_version.py checks the tag against every package's version and the ranges between them, then the tests, scripts/check_dist.py and twine check run, and the wheels and sdists are attached to a GitHub Release (marked as a pre-release for pre-release tags).

Logging

The packages log at DEBUG only, through loggers named after their modules (lambdafeedback.expression_parsing.preprocessing, lambdafeedback.units.quantity, ...): how a response was preprocessed and read, how a quantity was split into value and unit, which evaluations a criteria graph ran. Failures are exceptions, not log records. To see them:

import logging

logging.basicConfig(level=logging.DEBUG)
logging.getLogger("lambdafeedback.slr_parsing").setLevel(logging.INFO)  # the parser trace is verbose

Feedback decoupling

The parsing/units code does not resolve feedback text itself, and raises typed exceptions (expression_parsing.ExpressionParsingError, units.QuantityError, both ValueErrors with structured fields such as .expression) rather than returning feedback tags for failures. Non-fatal diagnostics stay as plain structured facts instead: units.PhysicalQuantity exposes unit-like text found inside a value as reverted_units: list[RevertedUnit] (before/marked/after). Consumers decide entirely on their own whether/how to surface any of this as feedback. CriteriaGraph.export_feedback turns reached criteria into feedback on a result object such as lf_toolkit.evaluation.Result; subclass the graph and override it to export feedback differently.