GitHub actions implemntation - #4
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d: systems/docgpt/main.py
PR Change LogChanged Files SummaryOverall: 14 files changed, 1004 insertions(+), 3 deletions(-) Files Changed
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Dependency ChangesWarningsNo large file changes detected. Updated: 2026-02-11T18:24:25.011Z |
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This pull request introduces a comprehensive set of improvements to the project's automation, configuration, and error logging. The main focus is on setting up robust CI/CD workflows, automated PR labeling, dependency and security checks, and enhancing error reporting for document ingestion in the DocGPT subproject. These changes aim to streamline development processes, improve code quality, and provide clearer diagnostics.
CI/CD and Automation Workflows:
.github/workflows/ci.ymlto run linting, formatting, type checks, and tests across multiple Python versions, including specialized testing for the DocGPT subproject..github/workflows/codeql.ymlfor automated CodeQL security analysis on pushes, pull requests, and a weekly schedule..github/workflows/dependency-review.ymlto review dependencies on PRs, fail on high-severity vulnerabilities, and deny GPL/AGPL licenses..github/workflows/stale.ymlto automatically mark and close stale issues and PRs based on inactivity, with configurable exemption labels..github/workflows/pr-changelog.ymlto automatically comment a detailed change log summary on PRs, including file stats, directory breakdown, dependency changes, and large file warnings.Automated Labeling:
.github/labeler.ymlto map labels to file path patterns for core framework, metrics, data ingestion, DocGPT, tests, documentation, CI/CD, dependencies, config, and Docker..github/workflows/auto-label.ymlto automatically label PRs based on changed files and PR size, removing old size labels and applying new ones according to the number of code changes.Configuration and Dependency Management:
pyproject.tomlfor project metadata, dependencies, and tool configurations (Ruff, Mypy, Pytest), supporting optional dependencies for development, Excel, and BibTeX.DocGPT Error Logging Improvements:
systems/docgpt/main.pyby replacing print statements with structured logging, capturing detailed metadata and exception info for failed document ingestions, and summarizing failed files for easier debugging. [1] [2] [3]Testing Infrastructure:
tests/conftest.pywith shared pytest fixtures for RAG evaluation, including default and faithfulness-only evaluators, sample data, and batch data for testing.