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‎.cursorignore

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.vscode
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.venv
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.env
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.cursorignore
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.gitignore
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.git
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.github
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uv.lock

‎.github/workflows/publish.yaml

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name: Automated Release Process
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on:
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push:
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branches:
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- main
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jobs:
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publish:
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runs-on: ubuntu-latest
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permissions:
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contents: write
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id-token: write
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Install uv
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uses: astral-sh/setup-uv@v5
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with:
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enable-cache: true
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python-version: "3.11"
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- name: Determine Version Change
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id: version_check
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run: |
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VERSION="v$(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version)"
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echo "Current version: $VERSION"
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LATEST_RELEASE=$(curl -s -H "Authorization: token ${{ github.token }}" \
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https://api.github.com/repos/${{ github.repository }}/releases/latest | jq -r '.tag_name')
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echo "Latest release version: $LATEST_RELEASE"
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if [ "$VERSION" != "$LATEST_RELEASE" ]; then
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echo "Version has changed."
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echo "version_changed=true" >> $GITHUB_OUTPUT
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echo "new_version=$VERSION" >> $GITHUB_OUTPUT
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else
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echo "No version change detected."
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echo "version_changed=false" >> $GITHUB_OUTPUT
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fi
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- name: Create Release
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if: steps.version_check.outputs.version_changed == 'true'
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uses: softprops/action-gh-release@v2
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with:
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tag_name: ${{ steps.version_check.outputs.new_version }}
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generate_release_notes: True
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- name: mint API token
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id: mint-token
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run: |
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# retrieve the ambient OIDC token
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resp=$(curl -H "Authorization: bearer $ACTIONS_ID_TOKEN_REQUEST_TOKEN" \
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"$ACTIONS_ID_TOKEN_REQUEST_URL&audience=pypi")
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oidc_token=$(jq -r '.value' <<< "${resp}")
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# exchange the OIDC token for an API token
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resp=$(curl -X POST https://pypi.org/_/oidc/mint-token -d "{\"token\": \"${oidc_token}\"}")
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api_token=$(jq -r '.token' <<< "${resp}")
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# mask the newly minted API token, so that we don't accidentally leak it
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echo "::add-mask::${api_token}"
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# see the next step in the workflow for an example of using this step output
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echo "api-token=${api_token}" >> "${GITHUB_OUTPUT}"
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- name: Build and publish to PyPI
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if: steps.version_check.outputs.version_changed == 'true'
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run: |
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uv build
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uv publish --token ${{ steps.mint-token.outputs.api-token }}

‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# UV
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# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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#uv.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
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.pdm.toml
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.pdm-python
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.pdm-build/
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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# Ruff stuff:
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.ruff_cache/
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# PyPI configuration file
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.pypirc

‎.python-version

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3.11

‎.vscode/settings.json

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{
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"editor.formatOnSave": true,
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"python.languageServer": "Pylance",
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"python.analysis.typeCheckingMode": "strict",
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"ruff.importStrategy": "fromEnvironment"
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}

‎LICENSE

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MIT License
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Copyright (c) 2025 Seonu Jang
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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# LLM Bridge MCP
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LLM Bridge MCP allows AI agents to interact with multiple large language models through a standardized interface. It leverages the Message Control Protocol (MCP) to provide seamless access to different LLM providers, making it easy to switch between models or use multiple models in the same application.
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## Features
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- Unified interface to multiple LLM providers:
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- OpenAI (GPT models)
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- Anthropic (Claude models)
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- Google (Gemini models)
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- DeepSeek
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- ...
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- Built with Pydantic AI for type safety and validation
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- Supports customizable parameters like temperature and max tokens
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- Provides usage tracking and metrics
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## Tools
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The server implements the following tool:
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```
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run_llm(
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prompt: str,
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model_name: KnownModelName = "openai:gpt-4o-mini",
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temperature: float = 0.7,
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max_tokens: int = 8192,
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system_prompt: str = "",
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) -> LLMResponse
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```
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- `prompt`: The text prompt to send to the LLM
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- `model_name`: Specific model to use (default: "openai:gpt-4o-mini")
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- `temperature`: Controls randomness (0.0 to 1.0)
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- `max_tokens`: Maximum number of tokens to generate
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- `system_prompt`: Optional system prompt to guide the model's behavior
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## Installation
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1. Clone the repository:
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```bash
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git clone https://github.com/yourusername/llm-bridge-mcp.git
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cd llm-bridge-mcp
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```
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2. Install [uv](https://github.com/astral-sh/uv) (if not already installed):
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```bash
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# On macOS and Linux
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curl -LsSf https://astral.sh/uv/install.sh | sh
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# On Windows
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powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
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```
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## Configuration
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Create a `.env` file in the root directory with your API keys:
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```
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OPENAI_API_KEY=your_openai_api_key
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ANTHROPIC_API_KEY=your_anthropic_api_key
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GOOGLE_API_KEY=your_google_api_key
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DEEPSEEK_API_KEY=your_deepseek_api_key
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```
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## Usage
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### Using with Claude Desktop or Cursor
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Add a server entry to your Claude Desktop configuration file or `.cursor/mcp.json`:
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```json
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"mcpServers": {
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"llm-bridge": {
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"command": "uvx",
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"args": [
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"llm-bridge-mcp"
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],
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"env": {
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"OPENAI_API_KEY": "your_openai_api_key",
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"ANTHROPIC_API_KEY": "your_anthropic_api_key",
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"GOOGLE_API_KEY": "your_google_api_key",
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"DEEPSEEK_API_KEY": "your_deepseek_api_key"
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}
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}
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}
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```
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## License
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This project is licensed under the MIT License - see the LICENSE file for details.

‎llm_bridge_mcp/__init__.py

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from .main import main
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__all__ = ["main"]

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