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doc: architectural docs
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.github/workflows/publish.yml

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name: Publish Python Package
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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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tag-and-publish:
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runs-on: ubuntu-latest
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steps:
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# Checkout the code
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- name: Checkout code
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uses: actions/checkout@v3
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# Set up Python
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: "3.6"
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# Install dependencies
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install setuptools wheel twine
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# Extract version from setup.py
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- name: Extract version
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id: extract_version
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run: |
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VERSION=$(python setup.py --version)
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echo "PACKAGE_VERSION=$VERSION" >> $GITHUB_ENV
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# Create Git tag
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- name: Create and push Git tag
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if: ${{ github.ref_name == 'main' }}
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env:
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PACKAGE_VERSION: ${{ env.PACKAGE_VERSION }}
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run: |
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git config user.name "GitHub Actions"
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git config user.email "actions@github.com"
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git tag -a "v$PACKAGE_VERSION" -m "Release version $PACKAGE_VERSION"
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git push origin "v$PACKAGE_VERSION"
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# Build the package
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- name: Build package
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run: |
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python setup.py sdist bdist_wheel
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# Publish to PyPI
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- name: Publish package to PyPI
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env:
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TWINE_USERNAME: __token__
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TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
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run: |
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python -m twine upload dist/*

README.md

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# ChatWithSQL
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**ChatWithSQL** is a Python library that bridges the gap between natural language queries and SQL databases. Designed for reliability, security, and performance, ChatWithSQL allows developers to leverage advanced Language Learning Models (LLMs) like OpenAI, Gemini, and more to retrieve database data using simple, intuitive natural language prompts.
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![Banner.png](docs_src%2Fimages%2FBanner.png)
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## 🎯 Why ChatWithSQL?
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]
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```
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## 🗺️ Architecture
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![architecture-diagram.png](docs_src%2Fimages%2Farchitecture-diagram.png)
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## 🐛 Logging and Debugging
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ChatWithSQL includes extensive logging for better observability:

docs_src/images/Banner.png

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