Fix RAG inference
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@@ -16,9 +16,7 @@ jobs:
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runs-on: Windows
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# *** KEY CHANGE ***
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# Changed the shell from 'pwsh' to 'powershell' to use the default
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# Windows PowerShell, which should be available on your runner.
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# Set the default shell to PowerShell, which is native to your Windows runner.
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defaults:
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run:
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shell: powershell
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@@ -41,24 +39,38 @@ jobs:
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if (-not (Test-Path -Path ".venv")) {
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python -m venv .venv
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}
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# The activation command is different for PowerShell
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.\.venv\Scripts\Activate.ps1
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# Step 4: Install or update dependencies
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- name: Install dependencies
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run: |
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# The venv is now active for this shell session, so we can call pip directly.
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pip install --upgrade pip
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pip install -r requirements.txt
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# Step 5: Run the indexing script within the virtual environment
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# Step 5: *** NEW - GPU Diagnostics ***
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# This step will help us see if the runner can access the GPU and CUDA.
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- name: Check GPU and CUDA status
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run: |
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echo "--- Checking for nvidia-smi ---"
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# The '|| $true' part ensures the workflow doesn't fail if the command isn't found
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nvidia-smi || $true
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echo "--- Checking PyTorch CUDA availability ---"
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# This command will explicitly tell us if PyTorch can see the GPU.
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python -c "import torch; print(f'PyTorch version: {torch.__version__}'); print(f'CUDA available: {torch.cuda.is_available()}'); print(f'CUDA version: {torch.version.cuda}'); print(f'Device count: {torch.cuda.device_count()}')"
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# Step 6: Run the indexing script within the virtual environment
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- name: Run indexing script
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run: |
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# Call python directly, as the correct one is now on the PATH from the activated venv.
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python create_index.py
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env:
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GITHUB_REPOSITORY: ${{ github.repository }}
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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# Optional: Specify the working directory if your bot lives in a subfolder
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# working-directory: ./path/to/your/bot
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# Step 7: Upload the database as an artifact
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- name: Upload database artifact
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uses: actions/upload-artifact@v4
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with:
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name: chroma_db_artifact
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path: ./chroma_db
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