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72 lines
2.0 KiB
YAML
72 lines
2.0 KiB
YAML
name: test-notebook
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on:
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workflow_call:
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inputs:
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notebook-location:
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description: "Location of Jupyter notebook to run"
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required: true
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type: string
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ci-image:
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required: false
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type: string
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default: ''
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secrets:
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#LLM_MODEL:
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# required: true
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#LLM_ENDPOINT:
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# required: true
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LLM_API_KEY:
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required: true
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LLM_ARGS:
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required: false
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OPENAI_API_KEY:
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required: true
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#LLM_API_VERSION:
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# required: true
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EMBEDDING_MODEL:
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required: true
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EMBEDDING_API_KEY:
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required: true
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env:
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RUNTIME__LOG_LEVEL: ERROR
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jobs:
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run_notebook_test:
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name: test
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runs-on: ubuntu-22.04
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container: ${{ inputs.ci-image != '' && fromJSON(format('{{"image":"{0}","credentials":{{"username":"{1}","password":"{2}"}}}}', inputs.ci-image, github.actor, github.token)) || null }}
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defaults:
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run:
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shell: bash
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steps:
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- name: Check out
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uses: actions/checkout@master
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: ${{ inputs.python-version }}
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extra-dependencies: "notebook"
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- name: Execute Jupyter Notebook
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env:
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ENV: 'dev'
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#LLM_MODEL: ${{ secrets.LLM_MODEL }}
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#LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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LLM_ARGS: ${{ secrets.LLM_ARGS }}
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} # Use OpenAI Until a multimedia model is deployed and DeepEval support for other models is added
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#LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_DIMENSIONS: 300
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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run: |
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uv run jupyter nbconvert \
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--to notebook \
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--execute ${{ inputs.notebook-location }} \
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--output executed_notebook.ipynb \
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--ExecutePreprocessor.timeout=1200
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