cognee-mcp

name: test-notebook on: workflow_call: inputs: notebook-location: description: "Location of Jupyter notebook to run" required: true type: string secrets: #LLM_MODEL: # required: true #LLM_ENDPOINT: # required: true LLM_API_KEY: required: true OPENAI_API_KEY: required: true #LLM_API_VERSION: # required: true EMBEDDING_MODEL: required: true EMBEDDING_ENDPOINT: required: true EMBEDDING_API_KEY: required: true EMBEDDING_API_VERSION: required: true env: RUNTIME__LOG_LEVEL: ERROR jobs: run_notebook_test: name: test runs-on: ubuntu-22.04 defaults: run: shell: bash steps: - name: Check out uses: actions/checkout@master - name: Cognee Setup uses: ./.github/actions/cognee_setup with: python-version: ${{ inputs.python-version }} extra-dependencies: "notebook" - name: Execute Jupyter Notebook env: ENV: 'dev' #LLM_MODEL: ${{ secrets.LLM_MODEL }} #LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }} OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} # Use OpenAI Until a multimedia model is deployed and DeepEval support for other models is added #LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} run: | uv run jupyter nbconvert \ --to notebook \ --execute ${{ inputs.notebook-location }} \ --output executed_notebook.ipynb \ --ExecutePreprocessor.timeout=1200