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feat: docker set-up for neo4j
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‎.github/workflows/docker-build.yml‎

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name: Build and Push Docker Image for Quickstart
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on:
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release:
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type: [published]
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#trigger on each commit of each branch and push with latest tag
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push :
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# pull_request:
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# paths:
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# - 'pyproject.toml'
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# - './docker/**'
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# - '.github/workflows/**'
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env:
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REGISTRY: ghcr.io
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jobs:
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build-image:
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runs-on: ubuntu-latest
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#if: github.ref != 'refs/heads/main'
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permissions:
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contents: read
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packages: write
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steps:
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# https://github.com/actions/checkout
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- name: checkout repository
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uses: actions/checkout@v4
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- name: lowercase image name
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run: |
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echo "IMAGE_NAME=${GITHUB_REPOSITORY,,}" >> ${GITHUB_ENV}
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# https://github.com/docker/setup-qemu-action
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v3.0.0
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# https://github.com/docker/setup-buildx-action
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- name: Set up Docker Buildx
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id: buildx
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uses: docker/setup-buildx-action@v3.0.0
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- name: Get current release version
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id: release-version
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run: |
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version=$(grep -E '^version += +' pyproject.toml | sed -E 's/.*= +//' | sed "s/['\"]//g")
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echo "version=${version}" >> $GITHUB_OUTPUT
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echo "version_build=${version}_"$(git rev-parse --short "$GITHUB_SHA") >> $GITHUB_OUTPUT
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# https://github.com/docker/build-push-action
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- name: Build Docker image
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uses: docker/build-push-action@v5.0.0
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with:
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context: .
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platforms: linux/amd64
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file: tools/images/Dockerfile
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push: false
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tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ steps.release-version.outputs.version_build }}
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build-args: PLATFORM=cu124
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outputs: type=image,annotation-index.org.opencontainers.image.description=Run a Graph Neural Network.
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push-image:
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runs-on: ubuntu-latest
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#if: github.ref == 'refs/heads/main'
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permissions:
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contents: read
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packages: write
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steps:
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# https://github.com/actions/checkout
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- name: checkout repository
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uses: actions/checkout@v4
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- name: lowercase image name
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run: |
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echo "IMAGE_NAME=${GITHUB_REPOSITORY,,}" >> ${GITHUB_ENV}
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# https://github.com/docker/setup-qemu-action
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v3.0.0
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# https://github.com/docker/setup-buildx-action
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- name: Set up Docker Buildx
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id: buildx
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uses: docker/setup-buildx-action@v3.0.0
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- name: Get current release version
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id: release-version
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run: |
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version=$(grep -E '^version += +' pyproject.toml | sed -E 's/.*= +//' | sed "s/['\"]//g")
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echo "version=${version}" >> $GITHUB_OUTPUT
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echo "version_build=${version}_"$(git rev-parse --short "$GITHUB_SHA") >> $GITHUB_OUTPUT
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# https://github.com/docker/login-action
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- name: Log in to the Container registry
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uses: docker/login-action@v3.0.0
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with:
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registry: ${{ env.REGISTRY }}
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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# https://github.com/docker/metadata-action
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- name: Extract metadata (tags, labels) for Docker
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id: meta
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uses: docker/metadata-action@v5.0.0
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with:
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
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tags: |
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type=raw,value=latest,enable=${{ github.event_name == 'push' }}
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type=raw,value=${{ needs.build-image.outputs.version }},enable=${{ github.event_name == 'release' }}
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# https://github.com/docker/build-push-action
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- name: Push Docker image
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uses: docker/build-push-action@v5.0.0
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with:
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context: .
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platforms: linux/amd64
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file: tools/images/Dockerfile
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push: true
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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build-args: PLATFORM=cu124
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outputs: type=image,annotation-index.org.opencontainers.image.description=Run a Graph Neural Network.

‎src/neo4j-quickstart/README.md‎

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1. Create virtual environment: `uv venv`
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2. Activate it: `source .venv/bin/activate`
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3. Get all predefined dependencies from the `uv.lock` file by running the command: `uv sync`
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3. Get all predefined dependencies from the `uv.lock` file by running the command: `uv sync`
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## Build docker
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Locally: `docker build -f tools/images/Dockerfile -t test .`
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Else there is an integrated github CI in the github workflows of this repository.

‎src/neo4j-quickstart/quickstart.ipynb‎

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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8dfd7fef",
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils.builder_models import df_to_pydantic_models\n",
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"graph = df_to_pydantic_models(df.head(200), relationships)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4f5f2f38",
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"metadata": {},
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"source": [
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"## Visualizing the Graph\n",
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"\n",
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"Here is some code snippet for visualizing the graph you have downloaded from Neo4J."
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"Here is some code snippet for visualizing the graph you have downloaded from Neo4J.\n",
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"\n",
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"**Disclaimer: if you run visualization on an entire graph of over 5000 nodes, it may take a long time or may give unreadable graphs. Be smart in your visualizations.**\n",
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"\n",
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"For CUSTOM visualizations, if you are familiar with networkx, there is a `create_networkx_graph` function in `visualization.py` from which you can kick off other graph manipulations or visualizations."
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]
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},
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{
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"cell_type": "markdown",
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"id": "ccef6835",
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"metadata": {},
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"source": [
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"### Visualize the entire graph or a subpart of the graph. \n",
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"\n",
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"We suggest: \n",
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"1. select from the dataframe (classic pandas operations)\n",
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"2. convert to the pydantic models \n",
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"3. run visualizations"
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]
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},
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{
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"id": "7df5ca1c",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"from utils.visualization import visualize_graph\n",
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"from pathlib import Path\n",
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"output_path = Path(\"plots/graphs/graph_visualization.png\")\n",
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"visualize_graph(graph, output_path)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8ad91bf7",
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"metadata": {},
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"source": [
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"### Visualize Clusters in the graph\n",
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"This function will make clusters based on the nodes that are handed over. \n",
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"We use the function from networkx `nx.weakly_connected_components(G)`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "581739d7",
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils.visualization import visualize_clusters\n",
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"from pathlib import Path\n",
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"output_dir = Path(\"plots/clusters/\")\n",
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"visualize_clusters(graph, output_dir)"
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]
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}
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],
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"metadata": {

‎tools/images/Dockerfile‎

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FROM python:3.10-slim
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# TO-DO how to generalize for other tools. i.e. CHAOSS and a graph database access
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# Install uv
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COPY --from=ghcr.io/astral-sh/uv:0.6.12 /uv /uvx /bin/
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# Copy the project into the image
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ADD src/neo4j-quickstart /app/neo4j-quickstart
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# Sync the project into a new environment, using the frozen lockfile
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WORKDIR /app
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RUN cd neo4j-quickstart && uv sync
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# make uv's python the default python for the image
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ENV PATH="/app/neo4j-quickstart/.venv/bin:$PATH"
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# Set PYTHONPATH for imports
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ENV PYTHONPATH=/app/neo4j-quickstart
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