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Azure AI Document Processing Samples - devcontainer

This devcontainer provides a development environment for running the samples in this repository. It includes all of the necessary tools and dependencies to setup and run every sample provided.

Note

If there are any issues with the devcontainer, please open an issue in the repository.

Tools

The following tools are included in the devcontainer:

  • Git - Used for version control.
  • PowerShell Core - Used for running deployment scripts for the necessary infrastructure.
  • Azure CLI - Used to managed the Azure resources.
  • Azure Developer CLI - Used to manage the Azure resources.
  • Python 3.12 - Used for Python samples.
  • GitHub CLI - Used to interact with the GitHub repository.
  • Docker-in-Docker - Used to run Docker containers from within the devcontainer.

VS Code Extensions

The following VS Code extensions are included in the devcontainer:

  • Python - Python language support.
  • Pylance - Python language server.
  • Python Debugger - Python debugging support.
  • Jupyter - Jupyter notebook support.
  • Bicep - Bicep language support.
  • Azure Tools - Azure resource management support.
  • PowerShell - PowerShell language support.
  • GitHub Pull Requests - GitHub pull request support.

Python Dependencies

The following Python dependencies are included in the devcontainer:

  • azure-ai-documentintelligence - To interact with the Azure AI Document Intelligence service.
  • azure-core - To interact with the Azure services.
  • azure-identity - To authenticate with the Azure services.
  • azure-storage-blob - To interact with the Azure Blob Storage service.
  • ipycanvas - To create interactive canvases in Jupyter notebooks.
  • ipykernel - To create Jupyter kernels.
  • matplotlib - To create plots in Jupyter notebooks.
  • notebook - To create Jupyter notebooks.
  • numpy - To work with numerical data.
  • openai - To interact with the Azure OpenAI service.
  • opencv-python - To work with images.
  • openpyxl - To work with Excel files.
  • pandas - To work with data.
  • pdf2image - To convert PDF files to images.
  • pydantic - To work with data models.
  • pytesseract - To extract text from images.
  • python-dotenv - To load environment variables from a .env file.
  • seaborn - To create plots in Jupyter notebooks.
  • tabulate - To create tables in Jupyter notebooks.
  • tiktoken - To calculate confidence scores for structured outputs using OpenAI's logprobs.