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Developing

Requirements

  • Python 3.11 (Download)

  • poetry (Download)

  • wkhtmltopdf (used to generate PDF reports)

    • Windows: (Download)

    • Linux: sudo apt-get install wkhtmltopdf

    • MacOS: brew install homebrew/cask/wkhtmltopdf

Install Dependencies

poetry install

LLM API access

Default values:

OPENAI_API_MODEL="gpt-4o-2024-08-06"
OPENAI_TYPE="OpenAI"
AZURE_AUTH_TYPE="Azure Key" # if OPENAI_TYPE==Azure OpenAI
DEFAULT_EMBEDDING_MODEL = "text-embedding-ada-002"

Azure OpenAI

OPENAI_TYPE="Azure OpenAI"
AZURE_OPENAI_VERSION=2023-12-01-preview
AZURE_OPENAI_ENDPOINT="https://<ENDPOINT>.azure.com/"
OPENAI_API_KEY=<OPENAI_API_KEY>
AZURE_AUTH_TYPE="Managed Identity" # if not default Azure Key

OpenAI

OPENAI_API_KEY=<OPENAI_API_KEY>

Running code-only

  • Detect Case Patterns

    • Example: See an example of how to run the code with your data to obtain results without the need to run the UI.

  • Query Text Data

    • Example: See an example of how to run the code with your data to obtain results without the need to run the UI.

  • Detect Entity Networks

    • Example: See an example of how to run the code with your data to obtain results without the need to run the UI.

🚧 Code-only workflows in progress:

  • Anonymize Case Data
  • Compare Case Groups
  • Match Entity Records

Running the UI (Streamlit)

Running via shell

poetry run poe run_streamlit

Running with docker

Download and install docker: https://www.docker.com/products/docker-desktop/

Then, via shell, in the root folder, run:

docker build . -t intelligence-toolkit

After building, run the docker container:

  • via shell:

    docker run -d -p 80:80 intelligence-toolkit --name intelligence-toolkit

  • via Docker GUI:

Open localhost:80

Do you want to share it? No need to build again.

Run

docker save --output="intelligence_toolkit.tar" intelligence-toolkit

then share the .tar file as you'd like.

In the target environment, in the same folder as the .tar file, run:

docker load --input intelligence_toolkit.tar

Once done, use the same command as above to run it.

Deploying with AWS

Wait for step 1 to be set as complete before starting step 2. The whole process will take up to 20 minutes.

  1. Launch the infrastructure deploy:

    • Give it a sugestive name since you'll be using it in the next step.

    launch-stack

  2. Launch the code deploy

    • In VPC Configuration, you should select the resources created by the previous step: VPCId, PublicSubnetAId, PublicSubnetBId, PrivateSubnetAId, PrivateSubnetBId

    launch-stack

Once step 2 it's complete, in the output tab, you'll see the deployed URL.

Note: This code doesn't have auth, so this URL will be open to the internet.

Deploying with Azure

In this tutorial, you can learn how to create the necessary services in azure.

From there, you can deploy it manually as described, or use our YAML file to automatically deploy to your environment.

Lifecycle Scripts

For Lifecycle scripts it utilizes Poetry and poethepoet to manage build scripts.

Available scripts are:

  • poetry run poe test - This will execute unit tests.
  • poetry run poe check - This will perform a suite of static checks across the package, including:
    • formatting
    • documentation formatting
    • linting
    • security patterns
    • type-checking
  • poetry run poe fix - This will apply any available auto-fixes to the package. Usually this is just formatting fixes.
  • poetry run poe fix_unsafe - This will apply any available auto-fixes to the package, including those that may be unsafe.
  • poetry run poe format - Explicitly run the formatter across the package.