intelligence-toolkit 0.1.2

Interactive workflows for generating AI intelligence reports from real-world data sources using GPT models

Developing

Requirements

• Python 3.11 or 3.12 (Download)

• uv (Download)

• wkhtmltopdf (used to generate PDF reports)

  • Windows: (Download)

  • Linux: sudo apt-get install wkhtmltopdf

  • MacOS: brew install homebrew/cask/wkhtmltopdf

Running the app

GPT settings

You can configure your OpenAI access when running the app via Settings page, or using environment variables.

Default values:

OPENAI_API_MODEL="gpt-4.1-mini"
OPENAI_TYPE="OpenAI" ## Other option available: Azure OpenAI
AZURE_AUTH_TYPE="Azure Key" # if OPENAI_TYPE==Azure OpenAI
DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small"

OpenAI

OPENAI_API_KEY=<OPENAI_API_KEY>

Azure OpenAI

OPENAI_TYPE="Azure OpenAI"
AZURE_OPENAI_VERSION=2023-12-01-preview
AZURE_OPENAI_ENDPOINT="https://<ENDPOINT>.azure.com/"
OPENAI_API_KEY=<AZURE_OPENAI_API_KEY>

#If Azure OpenAI using Managed Identity:
AZURE_AUTH_TYPE="Managed Identity"

Running locally

Windows: Search and open the app Windows Powershell on Windows start menu

Linux and Mac: Open Terminal

For any OS:

Navigate to the folder where you cloned this repo.

Use cd + the path to the folder. For example:

cd C:\Users\user01\projects\intelligence-toolkit

Run uv sync --extra dev and wait for the packages installation.

Run the app:

Run uv run poe run_streamlit, and it will automatically open the app in your default browser in localhost:8081

Use the API

You can also replicate the examples in your own environment running pip install intelligence-toolkit or uv add intelligence-toolkit.

See the documentation and an example of how to run the code with your data to obtain results without the need to run the UI.

• Anonymize Case Data

  • Example

• Compare Case Groups

  • Example

• Detect Case Patterns

  • Example

• Detect Entity Networks

  • Example

• Extract Record Data

  • Example

• Generate Mock Data

  • Example

• Match Entity Records

  • Example

• Query Text Data

  • Example

Running with docker

Recommended configuration:

• Minimum disk space: 8GB
• Minimum memory: 4GB

Download, install and then open docker app: https://www.docker.com/products/docker-desktop/

Then, open a terminal: Windows: Search and open the app Windows Powershell on Windows start menu

Linux and Mac: Open Terminal

For any OS:

Navigate to the folder where you cloned this repo.

Use cd + the path to the folder. For example:

cd C:\Users\user01\projects\intelligence-toolkit

Build the container:

docker build . -t intelligence-toolkit

Once the build is finished, run the docker container:

• via terminal:

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

Open localhost:80

Note that docker might sleep and you might need to start it again. Open Docker Desktop, in the left menu click on Container and press play on intelligence-toolkit.

Lifecycle Scripts

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

Available scripts are:

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