Credo AI Use Case Custom Field Patcher

Given a list of custom field names, this utility updates their values for corresponding use cases in the Credo AI platform. It operates in 3 phases:

1. Read mapping data from an input CSV file.
2. Map field-value pairs to their corresponding use cases.
3. Submit PATCH requests to update custom field values for specified use cases through the Credo AI API.

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🗺️ Overview

For each use case (row) in the provided CSV:

• The script identifies the use case by its use_case_id (an alias for the default id column).
• For each field specified in the configuration, it:
  • Extracts the corresponding value from the CSV row.
  • Casts that value as a string.
  • Sends a PATCH request(s) to update the custom field(s) for that specific use case.

This allows for batch updating multiple custom fields across many use cases in an automated and reliable manner. For example, if each use case had 3 custom fields to update, the script would update field_1, field_2, and field_3 on use_case_1 before moving to use_case_2.

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⚡ Quickstart

1. Install Python 3.13.2.
2. Install Git Bash (Windows only). On macOS/Linux, use the native terminal.
3. Open an IDE (e.g., VS Code).
4. Clone this repository.
5. Set up a virtual environment.
   1. Ensure virtualenv is installed: pip install virtualenv.
   2. Create a virtual environment: virtualenv venv --python=python3.13.2
   3. Activate the newly-created virtual environment:
      1. On macOS/Linux: venv/bin/activate
      2. On Windows (Git Bash or PowerShell): source venv/Scripts/activate
   4. Install dependencies: pip install -r requirements.txt
   5. Adjust the config.yaml to your current needs.
   6. Run in dry-run mode: python patcher.py config.yaml --dry-run

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🧰 Requirements

• Python 3.13.2
• Install required packages with:

  pip install -r requirements.txt

• See requirements.txt for specifics.

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⚙️ Configuration

Create a config.yaml file with the following structure:

# Path to the CSV file
csv_path: "path/to/your_file.csv"

# Base URL
base_url: "https://api.your-url.com"

# API authentication token (which should be read from an environment variable)
api_token: "${CREDO_AI_API_TOKEN}"

# Tenant identifier for the API
tenant: "your_tenant_id_here"

# Number of IDs (rows) to process. Leave empty or null to process all.
num_ids:

# List of custom field names to update
custom_field_names:
  - "Deployment Date"
  - "Business Type"
  - ...

📖 Key Definitions:

• csv_path (str): Path to the input CSV file.
• base_url (str): Base URL to CredoAI API/app server
• api_token (str): Bearer token used to authenticate with the Credo AI API.
• tenant (str): The organization or workspace identifier used to form the API path.
• num_ids (Union[int, NoneType]): The number of use cases to process, starting from the first row of data the input CSV (i.e., row 1, since row 0 is the header).
• custom_field_names (List[str]): A list of field names you intend to update. These fields must exist as column headers in the CSV.

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🧮 CSV Format

The CSV input file must include:

• An id column (which is renamed to use_case_id in the code for clarity).
• A column for each field specified in custom_field_names.

Example:

id	Deployment Date	Business Type	...	Is a Vendor
2p8KNZ7YShyEqkdSUhinT2	2024-12-01	Research	...	TRUE
Q5bGNjz8HDzAhg4ndE2pum	2025-01-15	Commercial	...	FALSE
...	...	...	...	...
Q5bGNjz8HDzAhg4ndE2pum	2025-02-29	IOPS	...	TRUE

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🖥️ Usage

Run this script in dry-run mode first (i.e. with --dry-run). In dry-run mode, the script logs the intended PATCH requests, including URLs and payloads, without sending them to the server.

🧪 Dry-run Mode

To simulate the API requests without making actual changes (for validation/testing):

python patcher.py config.yaml --dry-run

✅ Basic Run

To run the script and perform real PATCH operations:

python patcher.py config.yaml

🎛️ Default Config

If no config file is specified, it will default to config.yaml:

python patcher.py --dry-run

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🎯 Key Script Characteristics

1. Validation: The script checks that all fields listed in the config exist in the CSV.
2. Progress Tracking: Displays a dynamic progress bar for real-time feedback.
3. Error Handling: Logs clear error messages for missing config values, CSV read errors, and API call failures.
4. Request Throttling: Introduces a small wait time (0.5 seconds) between API calls to prevent server overload.

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📞 Support

For questions or technical issues, please contact your Credo AI technical representative.

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