Example: Creating Editor Agents

The following example uses ChatGPT-4o to automatically update classifications in Encord each time the Agent triggers. The example uses FastAPI as a server to host and run the code. The server returns an empty 200 response, indicating that the request has succeeded and causing the Label Editor to refresh.

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Assumptions

This example makes the following assumptions:

• Encord’s payload uses the application/json content type in its headers.

• The payload includes the following data fields:

  • projectHash: A unique identifier for the project.
  • dataHash: A unique identifier for the data item.
  • frame: The frame number within the data item.

Here’s an example of the payload structure:

{
  "dataHash": "038ed92d-dbe8-4991-a3aa-6ede35d6e62d",
  "projectHash": "027e0c65-c53f-426d-9f02-04fafe8bd80e",
  "frame": 10
}

• You can authenticate with OpenAI. This example sets an environment variable.
• You can authenticate with Encord using an API key. This example sets an environment variable.

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Step 1: Create a directory to host your code

Create a directory containing the following files.

• requirements.txt: Installs all the dependencies necessary for the example to work.
• openai_api_agent.py: Makes the call to the OpenAI API. Ensure that you replace <classification_name> with the name of the classification you want GPT 4o to update.
• data_models.py: Ensures that the response from OpenAI has the specific format required by Encord.
• dependencies.py: Retrieves the information necessary for the query to be made and the label row to be updated.
• main.py: Runs the program in the correct order.

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Step 2: Test your Agent locally

We strongly recommend you test your Agent locally before deploying it to your server.

1. Create a new virtual environment and install all the requirements using the following terminal commands:

$ python -m venv venv
$ source venv/bin/activate
$ python -m pip install -r requirements.txt

2. Run your Agent and server locally using the following terminal command. Ensure that you replace:

• <private_key_path> with the path to your private Encord API key.
• <openai_key_path> with the path / key to your OpenAI API key.

ENCORD_SSH_KEY_FILE=\<private_key_path> OPENAI_API_KEY='<openai_key_path>' fastapi dev main.py

3. In a new terminal window, point the server to a specific data asset in Encord. This step replicates the triggering of the Agent in the Encord platform. Ensure that you:

• Replace <project_hash> with the hash of your Project.
• Replace <data_hash> with the hash of the data unit you want to run your Agent on.

curl localhost:8000 -H "Content-type: application/json" -d '{
    "projectHash": "<project_hash>",
    "dataHash": "<data_hash>",
    "frame": 0
}'

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Step 3: Deploy the agent to your server

The Agent deploys to your server using a container image, such as Docker. The exact method of deployment varies depending on your choice of server.

• Install the requirements from the requirements.txt file. Documentation for creating a Docker image for FastAPI can be found here.

• Ensure that you set up environment variables to authenticate with Encord and OpenAI. Information on authenticating with OpenAI can be found in their documentation here.

  • ENCORD_SSH_KEY_FILE=/path/to/your/private_key_file
  • OPENAI_API_KEY=<your_openai_api_key>