Multimodal Data with NeMo Guardrails#

About Working with Multimodal Data#

NeMo Guardrails toolkit supports adding safety checks to multimodal content—images and text. The support is for input and output guardrails only. Depending on the image reasoning model, you can specify the image to check as a base64 encoded data or as a URL.

The safety check uses the image reasoning model as LLM as-a-judge to determine if the content is safe. The OpenAI, Llama Vision, and Llama Guard models can accept multimodal input and act as a judge model.

You must ensure the image size and prompt length do not exceed the maximum context length of the model.

Sample Configuration#

  1. Create a directory, such as configs/content_safety_vision, and add a config.yml file with the following content:

  2. Add a configs/content_safety_vision/prompts.yml file with the following content:

Example#

The following sample code uses the preceding configuration and sends requests to OpenAI endpoints. The sample image is a handgun.

  1. Set the OpenAI environment variable with your token:

    export OPENAI_API_KEY=<api-key>

  2. Import required libraries:

  3. Load the vision content safety configuration:

  4. Send an image reasoning request:

  5. Send a potentially unsafe request:

Tips for Base 64 Encoded Images#

Some models, such as the Llama Vision models, do not read an image from a URL. For these models, encode the image in base 64 and provide the encoded image to the model.

The following code sample shows the common Python statements.

import base64
import json

from nemoguardrails import RailsConfig
from nemoguardrails.rails.llm.llmrails import LLMRails

config = RailsConfig.from_path("./content_safety_vision")
rails = LLMRails(config)

with open("<path-to-image>", "rb") as image_file:
  base64_image = base64.b64encode(image_file.read()).decode()

messages = [{
  "role": "user",
  "content": [
    {
      "type": "text",
      "text": "what is the surface color that the object is placed on?",
    },
    {
      "type": "image_url",
      "image_url": {
          "url": f"data:image/jpeg;base64,{base64_image}"
      },
    },
  ],
}]

response = rails.generate(messages=messages)
print(json.dumps(response, indent=2))