Quickstart: Groundedness detection (preview)

Article
05/29/2024

Follow this guide to use Azure AI Content Safety Groundedness detection to check whether the text responses of large language models (LLMs) are grounded in the source materials provided by the users.

Prerequisites

An Azure subscription - Create one for free
Once you have your Azure subscription, create a Content Safety resource in the Azure portal to get your key and endpoint. Enter a unique name for your resource, select your subscription, and select a resource group, supported region (East US, East US2, West US, Sweden Central), and supported pricing tier. Then select Create.
The resource takes a few minutes to deploy. After it does, go to the new resource. In the left pane, under Resource Management, select API Keys and Endpoints. Copy one of the subscription key values and endpoint to a temporary location for later use.
(Optional) If you want to use the reasoning feature, create an Azure OpenAI Service resource with a GPT model deployed.
cURL or Python installed.

Check groundedness without reasoning

In the simple case without the reasoning feature, the Groundedness detection API classifies the ungroundedness of the submitted content as true or false.

cURL
Python

This section walks through a sample request with cURL. Paste the command below into a text editor, and make the following changes.

Replace <endpoint> with the endpoint URL associated with your resource.
Replace <your_subscription_key> with one of the keys for your resource.

Optionally, replace the "query" or "text" fields in the body with your own text you'd like to analyze.

curl --location --request POST '<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview' \
--header 'Ocp-Apim-Subscription-Key: <your_subscription_key>' \
--header 'Content-Type: application/json' \
--data-raw '{
  "domain": "Generic",
  "task": "QnA",
  "qna": {
       "query": "How much does she currently get paid per hour at the bank?"
  },
  "text": "12/hour",
  "groundingSources": [
    "I'm 21 years old and I need to make a decision about the next two years of my life. Within a week. I currently work for a bank that requires strict sales goals to meet. IF they aren't met three times (three months) you're canned. They pay me 10/hour and it's not unheard of to get a raise in 6ish months. The issue is, **I'm not a salesperson**. That's not my personality. I'm amazing at customer service, I have the most positive customer service \"reports\" done about me in the short time I've worked here. A coworker asked \"do you ask for people to fill these out? you have a ton\". That being said, I have a job opportunity at Chase Bank as a part time teller. What makes this decision so hard is that at my current job, I get 40 hours and Chase could only offer me 20 hours/week. Drive time to my current job is also 21 miles **one way** while Chase is literally 1.8 miles from my house, allowing me to go home for lunch. I do have an apartment and an awesome roommate that I know wont be late on his portion of rent, so paying bills with 20hours a week isn't the issue. It's the spending money and being broke all the time.\n\nI previously worked at Wal-Mart and took home just about 400 dollars every other week. So I know i can survive on this income. I just don't know whether I should go for Chase as I could definitely see myself having a career there. I'm a math major likely going to become an actuary, so Chase could provide excellent opportunities for me **eventually**."
  ],
  "reasoning": false
}'

Open a command prompt and run the cURL command.

Create a new Python file named quickstart.py. Open the new file in your preferred editor or IDE.

Replace the contents of quickstart.py with the following code. Enter your endpoint URL and key in the appropriate fields. Optionally, replace the "query" or "text" fields in the body with your own text you'd like to analyze.

import http.client
import json

conn = http.client.HTTPSConnection("<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview")
payload = json.dumps({
  "domain": "Generic",
  "task": "QnA",
  "qna": {
    "query": "How much does she currently get paid per hour at the bank?"
  },
  "text": "12/hour",
  "groundingSources": [
    "I'm 21 years old and I need to make a decision about the next two years of my life. Within a week. I currently work for a bank that requires strict sales goals to meet. IF they aren't met three times (three months) you're canned. They pay me 10/hour and it's not unheard of to get a raise in 6ish months. The issue is, **I'm not a salesperson**. That's not my personality. I'm amazing at customer service, I have the most positive customer service \"reports\" done about me in the short time I've worked here. A coworker asked \"do you ask for people to fill these out? you have a ton\". That being said, I have a job opportunity at Chase Bank as a part time teller. What makes this decision so hard is that at my current job, I get 40 hours and Chase could only offer me 20 hours/week. Drive time to my current job is also 21 miles **one way** while Chase is literally 1.8 miles from my house, allowing me to go home for lunch. I do have an apartment and an awesome roommate that I know wont be late on his portion of rent, so paying bills with 20hours a week isn't the issue. It's the spending money and being broke all the time.\n\nI previously worked at Wal-Mart and took home just about 400 dollars every other week. So I know i can survive on this income. I just don't know whether I should go for Chase as I could definitely see myself having a career there. I'm a math major likely going to become an actuary, so Chase could provide excellent opportunities for me **eventually**."
  ],
  "reasoning": false
})
headers = {
  'Ocp-Apim-Subscription-Key': '<your_subscription_key>',
  'Content-Type': 'application/json'
}
conn.request("POST", "/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))

Important

Remember to remove the key from your code when you're done, and never post your key publicly. For production, use a secure way of storing and accessing your credentials. For more information, see Azure Key Vault.

Run the application with the python command:
```
python quickstart.py
```
Wait a few moments to get the response.

To test a summarization task instead of a question answering (QnA) task, use the following sample JSON body:

{
    "domain": "Medical",
    "task": "Summarization",
    "text": "Ms Johnson has been in the hospital after experiencing a stroke.",
    "groundingSources": ["Our patient, Ms. Johnson, presented with persistent fatigue, unexplained weight loss, and frequent night sweats. After a series of tests, she was diagnosed with Hodgkin’s lymphoma, a type of cancer that affects the lymphatic system. The diagnosis was confirmed through a lymph node biopsy revealing the presence of Reed-Sternberg cells, a characteristic of this disease. She was further staged using PET-CT scans. Her treatment plan includes chemotherapy and possibly radiation therapy, depending on her response to treatment. The medical team remains optimistic about her prognosis given the high cure rate of Hodgkin’s lymphoma."],
    "reasoning": false
}

The following fields must be included in the URL:

Name	Required	Description	Type
API Version	Required	This is the API version to be used. The current version is: api-version=2024-02-15-preview. Example: `<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview`	String

The parameters in the request body are defined in this table:

Name	Description	Type
domain	(Optional) `MEDICAL` or `GENERIC`. Default value: `GENERIC`.	Enum
task	(Optional) Type of task: `QnA`, `Summarization`. Default value: `Summarization`.	Enum
qna	(Optional) Holds QnA data when the task type is `QnA`.	String
- `query`	(Optional) This represents the question in a QnA task. Character limit: 7,500.	String
text	(Required) The LLM output text to be checked. Character limit: 7,500.	String
groundingSources	(Required) Uses an array of grounding sources to validate AI-generated text. See Input requirements for limits.	String array
reasoning	(Optional) Specifies whether to use the reasoning feature. The default value is `false`. If `true`, you need to bring your own Azure OpenAI GPT-4 Turbo (1106-preview) resources to provide an explanation. Be careful: using reasoning increases the processing time.	Boolean

Interpret the API response

After you submit your request, you'll receive a JSON response reflecting the Groundedness analysis performed. Here’s what a typical output looks like:

{
    "ungroundedDetected": true,
    "ungroundedPercentage": 1,
    "ungroundedDetails": [
        {
            "text": "12/hour."
        }
    ]
}

The JSON objects in the output are defined here:

Name	Description	Type
ungroundedDetected	Indicates whether the text exhibits ungroundedness.	Boolean
ungroundedPercentage	Specifies the proportion of the text identified as ungrounded, expressed as a number between 0 and 1, where 0 indicates no ungrounded content and 1 indicates entirely ungrounded content.	Float
ungroundedDetails	Provides insights into ungrounded content with specific examples and percentages.	Array
-`text`	The specific text that is ungrounded.	String

Check groundedness with reasoning

The Groundedness detection API provides the option to include reasoning in the API response. With reasoning enabled, the response includes a "reasoning" field that details specific instances and explanations for any detected ungroundedness.

Bring your own GPT deployment

Tip

At the moment, we only support Azure OpenAI GPT-4 Turbo (1106-preview) resources and do not support other GPT types. You have the flexibility to deploy your GPT-4 Turbo (1106-preview) resources in any region. However, to minimize potential latency and avoid any geographical boundary data privacy and risk concerns, we recommend situating them in the same region as your content safety resources. For comprehensive details on data privacy, please refer to the Data, privacy and security guidelines for Azure OpenAI Service and Data, privacy, and security for Azure AI Content Safety.

In order to use your Azure OpenAI GPT4-Turbo (1106-preview) resource to enable the reasoning feature, use Managed Identity to allow your Content Safety resource to access the Azure OpenAI resource:

Enable Managed Identity for Azure AI Content Safety.

Navigate to your Azure AI Content Safety instance in the Azure portal. Find the Identity section under the Settings category. Enable the system-assigned managed identity. This action grants your Azure AI Content Safety instance an identity that can be recognized and used within Azure for accessing other resources.
Assign role to Managed Identity.

Navigate to your Azure OpenAI instance, select Add role assignment to start the process of assigning an Azure OpenAI role to the Azure AI Content Safety identity.

Choose the User or Contributor role.

Make the API request

In your request to the Groundedness detection API, set the "reasoning" body parameter to true, and provide the other needed parameters:

 {
  "reasoning": true,
  "llmResource": {
    "resourceType": "AzureOpenAI",
    "azureOpenAIEndpoint": "<your_OpenAI_endpoint>",
    "azureOpenAIDeploymentName": "<your_deployment_name>"
  }
}

cURL
Python

This section walks through a sample request with cURL. Paste the command below into a text editor, and make the following changes.

Replace <endpoint> with the endpoint URL associated with your resource.
Replace <your_subscription_key> with one of the keys for your resource.

Optionally, replace the "query" or "text" fields in the body with your own text you'd like to analyze.

curl --location --request POST '<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview' \
--header 'Ocp-Apim-Subscription-Key: <your_subscription_key>' \
--header 'Content-Type: application/json' \
--data-raw '{
  "domain": "Generic",
  "task": "QnA",
  "qna": {
       "query": "How much does she currently get paid per hour at the bank?"
  },
  "text": "12/hour",
  "groundingSources": [
    "I'm 21 years old and I need to make a decision about the next two years of my life. Within a week. I currently work for a bank that requires strict sales goals to meet. IF they aren't met three times (three months) you're canned. They pay me 10/hour and it's not unheard of to get a raise in 6ish months. The issue is, **I'm not a salesperson**. That's not my personality. I'm amazing at customer service, I have the most positive customer service \"reports\" done about me in the short time I've worked here. A coworker asked \"do you ask for people to fill these out? you have a ton\". That being said, I have a job opportunity at Chase Bank as a part time teller. What makes this decision so hard is that at my current job, I get 40 hours and Chase could only offer me 20 hours/week. Drive time to my current job is also 21 miles **one way** while Chase is literally 1.8 miles from my house, allowing me to go home for lunch. I do have an apartment and an awesome roommate that I know wont be late on his portion of rent, so paying bills with 20hours a week isn't the issue. It's the spending money and being broke all the time.\n\nI previously worked at Wal-Mart and took home just about 400 dollars every other week. So I know i can survive on this income. I just don't know whether I should go for Chase as I could definitely see myself having a career there. I'm a math major likely going to become an actuary, so Chase could provide excellent opportunities for me **eventually**."
  ],
  "reasoning": true,
  "llmResource": {
        "resourceType": "AzureOpenAI",
        "azureOpenAIEndpoint": "<your_OpenAI_endpoint>",
        "azureOpenAIDeploymentName": "<your_deployment_name>"
}'

Open a command prompt and run the cURL command.

Create a new Python file named quickstart.py. Open the new file in your preferred editor or IDE.

import http.client
import json

conn = http.client.HTTPSConnection("<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview")
payload = json.dumps({
  "domain": "Generic",
  "task": "QnA",
  "qna": {
    "query": "How much does she currently get paid per hour at the bank?"
  },
  "text": "12/hour",
  "groundingSources": [
    "I'm 21 years old and I need to make a decision about the next two years of my life. Within a week. I currently work for a bank that requires strict sales goals to meet. IF they aren't met three times (three months) you're canned. They pay me 10/hour and it's not unheard of to get a raise in 6ish months. The issue is, **I'm not a salesperson**. That's not my personality. I'm amazing at customer service, I have the most positive customer service \"reports\" done about me in the short time I've worked here. A coworker asked \"do you ask for people to fill these out? you have a ton\". That being said, I have a job opportunity at Chase Bank as a part time teller. What makes this decision so hard is that at my current job, I get 40 hours and Chase could only offer me 20 hours/week. Drive time to my current job is also 21 miles **one way** while Chase is literally 1.8 miles from my house, allowing me to go home for lunch. I do have an apartment and an awesome roommate that I know wont be late on his portion of rent, so paying bills with 20hours a week isn't the issue. It's the spending money and being broke all the time.\n\nI previously worked at Wal-Mart and took home just about 400 dollars every other week. So I know i can survive on this income. I just don't know whether I should go for Chase as I could definitely see myself having a career there. I'm a math major likely going to become an actuary, so Chase could provide excellent opportunities for me **eventually**."
  ],
  "reasoning": True
  "llmResource": {
   "resourceType": "AzureOpenAI",
   "azureOpenAIEndpoint": "<your_OpenAI_endpoint>",
   "azureOpenAIDeploymentName": "<your_deployment_name>"
  }
})
headers = {
  'Ocp-Apim-Subscription-Key': '<your_subscription_key>',
  'Content-Type': 'application/json'
}
conn.request("POST", "/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))

Run the application with the python command:
```
python quickstart.py
```
Wait a few moments to get the response.

The parameters in the request body are defined in this table:

Name	Description	Type
domain	(Optional) `MEDICAL` or `GENERIC`. Default value: `GENERIC`.	Enum
task	(Optional) Type of task: `QnA`, `Summarization`. Default value: `Summarization`.	Enum
qna	(Optional) Holds QnA data when the task type is `QnA`.	String
- `query`	(Optional) This represents the question in a QnA task. Character limit: 7,500.	String
text	(Required) The LLM output text to be checked. Character limit: 7,500.	String
groundingSources	(Required) Uses an array of grounding sources to validate AI-generated text. See Input requirements for limits,	String array
reasoning	(Optional) Set to `true`, the service uses Azure OpenAI resources to provide an explanation. Be careful: using reasoning increases the processing time and incurs extra fees.	Boolean
llmResource	(Required) If you want to use your own Azure OpenAI GPT4-Turbo (1106-preview) resource to enable reasoning, add this field and include the subfields for the resources used.	String
- `resourceType`	Specifies the type of resource being used. Currently it only allows `AzureOpenAI`. We only support Azure OpenAI GPT-4 Turbo (1106-preview) resources and do not support other GPT types.	Enum
- `azureOpenAIEndpoint`	Your endpoint URL for Azure OpenAI service.	String
- `azureOpenAIDeploymentName`	The name of the specific GPT deployment to use.	String

Interpret the API response

After you submit your request, you'll receive a JSON response reflecting the Groundedness analysis performed. Here’s what a typical output looks like:

{
    "ungroundedDetected": true,
    "ungroundedPercentage": 1,
    "ungroundedDetails": [
        {
            "text": "12/hour.",
            "offset": {
                "utf8": 0,
                "utf16": 0,
                "codePoint": 0
            },
            "length": {
                "utf8": 8,
                "utf16": 8,
                "codePoint": 8
            },
            "reason": "None. The premise mentions a pay of \"10/hour\" but does not mention \"12/hour.\" It's neutral. "
        }
    ]
}

The JSON objects in the output are defined here:

Name	Description	Type
ungroundedDetected	Indicates whether the text exhibits ungroundedness.	Boolean
ungroundedPercentage	Specifies the proportion of the text identified as ungrounded, expressed as a number between 0 and 1, where 0 indicates no ungrounded content and 1 indicates entirely ungrounded content.	Float
ungroundedDetails	Provides insights into ungrounded content with specific examples and percentages.	Array
-`text`	The specific text that is ungrounded.	String
-`offset`	An object describing the position of the ungrounded text in various encoding.	String
- `offset > utf8`	The offset position of the ungrounded text in UTF-8 encoding.	Integer
- `offset > utf16`	The offset position of the ungrounded text in UTF-16 encoding.	Integer
- `offset > codePoint`	The offset position of the ungrounded text in terms of Unicode code points.	Integer
-`length`	An object describing the length of the ungrounded text in various encoding. (utf8, utf16, codePoint), similar to the offset.	Object
- `length > utf8`	The length of the ungrounded text in UTF-8 encoding.	Integer
- `length > utf16`	The length of the ungrounded text in UTF-16 encoding.	Integer
- `length > codePoint`	The length of the ungrounded text in terms of Unicode code points.	Integer
-`reason`	Offers explanations for detected ungroundedness.	String

Clean up resources

If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps

Combine Groundedness detection with other LLM safety features like Prompt Shields.

Prompt Shields quickstart

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Quickstart: Groundedness detection (preview)

Prerequisites

Check groundedness without reasoning

Interpret the API response

Check groundedness with reasoning

Bring your own GPT deployment

Make the API request

Interpret the API response

Clean up resources

Next steps

Feedback

Feedback

Additional resources