Portkey

Qualifire offers a comprehensive suite of AI safety and quality guardrails that help ensure your AI applications are safe, compliant, and high-quality. The platform provides 20+ different guardrail checks covering content safety, AI quality, and compliance requirements.

Add Qualifire Credentials to Portkey

Add your Qualifire API key to Portkey:

Click on the Admin Settings button on Sidebar
Navigate to Plugins tab under Organisation Settings
Click on the edit button for the Qualifire integration
Add your Qualifire API Key - obtain this from your Qualifire account at https://app.qualifire.ai/settings/api-keys/

Add Qualifire's Guardrail Checks

Create and configure guardrails:

Navigate to the Guardrails page and click the Create button
Search for any of the Qualifire guardrail checks and click Add
Configure the specific parameters for your chosen guardrail
Set any actions you want on your check, and create the Guardrail!

Guardrail Actions allow you to orchestrate your guardrails logic. You can learn more about them in the Portkey Guardrails documentation.

Add Guardrail ID to Config

Add the Guardrail ID to your Portkey Config:

When you save a Guardrail, you’ll get an associated Guardrail ID
Add this ID to the input_guardrails or output_guardrails params in your Portkey Config
Create these Configs in Portkey UI, save them, and get an associated Config ID to attach to your requests

{
  "input_guardrails": ["guardrails-id-xxx"],
  "output_guardrails": ["guardrails-id-yyy"]
}

Make Your Request

Use your Config ID in your requests. Your requests are now protected by Qualifire’s comprehensive guardrail system!

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",
    config: "pc-***" // Supports a string config id or a config object
});

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    config="pc-***" # Supports a string config id or a config object
)

const openai = new OpenAI({
  apiKey: 'OPENAI_API_KEY',
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    apiKey: "PORTKEY_API_KEY",
    config: "CONFIG_ID"
  })
});

client = OpenAI(
    api_key="OPENAI_API_KEY", # defaults to os.environ.get("OPENAI_API_KEY")
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key="PORTKEY_API_KEY", # defaults to os.environ.get("PORTKEY_API_KEY")
        config="CONFIG_ID"
    )
)

curl https://api.portkey.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-config: $CONFIG_ID" \
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [{
        "role": "user",
        "content": "Hello!"
      }]
  }'

For more details on Configs, refer to the Portkey Config documentation.

Available Guardrail Checks

Qualifire provides a comprehensive set of guardrail checks organized into five main categories:

Security

Check Name	Description	Parameters	Supported Hooks
PII Check	Checks that neither the user nor the model included PIIs	None	`beforeRequestHook`, `afterRequestHook`
Prompt Injections Check	Checks that the prompt does not contain any injections to the model	None	`beforeRequestHook`

Safety

Check Name	Description	Parameters	Supported Hooks
Content Moderation Check	Checks for harmful content including sexual content, harassment, hate speech, and dangerous content in the user input or model output	None	`beforeRequestHook`, `afterRequestHook`

Reliability

Check Name	Description	Parameters	Supported Hooks
Instruction Following Check	Checks that the model followed the instructions provided in the prompt	None	`afterRequestHook`
Grounding Check	Checks that the model is grounded in the context provided	`mode` (optional) - See Mode Parameter	`afterRequestHook`
Hallucinations Check	Checks that the model did not hallucinate	`mode` (optional) - See Mode Parameter	`afterRequestHook`
Tool Use Quality Check	Checks the model’s tool use quality. Including correct tool selection, parameters and values	`mode` (optional) - See Mode Parameter	`afterRequestHook`

Policy

Check Name	Description	Parameters	Supported Hooks
Policy Violations Check	Checks that the prompt and response didn’t violate any given policies	`policies` (array of strings) - See Policy Violations Check `mode` (optional) - See Mode Parameter `policy_target` (optional) - See Policy Violations Check	`beforeRequestHook`, `afterRequestHook`

Configuration Examples

Mode Parameter

Several guardrail checks support a mode parameter that controls the trade-off between accuracy and speed:

quality: Highest accuracy, slower processing
balanced: Good balance between accuracy and speed (default)
speed: Fastest processing, lower accuracy

{
  "mode": "quality"
}

Policy Violations Check

For the Policy Violations Check, you can specify custom policies to enforce, the mode, and the target:

{
  "policies": [
    "The model cannot provide any discount to the user",
    "The model must not share internal company information",
    "The model must respond in a professional tone"
  ],
  "mode": "balanced",
  "policy_target": "both"
}

Parameters

policies (required): Array of strings defining custom policies to enforce
mode (optional): One of quality, balanced, or speed. Default: balanced
policy_target (optional): One of input, output, or both. Specifies whether to run the policy check on the request, response, or both. This must match the configured hooks:
- input: Only for beforeRequestHook
- output: Only for afterRequestHook
- both: For both beforeRequestHook and afterRequestHook

Use Cases

Content Moderation

Filter harmful or inappropriate content in user inputs and AI responses

Compliance

Ensure AI responses adhere to company policies and regulatory requirements

Quality Assurance

Detect hallucinations, instruction violations, and poor tool usage

Data Protection

Prevent PII exposure and ensure data privacy

Observability

You can see the verdict and any actions taken directly in your Portkey logs! Monitor guardrail results to understand how your AI applications are performing and identify areas for improvement.

Start with a monitor action on your guardrails to observe results before switching to block in production. This helps you tune thresholds without disrupting users.

Get Support

If you face any issues with the Qualifire integration, join the Portkey community forum for assistance. For Qualifire-specific support, visit the Qualifire Documentation or contact the Qualifire support team.

Get Started

Evaluations

Guardrails

Observability

Prompt Management

Integrations

Available Guardrail Checks

Configuration Examples

Mode Parameter

Policy Violations Check

Parameters

Use Cases

Content Moderation

Compliance

Quality Assurance

Data Protection

Observability

Get Support

Additional Resources

​Available Guardrail Checks

​Configuration Examples

​Mode Parameter

​Policy Violations Check

​Parameters

​Use Cases

Content Moderation

Compliance

Quality Assurance

Data Protection

​Observability

​Get Support

​Additional Resources

Available Guardrail Checks

Configuration Examples

Mode Parameter

Policy Violations Check

Parameters

Use Cases

Observability

Get Support

Additional Resources