Qualifire

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Using Qualifire with Portkey

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1. Add Qualifire Credentials 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/

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2. Add Qualifire’s Guardrail Checks

• 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!

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Available Guardrail Checks

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Security

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Safety

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Reliability

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Policy

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Configuration Examples

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Mode Parameter

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

{
  "mode": "quality"
}

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Policy Violations Check

{
  "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"
}

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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

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Add Guardrail ID to a Config and Make Your Request

• 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. More here.

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

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!"
      }]
  }'

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Use Cases

• Content Moderation: Filtering harmful or inappropriate content in user inputs and AI responses
• Compliance: Ensuring AI responses adhere to company policies and regulatory requirements
• Quality Assurance: Detecting hallucinations, instruction violations, and poor tool usage
• Data Protection: Preventing PII exposure and ensuring data privacy

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