> ## Documentation Index
> Fetch the complete documentation index at: https://docs.enkryptai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Guardrails Relevancy



## OpenAPI

````yaml POST /guardrails/relevancy
openapi: 3.0.0
info:
  title: Enkrypt AI APIs
  version: 2.0.0
servers:
  - url: https://api.enkryptai.com
security:
  - apiKeyAuth: []
tags:
  - name: Guardrails
  - name: Code of Conduct
  - name: Endpoints
  - name: Datasets
  - name: Redteam
  - name: Deployments
  - name: AI Proxy
  - name: Leaderboard
  - name: Archived
  - name: MCP Hub
    description: >-
      MCP Hub vulnerability scanning APIs. Submitting scans (the POST endpoints)
      is open to all authenticated callers. The scan **retrieval** APIs — Get
      Scan Job Status, Get Complete Scan Results, List Scans, and Get MCP Hub
      Scan Statistics (the GET endpoints) — are an **enterprise data-license
      feature**: they require your organization to have MCP Hub API access
      enabled by Enkrypt, otherwise they return `403`. Contact us at
      support@enkryptai.com to enable access.
  - name: MCP Registry Servers
  - name: MCP Gateways
  - name: MCP Playground
paths:
  /guardrails/relevancy:
    post:
      tags:
        - Guardrails
      summary: Guardrails Relevancy
      operationId: guardrails_relevancy_guardrails_relevancy_post
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/GuardrailsRelevancyRequest'
            examples:
              relevancy_example:
                summary: Example demonstrating relevancy detection
                value:
                  question: What is CV Raman known for?
                  llm_answer: >-
                    C.V. Raman won the Nobel Prize for Physics in 1930 for his
                    work on light scattering
        required: true
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/GuardrailsRelevancyResponse'
              examples:
                relevancy_successful_response:
                  summary: Example of a successful relevancy detection response
                  value:
                    relevancy_score: 1
components:
  schemas:
    GuardrailsRelevancyRequest:
      title: GuardrailsRelevancyRequest
      type: object
      required:
        - question
        - llm_answer
      properties:
        question:
          type: string
          title: Question
        llm_answer:
          type: string
          title: LLM Answer
    GuardrailsRelevancyResponse:
      title: GuardrailsRelevancyResponse
      type: object
      required:
        - summary
      properties:
        summary:
          type: object
          title: GuardrailsRelevancyResponseSummary
          properties:
            relevancy_score:
              type: number
              title: Relevancy Score
              description: Overall relevancy score between 0 and 1
              minimum: 0
              maximum: 1
          required:
            - relevancy_score
        details:
          type: object
          title: Analysis Details
          properties:
            atomic_facts:
              type: array
              items:
                type: string
              description: List of extracted atomic facts
            relevancy_list:
              type: array
              items:
                type: number
              description: Individual relevancy scores for each fact
            relevancy_response:
              type: string
              description: Detailed relevancy analysis in JSON format
            relevancy_latency:
              type: number
              description: Processing time in seconds
  securitySchemes:
    apiKeyAuth:
      type: apiKey
      in: header
      name: apikey

````