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Overview

Aporia’s REST API method involves explicit API calls to enforce guardrails before and after LLM interactions, suitable for applications requiring a high level of customization and control over content policy enforcement.

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Prerequisites

Before you begin, ensure you have created an Aporia Guardrails project.

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Integration Guide

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Step 1: Gather Aporia’s API Key

  1. Log into the Aporia dashboard and select your project.
  2. Click on the Integration tab.
  3. Ensure that REST API is activated.
  4. Note down the API Key displayed.

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Step 2: Integrate into Your Code

  1. Locate where your code makes LLM calls, such as OpenAI API calls.
  2. Before sending the prompt to the LLM, and after receiving the LLM’s response, incorporate calls to Aporia’s REST API to enforce the respective guardrails.

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API Endpoint and JSON Structure

Endpoint: POST https://gr-prd.aporia.com/<PROJECT_ID>/validate

Headers:

  • Content-Type: application/json
  • X-APORIA-API-KEY: Your copied Aporia API key

Request Fields:

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messages
array
required

OpenAI-compatible array of messages. Each message should include role and content.

Possible role values are system, user, assistant, or other for any unsupported roles.

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validation_target
string
default: "both"required

The target of the validation which can be prompt, response, or both.

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response
string

The raw response from the LLM before any modifications. It is required if ‘validation_target’ includes ‘response’.

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explain
boolean
default: "false"

Whether to return detailed explanations for the actions taken by the guardrails.

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session_id
string

An optional session ID to track related interactions across multiple requests.

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user
string

An optional user ID to associate sessions with specific user and monitor user activity.

Response Fields:

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action
string
required

The action taken by the guardrails, possible values are modify, passthrough, block, rephrase.

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revised_response
string
required

The revised version of the LLM’s response based on the applied guardrails.

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explain_log
array

A detailed log of each policy’s application, including the policy ID, target, result, and details of the action taken.

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policy_execution_result
object

The final result of the policy execution, detailing the log of policies applied and the specific actions taken for each.

Request JSON Example:

{
  "messages": [
    {
      "role": "user",
      "content": "This is a test prompt"
    }
  ],
  "response": "Response from LLM here",

  // Optional
  // "validation_target": "both",
  // "explain": false,
  // "session_id": "optional-session-id"
  // "user": "optional-user-id"
}

Response JSON Example:

{
  "action": "modify",
  "revised_response": "Modified response based on policy",
  "explain_log": [
    {
      "policy_id": "001",
      "target": "response",
      "result": "issue_detected",
      "details": {
        ...
      }
    },
    ...
  ],
  "policy_execution_result": {
    "policy_log": [
      {
        "policy_id": "001",
        "policy_type": "content_check",
        "target": "response"
      }
    ],
    "action": {
      "type": "modify",
      "revised_message": "Modified response based on policy"
    }
  }
}

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Best practices

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Request timeout

Set up a timeout of 5 second to the HTTP request in case there’s any failure on Aporia’s side.

If you are using the fetch API in JavaScript, you can provide an abort signal using the AbortController API and trigger it with setTimeout. See this example.

If you are using the requests library in Python, you can simply provide a timeout argument:

import requests

requests.post(
   "https://gr-prd.aporia.com/<PROJECT_ID>/validate",
   timeout=5,
   ...
)
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