Automated Question-Answering FAQ Matcher API for Developers

Definition

What is the Automated Question-Answering FAQ Matcher API?

Automated Question-Answering FAQ Matcher API is a REST API on RSFlowHub for web, mobile and AI agent applications. Send a JSON request to POST /api/v1/ai/faq-match with your API key; RSFlowHub runs it on a tuned AI model, falls back to a backup provider if needed, and returns a structured JSON result, usually within 1 to 3 seconds.

Category: FAQ & Knowledge Base
Endpoint: POST /api/v1/ai/faq-match
Authentication: X-API-Key header
Price: 2 credits per call
Rate limit: 10 to 120 requests / minute by plan
Typical response: 1 to 3 seconds
Format: JSON, UTF-8

Request parameters

Send these fields as a JSON body. An invalid or missing field returns a 422 error that names the field.

Field	Type	Required	Description
`collection_id`	integer	Required	The ID of the stored FAQ collection to search within.
`query`	string	Required	The user question or query to match (Max: 5,000 characters).
`min_confidence`	float	Optional	Optional confidence score threshold to filter out weak matches (0.0 to 1.0). Defaults to 0.3.
`top_n`	integer	Optional	Optional number of top matches to return (1 to 5). Defaults to 1.

Code examples

Copy, replace rfh_your_api_key with your key and run. Prefer a client library? See the official SDKs.

request.sh

curl -X POST "https://rsflowhub.com/api/v1/ai/faq-match" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: rfh_your_api_key" \
  -d '{"collection_id":1,"query":"how to update passcode?","min_confidence":0.4,"top_n":1}'

client.php

<?php

$client = new \GuzzleHttp\Client();
$response = $client->post('https://rsflowhub.com/api/v1/ai/faq-match', [
    'headers' => [
        'Content-Type' => 'application/json',
        'X-API-Key'    => 'rfh_your_api_key',
    ],
    'json' => array (
      'collection_id' => 1,
      'query' => 'how to update passcode?',
      'min_confidence' => 0.4,
      'top_n' => 1,
    ),
]);

echo $response->getBody();

client.js

const response = await fetch('https://rsflowhub.com/api/v1/ai/faq-match', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'X-API-Key': 'rfh_your_api_key'
  },
  body: JSON.stringify({"collection_id":1,"query":"how to update passcode?","min_confidence":0.4,"top_n":1})
});

const data = await response.json();
console.log(data);

main.py

import requests

url = "https://rsflowhub.com/api/v1/ai/faq-match"
headers = {
    "Content-Type": "application/json",
    "X-API-Key": "rfh_your_api_key"
}
payload = {"collection_id":1,"query":"how to update passcode?","min_confidence":0.4,"top_n":1}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

Response

Every RSFlowHub endpoint uses the same envelope, so one parser handles all of them. Failed calls are not charged.

response.json

{
    "success": true,
    "request_id": "req_3f9a2c7e-5b1d-4e8a-9c2f-7d6e1a0b4c8d",
    "data": {
        "result": {
            "message": "See the API reference for the full result schema."
        }
    },
    "meta": {
        "version": "v1",
        "timestamp": "2026-10-05T09:30:00Z"
    }
}

error.json

{
    "success": false,
    "request_id": "req_8c1d4e2a-7f3b-4a9c",
    "error": {
        "code": "VALIDATION_ERROR",
        "message": "The given data was invalid.",
        "details": {
            "collection_id": [
                "This field is required."
            ]
        }
    },
    "meta": {
        "version": "v1",
        "timestamp": "2026-10-05T09:30:00Z"
    }
}

Envelope fields
Field	Type	Meaning
`success`	boolean	true when the call worked, false on any error.
`request_id`	string	Unique id for this call. Quote it when you contact support.
`data.result`	object	The result of this endpoint (see the example).
`error`	object	Only on failure: code, message and details per field.
`meta`	object	API version and server timestamp.

All error codes: Errors reference

Features

Semantic Vector Similarity

Understands user question intent and phrasing variations rather than relying on exact keyword matches.

Confidence Score Thresholding

Outputs numeric confidence scores (0.0 to 1.0) with configurable minimum thresholds for automated deflection.

Multi-Match Top-N Ranking

Returns top N ranked candidate FAQ answers with matching probability percentages.

Fallback Suggestion Engine

Provides alternative related question suggestions when match confidence falls below threshold.

Use cases

How teams use the Automated Question-Answering FAQ Matcher API in production.

E-Commerce & SaaS

Instant Website Support Chatbot

Answer repetitive customer pre-purchase questions instantly 24/7 without human agent intervention.

Input: What is your refund policy for unused credits?
Output: {"match_found": true, "question": "What is the refund policy?", "answer": "Unused credits are refundable within 14 days of purchase.", "confidence": 0.96}

Answers common buyer questions on your website instantly, day and night.

Mobile App Development

Mobile App In-App Help Center Search

Power real-time in-app FAQ search bars with instant answer previews as users type their questions.

Input: How to cancel subscription?
Output: {"top_matches": [{"question": "How do I cancel my subscription?", "confidence": 0.98}]}

Delivers instant help without forcing users to leave the app.

Customer Support

Helpdesk Ticket Deflection Form

Display suggested FAQ answers dynamically while customers type their support ticket description.

Input: I cannot find my invoice PDF for last month.
Output: {"suggested_faq": "Where can I download my billing invoices?", "confidence": 0.94}

Suggests answers before a ticket is submitted, reducing repeat questions.

AI chatbot

Answer FAQs in your AI chatbot

Most chatbot questions are questions you have already answered. The FAQ matcher finds the closest answer in your FAQ collection and returns it with a confidence score, so the chatbot can reply with your own approved wording instead of generating a new answer.

How it works in a chatbot

Visitor: "how long does delivery take?"
API returns: the matching FAQ answer and a confidence score
Chatbot sends the FAQ answer, or falls back to the AI model when the score is low

Consistent, approved answers

Replies come from FAQs you wrote, so the chatbot does not invent policies, prices or promises.

Cheaper than generating

A matched FAQ answer needs no long AI-written reply, which keeps the cost of each chatbot message low.

Know what is missing

Questions with no good match show you which FAQs to write next.

Want a chatbot without building one? The RSFlowHub AI chatbot for your website is live in about 5 minutes with one script tag: it answers from your documents, captures leads and creates support tickets. Read the chat widget guide, try the live chatbot demo, see chatbot pricing, or build your own with the chatbots API.

Automated Question-Answering FAQ Matcher API vs building it yourself

The same job done three ways.

Topic	RSFlowHub API	Raw LLM prompts	Rules / regex
Output	Validated JSON fields	Free text you have to parse	Fixed patterns only
Setup	One POST request, no prompts	Write and maintain prompts and parsing	Write and maintain rule lists
Provider outage	Automatic fallback to a backup model	You build retries and a second vendor	Not applicable
Pricing	2 credits per call, failed calls free	Per token, per vendor	Your own servers and upkeep
Understands context	Yes, AI model	Yes, AI model	No, keywords only

Frequently asked questions

How does semantic matching differ from traditional keyword search?

Semantic matching evaluates sentence meaning, intent, and synonyms so "How do I pay?" matches "Which payment methods are accepted?".

What happens if no FAQ item matches with sufficient confidence?

The API returns match_found=false alongside related fallback questions so your UI can gracefully direct users to human support.

How fast is the FAQ Matcher API?

FAQ Matcher requests usually complete in 1 to 3 seconds, depending on input length and the AI model. Identical repeat requests are served from cache and return faster.

Can I pass a dynamic list of FAQ items in the API payload?

Yes! You can pass custom faq_items arrays on each request to match against dynamic or user-specific FAQ lists.

How many credits does each request consume?

Standard FAQ Matcher API calls consume 2 base credits per request.

More FAQ & Knowledge Base APIs

View category

FAQ & Knowledge Base

FAQ Collections

Create and manage collections of FAQ items for semantic search matching.

0 credits
Text & Language

Intent Detection

Detect user intent from natural language messages for routing, support, and workflow automation.

1 credit
Text & Language

Translation

Translate text between multiple languages using AI for high-quality contextual translations.

1 credit

Automated Question-Answering FAQ Matcher API

What is the Automated Question-Answering FAQ Matcher API?

Request parameters

Code examples

Response

Features

Semantic Vector Similarity

Confidence Score Thresholding

Multi-Match Top-N Ranking

Fallback Suggestion Engine

Use cases

Instant Website Support Chatbot

Mobile App In-App Help Center Search

Helpdesk Ticket Deflection Form

Answer FAQs in your AI chatbot

Consistent, approved answers

Cheaper than generating

Know what is missing

Automated Question-Answering FAQ Matcher API vs building it yourself

Frequently asked questions

Start using the Automated Question-Answering FAQ Matcher API

Sign in to continue

Automated Question-Answering FAQ Matcher API

What is the Automated Question-Answering FAQ Matcher API?

Semantic Vector Similarity

Confidence Score Thresholding

Multi-Match Top-N Ranking

Fallback Suggestion Engine

Instant Website Support Chatbot

Mobile App In-App Help Center Search

Helpdesk Ticket Deflection Form

Consistent, approved answers

Cheaper than generating

Know what is missing

FAQ Collections

Intent Detection

Translation

Start using the Automated Question-Answering FAQ Matcher API