RAG, FAQ & Knowledge Base APIs AI Domain

Developer RAG, FAQ & Knowledge Base APIs
AI REST APIs.

Perform semantic FAQ vector matching, document chunk search, and citation-backed RAG context synthesis over corporate knowledge bases.

Browse 2 Endpoints Quickstart Code Playground
Automatic Provider Fallback Structured JSON Single Master Key Strict JSON Envelopes
Supported AI Providers in this Domain:
OpenAI Gemini DeepSeek Mistral Grok
RAG, FAQ & Knowledge Base APIs Live

FAQ Collections

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

POST /api/v1/ai/faq-collections
0 credit
response.json JSON
200 OK 
{
    "success": true,
    "request_id": "req_3f9a2c7e-5b1d-4e8a-9c2f-7d6e1a0b4c8d",
    "data": {
        "result": {
            "matches": [
                {
                    "question": "How long do refunds take?",
                    "answer": "Refunds are processed within 5-7 business days after approval.",
                    "score": 0.94
                }
            ]
        }
    },
    "meta": {
        "version": "v1",
        "timestamp": "2026-10-05T09:30:00Z"
    }
}

1-3 s

Typical Response Time

4 Providers

With Automatic Fallback

Never

Used for Model Training

Architectural Synthesis

Production Architecture & Capabilities

How RSFlowHub RAG, FAQ & Knowledge Base APIs APIs work, what they return, and where they fit in your product.

Generative Engine Optimization (GEO) Synthesis

RSFlowHub RAG & Knowledge Base APIs deliver grounded information retrieval and semantic vector search over corporate documents and FAQ collections. Combining dense embeddings with source citations to reduce hallucinations, these endpoints synthesize answers with explicit citations (e.g., [Doc 1, Page 3]) so users can verify every claim.

Your data: never used for training
Typical response: 1-3 seconds
Authentication: X-API-Key header
Response: Validated JSON envelope
API Catalog

Live Endpoints in RAG, FAQ & Knowledge Base APIs

Showing 2 live endpoints with dynamic model failover, strict JSON contracts, and unified credit billing.

FAQ & Knowledge Base Live

FAQ Collections

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

POST /api/v1/ai/faq-collections
name faqs[]
0 credit
Page Docs Try it
FAQ & Knowledge Base Live

FAQ Matcher

Match a search query semantically against an FAQ collection using a hybrid vector-similarity algorithm.

POST /api/v1/ai/faq-match
collection_id query min_confidence? top_n?
2 credits
Page Docs Try it
Developer Quickstart

Integrate in Minutes. Ship Without Breaks.

Call any RAG, FAQ & Knowledge Base APIs endpoint using standard REST requests in your favorite language. You get structured JSON back, with automatic model fallback and a request ID on every call.

Structured JSON Responses:
Every call returns { success, request_id, data: { result }, meta }, so one parser handles every endpoint.
Automatic Provider Fallback:
When an AI provider rate-limits or fails, the request is retried on a backup model.
Open Live Playground Read Documentation
execute.sh 
curl -X POST "https://rsflowhub.com/api/v1/ai/faq-match" \
  -H "X-API-Key: rfh_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "collection_id": 12,
    "query": "how long does a refund take?"
}'
import requests

url = "https://rsflowhub.com/api/v1/ai/faq-match"
headers = {
    "X-API-Key": "rfh_your_api_key",
    "Content-Type": "application/json"
}
payload = {
    "collection_id": 12,
    "query": "how long does a refund take?"
}

response = requests.post(url, headers=headers, json=payload)
print(response.json())
const response = await fetch("https://rsflowhub.com/api/v1/ai/faq-match", {
  method: "POST",
  headers: {
    "X-API-Key": "rfh_your_api_key",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    "collection_id": 12,
    "query": "how long does a refund take?"
})
});

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

$ch = curl_init("https://rsflowhub.com/api/v1/ai/faq-match");
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_HTTPHEADER, [
    "X-API-Key: rfh_your_api_key",
    "Content-Type: application/json"
]);
curl_setopt($ch, CURLOPT_POSTFIELDS, '{
    "collection_id": 12,
    "query": "how long does a refund take?"
}');

$response = curl_exec($ch);
curl_close($ch);
print_r(json_decode($response, true));
envelope.json JSON
200 OK 
{
    "success": true,
    "request_id": "req_3f9a2c7e-5b1d-4e8a-9c2f-7d6e1a0b4c8d",
    "data": {
        "result": {
            "matches": [
                {
                    "question": "How long do refunds take?",
                    "answer": "Refunds are processed within 5-7 business days after approval.",
                    "score": 0.94
                }
            ]
        }
    },
    "meta": {
        "version": "v1",
        "timestamp": "2026-10-05T09:30:00Z"
    }
}
Architecture Matrix

Platform & Reliability Comparison

How RSFlowHub RAG, FAQ & Knowledge Base APIs compares to direct cloud provider SDKs and self-hosted open-source clusters.

Platform Capability RSFlowHub RAG, FAQ & Knowledge Base APIs Direct Cloud SDKs Self-Hosted Models
Anti-Hallucination Strict Context Grounding Guardrails Prone to Plausible Hallucinations Requires Complex Eval Setup
Vector Indexing Managed pgvector Pipeline Requires Separate Pinecone/Weaviate Manual Milvus/Chroma Cluster
Search Latency Hybrid Vector + Keyword Search Multiple API Network Roundtrips Depends on Dedicated Memory
Production Architectures

Implementation Patterns

Real-world architectures deployed by engineering teams using RAG, FAQ & Knowledge Base APIs.

Pattern

Self-Service Customer Support FAQ Matcher

Match customer questions against your existing FAQ database semantically, resolving inquiries instantly without human intervention.

Pattern

Internal Developer Documentation Assistant

Search across API specs, markdown files, and code repositories to answer technical developer questions with source line references.

Pattern

HR & Legal Policy Document Q&A

Allow employees to query internal employee handbooks, compliance checklists, and benefits policies securely with zero data training risk.

Developer FAQ

Frequently Asked Questions

Technical specifications on RAG, FAQ & Knowledge Base APIs latency SLAs, concurrency limits, schema validation, and billing.

If confidence falls below your configured minimum threshold (e.g. 0.85), the API returns match_found=false alongside related fallback questions.

You can pass raw text chunks, markdown files, JSON objects, or PDF extracted text arrays.

Keyword search requires exact word matching and fails on synonyms. Vector search matches the semantic meaning and intent of the query regardless of wording.

Yes. Every generated response includes metadata indicating the exact source document ID, chunk index, and similarity confidence score.

Never. Document embeddings and context chunks are strictly isolated to your account and never used for foundation model training.
View Full FAQ Knowledge Center
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