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RAG, FAQ & Knowledge Base APIs · 2 APIs

Enterprise RAG, FAQ Vector Matching & Knowledge Search APIs

RAG synthesis and semantic vector search APIs with source citations to reduce hallucinations.

Typical Response
1-3 s
Hallucination Rate
<0.2% Enforced
Citations
100% Grounded
POST /api/v1/ai/faq-match
{
    "collection_id": 12,
    "query": "how long does a refund take?"
}
200 OK response.json
{
    "data": {
        "result": {
            "matches": [
                {
                    "question": "How long do refunds take?",
                    "answer": "Refunds are processed within 5-7 business days after approval.",
                    "score": 0.94
                }
            ]
        }
    }
}

RAG, FAQ & Knowledge Base APIs: 2 APIs

2 credits per call. Each one has its own page with parameters, code and examples.

  • Live

    FAQ Collections

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

    POST /api/v1/ai/faq-collections
    Free Docs Try it
  • Live

    FAQ Matcher

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

    POST /api/v1/ai/faq-match
    2 credits Docs Try it
Definition

What are the RAG, FAQ & Knowledge Base APIs?

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.

APIs
2
Price
2 credits per call
Authentication
X-API-Key header
Format
JSON in, JSON out
Typical response
1 to 3 seconds
Fallback
Automatic, failed calls not charged

Quickstart

A real request to POST /api/v1/ai/faq-match. Every API in this category works the same way.

request.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?"}'
response.json
{
    "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"
    }
}

Why teams use RAG, FAQ & Knowledge Base APIs

Semantic Vector Similarity

Matches user questions against FAQ databases by sentence intent and concept rather than exact keyword strings.

Citation & Source Grounding

Appends explicit source citations (e.g. [Doc 1, Page 4]) to every generated answer in RAG pipelines.

Anti-Hallucination Guardrails

Constrains generated answers strictly to facts present in provided context chunks.

Use cases

Typical pipelines built with RAG, FAQ & Knowledge Base APIs.

Self-Service Customer Support FAQ Matcher

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

  1. User Question
  2. FAQ Matcher API (semantic vector)
  3. Direct Answer Modal

Internal Developer Documentation Assistant

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

  1. Developer Prompt
  2. RAG Synthesis API
  3. Grounded Code Answer with Docs Link

HR & Legal Policy Document Q&A

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

  1. Employee Query
  2. Document Search API
  3. Cited Policy Excerpt

RSFlowHub vs provider SDKs vs self-hosting

How the RAG, FAQ & Knowledge Base APIs compare with calling a provider directly or running your own models.

TopicRSFlowHubProvider SDKSelf-hosted
Anti-Hallucination Strict Context Grounding GuardrailsProne to Plausible HallucinationsRequires Complex Eval Setup
Vector Indexing Managed pgvector PipelineRequires Separate Pinecone/WeaviateManual Milvus/Chroma Cluster
Search Latency Hybrid Vector + Keyword SearchMultiple API Network RoundtripsDepends on Dedicated Memory

Frequently asked questions

How does the FAQ Matcher API handle unmatched questions?
If confidence falls below your configured minimum threshold (e.g. 0.85), the API returns match_found=false alongside related fallback questions.
What document formats can be passed for Knowledge Base search?
You can pass raw text chunks, markdown files, JSON objects, or PDF extracted text arrays.
How does semantic vector search differ from database keyword search?
Keyword search requires exact word matching and fails on synonyms. Vector search matches the semantic meaning and intent of the query regardless of wording.
Does the RAG engine quote exact source material?
Yes. Every generated response includes metadata indicating the exact source document ID, chunk index, and similarity confidence score.
Are our uploaded documents used to train third-party foundation models?
Never. Document embeddings and context chunks are strictly isolated to your account and never used for foundation model training.

Other API categories

All categories

Build with RAG, FAQ & Knowledge Base APIs

Free plan with 100 credits/month + 1,000 bonus credits when you sign in before 23 Nov. One key works for every API in every category.

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