RAG API for customer support

RAG API for Customer Support | Ragex

Build accurate customer support bots grounded in your documentation with a RAG API for customer support. Upload files and search in under five minutes.

Ragex support grounds your bot's answers in your actual documentation instead of letting the LLM hallucinate. Upload your support docs, call a search endpoint, and feed the results to any LLM as context. Your bot quotes your real policies instead of inventing them.

The Customer Support Bot Problem

Customer support bots powered by LLMs hallucinate when they lack access to company-specific documentation. Without retrieval grounding, a bot trained on general knowledge will confidently fabricate return policies, pricing details, and product capabilities. Research from industry analysts estimates that LLMs hallucinate in 15-20% of responses when operating without retrieval context. For customer support, that means roughly 1 in 5 answers could be wrong.

The consequences go beyond one frustrated customer. A single incorrect answer about billing, refund eligibility, or product capabilities creates support tickets, escalations, and eroded trust. For B2B products, a hallucinated answer about feature availability can derail a sales conversation entirely. Teams building chatbots without retrieval grounding spend more time correcting their bot than benefiting from it.

RAG (Retrieval-Augmented Generation) solves this by adding a retrieval step before the LLM responds. When a customer asks "What's your refund policy?", the system searches your knowledge base for relevant paragraphs, ranks results by semantic relevance, and passes the top matches to the LLM as context. The LLM generates an answer based on what your docs actually say, not what it imagines.

How It Works

A typical DIY RAG pipeline for customer support requires choosing and configuring 5+ components: a document parser, text chunker, embedding model, vector database, and reranker. Each has its own vendor, API key, and failure mode. With Ragex, the entire pipeline is three API calls:

import requests

API_KEY = "your-api-key"
BASE = "https://api.useragex.com/api/v1"
headers = {"Authorization": f"Bearer {API_KEY}"}

kb = requests.post(                        # create a knowledge base
    f"{BASE}/knowledge-bases",
    headers=headers,
    json={"name": "Support Docs"}).json()

requests.post(                             # upload your support docs
    f"{BASE}/knowledge-bases/{kb['id']}/documents",
    headers=headers,
    files={"file": open("return-policy.pdf", "rb")})

results = requests.post(                   # search with a customer question
    f"{BASE}/knowledge-bases/{kb['id']}/search",
    headers=headers,
    json={"query": "What is your return policy?", "top_k": 5}).json()

context = "\n".join([r["content"] for r in results["results"]])

The API processes your documents asynchronously -- parsing PDFs (including scanned documents via OCR), splitting text into chunks that preserve table structures, generating embeddings, and indexing for fast search. A text file processes in about 4 seconds. A 10-page PDF completes in under 60 seconds. You don't choose models, configure vector dimensions, or manage indexing parameters. When better components become available, your retrieval quality improves without a code change.

If you use a framework like LangChain or LlamaIndex, the API integrates directly as a retriever -- no need to swap out your existing application structure.

What You Can Build

Ragex support enables concrete applications that go beyond a basic FAQ chatbot:

Instant answers from thousands of articles. A support team using Slack or Intercom connects the API to their help desk. When a customer asks a question, the bot searches across 10,000 knowledge base articles, product manuals, and troubleshooting guides, then returns a grounded answer in seconds. No more digging through docs manually.

Tier-1 ticket deflection. An e-commerce company uploads their return policies, shipping FAQs, and product specs. The bot handles 60-70% of incoming tickets without human intervention -- questions like "Where's my order?" or "Can I return this after 30 days?" get accurate, policy-grounded answers. Agents focus on complex issues instead of repetitive ones.

Internal support for technical teams. Engineering teams use the same RAG API pattern for internal knowledge bases -- searching across runbooks, post-mortems, and architecture docs. When an on-call engineer needs to troubleshoot at 2am, they get answers from your actual documentation instead of guessing.

Your Documents Work Out of the Box

Support knowledge bases are rarely clean text files. They are a mix of PDF manuals with tables and headers, Word documents with embedded images, HTML help center exports with navigation chrome, and scanned documents that need OCR.

The API supports 16 file types automatically. PDFs, DOCX, PPTX, and XLSX files are parsed with layout-aware extraction that keeps tables intact -- critical for support content where a partial pricing table or truncated troubleshooting matrix is worse than no table at all. Text-based formats like TXT, MD, HTML, CSV, and JSON are ingested directly. For customer support specifically, the most common document types are FAQ documents, product manuals, return policies, troubleshooting guides, and knowledge base articles.

Tables are never split across chunks. A pricing table, feature comparison matrix, or troubleshooting decision tree stays intact as a single retrievable unit. This matters for document search across any document format, but it is especially important for support content where partial answers create more confusion than no answer.

Works With Your Stack

The API is a building block, not a walled garden. You bring your documents and your LLM -- Ragex handles everything in between. Common patterns for customer support include:

  • Any LLM: Feed search results as context to GPT-5-mini, Claude, Gemini, Llama, or any model you prefer. The API returns text chunks, not model-specific formats.
  • Framework integration: Use the API as a retriever in LangChain, LlamaIndex, or the Vercel AI SDK for streaming chat interfaces.
  • Help desk platforms: Connect to Intercom, Zendesk, or Freshdesk through their webhook or API layer. The search endpoint returns JSON -- any platform that can make HTTP requests can use it.
  • Confidence-based routing: The search endpoint returns a relevance score (0-1) for each result. Set a score_threshold to filter low-confidence results. When nothing meets the threshold, route to a human agent instead of guessing.

Teams evaluating their retrieval stack often compare options like Ragex vs Pinecone -- the key difference is that a vector database is one component, while Ragex handles the entire pipeline from document parsing to ranked search results. For teams exploring the broader landscape, our Pinecone alternatives overview covers the tradeoffs.

Getting Started

You can go from zero to a working support bot in under five minutes. Install the SDK, create a knowledge base, upload your documents, and run your first search. The API handles all parsing, chunking, embedding, and indexing behind the scenes -- you just call three endpoints and feed the results to your LLM.

Install the Python or TypeScript SDK to get started:

pip install ragex

from ragex import Ragex

client = Ragex(api_key="your-api-key")

kb = client.knowledge_bases.create(name="Support Docs")
client.documents.upload(kb.id, file_path="support-faq.pdf")
results = client.search(kb.id, query="What is the return policy?")

for result in results:
    print(result.content)

import { Ragex } from "ragex";

const client = new Ragex({ apiKey: "your-api-key" });

const kb = await client.knowledgeBases.create({ name: "Support Docs" });
await client.documents.upload(kb.id, { filePath: "support-faq.pdf" });

const results = await client.search(kb.id, {
  query: "What is the return policy?",
});
results.forEach((r) => console.log(r.content));

Five API calls from zero to a working, knowledge-grounded support bot. No vector database to manage, no embedding pipeline to debug, no parsing infrastructure to maintain.

Pricing

Most support bots start on the Starter plan at $29/mo, which includes 500 pages and 5,000 queries -- enough for early-stage bots with smaller doc sets. As query volume grows past 5,000 monthly queries, the Pro plan at $79/mo gives you 2,000 pages and 15,000 queries. Growing teams with larger knowledge bases move to the Business plan at $229/mo for 6,500 pages and 50,000 queries. High-volume support teams can scale to $499/mo for 15,000 pages and 120,000 queries.

Pricing is all-inclusive. Parsing, embedding, reranking, and search are included in every plan. No separate bills for storage or API calls to third-party embedding services. Organizations in regulated industries like healthcare often start on Pro for the additional capacity.

FAQ

How quickly can I get a support bot running with this API?

Under 5 minutes to first query. Create a knowledge base, upload your support docs, wait for processing (about 4 seconds for text, under 60 seconds for a 10-page PDF), and run your first search. The API handles parsing, chunking, embedding, and indexing automatically. No infrastructure to provision, no models to select.

What happens when the bot cannot find a relevant answer?

The search endpoint returns relevance scores for each result. You can set a score_threshold (0-1) to filter out low-confidence results. When no results meet the threshold, your application can gracefully fall back to "I'll connect you with a human agent" instead of hallucinating an answer. This confidence-based routing is a better experience than a confidently wrong response.

Can I update support docs without downtime?

Yes. Upload a new version of any document via the update endpoint. The API re-processes it asynchronously while existing search continues working against the current version. The search cache invalidates automatically when processing completes, so your bot always serves the latest information without any interruption.

How does reranking improve support bot accuracy?

Reranking re-scores search results by deep semantic relevance, not just vector similarity. For customer support, this means the most contextually relevant paragraph surfaces first -- even if a different paragraph shares more surface-level keywords. For example, a question about "cancellation" correctly matches your "Subscription Termination" policy instead of a billing FAQ that happens to mention the word "cancel." Reranking is on by default.

Last updated: February 20, 2026

Try it yourself

First query in under 5 minutes. No credit card required.

Get Started FreeRead the Docs