RAG API for chatbots
RAG API for Chatbots | Ragex
Use a RAG API for chatbots to ground every response in your actual documents. Supports 16 file types, first query in under 5 minutes. No pipeline to build.
Ragex grounds every response in your actual documents instead of letting the LLM hallucinate. Upload your knowledge base, call a search endpoint, and pass ranked results as context to your model. Your chatbot answers from real data, not guesswork.
The Chatbot Hallucination Problem
LLMs generate convincing text, but they don't know your data. Ask a chatbot about your product's return policy and it will invent terms that sound plausible. Ask about pricing and it will fabricate numbers. This is how language models work — they predict likely next tokens, not factual ones.
For production chatbots, hallucination creates real costs. A wrong answer about billing generates a support ticket. A fabricated feature claim disappoints a customer mid-purchase. An incorrect compliance response creates legal exposure. Building your own retrieval pipeline to solve this means assembling five or more components — an embedding model, a vector database, a reranker, a document parser, and a chunking strategy — each from a different vendor with its own configuration and failure modes. Most teams spend weeks on this integration before writing a single line of chatbot logic. Ragex collapses that entire stack into a few API calls, which is the same approach that powers customer support bots and internal knowledge assistants.
How a RAG API for Chatbots Works
RAG (Retrieval-Augmented Generation) inserts a search step between the user's message and the LLM's response. The chatbot searches your knowledge base for relevant passages, ranks them by relevance, and passes the top results to the LLM as context. The LLM generates its answer from your actual documents rather than its training data.
With Ragex, you don't select embedding models, tune index parameters, or manage reranker inference. When better components become available, your retrieval quality improves without any code changes. The workflow takes three API calls:
POST /v1/knowledge-bases -> Create a knowledge base
POST /v1/knowledge-bases/:kb_id/documents -> Upload your docs
POST /v1/knowledge-bases/:kb_id/search -> Query with user's message
The search request accepts parameters for fine-grained control:
{
"query": "What is your refund policy?",
"top_k": 5,
"rerank": true,
"score_threshold": 0.4
}
The API returns ranked passages with relevance scores. Your chatbot passes these to the LLM as context, and the model generates a grounded response. Time to first query is under 5 minutes. The API integrates directly with LangChain, LlamaIndex, and the Vercel AI SDK.
What You Can Build
Customer-facing support bot. A SaaS company uploads its help center, product docs, and policy documents. When a user asks "can I cancel mid-billing-cycle?", the chatbot retrieves the exact cancellation policy and generates an accurate answer. No more escalations caused by hallucinated policies — the same pattern behind dedicated customer support RAG implementations.
Internal knowledge assistant. An engineering team feeds runbooks, architecture docs, and incident postmortems into the knowledge base. New hires ask "how do we handle database failovers?" and get answers sourced from the on-call runbook. Organizations using internal knowledge bases report faster onboarding and fewer repeated questions.
Product advisor chatbot. An e-commerce company uploads product spec sheets and comparison guides. A shopper asks "which laptop has the longest battery life under $1,000?" and the chatbot pulls from real spec data instead of guessing from stale training data.
Your Documents Work Out of the Box
Chatbot knowledge bases are rarely clean text files. They include product manuals as PDFs, help center articles in HTML, policy documents in Word format, and onboarding guides with embedded tables.
The API supports 16 file types. Tier 1 files (PDF, DOCX, PPTX, XLSX, images) get layout-aware parsing with OCR for scanned text. Tier 2 files (TXT, MD, HTML, CSV, JSON, TSV, XML) are ingested directly with no parsing overhead.
For chatbot use cases, tables are never split across chunks — a pricing table or feature comparison stays intact as a single retrievable unit. Scanned PDFs and images are processed with OCR, so legacy documentation remains searchable. Maximum file size is 50MB or 500 pages. Webhooks notify your application when document processing completes, so your chatbot knows when new content is available. For teams dealing with large document collections, the same pipeline scales without configuration changes.
Works With Your Stack
Ragex is a building block, not a walled garden. It handles retrieval and returns ranked passages — you choose which LLM generates the response and which framework orchestrates the conversation.
Common pairings for chatbot projects: LangChain for conversation chains with memory, LlamaIndex for document-heavy applications with structured extraction, and the Vercel AI SDK for streaming chat interfaces. Any LLM works as the generation layer — OpenAI, Anthropic, Mistral, or open-source models. You bring your documents and your LLM; the API handles everything in between.
Getting Started
Install the SDK and run your first grounded chatbot query in under 5 minutes. Create a knowledge base, upload documents, and search — all through the Python or TypeScript SDK.
from ragex import RagexClient
client = RagexClient(api_key="your-api-key")
kb = client.knowledge_bases.create(name="support-docs")
client.documents.upload(kb.id, file_path="help-center.pdf")
results = client.search(
kb.id,
query="What is your refund policy?",
top_k=5,
rerank=True,
score_threshold=0.4
)
context = "\n".join([r.text for r in results])
Pass the context variable into your LLM prompt alongside the user's question. The model generates a response grounded in your actual documents. Five API calls from zero to a working, knowledge-grounded chatbot. Compare this approach to building your own pipeline to see the effort difference.
Pricing
| Plan | Monthly | Pages | Queries/mo | Chatbot Fit |
|---|---|---|---|---|
| Starter | $29 | 500 | 5,000 | Prototyping, internal bots |
| Pro | $79 | 2,000 | 15,000 | Growing chatbots |
| Business | $229 | 6,500 | 50,000 | Production chatbots |
| Scale | $499 | 15,000 | 120,000 | High-traffic chatbots |
Recommended for most chatbots: Business ($229/mo). Production chatbots typically handle 10-40K queries per month. Business provides 50K queries and 6,500 pages -- enough headroom for growth. Pro ($79/mo) works for smaller chatbots with up to 15K queries. Starter ($29/mo) works for prototyping or low-traffic internal bots. Pricing is all-inclusive: parsing, embedding, reranking, and search are bundled. Browse alternative managed approaches if you are evaluating options.
FAQ
How long does it take to add RAG to an existing chatbot?
Under 5 minutes to your first grounded query. Create a knowledge base with one API call, upload your documents (processing takes roughly 4 seconds for text files, under 60 seconds for a 10-page PDF), then call the search endpoint with your user's question. The API handles parsing, chunking, embedding, and indexing automatically — no infrastructure to configure and no models to select.
What happens when a user asks something not covered in my documents?
The search endpoint returns a relevance score (0 to 1) for each result. Set a score_threshold parameter to filter out low-confidence matches. When no results meet your threshold, your chatbot can respond with a fallback — like "I don't have information on that, let me connect you with a team member" — instead of generating a hallucinated response. This pattern is critical for customer support bots where wrong answers create real costs.
Can I update the chatbot's knowledge base without downtime?
Yes. Upload new or updated documents at any time. The API re-processes them asynchronously while your existing search index stays live — no downtime and no manual cache clearing. When processing completes, new content becomes searchable automatically. Register webhooks for completion events to trigger downstream workflows in your chatbot application.
Does the API work with any LLM provider?
Yes. The API handles retrieval and returns ranked passages with relevance scores. You pass those passages as context to whichever LLM you prefer. Frameworks like LangChain and LlamaIndex make this integration straightforward, and the Vercel AI SDK works well for streaming chat interfaces. Organizations in regulated industries like healthcare often pair the API with private LLM deployments for additional data control.
Last updated: February 20, 2026