Getting Started

Upload your first document and search it with natural language in under 5 minutes.

Prerequisites

RAG Engine is a built-in feature of MCP Gateway Pro. To use it, you need:

1

Open RAG Engine

Log in to the AppXen Console and navigate to RAG Engine in the sidebar. You will see the Overview page with your knowledge base stats.

If you have not uploaded any documents yet, the stats will show zero sources and zero chunks.

2

Upload a Document

Go to the Sources tab. You can upload documents in two ways:

File Upload

Drag and drop a file onto the upload area, or click to browse. Supports PDF, DOCX, HTML, Markdown, CSV, JSON, plain text, and images (PNG, JPEG, TIFF, BMP).

Text Ingestion

Paste or type text content directly. Give it a filename and click ingest. The text is processed immediately (synchronous).

File uploads are processed asynchronously. The source status will progress through:

queued → processing → ready

If parsing fails, the status changes to failed with an error message.

3

Search Your Knowledge Base

Go to the Search tab. Type a natural language query and hit search. RAG Engine will return the most relevant document chunks ranked by semantic similarity. 

# Example query
What is the refund policy for enterprise customers?

# Returns matching passages from your uploaded documents,
# ranked by relevance with source citations

Adjust the top-k dropdown to control how many results are returned (3, 5, or 10). Your search history is preserved for the session.

4

Use from AI Agents

Any AI agent connected to your MCP Gateway can search your knowledge base using the built-in rag-engine MCP tools. No extra configuration needed. 

curl -X POST https://api.appxen.ai/mcp \
  -H "Authorization: Bearer axgw_live_k1_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "rag-engine/knowledge_query",
      "arguments": {
        "query": "What is the refund policy?",
        "top_k": 3
      }
    },
    "id": 1
  }'

The gateway returns matching document chunks with citations. Your agent can use these results to answer questions grounded in your documents.

What's Next?