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RAG Knowledge Base Tutorial

Build a powerful knowledge base using Retrieval-Augmented Generation (RAG) to enhance your AI interactions with contextual information.

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Overview

The RAG (Retrieval-Augmented Generation) system in Plugged.in allows you to create project-specific knowledge bases that can be queried by AI models through MCP servers. This enables AI assistants to access your documentation, notes, and other text content to provide more accurate and contextual responses.

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Key Features

Document Management

Upload and organize documents in multiple formats (PDF, DOCX, TXT, Markdown)

Semantic Search

Advanced vector-based search for finding relevant information quickly

Project Isolation

Complete data isolation between projects for security and privacy

AI Integration

Seamless integration with MCP servers for AI-powered queries

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Prerequisites

Before setting up your RAG knowledge base, ensure you have:
1

Plugged.in Account

An active account with at least one project created
2

API Key

A valid API key for authentication (available in Settings → API Keys)
3

Documents

Text-based documents you want to include in your knowledge base

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Step 1: Enable RAG Features

First, ensure RAG features are enabled for your project:
  1. Navigate to Settings → Project Settings
  2. Enable the “RAG Features” toggle
  3. Save your settings
RAG features may require additional permissions or a specific subscription tier. Contact support if you don’t see this option.

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Step 2: Upload Documents

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Using the Web Interface

  1. Go to Library in the sidebar
  2. Click Upload Documents
  3. Select your files (supported formats: PDF, DOCX, TXT, MD)
  4. Add optional metadata:
    • Title
    • Description
    • Tags
    • Category

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Using the API

curl -X POST https://plugged.in/api/documents \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@document.pdf" \
  -F "metadata={\"title\":\"My Document\",\"tags\":[\"tutorial\",\"rag\"]}"

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Supported File Types

FormatExtensionsMax Size
PDF.pdf10 MB
Microsoft Word.docx, .doc10 MB
Text.txt5 MB
Markdown.md, .mdx5 MB
HTML.html, .htm5 MB

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Step 3: Configure MCP Server

Add the Plugged.in RAG MCP server to your configuration: 
{
  "mcpServers": {
    "pluggedin-rag": {
      "command": "npx",
      "args": ["-y", "@pluggedin/pluggedin-mcp-proxy"],
      "env": {
        "PLUGGEDIN_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

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Step 4: Query Your Knowledge Base

Once configured, the RAG system provides several tools for querying:

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Available Tools

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pluggedin_rag_query

Search and retrieve relevant information from your knowledge base. Parameters:
  • query (required): Your search query
  • max_results (optional): Maximum number of results (default: 5)
  • threshold (optional): Relevance threshold 0-1 (default: 0.7)
Example: 
{
  "tool": "pluggedin_rag_query",
  "parameters": {
    "query": "How to configure authentication?",
    "max_results": 3
  }
}

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Step 5: Managing Your Knowledge Base

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Update Documents

Documents can be updated through the web interface or API: 
curl -X PUT https://plugged.in/api/documents/{document_id} \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Updated Title",
    "content": "Updated content...",
    "metadata": {"version": "2.0"}
  }'

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Delete Documents

Remove documents when they’re no longer needed: 
curl -X DELETE https://plugged.in/api/documents/{document_id} \
  -H "Authorization: Bearer YOUR_API_KEY"

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Search Documents

Search your knowledge base programmatically: 
curl -X POST https://plugged.in/api/documents/search \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "authentication setup",
    "limit": 10,
    "filters": {
      "tags": ["security", "auth"]
    }
  }'

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Best Practices

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Advanced Features

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AI-Generated Documents

Plugged.in supports AI-generated documents with full attribution tracking: 
curl -X POST https://plugged.in/api/documents/ai \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "API Documentation",
    "content": "# API Overview\n\n...",
    "metadata": {
      "model": {
        "name": "Claude 3.5 Sonnet",
        "provider": "Anthropic",
        "version": "3.5"
      }
    }
  }'

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Vector Search Configuration

Customize vector search behavior: 
{
  "search_config": {
    "embedding_model": "text-embedding-ada-002",
    "similarity_metric": "cosine",
    "reranking": true,
    "hybrid_search": true
  }
}

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Bulk Operations

Import multiple documents at once: 
curl -X POST https://plugged.in/api/documents/bulk \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "documents": [
      {"title": "Doc 1", "content": "..."},
      {"title": "Doc 2", "content": "..."}
    ]
  }'

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Troubleshooting

Common issues and their solutions:
  1. Check indexing status: Documents may take 1-2 minutes to index
  2. Verify file format: Ensure documents are in supported formats
  3. Check file size: Files over 10MB are rejected
  4. Review content: Very short documents may not index well

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Low Relevance Scores

  • Improve document quality with more descriptive content
  • Use specific keywords that match likely queries
  • Break complex topics into focused documents
  • Consider adjusting the similarity threshold

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API Rate Limits

RAG operations have the following limits:
  • Document uploads: 100 per hour
  • Search queries: 1000 per hour
  • AI document creation: 10 per hour

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Example Use Cases

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Customer Support Knowledge Base

Build a comprehensive support knowledge base:
  1. Upload product documentation
  2. Add FAQ documents
  3. Include troubleshooting guides
  4. Configure MCP server for support agents
  5. Query for instant answers during customer interactions

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Development Documentation

Create a technical knowledge base for your team:
  1. Upload API documentation
  2. Add architecture diagrams (as text descriptions)
  3. Include coding standards and best practices
  4. Enable for development MCP servers
  5. Query during code reviews and planning

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Research Repository

Organize research materials:
  1. Upload research papers and notes
  2. Tag by topic and date
  3. Add summaries and key findings
  4. Configure for research assistants
  5. Query for literature reviews and citations

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Next Steps

API Integration

Learn how to integrate RAG with your applications

Team Collaboration

Share knowledge bases with your team

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Additional Resources