Description

n8n-nodes-rag

Advanced RAG (Retrieval-Augmented Generation) knowledge base nodes for n8n.

Features

This package provides two powerful nodes for building RAG applications:

🗄️ RAG Knowledge Base Node

• Text Processing: Clean and preprocess text data with customizable options
• Intelligent Chunking: Multiple strategies (fixed size, sentence-based, paragraph-based)
• Vector Embeddings: Support for OpenAI, Hugging Face, and custom embedding providers
• Flexible Storage: Plugin interface for various vector databases (Qdrant, Milvus)
• Operations: Store, delete, and count documents

🔍 RAG Retrieval Node

• Multiple Search Types: Vector search, full-text search, and hybrid search
• Configurable Results: Customizable limits, score thresholds, and filtering
• Metadata Support: Include or exclude metadata in search results
• AI-Ready Output: Formatted results perfect for AI agent workflows

Installation

npm install n8n-nodes-rag

Supported Vector Databases

• Qdrant: Cloud and self-hosted vector database
• Milvus: Open-source vector database
• Extensible: Easy to add new vector store adapters

Supported Embedding Providers

• OpenAI: text-embedding-ada-002 and other models
• Hugging Face: Wide range of open-source models
• Custom API: Bring your own embedding service

Quick Start

1. Setup Vector Database

Start with Qdrant (easiest option):

docker run -p 6333:6333 qdrant/qdrant

2. Create Knowledge Base

1. Add RAG Knowledge Base node to your workflow
2. Connect your text data source
3. Configure chunking strategy and embedding provider
4. Set vector database connection details
5. Execute to process and store your documents

3. Retrieve Information

1. Add RAG Retrieval node to your workflow
2. Configure the same vector database settings
3. Set your search query and parameters
4. Choose search type (vector, full-text, or hybrid)
5. Execute to get relevant results

Configuration Examples

Basic Text Processing

{
  "operation": "store",
  "chunkingStrategy": "sentence",
  "chunkSize": 1000,
  "overlap": 200,
  "generateEmbeddings": true,
  "embeddingProvider": "openai"
}

Vector Search

{
  "searchType": "vector",
  "limit": 10,
  "threshold": 0.7,
  "includeMetadata": true
}

Hybrid Search

{
  "searchType": "hybrid",
  "limit": 10,
  "alpha": 0.5,
  "threshold": 0.6
}

Use Cases

📚 Document Q&A Systems

Build intelligent document search and question-answering systems.

🤖 AI Agent Knowledge Base

Provide contextual information to AI agents and chatbots.

🔍 Semantic Search

Create powerful semantic search experiences for your applications.

📊 Content Analytics

Analyze and categorize large collections of text documents.

Architecture

Text Processing Pipeline

1. Input Validation: Ensure text data is properly formatted
2. Text Cleaning: Remove extra whitespace, normalize line breaks
3. Chunking: Split text using configurable strategies
4. Embedding Generation: Create vector representations
5. Storage: Save to vector database with metadata

Search Pipeline

1. Query Processing: Generate embeddings for search queries
2. Vector Search: Find semantically similar content
3. Full-text Search: Keyword-based matching
4. Hybrid Search: Combine vector and full-text results
5. Result Ranking: Score and filter results

Advanced Features

Custom Metadata Filtering

Filter search results based on document metadata:

{
  "source": "documentation",
  "category": "technical",
  "date": { "$gte": "2024-01-01" }
}

Chunking Strategies

• Fixed Size: Split by character count with overlap
• Sentence: Respect sentence boundaries
• Paragraph: Maintain paragraph structure
• Semantic: AI-powered semantic chunking (future)

Vector Store Adapters

Easily extend support for additional vector databases by implementing the VectorStoreAdapter interface.

API Reference

RAG Knowledge Base Node Parameters

RAG Retrieval Node Parameters

Troubleshooting

Common Issues

Empty results from vector search

• Check that embeddings were generated during storage
• Verify embedding provider settings match between store and search
• Adjust similarity threshold (try lower values like 0.5)

API rate limits

• Use batch processing for large documents
• Implement delays between API calls
• Consider using local embedding models

Vector database connection errors

• Verify endpoint URL and API key
• Check network connectivity
• Ensure collection/index exists

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

MIT License – see LICENSE for details.

Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

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