n8n-nodes-puter-ai v2.0.0 🚀

An advanced n8n community node for Puter.js AI with RAG agentic capabilities, document processing, Supabase integration, and cost-optimized model selection.

🌟 New in v2.0.0

🤖 Agentic RAG: Intelligent document-based reasoning and synthesis 📄 Document Processing: Auto-detect and process files from Telegram/other sources 🗄️ Supabase Integration: Vector storage with pgvector for semantic search 💰 Cost Optimization: Starting from $0.10 with google/gemma-2-27b-it 🔍 Vector Search: Semantic document search with similarity scoring 📱 Auto-Detection: Automatically process documents from input data

🚀 Features

🤖 AI Operations

• Chat Completion: Standard AI chat with cost-optimized models
• RAG Chat: Enhanced responses with document context
• Agentic RAG: Intelligent document-based reasoning
• Vector Search: Semantic document search

📄 Document Processing

• Multi-Format Support: PDF, DOCX, TXT, MD files
• Auto-Detection: Process files from Telegram/other sources
• Text Extraction: Advanced content parsing
• Vector Embeddings: Generate embeddings for semantic search

🗄️ Database Integration

• Supabase Integration: Vector storage with pgvector
• Document Storage: Organized with metadata and tags
• Similarity Search: Fast vector-based retrieval
• Auto-Indexing: Automatic embedding generation

💰 Cost Optimization

• google/gemma-2-27b-it: $0.10 (most cost-effective)
• gemini-1.5-flash: $0.225
• gemini-2.0-flash: $0.30
• gpt-4o-mini: $0.375
• Smart Fallback: Automatic model switching

🔐 Account Management

• Multiple Account Fallback: Primary + 2 fallback accounts
• Smart Strategies: Sequential or random selection
• Enhanced Tracking: Monitor costs and usage across accounts
• Robust Error Handling: Comprehensive retry logic

Installation

Community Nodes (Recommended)

1. Go to Settings > Community Nodes in your n8n instance
2. Select Install a community node
3. Enter n8n-nodes-puter-ai
4. Click Install

Manual Installation

# In your n8n root folder
npm install [email protected]

🎯 Operations

1. Document Processing

Process and store documents for RAG functionality:

• File Upload: Process files from Telegram or other sources
• Text Content: Process raw text content
• URL/Link: Download and process documents from URLs
• Auto-Storage: Automatically store in Supabase with embeddings

2. Vector Search

Search documents by semantic similarity:

• Natural Language Queries: Search using plain English
• Similarity Scoring: Get relevance scores for results
• Configurable Results: Control number of documents returned
• Fast Retrieval: Optimized vector search with HNSW indexing

3. Agentic RAG

Intelligent document-based reasoning:

• Context Building: Automatically retrieve relevant documents
• Multi-Source Synthesis: Combine information from multiple documents
• Citation Support: Track which documents were used
• Intelligent Responses: AI reasoning over document context

4. Chat Completion

Standard AI chat with cost optimization:

• Cost-Optimized Models: Automatic selection of cheapest effective model
• Model Fallback: Try alternative models if primary fails
• Account Fallback: Switch accounts automatically
• Usage Tracking: Monitor costs and token consumption

5. RAG Chat

Enhanced chat with document context:

• Context Integration: Include relevant documents in responses
• Smart Retrieval: Automatically find related content
• Enhanced Accuracy: More accurate responses with document backing
• Flexible Context: Control how much context to include

Configuration

1. Supabase Setup (Required for RAG)

1. Create Supabase Project: Go to supabase.com and create a new project
2. Enable Vector Extension: Run the provided supabase-setup.sql script in your SQL editor
3. Configure Supabase Credentials in n8n:
   • Supabase URL: https://your-project.supabase.co
   • Anon Key: Your public anon key
   • Service Role Key: Your service role key (for admin operations)
   • Enable Vector Storage: ✅ True
   • Documents Table: documents
   • Embeddings Table: document_embeddings
   • Vector Dimension: 1536
   • Similarity Threshold: 0.7
   • Max Documents Retrieved: 5

2. Puter AI Credentials

1. Go to Credentials in your n8n instance
2. Click Add Credential
3. Search for Puter AI API
4. Configure with cost-optimized model priorities:
   • Primary Account: Your main Puter.js username/password
   • Primary Models: google/gemma-2-27b-it, gemini-1.5-flash, gemini-2.0-flash, gpt-4o-mini
   • Fallback Account 1: Backup username/password
   • Fallback 1 Models: gemini-1.5-flash, gpt-4o-mini, gemini-2.0-flash
   • Fallback Account 2: Second backup username/password
   • Fallback 2 Models: google/gemma-2-27b-it, gemini-1.5-flash
   • Enable Auto Fallback: ✅ True
   • Fallback Strategy: Sequential (recommended)

2. Add the Node

1. In your workflow, click Add Node
2. Search for Puter AI
3. Configure the node parameters

Node Parameters

Operation

• Chat Completion: Standard AI chat with cost optimization
• RAG Chat: Chat with document context for enhanced responses
• Document Processing: Process and store documents for RAG
• Vector Search: Search documents by semantic similarity
• Agentic RAG: Intelligent document-based reasoning

Model Strategy

• Use Credential Priority (Recommended): Uses cost-optimized model order from credentials
• Override with Specific Model: Choose a specific model
• Auto (Smart Selection): Automatically select best model

Cost-Optimized Models (by price)

• google/gemma-2-27b-it ($0.10): Most cost-effective
• gemini-1.5-flash ($0.225): Good balance of cost/performance
• gemini-2.0-flash ($0.30): Latest Gemini model
• gpt-5-nano ($0.35): Ultra-low-cost tier
• gpt-4o-mini ($0.375): OpenAI's efficient model
• o4-mini (~$0.40): Balanced performance
• gpt-4.1-nano (~$0.45): Advanced reasoning at low cost
• meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo ($0.88): Open-source option
• Auto (Smart Selection): Automatically selects the best available model

Response Format

• Simple Text: Just the AI response
• Formatted with Metadata: Includes model, usage, and timing info
• Telegram Ready: Pre-formatted for Telegram bots with emojis and styling
• Raw Response: Complete API response

Usage Examples

Basic Chat

{
  "operation": "chatCompletion",
  "model": "gpt-4o",
  "message": "Hello, how are you?",
  "responseFormat": "simple"
}

RAG-Enhanced Chat

{
  "operation": "ragChat",
  "model": "claude-3-5-sonnet",
  "message": "What are the legal requirements?",
  "ragContext": "Legal document content here...",
  "responseFormat": "formatted"
}

Telegram Bot Integration

{
  "operation": "chatCompletion",
  "model": "auto",
  "message": "{{$json.message.text}}",
  "responseFormat": "telegram"
}

Error Handling

The node automatically handles:

• Authentication failures: Retries with fresh tokens
• Rate limits: Switches to fallback account
• Model unavailability: Tries alternative models
• Usage limits: Seamlessly switches accounts

Fallback Logic

1. Primary Account: Attempts request with main account
2. Account Fallback: On 400 errors, switches to fallback account
3. Model Fallback: If model fails, tries alternatives in priority order:
   • o3 → o1-pro → gpt-4o → claude-3-5-sonnet → o1 → gpt-4o-mini → gemini-2.0-flash

License

MIT

Support

For issues and feature requests, please visit: GitHub Repository