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

$3

- 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

$3

- 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

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

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- 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

$3

- 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

$3

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

$3

``bash


In your n8n root folder


npm install n8n-nodes-puter-ai@2.0.0
`

🎯 Operations

$3

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

$3

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

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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

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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

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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

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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

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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)

$3

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

Node Parameters

$3

- 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

$3

- 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

$3

- 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

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- 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

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

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

$3

`json
{
"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