Overview
This workflow automates the creation of a documentation expert bot by integrating Google Gemini's Retrieval-Augmented Generation (RAG) with Supabase vector storage. It processes, cleans, and indexes official n8n documentation, enabling advanced conversational AI support.
Key Features
- Automated Data Loading: Fetches and loads all n8n documentation links using HTTP requests and data loaders.
- Content Processing: Splits, cleans, and deduplicates documentation content for optimal vectorization.
- Vector Storage: Stores processed documentation in Supabase for efficient semantic search and retrieval.
- Conversational AI: Utilizes Gemini 2.5 Flash for intelligent, context-aware responses based on documentation.
- Memory Management: Implements memory buffers for maintaining conversation context.
Benefits
- Enhanced Support: Provides instant, accurate answers to documentation queries, reducing manual support workload.
- Scalable Knowledge Base: Easily updates as documentation evolves, ensuring up-to-date responses.
- Time Savings: Automates data ingestion, cleaning, and AI response generation, streamlining support processes.
Use Cases
- Internal support bots for teams using n8n.
- Customer-facing documentation assistants.
- Automated onboarding and training tools.
Integrations
- Google Gemini (AI chat)
- Supabase (vector database)
- n8n official documentation
- LangChain nodes for processing and memory
