Templates / Customer support chatbot with RAG using OpenAI and Pinecone

Customer support chatbot with RAG using OpenAI and Pinecone

Ready for 3.0

by Ilyass Kanissi βœ“ Β· 2,465 views Β· created 2025-08-19 Β· AI RAG, Multimodal AI

Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.

Nodes used (11)

Google DrivePinecone Vector StoreEmbeddings OpenAIDefault Data LoaderRecursive Character Text SplitterAI AgentOpenAI Chat ModelSimple MemoryReranker Cohere

What it does

πŸ€– Simple RAG Customer Support Chatbot πŸ“‹ Overview This intelligent customer support chatbot leverages Retrieval-Augmented Generation (RAG) to provide accurate, contextual responses by combining your knowledge base with AI capabilities. The system automatically retrieves relevant documents from your Pinecone vector store and uses them to generate informed responses through OpenAI's language models. ⚑ Quick Setup 1. Import Workflow Import this workflow template into your n8n instance 2. Configure Credentials Add the following API credentials: - OpenAI API Key: For chat completions and embeddings - Pinecone API Key: For vector database operations - Google Drive: For document auto ingestion 3. Initialize Vector Store Use the "Insert documents into Pinecone" workflow to populate your knowledge base 5. Activate Workflow Enable the main chat workflow to start receiving requests πŸ”§ How it Works Main Chat Flow (Agent Workflow) User Message β†’ Memory Retrieval β†’ Vector Search β†’ Context Assembly β†’ AI Response β†’ Memory Update β†’ Response Process Flow: Message Reception: Webhook receives user chat messages with session management Memory Retrieval: Loads conversation history for context continuity Semantic Search: Queries Pinecone vector store for relevant documents Context Assembly: Combines retrieved documents with conversation history AI Generation: OpenAI generates contextual response using assembled context Memory Storage: Updates conversation memory for future interactions Response Delivery: Returns formatted response to user interface Document Ingestion Flow Document Source β†’ Text Extraction β†’ Chunking β†’ Embedding β†’ Vector Storage Process Flow: Document Trigger: Google Drive or manual file upload detection Content Extraction: Extracts text from various file formats (PDF, DOC, TXT) Text Chunking: Splits documents into optimal chunks for embedding Embedding Generation: Creates vector embeddings using OpenAI Vector Storage: Stores embeddings in Pinecone with metadata Index Update: Updates search index for immediate availability

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