🔍 Customer support chatbot with RAG using OpenAI and Pinecone
⚡ 2,057 views · 🔍 AI RAG & Knowledge Retrieval
Description
🤖 Simple RAG Customer Support Chatbot
📋 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
- Import Workflow Import this workflow template into your n8n instance
- 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
- Initialize Vector Store Use the “Insert documents into Pinecone” workflow to populate your knowledge base
- Activate Workflow Enable the main chat workflow to start receiving requests
🔧 How it Works
Main Chat Flow (Agent Workflow)
User Message → Memory Retrieval → Vector Search → Context Assembly → AI Response → Memory Update → 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 → Text Extraction → Chunking → Embedding → 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
🔗 Nodes Used
Google Drive, Google Drive Trigger, AI Agent, Embeddings OpenAI, OpenAI Chat Model, Simple Memory
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File