Customer Support Chatbot with RAG using OpenAI and Pinecone

šŸ¤– Simple RAG Customer Support Chatbot

šŸ“‹ 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

šŸ”§ 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

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Quality Score
intermediate
Complexity
Author:Ilyass Kanissi(View Original →)
Created:9/10/2025
Updated:9/24/2025

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