Customer support chatbot with RAG using OpenAI and Pinecone

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Simple RAG Customer Support Chatbot Overview This intelligent customer support chatbot leverages Retrieval Augmented Generation (RAG) to provide accurate, contextual responses by combining yo...

Best for

AI RAG automation workflows
Multimodal AI automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.googledrivetrigger, n8n-nodes-base.googledrive, @n8n/n8n-nodes-langchain.vectorstorepinecone, @n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Ilyass Kanissi.

Original n8n.io source

1.1 Workflow description

Title: Customer support chatbot with RAG using OpenAI and Pinecone
Workflow name: Customer support chatbot with RAG using OpenAI and Pinecone

🤖 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

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Google Drive Trigger

Type / Role: n8n-nodes-base.googleDriveTrigger - googleDriveTrigger
Config choices: Version 1

Block 2 - Download file

Type / Role: n8n-nodes-base.googleDrive - googleDrive
Config choices: Version 3

Block 3 - Pinecone Vector Store

Type / Role: @n8n/n8n-nodes-langchain.vectorStorePinecone - vectorStorePinecone
Config choices: Version 1.3

Block 4 - Embeddings OpenAI

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices: Version 1.2

Block 5 - Default Data Loader

Type / Role: @n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices: Version 1.1

Block 6 - Recursive Character Text Splitter

Type / Role: @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - textSplitterRecursiveCharacterTextSplitter
Config choices: Version 1

Block 7 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2.2

Block 8 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.2

Block 9 - Simple Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 10 - Embeddings OpenAI1

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices: Version 1.2

Block 11 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 12 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 13 - When chat message received

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.3

Block 14 - Reranker Cohere

Type / Role: @n8n/n8n-nodes-langchain.rerankerCohere - rerankerCohere
Config choices: Version 1

Block 15 - Vector Store

Type / Role: @n8n/n8n-nodes-langchain.vectorStorePinecone - vectorStorePinecone
Config choices: Version 1.3

3. Summary Table

Workflow	Customer support chatbot with RAG using OpenAI and Pinecone
Complexity	advanced
Nodes	15
Categories	AI RAG, Multimodal AI
Author	Ilyass Kanissi
Published	19 Aug 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/7561/7561.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source AI RAG workflows Multimodal AI workflows

Frequently asked questions

What does Customer support chatbot with RAG using OpenAI and Pinecone do?

Simple RAG Customer Support Chatbot Overview This intelligent customer support chatbot leverages Retrieval Augmented Generation (RAG) to provide accurate, contextual responses by combining yo...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your AI RAG, Multimodal AI use case.

Ilyass Kanissi

4 workflows

Nodes

n8n-nodes-base.googledrivetrigger n8n-nodes-base.googledrive @n8n/n8n-nodes-langchain.vectorstorepinecone @n8n/n8n-nodes-langchain.embeddingsopenai @n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatopenai

Complexity

advanced

Published 19 Aug 2025

Likes 0

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Install path: /data/workflows/7561/7561.json

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Customer support chatbot with RAG using OpenAI and Pinecone

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

🤖 Simple RAG Customer Support Chatbot

📋 Overview

⚡ Quick Setup

🔧 How it Works

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Google Drive Trigger

Block 2 - Download file

Block 3 - Pinecone Vector Store

Block 4 - Embeddings OpenAI

Block 5 - Default Data Loader

Block 6 - Recursive Character Text Splitter

Block 7 - AI Agent

Block 8 - OpenAI Chat Model

Block 9 - Simple Memory

Block 10 - Embeddings OpenAI1

Block 11 - Sticky Note

Block 12 - Sticky Note1

Block 13 - When chat message received

Block 14 - Reranker Cohere

Block 15 - Vector Store

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions