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AI & researchAgentDocument Default Data LoaderEmbeddings Open AiGoogle DriveHTTP Request

Business WhatsApp AI RAG Chatbot

Configured with Agent, Document Default Data Loader, Embeddings Open Ai, Google Drive, and additional nodes. Review the connections and setup before adapting this template.

Download workflow JSONView files on GitHub ↗18 nodes · MIT
Workflow preview
Web / APIRespond to WebhookRespond To WebhookAI & modelsAI AgentAgentAI & modelsOpenAI Chat ModelOpenAI Chat ModelTriggerWhen clicking ‘Testworkflow’Manual TriggerData storeQdrant Vector StoreVector Store QdrantWeb / APICreate collectionHTTP RequestWeb / APIRefresh collectionHTTP RequestFiles / mediaGet folderGoogle DriveFiles / mediaDownload FilesGoogle DriveAI & modelsEmbeddings OpenAIEmbeddings Open AiFiles / mediaDefault Data LoaderDocument Default Data Lo…TransformToken SplitterText Splitter Token Spli…TriggerVerifyWebhookTriggerRespondWebhookRoutingis Message?IfMessagingOnly messageWhats AppMessagingSendWhats AppData storeWindow Buffer MemoryMemory Buffer Window

Node categories in this workflow

  • Web / API
  • AI & models
  • Trigger
  • Data store
  • Files / media
  • Transform
  • Routing
  • Messaging

Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.

Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.

How it works

The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.

Workflow node names, types, and operations
NodeCategoryTypeOperation
Respond to WebhookWeb / APIRespond To WebhookSee node parameters
AI AgentAI & modelsAgentSee node parameters
OpenAI Chat ModelAI & modelsOpenAI Chat ModelSee node parameters
When clicking ‘Test workflow’TriggerManual TriggerSee node parameters
Qdrant Vector StoreData storeVector Store QdrantSee node parameters
Create collectionWeb / APIHTTP RequestPOST
Refresh collectionWeb / APIHTTP RequestPOST
Get folderFiles / mediaGoogle DrivefileFolder
Download FilesFiles / mediaGoogle Drivedownload
Embeddings OpenAIAI & modelsEmbeddings Open AiSee node parameters
Default Data LoaderFiles / mediaDocument Default Data LoaderSee node parameters
Token SplitterTransformText Splitter Token SplitterSee node parameters
VerifyTriggerWebhookSee node parameters
RespondTriggerWebhookSee node parameters
is Message?RoutingIfSee node parameters
Only messageMessagingWhats Appsend
SendMessagingWhats Appsend
Window Buffer MemoryData storeMemory Buffer WindowSee node parameters

Setup

  1. Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
  2. Connect your own credentials through n8n’s credential store. Replace REPLACE_WITH_* values with your resource identifiers.
  3. Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
  4. Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.

Requirements

An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.

Credential types declared in the source

  • googleDriveOAuth2Api
  • httpHeaderAuth
  • openAiApi
  • qdrantApi
  • whatsAppApi

Workflow notes

STEP 4

STEP 4

RAG System

  • Respond webhook receives various POST Requests from Meta regarding WhatsApp messages (user messages + status notifications)
  • Check if the incoming JSON contains user message
  • Echo back the text message to the user. This is a custom message, not a WhatsApp Business template message
Important!

Important!

Configure the webhook nodes this way:

  • Make sure that both Verify and Respond have the same URL
  • Verify should have GET HTTP Method
  • Respond should have POST HTTP Method
STEP 3

STEP 3

Create Webhook

  • Go to your Meta for Developers App page, navigate to the App settings
  • Add a production webhook URL as a new Callback URL
  • Verify webhook receives a GET Request and sends back a verification code
  • After that you can delete this
STEP 1

STEP 1

Create Qdrant Collection

Change:

  • QDRANTURL
  • COLLECTION
STEP 2

STEP 2

Documents vectorization with Qdrant and Google Drive

Change:

  • QDRANTURL
  • COLLECTION
Configure AI Agent

Configure AI Agent

Set System prompt and chat model. If you want you can set any tools

Business WhatsApp AI RAG Chatbot

Business WhatsApp AI RAG Chatbot

Purpose and structure

This folder contains the Business WhatsApp AI RAG Chatbot workflow. Its executable nodes use agent, documentDefaultDataLoader, embeddingsOpenAi, googleDrive, httpRequest, if, lmChatOpenAi, manualTrigger, memoryBufferWindow, respondToWebhook, textSplitterTokenSplitter, vectorStoreQdrant, webhook, whatsApp. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.

Setup

Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.

Entry points

When clicking ‘Test workflow’, Verify, Respond

Verification

Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.

Attribution

https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Webhook/1385_Webhook_Respondtowebhook_Automate_Webhook.json MIT; preserve source notices.

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