Slack AI Chatbot with RAG for company staff
Configured with Agent, Document Default Data Loader, Embeddings Open Ai, Google Drive, and additional nodes. Review the connections and setup before adapting this template.
Node categories in this workflow
- AI & models
- Data store
- Integration
- Trigger
- Messaging
- Web / API
- Files / media
- Transform
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.
| Node | Category | Type | Operation |
|---|---|---|---|
| AI Agent | AI & models | Agent | See node parameters |
| Simple Memory | Data store | Memory Buffer Window | See node parameters |
| Embeddings OpenAI1 | AI & models | Embeddings Open Ai | See node parameters |
| RAG | Data store | Vector Store Qdrant | See node parameters |
| Calculator | Integration | Tool Calculator | See node parameters |
| Get message | Trigger | Slack Trigger | See node parameters |
| Send message | Messaging | Slack | See node parameters |
| When clicking ‘Test workflow’ | Trigger | Manual Trigger | See node parameters |
| Create collection | Web / API | HTTP Request | POST |
| Refresh collection | Web / API | HTTP Request | POST |
| Get folder | Files / media | Google Drive | fileFolder |
| Download Files | Files / media | Google Drive | download |
| Default Data Loader | Files / media | Document Default Data Loader | See node parameters |
| Token Splitter | Transform | Text Splitter Token Splitter | See node parameters |
| Qdrant Vector Store1 | Data store | Vector Store Qdrant | See node parameters |
| Embeddings OpenAI2 | AI & models | Embeddings Open Ai | See node parameters |
| Anthropic Chat Model | AI & models | Anthropic Chat Model | See node parameters |
Setup
- Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
- Connect your own credentials through n8n’s credential store. Replace
REPLACE_WITH_*values with your resource identifiers. - Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
- 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
anthropicApigoogleDriveOAuth2ApihttpHeaderAuthopenAiApiqdrantApislackApi
Workflow notes
STEP 1
STEP 1
Create Qdrant Collection
Change:
- QDRANTURL
- COLLECTION
STEP 2
STEP 2
Documents vectorization with Qdrant and Google Drive
Change:
- QDRANTURL
- COLLECTION
Slack AI Chatbot Workflow with RAG
Slack AI Chatbot Workflow with RAG
Imagine having an AI chatbot on Slack that seamlessly integrates with your company’s workflow, automating repetitive requests. No more digging through emails or documents to find answers about IT requests, company policies, or vacation days—just ask the bot, and it will instantly provide the right information.
With its 24/7 availability, the chatbot ensures that team members get immediate support without waiting for a colleague to be online, making assistance faster and more efficient.
STEP 3
STEP 3
Create a Slack Bot here and add it to your Slack (Private o Public) channel.
Set "Scope Subscribe to Bot Event":
- app_mention
- message.channels
Set Bot Token Scopes:
- app_mentions:read
- channels:history
- channels:manage
- channels:read
- chat:write
- files:read
- groups:history
- groups:read
- im:history
- im:read
- mpim:history
- mpim:read
- reactions:read
- reactions:write
- usergroups:read
- users:read
In RAG Qdrant node change:
- COLLECTION
Slack AI Chatbot with RAG for company staff
Slack AI Chatbot with RAG for company staff
Purpose and structure
This folder contains the Slack AI Chatbot with RAG for company staff workflow. Its executable nodes use agent, documentDefaultDataLoader, embeddingsOpenAi, googleDrive, httpRequest, lmChatAnthropic, manualTrigger, memoryBufferWindow, slack, slackTrigger, textSplitterTokenSplitter, toolCalculator, vectorStoreQdrant. 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
Get message, When clicking ‘Test workflow’
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/Slack/1643_Slack_Manual_Automate_Webhook.json MIT; preserve source notices.