Block 1 - Set ID
- Type / Role
- n8n-nodes-base.set - set
- Config choices
- Version 3.4
This workflow is provided as-is. Please review and test before using in production.
Turn your docs into an AI powered internal or public facing assistant. This chatbot workflow uses RAG (Retrieval Augmented Generation) with Supabase vector search to answer employee or customer que...
n8n-nodes-base.set, @n8n/n8n-nodes-langchain.vectorstoresupabase, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter, @n8n/n8n-nodes-langchain.embeddingsopenai, n8n-nodes-base.googledrivetrigger, n8n-nodes-base.googledrive, n8n-nodes-base.stickynote
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Abdul Mir.
Original n8n.io sourceTurn your docs into an AI-powered internal or public-facing assistant. This chatbot workflow uses RAG (Retrieval-Augmented Generation) with Supabase vector search to answer employee or customer questions based on your company documents—automatically updated via Google Drive.
Whether it’s deployed in Telegram or embedded on your website, this agent supports voice and text input, transcribes voice messages, pulls relevant context from your internal files, and responds with a helpful, AI-generated answer. Two additional workflows listen for file changes in a shared Google Drive folder, convert them into embeddings using OpenAI, and sync them with your Supabase vector DB—so your knowledge base is always up to date.
> You upload your internal policy docs and client FAQs into a Google Drive folder.
>
> Employees or customers can now ask:
> - “What’s the refund policy for annual plans?”
> - “How do I request a day off?”
> - “What tools are approved for use by the engineering team?”
>
> The chatbot instantly pulls up the right section and responds with a smart, confident answer.
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.
Showing the first 24 of 49 workflow blocks. Download the JSON for the full node graph.
| Workflow | Company knowledge base agent (RAG) |
|---|---|
| Complexity | advanced |
| Nodes | 49 |
| Categories | Internal Wiki, AI RAG |
| Author | Abdul Mir |
| Published | 27 Jul 2025 |
Use the JSON export at /data/workflows/6538/6538.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
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.
Turn your docs into an AI powered internal or public facing assistant. This chatbot workflow uses RAG (Retrieval Augmented Generation) with Supabase vector search to answer employee or customer que...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
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 Internal Wiki, AI RAG use case.