RAG chatbot with Supabase + TogetherAI + Openrouter
RUN the FIRST WORKFLOW ONLY ONCE (as it will convert your content in Embedding format and save it in DB and is ready for the RAG Chat) π Telegram Trigger Type: telegramTrigger Purpose: Waits for new Telegram messages to trigger the workflow. Note: Currently disabled. --- π Content for the Training Type: googleDocs Purpose: Fetches document content from Google Docs using its URL. Details: Uses Service Account authentication. --- βοΈ Sp
At a glance
RAG chatbot with Supabase + TogetherAI + Openrouter is a ready-made n8n workflow you import as a workflow JSON file β no build required. It connects Telegram. It's free to download. Follow the 5-step import below to go live in minutes.
- Platform
- n8n
- Connects
- Telegram
- Modules
- 11
- Price
- Free
- Version
- v1.0

About this workflow
RUN the FIRST WORKFLOW ONLY ONCE (as it will convert your content in Embedding format and save it in DB and is ready for the RAG Chat) π Telegram Trigger Type: telegramTrigger Purpose: Waits for new Telegram messages to trigger the workflow. Note: Currently disabled. --- π Content for the Training Type: googleDocs Purpose: Fetches document content from Google Docs using its URL. Details: Uses Service Account authentication. --- βοΈ Splitting into Chunks Type: code Purpose: Splits the fetched document text into smaller chunks (1000 chars each) for processing. Logic: Loops over text and slices it. --- π§ Embedding Uploaded Document Type: httpRequest Purpose: Calls Together AI embedding API to get vector embeddings for each text chunk. Details: Sends JSON with model name and chunk as input. --- π’ Save the embedding in DB Type: supabase Purpose: Saves each text chunk and its embedding vector into the Supabase embed table. SECOND WORKFLOW EXPLAINATION: π¬ When chat message received Type: chatTrigger Purpose: Starts the workflow when a user sends a chat message. Details: Sends an initial greeting message to the user. --- π§© Embend User Message Type: httpRequest Purpose: Generates embedding for the userβs input message. Details: Calls Together AI embeddings API. --- π Search Embeddings Type: httpRequest Purpose: Searches Supabase DB for the top 5 most similar text chunks based on the generated embedding. Details: Calls Supabase RPC function matchembeddings1. --- π¦ Aggregate Type: aggregate Purpose: Combines all retrieved text chunks into a single aggregated context for the LLM. --- π§ Basic LLM Chain Type: chainLlm Purpose: Passes the user's question + aggregated context to the LLM to generate a detailed answer. Details: Contains prompt instructing the LLM to answer only based on context. --- π€ OpenRouter Chat Model Type: lmChatOpenRouter Purpose: Provides the actual AI language model that processes the prompt. Details: Uses qwen/qwen3-8b:free model via OpenRouter and you can use any of your choice.
How to import this n8n workflow
- 1
Download the workflow JSON file after purchase.
- 2
Open n8n β click the menu β Import from File.
- 3
Select the downloaded JSON and import.
- 4
Set up credentials for each node that requires them.
- 5
Click Execute Workflow to test, then activate.
Setup guide
Setup guide included
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- JSON blueprint β instant download
- Setup guide PDF included
- 5 downloads Β· valid 30 days
- Works with n8n
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