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RAG chatbot with Supabase + TogetherAI + Openrouter

n8nn8n11 modulesv1.0
Telegram

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) πŸ“Œ Telegram Trigger Type: telegramTrigger Purpose: Waits for new Telegram messages to trigger the workflow. Note: Currently disabled. --- πŸ“„ Content for the Training Type: googleDocs Purpose: Fetches document content from Google Docs using its URL. Details: Uses Service Account authentication. --- βœ‚οΈ 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
RAG chatbot with Supabase + TogetherAI + Openrouter workflow diagram

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) πŸ“Œ Telegram Trigger Type: telegramTrigger Purpose: Waits for new Telegram messages to trigger the workflow. Note: Currently disabled. --- πŸ“„ Content for the Training Type: googleDocs Purpose: Fetches document content from Google Docs using its URL. Details: Uses Service Account authentication. --- βœ‚οΈ 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. --- 🧠 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. --- πŸ›’ Save the embedding in DB Type: supabase Purpose: Saves each text chunk and its embedding vector into the Supabase embed table. SECOND WORKFLOW EXPLAINATION: πŸ’¬ 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. --- 🧩 Embend User Message Type: httpRequest Purpose: Generates embedding for the user’s input message. Details: Calls Together AI embeddings API. --- πŸ” 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. --- πŸ“¦ Aggregate Type: aggregate Purpose: Combines all retrieved text chunks into a single aggregated context for the LLM. --- 🧠 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. --- πŸ€– 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.

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How to import this n8n workflow

  1. 1

    Download the workflow JSON file after purchase.

  2. 2

    Open n8n β†’ click the menu β†’ Import from File.

  3. 3

    Select the downloaded JSON and import.

  4. 4

    Set up credentials for each node that requires them.

  5. 5

    Click Execute Workflow to test, then activate.

Setup guide

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