RAG Chatbot with Supabase + TogetherAI + Openrouter Workflow Solution

Workflow overview

Why this workflow matters

Supports knowledge capture and document intelligence use cases.

⚠️ 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) 📌 Telegram Trigger Type:** telegramTrigger Purpose:** Waits for new Telegram messages to trigger the workflow. Note:** Currently disabled. 📄 Content for the Training Type:** googleDocs Purpose:** Fetches document content from Google Docs using its URL. Details:** Uses Service Account authentication. ✂️ 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. 🛢 Save the embedding in DB Type:** supabase Purpose:** Saves each text chunk and its embedding vector into the Supabase embed table. SECOND WORKFLOW EXPLAINATION: 💬 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. 🔍 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. 📦 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. 🤖 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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