RAG Chatbot with Supabase + TogetherAI + Openrouter
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iamvaar
Description
⚠️ 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.
This workflow was created by iamvaar and shared publicly on the n8n community template library. n8nFind indexes it for discovery — the workflow remains the property of its original creator.