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)

📌 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.