RAG Chatbot with Supabase + TogetherAI + OpenRouter

## RUN the FIRST WORKFLOW ONLY ONCE (as it will convert your content into 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 EXPLANATION: ## 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. --- ## Embed 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.