RAG-powered AI voice customer support agent (Supabase + Gemini + ElevenLabs)

Execution video: Youtube Link

I built an AI voice-triggered RAG assistant where ElevenLabs’ conversational model acts as the front end and n8n handles the brain....here’s the real breakdown of what’s happening in that workflow:

1. Webhook (/inf)

• ▪Gets hit by ElevenLabs once the user finishes talking.
• ▪Payload includes user_question.

1. Embed User Message (Together API - BAAI/bge-large-en-v1.5)

• ▪Turns the spoken question into a dense vector embedding.
• ▪This embedding is the query representation for semantic search.

1. Search Embeddings (Supabase RPC)

• ▪Calls matchembeddings1 to find the top 5 most relevant context chunks from your stored knowledge base.

1. Aggregate

• ▪Merges all retrieved chunk values into one block of text so the LLM gets full context at once.

1. Basic LLM Chain (LangChain node)

• ▪Prompt forces the model to only answer from the retrieved context and to sound human-like without saying “based on the context”....
• ▪Uses Google Vertex Gemini 2.5 Flash as the actual model.

1. Respond to Webhook

• ▪Sends the generated answer back instantly to the webhook call, so ElevenLabs can speak it back.

You essentially have:

Voice → Text → Embedding → Vector Search → Context Injection → LLM → Response → Voice