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

⚡ 362 views · 💬 Support Chatbots

Description

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.

  2. 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.

  3. Search Embeddings (Supabase RPC)

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

  4. Aggregate

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

  5. 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.

  6. 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

🔗 Nodes Used

HTTP Request, Webhook, Google Docs, Supabase, Basic LLM Chain, Google Vertex Chat Model

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup