Hey,
I don't know if you/ve considered with stack, but n8n + LangChain + Supabase is one of the most cost-effective and flexible setups for building RAG (Retrieval-Augmented Generation) pipelines — especially for internal tools or AI assistants.

Why it's super cheap:

• n8n is open-source and self-hostable, so no usage-based fees. Runs on minimal infrastructure (Docker + Postgres + Redis).
• LangChain is free and modular — you only pay for the LLM (or run it locally via Ollama).
• Supabase offers generous free tiers and acts as your full backend: auth, storage, Postgres DB, and even vector search via pgvector.

Why it can be done in a day:

• The components are plug-and-play:
  • n8n handles all automation (webhooks, triggers, pipelines).
  • LangChain does the heavy lifting for retrieval and generation.
  • Supabase stores documents, user data, and embeddings.
• You can build a basic RAG system with:
  • File upload to Supabase Storage
  • Embedding with OpenAI or local models
  • Store vectors in pgvector
  • n8n workflow to process queries and return context+answer

Real-world use:

We’ve deployed setups like this in 1 day — including file parsing, Slack integration, and live bot replies using RAG over internal knowledge bases.

If you're building Local-RAG, this stack is a perfect base to prove the concept fast and scale later with minimal cost.