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Architecture

ragdesk is a multi-tenant, AI-powered knowledge SaaS. Teams create a workspace, upload documents, and chat with an assistant that answers only from their documents, with citations — a Retrieval-Augmented Generation (RAG) product.

Services

flowchart LR
    User([User]) --> Web[web · Next.js 16 + TS]
    Web -->|REST / SSE| API[api · Go + chi]
    API --> PG[(Postgres + pgvector)]
    API --> Redis[(Redis · cache + queue)]
    API -->|ingest / chat| AI[ai · Python + FastAPI]
    AI --> PG
    AI -->|provider-agnostic| LLM{{Ollama · Gemini · Claude}}
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Service Stack Responsibility
web Next.js 16, TypeScript, Tailwind Auth UI, workspace dashboard, streaming chat, billing portal
api Go, chi, pgx, go-redis Auth/JWT, multi-tenancy, documents, Stripe billing, usage metering, rate limiting
ai Python, FastAPI Document ingestion (chunk → embed → pgvector), retrieval, RAG chat, provider-agnostic LLM
postgres pgvector/pg16 Relational data and vector embeddings in one store
redis redis:7 Cache, rate limiting, background job queue

Why this split

  • Go core, Python AI mirrors how real AI products are built: a fast, strongly-typed service layer for tenancy/billing, and Python where the LLM/embedding ecosystem lives.
  • One Postgres for rows and vectors (via pgvector) keeps the $0 footprint small and avoids a separate vector database.
  • Provider-agnostic LLM — the model is an implementation detail behind an interface. Local Ollama for $0 development, a free-tier hosted provider for cloud demos, and Claude when there is budget. This is the production pattern (model routing + fallback), not a single hard-coded vendor.

$0 deployment target

Concern Free option
Frontend Vercel free tier
Postgres + auth + storage Supabase free tier
Redis Upstash free tier
Backend services Render / Koyeb free tier
LLM Ollama (local) / Gemini free tier
CI/CD GitHub Actions (public repo)
Billing Stripe test mode