product-dev-os-OPEN/specs/2_systems_s/architecture.md

Long-Term Tech Stack

This is designed to be:

• Simple
• Durable
• Modular
• Vendor-independent
• AI-native
• Low-maintenance
• Future-proof

Core Backend Layer (Stays Python Forever)

FastAPI

Your main backend framework indefinitely.

• Lightweight
• Fast
• Async
• Great docs
• Perfect for APIs
• Perfect for JSON-store backends

Pydantic (v2) {#pydantic-(v2)}

For:

• Validation
• Schema definition
• Type enforcement
• Parsing & serializing JSON

This gives you the “AI-Native Data Object” foundation.

Python typing

Static typing + Pydantic gives you:

• Safety
• Predictable data models
• AI-generated UIs
• Clean code

JSON object datastore on disk

Your architecture:

/srv/data/json-db/lod/customers/*.json

/srv/data/json-db/lod/files/

/srv/data/json-db/bc/hubs/*.json

This becomes your source of truth — and it’s timeless:

• No SQL migration pain
• AI-friendly
• Human-readable
• Portable across servers
• Works with agents
• Simple backups
• Easy to replicate across clouds

Systemd services

For:

• Backend uptime
• Simple restarts
• Zero Docker complexity
• Predictability

CAddy

One global reverse proxy.

• SSL termination
• Routing
• Static file serving
• Stable
• Minimal overhead

This will last you a decade.

Frontend Layer (Optional TS Later) {#frontend-layer-(optional-ts-later)}

For now:

• Simple JS
• Plain HTML + small modals
• Works fine for LOD

Long term (as projects expand):

• React + TypeScript
• HTMX for hybrid pages
• Shared component library
• Shared TypeScript types autogenerated from Pydantic
• Local static hosting (/srv/apps/ui/…)

You only bring in TS when:

• The UIs become larger
• You need better autocompletion
• Indexing dashboards get complex
• You build cross-app shared components

There is no immediate need.

AI Layer (Strong Long-Term Direction) {#ai-layer-(strong-long-term-direction)}

Python for AI operations

Perfect for:

• Embeddings
• Chunking
• Vectorization
• STT
• LLM calls
• Agents
• Parsing inbound data
• Publishing pipelines

Agentic pipelines {#agentic-pipelines-(future)}

• Chunking → JSONL files
• Vectorization (Embeddings) → Python
  • OpenAI embeddings
  • Jina embedding
  • Cohere
  • Local embeddings later (GGUF)
  • SentenceTransformers
  • Python is the correct home for:
• running the embedder
• transforming the JSONL chunks
• updating embeddings
• building vector stores
• MCP
• OpenAI’s new agent tools
• Event-driven systems
• Scheduled analytical tasks (weeklies)
• Lightweight Database for Metadata + Embeddings
  • SQLite + DuckDB
  • Qdrant
  • LanceDB
  • Weaviate (local mode)
  • ChromaDB
• The simplest long-term option I recommend for you:
  • DuckDB or SQLite for metadata
  • LanceDB or Qdrant for vectors
  • Why?
    • Very fast
    • No server needed
    • Easy to copy/backup
    • Python-native
    • AI-friendly
    • Perfect with JSONL chunk pipelines
• Your JSONL holds the raw chunks, Your small local DB holds:
• chunk_id
• metadata (source, tags, time ranges)
• vector embeddings
• up-to-date indexes
• Rag Layer - Python
• Consiuder agentic RAG sitting in this - see index main product development document https://docs.google.com/document/d/1GedfgKY78INGREJ5lgNSOv0OIAMjm0Lj268I40u0JuY/edit?tab=t.0#heading=h.ksb8pdnacvtr

Python scripts & CLIs {#python-scripts-&-clis}

For:

• Import/export
• Data normalization
• Periodic JSON cleanup
• Building indexes
• Summaries
• AI-native publishing

These will accumulate value into your system over years.

API-Driven

Even inside your system, set up so the pieces talk to each other via API, rather than tight coupling so that modules can be upgraded, replaced, outsourced, etc.