2026-04-20 18:30:51 +00:00
# **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* {#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* {#python-typing}
Static typing \+ Pydantic gives you:
* Safety
* Predictable data models
* AI-generated UIs
* Clean code
### *JSON object datastore on disk* {#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’
* No SQL migration pain
* AI-friendly
* Human-readable
* Portable across servers
* Works with agents
* Simple backups
* Easy to replicate across clouds
### *Systemd services* {#systemd-services}
For:
* Backend uptime
* Simple restarts
* Zero Docker complexity
* Predictability
### *CAddy* {#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* {#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’
* 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* {#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.
