# **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’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* {#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’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* {#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.