Structured Intelligence Architecture

Finding a generic name for all of the AI Models

They are all essentially different. What can we call them? Big and small, local and propriatory.

There are

reasoning engines → when they interpret, explain, decide

language engines → when they read/write human text

signal processors → when they score, classify, embed

assistive runtimes → when they sit behind your API and respond

Structured Intelligence Architecture

That fits the whole beast:

Backdrop = input + display
Python = structure + deterministic logic
Local signal layer = matching/scoring/classification
Interpreter layer = rare escalation

Structured Intelligence Pipeline

Use this when describing the flow.

A more ops-flavoured version:

Deterministic-First Intelligence Stack

That one says the important thing:

We compute what we can prove, and escalate only what we cannot resolve.

Deterministic-First Structured Intelligence

local models
external models
reasoning engines
language engines
signal processors
assistive runtimes

And the collective name for all those “models/tools/runtimes”?

Interpretive Engines

Nice umbrella. Big, small, local, proprietary, text, reasoning, embedding. They all interpret signals into useful output.

So your clean framing becomes:

This is a Deterministic-First Structured Intelligence system.
Backdrop handles input and display. Python owns structure, scoring, and truth. Local signal processors handle semantic matching. Interpretive engines are used only for low-confidence edge cases.

Deterministic first.
Local where possible.
Interpretive only by escalation.

Backdrop = input + display
Python = structure + logic

Semantic matching (local wins here)

This is where you don’t need external anything.

Use:

sentence-transformers
or lightweight embedding models

For:

“choy + chicken but no tomato”
fuzzy matching
clustering similar records

This is your signal layer.

Fast. Local. Deterministic-ish.

“We use local signal processing for structure and scoring, and bring in external interpreters only when meaning cannot be derived deterministically.”

Structured reasoning (where we don't outsource ever)

The logic like:

scoring pairs
Wilson confidence
rule thresholds

👉 stays in Python

But!? For deeper dives...

Edge-case interpretation (surgical use)

This is the fun one.

When something breaks your rules:

weird receipt line
ambiguous ingredient
malformed text

We can do:

if low_confidence:
    call_interpreter()

if low_confidence:
call_interpreter()

That’s not dependency. That’s escalation.

Structured reasoning to edge-case interpretation

Structured Reasoning:

define truth
compute totals
assign final labels

Interpreter:

explain results
rephrase output
generate readable insights

[Backdrop Form]
        ↓
[Python: shape data]
        ↓
[Python: compute signals]
        ↓
[Optional: external interpreter]
        ↓
[Python: validate + finalize]
        ↓
[Backdrop display]

We’re not “integrating AI into a CMS.”

We’re doing:

Backdrop = structured input + controlled display
Python = deterministic reasoning engine
Interpreter = escalation layer (rare, deliberate)

This is a three-tier cognition system, not a plugin.

Yes. Clean, minimal, no ceremony. Let’s wire a local Python “brain” behind your Backdrop content type.

You already have the “form” (Page content type). Now we add a door.

🧭 Shape of the flow (your mental model)

Backdrop (node form)
   ↓
POST /api/recipe/enrich
   ↓
Python (local service)
   ↓
JSON response (signals, tags, metadata)
   ↓
Backdrop (stores or displays)

No magic. Just a handshake 🤝

🐍 1. Minimal Python service (Flask)

Run this locally on the same box (Camelot-style, nice and contained):

# app.py
from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route("/api/recipe/enrich", methods=["POST"])
def enrich():
    data = request.json

    # extract only what you care about
    title = data.get("title")
    body = data.get("body")
    tags = data.get("dish", [])

    # pretend "intelligence"
    response = {
        "summary": f"{title} analyzed",
        "signals": {
            "has_protein": "chicken" in body.lower(),
            "complexity": "low" if len(body) < 500 else "medium"
        },
        "enriched_tags": tags + ["processed"]
    }

    return jsonify(response)

if __name__ == "__main__":
    app.run(host="127.0.0.1", port=5000)

Run:

python3 app.py

Now you have:

http://127.0.0.1:5000/api/recipe/enrich

🌐 2. Backdrop side (quick prototype)

You don’t need a full module yet. Start with a hook or simple POST trigger.

Option A (fastest): use a custom module with `hook_node_insert`

function mymodule_node_insert($node) {
  if ($node->type == 'page') {

    $payload = [
      'title' => $node->title,
      'body' => $node->body[LANGUAGE_NONE][0]['value'],
      'dish' => array_column($node->field_tags[LANGUAGE_NONE] ?? [], 'tid'),
    ];

    $options = [
      'method' => 'POST',
      'data' => json_encode($payload),
      'headers' => [
        'Content-Type' => 'application/json',
      ],
      'timeout' => 2,
    ];

    $response = drupal_http_request('http://127.0.0.1:5000/api/recipe/enrich', $options);

    if ($response->code == 200) {
      $result = json_decode($response->data, TRUE);

      // Example: store result back into a field
      $node->field_method[LANGUAGE_NONE][0]['value'] = $result['summary'];

      node_save($node);
    }
  }
}

🧪 3. Test it manually first (important)

Before wiring Backdrop, hit it directly:

curl -X POST http://127.0.0.1:5000/api/recipe/enrich \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Test Dish",
    "body": "Chicken with herbs",
    "dish": ["dinner"]
  }'

If that works → your door is alive.

🧱 4. What you’ve just built (important)

You now have:

Backdrop = data + UI
Python = interpretation layer
HTTP = contract

This is the core pattern you were aiming for:

“We enrich a Backdrop content type by round-tripping it through a local Python service.”

🔧 Next logical upgrades (not required yet)

Keep it lean, but here’s where it goes:

Add /api/recipe/query (search/filter endpoint)
Normalize payloads (consistent JSON schema)
Move from node_insert → button-trigger or async queue
Add nginx reverse proxy:
```
/api/ → 127.0.0.1:5000
```

⚡ The important insight

You didn’t bolt AI into Backdrop.

You built a clean seam:

Backdrop doesn’t “think”
Python doesn’t “store”
They handshake like two professionals who don’t overshare

If you want next step:
We can define a clean JSON contract (your recipe prototype: includes, excludes, provides, region) and make both sides obey it like a treaty.

$response = backdrop_http_request('http://127.0.0.1:5000/api/query', array(
  'method' => 'POST',
  'data' => json_encode(array('text' => $input)),
  'headers' => array('Content-Type' => 'application/json'),
));

$result = json_decode($response->data, TRUE);

Perfect. Now you’re thinking in intent, not strings. That’s the real switch flipping ⚡

“Chicken soup” was just a heartbeat check.
What you actually want is:

“I have this thing… what can I do with it?”

That’s a completely different contract.

🧭 Let’s reshape the call (closer to your world)

Instead of:

'text' => $input

You send something like:

$payload = array(
  'intent' => 'use_ingredient',
  'ingredient' => 'chicken broth',
  'quantity' => '1 litre',
  'constraints' => array(
    'already_thawed' => TRUE,
    'avoid_refreeze' => TRUE
  )
);

$response = backdrop_http_request('http://127.0.0.1:5000/api/query', array(
  'method' => 'POST',
  'data' => json_encode($payload),
  'headers' => array('Content-Type' => 'application/json'),
));

$result = json_decode($response->data, TRUE);
dpm($result);

🐍 Python now speaks your language

@app.route("/api/query", methods=["POST"])
def query():
    data = request.json

    if data.get("intent") == "use_ingredient":
        ingredient = data.get("ingredient")
        qty = data.get("quantity")

        # stub logic for now
        return jsonify({
            "suggestions": [
                f"Soup using {ingredient}",
                f"Sauce reduction ({qty})",
                f"Grain cooking base with {ingredient}"
            ],
            "note": "Use it today (already thawed)"
        })

    return jsonify({"status": "unknown intent"})

🧠 What just happened (this is the important bit)

You moved from:

🔤 “search string”

to:

🧩 “structured situation”

That unlocks everything later:

filtering recipes
excluding ingredients (“no tomatoes”)
time constraints (“< 45 min”)
inventory-aware cooking

🧱 Where your Backdrop fields come in

From your screenshot, you already have:

Dish (tags)
Method
Stage
Body
(and your now-removed food pics 😄)

Those become your dataset, not your query.

The Python side becomes:

“Given ingredient X, find nodes where X appears in Body or tags”

⚡ Ultra-practical next step

Don’t overbuild. Just fake it once:

Hardcode 2–3 “recipes” in Python:

recipes = [
    {"title": "Chicken Rice", "ingredients": ["chicken broth", "rice"]},
    {"title": "Veg Soup", "ingredients": ["vegetables", "chicken broth"]},
]

Then filter:

matches = [r for r in recipes if "chicken broth" in r["ingredients"]]

Return that.

🧭 Mental model (keep this)

Backdrop = pantry + cookbook 📚
Python = the cook thinking out loud 🍳

You’re not searching text.
You’re asking:

“What can I do with what I’ve got?”

If you want next, we can wire this to:

actual Backdrop node exports
or a tiny JSON file generated from your site

and make recipes.fred.ca feel like it’s thinking instead of listing.

This is the seam where architecture turns into plumbing. The design is clean. Now you need a door that Backdrop can knock on without dragging Python into its basement.

Short answer: yes — take the API step now. Not because it’s trendy, but because it locks your boundary early and prevents the CMS from “helpfully” absorbing your logic later.

“I already have a local Python service that can normalize ingredients and reason over a small recipe dataset.
I’m wondering if Backdrop could act as the content layer and just call that service.”

The Wiring Pattern (no surprises, no magic)

Think of this as a controlled corridor, not a free-for-all:

Backdrop (Webform or page)
        ↓ POST /api/query
Python API (Flask/FastAPI)
        ↓
[shape → score → thresholds]
        ↓
if low_confidence → interpreter()
        ↓
[validate + finalize]
        ↓ JSON
Backdrop renders result

Backdrop never “thinks.”
Python never “renders.”
That separation is your leverage.

⚙️ Step 1 — Stand up the Python door (minimal, surgical)

Use something like FastAPI or Flask. Keep it boring.

Example:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Query(BaseModel):
    text: str

@app.post("/api/query")
def process(query: Query):
    shaped = shape_data(query.text)
    signals = compute_signals(shaped)

    if signals["confidence"] < 0.6:
        signals = call_interpreter(signals)

    result = finalize(signals)

    return result

Run it locally:

uvicorn app:app --host 127.0.0.1 --port 5000

That’s your castle gate.

🧩 Step 2 — Backdrop as the caller (not the brain)

You’ve got two clean options:

Option A — Webform (fastest path)

Build a form
Submit to a custom handler
That handler does an HTTP POST to your Python API

Option B — Backdrop CMS Services module (cleaner long-term)

Expose a Backdrop endpoint like /api/proxy
That endpoint forwards requests to Python
Gives you control, logging, auth later

🔌 Step 3 — The actual call (this is the only “integration”)

In Backdrop (PHP side), you’re basically doing:

$response = backdrop_http_request('http://127.0.0.1:5000/api/query', array(
  'method' => 'POST',
  'data' => json_encode(array('text' => $input)),
  'headers' => array('Content-Type' => 'application/json'),
));

$result = json_decode($response->data, TRUE);

That’s it.

No SDKs. No ceremony. Just a POST.

🧠 Step 4 — Respect your escalation model

This is where your design shines. Keep it strict:

Backdrop → never decides anything
Python core → deterministic truth
Interpreter → only when summoned

Your pattern:

if low_confidence:
    call_interpreter()

That’s not “AI-powered.”
That’s exception handling with a brain.

🧪 Step 5 — Validate the loop early

Before adding complexity, prove this loop:

Type text in Backdrop form
See JSON come back
Render 2–3 fields on screen

If that works, the system is alive.

Everything else is refinement.

🚧 When not to overbuild (right now)

Skip for now:

auth layers
queueing systems
async pipelines
CMS-side AI modules (like OpenAI plugins)

They blur your boundary.

You want a tight loop first.

🧭 Strategic read (this matters more than code)

You’re not “integrating AI into a CMS.”

You’re doing this:

Backdrop = structured input + controlled display
Python = deterministic reasoning engine
Interpreter = escalation layer (rare, deliberate)

That’s a three-tier cognition system, not a plugin.

🔥 Decision checkpoint

You’re at a fork:

Keep prototyping inside Python → faster experimentation
Or wire Backdrop now → validates the real architecture

Given how strongly you care about control and separation,
👉 wire it now, but keep it thin.

Now that framing clicks. You’re not pitching a feature. You’re defining governance + architecture in one stroke.

Let’s write it the way a Drupal/Backdrop audience will actually absorb it.

📜 Backdrop’s New AI Usage Policy (Draft)

1. Core Principle

AI is not a data source. It is an escalation tool.

Backdrop remains the system of record.
All structured reasoning and decision logic must remain deterministic and auditable.

2. Architectural Model

This system introduces a Structured Intelligence Architecture deployed alongside Backdrop.

Backdrop CMS (PHP)
  - Forms (input)
  - Content (database)
  - Display (theme/views)
        ↓ HTTP (local)

Python Intelligence Service (same host or internal network)
  - Data shaping (structure normalization)
  - Signal processing (scoring, matching, embeddings)
  - Deterministic reasoning (rules, thresholds, confidence)
  - Optional interpreter escalation (external or local)
        ↓ JSON

Backdrop CMS
  - Final rendering

Key constraint:

Python is a local service, not a SaaS dependency
Database remains under Backdrop control
Network calls are internal unless explicitly escalated

3. Responsibility Boundaries

Backdrop (System of Record)

Owns all content and schemas
Handles all user interaction
Controls final rendering and display
Never delegates decision authority

Python Service (Deterministic Intelligence Layer)

Shapes incoming data into structured form
Computes signals:
- pair scoring
- clustering / similarity
- rule thresholds
- confidence scoring (e.g., Wilson)
Produces repeatable, testable outputs

👉 This replaces:

complex Views logic
ad hoc PHP decision code

Interpreter Layer (Escalation Only)

Used only when deterministic confidence fails

if confidence < threshold:
    call_interpreter()

Allowed uses:

ambiguous text interpretation
malformed input recovery
explanation / summarization

Not allowed:

primary decision making
replacing deterministic scoring
acting as source of truth

4. Data Flow Contract

All communication between Backdrop and Python follows a strict API boundary.

Request

{
  "query": "chicken + choy, no tomato",
  "context": {...}
}

Response

{
  "results": [...],
  "confidence": 0.87,
  "explanation": "...",
  "source": "deterministic | interpreter"
}

Rule:

Backdrop renders results
Python never returns HTML

5. Local-First Requirement

The system must prioritize:

local models (e.g., embeddings via sentence-transformers)
local computation for scoring and matching
zero external calls for standard operations

External interpreters are:

optional
explicitly invoked
auditable

6. Deterministic First Policy

All logic must follow this order:

1. Structure the data
2. Compute signals
3. Apply rules
4. Assign confidence
5. Escalate only if required

This ensures:

reproducibility
explainability
testability

7. Deployment Model

Python service runs:
- on same host as Backdrop or
- on internal network (no public exposure required)
Communication:
- HTTP (loopback or internal)
- no browser-side JavaScript dependency required
Database:
- remains separate and unchanged
- accessed only by Backdrop (preferred)

8. What This Is Not

Not a ChatGPT integration
Not a plugin replacing Views
Not a black-box AI system

9. What This Enables

semantic querying beyond SQL
controlled interpretation of messy data
auditable decision pipelines
gradual, safe adoption of AI capabilities

10. Guiding Statement

“Backdrop owns the data.
Python owns the logic.
Interpretation is invoked, never assumed.”

⚙️ Final note (the subtle power move)

This policy does something important:

It reframes “AI usage” from:

“Which model do we call?”

to:

“At what point do we allow interpretation into a deterministic system?”

That’s a much more mature conversation — and Drupal folks will feel that shift immediately.

Structured Intelligence Architecture

Finding a generic name for all of the AI Models