Overview

@forgespace/siza-gen is the AI brain extracted from siza-mcp. It provides:

• Framework generators — React, Vue, Angular, Svelte, HTML
• Component registry — 502 curated snippets (357 component + 85 animation + 60 backend)
• ML quality scoring — Embeddings, quality validation, anti-generic rules
• Feedback system — Self-learning, pattern promotion, feedback-boosted search
• Template compositions — Pre-built page templates with quality gating
• Brand integration — Transform branding-mcp tokens into design context
• LLM providers — Ollama, OpenAI, Anthropic, Gemini with auto-fallback

Installation

npm install @forgespace/siza-gen

Usage

import {
  searchComponents,
  initializeRegistry,
  GeneratorFactory,
} from '@forgespace/siza-gen';

await initializeRegistry();
const results = searchComponents('hero section');
const generator = GeneratorFactory.create('react');

What's inside

| Module | Description | | ------------- | ----------------------------------------------------------------- | | generators/ | React, Vue, Angular, Svelte, HTML code generators | | registry/ | 502 snippets — 357 component + 85 animation + 60 backend | | ml/ | Embeddings (all-MiniLM-L6-v2), quality scoring, training pipeline | | feedback/ | Self-learning loop, pattern promotion, feedback-boosted search | | quality/ | Anti-generic rules, diversity tracking | | artifacts/ | Generated artifact storage and learning loop |

LLM Providers

Built-in multi-provider support with auto-fallback:

import { createProviderWithFallback } from '@forgespace/siza-gen';

// Tries Ollama first (local), falls back to OpenAI/Anthropic/Gemini
const provider = await createProviderWithFallback();

Supports: Ollama (local), OpenAI, Anthropic, Gemini (via OpenAI adapter).

Brand Integration

Transform branding-mcp tokens into design context:

import { brandToDesignContext } from '@forgespace/siza-gen';

const designContext = brandToDesignContext(brandIdentity);

Python ML Sidecar

An optional Python FastAPI sidecar handles compute-intensive ML operations. When unavailable, the system gracefully degrades to Transformers.js and heuristics.

cd python && pip install -e ".[dev]"
python -m uvicorn siza_ml.app:app --port 8100

Or via npm:

npm run sidecar:start     # Launch Python sidecar
npm run sidecar:test      # Run Python tests (41 tests)

| Endpoint | Description | | --------------------- | ------------------------------- | | POST /embed | Sentence-transformer embeddings | | POST /embed/batch | Batch embeddings | | POST /vector/search | FAISS k-NN similarity search | | POST /score | LLM-based quality scoring | | POST /enhance | LLM-based prompt enhancement | | POST /train/start | LoRA fine-tuning via PEFT | | GET /health | Liveness check | | GET /metrics/report | ML observability metrics |

Fallback chain: Python sidecar → Transformers.js/local LLM → heuristics.

Development

npm install && npm run build
npm test                  # 424 tests, 21 suites
npm run validate          # lint + format + typecheck + test
npm run registry:stats    # Report snippet counts

License

MIT