@forgespace/siza-gen

@forgespace/siza-gen

Siza AI generation engine — multi-framework code generation, component registry, and ML-powered quality scoring.

Overview

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

• Framework generators — React, Vue, Angular, Svelte, HTML
• Component registry — 518 curated snippets (400 component + 85 animation + 60 backend) with AI chat and data display molecules
• ML quality scoring — Hybrid semantic+keyword search, 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

Lightweight Entry (/lite)

A zero-native-dependency entry point for edge runtimes (Cloudflare Workers, Deno, Bun). Provides context assembly without the registry/database/ML stack.

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

const ctx = assembleContext({
  framework: 'react',
  componentLibrary: 'shadcn',
  tokenBudget: 4000,
});
// ctx.systemPrompt — ready to use as LLM system prompt

43 KB vs 1.87 MB full bundle. Includes brandToDesignContext, designContextStore, and all core types.

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                  # 503 tests, 25 suites
npm run validate          # lint + format + typecheck + test
npm run format            # apply repo-wide Prettier formatting
npm run registry:stats    # Report snippet counts

Distribution

• npm — published as @forgespace/siza-gen
• GitHub — source, issues, and releases live at Forge-Space/siza-gen
• Docs and ecosystem context — start from docs.forgespace.co/docs

SonarCloud duplication configuration

sonar-project.properties includes targeted CPD exclusions for src/registry/component-registry/molecules/ai-patterns.ts and src/registry/component-registry/molecules/data-display.ts. These files contain intentional registry template repetition and are excluded from duplication quality-gate calculations only.

AI Benchmarks

Run the benchmark suite to compare LLM providers on generation quality, scoring accuracy, prompt enhancement effectiveness, and cost:

npm run bench:dry    # Preview plan without API calls
npm run bench        # Run full benchmark (requires API keys or Ollama)

Set provider API keys as environment variables:

export ANTHROPIC_API_KEY=sk-...
export OPENAI_API_KEY=sk-...
export GEMINI_API_KEY=...

Results are saved to benchmarks/report-{date}.json with a console summary.

Community

• GitHub Discussions
• Issue tracker

License

MIT