Azure AI Foundry

import json
from dataclasses import dataclass, field
from typing import List, Dict, Any

# ─── Azure AI Foundry Simulation ───────────────────────────
@dataclass
class FoundryModel:
    name:        str
    provider:    str
    family:      str
    input_cost:  float  # per 1K tokens
    output_cost: float

CATALOG = [
    FoundryModel("gpt-4o",           "OpenAI",    "GPT-4",  0.005,  0.015),
    FoundryModel("gpt-35-turbo",     "OpenAI",    "GPT-3",  0.0005, 0.0015),
    FoundryModel("llama-3-70b",      "Meta",      "Llama",  0.001,  0.003),
    FoundryModel("mistral-large",    "Mistral",   "Mistral",0.004,  0.012),
    FoundryModel("phi-3-mini",       "Microsoft", "Phi",    0.0001, 0.0002),
    FoundryModel("claude-3-sonnet",  "Anthropic", "Claude", 0.003,  0.015),
]

@dataclass
class PromptFlowStep:
    name:   str
    kind:   str    # "llm", "python", "prompt", "search"
    config: Dict[str, Any] = field(default_factory=dict)

@dataclass
class PromptFlow:
    name:  str
    steps: List[PromptFlowStep] = field(default_factory=list)

    def add_step(self, name: str, kind: str, **cfg) -> "PromptFlow":
        self.steps.append(PromptFlowStep(name, kind, config=cfg))
        return self

    def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        context = dict(inputs)
        print(f"  🔄 تشغيل Prompt Flow: {self.name}")
        for step in self.steps:
            print(f"     [{step.kind.upper()}] {step.name}...", end=" ")
            if step.kind == "search":
                context["retrieved_docs"] = [
                    f"وثيقة ذات صلة بـ '{context.get('query', '')}' #{i}" for i in range(3)
                ]
                context["num_docs"] = 3
            elif step.kind == "llm":
                q    = context.get("query", "")
                docs = context.get("retrieved_docs", [])
                context["answer"] = f"[{step.config.get('model','gpt-4o')}] إجابة على '{q[:40]}' بناءً على {len(docs)} وثيقة"
            elif step.kind == "python":
                fn = step.config.get("fn", lambda c: c)
                context = fn(context)
            elif step.kind == "prompt":
                pass  # تنسيق الـ prompt
            print("✅")
        return context

class AIFoundryProject:
    def __init__(self, hub: str, name: str):
        self.hub   = hub
        self.name  = name
        self.flows: List[PromptFlow] = []
        self._eval_results: List[Dict] = []

    def create_flow(self, flow_name: str) -> PromptFlow:
        flow = PromptFlow(flow_name)
        self.flows.append(flow)
        print(f"  📊 Flow: {flow_name}")
        return flow

    def evaluate(self, flow: PromptFlow, test_cases: List[Dict]) -> Dict:
        scores = []
        for tc in test_cases:
            result  = flow.run(tc)
            quality = 0.85 + len(result.get("answer", "")) * 0.0001
            scores.append(min(0.99, quality))
        avg = sum(scores) / len(scores)
        self._eval_results.append({"flow": flow.name, "avg_score": avg, "cases": len(test_cases)})
        return {"flow": flow.name, "avg_score": round(avg, 4), "num_cases": len(test_cases)}

# ─── بناء RAG Pipeline ─────────────────────────────────────
print("🔵 Azure AI Foundry — بناء RAG Chatbot:")
print("=" * 52)

# عرض Model Catalog
print("\n📦 Model Catalog (مختارات):")
for m in CATALOG:
    in_s  = "$" + f"{m.input_cost:.4f}"
    out_s = "$" + f"{m.output_cost:.4f}"
    print(f"  [{m.provider:<12}] {m.name:<22} in:{in_s} out:{out_s}")

# إنشاء المشروع
print(f"\n\n🏗️  إنشاء AI Foundry Project:")
project = AIFoundryProject("hub-enterprise-001", "customer-support-ai")

# بناء RAG Flow
print(f"\n📊 بناء Prompt Flow (RAG):")
rag_flow = (
    project.create_flow("customer-support-rag")
    .add_step("format-query",      "prompt",  template="Answer this: {query}")
    .add_step("search-knowledge",  "search",  index="support-docs", top_k=3)
    .add_step("generate-answer",   "llm",     model="gpt-4o", temp=0.3)
    .add_step("post-process",      "python",
              fn=lambda c: {**c, "formatted": c.get("answer","")[:100] + "..."})
)
print(f"  الخطوات: {len(rag_flow.steps)}")

# تشغيل
print(f"\n▶️  تشغيل Pipeline:")
result = rag_flow.run({"query": "كيف أعيد ضبط كلمة المرور؟", "lang": "ar"})
print(f"  الإجابة: {result.get('answer','')[:80]}...")
print(f"  الوثائق المسترجعة: {result.get('num_docs', 0)}")

# التقييم
print(f"\n📊 تقييم الـ Flow:")
test_cases = [
    {"query": "كيف أعيد ضبط كلمة المرور؟"},
    {"query": "ما أوقات الدعم الفني؟"},
    {"query": "كيف أطلب استرداد المبلغ؟"},
]
eval_r = project.evaluate(rag_flow, test_cases)
score_s = f"{eval_r['avg_score']:.4f}"
print(f"  النتيجة: {score_s} ({eval_r['num_cases']} حالات اختبار)")
print(f"\n✅ Azure AI Foundry RAG Pipeline جاهز للإنتاج!")