from datasets import Dataset
from ragas import evaluate
from ragas.llms import llm_factory
from ragas.metrics.collections import Faithfulness, ResponseRelevancy, ContextPrecision
from src.agent import app

# Ragas 2025: Experiment-based factory
judge_llm = llm_factory("gemini-2.0-flash")

def evaluate_agent(questions: list, references: list):
    """MNC-grade verification of RAG pipeline quality."""
    results = []
    for q, r in zip(questions, references):
        output = app.invoke({"messages": [("user", q)]})
        results.append({
            "user_input": q,
            "response": output["messages"][-1].content,
            "retrieved_contexts": [m.content for m in output["messages"] if hasattr(m, "tool_call_id")],
            "reference": r
        })
    
    dataset = Dataset.from_list(results)
    metrics = [Faithfulness(), ResponseRelevancy(), ContextPrecision()]
    
    # Evaluate with Gemini judge
    return evaluate(dataset=dataset, metrics=metrics, llm=judge_llm)