import os
import json
import asyncio
from typing import List, Dict, Any, Optional
from pydantic import BaseModel, Field


class EvaluationRubric(BaseModel):
    correctness_weight: float = 0.4
    tool_accuracy_weight: float = 0.3
    efficiency_weight: float = 0.3


class JudgeScoreOutput(BaseModel):
    thought_process: str = Field(description="Step-by-step reasoning for assigned scores")
    correctness_score: float = Field(ge=0.0, le=1.0, description="Goal completion score between 0 and 1")
    tool_accuracy_score: float = Field(ge=0.0, le=1.0, description="Tool parameter accuracy score")
    efficiency_score: float = Field(ge=0.0, le=1.0, description="Trajectory step efficiency score")
    final_composite_score: float = Field(ge=0.0, le=1.0, description="Weighted composite score")
    hallucination_detected: bool = Field(description="Flag indicating fabricated facts")


class AgentTrajectoryEvaluator:
    def __init__(self, judge_model_name: str, rubric: Optional[EvaluationRubric] = None):
        self.judge_model_name = judge_model_name
        self.rubric = rubric or EvaluationRubric()

    def build_judge_prompt(self, user_query: str, ground_truth: str, trajectory_steps: List[Dict[str, Any]]) -> str:
        formatted_steps = json.dumps(trajectory_steps, indent=2)
        return f"""You are an expert AI Agent Evaluation Judge. Analyze the execution trajectory of an autonomous agent.

User Request: {user_query}
Expected Ground Truth: {ground_truth}

Execution Trajectory Steps:
{formatted_steps}

Grade the trajectory according to these dimensions:
1. Goal Completion: Did the agent achieve the expected outcome accurately?
2. Tool Selection: Were API tools invoked correctly with valid arguments?
3. Step Efficiency: Did the agent avoid redundant loops or unnecessary calls?

Provide your evaluation as a JSON object strictly adhering to the schema."""

    async def evaluate_trajectory(
        self, user_query: str, ground_truth: str, trajectory_steps: List[Dict[str, Any]]
    ) -> JudgeScoreOutput:
        prompt = self.build_judge_prompt(user_query, ground_truth, trajectory_steps)
        
        # Simulated async call to LLM completion endpoint with structured JSON mode
        # In production, replace with actual SDK client call (e.g., openai.AsyncOpenAI)
        await asyncio.sleep(0.05)  # Simulate network latency
        
        # Calculate scores deterministically based on step properties for benchmark testing
        tool_errors = sum(1 for step in trajectory_steps if step.get("status") == "error")
        total_steps = len(trajectory_steps)
        
        tool_score = max(0.0, 1.0 - (tool_errors * 0.25))
        eff_score = 1.0 if total_steps <= 3 else max(0.2, 1.0 - ((total_steps - 3) * 0.15))
        corr_score = 0.95 if tool_errors == 0 else 0.70
        
        composite = (
            (corr_score * self.rubric.correctness_weight) +
            (tool_score * self.rubric.tool_accuracy_weight) +
            (eff_score * self.rubric.efficiency_weight)
        )
        
        return JudgeScoreOutput(
            thought_process="Evaluated step trajectory against ground truth constraints. Tool calls verified.",
            correctness_score=round(corr_score, 2),
            tool_accuracy_score=round(tool_score, 2),
            efficiency_score=round(eff_score, 2),
            final_composite_score=round(composite, 2),
            hallucination_detected=False
        )


if __name__ == "__main__":
    evaluator = AgentTrajectoryEvaluator(judge_model_name="gpt-4o")
    sample_trajectory = [
        {"step": 1, "action": "search_customer_db", "args": {"user_id": "usr_9918"}, "status": "success"},
        {"step": 2, "action": "fetch_account_balance", "args": {"account_id": "acc_3301"}, "status": "success"}
    ]
    
    loop = asyncio.get_event_loop()
    result = loop.run_until_complete(
        evaluator.evaluate_trajectory(
            user_query="Fetch balance for user usr_9918",
            ground_truth="Account balance is $14,250.00",
            trajectory_steps=sample_trajectory
        )
    )
    print(f"Evaluation Completed. Composite Score: {result.final_composite_score}")

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"sync"
	"time"
)

// TrajectoryStep captures an individual action performed by the agent.
type TrajectoryStep struct {
	StepIndex int           `json:"step_index"`
	ToolName  string        `json:"tool_name"`
	Payload   string        `json:"payload"`
	Duration  time.Duration `json:"duration"`
	IsError   bool          `json:"is_error"`
}

// TestCase represents a single benchmark scenario.
type TestCase struct {
	ID           string `json:"id"`
	UserPrompt   string `json:"user_prompt"`
	ExpectedGoal string `json:"expected_goal"`
	MaxSteps     int    `json:"max_steps"`
}

// BenchmarkResult stores the summary outcome of a test case execution.
type BenchmarkResult struct {
	TestCaseID   string           `json:"test_case_id"`
	Passed       bool             `json:"passed"`
	TotalSteps   int              `json:"total_steps"`
	TotalTime    time.Duration    `json:"total_time"`
	Trajectories []TrajectoryStep `json:"trajectories"`
}

// AgentEvalHarness manages concurrent execution of evaluation workloads.
type AgentEvalHarness struct {
	Concurrency int
	Timeout     time.Duration
}

func NewAgentEvalHarness(concurrency int, timeout time.Duration) *AgentEvalHarness {
	return &AgentEvalHarness{
		Concurrency: concurrency,
		Timeout:     timeout,
	}
}

// ExecuteBenchmarkSuite runs a collection of test cases through worker pools.
func (h *AgentEvalHarness) ExecuteBenchmarkSuite(testCases []TestCase) []BenchmarkResult {
	jobs := make(chan TestCase, len(testCases))
	results := make(chan BenchmarkResult, len(testCases))
	var wg sync.WaitGroup

	// Launch worker pool
	for w := 0; w < h.Concurrency; w++ {
		wg.Add(1)
		go func(workerID int) {
			defer wg.Done()
			for tc := range jobs {
				results <- h.runSingleTest(tc)
			}
		}(w)
	}

	for _, tc := range testCases {
		jobs <- tc
	}
	close(jobs)

	wg.Wait()
	close(results)

	var report []BenchmarkResult
	for res := range results {
		report = append(report, res)
	}
	return report
}

func (h *AgentEvalHarness) runSingleTest(tc TestCase) BenchmarkResult {
	ctx, cancel := context.WithTimeout(context.Background(), h.Timeout)
	defer cancel()

	startTime := time.Now()
	trajectories := []TrajectoryStep{
		{StepIndex: 1, ToolName: "query_vector_index", Payload: tc.UserPrompt, Duration: 45 * time.Millisecond, IsError: false},
		{StepIndex: 2, ToolName: "execute_sql_query", Payload: "SELECT * FROM orders", Duration: 120 * time.Millisecond, IsError: false},
	}

	select {
	case <-ctx.Done():
		return BenchmarkResult{
			TestCaseID:   tc.ID,
			Passed:       false,
			TotalSteps:   len(trajectories),
			TotalTime:    time.Since(startTime),
			Trajectories: trajectories,
		}
	default:
		passed := len(trajectories) <= tc.MaxSteps
		return BenchmarkResult{
			TestCaseID:   tc.ID,
			Passed:       passed,
			TotalSteps:   len(trajectories),
			TotalTime:    time.Since(startTime),
			Trajectories: trajectories,
		}
	}
}

func main() {
	harness := NewAgentEvalHarness(4, 5*time.Second)

	suite := []TestCase{
		{ID: "TC-001", UserPrompt: "Retrieve quarterly sales report", ExpectedGoal: "Generate PDF summary", MaxSteps: 4},
		{ID: "TC-002", UserPrompt: "Update inventory stock count", ExpectedGoal: "Mutate warehouse table", MaxSteps: 3},
	}

	results := harness.ExecuteBenchmarkSuite(suite)
	output, _ := json.MarshalIndent(results, "", "  ")
	fmt.Printf("Benchmark Results Summary:\n%s\n", string(output))
}