"""
Configuration for RGB RAG Evaluation
"""

import os
from typing import List

# Data directory
DATA_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data")

# Results directory
RESULTS_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "results")

# Dataset files
DATASETS = {
    "noise_robustness": "en_refine.json",
    "negative_rejection": "en_refine.json",
    "information_integration": "en_int.json",
    "counterfactual_robustness": "en_fact.json",
}

# Dataset URLs (from GitHub)
DATASET_URLS = {
    "en_refine.json": "https://raw.githubusercontent.com/chen700564/RGB/main/data/en_refine.json",
    "en_int.json": "https://raw.githubusercontent.com/chen700564/RGB/main/data/en_int.json",
    "en_fact.json": "https://raw.githubusercontent.com/chen700564/RGB/main/data/en_fact.json",
}

# Default models to evaluate (first 5 as primary)
DEFAULT_MODELS: List[str] = [
    "meta-llama/llama-4-maverick-17b-128e-instruct",  # Llama 4 Maverick 17B
    "meta-llama/llama-prompt-guard-2-86m",            # Llama Prompt Guard 2 86M
    "llama-3.1-8b-instant",                           # Llama 3.1 8B - Fast
    "openai/gpt-oss-120b",                            # GPT OSS 120B
    "moonshotai/kimi-k2-instruct",                    # Moonshot Kimi K2 Instruct
]

# Additional available models
ADDITIONAL_MODELS: List[str] = [
    "moonshotai/kimi-k2-instruct-0905",  # Kimi K2 Instruct 0905
    "moonshotai/kimi-k2-instruct",       # Kimi K2 Instruct
    "llama-3.3-70b-versatile",           # Llama 3.3 70B
    "meta-llama/llama-4-scout-17b-16e-instruct",  # Llama 4 Scout 17B
    "qwen/qwen3-32b",                   # Qwen 3 32B
]

# All available models (for UI dropdown)
ALL_MODELS: List[str] = DEFAULT_MODELS + ADDITIONAL_MODELS

# Evaluation settings
EVALUATION_CONFIG = {
    "temperature": 0.0,           # Use deterministic outputs for reproducibility
    "max_tokens": 1024,           # Maximum response tokens
    "rate_limit_delay": 0.5,      # Seconds between API calls
    "retry_count": 3,             # Number of retries on failure
}

# Metrics to report
METRICS = {
    "noise_robustness": ["accuracy"],
    "negative_rejection": ["rejection_rate"],
    "information_integration": ["accuracy"],
    "counterfactual_robustness": ["error_detection_rate", "error_correction_rate"],
}