agent_studies.py

import os
import json
import uuid
import random
import yaml
import litellm
import tqdm
import concurrent.futures
from typing import List, Dict, Any, Optional, Tuple
from datetime import datetime
from pathlib import Path

# Reuse components from the existing codebase
from prompts import PROMPTS, format_prompt, load_prompts
from utils import save_results, generate_user_id
from ibfs import generate_strategies, answer_query
from zero_shot import zero_shot_answer

# Load environment variables and prompts
load_prompts()


class UserAgent:
    """Simulates a user with a preferred answer and decision-making behavior."""

    def __init__(self, llm_model: str = "gpt-4o", epsilon: float = 0.2):
        """
        Initialize the UserAgent with properties.

        Args:
            llm_model: The LLM model to use for agent decisions
            epsilon: Probability of making a random choice instead of optimal
        """
        self.llm_model = llm_model
        self.epsilon = epsilon
        self.preferred_answer = None
        self.query = None
        self.id = generate_user_id()

    def set_preferences(self, query: str):
        """
        Set a query and generate the preferred answer for this user.

        Args:
            query: The question the user wants answered
        """
        self.query = query

        # Generate the user's preferred answer using the LLM
        messages = [
            {"role": "system",
             "content": "You are generating a preferred answer that a user has in mind for their query. This represents what the user is hoping to learn or the perspective they're hoping to see."},
            {"role": "user",
             "content": f"For the query: '{query}', generate a detailed, thoughtful answer that will serve as the user's preferred answer. This is the information or perspective they are hoping to find. Make it 20 words."}
        ]

        response = litellm.completion(
            model=self.llm_model,
            messages=messages,
            max_tokens=1000
        )

        self.preferred_answer = response.choices[0].message.content
        return self.preferred_answer

    def choose_strategy(self, strategies: List[str]) -> int:
        """
        Choose a strategy from the provided options.

        Args:
            strategies: List of strategy descriptions

        Returns:
            Index of the chosen strategy (0-based)
        """
        # With probability epsilon, make a random choice
        if random.random() < self.epsilon:
            return random.randint(0, len(strategies) - 1)

        # Otherwise, evaluate which strategy gets closest to preferred answer
        if not self.preferred_answer or not strategies:
            return 0  # Default to first option if no preference or strategies

        # Prompt the LLM to rank the strategies based on similarity to preferred answer
        strategy_list = "\n".join([f"{i + 1}. {s}" for i, s in enumerate(strategies)])

        messages = [
            {"role": "system",
             "content": "You are helping a user select the strategy that would most likely lead to their preferred answer."},
            {"role": "user", "content": f"""
Query: {self.query}

User's preferred answer: {self.preferred_answer}

Available strategies:
{strategy_list}

Which strategy (provide the number only) would most likely lead to an answer that matches the user's preferred answer? Respond with only a single number representing your choice.
"""}
        ]

        try:
            response = litellm.completion(
                model=self.llm_model,
                messages=messages,
                temperature=0.2,
                max_tokens=10
            )

            # Extract the chosen strategy number
            content = response.choices[0].message.content.strip()
            # Find the first number in the response
            import re
            match = re.search(r'\d+', content)
            if match:
                choice = int(match.group()) - 1  # Convert to 0-based index
                # Ensure it's within bounds
                if 0 <= choice < len(strategies):
                    return choice

            # If we couldn't parse the response or it's out of bounds, make a random choice
            return random.randint(0, len(strategies) - 1)

        except Exception as e:
            print(f"Error in choosing strategy: {e}")
            # Fall back to random choice
            return random.randint(0, len(strategies) - 1)


class IBFSAgent:
    """Implements the Interactive Best-First Search process."""

    def __init__(self,
                 llm_model: str = "gpt-4o",
                 diversity_level: str = "medium",
                 branching_factor: int = 4,
                 max_depth: int = 2):
        """
        Initialize the IBFSAgent with properties.

        Args:
            llm_model: The LLM model to use for generating candidates
            diversity_level: How diverse the generated candidates should be (low, medium, high)
            branching_factor: Number of candidates to generate at each step
            max_depth: Maximum depth/iterations of the IBFS process
        """
        self.llm_model = llm_model
        self.diversity_level = diversity_level
        self.branching_factor = branching_factor
        self.max_depth = max_depth
        self.id = generate_user_id()

        # Set up diversity-specific prompts
        self._setup_prompts()

    def _setup_prompts(self):
        """Set up the candidate generation and refinement prompts based on diversity level."""
        # Load the base prompts from PROMPTS dictionary
        self.base_system_prompt = PROMPTS["ibfs"]["initial_strategies"]["system"]
        self.base_user_prompt = PROMPTS["ibfs"]["initial_strategies"]["user"]
        self.refinement_system_prompt = PROMPTS["ibfs"]["continuation_strategies"]["system"]
        self.refinement_user_prompt = PROMPTS["ibfs"]["continuation_strategies"]["user"]

        # Augment with diversity-specific instructions
        diversity_instructions = {
            "low": """
The strategies you generate can be similar to each other and explore related approaches.
There's no need to make them very different from each other.
""",
            "medium": """
Each strategy should represent a somewhat different approach to answering the question.
Try to include some variety in the approaches.
""",
            "high": """
Each strategy should represent a substantially different approach to answering the question.
Make sure the strategies are maximally diverse from each other - consider entirely different angles, 
methodologies, perspectives, and areas of knowledge.
"""
        }

        # Add diversity instructions to the prompts
        self.diversity_instructions = diversity_instructions[self.diversity_level]

    def generate_strategies(self, query: str, current_path: List[str] = None) -> List[str]:
        """
        Generate strategy options for the current step.

        Args:
            query: The user's query
            current_path: List of previously selected strategies

        Returns:
            List of strategy descriptions
        """
        if not current_path or len(current_path) == 0:
            # Initial generation
            system_prompt = self.base_system_prompt + "\n" + self.diversity_instructions
            user_prompt = self.base_user_prompt

            # Format the prompts
            format_args = {
                "query": query,
                "k": self.branching_factor
            }
        else:
            # Refinement of previously selected strategy
            system_prompt = self.refinement_system_prompt + "\n" + self.diversity_instructions
            user_prompt = self.refinement_user_prompt

            # Format the prompts
            format_args = {
                "query": query,
                "selected_strategy": current_path[-1],
                "k": self.branching_factor
            }

        # Format the prompts
        system_message = format_prompt(system_prompt, **format_args)
        user_message = format_prompt(user_prompt, **format_args)

        messages = [
            {"role": "system", "content": system_message},
            {"role": "user", "content": user_message}
        ]

        try:
            response = litellm.completion(
                model=self.llm_model,
                messages=messages,
                temperature=0.7,
                max_tokens=1000
            )

            content = response.choices[0].message.content

            # Use the strategy parsing from ibfs.py
            # Parse strategies using regex
            import re
            strategies = re.findall(r'\d+\.\s*(I can answer by[^\n\d]*(?:\n(?!\d+\.)[^\n]*)*)', content, re.IGNORECASE)

            # If we didn't find enough strategies with that format, try alternative parsing
            if len(strategies) < self.branching_factor:
                strategies = re.findall(r'(?:^|\n)(I can answer by[^\n]*(?:\n(?!I can answer by)[^\n]*)*)', content,
                                        re.IGNORECASE)

            # Clean up the strategies
            strategies = [s.strip() for s in strategies]

            # Ensure we have exactly b strategies
            if len(strategies) > self.branching_factor:
                strategies = strategies[:self.branching_factor]

            # If we still don't have enough strategies, create generic ones
            while len(strategies) < self.branching_factor:
                strategies.append(
                    f"I can answer by using approach #{len(strategies) + 1} (Note: Strategy generation incomplete)")

            return strategies

        except Exception as e:
            print(f"Error generating strategies: {e}")
            # Return fallback strategies
            return [f"I can answer by approach #{i + 1} (Error: Could not generate strategies)" for i in
                    range(self.branching_factor)]

    def generate_final_answer(self, query: str, strategy_path: List[str]) -> str:
        """
        Generate the final answer based on the selected strategy path.

        Args:
            query: The original user query
            strategy_path: List of selected strategies

        Returns:
            Final answer to the query
        """
        if not strategy_path:
            return "No strategy was selected to generate an answer."

        final_strategy = strategy_path[-1]

        # Use the answer_query function from ibfs.py
        return answer_query(query, final_strategy)


def run_simulation(query: str,
                   user_agent: UserAgent,
                   ibfs_agent: IBFSAgent) -> Dict[str, Any]:
    """
    Run a full simulation of a user interacting with the IBFS system.

    Args:
        query: The question to be answered
        user_agent: The UserAgent instance
        ibfs_agent: The IBFSAgent instance

    Returns:
        Dictionary containing the simulation results
    """
    # Set up the user's preferred answer
    user_agent.set_preferences(query)

    # Initialize the strategy path
    strategy_path = []

    # Record all strategies presented and choices made
    history = []

    # Run through the IBFS process up to max_depth
    for depth in range(ibfs_agent.max_depth):
        # Generate strategies at this step
        strategies = ibfs_agent.generate_strategies(query, strategy_path)

        # Have the user agent choose a strategy
        choice_idx = user_agent.choose_strategy(strategies)
        chosen_strategy = strategies[choice_idx]

        # Record this step
        history.append({
            "depth": depth,
            "strategies": strategies,
            "choice_idx": choice_idx,
            "chosen_strategy": chosen_strategy
        })

        # Update the strategy path
        strategy_path.append(chosen_strategy)

    # Generate the final answer
    final_answer = ibfs_agent.generate_final_answer(query, strategy_path)

    # Create the simulation result
    result = {
        "query": query,
        "user_id": user_agent.id,
        "ibfs_id": ibfs_agent.id,
        "user_preferred_answer": user_agent.preferred_answer,
        "final_answer": final_answer,
        "strategy_path": strategy_path,
        "history": history,
        "ibfs_config": {
            "diversity_level": ibfs_agent.diversity_level,
            "branching_factor": ibfs_agent.branching_factor,
            "max_depth": ibfs_agent.max_depth
        },
        "user_config": {
            "epsilon": user_agent.epsilon
        },
        "timestamp": datetime.now().isoformat()
    }

    return result


def evaluate_answer_similarity(answer1: str, answer2: str) -> float:
    """
    Evaluate the similarity between two answers using the LLM.

    Args:
        answer1: First answer
        answer2: Second answer

    Returns:
        Similarity score (0-1)
    """
    messages = [
        {"role": "system",
         "content": "You are evaluating the similarity between a user's preferred answer and a generated answer. Provide a similarity score from 0 to 1, where 1 means identical in content and perspective, and 0 means completely different."},
        {"role": "user", "content": f"""
Answer 1:
{answer1}

Answer 2:
{answer2}

On a scale from 0 to 1, how similar are these answers in terms of content, perspective, and key information? 
Provide only a single number as your response.
"""}
    ]

    try:
        response = litellm.completion(
            model="gpt-4o",
            messages=messages,
            temperature=0.1,
            max_tokens=10
        )

        content = response.choices[0].message.content.strip()
        # Extract the score from the response
        import re
        match = re.search(r'(\d+(\.\d+)?)', content)
        if match:
            score = float(match.group(1))
            # Ensure it's in the range [0, 1]
            return max(0, min(score, 1))
        else:
            # Default score if parsing fails
            return 0.5

    except Exception as e:
        print(f"Error evaluating similarity: {e}")
        return 0.5


def process_simulation(args):
    """
    Process a single simulation for parallel execution.

    Args:
        args: Tuple containing (query, user_config, ibfs_config, experiment_id, sim_count)

    Returns:
        Simulation result
    """
    query, user_config, ibfs_config, experiment_id, sim_count = args

    try:
        # Create agents with the current configuration
        user_agent = UserAgent(epsilon=user_config["epsilon"])
        ibfs_agent = IBFSAgent(
            diversity_level=ibfs_config["diversity_level"],
            branching_factor=ibfs_config["branching_factor"],
            max_depth=ibfs_config["max_depth"]
        )

        # Run the simulation
        result = run_simulation(query, user_agent, ibfs_agent)

        # Add evaluation of similarity between preferred and final answers
        similarity = evaluate_answer_similarity(
            user_agent.preferred_answer,
            result["final_answer"]
        )
        result["similarity_score"] = similarity

        # Add metadata
        result["experiment_id"] = experiment_id
        result["simulation_id"] = sim_count

        # Save individual result
        os.makedirs("experiment_results", exist_ok=True)
        simulation_id = f"{experiment_id}_sim_{sim_count}"
        with open(f"experiment_results/{simulation_id}.json", "w") as f:
            json.dump(result, f, indent=2)

        return result

    except Exception as e:
        print(f"Error in simulation {sim_count}: {e}")
        return {
            "error": str(e),
            "experiment_id": experiment_id,
            "simulation_id": sim_count,
            "query": query
        }


def run_experiment(queries: List[str],
                   diversity_levels: List[str],
                   branching_factors: List[int],
                   max_depths: List[int],
                   epsilon_values: List[float],
                   repetitions: int = 3,
                   max_workers: int = 4) -> List[Dict[str, Any]]:
    """
    Run a full experiment with different configurations using parallel processing.

    Args:
        queries: List of queries to test
        diversity_levels: List of diversity levels to test
        branching_factors: List of branching factors to test
        max_depths: List of max depths to test
        epsilon_values: List of epsilon values to test
        repetitions: Number of repetitions for each configuration
        max_workers: Maximum number of parallel workers

    Returns:
        List of results for all simulations
    """
    # Create the results directory if it doesn't exist
    os.makedirs("experiment_results", exist_ok=True)

    # Generate a unique experiment ID
    experiment_id = f"exp_{datetime.now().strftime('%Y%m%d_%H%M%S')}"

    # Create a config log for this experiment
    config = {
        "experiment_id": experiment_id,
        "queries": queries,
        "diversity_levels": diversity_levels,
        "branching_factors": branching_factors,
        "max_depths": max_depths,
        "epsilon_values": epsilon_values,
        "repetitions": repetitions,
        "timestamp": datetime.now().isoformat()
    }

    # Save the configuration
    with open(f"experiment_results/{experiment_id}_config.json", "w") as f:
        json.dump(config, f, indent=2)

    # Prepare all simulation configurations
    simulation_args = []
    sim_count = 0

    for query in queries:
        for diversity in diversity_levels:
            for branching in branching_factors:
                for depth in max_depths:
                    for epsilon in epsilon_values:
                        for rep in range(repetitions):
                            # Create configuration for this simulation
                            user_config = {"epsilon": epsilon}
                            ibfs_config = {
                                "diversity_level": diversity,
                                "branching_factor": branching,
                                "max_depth": depth
                            }

                            # Add to arguments list
                            simulation_args.append((query, user_config, ibfs_config, experiment_id, sim_count))
                            sim_count += 1

    # Calculate total simulations
    total_simulations = len(simulation_args)

    # Run simulations in parallel with progress bar
    results = []
    with tqdm.tqdm(total=total_simulations, desc="Running simulations") as pbar:
        with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor:
            # Submit all simulations and process as they complete
            future_to_sim = {executor.submit(process_simulation, args): args for args in simulation_args}

            for future in concurrent.futures.as_completed(future_to_sim):
                result = future.result()
                results.append(result)
                pbar.update(1)

    # Save aggregated results
    with open(f"experiment_results/{experiment_id}_all_results.json", "w") as f:
        json.dump(results, f, indent=2)

    return results


def analyze_results(experiment_id: str) -> Dict[str, Any]:
    """
    Analyze the results of an experiment.

    Args:
        experiment_id: ID of the experiment to analyze

    Returns:
        Dictionary with analysis results
    """
    # Load all simulation results for this experiment
    results = []
    for filename in os.listdir("experiment_results"):
        if filename.startswith(experiment_id) and not filename.endswith("config.json") and not filename.endswith(
                "all_results.json") and not filename.endswith("analysis.json"):
            with open(f"experiment_results/{filename}", "r") as f:
                try:
                    results.append(json.load(f))
                except json.JSONDecodeError:
                    print(f"Error loading {filename}")

    # Aggregate metrics by configuration
    aggregated = {}

    for result in results:
        if "error" in result:
            continue  # Skip failed simulations

        # Create a key for this configuration
        config_key = (
            result["ibfs_config"]["diversity_level"],
            result["ibfs_config"]["branching_factor"],
            result["ibfs_config"]["max_depth"],
            result["user_config"]["epsilon"]
        )

        # Initialize if this is the first result with this config
        if config_key not in aggregated:
            aggregated[config_key] = {
                "similarity_scores": [],
                "config": {
                    "diversity_level": result["ibfs_config"]["diversity_level"],
                    "branching_factor": result["ibfs_config"]["branching_factor"],
                    "max_depth": result["ibfs_config"]["max_depth"],
                    "epsilon": result["user_config"]["epsilon"]
                },
                "queries": []
            }

        # Add the similarity score
        aggregated[config_key]["similarity_scores"].append(result["similarity_score"])

        # Track queries for this configuration
        if result["query"] not in aggregated[config_key]["queries"]:
            aggregated[config_key]["queries"].append(result["query"])

    # Calculate summary statistics
    summary = []
    for config_key, data in aggregated.items():
        scores = data["similarity_scores"]
        summary.append({
            "config": data["config"],
            "avg_similarity": sum(scores) / len(scores) if scores else 0,
            "min_similarity": min(scores) if scores else 0,
            "max_similarity": max(scores) if scores else 0,
            "std_deviation": (sum((x - (sum(scores) / len(scores))) ** 2 for x in scores) / len(
                scores)) ** 0.5 if scores else 0,
            "num_samples": len(scores),
            "queries_tested": len(data["queries"])
        })

    # Sort by average similarity (descending)
    summary.sort(key=lambda x: x["avg_similarity"], reverse=True)

    # Add query-specific analysis
    query_analysis = {}
    for result in results:
        if "error" in result:
            continue

        query = result["query"]
        if query not in query_analysis:
            query_analysis[query] = {
                "best_config": None,
                "best_similarity": -1,
                "configs_tested": 0,
                "avg_similarity": 0,
                "all_scores": []
            }

        # Track all scores for this query
        query_analysis[query]["all_scores"].append(result["similarity_score"])

        # Update best configuration for this query
        if result["similarity_score"] > query_analysis[query]["best_similarity"]:
            query_analysis[query]["best_similarity"] = result["similarity_score"]
            query_analysis[query]["best_config"] = {
                "diversity_level": result["ibfs_config"]["diversity_level"],
                "branching_factor": result["ibfs_config"]["branching_factor"],
                "max_depth": result["ibfs_config"]["max_depth"],
                "epsilon": result["user_config"]["epsilon"]
            }

    # Calculate query statistics
    for query, data in query_analysis.items():
        scores = data["all_scores"]
        data["avg_similarity"] = sum(scores) / len(scores) if scores else 0
        data["configs_tested"] = len(scores)
        # Remove the raw scores to keep the analysis file smaller
        del data["all_scores"]

    # Save the analysis
    analysis = {
        "experiment_id": experiment_id,
        "total_simulations": len(results),
        "summary": summary,
        "query_analysis": query_analysis,
        "timestamp": datetime.now().isoformat()
    }

    with open(f"experiment_results/{experiment_id}_analysis.json", "w") as f:
        json.dump(analysis, f, indent=2)

    return analysis


def compare_to_zero_shot(experiment_id: str, queries: List[str]) -> Dict[str, Any]:
    """
    Compare IBFS results to zero-shot answers.

    Args:
        experiment_id: ID of the experiment to compare
        queries: List of queries to test with zero-shot

    Returns:
        Comparison results
    """
    # First, get zero-shot answers for all queries
    zero_shot_results = []

    print("Generating zero-shot answers...")
    for query in tqdm.tqdm(queries):
        try:
            # Generate zero-shot answer using function from ibfs.py
            answer = zero_shot_answer(query)

            # Save the result
            zero_shot_results.append({
                "query": query,
                "zero_shot_answer": answer
            })
        except Exception as e:
            print(f"Error generating zero-shot answer for '{query}': {e}")
            zero_shot_results.append({
                "query": query,
                "zero_shot_answer": f"Error: {str(e)}",
                "error": True
            })

    # Load the IBFS experiment results
    analysis = analyze_results(experiment_id)

    # Load the best configurations from the analysis
    best_config = analysis["summary"][0]["config"] if analysis["summary"] else None

    # For each query, compare the best IBFS result to the zero-shot answer
    comparison = []

    print("Comparing zero-shot to IBFS results...")
    for zero_shot_result in tqdm.tqdm(zero_shot_results):
        query = zero_shot_result["query"]
        zero_shot_answer = zero_shot_result["zero_shot_answer"]

        # Find the best IBFS result for this query
        query_data = analysis.get("query_analysis", {}).get(query, {})
        best_config_for_query = query_data.get("best_config", best_config)

        if best_config_for_query:
            # Find a simulation with this configuration and query
            matching_results = []
            for filename in os.listdir("experiment_results"):
                if filename.startswith(experiment_id) and not filename.endswith(
                        "config.json") and not filename.endswith("all_results.json") and not filename.endswith(
                        "analysis.json"):
                    try:
                        with open(f"experiment_results/{filename}", "r") as f:
                            result = json.load(f)
                            if (result.get("query") == query and
                                    result.get("ibfs_config", {}).get("diversity_level") == best_config_for_query.get(
                                        "diversity_level") and
                                    result.get("ibfs_config", {}).get("branching_factor") == best_config_for_query.get(
                                        "branching_factor") and
                                    result.get("ibfs_config", {}).get("max_depth") == best_config_for_query.get(
                                        "max_depth") and
                                    result.get("user_config", {}).get("epsilon") == best_config_for_query.get(
                                        "epsilon")):
                                matching_results.append(result)
                    except:
                        continue

            # Use the best matching result (if any)
            if matching_results:
                # Sort by similarity score (descending)
                matching_results.sort(key=lambda x: x.get("similarity_score", 0), reverse=True)
                best_ibfs_result = matching_results[0]

                # Compare zero-shot to user's preferred answer
                preferred_answer = best_ibfs_result.get("user_preferred_answer", "")
                zero_shot_similarity = evaluate_answer_similarity(preferred_answer, zero_shot_answer)

                # Get the IBFS similarity (already calculated)
                ibfs_similarity = best_ibfs_result.get("similarity_score", 0)

                comparison.append({
                    "query": query,
                    "zero_shot_similarity": zero_shot_similarity,
                    "ibfs_similarity": ibfs_similarity,
                    "difference": ibfs_similarity - zero_shot_similarity,
                    "ibfs_config": best_config_for_query
                })
        else:
            print(f"No valid configuration found for query: {query}")

    # Calculate overall metrics
    zero_shot_avg = sum(item["zero_shot_similarity"] for item in comparison) / len(comparison) if comparison else 0
    ibfs_avg = sum(item["ibfs_similarity"] for item in comparison) / len(comparison) if comparison else 0
    avg_difference = sum(item["difference"] for item in comparison) / len(comparison) if comparison else 0

    # Save the comparison results
    comparison_results = {
        "experiment_id": experiment_id,
        "zero_shot_avg_similarity": zero_shot_avg,
        "ibfs_avg_similarity": ibfs_avg,
        "avg_difference": avg_difference,
        "query_comparisons": comparison,
        "timestamp": datetime.now().isoformat()
    }

    with open(f"experiment_results/{experiment_id}_zero_shot_comparison.json", "w") as f:
        json.dump(comparison_results, f, indent=2)

    return comparison_results


if __name__ == "__main__":
    # Ensure the prompts from YAML file are loaded
    if not PROMPTS:
        load_prompts()

    # Sample queries to test
    queries = [
        "What are the environmental impacts of electric vehicles compared to traditional gasoline vehicles?",
        "How has artificial intelligence changed the job market in the past decade?",
        "What are the most effective strategies for reducing stress and anxiety?",
        "What are the arguments for and against universal basic income?",
    ]

    # Experiment parameters
    diversity_levels = ["low", "medium", "high"]
    branching_factors = [2, 4, 8]
    max_depths = [1, 2, 4]
    epsilon_values = [0.1, 0.3]

    # Run a smaller test experiment
    print("Running test experiment...")
    test_results = run_experiment(
        queries=queries[:1],  # Just use the first query for testing
        diversity_levels=diversity_levels[:2],  # Test low and medium diversity
        branching_factors=[2, 4],  # Test b=2 and b=4
        max_depths=[1, 2],  # Test m=1 and m=2
        epsilon_values=[0.2],  # Test epsilon=0.2
        repetitions=10,  # Just 2 repetitions for testing
        max_workers=7  # Use 2 parallel workers for testing
    )