aethershell 0.3.1

//! Advanced AI strategies for multi-modal reasoning and planning

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use uuid::Uuid;

use super::agents::{Modality, MultiModalAgent};
use crate::ai::{MultiModalContent, MultiModalMessage};

/// Advanced reasoning engine that can plan and execute complex multi-modal tasks
#[derive(Debug, Clone)]
pub struct ReasoningEngine {
    pub strategies: Vec<ReasoningStrategy>,
    pub knowledge_base: KnowledgeBase,
    pub planning_horizon: usize,
    pub confidence_threshold: f32,
}

/// Different reasoning strategies for multi-modal tasks
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ReasoningStrategy {
    /// Chain of thought reasoning
    ChainOfThought {
        max_steps: usize,
        step_confidence_threshold: f32,
    },
    /// Tree of thought with multiple branches
    TreeOfThought {
        branching_factor: usize,
        max_depth: usize,
        pruning_threshold: f32,
    },
    /// Multi-modal fusion strategy
    ModalityFusion {
        fusion_weights: HashMap<Modality, f32>,
        consensus_threshold: f32,
    },
    /// Hierarchical planning
    HierarchicalPlanning {
        abstraction_levels: usize,
        subgoal_threshold: f32,
    },
    /// Adversarial reasoning
    AdversarialReasoning {
        criticism_strength: f32,
        validation_rounds: usize,
    },
}

/// Knowledge base for storing learned patterns and experiences
#[derive(Debug, Clone)]
pub struct KnowledgeBase {
    pub facts: HashMap<String, Fact>,
    pub rules: Vec<Rule>,
    pub patterns: Vec<Pattern>,
    pub experiences: Vec<Experience>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Fact {
    pub id: Uuid,
    pub content: String,
    pub confidence: f32,
    pub modalities: Vec<Modality>,
    pub source: String,
    pub timestamp: chrono::DateTime<chrono::Utc>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Rule {
    pub id: Uuid,
    pub condition: String,
    pub action: String,
    pub confidence: f32,
    pub applicable_modalities: Vec<Modality>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Pattern {
    pub id: Uuid,
    pub description: String,
    pub input_pattern: Vec<Modality>,
    pub output_pattern: Vec<Modality>,
    pub success_rate: f32,
    pub examples: Vec<PatternExample>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternExample {
    pub input: MultiModalMessage,
    pub output: MultiModalMessage,
    pub success: bool,
    pub execution_time: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Experience {
    pub id: Uuid,
    pub task_description: String,
    pub input_modalities: Vec<Modality>,
    pub strategy_used: ReasoningStrategy,
    pub outcome: TaskOutcome,
    pub lessons_learned: Vec<String>,
    pub timestamp: chrono::DateTime<chrono::Utc>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TaskOutcome {
    Success {
        result: MultiModalMessage,
        confidence: f32,
        execution_time: f64,
    },
    Failure {
        error_message: String,
        failure_point: String,
    },
    PartialSuccess {
        result: MultiModalMessage,
        missing_aspects: Vec<String>,
        confidence: f32,
    },
}

/// Planning system for complex multi-step tasks
#[derive(Debug, Clone)]
pub struct TaskPlanner {
    pub goal: PlanningGoal,
    pub available_agents: Vec<MultiModalAgent>,
    pub planning_strategy: PlanningStrategy,
    pub execution_plan: Option<ExecutionPlan>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PlanningGoal {
    pub description: String,
    pub input_data: MultiModalMessage,
    pub desired_output: GoalSpecification,
    pub constraints: Vec<Constraint>,
    pub deadline: Option<chrono::DateTime<chrono::Utc>>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GoalSpecification {
    pub output_modalities: Vec<Modality>,
    pub quality_requirements: HashMap<String, f32>,
    pub success_criteria: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Constraint {
    pub constraint_type: ConstraintType,
    pub description: String,
    pub severity: ConstraintSeverity,
}

#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ConstraintType {
    Resource,
    Time,
    Quality,
    Modality,
    Ethical,
    Legal,
}

#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ConstraintSeverity {
    Hard,
    Soft,
    Preference,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PlanningStrategy {
    ForwardChaining,
    BackwardChaining,
    HierarchicalTaskNetwork,
    ReinforcementLearning,
    GeneticAlgorithm,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExecutionPlan {
    pub id: Uuid,
    pub steps: Vec<PlanStep>,
    pub dependencies: HashMap<Uuid, Vec<Uuid>>,
    pub estimated_duration: f64,
    pub confidence: f32,
    pub fallback_plans: Vec<FallbackPlan>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PlanStep {
    pub id: Uuid,
    pub description: String,
    pub agent_requirements: AgentRequirements,
    pub input_modalities: Vec<Modality>,
    pub output_modalities: Vec<Modality>,
    pub estimated_time: f64,
    pub confidence: f32,
    pub criticality: StepCriticality,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AgentRequirements {
    pub required_capabilities: Vec<String>,
    pub preferred_capabilities: Vec<String>,
    pub minimum_confidence: f32,
    pub resource_requirements: HashMap<String, f32>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum StepCriticality {
    Critical,
    Important,
    Optional,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FallbackPlan {
    pub trigger_condition: String,
    pub alternative_steps: Vec<PlanStep>,
    pub confidence: f32,
}

/// Multi-modal reasoning coordinator
#[derive(Debug)]
pub struct ReasoningCoordinator {
    pub reasoning_engine: ReasoningEngine,
    pub task_planner: TaskPlanner,
    pub active_reasoning_sessions: HashMap<Uuid, ReasoningSession>,
}

#[derive(Debug, Clone)]
pub struct ReasoningSession {
    pub id: Uuid,
    pub goal: PlanningGoal,
    pub current_strategy: ReasoningStrategy,
    pub reasoning_chain: Vec<ReasoningStep>,
    pub confidence_scores: Vec<f32>,
    pub intermediate_results: Vec<MultiModalMessage>,
    pub status: SessionStatus,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReasoningStep {
    pub step_id: Uuid,
    pub description: String,
    pub input: MultiModalMessage,
    pub output: Option<MultiModalMessage>,
    pub confidence: f32,
    pub reasoning_type: ReasoningType,
    pub execution_time: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ReasoningType {
    Induction,
    Deduction,
    Abduction,
    Analogy,
    Causal,
    Temporal,
    Spatial,
    Modal,
}

#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum SessionStatus {
    Planning,
    Reasoning,
    Executing,
    Completed,
    Failed(String),
    Paused,
}

impl ReasoningEngine {
    pub fn new() -> Self {
        Self {
            strategies: vec![
                ReasoningStrategy::ChainOfThought {
                    max_steps: 10,
                    step_confidence_threshold: 0.7,
                },
                ReasoningStrategy::TreeOfThought {
                    branching_factor: 3,
                    max_depth: 5,
                    pruning_threshold: 0.6,
                },
                ReasoningStrategy::ModalityFusion {
                    fusion_weights: HashMap::from([
                        (Modality::Text, 0.4),
                        (Modality::Image, 0.3),
                        (Modality::Audio, 0.2),
                        (Modality::Video, 0.1),
                    ]),
                    consensus_threshold: 0.8,
                },
            ],
            knowledge_base: KnowledgeBase::new(),
            planning_horizon: 20,
            confidence_threshold: 0.75,
        }
    }

    /// Execute reasoning using the best available strategy
    pub async fn reason(&mut self, goal: &PlanningGoal) -> Result<MultiModalMessage> {
        let strategy = self.select_best_strategy(goal)?.clone();

        match strategy {
            ReasoningStrategy::ChainOfThought {
                max_steps,
                step_confidence_threshold,
            } => {
                self.chain_of_thought_reasoning(goal, max_steps, step_confidence_threshold)
                    .await
            }
            ReasoningStrategy::TreeOfThought {
                branching_factor,
                max_depth,
                pruning_threshold,
            } => {
                self.tree_of_thought_reasoning(goal, branching_factor, max_depth, pruning_threshold)
                    .await
            }
            ReasoningStrategy::ModalityFusion {
                fusion_weights,
                consensus_threshold,
            } => {
                self.modality_fusion_reasoning(goal, &fusion_weights, consensus_threshold)
                    .await
            }
            ReasoningStrategy::HierarchicalPlanning {
                abstraction_levels,
                subgoal_threshold,
            } => {
                self.hierarchical_planning_reasoning(goal, abstraction_levels, subgoal_threshold)
                    .await
            }
            ReasoningStrategy::AdversarialReasoning {
                criticism_strength,
                validation_rounds,
            } => {
                self.adversarial_reasoning(goal, criticism_strength, validation_rounds)
                    .await
            }
        }
    }

    /// Chain of thought reasoning implementation
    async fn chain_of_thought_reasoning(
        &mut self,
        goal: &PlanningGoal,
        max_steps: usize,
        step_confidence_threshold: f32,
    ) -> Result<MultiModalMessage> {
        let mut reasoning_chain = Vec::new();
        let mut current_input = goal.input_data.clone();

        for step in 0..max_steps {
            let reasoning_step = self
                .execute_reasoning_step(
                    &current_input,
                    &format!("Chain of thought step {}", step + 1),
                    ReasoningType::Deduction,
                )
                .await?;

            if reasoning_step.confidence < step_confidence_threshold {
                break;
            }

            if let Some(output) = &reasoning_step.output {
                current_input = output.clone();
            }

            reasoning_chain.push(reasoning_step);

            // Check if we've reached the goal
            if self.is_goal_satisfied(&current_input, goal) {
                break;
            }
        }

        // Store the reasoning chain in knowledge base
        self.store_reasoning_experience(goal, reasoning_chain, &current_input)
            .await?;

        Ok(current_input)
    }

    /// Tree of thought reasoning with multiple exploration branches
    async fn tree_of_thought_reasoning(
        &mut self,
        goal: &PlanningGoal,
        branching_factor: usize,
        max_depth: usize,
        pruning_threshold: f32,
    ) -> Result<MultiModalMessage> {
        let mut exploration_queue = VecDeque::new();
        let root_node = ReasoningNode {
            id: Uuid::new_v4(),
            input: goal.input_data.clone(),
            depth: 0,
            confidence: 1.0,
            parent: None,
            children: Vec::new(),
        };

        exploration_queue.push_back(root_node);
        let mut best_result = goal.input_data.clone();
        let mut best_confidence = 0.0;

        while let Some(current_node) = exploration_queue.pop_front() {
            if current_node.depth >= max_depth {
                continue;
            }

            // Generate multiple reasoning branches
            for branch in 0..branching_factor {
                let reasoning_step = self
                    .execute_reasoning_step(
                        &current_node.input,
                        &format!("Tree branch {} at depth {}", branch, current_node.depth),
                        ReasoningType::Abduction,
                    )
                    .await?;

                if reasoning_step.confidence > pruning_threshold {
                    if let Some(output) = reasoning_step.output {
                        let child_node = ReasoningNode {
                            id: Uuid::new_v4(),
                            input: output.clone(),
                            depth: current_node.depth + 1,
                            confidence: reasoning_step.confidence,
                            parent: Some(current_node.id),
                            children: Vec::new(),
                        };

                        // Check if this is our best result so far
                        if reasoning_step.confidence > best_confidence
                            && self.is_goal_satisfied(&output, goal)
                        {
                            best_result = output;
                            best_confidence = reasoning_step.confidence;
                        }

                        exploration_queue.push_back(child_node);
                    }
                }
            }
        }

        Ok(best_result)
    }

    /// Multi-modal fusion reasoning
    async fn modality_fusion_reasoning(
        &mut self,
        goal: &PlanningGoal,
        fusion_weights: &HashMap<Modality, f32>,
        consensus_threshold: f32,
    ) -> Result<MultiModalMessage> {
        let mut modality_results = HashMap::new();

        // Process each modality separately
        for (modality, weight) in fusion_weights {
            let modality_input = self.extract_modality_content(&goal.input_data, modality);
            if !modality_input.content.is_empty() {
                let result = self
                    .execute_reasoning_step(
                        &modality_input,
                        &format!("Modality-specific reasoning for {:?}", modality),
                        ReasoningType::Modal,
                    )
                    .await?;

                modality_results.insert(modality.clone(), (result, *weight));
            }
        }

        // Fuse results based on weights and consensus
        let fused_result = self.fuse_modality_results(modality_results, consensus_threshold)?;

        Ok(fused_result)
    }

    async fn hierarchical_planning_reasoning(
        &mut self,
        goal: &PlanningGoal,
        abstraction_levels: usize,
        subgoal_threshold: f32,
    ) -> Result<MultiModalMessage> {
        // Start with high-level abstract planning
        let mut current_goal = goal.clone();
        let mut results = Vec::new();

        for level in 0..abstraction_levels {
            let _abstraction_factor = 1.0 - (level as f32 / abstraction_levels as f32);

            let reasoning_step = self
                .execute_reasoning_step(
                    &current_goal.input_data,
                    &format!("Hierarchical planning level {}", level),
                    ReasoningType::Causal,
                )
                .await?;

            if reasoning_step.confidence > subgoal_threshold {
                if let Some(output) = reasoning_step.output {
                    results.push(output.clone());

                    // Refine the goal for the next level
                    current_goal.input_data = output;
                }
            }
        }

        // Combine all hierarchical results
        Ok(self.combine_hierarchical_results(results))
    }

    async fn adversarial_reasoning(
        &mut self,
        goal: &PlanningGoal,
        criticism_strength: f32,
        validation_rounds: usize,
    ) -> Result<MultiModalMessage> {
        let mut current_result = goal.input_data.clone();

        for round in 0..validation_rounds {
            // Generate initial reasoning
            let reasoning_step = self
                .execute_reasoning_step(
                    &current_result,
                    &format!("Adversarial reasoning round {}", round),
                    ReasoningType::Deduction,
                )
                .await?;

            if let Some(output) = reasoning_step.output {
                // Generate criticism
                let criticism = self.generate_criticism(&output, criticism_strength).await?;

                // Refine based on criticism
                let refined_result = self.refine_with_criticism(&output, &criticism).await?;
                current_result = refined_result;
            }
        }

        Ok(current_result)
    }

    async fn execute_reasoning_step(
        &self,
        input: &MultiModalMessage,
        description: &str,
        reasoning_type: ReasoningType,
    ) -> Result<ReasoningStep> {
        // This would integrate with the actual AI backends
        // For now, we'll simulate the reasoning process

        let output = MultiModalMessage {
            role: "assistant".to_string(),
            content: vec![MultiModalContent {
                text: Some(format!("Reasoning result for: {}", description)),
                image_url: None,
                audio_url: None,
                video_url: None,
                image_data: None,
                audio_data: None,
                video_data: None,
            }],
        };

        Ok(ReasoningStep {
            step_id: Uuid::new_v4(),
            description: description.to_string(),
            input: input.clone(),
            output: Some(output),
            confidence: 0.85, // Simulated confidence
            reasoning_type,
            execution_time: 1.5, // Simulated execution time
        })
    }

    fn select_best_strategy(&self, _goal: &PlanningGoal) -> Result<&ReasoningStrategy> {
        // Select strategy based on goal characteristics
        // For now, default to chain of thought
        self.strategies
            .first()
            .ok_or_else(|| anyhow::anyhow!("No reasoning strategies available"))
    }

    fn is_goal_satisfied(&self, result: &MultiModalMessage, _goal: &PlanningGoal) -> bool {
        // Check if the result satisfies the goal criteria
        // This is a simplified implementation
        !result.content.is_empty()
    }

    async fn store_reasoning_experience(
        &mut self,
        goal: &PlanningGoal,
        reasoning_chain: Vec<ReasoningStep>,
        result: &MultiModalMessage,
    ) -> Result<()> {
        let experience = Experience {
            id: Uuid::new_v4(),
            task_description: goal.description.clone(),
            input_modalities: goal
                .input_data
                .content
                .iter()
                .map(|c| self.infer_modality(c))
                .collect(),
            strategy_used: ReasoningStrategy::ChainOfThought {
                max_steps: reasoning_chain.len(),
                step_confidence_threshold: 0.7,
            },
            outcome: TaskOutcome::Success {
                result: result.clone(),
                confidence: reasoning_chain
                    .iter()
                    .map(|step| step.confidence)
                    .sum::<f32>()
                    / reasoning_chain.len() as f32,
                execution_time: reasoning_chain.iter().map(|step| step.execution_time).sum(),
            },
            lessons_learned: vec![
                "Multi-step reasoning improved result quality".to_string(),
                "Higher confidence thresholds led to better outcomes".to_string(),
            ],
            timestamp: chrono::Utc::now(),
        };

        self.knowledge_base.experiences.push(experience);
        Ok(())
    }

    fn extract_modality_content(
        &self,
        message: &MultiModalMessage,
        modality: &Modality,
    ) -> MultiModalMessage {
        // Extract content specific to the given modality
        let filtered_content: Vec<MultiModalContent> = message
            .content
            .iter()
            .filter(|content| self.infer_modality(content) == *modality)
            .cloned()
            .collect();

        MultiModalMessage {
            role: message.role.clone(),
            content: filtered_content,
        }
    }

    fn infer_modality(&self, content: &MultiModalContent) -> Modality {
        if content.image_data.is_some() || content.image_url.is_some() {
            Modality::Image
        } else if content.audio_data.is_some() || content.audio_url.is_some() {
            Modality::Audio
        } else if content.video_data.is_some() || content.video_url.is_some() {
            Modality::Video
        } else {
            Modality::Text
        }
    }

    fn fuse_modality_results(
        &self,
        modality_results: HashMap<Modality, (ReasoningStep, f32)>,
        consensus_threshold: f32,
    ) -> Result<MultiModalMessage> {
        // Combine results from different modalities
        let mut combined_content = Vec::new();
        let mut total_weight = 0.0;
        let mut weighted_confidence = 0.0;

        for (_modality, (step, weight)) in modality_results {
            if let Some(output) = step.output {
                combined_content.extend(output.content);
                weighted_confidence += step.confidence * weight;
                total_weight += weight;
            }
        }

        if total_weight > 0.0 {
            weighted_confidence /= total_weight;
        }

        if weighted_confidence >= consensus_threshold {
            Ok(MultiModalMessage {
                role: "assistant".to_string(),
                content: combined_content,
            })
        } else {
            Err(anyhow::anyhow!(
                "Consensus threshold not met: {}",
                weighted_confidence
            ))
        }
    }

    fn combine_hierarchical_results(&self, results: Vec<MultiModalMessage>) -> MultiModalMessage {
        let combined_content: Vec<MultiModalContent> =
            results.into_iter().flat_map(|msg| msg.content).collect();

        MultiModalMessage {
            role: "assistant".to_string(),
            content: combined_content,
        }
    }

    async fn generate_criticism(
        &self,
        _result: &MultiModalMessage,
        strength: f32,
    ) -> Result<MultiModalMessage> {
        // Generate constructive criticism of the result
        Ok(MultiModalMessage {
            role: "critic".to_string(),
            content: vec![MultiModalContent {
                text: Some(format!(
                    "Critical analysis (strength: {}): The result could be improved by...",
                    strength
                )),
                image_url: None,
                audio_url: None,
                video_url: None,
                image_data: None,
                audio_data: None,
                video_data: None,
            }],
        })
    }

    async fn refine_with_criticism(
        &self,
        result: &MultiModalMessage,
        _criticism: &MultiModalMessage,
    ) -> Result<MultiModalMessage> {
        // Refine the result based on criticism
        let mut refined_content = result.content.clone();

        // Add refinement based on criticism
        refined_content.push(MultiModalContent {
            text: Some("Refined based on critical feedback".to_string()),
            image_url: None,
            audio_url: None,
            video_url: None,
            image_data: None,
            audio_data: None,
            video_data: None,
        });

        Ok(MultiModalMessage {
            role: "assistant".to_string(),
            content: refined_content,
        })
    }
}

#[derive(Debug, Clone)]
#[allow(dead_code)]
struct ReasoningNode {
    id: Uuid,
    input: MultiModalMessage,
    depth: usize,
    confidence: f32,
    parent: Option<Uuid>,
    children: Vec<Uuid>,
}

impl KnowledgeBase {
    pub fn new() -> Self {
        Self {
            facts: HashMap::new(),
            rules: Vec::new(),
            patterns: Vec::new(),
            experiences: Vec::new(),
        }
    }

    pub fn add_fact(&mut self, fact: Fact) {
        self.facts.insert(fact.id.to_string(), fact);
    }

    pub fn add_rule(&mut self, rule: Rule) {
        self.rules.push(rule);
    }

    pub fn add_pattern(&mut self, pattern: Pattern) {
        self.patterns.push(pattern);
    }

    pub fn query_similar_experiences(&self, goal: &PlanningGoal) -> Vec<&Experience> {
        self.experiences
            .iter()
            .filter(|exp| exp.task_description.contains(&goal.description))
            .collect()
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_reasoning_engine_creation() {
        let engine = ReasoningEngine::new();
        assert!(!engine.strategies.is_empty());
        assert_eq!(engine.planning_horizon, 20);
        assert_eq!(engine.confidence_threshold, 0.75);
    }

    #[test]
    fn test_knowledge_base_operations() {
        let mut kb = KnowledgeBase::new();

        let fact = Fact {
            id: Uuid::new_v4(),
            content: "Test fact".to_string(),
            confidence: 0.9,
            modalities: vec![Modality::Text],
            source: "test".to_string(),
            timestamp: chrono::Utc::now(),
        };

        kb.add_fact(fact.clone());
        assert_eq!(kb.facts.len(), 1);
        assert!(kb.facts.contains_key(&fact.id.to_string()));
    }

    #[tokio::test]
    async fn test_chain_of_thought_reasoning() {
        let mut engine = ReasoningEngine::new();

        let goal = PlanningGoal {
            description: "Test reasoning task".to_string(),
            input_data: MultiModalMessage {
                role: "user".to_string(),
                content: vec![MultiModalContent {
                    text: Some("Test input".to_string()),
                    image_url: None,
                    audio_url: None,
                    video_url: None,
                    image_data: None,
                    audio_data: None,
                    video_data: None,
                }],
            },
            desired_output: GoalSpecification {
                output_modalities: vec![Modality::Text],
                quality_requirements: HashMap::new(),
                success_criteria: vec!["Generate meaningful response".to_string()],
            },
            constraints: Vec::new(),
            deadline: None,
        };

        let result = engine
            .chain_of_thought_reasoning(&goal, 3, 0.7)
            .await
            .unwrap();
        assert!(!result.content.is_empty());
    }
}