oxirs-chat 0.2.4

//! Advanced Reasoning Module for OxiRS Chat RAG System
//!
//! Implements sophisticated reasoning capabilities including:
//! - Multi-step logical inference
//! - Causal reasoning chains
//! - Probabilistic reasoning with uncertainty quantification
//! - Analogical reasoning for pattern matching
//! - Temporal reasoning for time-sensitive queries

use crate::rag::AssembledContext;
use anyhow::{anyhow, Result};
use chrono::{DateTime, Utc};
use oxirs_core::model::triple::Triple;
use regex::Regex;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use tracing::{debug, info};

/// Configuration for advanced reasoning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReasoningConfig {
    pub max_inference_depth: usize,
    pub confidence_threshold: f64,
    pub enable_causal_reasoning: bool,
    pub enable_temporal_reasoning: bool,
    pub enable_analogical_reasoning: bool,
    pub uncertainty_quantification: bool,
}

impl Default for ReasoningConfig {
    fn default() -> Self {
        Self {
            max_inference_depth: 5,
            confidence_threshold: 0.7,
            enable_causal_reasoning: true,
            enable_temporal_reasoning: true,
            enable_analogical_reasoning: true,
            uncertainty_quantification: true,
        }
    }
}

/// Types of reasoning patterns
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ReasoningType {
    /// Direct logical inference (A → B, B → C, therefore A → C)
    Deductive,
    /// Pattern-based inference from examples
    Inductive,
    /// Cause-and-effect reasoning
    Causal,
    /// Time-based sequential reasoning
    Temporal,
    /// Similarity-based reasoning
    Analogical,
    /// Probabilistic inference with uncertainty
    Probabilistic,
}

/// A single reasoning step in a chain
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReasoningStep {
    pub step_id: String,
    pub reasoning_type: ReasoningType,
    pub premise_triples: Vec<Triple>,
    pub conclusion_triple: Option<Triple>,
    pub confidence: f64,
    pub explanation: String,
    pub timestamp: DateTime<Utc>,
}

/// A complete reasoning chain from premise to conclusion
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReasoningChain {
    pub chain_id: String,
    pub query: String,
    pub steps: Vec<ReasoningStep>,
    pub final_conclusion: Option<Triple>,
    pub overall_confidence: f64,
    pub reasoning_time_ms: u64,
    pub alternative_chains: Vec<AlternativeChain>,
}

/// Alternative reasoning paths with different conclusions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlternativeChain {
    pub chain_id: String,
    pub steps: Vec<ReasoningStep>,
    pub conclusion: Option<Triple>,
    pub confidence: f64,
    pub divergence_point: usize,
}

/// Result of reasoning analysis
#[derive(Debug, Clone)]
pub struct ReasoningResult {
    pub primary_chain: ReasoningChain,
    pub supporting_evidence: Vec<Triple>,
    pub contradicting_evidence: Vec<Triple>,
    pub uncertainty_factors: Vec<UncertaintyFactor>,
    pub reasoning_quality: ReasoningQuality,
}

/// Factors contributing to reasoning uncertainty
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UncertaintyFactor {
    pub factor_type: UncertaintyType,
    pub description: String,
    pub impact_score: f64,
    pub mitigation_strategy: Option<String>,
}

/// Types of uncertainty in reasoning
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum UncertaintyType {
    /// Insufficient evidence for conclusion
    InsufficientEvidence,
    /// Conflicting evidence exists
    ConflictingEvidence,
    /// Temporal inconsistencies
    TemporalInconsistency,
    /// Causal chain gaps
    CausalGaps,
    /// Statistical uncertainty
    StatisticalUncertainty,
}

/// Quality assessment of reasoning process
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReasoningQuality {
    pub logical_consistency: f64,
    pub evidence_strength: f64,
    pub chain_completeness: f64,
    pub temporal_coherence: f64,
    pub overall_quality: f64,
}

/// Advanced reasoning engine
pub struct AdvancedReasoningEngine {
    config: ReasoningConfig,
    reasoning_patterns: HashMap<String, ReasoningPattern>,
    causal_knowledge: CausalKnowledgeBase,
    temporal_model: TemporalReasoningModel,
    analogical_matcher: AnalogicalMatcher,
}

/// Reasoning pattern template
#[derive(Debug, Clone)]
struct ReasoningPattern {
    pattern_id: String,
    pattern_type: ReasoningType,
    premise_template: String,
    conclusion_template: String,
    confidence_modifier: f64,
}

/// Causal knowledge base for cause-effect reasoning
#[derive(Debug, Clone)]
struct CausalKnowledgeBase {
    causal_relations: HashMap<String, Vec<CausalRelation>>,
}

/// Temporal reasoning model
#[derive(Debug, Clone)]
struct TemporalReasoningModel {
    temporal_relations: HashMap<String, TemporalRelation>,
    time_constraints: Vec<TimeConstraint>,
}

/// Analogical pattern matcher
#[derive(Debug, Clone)]
struct AnalogicalMatcher {
    similarity_patterns: HashMap<String, Vec<AnalogicalPattern>>,
}

#[derive(Debug, Clone)]
struct CausalRelation {
    cause: String,
    effect: String,
    strength: f64,
    conditions: Vec<String>,
}

#[derive(Debug, Clone)]
struct TemporalRelation {
    relation_type: String,
    before_entity: String,
    after_entity: String,
    time_interval: Option<std::time::Duration>,
}

#[derive(Debug, Clone)]
struct TimeConstraint {
    constraint_type: String,
    entities: Vec<String>,
    temporal_bound: DateTime<Utc>,
}

#[derive(Debug, Clone)]
struct AnalogicalPattern {
    source_domain: String,
    target_domain: String,
    mapping_strength: f64,
    structural_similarity: f64,
}

impl AdvancedReasoningEngine {
    /// Create a new advanced reasoning engine
    pub fn new(config: ReasoningConfig) -> Self {
        Self {
            config,
            reasoning_patterns: Self::initialize_reasoning_patterns(),
            causal_knowledge: CausalKnowledgeBase {
                causal_relations: HashMap::new(),
            },
            temporal_model: TemporalReasoningModel {
                temporal_relations: HashMap::new(),
                time_constraints: Vec::new(),
            },
            analogical_matcher: AnalogicalMatcher {
                similarity_patterns: HashMap::new(),
            },
        }
    }

    /// Initialize standard reasoning patterns
    fn initialize_reasoning_patterns() -> HashMap<String, ReasoningPattern> {
        let mut patterns = HashMap::new();

        // Deductive reasoning patterns
        patterns.insert(
            "modus_ponens".to_string(),
            ReasoningPattern {
                pattern_id: "modus_ponens".to_string(),
                pattern_type: ReasoningType::Deductive,
                premise_template: "If {P} then {Q}; {P} is true".to_string(),
                conclusion_template: "Therefore {Q} is true".to_string(),
                confidence_modifier: 0.95,
            },
        );

        // Causal reasoning patterns
        patterns.insert(
            "causal_chain".to_string(),
            ReasoningPattern {
                pattern_id: "causal_chain".to_string(),
                pattern_type: ReasoningType::Causal,
                premise_template: "{A} causes {B}; {B} causes {C}".to_string(),
                conclusion_template: "{A} causes {C}".to_string(),
                confidence_modifier: 0.8,
            },
        );

        // Temporal reasoning patterns
        patterns.insert(
            "temporal_sequence".to_string(),
            ReasoningPattern {
                pattern_id: "temporal_sequence".to_string(),
                pattern_type: ReasoningType::Temporal,
                premise_template: "{A} happens before {B}; {B} happens before {C}".to_string(),
                conclusion_template: "{A} happens before {C}".to_string(),
                confidence_modifier: 0.9,
            },
        );

        patterns
    }

    /// Perform advanced reasoning on assembled context
    pub async fn reason(
        &mut self,
        query: &str,
        context: &AssembledContext,
    ) -> Result<ReasoningResult> {
        let start_time = std::time::Instant::now();
        info!("Starting advanced reasoning for query: {}", query);

        // Build reasoning chains from different perspectives
        let mut reasoning_chains = Vec::new();

        // Deductive reasoning chain
        if let Some(deductive_chain) = self.build_deductive_chain(query, context).await? {
            reasoning_chains.push(deductive_chain);
        }

        // Causal reasoning chain
        if self.config.enable_causal_reasoning {
            if let Some(causal_chain) = self.build_causal_chain(query, context).await? {
                reasoning_chains.push(causal_chain);
            }
        }

        // Temporal reasoning chain
        if self.config.enable_temporal_reasoning {
            if let Some(temporal_chain) = self.build_temporal_chain(query, context).await? {
                reasoning_chains.push(temporal_chain);
            }
        }

        // Analogical reasoning chain
        if self.config.enable_analogical_reasoning {
            if let Some(analogical_chain) = self.build_analogical_chain(query, context).await? {
                reasoning_chains.push(analogical_chain);
            }
        }

        // Select the best reasoning chain
        let primary_chain = self.select_best_chain(reasoning_chains)?;

        // Gather supporting and contradicting evidence
        let (supporting_evidence, contradicting_evidence) =
            self.gather_evidence(&primary_chain, context).await?;

        // Quantify uncertainty if enabled
        let uncertainty_factors = if self.config.uncertainty_quantification {
            self.quantify_uncertainty(&primary_chain, context).await?
        } else {
            Vec::new()
        };

        // Assess reasoning quality
        let reasoning_quality = self
            .assess_reasoning_quality(&primary_chain, context)
            .await?;

        let reasoning_time = start_time.elapsed().as_millis() as u64;
        info!("Advanced reasoning completed in {}ms", reasoning_time);

        Ok(ReasoningResult {
            primary_chain,
            supporting_evidence,
            contradicting_evidence,
            uncertainty_factors,
            reasoning_quality,
        })
    }

    /// Build deductive reasoning chain
    async fn build_deductive_chain(
        &self,
        query: &str,
        context: &AssembledContext,
    ) -> Result<Option<ReasoningChain>> {
        debug!("Building deductive reasoning chain");

        let mut steps = Vec::new();
        let mut current_premises = context
            .semantic_results
            .iter()
            .map(|r| r.triple.clone())
            .collect::<Vec<_>>();

        // Apply modus ponens pattern iteratively
        for depth in 0..self.config.max_inference_depth {
            if let Some(new_conclusion) = self.apply_modus_ponens(&current_premises)? {
                let step = ReasoningStep {
                    step_id: format!("deductive_step_{depth}"),
                    reasoning_type: ReasoningType::Deductive,
                    premise_triples: current_premises.clone(),
                    conclusion_triple: Some(new_conclusion.clone()),
                    confidence: 0.9 - (depth as f64 * 0.1),
                    explanation: format!("Applied deductive inference at depth {depth}"),
                    timestamp: Utc::now(),
                };
                steps.push(step);
                current_premises.push(new_conclusion);
            } else {
                break;
            }
        }

        if steps.is_empty() {
            return Ok(None);
        }

        let overall_confidence = steps
            .iter()
            .map(|s| s.confidence)
            .fold(1.0, |acc, conf| acc * conf);

        Ok(Some(ReasoningChain {
            chain_id: uuid::Uuid::new_v4().to_string(),
            query: query.to_string(),
            steps,
            final_conclusion: current_premises.last().cloned(),
            overall_confidence,
            reasoning_time_ms: 0, // Will be set by caller
            alternative_chains: Vec::new(),
        }))
    }

    /// Build causal reasoning chain
    async fn build_causal_chain(
        &self,
        query: &str,
        context: &AssembledContext,
    ) -> Result<Option<ReasoningChain>> {
        debug!("Building causal reasoning chain");

        // Look for causal relationships in the context
        let causal_triples = context
            .semantic_results
            .iter()
            .filter(|r| self.is_causal_relation(&r.triple))
            .map(|r| r.triple.clone())
            .collect::<Vec<_>>();

        if causal_triples.is_empty() {
            return Ok(None);
        }

        let mut steps = Vec::new();
        let mut causal_chain = Vec::new();

        // Build causal chain step by step
        for (i, triple) in causal_triples.iter().enumerate() {
            let step = ReasoningStep {
                step_id: format!("causal_step_{i}"),
                reasoning_type: ReasoningType::Causal,
                premise_triples: vec![triple.clone()],
                conclusion_triple: None, // Will be derived from causal inference
                confidence: 0.8,
                explanation: format!("Identified causal relationship: {}", triple.object()),
                timestamp: Utc::now(),
            };
            steps.push(step);
            causal_chain.push(triple.clone());
        }

        let overall_confidence = 0.8_f64.powi(steps.len() as i32);

        Ok(Some(ReasoningChain {
            chain_id: uuid::Uuid::new_v4().to_string(),
            query: query.to_string(),
            steps,
            final_conclusion: causal_chain.last().cloned(),
            overall_confidence,
            reasoning_time_ms: 0,
            alternative_chains: Vec::new(),
        }))
    }

    /// Build temporal reasoning chain
    async fn build_temporal_chain(
        &self,
        query: &str,
        context: &AssembledContext,
    ) -> Result<Option<ReasoningChain>> {
        debug!("Building temporal reasoning chain");

        // Look for temporal relationships
        let temporal_triples = context
            .semantic_results
            .iter()
            .filter(|r| self.is_temporal_relation(&r.triple))
            .map(|r| r.triple.clone())
            .collect::<Vec<_>>();

        if temporal_triples.is_empty() {
            return Ok(None);
        }

        // Sort by temporal order if possible
        let mut sorted_triples = temporal_triples;

        // Implement temporal sorting based on timestamps and sequential relationships
        sorted_triples.sort_by(|a, b| {
            // Try to extract temporal information from the triple objects
            let a_temporal_score = self.extract_temporal_score(a);
            let b_temporal_score = self.extract_temporal_score(b);

            a_temporal_score
                .partial_cmp(&b_temporal_score)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        let mut steps = Vec::new();
        for (i, triple) in sorted_triples.iter().enumerate() {
            let step = ReasoningStep {
                step_id: format!("temporal_step_{i}"),
                reasoning_type: ReasoningType::Temporal,
                premise_triples: vec![triple.clone()],
                conclusion_triple: None,
                confidence: 0.85,
                explanation: format!("Temporal sequence element: {}", triple.object()),
                timestamp: Utc::now(),
            };
            steps.push(step);
        }

        let overall_confidence = 0.85_f64.powi(steps.len() as i32);

        Ok(Some(ReasoningChain {
            chain_id: uuid::Uuid::new_v4().to_string(),
            query: query.to_string(),
            steps,
            final_conclusion: sorted_triples.last().cloned(),
            overall_confidence,
            reasoning_time_ms: 0,
            alternative_chains: Vec::new(),
        }))
    }

    /// Build analogical reasoning chain
    async fn build_analogical_chain(
        &self,
        query: &str,
        context: &AssembledContext,
    ) -> Result<Option<ReasoningChain>> {
        debug!("Building analogical reasoning chain");

        // Find analogical patterns in the data
        let analogical_candidates = context
            .semantic_results
            .iter()
            .filter(|r| self.has_analogical_potential(&r.triple))
            .map(|r| r.triple.clone())
            .collect::<Vec<_>>();

        if analogical_candidates.is_empty() {
            return Ok(None);
        }

        let mut steps = Vec::new();
        for (i, triple) in analogical_candidates.iter().enumerate() {
            let step = ReasoningStep {
                step_id: format!("analogical_step_{i}"),
                reasoning_type: ReasoningType::Analogical,
                premise_triples: vec![triple.clone()],
                conclusion_triple: None,
                confidence: 0.7, // Lower confidence for analogical reasoning
                explanation: format!("Analogical pattern identified: {}", triple.object()),
                timestamp: Utc::now(),
            };
            steps.push(step);
        }

        let overall_confidence = 0.7_f64.powi(steps.len() as i32);

        Ok(Some(ReasoningChain {
            chain_id: uuid::Uuid::new_v4().to_string(),
            query: query.to_string(),
            steps,
            final_conclusion: analogical_candidates.last().cloned(),
            overall_confidence,
            reasoning_time_ms: 0,
            alternative_chains: Vec::new(),
        }))
    }

    /// Apply modus ponens reasoning pattern
    fn apply_modus_ponens(&self, premises: &[Triple]) -> Result<Option<Triple>> {
        // Simplified modus ponens: look for implication patterns
        // In a real implementation, this would involve sophisticated logical inference

        for premise in premises {
            // Look for "implies" or similar predicates
            let predicate_str = premise.predicate().to_string();
            if predicate_str.contains("implies") || predicate_str.contains("causes") {
                // Extract conclusion from implication
                // This is a simplified version - real implementation would be more sophisticated
                return Ok(Some(premise.clone()));
            }
        }

        Ok(None)
    }

    /// Check if a triple represents a causal relation
    fn is_causal_relation(&self, triple: &Triple) -> bool {
        let predicate = triple.predicate().to_string().to_lowercase();
        predicate.contains("cause")
            || predicate.contains("result")
            || predicate.contains("lead")
            || predicate.contains("effect")
    }

    /// Check if a triple represents a temporal relation
    fn is_temporal_relation(&self, triple: &Triple) -> bool {
        let predicate = triple.predicate().to_string().to_lowercase();
        predicate.contains("before")
            || predicate.contains("after")
            || predicate.contains("during")
            || predicate.contains("when")
            || predicate.contains("time")
    }

    /// Check if a triple has analogical potential
    fn has_analogical_potential(&self, triple: &Triple) -> bool {
        let predicate = triple.predicate().to_string().to_lowercase();
        predicate.contains("similar")
            || predicate.contains("like")
            || predicate.contains("analogy")
            || predicate.contains("resemble")
    }

    /// Select the best reasoning chain from candidates
    fn select_best_chain(&self, chains: Vec<ReasoningChain>) -> Result<ReasoningChain> {
        if chains.is_empty() {
            return Err(anyhow!("No valid reasoning chains found"));
        }

        // Select chain with highest confidence above threshold
        let best_chain = chains
            .into_iter()
            .filter(|chain| chain.overall_confidence >= self.config.confidence_threshold)
            .max_by(|a, b| {
                a.overall_confidence
                    .partial_cmp(&b.overall_confidence)
                    .unwrap_or(std::cmp::Ordering::Equal)
            });

        best_chain.ok_or_else(|| anyhow!("No reasoning chain meets confidence threshold"))
    }

    /// Gather supporting and contradicting evidence
    async fn gather_evidence(
        &self,
        _chain: &ReasoningChain,
        context: &AssembledContext,
    ) -> Result<(Vec<Triple>, Vec<Triple>)> {
        let mut supporting = Vec::new();
        let mut contradicting = Vec::new();

        // Simple evidence gathering based on semantic similarity
        for result in &context.semantic_results {
            if result.score > 0.8 {
                supporting.push(result.triple.clone());
            } else if result.score < 0.3 {
                contradicting.push(result.triple.clone());
            }
        }

        Ok((supporting, contradicting))
    }

    /// Quantify uncertainty in reasoning
    async fn quantify_uncertainty(
        &self,
        chain: &ReasoningChain,
        context: &AssembledContext,
    ) -> Result<Vec<UncertaintyFactor>> {
        let mut factors = Vec::new();

        // Check for insufficient evidence
        if context.semantic_results.len() < 3 {
            factors.push(UncertaintyFactor {
                factor_type: UncertaintyType::InsufficientEvidence,
                description: "Limited evidence available for reasoning".to_string(),
                impact_score: 0.3,
                mitigation_strategy: Some("Gather more relevant information".to_string()),
            });
        }

        // Check for conflicting evidence
        let confidence_variance = chain
            .steps
            .iter()
            .map(|s| s.confidence)
            .fold((0.0, 0.0), |acc, conf| (acc.0 + conf, acc.1 + conf * conf));

        let mean_confidence = confidence_variance.0 / chain.steps.len() as f64;
        let variance =
            (confidence_variance.1 / chain.steps.len() as f64) - mean_confidence * mean_confidence;

        if variance > 0.1 {
            factors.push(UncertaintyFactor {
                factor_type: UncertaintyType::ConflictingEvidence,
                description: "High variance in step confidences".to_string(),
                impact_score: variance,
                mitigation_strategy: Some("Resolve conflicting information".to_string()),
            });
        }

        Ok(factors)
    }

    /// Assess overall reasoning quality
    async fn assess_reasoning_quality(
        &self,
        chain: &ReasoningChain,
        context: &AssembledContext,
    ) -> Result<ReasoningQuality> {
        // Logical consistency
        let logical_consistency = chain
            .steps
            .iter()
            .map(|s| s.confidence)
            .fold(0.0, |acc, conf| acc + conf)
            / chain.steps.len() as f64;

        // Evidence strength
        let evidence_strength = context
            .semantic_results
            .iter()
            .map(|r| r.score as f64)
            .fold(0.0, |acc, score| acc + score)
            / context.semantic_results.len().max(1) as f64;

        // Chain completeness
        let chain_completeness = if chain.final_conclusion.is_some() {
            1.0
        } else {
            0.5
        };

        // Temporal coherence (enhanced analysis)
        let temporal_coherence = self.analyze_temporal_coherence(chain);

        let overall_quality =
            (logical_consistency + evidence_strength + chain_completeness + temporal_coherence)
                / 4.0;

        Ok(ReasoningQuality {
            logical_consistency,
            evidence_strength,
            chain_completeness,
            temporal_coherence,
            overall_quality,
        })
    }

    /// Extract temporal score from a triple for sorting purposes
    fn extract_temporal_score(&self, triple: &Triple) -> f64 {
        let object_str = triple.object().to_string().to_lowercase();

        // Look for temporal keywords and assign scores
        if object_str.contains("before")
            || object_str.contains("first")
            || object_str.contains("initial")
        {
            0.0
        } else if object_str.contains("during")
            || object_str.contains("while")
            || object_str.contains("concurrent")
        {
            0.5
        } else if object_str.contains("after")
            || object_str.contains("then")
            || object_str.contains("following")
        {
            1.0
        } else if object_str.contains("finally")
            || object_str.contains("last")
            || object_str.contains("end")
        {
            2.0
        } else {
            // Try to extract year or date information
            if let Some(year) = self.extract_year_from_string(&object_str) {
                year as f64 / 10000.0 // Normalize to smaller range
            } else {
                0.5 // Default middle position
            }
        }
    }

    /// Extract year from string if present
    fn extract_year_from_string(&self, text: &str) -> Option<i32> {
        // Simple regex to find 4-digit years between 1000-2100
        let year_regex = Regex::new(r"\b(1[0-9]{3}|20[0-9]{2}|21[0-9]{2})\b").ok()?;
        if let Some(captures) = year_regex.find(text) {
            captures.as_str().parse().ok()
        } else {
            None
        }
    }

    /// Enhanced temporal coherence analysis
    fn analyze_temporal_coherence(&self, chain: &ReasoningChain) -> f64 {
        if chain.steps.len() < 2 {
            return 1.0; // Single step is coherent
        }

        let mut coherence_scores = Vec::new();

        for i in 1..chain.steps.len() {
            let prev_step = &chain.steps[i - 1];
            let curr_step = &chain.steps[i];

            // Check if temporal order makes sense
            let prev_temporal = self.extract_temporal_info_from_step(prev_step);
            let curr_temporal = self.extract_temporal_info_from_step(curr_step);

            let coherence = if prev_temporal <= curr_temporal {
                1.0 // Correct temporal order
            } else {
                0.3 // Potential temporal inconsistency
            };

            coherence_scores.push(coherence);
        }

        coherence_scores.iter().sum::<f64>() / coherence_scores.len() as f64
    }

    /// Extract temporal information from a reasoning step
    fn extract_temporal_info_from_step(&self, step: &ReasoningStep) -> f64 {
        if let Some(conclusion) = &step.conclusion_triple {
            self.extract_temporal_score(conclusion)
        } else if !step.premise_triples.is_empty() {
            self.extract_temporal_score(&step.premise_triples[0])
        } else {
            0.5 // Default neutral position
        }
    }
}

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

    #[tokio::test]
    async fn test_reasoning_engine_creation() {
        let config = ReasoningConfig::default();
        let engine = AdvancedReasoningEngine::new(config);

        assert_eq!(engine.config.max_inference_depth, 5);
        assert_eq!(engine.config.confidence_threshold, 0.7);
    }

    #[test]
    fn test_reasoning_patterns_initialization() {
        let patterns = AdvancedReasoningEngine::initialize_reasoning_patterns();

        assert!(patterns.contains_key("modus_ponens"));
        assert!(patterns.contains_key("causal_chain"));
        assert!(patterns.contains_key("temporal_sequence"));
    }

    #[test]
    fn test_causal_relation_detection() {
        let _engine = AdvancedReasoningEngine::new(ReasoningConfig::default());

        // This test would require actual Triple instances
        // In a real implementation, you'd create test triples with causal predicates
    }
}