oxirs-chat 0.2.4

//! Explanation and transparency module for OxiRS Chat
//!
//! This module provides explainable AI capabilities including:
//! - Response explanation and source attribution
//! - Confidence indicators and reasoning paths
//! - Interactive clarification and ambiguity handling
//! - Evidence presentation and uncertainty quantification
//! - Alternative viewpoints and assumption validation

use crate::types::Message;
use anyhow::Result;
use serde::{Deserialize, Serialize};

/// Explanation result with attribution and reasoning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExplanationResult {
    pub response_explanation: ResponseExplanation,
    pub source_attribution: SourceAttribution,
    pub reasoning_path: ReasoningPath,
    pub confidence_indicators: ConfidenceIndicators,
    pub evidence: Vec<Evidence>,
    pub uncertainty_quantification: UncertaintyQuantification,
    pub alternative_views: Vec<AlternativeView>,
}

/// Response explanation with reasoning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResponseExplanation {
    pub summary: String,
    pub reasoning_steps: Vec<ReasoningStep>,
    pub methodology: String,
    pub assumptions: Vec<String>,
    pub limitations: Vec<String>,
    pub decision_factors: Vec<DecisionFactor>,
}

/// Reasoning step in the explanation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReasoningStep {
    pub step_number: u32,
    pub description: String,
    pub reasoning_type: ReasoningType,
    pub inputs: Vec<String>,
    pub outputs: Vec<String>,
    pub confidence: f32,
    pub sources: Vec<String>,
}

/// Types of reasoning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ReasoningType {
    Deduction,
    Induction,
    Abduction,
    Analogy,
    Retrieval,
    Inference,
    Aggregation,
}

/// Decision factors that influenced the response
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DecisionFactor {
    pub factor: String,
    pub weight: f32,
    pub impact: FactorImpact,
    pub explanation: String,
}

/// Impact levels of decision factors
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FactorImpact {
    High,
    Medium,
    Low,
    Neutral,
}

/// Source attribution information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SourceAttribution {
    pub primary_sources: Vec<DataSource>,
    pub secondary_sources: Vec<DataSource>,
    pub citation_style: CitationStyle,
    pub source_quality: SourceQuality,
    pub traceability: Vec<TraceabilityLink>,
}

/// Data source information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataSource {
    pub source_id: String,
    pub source_type: SourceType,
    pub uri: Option<String>,
    pub title: String,
    pub description: String,
    pub reliability_score: f32,
    pub last_updated: chrono::DateTime<chrono::Utc>,
    pub access_date: chrono::DateTime<chrono::Utc>,
    pub relevance_score: f32,
}

/// Types of data sources
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SourceType {
    KnowledgeGraph,
    Database,
    Document,
    API,
    UserInput,
    InferredKnowledge,
    CachedResult,
}

/// Citation styles
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CitationStyle {
    Inline,
    Footnote,
    Bibliography,
    Hyperlink,
    Structured,
}

/// Source quality assessment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SourceQuality {
    pub overall_score: f32,
    pub completeness: f32,
    pub accuracy: f32,
    pub freshness: f32,
    pub authority: f32,
    pub consistency: f32,
}

/// Traceability link for data lineage
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TraceabilityLink {
    pub from_source: String,
    pub to_source: String,
    pub transformation: String,
    pub confidence: f32,
    pub timestamp: chrono::DateTime<chrono::Utc>,
}

/// Reasoning path through the analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReasoningPath {
    pub path_id: String,
    pub start_query: String,
    pub end_response: String,
    pub intermediate_steps: Vec<IntermediateStep>,
    pub path_confidence: f32,
    pub alternative_paths: Vec<AlternativePath>,
}

/// Intermediate step in reasoning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IntermediateStep {
    pub step_id: String,
    pub operation: String,
    pub input_data: String,
    pub output_data: String,
    pub processing_time_ms: u64,
    pub confidence: f32,
    pub explanation: String,
}

/// Alternative reasoning path
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlternativePath {
    pub path_description: String,
    pub confidence: f32,
    pub why_not_chosen: String,
    pub potential_outcome: String,
}

/// Confidence indicators for the response
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConfidenceIndicators {
    pub overall_confidence: f32,
    pub data_confidence: f32,
    pub reasoning_confidence: f32,
    pub source_confidence: f32,
    pub completeness_confidence: f32,
    pub consistency_confidence: f32,
    pub factors: Vec<ConfidenceFactor>,
}

/// Factor affecting confidence
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConfidenceFactor {
    pub factor_name: String,
    pub impact: f32,
    pub explanation: String,
    pub factor_type: ConfidenceFactorType,
}

/// Types of confidence factors
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConfidenceFactorType {
    DataQuality,
    SourceReliability,
    ReasoningComplexity,
    QueryAmbiguity,
    ContextCompleteness,
    Methodological,
}

/// Evidence supporting the response
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Evidence {
    pub evidence_id: String,
    pub evidence_type: EvidenceType,
    pub content: String,
    pub strength: f32,
    pub source: String,
    pub relevance: f32,
    pub verification_status: VerificationStatus,
    pub supporting_claims: Vec<String>,
}

/// Types of evidence
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EvidenceType {
    Direct,
    Indirect,
    Circumstantial,
    Statistical,
    Anecdotal,
    Expert,
    Derived,
}

/// Evidence verification status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum VerificationStatus {
    Verified,
    PartiallyVerified,
    Unverified,
    Disputed,
    Unknown,
}

/// Uncertainty quantification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UncertaintyQuantification {
    pub uncertainty_level: UncertaintyLevel,
    pub uncertainty_sources: Vec<UncertaintySource>,
    pub confidence_intervals: Vec<ConfidenceInterval>,
    pub sensitivity_analysis: SensitivityAnalysis,
    pub error_bounds: ErrorBounds,
}

/// Levels of uncertainty
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum UncertaintyLevel {
    Low,
    Moderate,
    High,
    VeryHigh,
    Unknown,
}

/// Sources of uncertainty
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UncertaintySource {
    pub source: String,
    pub contribution: f32,
    pub description: String,
    pub mitigation: Option<String>,
}

/// Confidence interval for uncertainty
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConfidenceInterval {
    pub parameter: String,
    pub lower_bound: f32,
    pub upper_bound: f32,
    pub confidence_level: f32,
}

/// Sensitivity analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SensitivityAnalysis {
    pub most_sensitive_factors: Vec<String>,
    pub robustness_score: f32,
    pub critical_assumptions: Vec<String>,
}

/// Error bounds estimation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ErrorBounds {
    pub estimated_error: f32,
    pub error_type: ErrorType,
    pub error_sources: Vec<String>,
}

/// Types of errors
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ErrorType {
    Systematic,
    Random,
    ModelError,
    DataError,
    ProcessingError,
}

/// Alternative viewpoint or interpretation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlternativeView {
    pub viewpoint_id: String,
    pub title: String,
    pub description: String,
    pub supporting_evidence: Vec<String>,
    pub likelihood: f32,
    pub implications: Vec<String>,
    pub why_alternative: String,
}

/// Clarification request handling
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClarificationRequest {
    pub request_id: String,
    pub ambiguity_type: AmbiguityType,
    pub clarification_question: String,
    pub options: Vec<ClarificationOption>,
    pub context: String,
    pub urgency: ClarificationUrgency,
}

/// Types of ambiguity
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AmbiguityType {
    Lexical,     // Word meaning ambiguity
    Syntactic,   // Grammar structure ambiguity
    Semantic,    // Meaning ambiguity
    Pragmatic,   // Context-dependent ambiguity
    Referential, // What something refers to
    Scope,       // Scope of quantifiers/modifiers
}

/// Clarification option
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClarificationOption {
    pub option_id: String,
    pub description: String,
    pub interpretation: String,
    pub likelihood: f32,
    pub example: Option<String>,
}

/// Urgency levels for clarification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ClarificationUrgency {
    Critical, // Must clarify to proceed
    High,     // Strongly recommended
    Medium,   // Helpful but not required
    Low,      // Optional clarification
}

/// Explanation engine for generating explanations
pub struct ExplanationEngine {
    citation_generator: CitationGenerator,
    confidence_calculator: ConfidenceCalculator,
    ambiguity_detector: AmbiguityDetector,
    uncertainty_analyzer: UncertaintyAnalyzer,
}

impl ExplanationEngine {
    /// Create a new explanation engine
    pub fn new() -> Self {
        Self {
            citation_generator: CitationGenerator::new(),
            confidence_calculator: ConfidenceCalculator::new(),
            ambiguity_detector: AmbiguityDetector::new(),
            uncertainty_analyzer: UncertaintyAnalyzer::new(),
        }
    }

    /// Generate comprehensive explanation for a response
    pub async fn explain_response(
        &self,
        query: &str,
        response: &str,
        context: &[Message],
        sources: &[DataSource],
        reasoning_trace: &[IntermediateStep],
    ) -> Result<ExplanationResult> {
        // Generate response explanation
        let response_explanation = self
            .generate_response_explanation(query, response, reasoning_trace)
            .await?;

        // Generate source attribution
        let source_attribution = self
            .citation_generator
            .generate_attribution(sources, &response_explanation)
            .await?;

        // Build reasoning path
        let reasoning_path = self
            .build_reasoning_path(query, response, reasoning_trace)
            .await?;

        // Calculate confidence indicators
        let confidence_indicators = self
            .confidence_calculator
            .calculate_confidence(sources, reasoning_trace, context)
            .await?;

        // Collect evidence
        let evidence = self
            .collect_evidence(sources, reasoning_trace, response)
            .await?;

        // Quantify uncertainty
        let uncertainty_quantification = self
            .uncertainty_analyzer
            .quantify_uncertainty(&confidence_indicators, sources, reasoning_trace)
            .await?;

        // Generate alternative views
        let alternative_views = self
            .generate_alternative_views(query, response, sources)
            .await?;

        Ok(ExplanationResult {
            response_explanation,
            source_attribution,
            reasoning_path,
            confidence_indicators,
            evidence,
            uncertainty_quantification,
            alternative_views,
        })
    }

    /// Detect ambiguities in user query
    pub async fn detect_ambiguities(
        &self,
        query: &str,
        context: &[Message],
    ) -> Result<Vec<ClarificationRequest>> {
        self.ambiguity_detector
            .detect_ambiguities(query, context)
            .await
    }

    /// Generate clarification questions
    pub async fn generate_clarification_questions(
        &self,
        ambiguities: &[ClarificationRequest],
    ) -> Result<Vec<String>> {
        let mut questions = Vec::new();

        for ambiguity in ambiguities {
            let question = match ambiguity.ambiguity_type {
                AmbiguityType::Lexical => {
                    format!(
                        "Could you clarify what you mean by '{}'? {}",
                        self.extract_ambiguous_term(&ambiguity.context),
                        ambiguity.clarification_question
                    )
                }
                AmbiguityType::Semantic => {
                    format!(
                        "I want to make sure I understand correctly. {}",
                        ambiguity.clarification_question
                    )
                }
                AmbiguityType::Referential => {
                    format!(
                        "When you mention '{}', which specific entity are you referring to?",
                        self.extract_reference(&ambiguity.context)
                    )
                }
                _ => ambiguity.clarification_question.clone(),
            };
            questions.push(question);
        }

        Ok(questions)
    }

    /// Validate assumptions made in reasoning
    pub async fn validate_assumptions(
        &self,
        assumptions: &[String],
        sources: &[DataSource],
    ) -> Result<Vec<AssumptionValidation>> {
        let mut validations = Vec::new();

        for assumption in assumptions {
            let validation = AssumptionValidation {
                assumption: assumption.clone(),
                validation_status: self.check_assumption_validity(assumption, sources).await?,
                supporting_evidence: self.find_supporting_evidence(assumption, sources).await?,
                contradicting_evidence: self
                    .find_contradicting_evidence(assumption, sources)
                    .await?,
                confidence: self
                    .calculate_assumption_confidence(assumption, sources)
                    .await?,
            };
            validations.push(validation);
        }

        Ok(validations)
    }

    // Private helper methods

    async fn generate_response_explanation(
        &self,
        _query: &str,
        _response: &str,
        reasoning_trace: &[IntermediateStep],
    ) -> Result<ResponseExplanation> {
        let reasoning_steps: Vec<_> = reasoning_trace
            .iter()
            .enumerate()
            .map(|(i, step)| {
                ReasoningStep {
                    step_number: i as u32 + 1,
                    description: step.explanation.clone(),
                    reasoning_type: self.classify_reasoning_type(&step.operation),
                    inputs: vec![step.input_data.clone()],
                    outputs: vec![step.output_data.clone()],
                    confidence: step.confidence,
                    sources: vec![], // Would be populated from actual sources
                }
            })
            .collect();

        Ok(ResponseExplanation {
            summary: format!(
                "Response generated through {} reasoning steps",
                reasoning_steps.len()
            ),
            reasoning_steps,
            methodology: "Retrieval-Augmented Generation with semantic reasoning".to_string(),
            assumptions: vec![
                "Knowledge graph data is accurate".to_string(),
                "Query intent correctly interpreted".to_string(),
            ],
            limitations: vec![
                "Response limited to available data sources".to_string(),
                "May not reflect most recent updates".to_string(),
            ],
            decision_factors: vec![
                DecisionFactor {
                    factor: "Source reliability".to_string(),
                    weight: 0.3,
                    impact: FactorImpact::High,
                    explanation: "Prioritized high-reliability sources".to_string(),
                },
                DecisionFactor {
                    factor: "Query relevance".to_string(),
                    weight: 0.4,
                    impact: FactorImpact::High,
                    explanation: "Selected most relevant information".to_string(),
                },
            ],
        })
    }

    async fn build_reasoning_path(
        &self,
        query: &str,
        response: &str,
        reasoning_trace: &[IntermediateStep],
    ) -> Result<ReasoningPath> {
        let path_confidence = reasoning_trace
            .iter()
            .map(|step| step.confidence)
            .sum::<f32>()
            / reasoning_trace.len() as f32;

        Ok(ReasoningPath {
            path_id: uuid::Uuid::new_v4().to_string(),
            start_query: query.to_string(),
            end_response: response.to_string(),
            intermediate_steps: reasoning_trace.to_vec(),
            path_confidence,
            alternative_paths: vec![AlternativePath {
                path_description: "Direct database lookup".to_string(),
                confidence: 0.7,
                why_not_chosen: "Less comprehensive than RAG approach".to_string(),
                potential_outcome: "More precise but potentially incomplete answer".to_string(),
            }],
        })
    }

    async fn collect_evidence(
        &self,
        sources: &[DataSource],
        _reasoning_trace: &[IntermediateStep],
        response: &str,
    ) -> Result<Vec<Evidence>> {
        let mut evidence = Vec::new();

        for (i, source) in sources.iter().enumerate() {
            evidence.push(Evidence {
                evidence_id: format!("evidence_{i}"),
                evidence_type: EvidenceType::Direct,
                content: format!("Source: {}", source.title),
                strength: source.reliability_score,
                source: source.source_id.clone(),
                relevance: source.relevance_score,
                verification_status: VerificationStatus::Verified,
                supporting_claims: vec![response.chars().take(100).collect()],
            });
        }

        Ok(evidence)
    }

    async fn generate_alternative_views(
        &self,
        _query: &str,
        _response: &str,
        _sources: &[DataSource],
    ) -> Result<Vec<AlternativeView>> {
        Ok(vec![AlternativeView {
            viewpoint_id: "alt_1".to_string(),
            title: "Alternative interpretation".to_string(),
            description: "Different perspective on the same data".to_string(),
            supporting_evidence: vec!["Alternative source analysis".to_string()],
            likelihood: 0.3,
            implications: vec!["Would lead to different conclusion".to_string()],
            why_alternative: "Based on different weighting of evidence".to_string(),
        }])
    }

    fn classify_reasoning_type(&self, operation: &str) -> ReasoningType {
        match operation.to_lowercase().as_str() {
            op if op.contains("retrieve") => ReasoningType::Retrieval,
            op if op.contains("infer") => ReasoningType::Inference,
            op if op.contains("aggregate") => ReasoningType::Aggregation,
            op if op.contains("deduce") => ReasoningType::Deduction,
            _ => ReasoningType::Retrieval,
        }
    }

    fn extract_ambiguous_term(&self, context: &str) -> String {
        // Simple extraction - in practice would use NLP
        context
            .split_whitespace()
            .next()
            .unwrap_or("term")
            .to_string()
    }

    fn extract_reference(&self, _context: &str) -> String {
        // Simple extraction - in practice would use NLP
        "reference".to_string()
    }

    async fn check_assumption_validity(
        &self,
        _assumption: &str,
        _sources: &[DataSource],
    ) -> Result<ValidationStatus> {
        // Check if assumption is supported by sources
        Ok(ValidationStatus::Supported)
    }

    async fn find_supporting_evidence(
        &self,
        _assumption: &str,
        _sources: &[DataSource],
    ) -> Result<Vec<String>> {
        Ok(vec!["Supporting evidence from reliable sources".to_string()])
    }

    async fn find_contradicting_evidence(
        &self,
        _assumption: &str,
        _sources: &[DataSource],
    ) -> Result<Vec<String>> {
        Ok(vec![])
    }

    async fn calculate_assumption_confidence(
        &self,
        _assumption: &str,
        _sources: &[DataSource],
    ) -> Result<f32> {
        Ok(0.8)
    }
}

/// Assumption validation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AssumptionValidation {
    pub assumption: String,
    pub validation_status: ValidationStatus,
    pub supporting_evidence: Vec<String>,
    pub contradicting_evidence: Vec<String>,
    pub confidence: f32,
}

/// Validation status for assumptions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ValidationStatus {
    Supported,
    PartiallySupported,
    Unsupported,
    Contradicted,
    Inconclusive,
}

/// Citation generator for source attribution
pub struct CitationGenerator {}

impl CitationGenerator {
    fn new() -> Self {
        Self {}
    }

    async fn generate_attribution(
        &self,
        sources: &[DataSource],
        _explanation: &ResponseExplanation,
    ) -> Result<SourceAttribution> {
        let primary_sources: Vec<DataSource> = sources
            .iter()
            .filter(|s| s.relevance_score > 0.7)
            .cloned()
            .collect();

        let secondary_sources: Vec<DataSource> = sources
            .iter()
            .filter(|s| s.relevance_score <= 0.7)
            .cloned()
            .collect();

        let overall_quality =
            sources.iter().map(|s| s.reliability_score).sum::<f32>() / sources.len() as f32;

        Ok(SourceAttribution {
            primary_sources,
            secondary_sources,
            citation_style: CitationStyle::Inline,
            source_quality: SourceQuality {
                overall_score: overall_quality,
                completeness: 0.8,
                accuracy: 0.9,
                freshness: 0.7,
                authority: 0.8,
                consistency: 0.85,
            },
            traceability: vec![],
        })
    }
}

/// Confidence calculator
pub struct ConfidenceCalculator {}

impl ConfidenceCalculator {
    fn new() -> Self {
        Self {}
    }

    async fn calculate_confidence(
        &self,
        sources: &[DataSource],
        reasoning_trace: &[IntermediateStep],
        _context: &[Message],
    ) -> Result<ConfidenceIndicators> {
        let data_confidence =
            sources.iter().map(|s| s.reliability_score).sum::<f32>() / sources.len() as f32;

        let reasoning_confidence = reasoning_trace.iter().map(|s| s.confidence).sum::<f32>()
            / reasoning_trace.len() as f32;

        let overall_confidence = (data_confidence + reasoning_confidence) / 2.0;

        Ok(ConfidenceIndicators {
            overall_confidence,
            data_confidence,
            reasoning_confidence,
            source_confidence: data_confidence,
            completeness_confidence: 0.75,
            consistency_confidence: 0.8,
            factors: vec![
                ConfidenceFactor {
                    factor_name: "Data quality".to_string(),
                    impact: 0.3,
                    explanation: "High-quality sources used".to_string(),
                    factor_type: ConfidenceFactorType::DataQuality,
                },
                ConfidenceFactor {
                    factor_name: "Source reliability".to_string(),
                    impact: 0.25,
                    explanation: "Reliable sources with good track record".to_string(),
                    factor_type: ConfidenceFactorType::SourceReliability,
                },
            ],
        })
    }
}

/// Ambiguity detector
pub struct AmbiguityDetector {}

impl AmbiguityDetector {
    fn new() -> Self {
        Self {}
    }

    async fn detect_ambiguities(
        &self,
        query: &str,
        _context: &[Message],
    ) -> Result<Vec<ClarificationRequest>> {
        let mut requests = Vec::new();

        // Detect lexical ambiguity
        if self.has_ambiguous_terms(query) {
            requests.push(ClarificationRequest {
                request_id: uuid::Uuid::new_v4().to_string(),
                ambiguity_type: AmbiguityType::Lexical,
                clarification_question: "Which meaning of this term do you intend?".to_string(),
                options: self.generate_term_options(query),
                context: query.to_string(),
                urgency: ClarificationUrgency::Medium,
            });
        }

        // Detect referential ambiguity
        if self.has_unclear_references(query) {
            requests.push(ClarificationRequest {
                request_id: uuid::Uuid::new_v4().to_string(),
                ambiguity_type: AmbiguityType::Referential,
                clarification_question: "Which specific entity are you referring to?".to_string(),
                options: self.generate_reference_options(query),
                context: query.to_string(),
                urgency: ClarificationUrgency::High,
            });
        }

        Ok(requests)
    }

    fn has_ambiguous_terms(&self, query: &str) -> bool {
        // Simple check for common ambiguous words
        let ambiguous_words = ["bank", "right", "left", "table", "class"];
        ambiguous_words
            .iter()
            .any(|word| query.to_lowercase().contains(word))
    }

    fn has_unclear_references(&self, query: &str) -> bool {
        // Check for pronouns and demonstratives
        let reference_words = ["this", "that", "it", "they", "them"];
        reference_words
            .iter()
            .any(|word| query.to_lowercase().contains(word))
    }

    fn generate_term_options(&self, _query: &str) -> Vec<ClarificationOption> {
        vec![
            ClarificationOption {
                option_id: "opt_1".to_string(),
                description: "Financial institution".to_string(),
                interpretation: "Banking context".to_string(),
                likelihood: 0.6,
                example: Some("Commercial bank".to_string()),
            },
            ClarificationOption {
                option_id: "opt_2".to_string(),
                description: "River bank".to_string(),
                interpretation: "Geographic context".to_string(),
                likelihood: 0.4,
                example: Some("Riverbank".to_string()),
            },
        ]
    }

    fn generate_reference_options(&self, _query: &str) -> Vec<ClarificationOption> {
        vec![ClarificationOption {
            option_id: "ref_1".to_string(),
            description: "Previous entity mentioned".to_string(),
            interpretation: "Referring to earlier context".to_string(),
            likelihood: 0.7,
            example: None,
        }]
    }
}

/// Uncertainty analyzer
pub struct UncertaintyAnalyzer {}

impl UncertaintyAnalyzer {
    fn new() -> Self {
        Self {}
    }

    async fn quantify_uncertainty(
        &self,
        confidence: &ConfidenceIndicators,
        _sources: &[DataSource],
        _reasoning_trace: &[IntermediateStep],
    ) -> Result<UncertaintyQuantification> {
        let uncertainty_level = match confidence.overall_confidence {
            x if x > 0.8 => UncertaintyLevel::Low,
            x if x > 0.6 => UncertaintyLevel::Moderate,
            x if x > 0.4 => UncertaintyLevel::High,
            _ => UncertaintyLevel::VeryHigh,
        };

        Ok(UncertaintyQuantification {
            uncertainty_level,
            uncertainty_sources: vec![
                UncertaintySource {
                    source: "Data incompleteness".to_string(),
                    contribution: 0.3,
                    description: "Some relevant data may be missing".to_string(),
                    mitigation: Some("Seek additional sources".to_string()),
                },
                UncertaintySource {
                    source: "Model limitations".to_string(),
                    contribution: 0.2,
                    description: "Reasoning model has inherent limitations".to_string(),
                    mitigation: Some("Use ensemble methods".to_string()),
                },
            ],
            confidence_intervals: vec![ConfidenceInterval {
                parameter: "Response accuracy".to_string(),
                lower_bound: confidence.overall_confidence - 0.1,
                upper_bound: confidence.overall_confidence + 0.1,
                confidence_level: 0.95,
            }],
            sensitivity_analysis: SensitivityAnalysis {
                most_sensitive_factors: vec!["Source quality".to_string()],
                robustness_score: 0.75,
                critical_assumptions: vec!["Data accuracy assumption".to_string()],
            },
            error_bounds: ErrorBounds {
                estimated_error: 1.0 - confidence.overall_confidence,
                error_type: ErrorType::ModelError,
                error_sources: vec!["Incomplete knowledge".to_string()],
            },
        })
    }
}

impl Default for ExplanationEngine {
    fn default() -> Self {
        Self::new()
    }
}

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

    #[tokio::test]
    async fn test_explanation_engine_creation() {
        let _engine = ExplanationEngine::new();
        // Basic creation test - engine creation should not panic
    }

    #[tokio::test]
    async fn test_ambiguity_detection() {
        let engine = ExplanationEngine::new();
        let query = "Show me the bank data";
        let context = vec![];

        let ambiguities = engine
            .detect_ambiguities(query, &context)
            .await
            .expect("should succeed");
        assert!(!ambiguities.is_empty());
    }

    #[tokio::test]
    async fn test_confidence_calculation() {
        let calculator = ConfidenceCalculator::new();
        let sources = vec![DataSource {
            source_id: "test".to_string(),
            source_type: SourceType::KnowledgeGraph,
            uri: None,
            title: "Test Source".to_string(),
            description: "Test description".to_string(),
            reliability_score: 0.9,
            last_updated: chrono::Utc::now(),
            access_date: chrono::Utc::now(),
            relevance_score: 0.8,
        }];

        let reasoning_trace = vec![IntermediateStep {
            step_id: "step1".to_string(),
            operation: "retrieve".to_string(),
            input_data: "query".to_string(),
            output_data: "result".to_string(),
            processing_time_ms: 100,
            confidence: 0.9,
            explanation: "Retrieved relevant data".to_string(),
        }];

        let context = vec![];
        let confidence = calculator
            .calculate_confidence(&sources, &reasoning_trace, &context)
            .await
            .expect("should succeed");

        assert!(confidence.overall_confidence > 0.0);
        assert!(confidence.overall_confidence <= 1.0);
    }
}