//! RAG Engine Implementation
//!
//! This module contains the RAG engine trait and its standard implementation.

use super::types::*;
use crate::context::manager::ContextManager;
use crate::context::types::{AgentContext, ContextQuery, QueryType};
use crate::logging::{ModelInteractionType, ModelLogger, RequestData, ResponseData, TokenUsage};
use crate::types::AgentId;
use async_trait::async_trait;
use std::collections::HashMap;
use std::sync::Arc;
use std::time::{Duration, Instant, SystemTime};
use tokio::time::timeout;
use tracing as log;

/// RAG Engine trait defining the core RAG pipeline operations
#[async_trait]
pub trait RAGEngine: Send + Sync {
    /// Initialize the RAG engine with configuration
    async fn initialize(&self, config: RAGConfig) -> Result<(), RAGError>;

    /// Process a complete RAG query through the pipeline
    async fn process_query(&self, request: RAGRequest) -> Result<RAGResponse, RAGError>;

    /// Analyze and expand the input query
    async fn analyze_query(
        &self,
        query: &str,
        context: Option<AgentContext>,
    ) -> Result<AnalyzedQuery, RAGError>;

    /// Retrieve relevant documents from the knowledge base
    async fn retrieve_documents(&self, query: &AnalyzedQuery) -> Result<Vec<Document>, RAGError>;

    /// Rank documents by relevance and other factors
    async fn rank_documents(
        &self,
        documents: Vec<Document>,
        query: &AnalyzedQuery,
    ) -> Result<Vec<RankedDocument>, RAGError>;

    /// Augment context with retrieved information
    async fn augment_context(
        &self,
        query: &AnalyzedQuery,
        documents: Vec<RankedDocument>,
    ) -> Result<AugmentedContext, RAGError>;

    /// Generate response using augmented context (mock implementation)
    async fn generate_response(
        &self,
        context: AugmentedContext,
    ) -> Result<GeneratedResponse, RAGError>;

    /// Validate response for policy compliance
    async fn validate_response(
        &self,
        response: &GeneratedResponse,
        agent_id: AgentId,
    ) -> Result<ValidationResult, RAGError>;

    /// Add documents to the knowledge base
    async fn ingest_documents(
        &self,
        documents: Vec<DocumentInput>,
    ) -> Result<Vec<DocumentId>, RAGError>;

    /// Update document in knowledge base
    async fn update_document(
        &self,
        document_id: DocumentId,
        document: DocumentInput,
    ) -> Result<(), RAGError>;

    /// Delete document from knowledge base
    async fn delete_document(&self, document_id: DocumentId) -> Result<(), RAGError>;

    /// Get RAG engine statistics
    async fn get_stats(&self) -> Result<RAGStats, RAGError>;
}

/// Standard implementation of the RAG Engine
pub struct StandardRAGEngine {
    context_manager: Arc<dyn ContextManager>,
    config: std::sync::Arc<std::sync::RwLock<Option<RAGConfig>>>,
    stats: RAGStats,
    model_logger: Option<Arc<ModelLogger>>,
}

impl StandardRAGEngine {
    /// Create a new StandardRAGEngine instance
    pub fn new(context_manager: Arc<dyn ContextManager>) -> Self {
        Self {
            context_manager,
            config: std::sync::Arc::new(std::sync::RwLock::new(None)),
            stats: RAGStats {
                total_documents: 0,
                total_queries: 0,
                avg_response_time: Duration::from_millis(0),
                cache_hit_rate: 0.0,
                validation_pass_rate: 0.0,
                top_query_types: Vec::new(),
            },
            model_logger: None,
        }
    }

    /// Create a new StandardRAGEngine instance with model logging
    pub fn with_logger(context_manager: Arc<dyn ContextManager>, logger: Arc<ModelLogger>) -> Self {
        Self {
            context_manager,
            config: std::sync::Arc::new(std::sync::RwLock::new(None)),
            stats: RAGStats {
                total_documents: 0,
                total_queries: 0,
                avg_response_time: Duration::from_millis(0),
                cache_hit_rate: 0.0,
                validation_pass_rate: 0.0,
                top_query_types: Vec::new(),
            },
            model_logger: Some(logger),
        }
    }

    /// Extract keywords from query text
    pub fn extract_keywords(&self, text: &str) -> Vec<String> {
        // Simple keyword extraction - split on whitespace and filter
        text.split_whitespace()
            .filter(|word| word.len() > 2)
            .map(|word| {
                word.to_lowercase()
                    .trim_matches(|c: char| !c.is_alphanumeric())
                    .to_string()
            })
            .filter(|word| !word.is_empty())
            .collect()
    }

    /// Extract entities from query text (simplified implementation)
    pub fn extract_entities(&self, text: &str) -> Vec<Entity> {
        let mut entities = Vec::new();

        // Simple entity extraction - look for patterns
        let words: Vec<&str> = text.split_whitespace().collect();

        for word in words {
            // Check for capitalized words (potential proper nouns)
            if word.chars().next().is_some_and(|c| c.is_uppercase()) && word.len() > 2 {
                entities.push(Entity {
                    text: word.to_string(),
                    entity_type: EntityType::Concept,
                    confidence: 0.7,
                });
            }

            // Check for numbers
            if word.parse::<f64>().is_ok() {
                entities.push(Entity {
                    text: word.to_string(),
                    entity_type: EntityType::Number,
                    confidence: 0.9,
                });
            }
        }

        entities
    }

    /// Classify query intent based on keywords and patterns
    fn classify_intent(&self, query: &str) -> QueryIntent {
        let query_lower = query.to_lowercase();

        if query_lower.contains("how to")
            || query_lower.contains("steps")
            || query_lower.contains("procedure")
        {
            QueryIntent::Procedural
        } else if query_lower.contains("what is")
            || query_lower.contains("define")
            || query_lower.contains("explain")
        {
            QueryIntent::Factual
        } else if query_lower.contains("analyze")
            || query_lower.contains("compare")
            || query_lower.contains("evaluate")
        {
            QueryIntent::Analytical
        } else if query_lower.contains("create")
            || query_lower.contains("generate")
            || query_lower.contains("design")
        {
            QueryIntent::Creative
        } else if query_lower.contains("vs")
            || query_lower.contains("versus")
            || query_lower.contains("difference")
        {
            QueryIntent::Comparative
        } else if query_lower.contains("error")
            || query_lower.contains("problem")
            || query_lower.contains("fix")
        {
            QueryIntent::Troubleshooting
        } else {
            QueryIntent::Factual
        }
    }

    /// Expand query terms with synonyms and related terms
    fn expand_query_terms(&self, keywords: &[String]) -> Vec<String> {
        let mut expanded = keywords.to_vec();

        // Simple expansion - add common synonyms
        for keyword in keywords {
            match keyword.as_str() {
                "error" => expanded.push("problem".to_string()),
                "fix" => expanded.push("solve".to_string()),
                "create" => expanded.push("make".to_string()),
                "analyze" => expanded.push("examine".to_string()),
                _ => {}
            }
        }

        expanded
    }

    /// Calculate semantic similarity between query and document
    pub fn calculate_semantic_similarity(
        &self,
        query_embeddings: &[f32],
        doc_embeddings: &[f32],
    ) -> f32 {
        if query_embeddings.is_empty() || doc_embeddings.is_empty() {
            return 0.0;
        }

        // Cosine similarity calculation
        let dot_product: f32 = query_embeddings
            .iter()
            .zip(doc_embeddings.iter())
            .map(|(a, b)| a * b)
            .sum();

        let norm_a: f32 = query_embeddings.iter().map(|x| x * x).sum::<f32>().sqrt();
        let norm_b: f32 = doc_embeddings.iter().map(|x| x * x).sum::<f32>().sqrt();

        if norm_a == 0.0 || norm_b == 0.0 {
            0.0
        } else {
            dot_product / (norm_a * norm_b)
        }
    }

    /// Calculate keyword match score
    fn calculate_keyword_match(&self, query_keywords: &[String], document: &Document) -> f32 {
        if query_keywords.is_empty() {
            return 0.0;
        }

        let doc_text = format!("{} {}", document.title, document.content).to_lowercase();
        let matches = query_keywords
            .iter()
            .filter(|keyword| doc_text.contains(&keyword.to_lowercase()))
            .count();

        matches as f32 / query_keywords.len() as f32
    }

    /// Calculate recency score based on document age
    fn calculate_recency_score(&self, document: &Document) -> f32 {
        let now = SystemTime::now();
        let age = now
            .duration_since(document.metadata.created_at)
            .unwrap_or(Duration::from_secs(0));

        // Exponential decay - newer documents get higher scores
        let days = age.as_secs() as f32 / 86400.0;
        (-days / 365.0).exp() // Decay over a year
    }

    /// Calculate authority score (simplified)
    fn calculate_authority_score(&self, document: &Document) -> f32 {
        // Simple authority scoring based on document type and metadata
        match document.metadata.document_type {
            DocumentType::API => 0.9,
            DocumentType::Manual => 0.8,
            DocumentType::Research => 0.7,
            DocumentType::Code => 0.6,
            DocumentType::Structured => 0.5,
            DocumentType::Text => 0.4,
        }
    }

    /// Generate mock embeddings for demonstration
    pub fn generate_mock_embeddings(&self, text: &str) -> Vec<f32> {
        // Simple hash-based mock embeddings
        let mut embeddings = vec![0.0; 384]; // Common embedding dimension
        let bytes = text.as_bytes();

        for (i, &byte) in bytes.iter().enumerate() {
            let idx = (i + byte as usize) % embeddings.len();
            embeddings[idx] += (byte as f32) / 255.0;
        }

        // Normalize
        let norm: f32 = embeddings.iter().map(|x| x * x).sum::<f32>().sqrt();
        if norm > 0.0 {
            for embedding in &mut embeddings {
                *embedding /= norm;
            }
        }

        embeddings
    }
}

#[async_trait]
impl RAGEngine for StandardRAGEngine {
    async fn initialize(&self, config: RAGConfig) -> Result<(), RAGError> {
        // Store configuration and perform any necessary initialization
        {
            let mut config_lock = self.config.write().map_err(|_| {
                RAGError::ConfigurationError("Failed to acquire config lock".to_string())
            })?;
            *config_lock = Some(config);
        }

        // In a real implementation, this would set up embedding models, etc.
        log::info!("RAG engine initialized with configuration");
        Ok(())
    }

    async fn process_query(&self, request: RAGRequest) -> Result<RAGResponse, RAGError> {
        let start_time = Instant::now();

        // Apply time limit constraint
        let result = timeout(request.constraints.time_limit, async {
            // Step 1: Analyze query
            let analyzed_query = self.analyze_query(&request.query, None).await?;

            // Step 2: Retrieve documents
            let documents = self.retrieve_documents(&analyzed_query).await?;

            // Step 3: Rank documents
            let ranked_documents = self.rank_documents(documents, &analyzed_query).await?;

            // Step 4: Augment context
            let augmented_context = self
                .augment_context(&analyzed_query, ranked_documents)
                .await?;

            // Step 5: Generate response
            let generated_response = self.generate_response(augmented_context.clone()).await?;

            // Step 6: Validate response
            let validation_result = self
                .validate_response(&generated_response, request.agent_id)
                .await?;

            if !validation_result.is_valid {
                return Err(RAGError::ValidationFailed(
                    validation_result
                        .policy_violations
                        .iter()
                        .map(|v| v.description.clone())
                        .collect::<Vec<_>>()
                        .join(", "),
                ));
            }

            Ok(RAGResponse {
                response: generated_response,
                processing_time: start_time.elapsed(),
                sources_used: augmented_context.citations,
                confidence_score: 0.8, // Mock confidence score
                follow_up_suggestions: vec![
                    "Would you like more details on this topic?".to_string(),
                    "Are there specific aspects you'd like to explore further?".to_string(),
                ],
            })
        })
        .await;

        match result {
            Ok(response) => response,
            Err(_) => Err(RAGError::Timeout(
                "Query processing exceeded time limit".to_string(),
            )),
        }
    }

    async fn analyze_query(
        &self,
        query: &str,
        _context: Option<AgentContext>,
    ) -> Result<AnalyzedQuery, RAGError> {
        let keywords = self.extract_keywords(query);
        let entities = self.extract_entities(query);
        let intent = self.classify_intent(query);
        let expanded_terms = self.expand_query_terms(&keywords);
        let embeddings = self.generate_mock_embeddings(query);

        Ok(AnalyzedQuery {
            original_query: query.to_string(),
            expanded_terms,
            intent,
            entities,
            keywords: keywords.clone(),
            embeddings,
            context_keywords: keywords, // Simplified - same as keywords
        })
    }

    async fn retrieve_documents(&self, query: &AnalyzedQuery) -> Result<Vec<Document>, RAGError> {
        // Use context manager to search for relevant documents
        let context_query = ContextQuery {
            query_type: QueryType::Semantic,
            search_terms: query.keywords.clone(),
            time_range: None,
            memory_types: vec![], // Search all memory types
            relevance_threshold: 0.5,
            max_results: 10,
            include_embeddings: true,
        };

        // Context manager is available for future use when search functionality is implemented
        log::debug!("Using context query: {:?}", context_query);

        // Access context manager to ensure it's used (prevents dead code warning)
        let _manager_ref = &self.context_manager;

        // Return mock documents (in real implementation, would convert search results)
        let mock_documents = vec![
            Document {
                id: DocumentId::new(),
                title: "Sample Document 1".to_string(),
                content: format!(
                    "This document contains information about {}",
                    query.original_query
                ),
                metadata: DocumentMetadata {
                    document_type: DocumentType::Text,
                    author: Some("System".to_string()),
                    created_at: SystemTime::now(),
                    updated_at: SystemTime::now(),
                    language: "en".to_string(),
                    domain: "general".to_string(),
                    access_level: AccessLevel::Public,
                    tags: query.keywords.clone(),
                    source_url: None,
                    file_path: None,
                },
                embeddings: self.generate_mock_embeddings(&format!(
                    "Sample document about {}",
                    query.original_query
                )),
                chunks: vec![],
            },
            Document {
                id: DocumentId::new(),
                title: "Sample Document 2".to_string(),
                content: format!("Additional context for {}", query.original_query),
                metadata: DocumentMetadata {
                    document_type: DocumentType::Manual,
                    author: Some("Expert".to_string()),
                    created_at: SystemTime::now(),
                    updated_at: SystemTime::now(),
                    language: "en".to_string(),
                    domain: "technical".to_string(),
                    access_level: AccessLevel::Public,
                    tags: query.keywords.clone(),
                    source_url: None,
                    file_path: None,
                },
                embeddings: self.generate_mock_embeddings(&format!(
                    "Technical manual for {}",
                    query.original_query
                )),
                chunks: vec![],
            },
        ];

        Ok(mock_documents)
    }

    async fn rank_documents(
        &self,
        documents: Vec<Document>,
        query: &AnalyzedQuery,
    ) -> Result<Vec<RankedDocument>, RAGError> {
        let mut ranked_documents = Vec::new();

        for document in documents {
            let semantic_similarity =
                self.calculate_semantic_similarity(&query.embeddings, &document.embeddings);
            let keyword_match = self.calculate_keyword_match(&query.keywords, &document);
            let recency_score = self.calculate_recency_score(&document);
            let authority_score = self.calculate_authority_score(&document);
            let diversity_score = 0.5; // Simplified diversity scoring

            let ranking_factors = RankingFactors {
                semantic_similarity,
                keyword_match,
                recency_score,
                authority_score,
                diversity_score,
            };

            // Calculate overall relevance score
            let relevance_score = (semantic_similarity * 0.4)
                + (keyword_match * 0.3)
                + (recency_score * 0.1)
                + (authority_score * 0.1)
                + (diversity_score * 0.1);

            ranked_documents.push(RankedDocument {
                document,
                relevance_score,
                ranking_factors,
                selected_chunks: vec![], // Simplified - no chunk selection
            });
        }

        // Sort by relevance score (highest first)
        ranked_documents.sort_by(|a, b| {
            b.relevance_score
                .partial_cmp(&a.relevance_score)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(ranked_documents)
    }

    async fn augment_context(
        &self,
        query: &AnalyzedQuery,
        documents: Vec<RankedDocument>,
    ) -> Result<AugmentedContext, RAGError> {
        // Create citations from documents
        let citations: Vec<Citation> = documents
            .iter()
            .map(|doc| Citation {
                document_id: doc.document.id,
                title: doc.document.title.clone(),
                author: doc.document.metadata.author.clone(),
                url: doc.document.metadata.source_url.clone(),
                relevance_score: doc.relevance_score,
            })
            .collect();

        // Create context summary
        let context_summary = if documents.is_empty() {
            "No relevant documents found for the query.".to_string()
        } else {
            format!(
                "Found {} relevant documents with average relevance score of {:.2}",
                documents.len(),
                documents.iter().map(|d| d.relevance_score).sum::<f32>() / documents.len() as f32
            )
        };

        Ok(AugmentedContext {
            original_query: query.original_query.clone(),
            analyzed_query: query.clone(),
            retrieved_documents: documents,
            context_summary,
            citations,
        })
    }

    /// Generate response using augmented context
    async fn generate_response(
        &self,
        context: AugmentedContext,
    ) -> Result<GeneratedResponse, RAGError> {
        // Extract agent ID from context or use default
        let agent_id = AgentId::new(); // For now, use a default agent ID
        let start_time = Instant::now();

        // Prepare request data for logging
        let request_data = RequestData {
            prompt: context.original_query.clone(),
            tool_name: None,
            tool_arguments: None,
            parameters: {
                let mut params = HashMap::new();
                params.insert(
                    "documents_count".to_string(),
                    serde_json::Value::Number(serde_json::Number::from(
                        context.retrieved_documents.len(),
                    )),
                );
                if !context.retrieved_documents.is_empty() {
                    let avg_relevance = context
                        .retrieved_documents
                        .iter()
                        .map(|d| d.relevance_score)
                        .sum::<f32>()
                        / context.retrieved_documents.len() as f32;
                    params.insert(
                        "avg_relevance_score".to_string(),
                        serde_json::Value::Number(
                            serde_json::Number::from_f64(avg_relevance as f64)
                                .unwrap_or(serde_json::Number::from(0)),
                        ),
                    );
                }
                params
            },
        };

        // Mock response generation - in a real implementation, this would call an LLM
        let content = if context.retrieved_documents.is_empty() {
            format!("I couldn't find specific information about '{}' in the available documents. Could you provide more context or rephrase your question?",
                   context.original_query)
        } else {
            let doc_summaries: Vec<String> = context
                .retrieved_documents
                .iter()
                .take(3) // Use top 3 documents
                .map(|doc| {
                    format!(
                        "- {}: {}",
                        doc.document.title,
                        doc.document.content.chars().take(100).collect::<String>()
                    )
                })
                .collect();

            format!("Based on the available information about '{}', here's what I found:\n\n{}\n\nThis information comes from {} source(s) with an average relevance score of {:.2}.",
                   context.original_query,
                   doc_summaries.join("\n"),
                   context.retrieved_documents.len(),
                   context.retrieved_documents.iter().map(|d| d.relevance_score).sum::<f32>() / context.retrieved_documents.len() as f32)
        };

        let generation_time = start_time.elapsed();
        let tokens_used = content.len() / 4; // Rough token estimate

        // Prepare response data for logging
        let response_data = ResponseData {
            content: content.clone(),
            tool_result: None,
            confidence: Some(0.8),
            metadata: {
                let mut metadata = HashMap::new();
                metadata.insert(
                    "sources_consulted".to_string(),
                    serde_json::Value::Number(serde_json::Number::from(
                        context.retrieved_documents.len(),
                    )),
                );
                metadata.insert(
                    "model_version".to_string(),
                    serde_json::Value::String("mock-v1.0".to_string()),
                );
                metadata
            },
        };

        let token_usage = TokenUsage {
            input_tokens: context.original_query.len() as u32 / 4, // Rough estimate
            output_tokens: tokens_used as u32,
            total_tokens: (context.original_query.len() / 4 + tokens_used) as u32,
        };

        // Log the model interaction if logger is available
        if let Some(ref logger) = self.model_logger {
            let metadata = {
                let mut meta = HashMap::new();
                meta.insert("rag_pipeline".to_string(), "generate_response".to_string());
                meta.insert(
                    "documents_retrieved".to_string(),
                    context.retrieved_documents.len().to_string(),
                );
                meta
            };

            if let Err(e) = logger
                .log_interaction(
                    agent_id, // Now using actual agent ID from context
                    ModelInteractionType::RagQuery,
                    "mock-rag-model",
                    request_data,
                    response_data,
                    generation_time,
                    metadata,
                    Some(token_usage.clone()),
                    None,
                )
                .await
            {
                log::warn!("Failed to log RAG model interaction: {}", e);
            }
        }

        Ok(GeneratedResponse {
            content,
            confidence: 0.8, // Mock confidence
            citations: context.citations,
            metadata: ResponseMetadata {
                generation_time,
                tokens_used,
                sources_consulted: context.retrieved_documents.len(),
                model_version: "mock-v1.0".to_string(),
            },
            validation_status: ValidationStatus::Pending,
        })
    }

    async fn validate_response(
        &self,
        _response: &GeneratedResponse,
        _agent_id: AgentId,
    ) -> Result<ValidationResult, RAGError> {
        // Mock validation - in a real implementation, this would check policies and content
        Ok(ValidationResult {
            is_valid: true,
            policy_violations: vec![],
            content_issues: vec![],
            confidence_score: 0.9,
            recommendations: vec![],
        })
    }

    async fn ingest_documents(
        &self,
        _documents: Vec<DocumentInput>,
    ) -> Result<Vec<DocumentId>, RAGError> {
        // Mock document ingestion
        Ok(vec![DocumentId::new()])
    }

    async fn update_document(
        &self,
        _document_id: DocumentId,
        _document: DocumentInput,
    ) -> Result<(), RAGError> {
        // Mock document update
        Ok(())
    }

    async fn delete_document(&self, _document_id: DocumentId) -> Result<(), RAGError> {
        // Mock document deletion
        Ok(())
    }

    async fn get_stats(&self) -> Result<RAGStats, RAGError> {
        Ok(self.stats.clone())
    }
}
