//! Vector store trait and common implementations

use async_trait::async_trait;
use anyhow::Result;
use std::collections::HashMap;
use crate::types::{Document, SearchOptions, SearchResult, SearchFilter};

/// Main vector store trait that all implementations must follow
#[async_trait]
pub trait VectorStore: Send + Sync {
    /// Downcast to concrete type for specific functionality
    fn as_any(&self) -> &dyn std::any::Any;
    /// Initialize the vector store
    async fn initialize(&self) -> Result<()>;
    
    /// Check if the vector store is initialized
    async fn is_initialized(&self) -> bool;
    
    /// Set vector dimensions
    async fn set_dimensions(&self, dimensions: usize) -> Result<()>;
    
    /// Add a single document to a collection
    async fn add_document(&self, collection_name: &str, document: Document) -> Result<String>;
    
    /// Add multiple documents to a collection
    async fn add_documents(&self, collection_name: &str, documents: Vec<Document>) -> Result<Vec<String>>;
    
    /// Search for documents using vector similarity
    async fn search(
        &self,
        collection_name: &str,
        query_vector: Vec<f32>,
        options: SearchOptions,
    ) -> Result<Vec<SearchResult>>;
    
    /// Get a document by ID
    async fn get_document(&self, collection_name: &str, id: &str) -> Result<Option<Document>>;
    
    /// Update a document
    async fn update_document(&self, collection_name: &str, id: &str, document: Document) -> Result<()>;
    
    /// Delete a document
    async fn delete_document(&self, collection_name: &str, id: &str) -> Result<bool>;
    
    /// List documents with optional filtering
    async fn list_documents(
        &self,
        collection_name: &str,
        limit: Option<usize>,
        filter: Option<SearchFilter>,
    ) -> Result<Vec<Document>>;
    
    /// Create a new collection
    async fn create_collection(&self, name: &str, vector_size: usize) -> Result<()>;
    
    /// Delete a collection
    async fn delete_collection(&self, name: &str) -> Result<bool>;
    
    /// List all collections
    async fn list_collections(&self) -> Result<Vec<String>>;
    
    /// Get collection info
    async fn get_collection_info(&self, name: &str) -> Result<Option<CollectionInfo>>;
    
    /// Scroll through a collection (for chat history with 1D vectors)
    async fn scroll_collection(
        &self,
        collection_name: &str,
        filter: Option<SearchFilter>,
        limit: Option<usize>,
    ) -> Result<Vec<SearchResult>>;
    
    /// Get collection health information
    async fn get_collections_health(&self) -> Result<HashMap<String, CollectionHealth>>;
    
    /// Shutdown the vector store
    async fn shutdown(&self) -> Result<()>;
}

/// Collection information structure
#[derive(Debug, Clone)]
pub struct CollectionInfo {
    pub name: String,
    pub vector_size: usize,
    pub distance: String,
    pub points_count: usize,
    pub segments_count: Option<usize>,
    pub disk_data_size: Option<u64>,
    pub ram_data_size: Option<u64>,
}

/// Collection health information
#[derive(Debug, Clone)]
pub struct CollectionHealth {
    pub name: String,
    pub status: String,
    pub points_count: usize,
    pub segments_count: usize,
    pub disk_size: u64,
    pub ram_size: u64,
    pub last_updated: chrono::DateTime<chrono::Utc>,
}

/// Common utility functions for vector operations
pub mod utils {
    use anyhow::{Result, anyhow};
    
    /// Calculate cosine similarity between two vectors
    pub fn cosine_similarity(vec_a: &[f32], vec_b: &[f32]) -> Result<f32> {
        if vec_a.len() != vec_b.len() {
            return Err(anyhow!("Vectors must have the same dimensions"));
        }
        
        let dot_product: f32 = vec_a.iter().zip(vec_b.iter()).map(|(a, b)| a * b).sum();
        let norm_a: f32 = vec_a.iter().map(|a| a * a).sum::<f32>().sqrt();
        let norm_b: f32 = vec_b.iter().map(|b| b * b).sum::<f32>().sqrt();
        
        if norm_a == 0.0 || norm_b == 0.0 {
            return Ok(0.0);
        }
        
        Ok(dot_product / (norm_a * norm_b))
    }
    
    /// Calculate Euclidean distance between two vectors
    pub fn euclidean_distance(vec_a: &[f32], vec_b: &[f32]) -> Result<f32> {
        if vec_a.len() != vec_b.len() {
            return Err(anyhow!("Vectors must have the same dimensions"));
        }
        
        let sum_squared_diff: f32 = vec_a
            .iter()
            .zip(vec_b.iter())
            .map(|(a, b)| (a - b).powi(2))
            .sum();
        
        Ok(sum_squared_diff.sqrt())
    }
    
    /// Normalize a vector to unit length
    pub fn normalize_vector(vector: &mut [f32]) -> Result<()> {
        let norm: f32 = vector.iter().map(|x| x * x).sum::<f32>().sqrt();
        
        if norm == 0.0 {
            return Err(anyhow!("Cannot normalize zero vector"));
        }
        
        for x in vector.iter_mut() {
            *x /= norm;
        }
        
        Ok(())
    }
    
    /// Generate a dummy vector for chat history collection (1D)
    pub fn generate_dummy_vector() -> Vec<f32> {
        vec![1.0]
    }
}

#[cfg(test)]
mod tests {
    use super::utils::*;
    
    #[test]
    fn test_cosine_similarity() {
        let vec_a = vec![1.0, 2.0, 3.0];
        let vec_b = vec![4.0, 5.0, 6.0];
        
        let similarity = cosine_similarity(&vec_a, &vec_b).unwrap();
        assert!(similarity > 0.0 && similarity <= 1.0);
    }
    
    #[test]
    fn test_euclidean_distance() {
        let vec_a = vec![1.0, 2.0, 3.0];
        let vec_b = vec![4.0, 5.0, 6.0];
        
        let distance = euclidean_distance(&vec_a, &vec_b).unwrap();
        assert!(distance > 0.0);
    }
    
    #[test]
    fn test_normalize_vector() {
        let mut vector = vec![3.0, 4.0];
        normalize_vector(&mut vector).unwrap();
        
        // Check if normalized (magnitude should be 1.0)
        let magnitude: f32 = vector.iter().map(|x| x * x).sum::<f32>().sqrt();
        assert!((magnitude - 1.0).abs() < 1e-6);
    }
}