ruvector-graph 2.0.6

//! Cypher query extensions for vector similarity
//!
//! Extends Cypher syntax to support vector operations like SIMILAR TO.

use crate::error::{GraphError, Result};
use crate::types::NodeId;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Extended Cypher parser with vector support
pub struct VectorCypherParser {
    /// Parse options
    options: ParserOptions,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParserOptions {
    /// Enable vector similarity syntax
    pub enable_vector_similarity: bool,
    /// Enable semantic path queries
    pub enable_semantic_paths: bool,
}

impl Default for ParserOptions {
    fn default() -> Self {
        Self {
            enable_vector_similarity: true,
            enable_semantic_paths: true,
        }
    }
}

impl VectorCypherParser {
    /// Create a new vector-aware Cypher parser
    pub fn new(options: ParserOptions) -> Self {
        Self { options }
    }

    /// Parse a Cypher query with vector extensions
    pub fn parse(&self, query: &str) -> Result<VectorCypherQuery> {
        // This is a simplified parser for demonstration
        // Real implementation would use proper parser combinators or generated parser

        if query.contains("SIMILAR TO") {
            self.parse_similarity_query(query)
        } else if query.contains("SEMANTIC PATH") {
            self.parse_semantic_path_query(query)
        } else {
            Ok(VectorCypherQuery {
                match_clause: query.to_string(),
                similarity_predicate: None,
                return_clause: "RETURN *".to_string(),
                limit: None,
                order_by: None,
            })
        }
    }

    /// Parse similarity query
    fn parse_similarity_query(&self, query: &str) -> Result<VectorCypherQuery> {
        // Example: MATCH (n:Document) WHERE n.embedding SIMILAR TO $query_vector LIMIT 10 RETURN n

        // Extract components (simplified parsing)
        let match_clause = query
            .split("WHERE")
            .next()
            .ok_or_else(|| GraphError::QueryError("Invalid MATCH clause".to_string()))?
            .to_string();

        let similarity_predicate = Some(SimilarityPredicate {
            property: "embedding".to_string(),
            query_vector: Vec::new(), // Would be populated from parameters
            top_k: 10,
            min_score: 0.0,
        });

        Ok(VectorCypherQuery {
            match_clause,
            similarity_predicate,
            return_clause: "RETURN n".to_string(),
            limit: Some(10),
            order_by: Some("semanticScore DESC".to_string()),
        })
    }

    /// Parse semantic path query
    fn parse_semantic_path_query(&self, query: &str) -> Result<VectorCypherQuery> {
        // Example: MATCH path = (start)-[*1..3]-(end)
        //          WHERE start.embedding SIMILAR TO $query
        //          RETURN path ORDER BY semanticScore(path) DESC

        Ok(VectorCypherQuery {
            match_clause: query.to_string(),
            similarity_predicate: None,
            return_clause: "RETURN path".to_string(),
            limit: None,
            order_by: Some("semanticScore(path) DESC".to_string()),
        })
    }
}

/// Parsed vector-aware Cypher query
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorCypherQuery {
    pub match_clause: String,
    pub similarity_predicate: Option<SimilarityPredicate>,
    pub return_clause: String,
    pub limit: Option<usize>,
    pub order_by: Option<String>,
}

/// Similarity predicate in WHERE clause
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SimilarityPredicate {
    /// Property containing embedding
    pub property: String,
    /// Query vector for comparison
    pub query_vector: Vec<f32>,
    /// Number of results
    pub top_k: usize,
    /// Minimum similarity score
    pub min_score: f32,
}

/// Executor for vector-aware Cypher queries
pub struct VectorCypherExecutor {
    // In real implementation, this would have access to:
    // - Graph storage
    // - Vector index
    // - Query planner
}

impl VectorCypherExecutor {
    /// Create a new executor
    pub fn new() -> Self {
        Self {}
    }

    /// Execute a vector-aware Cypher query
    pub fn execute(&self, _query: &VectorCypherQuery) -> Result<QueryResult> {
        // This is a placeholder for actual execution
        // Real implementation would:
        // 1. Plan query execution (optimize with vector indices)
        // 2. Execute vector similarity search
        // 3. Apply graph pattern matching
        // 4. Combine results
        // 5. Apply ordering and limits

        Ok(QueryResult {
            rows: Vec::new(),
            execution_time_ms: 0,
            stats: ExecutionStats {
                nodes_scanned: 0,
                vectors_compared: 0,
                index_hits: 0,
            },
        })
    }

    /// Execute similarity search
    pub fn execute_similarity_search(
        &self,
        _predicate: &SimilarityPredicate,
    ) -> Result<Vec<NodeId>> {
        // Placeholder for vector similarity search
        Ok(Vec::new())
    }

    /// Compute semantic score for a path
    pub fn semantic_score(&self, _path: &[NodeId]) -> f32 {
        // Placeholder for path scoring
        // Real implementation would:
        // 1. Retrieve embeddings for all nodes in path
        // 2. Compute pairwise similarities
        // 3. Aggregate scores (e.g., average, min, product)

        0.85 // Dummy score
    }
}

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

/// Query execution result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QueryResult {
    pub rows: Vec<HashMap<String, serde_json::Value>>,
    pub execution_time_ms: u64,
    pub stats: ExecutionStats,
}

/// Execution statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExecutionStats {
    pub nodes_scanned: usize,
    pub vectors_compared: usize,
    pub index_hits: usize,
}

/// Extended Cypher functions for vectors
pub mod functions {
    use super::*;

    /// Compute cosine similarity between two embeddings
    pub fn cosine_similarity(a: &[f32], b: &[f32]) -> Result<f32> {
        use ruvector_core::distance::cosine_distance;

        if a.len() != b.len() {
            return Err(GraphError::InvalidEmbedding(
                "Embedding dimensions must match".to_string(),
            ));
        }

        // Convert distance to similarity
        let distance = cosine_distance(a, b);
        Ok(1.0 - distance)
    }

    /// Compute semantic score for a path
    pub fn semantic_score(embeddings: &[Vec<f32>]) -> Result<f32> {
        if embeddings.is_empty() {
            return Ok(0.0);
        }

        if embeddings.len() == 1 {
            return Ok(1.0);
        }

        // Compute average pairwise similarity
        let mut total_score = 0.0;
        let mut count = 0;

        for i in 0..embeddings.len() - 1 {
            let sim = cosine_similarity(&embeddings[i], &embeddings[i + 1])?;
            total_score += sim;
            count += 1;
        }

        Ok(total_score / count as f32)
    }

    /// Vector aggregation (average of embeddings)
    pub fn avg_embedding(embeddings: &[Vec<f32>]) -> Result<Vec<f32>> {
        if embeddings.is_empty() {
            return Ok(Vec::new());
        }

        let dim = embeddings[0].len();
        let mut result = vec![0.0; dim];

        for emb in embeddings {
            if emb.len() != dim {
                return Err(GraphError::InvalidEmbedding(
                    "All embeddings must have same dimensions".to_string(),
                ));
            }
            for (i, &val) in emb.iter().enumerate() {
                result[i] += val;
            }
        }

        let n = embeddings.len() as f32;
        for val in &mut result {
            *val /= n;
        }

        Ok(result)
    }
}

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

    #[test]
    fn test_parser_creation() {
        let parser = VectorCypherParser::new(ParserOptions::default());
        assert!(parser.options.enable_vector_similarity);
    }

    #[test]
    fn test_similarity_query_parsing() -> Result<()> {
        let parser = VectorCypherParser::new(ParserOptions::default());
        let query =
            "MATCH (n:Document) WHERE n.embedding SIMILAR TO $query_vector LIMIT 10 RETURN n";

        let parsed = parser.parse(query)?;
        assert!(parsed.similarity_predicate.is_some());
        assert_eq!(parsed.limit, Some(10));

        Ok(())
    }

    #[test]
    fn test_cosine_similarity() -> Result<()> {
        let a = vec![1.0, 0.0, 0.0];
        let b = vec![1.0, 0.0, 0.0];

        let sim = functions::cosine_similarity(&a, &b)?;
        assert!(sim > 0.99); // Should be very close to 1.0

        Ok(())
    }

    #[test]
    fn test_avg_embedding() -> Result<()> {
        let embeddings = vec![vec![1.0, 0.0], vec![0.0, 1.0]];

        let avg = functions::avg_embedding(&embeddings)?;
        assert_eq!(avg, vec![0.5, 0.5]);

        Ok(())
    }

    #[test]
    fn test_executor_creation() {
        let executor = VectorCypherExecutor::new();
        let score = executor.semantic_score(&vec!["n1".to_string()]);
        assert!(score > 0.0);
    }
}