velesdb-core 1.6.0

//! Graph API methods for Collection (EPIC-015 US-001).
//!
//! Exposes Knowledge Graph operations on Collection for use by
//! Tauri plugin, REST API, and other consumers.

use std::collections::HashSet;

use crate::collection::graph::{
    EdgeStore, GraphEdge, GraphSchema, TraversalConfig, TraversalResult,
};
use crate::collection::types::Collection;
use crate::error::{Error, Result};
use crate::index::VectorIndex;
use crate::point::{Point, SearchResult};
use crate::storage::{PayloadStorage, VectorStorage};

/// Returns `true` if the edge's label is accepted by the relationship filter.
///
/// An empty `rel_types` slice means "accept all".
#[inline]
fn edge_passes_rel_filter(edge: &GraphEdge, rel_types: &[&str]) -> bool {
    rel_types.is_empty() || rel_types.contains(&edge.label())
}

/// Collects unvisited, rel-type-filtered neighbor expansions for a node.
///
/// Each returned tuple is `(target_id, next_depth, path_to_target)`.
/// Visited targets are inserted into `visited` before returning, so
/// duplicate expansion is impossible even when the caller enqueues lazily.
#[inline]
fn collect_neighbor_expansions<'a>(
    edges: impl Iterator<Item = &'a GraphEdge>,
    depth: u32,
    path: &[u64],
    rel_types: &[&str],
    visited: &mut HashSet<u64>,
) -> Vec<(u64, u32, Vec<u64>)> {
    edges
        .filter(|e| edge_passes_rel_filter(e, rel_types))
        .filter(|e| visited.insert(e.target()))
        .map(|e| {
            let mut new_path = path.to_vec();
            new_path.push(e.id());
            (e.target(), depth + 1, new_path)
        })
        .collect()
}

/// Pushes unvisited, rel-type-filtered neighbors onto the DFS stack.
///
/// Iterates outgoing edges in reverse so that the first outgoing edge
/// is processed first after `stack.pop()` (LIFO order preservation).
#[inline]
fn expand_dfs_neighbors(
    store: &EdgeStore,
    node_id: u64,
    depth: u32,
    path: &[u64],
    rel_filter: &HashSet<&str>,
    visited: &HashSet<u64>,
    stack: &mut Vec<(u64, u32, Vec<u64>)>,
) {
    for edge in store.get_outgoing(node_id).into_iter().rev() {
        if !rel_filter.is_empty() && !rel_filter.contains(edge.label()) {
            continue;
        }
        if visited.contains(&edge.target()) {
            continue;
        }
        let mut new_path = path.to_vec();
        // Use edge IDs in path, consistent with bfs_traverse/bfs_stream.
        new_path.push(edge.id());
        stack.push((edge.target(), depth + 1, new_path));
    }
}

impl Collection {
    /// Adds an edge to the collection's knowledge graph.
    ///
    /// # Arguments
    ///
    /// * `edge` - The edge to add (id, source, target, label, properties)
    ///
    /// # Errors
    ///
    /// Returns `Error::EdgeExists` if an edge with the same ID already exists.
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// use velesdb_core::collection::graph::GraphEdge;
    ///
    /// let edge = GraphEdge::new(1, 100, 200, "KNOWS")?;
    /// collection.add_edge(edge)?;
    /// ```
    pub fn add_edge(&self, edge: GraphEdge) -> Result<()> {
        self.edge_store.write().add_edge(edge)?;
        // Bump write generation so any cached plan for this collection is
        // invalidated on the next query (CACHE-01).
        self.write_generation
            .fetch_add(1, std::sync::atomic::Ordering::Relaxed);
        Ok(())
    }

    /// Gets all edges from the collection's knowledge graph.
    ///
    /// Note: This iterates through all stored edges. For large graphs,
    /// consider using `get_edges_by_label` or `get_outgoing_edges` for
    /// more targeted queries.
    ///
    /// # Returns
    ///
    /// Vector of all edges in the graph (cloned).
    #[must_use]
    pub fn get_all_edges(&self) -> Vec<GraphEdge> {
        let store = self.edge_store.read();
        store.all_edges().into_iter().cloned().collect()
    }

    /// Gets edges filtered by label.
    ///
    /// # Arguments
    ///
    /// * `label` - The edge label (relationship type) to filter by
    ///
    /// # Returns
    ///
    /// Vector of edges with the specified label (cloned).
    #[must_use]
    pub fn get_edges_by_label(&self, label: &str) -> Vec<GraphEdge> {
        self.edge_store
            .read()
            .get_edges_by_label(label)
            .into_iter()
            .cloned()
            .collect()
    }

    /// Gets outgoing edges from a specific node.
    ///
    /// # Arguments
    ///
    /// * `node_id` - The source node ID
    ///
    /// # Returns
    ///
    /// Vector of edges originating from the specified node (cloned).
    #[must_use]
    pub fn get_outgoing_edges(&self, node_id: u64) -> Vec<GraphEdge> {
        self.edge_store
            .read()
            .get_outgoing(node_id)
            .into_iter()
            .cloned()
            .collect()
    }

    /// Gets incoming edges to a specific node.
    ///
    /// # Arguments
    ///
    /// * `node_id` - The target node ID
    ///
    /// # Returns
    ///
    /// Vector of edges pointing to the specified node (cloned).
    #[must_use]
    pub fn get_incoming_edges(&self, node_id: u64) -> Vec<GraphEdge> {
        self.edge_store
            .read()
            .get_incoming(node_id)
            .into_iter()
            .cloned()
            .collect()
    }

    /// Traverses the graph using BFS from a source node.
    ///
    /// # Arguments
    ///
    /// * `source` - Starting node ID
    /// * `max_depth` - Maximum traversal depth
    /// * `rel_types` - Optional filter by relationship types
    /// * `limit` - Maximum number of results
    ///
    /// # Returns
    ///
    /// Vector of traversal results with target nodes and paths.
    ///
    /// # Errors
    ///
    /// Returns an error if traversal fails.
    pub fn traverse_bfs(
        &self,
        source: u64,
        max_depth: u32,
        rel_types: Option<&[&str]>,
        limit: usize,
    ) -> Result<Vec<TraversalResult>> {
        use std::collections::VecDeque;

        let store = self.edge_store.read();
        let filter: &[&str] = rel_types.unwrap_or(&[]);
        let mut visited = HashSet::new();
        let mut queue = VecDeque::new();
        let mut results = Vec::new();

        visited.insert(source);
        queue.push_back((source, 0u32, Vec::new()));

        while let Some((node, depth, path)) = queue.pop_front() {
            if results.len() >= limit {
                break;
            }
            if depth >= max_depth {
                continue;
            }

            let neighbors = collect_neighbor_expansions(
                store.get_outgoing(node).into_iter(),
                depth,
                &path,
                filter,
                &mut visited,
            );

            for (target, next_depth, new_path) in neighbors {
                results.push(TraversalResult {
                    target_id: target,
                    depth: next_depth,
                    path: new_path.clone(),
                });
                if results.len() >= limit {
                    break;
                }
                queue.push_back((target, next_depth, new_path));
            }
        }

        Ok(results)
    }

    /// Traverses the graph using DFS from a source node.
    ///
    /// # Arguments
    ///
    /// * `source` - Starting node ID
    /// * `max_depth` - Maximum traversal depth
    /// * `rel_types` - Optional filter by relationship types
    /// * `limit` - Maximum number of results
    ///
    /// # Returns
    ///
    /// Vector of traversal results with target nodes and paths.
    ///
    /// # Errors
    ///
    /// Returns an error if traversal fails.
    pub fn traverse_dfs(
        &self,
        source: u64,
        max_depth: u32,
        rel_types: Option<&[&str]>,
        limit: usize,
    ) -> Result<Vec<TraversalResult>> {
        let store = self.edge_store.read();
        let filter: &[&str] = rel_types.unwrap_or(&[]);
        let mut visited = HashSet::new();
        let mut stack = Vec::new();
        let mut results = Vec::new();

        visited.insert(source);
        stack.push((source, 0u32, Vec::new()));

        while let Some((node, depth, path)) = stack.pop() {
            if results.len() >= limit {
                break;
            }
            if depth >= max_depth {
                continue;
            }

            let neighbors = collect_neighbor_expansions(
                store.get_outgoing(node).into_iter(),
                depth,
                &path,
                filter,
                &mut visited,
            );

            for (target, next_depth, new_path) in neighbors {
                results.push(TraversalResult {
                    target_id: target,
                    depth: next_depth,
                    path: new_path.clone(),
                });
                if results.len() >= limit {
                    break;
                }
                stack.push((target, next_depth, new_path));
            }
        }

        Ok(results)
    }

    /// Gets the in-degree and out-degree of a node.
    ///
    /// # Arguments
    ///
    /// * `node_id` - The node ID
    ///
    /// # Returns
    ///
    /// Tuple of (`in_degree`, `out_degree`).
    #[must_use]
    pub fn get_node_degree(&self, node_id: u64) -> (usize, usize) {
        let store = self.edge_store.read();
        let in_degree = store.get_incoming(node_id).len();
        let out_degree = store.get_outgoing(node_id).len();
        (in_degree, out_degree)
    }

    /// Removes an edge from the graph by ID.
    ///
    /// # Arguments
    ///
    /// * `edge_id` - The edge ID to remove
    ///
    /// # Returns
    ///
    /// `true` if the edge existed and was removed, `false` if it didn't exist.
    #[must_use]
    pub fn remove_edge(&self, edge_id: u64) -> bool {
        let mut store = self.edge_store.write();
        if store.contains_edge(edge_id) {
            store.remove_edge(edge_id);
            // Bump only when a mutation actually occurred (CACHE-01).
            // Releasing the write lock before the atomic bump is intentional:
            // the bump is a best-effort cache invalidation hint, not part of
            // the edge-store transaction.
            drop(store);
            self.write_generation
                .fetch_add(1, std::sync::atomic::Ordering::Relaxed);
            true
        } else {
            false
        }
    }

    /// Returns the total number of edges in the graph.
    #[must_use]
    pub fn edge_count(&self) -> usize {
        self.edge_store.read().len()
    }

    // -------------------------------------------------------------------------
    // Graph schema
    // -------------------------------------------------------------------------

    /// Returns the graph schema stored in the collection config, if any.
    #[must_use]
    pub fn graph_schema(&self) -> Option<GraphSchema> {
        self.config.read().graph_schema.clone()
    }

    /// Returns `true` if this collection was created as a graph collection.
    #[must_use]
    pub fn is_graph(&self) -> bool {
        self.config.read().graph_schema.is_some()
    }

    /// Returns `true` if this graph collection stores node embeddings.
    #[must_use]
    pub fn has_embeddings(&self) -> bool {
        self.config.read().embedding_dimension.is_some()
    }

    // -------------------------------------------------------------------------
    // Node payload (graph node properties)
    // -------------------------------------------------------------------------

    /// Stores a JSON payload for a graph node.
    ///
    /// # Errors
    ///
    /// Returns an error if storage fails.
    pub fn store_node_payload(&self, node_id: u64, payload: &serde_json::Value) -> Result<()> {
        let mut storage = self.payload_storage.write();
        storage.store(node_id, payload)?;
        // Bump write generation so any cached plan for this collection is
        // invalidated on the next query (CACHE-01).
        self.write_generation
            .fetch_add(1, std::sync::atomic::Ordering::Relaxed);
        Ok(())
    }

    /// Retrieves the JSON payload for a graph node.
    ///
    /// # Errors
    ///
    /// Returns an error if retrieval fails.
    pub fn get_node_payload(&self, node_id: u64) -> Result<Option<serde_json::Value>> {
        Ok(self.payload_storage.read().retrieve(node_id)?)
    }

    // -------------------------------------------------------------------------
    // Graph traversal with TraversalConfig
    // -------------------------------------------------------------------------

    /// BFS traversal using the core `bfs_stream` iterator.
    #[must_use]
    pub fn traverse_bfs_config(
        &self,
        source_id: u64,
        config: &TraversalConfig,
    ) -> Vec<TraversalResult> {
        use crate::collection::graph::{bfs_stream, StreamingConfig};
        let store = self.edge_store.read();
        let streaming = StreamingConfig {
            max_depth: config.max_depth,
            rel_types: config.rel_types.clone(),
            limit: Some(config.limit),
            max_visited_size: 100_000,
        };
        bfs_stream(&store, source_id, streaming)
            .filter(|result| result.depth >= config.min_depth)
            .take(config.limit)
            .collect()
    }

    /// DFS traversal (iterative) using `TraversalConfig`.
    #[must_use]
    pub fn traverse_dfs_config(
        &self,
        source_id: u64,
        config: &TraversalConfig,
    ) -> Vec<TraversalResult> {
        use std::collections::HashSet;
        let store = self.edge_store.read();
        let rel_filter: HashSet<&str> = config.rel_types.iter().map(String::as_str).collect();

        let mut results = Vec::new();
        let mut visited: HashSet<u64> = HashSet::new();
        let mut stack: Vec<(u64, u32, Vec<u64>)> = vec![(source_id, 0, Vec::new())];

        while let Some((node_id, depth, path)) = stack.pop() {
            if results.len() >= config.limit {
                break;
            }
            if !visited.insert(node_id) {
                continue;
            }
            if depth >= config.min_depth && depth > 0 {
                results.push(TraversalResult::new(node_id, path.clone(), depth));
                if results.len() >= config.limit {
                    break;
                }
            }
            if depth < config.max_depth {
                expand_dfs_neighbors(
                    &store,
                    node_id,
                    depth,
                    &path,
                    &rel_filter,
                    &visited,
                    &mut stack,
                );
            }
        }
        results
    }

    // -------------------------------------------------------------------------
    // Embedding search on graph nodes
    // -------------------------------------------------------------------------

    /// Searches for similar graph nodes by embedding vector.
    ///
    /// Only available if `has_embeddings()` returns `true`.
    ///
    /// # Errors
    ///
    /// Returns `Error::VectorNotAllowed` if no embeddings are configured,
    /// or `Error::DimensionMismatch` if the query dimension is wrong.
    pub fn search_by_embedding(&self, query: &[f32], k: usize) -> Result<Vec<SearchResult>> {
        let config = self.config.read();
        let emb_dim = config
            .embedding_dimension
            .ok_or_else(|| Error::VectorNotAllowed(config.name.clone()))?;
        drop(config);

        if query.len() != emb_dim {
            return Err(Error::DimensionMismatch {
                expected: emb_dim,
                actual: query.len(),
            });
        }

        // Reason: we reuse the existing HNSW index (dimension == emb_dim when created
        // via create_graph_collection_with_embeddings). For graph-without-embeddings
        // the HNSW has dimension 0 and the guard above already rejected the call.
        let metric = self.config.read().metric;
        let ids = self.index.search(query, k);
        let ids = self.merge_delta(ids, query, k, metric);

        // Acquire each lock once: collect vector data, then collect payload data.
        // This avoids holding vector_storage while locking payload_storage per item.
        let vectors: Vec<(u64, f32, Option<Vec<f32>>)> = {
            let vector_storage = self.vector_storage.read();
            ids.into_iter()
                .map(|sr| {
                    let vec = vector_storage.retrieve(sr.id).ok().flatten();
                    (sr.id, sr.score, vec)
                })
                .collect()
        };
        let results = {
            let payload_storage = self.payload_storage.read();
            vectors
                .into_iter()
                .filter_map(|(id, score, vector)| {
                    let vector = vector?;
                    let payload = payload_storage.retrieve(id).ok().flatten();
                    Some(SearchResult::new(
                        Point {
                            id,
                            vector,
                            payload,
                            sparse_vectors: None,
                        },
                        score,
                    ))
                })
                .collect()
        };
        Ok(results)
    }
}