ruve-db 0.1.1

use std::collections::{HashMap, HashSet};
use crate::bm25;
use crate::hnsw::index::HNSW;
use crate::index::{load_index, save_index, Index};
use crate::similarities::cosine_similarity;
use crate::storage::{append_record, retrieve_record};
use crate::types::Record;

const DEFAULT_CHECKPOINT_EVERY: usize = 100000;

pub struct Database {
    pub data_path: String,
    pub index_path: String,
    pub bm25_index_path: String,
    pub index: Index,
    pub bm25_index: bm25::index::Bm25Index,
    pub bm25_tokenizer: bm25::tokenizer::Tokenizer,
    pub hnsw: HNSW,
    pub graph_path: String,
    /// Flush indexes to disk every this many inserts. Default: 500.
    pub checkpoint_every: usize,
    dirty_count: usize,
}

impl Database {
    pub fn new(data_path: &str, index_path: &str, bm25_index_path: &str, hnsw_path: &str, graph_path: &str) -> Database {
        // ensure the data directory exists
        if let Some(dir) = std::path::Path::new(data_path).parent() {
            std::fs::create_dir_all(dir).unwrap();
        }
        let index = load_index(index_path);
        let bm25_index = bm25::index::Bm25Index::load(bm25_index_path);
        let bm25_tokenizer = bm25::tokenizer::Tokenizer::new();
        let hnsw = HNSW::load(hnsw_path, graph_path);

        Database {
            data_path: data_path.to_string(),
            index_path: index_path.to_string(),
            bm25_index_path: bm25_index_path.to_string(),
            index,
            bm25_index,
            bm25_tokenizer,
            hnsw,
            graph_path: graph_path.to_string(),
            checkpoint_every: DEFAULT_CHECKPOINT_EVERY,
            dirty_count: 0,
        }
    }

    /// Flush all indexes to disk immediately.
    pub fn save_all(&mut self) {
        save_index(&self.index_path, &self.index);
        self.bm25_index.save(&self.bm25_index_path);
        self.hnsw.save();
        self.dirty_count = 0;
    }

    /// Insert a record with a pre-computed embedding vector and text metadata.
    /// `id` pins the record to a specific key; pass `None` to auto-generate a UUID v7.
    /// The text is indexed into BM25 for full-text search; the vector is inserted into the HNSW graph.
    pub fn insert_raw(&mut self, vector: Vec<f32>, text: &str, id: Option<&str>) {
        let record = Record::new(vector, Some(text.to_string()), id.map(str::to_string));
        let tokens = self.bm25_tokenizer.tokenize(text);
        let offset = append_record(&record, &self.data_path);
        self.bm25_index.index_record(&record.id, &tokens);
        self.index.insert(record.id.clone(), offset);
        let (node_index, assigned_layer, prev_entry, prev_highest) = self.hnsw.insert(&record);
        self.make_connections(&record.vector, node_index, assigned_layer, prev_entry, prev_highest);
        self.dirty_count += 1;
        if self.dirty_count >= self.checkpoint_every {
            self.save_all();
        }
    }

    // find nearest neighbors for the new node at each of its layers and wire them up bidirectionally
    fn make_connections(&mut self, vector: &[f32], node_index: u32, assigned_layer: usize, prev_entry: Option<u32>, prev_highest: usize) {
        // first node ever — nothing to connect to
        let entry = match prev_entry {
            Some(ep) => ep,
            None => return,
        };

        // search using the pre-insert entry point and its highest layer so the
        // new (connectionless) node is never used as a starting point
        let candidates_by_layer = self.search_layers(vector, self.hnsw.max_neighbors_per_document * 2, Some((entry, prev_highest)));

        for (layer, candidates) in &candidates_by_layer {
            // only connect layers below the assigned one
            if *layer > assigned_layer {
                continue;
            }

            let neighbors: Vec<u32> = candidates.iter()
                .filter(|&&n| n != node_index) // filter out the node itself
                .take(self.hnsw.max_neighbors_per_document) // take first max neighbors per layer
                .cloned()
                .collect();

            // wire the node to its new best neighbors
            self.hnsw.set_neighbors(node_index, *layer, &neighbors);

            // wire bidirectionally
            for &neighbor in &neighbors {
                let mut existing = self.hnsw.get_neighbors(neighbor, *layer);

                if !existing.contains(&node_index) {
                    existing.push(node_index);

                    if existing.len() > self.hnsw.max_neighbors_per_document {
                        // keep only the best neighbors by similarity
                        let neighbor_uuid = &self.hnsw.index_to_id[neighbor as usize];
                        // reobtain the neighbor vector
                        let neighbor_emb = retrieve_record(*self.index.get(neighbor_uuid).unwrap(), &self.data_path).vector;
                        // score it
                        let mut scored: Vec<(f32, u32)> = existing.iter()
                            .map(|&n| {
                                let uuid = &self.hnsw.index_to_id[n as usize];
                                let vec = retrieve_record(*self.index.get(uuid).unwrap(), &self.data_path).vector;
                                (cosine_similarity(&neighbor_emb, &vec), n)
                            })
                            .collect();
                        scored.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap_or(std::cmp::Ordering::Equal));
                        // keep best scoring one
                        existing = scored.iter()
                            .take(self.hnsw.max_neighbors_per_document)
                            .map(|&(_, n)| n)
                            .collect();
                    }

                    self.hnsw.set_neighbors(neighbor, *layer, &existing);
                }
            }
        }
    }

    // given a query get the most relevant records using the BM25 index
    // then convert the doc ids to offsets using the index
    // and retrieve the records from storage
    pub fn text_search(&self, query: &str, k: usize) -> Vec<Record> {
        let tokens = self.bm25_tokenizer.tokenize(query);
        let scores = self.bm25_index.score(&tokens);

        scores.into_iter().take(k)
            .filter_map(|(doc_id, _)| self.index.get(&doc_id))
            .map(|offset| retrieve_record(*offset, &self.data_path))
            .collect()
    }

    /// Delete a record by UUID. Returns false if the ID was not found.
    pub fn delete(&mut self, id: &str) -> bool {
        let Some(offset) = self.index.get(id).copied() else { return false };

        let record = retrieve_record(offset, &self.data_path);
        let tokens = self.bm25_tokenizer.tokenize(record.metadata.as_deref().unwrap_or(""));

        self.bm25_index.remove_record(id, &tokens);
        self.index.remove(id);
        save_index(&self.index_path, &self.index);
        self.bm25_index.save(&self.bm25_index_path);
        true
    }

    /// Delete all records and wipe every index and data file.
    pub fn wipe(&mut self) {
        self.index.clear();
        self.bm25_index = bm25::index::Bm25Index::new();
        self.hnsw.wipe();
        self.dirty_count = 0;

        let _ = std::fs::remove_file(&self.data_path);
        let _ = std::fs::remove_file(&self.index_path);
        let _ = std::fs::remove_file(&self.bm25_index_path);
    }

    /// Brute-force cosine-similarity search over all stored vectors.
    /// Returns up to k records sorted by descending score.
    /// Useful for recall evaluation against HNSW results.
    pub fn search_scored(&self, query_vector: &[f32], k: usize) -> Vec<(f32, Record)> {
        let mut results: Vec<(f32, Record)> = self.index.iter()
            .map(|(_id, offset)| retrieve_record(*offset, &self.data_path))
            .map(|r| {
                let score = cosine_similarity(&r.vector, query_vector);
                (score, r)
            })
            .collect();

        results.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap());
        results.truncate(k);
        results
    }

    fn score_node(&self, node_index: u32, query_vector: &[f32]) -> Option<f32> {
        let uuid = self.hnsw.index_to_id.get(node_index as usize)?;
        let offset = self.index.get(uuid)?;
        let record = retrieve_record(*offset, &self.data_path);
        Some(cosine_similarity(&record.vector, query_vector))
    }

    /// HNSW approximate nearest-neighbour search.
    ///
    /// Returns records sorted by descending similarity.
    /// `ef` is the exploration factor — higher values improve recall at the cost of speed.
    pub fn search_hnsw(&self, query_vector: &[f32], ef: usize) -> Vec<Record> {
        if self.hnsw.node_offsets.is_empty() {
            return Vec::new();
        }

        let (mut current, top_layer) = match self.hnsw.entry_point {
            Some(ep) => (ep, self.hnsw.highest_layer),
            None => return Vec::new(),
        };

        // phase 1: greedy descent — upper layers are sparse, used to fast-forward close to the query
        for layer in (1..=top_layer).rev() {       // walk down from top to layer 1
            loop {
                let current_score = self.score_node(current, query_vector).unwrap_or(f32::NEG_INFINITY); // score of where we are now
                let best = self.hnsw.get_neighbors(current, layer).into_iter()
                    .filter_map(|n| Some((self.score_node(n, query_vector)?, n))) // score each neighbor
                    .filter(|(s, _)| *s > current_score)                          // keep only those better than current
                    .max_by(|a, b| a.0.partial_cmp(&b.0).unwrap_or(std::cmp::Ordering::Equal)); // pick the best
                match best {
                    Some((_, n)) => current = n, // found a better neighbor, move there and re-check
                    None => break,               // no neighbor beats current — local optimum, drop to next layer
                }
            }
        }

        // phase 2: beam search at layer 0 — explore the ef best candidates
        // both vecs are kept sorted descending by score
        let entrypoint_score = match self.score_node(current, query_vector) {
            Some(s) => s,
            None => return Vec::new(),
        };
        // nodes already scored — never enqueue or score twice
        let mut visited: HashSet<u32> = HashSet::from([current]);
        // frontier: nodes discovered but not yet expanded (neighbors not yet read)
        // sorted descending — front is always the most promising next step
        let mut candidates: Vec<(f32, u32)> = vec![(entrypoint_score, current)];
        // best ef nodes seen so far — what we return at the end
        // sorted descending — last element is the worst, evicted when results exceeds ef
        let mut results: Vec<(f32, u32)> = vec![(entrypoint_score, current)];

        while let Some(&(c_score, c_node)) = candidates.first() {
            // worst score currently in our result set
            let worst_result = results.last().map(|(s, _)| *s).unwrap_or(f32::NEG_INFINITY);
            // if the worst result is better than the candidate we are exploring, break
            if c_score < worst_result {
                break; // best remaining candidate can't improve the result set
            }
            candidates.remove(0);

            // get current candidate neighbors
            for neighbor in self.hnsw.get_neighbors(c_node, 0) {
                // if we haven't seen this neighbor before
                if visited.insert(neighbor) {
                    // evaluate the similarity between the neighbor and the given query vector
                    if let Some(n_score) = self.score_node(neighbor, query_vector) {
                        let worst = results.last().map(|(s, _)| *s).unwrap_or(f32::NEG_INFINITY);
                        // if the score is better than the worst in the results or the results is not yet full
                        if n_score > worst || results.len() < ef {
                            // insert into candidates keeping order
                            let pos = candidates.partition_point(|(s, _)| *s > n_score);
                            candidates.insert(pos, (n_score, neighbor));
                            // insert into results keeping order
                            let pos = results.partition_point(|(s, _)| *s > n_score);
                            results.insert(pos, (n_score, neighbor));
                            // if we are over the limits, pop the last one (worst)
                            if results.len() > ef {
                                results.pop();
                            }
                        }
                    }
                }
            }
        }

        results.iter()
            .filter_map(|(_, node)| {
                let uuid = self.hnsw.index_to_id.get(*node as usize)?;
                let offset = self.index.get(uuid)?;
                Some(retrieve_record(*offset, &self.data_path))
            })
            .collect()
    }

    // used during insertion: returns candidates per layer so make_connections can wire each one
    fn search_layers(&self, query_vector: &[f32], candidates_per_layer: usize, entry_override: Option<(u32, usize)>) -> HashMap<usize, Vec<u32>> {
        if self.hnsw.node_offsets.is_empty() {
            return HashMap::new();
        }

        let (entry_node, top_layer) = match entry_override {
            Some(pair) => pair,
            None => match self.hnsw.entry_point {
                Some(ep) => (ep, self.hnsw.highest_layer),
                None => return HashMap::new(),
            },
        };

        let mut current_candidates: Vec<u32> = vec![entry_node];
        let mut layer_candidates: HashMap<usize, Vec<u32>> = HashMap::new();

        for layer in (0..=top_layer).rev() {
            // collect current candidates + their neighbors, deduplicated
            let mut seen: HashSet<u32> = current_candidates.iter().cloned().collect();
            for &candidate in &current_candidates {
                for neighbor in self.hnsw.get_neighbors(candidate, layer) {
                    seen.insert(neighbor);
                }
            }

            // score all candidates by cosine similarity
            let mut scored: Vec<(f32, u32)> = seen.iter()
                .filter_map(|&node_index| {
                    // foreach neighbor
                    let uuid = &self.hnsw.index_to_id[node_index as usize];

                    // retrieve the actual record
                    let data_offset = self.index.get(uuid)?;
                    let record = retrieve_record(*data_offset, &self.data_path);

                    // evaluate the score against given vector
                    let score = cosine_similarity(&record.vector, query_vector);
                    Some((score, node_index))
                })
                .collect();

            // sort highest score first
            scored.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap());
            scored.truncate(candidates_per_layer);

            layer_candidates.insert(layer, scored.iter().map(|&(_, idx)| idx).collect());

            // pass top half down to next layer
            let next_size = (candidates_per_layer / 2).max(1);
            current_candidates = scored.iter().take(next_size).map(|&(_, idx)| idx).collect();
        }

        layer_candidates
    }

    /// Brute-force search returning the top-k records (without scores).
    pub fn search(&self, query_vector: &[f32], k: usize) -> Vec<Record> {
        let mut results = Vec::new();

        for (_id, offset) in &self.index {
            let record = retrieve_record(*offset, &self.data_path);
            let similarity = cosine_similarity(&record.vector, query_vector);
            results.push((similarity, record));
        }

        results.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap());
        results.into_iter().take(k).map(|(_, record)| record).collect()
    }
}

impl Drop for Database {
    fn drop(&mut self) {
        if self.dirty_count > 0 {
            self.save_all();
        }
    }
}