export class HnswLite {
    vectors = new Map();
    neighbors = new Map();
    dimensions;
    maxNeighbors;
    efConstruction;
    metric;
    constructor(dimensions, m, efConstruction, metric) {
        this.dimensions = dimensions;
        this.maxNeighbors = m;
        this.efConstruction = efConstruction;
        this.metric = metric;
    }
    get size() {
        return this.vectors.size;
    }
    add(id, vector) {
        this.vectors.set(id, vector);
        if (this.vectors.size === 1) {
            this.neighbors.set(id, new Set());
            return;
        }
        const nearest = this.findNearest(vector, this.maxNeighbors);
        const neighborSet = new Set();
        for (const n of nearest) {
            neighborSet.add(n.id);
            const nNeighbors = this.neighbors.get(n.id);
            if (nNeighbors) {
                nNeighbors.add(id);
                if (nNeighbors.size > this.maxNeighbors * 2) {
                    this.pruneNeighbors(n.id);
                }
            }
        }
        this.neighbors.set(id, neighborSet);
    }
    remove(id) {
        this.vectors.delete(id);
        const myNeighbors = this.neighbors.get(id);
        if (myNeighbors) {
            for (const nId of myNeighbors) {
                this.neighbors.get(nId)?.delete(id);
            }
        }
        this.neighbors.delete(id);
    }
    search(query, k, threshold) {
        if (this.vectors.size === 0)
            return [];
        if (this.vectors.size <= k * 2) {
            return this.bruteForce(query, k, threshold);
        }
        const visited = new Set();
        const candidates = [];
        let entryId;
        let bestScore = -1;
        for (const [id] of this.vectors) {
            const score = this.similarity(query, this.vectors.get(id));
            if (score > bestScore) {
                bestScore = score;
                entryId = id;
            }
            if (visited.size >= Math.min(this.efConstruction, this.vectors.size))
                break;
            visited.add(id);
            candidates.push({ id, score });
        }
        if (entryId) {
            const queue = [entryId];
            let idx = 0;
            while (idx < queue.length && visited.size < this.efConstruction * 2) {
                const currentId = queue[idx++];
                const currentNeighbors = this.neighbors.get(currentId);
                if (!currentNeighbors)
                    continue;
                for (const nId of currentNeighbors) {
                    if (visited.has(nId))
                        continue;
                    visited.add(nId);
                    const vec = this.vectors.get(nId);
                    if (!vec)
                        continue;
                    const score = this.similarity(query, vec);
                    candidates.push({ id: nId, score });
                    queue.push(nId);
                }
            }
        }
        candidates.sort((a, b) => b.score - a.score);
        let filtered = candidates;
        if (threshold !== undefined) {
            filtered = filtered.filter(c => c.score >= threshold);
        }
        return filtered.slice(0, k);
    }
    bruteForce(query, k, threshold) {
        const results = [];
        for (const [id, vec] of this.vectors) {
            const score = this.similarity(query, vec);
            if (threshold !== undefined && score < threshold)
                continue;
            results.push({ id, score });
        }
        results.sort((a, b) => b.score - a.score);
        return results.slice(0, k);
    }
    findNearest(query, k) {
        return this.bruteForce(query, k);
    }
    pruneNeighbors(id) {
        const myNeighbors = this.neighbors.get(id);
        if (!myNeighbors)
            return;
        const vec = this.vectors.get(id);
        if (!vec)
            return;
        const scored = [];
        for (const nId of myNeighbors) {
            const nVec = this.vectors.get(nId);
            if (!nVec)
                continue;
            scored.push({ id: nId, score: this.similarity(vec, nVec) });
        }
        scored.sort((a, b) => b.score - a.score);
        const keep = new Set(scored.slice(0, this.maxNeighbors).map(s => s.id));
        for (const nId of myNeighbors) {
            if (!keep.has(nId)) {
                myNeighbors.delete(nId);
            }
        }
    }
    similarity(a, b) {
        if (this.metric === 'dot')
            return dotProduct(a, b);
        if (this.metric === 'euclidean')
            return 1 / (1 + euclideanDistance(a, b));
        return cosineSimilarity(a, b);
    }
}
export function cosineSimilarity(a, b) {
    let dot = 0;
    let normA = 0;
    let normB = 0;
    for (let i = 0; i < a.length; i++) {
        dot += a[i] * b[i];
        normA += a[i] * a[i];
        normB += b[i] * b[i];
    }
    if (normA === 0 || normB === 0)
        return 0;
    return dot / (Math.sqrt(normA) * Math.sqrt(normB));
}
function dotProduct(a, b) {
    let sum = 0;
    for (let i = 0; i < a.length; i++) {
        sum += a[i] * b[i];
    }
    return sum;
}
function euclideanDistance(a, b) {
    let sum = 0;
    for (let i = 0; i < a.length; i++) {
        const d = a[i] - b[i];
        sum += d * d;
    }
    return Math.sqrt(sum);
}
//# sourceMappingURL=hnsw-lite.js.map