feat(server): memory v2 — BM25 + local embedding hybrid search

- Bm25Ranker: Okapi BM25 scoring (pure TS, no deps)
- Embedding module: ONNX-based local embeddings via onnxruntime-node
- Hybrid recall: BM25 (30%) + cosine similarity (70%) weighted merge
- Falls back to keyword-only via MEMORY_SEARCH=keyword env var
- extract_memory agent tool for persisting memory entries

apps/server/src/services/memory/bm25.ts

@@ -0,0 +1,67 @@
+// BM25 ranker — pure Okapi BM25 scoring. No external deps.
+
+interface Bm25Config {
+  k1?: number;
+  b?: number;
+}
+
+export class Bm25Ranker {
+  private k1: number;
+  private b: number;
+  private corpus: string[];
+  private avgDocLen: number;
+  private idfCache: Map<string, number>;
+  private docCount: number;
+
+  constructor(config?: Bm25Config) {
+    this.k1 = config?.k1 ?? 1.5;
+    this.b = config?.b ?? 0.75;
+    this.corpus = [];
+    this.avgDocLen = 0;
+    this.idfCache = new Map();
+    this.docCount = 0;
+  }
+
+  fit(docs: string[]): void {
+    this.corpus = docs;
+    this.docCount = docs.length;
+    const lengths = docs.map((d) => d.split(/\s+/).length);
+    this.avgDocLen = lengths.reduce((a, b) => a + b, 0) / lengths.length;
+    this.idfCache.clear();
+  }
+
+  private tokenize(text: string): string[] {
+    return text.toLowerCase().split(/\s+/).filter((t) => t.length > 0);
+  }
+
+  private idf(term: string): number {
+    const cached = this.idfCache.get(term);
+    if (cached !== undefined) return cached;
+    const docsWithTerm = this.corpus.filter((d) => this.tokenize(d).includes(term)).length;
+    const idf = Math.log(1 + (this.docCount - docsWithTerm + 0.5) / (docsWithTerm + 0.5));
+    this.idfCache.set(term, idf);
+    return idf;
+  }
+
+  score(query: string, docIndex: number): number {
+    if (docIndex < 0 || docIndex >= this.corpus.length) return 0;
+    const doc = this.corpus[docIndex]!;
+    const queryTerms = this.tokenize(query);
+    const docTokens = this.tokenize(doc);
+    const docLen = docTokens.length;
+
+    let total = 0;
+    for (const term of queryTerms) {
+      const tf = docTokens.filter((t) => t === term).length;
+      if (tf === 0) continue;
+      const idfVal = this.idf(term);
+      total += idfVal * ((tf * (this.k1 + 1)) / (tf + this.k1 * (1 - this.b + this.b * docLen / this.avgDocLen)));
+    }
+    return total;
+  }
+
+  rank(query: string, topN: number = 10): Array<{ index: number; score: number }> {
+    const scores = this.corpus.map((_, i) => ({ index: i, score: this.score(query, i) }));
+    return scores.sort((a, b) => b.score - a.score).slice(0, topN).filter((s) => s.score > 0);
+  }
+}