Method findRelatedSkill

packages/memos-core/src/skill/evolver.ts:128–195

* Search for an existing skill that is HIGHLY related to the given task. * * 1. Collect top 50 skill candidates by FTS + vector similarity (relaxed thresholds). * 2. Call LLM with task title/summary and each skill's name/description; strict rule: * only output ONE skill index if the t

(task: Task)

   *    otherwise output 0 (do not force a match).
   */
  private async findRelatedSkill(task: Task): Promise<Skill | null> {
    const query = task.summary.slice(0, 600);
    const owner = task.owner ?? "agent:main";
    // Relaxed thresholds to gather a larger candidate pool; LLM will do strict filtering
    const VEC_FLOOR = 0.35;
    const TOP_N = SkillEvolver.RELATED_SKILL_CANDIDATE_TOP;

    type Candidate = { skill: Skill; vecScore: number; ftsScore: number; combined: number };
    const candidateMap = new Map<string, Candidate>();

    // 1. FTS on skill name + description (take more candidates)
    try {
      const ftsHits = this.store.skillFtsSearch(query, TOP_N, "mix", owner);
      for (const hit of ftsHits) {
        const skill = this.store.getSkill(hit.skillId);
        if (skill && (skill.status === "active" || skill.status === "draft")) {
          candidateMap.set(skill.id, { skill, vecScore: 0, ftsScore: hit.score, combined: 0 });
        }
      }
    } catch (err) {
      this.ctx.log.warn(`SkillEvolver: skill FTS search failed: ${err}`);
    }

    // 2. Vector similarity: include all skills above a low floor to rank them
    if (this.embedder) {
      try {
        const queryVec = await this.embedder.embedQuery(query);
        const allSkillEmb = this.store.getSkillEmbeddings("mix", owner);
        for (const row of allSkillEmb) {
          const sim = cosineSimilarity(queryVec, row.vector);
          if (sim >= VEC_FLOOR) {
            const existing = candidateMap.get(row.skillId);
            if (existing) {
              existing.vecScore = sim;
            } else {
              const skill = this.store.getSkill(row.skillId);
              if (skill && (skill.status === "active" || skill.status === "draft")) {
                candidateMap.set(skill.id, { skill, vecScore: sim, ftsScore: 0, combined: 0 });
              }
            }
          }
        }
      } catch (err) {
        this.ctx.log.warn(`SkillEvolver: skill vector search failed: ${err}`);
      }
    }

    if (candidateMap.size === 0) return null;

    for (const c of candidateMap.values()) {
      c.combined = c.vecScore * 0.7 + c.ftsScore * 0.3;
    }

    const sorted = [...candidateMap.values()]
      .sort((a, b) => b.combined - a.combined)
      .slice(0, TOP_N);

    if (sorted.length === 0) return null;