| int32_t DenseIndex::add(const float* v) { | |
| double norm = 0.0; | |
| for (int i = 0; i < dim_; i++) norm += static_cast<double>(v[i]) * v[i]; | |
| norm = std::sqrt(norm); | |
| const float inv = (norm > 1e-12) ? static_cast<float>(1.0 / norm) : 0.0f; | |
| data_.reserve(data_.size() + dim_); | |
| for (int i = 0; i < dim_; i++) data_.push_back(v[i] * inv); | |
| return n_++; | |
| } | |
| std::vector<ScoredDoc> DenseIndex::search(const float* q, int k) const { | |
| std::vector<ScoredDoc> out; | |
| if (n_ == 0) return out; | |
| double qn = 0.0; | |
| for (int i = 0; i < dim_; i++) qn += static_cast<double>(q[i]) * q[i]; | |
| qn = std::sqrt(qn); | |
| const float qinv = (qn > 1e-12) ? static_cast<float>(1.0 / qn) : 0.0f; | |
| out.resize(n_); | |
| for (int32_t d = 0; d < n_; d++) { | |
| const float* row = data_.data() + static_cast<size_t>(d) * dim_; | |
| float dot = 0.0f; | |
| for (int i = 0; i < dim_; i++) dot += row[i] * q[i]; | |
| out[d] = {d, dot * qinv}; | |
| } | |
| const int kk = std::min<int>(k, n_); | |
| std::partial_sort(out.begin(), out.begin() + kk, out.end(), | |
| [](const ScoredDoc& a, const ScoredDoc& b){ return a.score > b.score; }); | |
| out.resize(kk); | |
| return out; | |
| } | |