| extern "C" { | |
| // --- Production Data Structures --- | |
| struct NodeMetadata { | |
| float centrality; | |
| float recency; | |
| float weight; | |
| }; | |
| // --- The Parallel Graph Walk Kernel --- | |
| // Inputs: | |
| // query_ptr: Pointer to query vector (Size D) | |
| // db_ptr: Pointer to ALL document vectors (Size N * D) | |
| // meta_ptr: Pointer to ALL metadata structs (Size N * 3) | |
| // n: Number of documents | |
| // d: Vector dimension | |
| // Output: | |
| // best_idx: Index of the winning document | |
| // best_score: The priority score of the winner | |
| void ov_hybrid_search( | |
| const float* query_ptr, | |
| const float* db_ptr, | |
| const float* meta_ptr, | |
| int n, | |
| int d, | |
| int* best_idx, | |
| float* best_score | |
| ) { | |
| float max_p = -1.0f; | |
| int winner = -1; | |
| // OpenMP pragma would go here for multi-core parallelism | |
| // #pragma omp parallel for | |
| for (int i = 0; i < n; ++i) { | |
| float dot = 0.0f; | |
| float norm_q = 0.0f; | |
| float norm_d = 0.0f; | |
| // Vector Dot Product | |
| const float* doc_vec = &db_ptr[i * d]; | |
| for (int j = 0; j < d; ++j) { | |
| dot += query_ptr[j] * doc_vec[j]; | |
| norm_q += query_ptr[j] * query_ptr[j]; | |
| norm_d += doc_vec[j] * doc_vec[j]; | |
| } | |
| // Cosine Similarity | |
| float similarity = dot / (std::sqrt(norm_q) * std::sqrt(norm_d) + 1e-9f); | |
| // Metadata Access | |
| // Structure layout: [C, R, W, C, R, W...] | |
| float C = meta_ptr[i * 3 + 0]; | |
| float R = meta_ptr[i * 3 + 1]; | |
| float W = meta_ptr[i * 3 + 2]; | |
| // Priority Formula | |
| float P = similarity * C * R * W; | |
| if (P > max_p) { | |
| max_p = P; | |
| winner = i; | |
| } | |
| } | |
| *best_idx = winner; | |
| *best_score = max_p; | |
| } | |
| } | |