Hamming distance based LLM without need for training

hamming_llm.cpp

#include <iostream>
#include <vector>
#include <string>
#include <random>
#include <unordered_map>
#include <arm_neon.h> // Specialized for your M2 chip

// 1024-bit vector = 128 bytes = 2 Cache Lines
// We use alignas(64) to ensure the CPU doesn't cross cache line boundaries
struct alignas(64) HyperVector {
    uint64_t bits[16];
};

class SemanticEngine {
private:
    std::unordered_map<std::string, HyperVector> dna_registry;

public:
    // Generates a deterministic random vector for any string
    HyperVector get_dna(const std::string& root) {
        if (dna_registry.find(root) != dna_registry.end()) return dna_registry[root];

        HyperVector hv;
        std::mt19937_64 gen(std::hash<std::string>{}(root));
        std::uniform_int_distribution<uint64_t> dist(0, 0xFFFFFFFFFFFFFFFF);

        for (int i = 0; i < 16; ++i) hv.bits[i] = dist(gen);
        dna_registry[root] = hv;
        return hv;
    }

    // Bundles multiple concepts into one Document Fingerprint using Bitwise OR
    // (Note: In a massive system, we'd use Majority Vote, but OR works for sparse roots)
    HyperVector create_fingerprint(const std::vector<std::string>& roots) {
        HyperVector doc = {0};
        for (const auto& r : roots) {
            HyperVector root_dna = get_dna(r);
            for (int i = 0; i < 16; ++i) {
                doc.bits[i] |= root_dna.bits[i];
            }
        }
        return doc;
    }

    // Optimized Hamming Distance for M2 (ARMv8)
    int compare(const HyperVector& a, const HyperVector& b) {
        int distance = 0;
        for (int i = 0; i < 16; ++i) {
            // Builtin_popcountll maps to the 'CNT' instruction on M2
            distance += __builtin_popcountll(a.bits[i] ^ b.bits[i]);
        }
        return distance;
    }
};

int main() {
    SemanticEngine engine;

    // 1. Define our concepts
    // Document A: Heart Inflammation (Cardio + Itis)
    std::vector<std::string> docA_roots = {"cardio", "itis"};
    
    // Document B: Heart Disease (Cardio + Patho)
    std::vector<std::string> docB_roots = {"cardio", "patho"};

    // Document C: Bone Disease (Osteo + Patho) - Unrelated to Heart
    std::vector<std::string> docC_roots = {"osteo", "patho"};

    // 2. Generate Fingerprints
    auto fingerA = engine.create_fingerprint(docA_roots);
    auto fingerB = engine.create_fingerprint(docB_roots);
    auto fingerC = engine.create_fingerprint(docC_roots);

    // 3. The Query: Find things related to "Heart" (Cardio)
    auto query = engine.get_dna("cardio");

    std::cout << "--- 1-Bit Semantic Search Benchmark (M2) ---" << std::endl;
    std::cout << "Target Query: 'cardio'" << std::endl;
    std::cout << "Distance to 'Cardio-Itis':   " << engine.compare(fingerA, query) << std::endl;
    std::cout << "Distance to 'Cardio-Patho':  " << engine.compare(fingerB, query) << std::endl;
    std::cout << "Distance to 'Osteo-Patho':   " << engine.compare(fingerC, query) << std::endl;
    std::cout << "(Lower score = higher similarity. Random noise is ~512)" << std::endl;

    return 0;
}