const std::vector<std::string>& texts,

const std::string& input_type,

char* api_key

) {

std::string url{"https://api.cohere.com/v2/embed"};

json data{

{"texts", texts},

{"model", "embed-v4.0"},

{"input_type", input_type},

{"embedding_types", {"ubinary"}}

};

cpr::Response r = cpr::Post(

cpr::Url{url},

cpr::Body{data.dump()},

cpr::Bearer{api_key},

cpr::Header{{"Content-Type", "application/json"}}

);

if (r.status_code != 200) {

throw std::runtime_error{"Bad status: " + std::to_string(r.status_code)};

}

json response = json::parse(r.text);

std::vector<std::string> embeddings;

for (const auto& v : response["embeddings"]["ubinary"]) {

std::stringstream buf;

for (uint8_t c : v) {

std::bitset<8> b{c};

buf << b.to_string();

}

embeddings.emplace_back(buf.str());

}

return embeddings;

char* api_key = std::getenv("CO_API_KEY");

if (!api_key) {

std::cout << "Set CO_API_KEY" << std::endl;

return 1;

}

pqxx::connection conn{"dbname=pgvector_example"};

pqxx::nontransaction tx{conn};

tx.exec("CREATE EXTENSION IF NOT EXISTS vector");

tx.exec("DROP TABLE IF EXISTS documents");

tx.exec("CREATE TABLE documents (id bigserial PRIMARY KEY, content text, embedding bit(1536))");

std::vector<std::string> input{

"The dog is barking", "The cat is purring", "The bear is growling"

};

std::vector<std::string> embeddings = embed(input, "search_document", api_key);

for (size_t i = 0; i < input.size(); i++) {

tx.exec(

"INSERT INTO documents (content, embedding) VALUES ($1, $2)",

pqxx::params{input[i], embeddings[i]}

);

}

std::string query{"forest"};

std::string query_embedding = embed({query}, "search_query", api_key)[0];

pqxx::result result = tx.exec(

"SELECT content FROM documents ORDER BY embedding <~> $1 LIMIT 5",

pqxx::params{query_embedding}

);

for (const auto& row : result) {

std::cout << row[0].as<std::string>() << std::endl;

}

return 0;