pgvector-cpp/examples/openai/example.cpp at master · pgvector/pgvector-cpp · GitHub

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#include <iostream>

#include <string>

#include <vector>

#include <cpr/cpr.h>

#include <nlohmann/json.hpp>

#include <pgvector/pqxx.hpp>

#include <pqxx/pqxx>

using json = nlohmann::json;

// https://platform.openai.com/docs/guides/embeddings/how-to-get-embeddings

// input can be an array with 2048 elements

std::vector<std::vector<float>> embed(const std::vector<std::string>& input, char* api_key) {

std::string url{"https://api.openai.com/v1/embeddings"};

json data{{"input", input}, {"model", "text-embedding-3-small"}};

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::vector<float>> embeddings;

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

embeddings.emplace_back(v["embedding"]);

}

return embeddings;

}

int main() {

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

if (!api_key) {

std::cout << "Set OPENAI_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 vector(1536))"

);

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

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

};

std::vector<std::vector<float>> embeddings = embed(input, 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], pgvector::Vector{embeddings[i]}}

);

}

std::string query{"forest"};

std::vector<float> query_embedding = embed({query}, api_key)[0];

pqxx::result result = tx.exec(

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

pqxx::params{pgvector::Vector{query_embedding}}

);

for (const auto& row : result) {

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

}

return 0;

}

pgvector-cpp/examples/openai/example.cpp at master · pgvector/pgvector-cpp · GitHub

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