108 lines
3.8 KiB
Perl
108 lines
3.8 KiB
Perl
# test for OpenAIAsync perl module
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use strict;
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use warnings;
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# Use the Test2::V0 module for testing
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use Test2::V0;
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use OpenAIAsync::Server;
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# Import the OpenAIAsync::Server module
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use OpenAIAsync::Server;
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# Use Object::Pad for object-oriented programming
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use Object::Pad;
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# Import IO::Async::Loop for managing loops and event loops
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use IO::Async::Loop;
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# Use Future::AsyncAwait for easier handling of asynchronous operations
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use Future::AsyncAwait;
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# Import JSON::MaybeXS for encoding and decoding JSON data
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use JSON::MaybeXS;
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# Import Net::Async::HTTP for asynchronous HTTP requests
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use Net::Async::HTTP;
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# Use the relative path './lib' for module lookup
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use lib::relative './lib';
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# Create an instance of IO::Async::Loop
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my $loop = IO::Async::Loop->new();
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# Set the OPENAI_API_KEY environment variable if it is not set or not equal to "12345"
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BEGIN {
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no warnings 'uninitialized';
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$ENV{OPENAI_API_KEY}="12345" unless $ENV{OPENAI_API_KEY}eq"12345";
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$ENV{OPENAI_API_KEY} = "12345" unless $ENV{OPENAI_API_KEY} eq "12345";
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}
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# Define a TestServer class that inherits from OpenAIAsync::Server
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class TestServer {
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# Inherit methods and properties from OpenAIAsync::Server
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inherit OpenAIAsync::Server;
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# Apply methods from various OpenAIAsync::Server::API::Test modules
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apply OpenAIAsync::Server::API::Test::ChatCompletion;
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apply OpenAIAsync::Server::API::Test::Audio;
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apply OpenAIAsync::Server::API::Test::Completions;
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apply OpenAIAsync::Server::API::Test::Embeddings;
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apply OpenAIAsync::Server::API::Test::File;
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apply OpenAIAsync::Server::API::Test::Image;
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apply OpenAIAsync::Server::API::Test::ModelList;
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apply OpenAIAsync::Server::API::Test::Moderations;
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}
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# Pick a random high port, TODO better scheme for this
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my $port = int(2048+rand(20480));
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# Pick a random high port number between 2048 and 22528
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my $port = int(2048 + rand(20480));
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# Create an instance of the TestServer class that listens on localhost with the chosen port
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my $server = TestServer->new(listen => '127.0.0.1', port => $port);
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# Create an instance of Net::Async::HTTP for making HTTP requests
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my $http_client = Net::Async::HTTP->new();
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# Add the $http_client and $server instances to the event loop
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$loop->add($http_client);
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$loop->add($server);
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# Define a hash ref for the chat completion request data
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my $chat_completion_input = {
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"model" => "gpt-3.5-turbo",
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"messages" => [
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{"role" => "user", "content" => "Say this is a test!"}
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],
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"temperature" => 0.7
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};
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# Subroutine to make an HTTP POST request to the server
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sub mk_req($uri, $content) {
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my $content_json = encode_json($content);
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return $http_client->POST("http://127.0.0.1:$port/v1".$uri, $content_json, content_type => 'application/json');
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my $content_json = encode_json($content); # Encode the content data as JSON
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return $http_client->POST("http://127.0.0.1:$port/v1{$uri}", $content_json, content_type => 'application/json'); # Make the POST request
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}
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# Make an HTTP POST request to the chat completions endpoint with the chat completion input data
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my $res_fut = mk_req("/chat/completions", $chat_completion_input);
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# Delay the loop for 5 seconds to allow the request to complete
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$loop->delay_future(after => 5)->get();
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# Get the response object from the future
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my $res = $res_fut->get();
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# Extract the response content
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my $content = $res->content;
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is($content, '{"choices":[],"created":"0","id":"24601","model":"GumbyBrain-llm","object":"text_completion","system_fingerprint":"SHODAN node 12 of 16 tertiary adjunct of unimatrix 42","usage":{"completion_tokens":9,"prompt_tokens":6,"total_tokens":42}}', "check marshalling of data directly");
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done_testing();
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# Compare the response content to the expected content and provide a test message
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is($content, '{"choices":[],"created":"0","id":"24601","model":"GumbyBrain-llm","object":"text_completion","
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