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| 1 | +use std::path::PathBuf; |
| 2 | + |
| 3 | +use async_openai::{ |
| 4 | + types::{CreateCompletionRequestArgs, CreateFileRequestArgs, CreateFineTuneRequestArgs}, |
| 5 | + Client, |
| 6 | + config::OpenAIConfig, |
| 7 | +}; |
| 8 | +use clap::{arg, Command}; |
| 9 | + |
| 10 | +// TODO: Constructive error handling |
| 11 | +async fn data(paths: Vec<&PathBuf>, client: Client<OpenAIConfig>) { |
| 12 | + if paths.len() > 2 { |
| 13 | + println!("pls provide the trainning file path and optionally a validation file path") |
| 14 | + } else { |
| 15 | + if paths.len() < 2 { |
| 16 | + let train_request = CreateFileRequestArgs::default() |
| 17 | + .file(paths[0]) |
| 18 | + .purpose("fine-tune") |
| 19 | + .build() |
| 20 | + .unwrap(); |
| 21 | + |
| 22 | + let trainning_data = client.files().create(train_request).await.unwrap(); |
| 23 | + |
| 24 | + let fine_tune_request = CreateFineTuneRequestArgs::default() |
| 25 | + .training_file(trainning_data.id) |
| 26 | + .build() |
| 27 | + .unwrap(); |
| 28 | + |
| 29 | + let job = client.fine_tunes().create(fine_tune_request).await.unwrap(); |
| 30 | + |
| 31 | + println!("Save the ft job ID: {:?}", job.id) // more constructive message can be used |
| 32 | + } else { |
| 33 | + let train_request = CreateFileRequestArgs::default() |
| 34 | + .file(paths[0]) |
| 35 | + .purpose("fine-tune") |
| 36 | + .build() |
| 37 | + .unwrap(); |
| 38 | + |
| 39 | + let validate_request = CreateFileRequestArgs::default() |
| 40 | + .file(paths[1]) |
| 41 | + .purpose("fine-tune") |
| 42 | + .build() |
| 43 | + .unwrap(); |
| 44 | + |
| 45 | + let trainning_data = client.files().create(train_request).await.unwrap(); |
| 46 | + |
| 47 | + let validation_data = client.files().create(validate_request).await.unwrap(); |
| 48 | + |
| 49 | + let fine_tune_request = CreateFineTuneRequestArgs::default() |
| 50 | + .training_file(trainning_data.id) |
| 51 | + .validation_file(validation_data.id) |
| 52 | + .build() |
| 53 | + .unwrap(); |
| 54 | + |
| 55 | + let job = client.fine_tunes().create(fine_tune_request).await.unwrap(); |
| 56 | + |
| 57 | + println!("Save the ft job ID: {:?}", job.id) // more constructive message can be used |
| 58 | + } |
| 59 | + } |
| 60 | +} |
| 61 | + |
| 62 | +async fn retrieve(job_id: String, client: Client<OpenAIConfig>) { |
| 63 | + let ss = client.fine_tunes().retrieve(&job_id).await.unwrap(); |
| 64 | + |
| 65 | + if let Some(ft_model) = ss.fine_tuned_model { |
| 66 | + println!("{:?}", ft_model) |
| 67 | + } else { |
| 68 | + println!("Please wait a while, your model is not done processing"); |
| 69 | + } |
| 70 | +} |
| 71 | + |
| 72 | +async fn completion(model: String, prompt: String, client: Client<OpenAIConfig>) { |
| 73 | + let request = CreateCompletionRequestArgs::default() |
| 74 | + .model(model) |
| 75 | + .prompt(prompt) |
| 76 | + .max_tokens(1_u16) |
| 77 | + .build() |
| 78 | + .unwrap(); |
| 79 | + |
| 80 | + let response = client.completions().create(request).await.unwrap(); |
| 81 | + |
| 82 | + println!("{:?}", response.choices[0]); |
| 83 | +} |
| 84 | + |
| 85 | +fn cli() -> Command { |
| 86 | + Command::new("ft") |
| 87 | + .about("Fine tune a model by OPENAI ") |
| 88 | + .subcommand_required(true) |
| 89 | + .arg_required_else_help(true) |
| 90 | + .subcommand( |
| 91 | + Command::new("data") |
| 92 | + .about("Provide training and validation (Optional) data") |
| 93 | + .arg_required_else_help(true) |
| 94 | + .arg( |
| 95 | + arg!(<PATH> ... "Path to trainning file and optionally validation file") |
| 96 | + .value_parser(clap::value_parser!(PathBuf)), |
| 97 | + ), |
| 98 | + ) |
| 99 | + .subcommand( |
| 100 | + Command::new("retrieve") |
| 101 | + .about("Retrieve completed fine tune model") |
| 102 | + .arg(arg!(<JOB_ID> "The fine tune job Id")) |
| 103 | + .arg_required_else_help(true), |
| 104 | + ) |
| 105 | + .subcommand( |
| 106 | + Command::new("test") |
| 107 | + .about("classify prompt as positive or negative") |
| 108 | + .arg(arg!(<FINE_TUNE_MODEL> "The remote to target")) |
| 109 | + .arg(arg!(<PROMPT> "Provide a completion prompt to test your model")) |
| 110 | + .arg_required_else_help(true), |
| 111 | + ) |
| 112 | +} |
| 113 | +#[tokio::main] |
| 114 | +async fn main() { |
| 115 | + let config = OpenAIConfig::new(); |
| 116 | + let client = Client::with_config(config); |
| 117 | + |
| 118 | + let matches = cli().get_matches(); |
| 119 | + |
| 120 | + match matches.subcommand() { |
| 121 | + Some(("data", sub_matches)) => { |
| 122 | + let paths = sub_matches |
| 123 | + .get_many::<PathBuf>("PATH") |
| 124 | + .into_iter() |
| 125 | + .flatten() |
| 126 | + .collect::<Vec<_>>(); |
| 127 | + data(paths, client).await |
| 128 | + } |
| 129 | + Some(("retrieve", sub_matches)) => { |
| 130 | + let job_id = sub_matches.get_one::<String>("JOB_ID").expect("required"); |
| 131 | + retrieve(job_id.to_owned(), client).await |
| 132 | + } |
| 133 | + Some(("test", sub_matches)) => { |
| 134 | + let model = sub_matches |
| 135 | + .get_one::<String>("FINE_TUNE_MODEL") |
| 136 | + .expect("required"); |
| 137 | + let prompt = sub_matches.get_one::<String>("PROMPT").expect("required"); |
| 138 | + |
| 139 | + completion(model.to_owned(), prompt.to_owned(), client).await |
| 140 | + } |
| 141 | + _ => unreachable!(), // If all subcommands are defined above, anything else is unreachable!() |
| 142 | + } |
| 143 | +} |
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