Added support for model suffix and added migrations for new OpenAI SDK (#169)

instructor/cli/jobs.py

@@ -7,6 +7,8 @@ from rich.live import Live
 from rich.table import Table
 from rich.console import Console
 from datetime import datetime
+from typing import cast
+from openai.types.fine_tuning import FineTuningJob
 
 client = OpenAI()
 app = typer.Typer()
@@ -15,7 +17,8 @@ console = Console()
 
 def generate_table(jobs):
     # Sorting the jobs by creation time
-    jobs = sorted(jobs, key=lambda x: x["created_at"], reverse=True)
+    jobs = sorted(jobs, key=lambda x: (cast(FineTuningJob, x)).created_at, reverse=True)
+    jobs = cast(List[FineTuningJob], jobs)
 
     table = Table(
         title="OpenAI Fine Tuning Job Monitoring",
@@ -37,23 +40,21 @@ def generate_table(jobs):
             "succeeded": "✅",
             "failed": "❌",
             "cancelled": "🚫",
-        }.get(job["status"], "❓")
+        }.get(job.status, "❓")
 
         finished_at = (
-            str(datetime.fromtimestamp(job["finished_at"]))
-            if job["finished_at"]
-            else "N/A"
+            str(datetime.fromtimestamp(job.finished_at)) if job.finished_at else "N/A"
         )
 
         table.add_row(
-            job["id"],
-            f"{status_emoji} [{status_color(job['status'])}]{job['status']}[/]",
-            str(datetime.fromtimestamp(job["created_at"])),
+            job.id,
+            f"{status_emoji} [{status_color(job.status)}]{job.status}[/]",
+            str(datetime.fromtimestamp(job.created_at)),
             finished_at,
-            job["fine_tuned_model"],
-            job["training_file"],
-            str(job["hyperparameters"]["n_epochs"]),
-            job["model"],
+            job.fine_tuned_model,
+            job.training_file,
+            str(job.hyperparameters.n_epochs),
+            job.model,
         )
 
     return table
@@ -66,12 +67,12 @@ def status_color(status: str) -> str:
 
 
 def get_jobs(limit: int = 5) -> List:
-    return client.fine_tuning.list(limit=limit)["data"]
+    return client.fine_tuning.jobs.list(limit=limit).data
 
 
 def get_file_status(file_id: str) -> str:
     response = client.files.retrieve(file_id)
-    return response["status"]
+    return response.status
 
 
 @app.command(
@@ -124,7 +125,7 @@ def create_from_id(
     with console.status(
         f"[bold green]Creating fine-tuning job from ID {id}...", spinner="dots"
     ):
-        job = client.fine_tuning.create(
+        job = client.fine_tuning.jobs.create(
             training_file=id,
             model=model,
             hyperparameters=hyperparameters_dict if hyperparameters_dict else None,
@@ -151,6 +152,7 @@ def create_from_file(
         None, help="Learning rate multiplier for fine-tuning", show_default=False
     ),
     validation_file: str = typer.Option(None, help="Path to the validation file"),
+    model_suffix: str = typer.Option(None, help="Suffix to identify the model"),
 ):
     hyperparameters_dict = {}
     if n_epochs is not None:
@@ -163,13 +165,13 @@ def create_from_file(
     with open(file, "rb") as file:
         response = client.files.create(file=file, purpose="fine-tune")
 
-    file_id = response["id"]
+    file_id = response.id
 
     validation_file_id = None
     if validation_file:
         with open(validation_file, "rb") as val_file:
             val_response = client.files.create(file=val_file, purpose="fine-tune")
-        validation_file_id = val_response["id"]
+        validation_file_id = val_response.id
 
     with console.status(f"Monitoring upload: {file_id} before finetuning...") as status:
         status.spinner_style = "dots"
@@ -190,19 +192,26 @@ def create_from_file(
 
             time.sleep(poll)
 
-    job = client.fine_tuning.create(
+    additional_params = {}
+    if hyperparameters_dict:
+        additional_params["hyperparameters"] = hyperparameters_dict
+    if validation_file:
+        additional_params["validation_file"] = validation_file
+    if model_suffix:
+        additional_params["suffix"] = model_suffix
+
+    job = client.fine_tuning.jobs.create(
         training_file=file_id,
         model=model,
-        hyperparameters=hyperparameters_dict if hyperparameters_dict else None,
-        validation_file=validation_file_id if validation_file else None,
+        **additional_params,
     )
     if validation_file_id:
         console.log(
-            f"[bold green]Fine-tuning job created with ID: {job['id']} from file ID: {file_id} and validation_file ID: {validation_file_id}"
+            f"[bold green]Fine-tuning job created with ID: {job.id} from file ID: {file_id} and validation_file ID: {validation_file_id}"
         )
     else:
         console.log(
-            f"[bold green]Fine-tuning job created with ID: {job['id']} from file ID: {file_id}"
+            f"[bold green]Fine-tuning job created with ID: {job.id} from file ID: {file_id}"
         )
     watch(limit=5, poll=poll, screen=False)
 
@@ -213,7 +222,7 @@ def create_from_file(
 def cancel(id: str = typer.Argument(..., help="ID of the fine-tuning job to cancel")):
     with console.status(f"[bold red]Cancelling job {id}...", spinner="dots"):
         try:
-            client.fine_tuning.cancel(id)
+            client.fine_tuning.jobs.cancel(id)
             console.log(f"[bold red]Job {id} cancelled successfully!")
         except Exception as e:
             console.log(f"[bold red]Error cancelling job {id}: {e}")