chore: better logging (#262)

* rebrand to docwrangler * refactor: rebranding to docwrangler * refactor: rebranding to docwrangler * refactor: edit vercel.json * fix: map optimizer should work * docs: update playground docs * chore: more thorough logging
ucbepic · Jan 3, 2025 · 242ae05 · 242ae05
1 parent f38fbb8
commit 242ae05
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Showing 2 changed files with 9 additions and 3 deletions.
diff --git a/docetl/operations/utils/api.py b/docetl/operations/utils/api.py
@@ -612,7 +612,7 @@ def _parse_llm_response_helper(
             for tool_call in tool_calls:
                 if response.choices[0].finish_reason == "content_filter":
                     raise InvalidOutputError(
-                        "Content filter triggered in LLM response",
+                        "Content filter triggered by LLM provider.",
                         "",
                         schema,
                         response.choices,

diff --git a/docetl/runner.py b/docetl/runner.py
@@ -375,7 +375,8 @@ def execute_step(
                 else:
                     input_data, cost = operation_instance.execute(input_data)
                 total_cost += cost
-                step_content += f"[green]✓[/green] Operation [cyan]{operation_name}[/cyan] completed (Cost: [green]${cost:.2f}[/green])\n"
+                step_content += f"[green]✓[/green] Operation [cyan]{operation_name}[/cyan] (Cost: [green]${cost:.2f}[/green])\n"
+                self.console.print(f"[green]✓[/green] Operation [cyan]{operation_name}[/cyan] completed (Cost: [green]${cost:.2f}[/green])")
 
             # Checkpoint after each operation
             if self.intermediate_dir:
@@ -442,7 +443,12 @@ def _load_from_checkpoint_if_exists(
                 self.datasets[f"{step_name}_{operation_name}"] = Dataset(
                     self, "file", checkpoint_path, "local"
                 )
-            return self.datasets[f"{step_name}_{operation_name}"].load()
+
+                self.console.print(
+                    f"[green]✓[/green] [italic]Loaded checkpoint for operation '{operation_name}' in step '{step_name}' from {checkpoint_path}[/italic]"
+                )
+
+                return self.datasets[f"{step_name}_{operation_name}"].load()
         return None
 
     def clear_intermediate(self) -> None: