Merge pull request #294 from christianlouis/copilot/fix-reprocessing-metadata-embed

Fix retry failure when file moved to processed directory

app/api/files.py

@@ -525,13 +525,34 @@ def _retry_pipeline_step(file_record: FileRecord, step_name: str, db: Session) -
         # Retrying embed requires re-running metadata extraction first, because
         # embed_metadata_into_pdf needs the actual metadata dict (not empty).
         # Re-trigger extract_metadata_with_gpt which will chain into embed_metadata_into_pdf.
-        if not file_record.local_filename or not os.path.exists(file_record.local_filename):
+
+        # Check for file in multiple locations:
+        # 1. Original location in tmp (file_record.local_filename)
+        # 2. Processed location (file_record.processed_file_path)
+        # 3. Fallback to workdir/tmp/<basename>
+        file_path = None
+        if file_record.local_filename and os.path.exists(file_record.local_filename):
+            file_path = file_record.local_filename
+        elif file_record.processed_file_path and os.path.exists(file_record.processed_file_path):
+            # File has been processed and moved to processed directory
+            file_path = file_record.processed_file_path
+        # Try fallback path in workdir/tmp
+        elif file_record.local_filename:
+            workdir = settings.workdir
+            tmp_dir = os.path.join(workdir, "tmp")
+            fallback_path = os.path.join(tmp_dir, os.path.basename(file_record.local_filename))
+            if os.path.exists(fallback_path):
+                file_path = fallback_path
+
+        if not file_path:
             raise HTTPException(
-                status_code=400, detail="Local file not found on disk. Cannot retry metadata embedding."
+                status_code=400,
+                detail="File not found in tmp or processed directory. Cannot retry metadata embedding."
             )
-        extracted_text = _extract_text_from_pdf(file_record.local_filename)
+
-        filename = os.path.basename(file_record.local_filename)
+        extracted_text = _extract_text_from_pdf(file_path)
-        task = extract_metadata_task.delay(filename, extracted_text, file_id)
+        # Pass the full path to the task so it can locate the file
+        task = extract_metadata_task.delay(file_path, extracted_text, file_id)
     else:
         raise HTTPException(status_code=400, detail=f"Unsupported pipeline step: {step_name}")
 

app/tasks/extract_metadata_with_gpt.py

@@ -47,17 +47,28 @@ def extract_json_from_text(text):
 
 @celery.task(base=BaseTaskWithRetry, bind=True)
 def extract_metadata_with_gpt(self, filename: str, cleaned_text: str, file_id: int = None):
-    """Uses OpenAI to classify document metadata."""
+    """
+    Uses OpenAI to classify document metadata.
+
+    Args:
+        filename: Can be either a basename (e.g., "file.pdf") or a full path (e.g., "/workdir/processed/file.pdf")
+        cleaned_text: The extracted text from the document
+        file_id: Optional file ID for tracking
+    """
     task_id = self.request.id
     logger.info(f"[{task_id}] Starting metadata extraction for: {filename}")
     log_task_progress(
-        task_id, "extract_metadata_with_gpt", "in_progress", f"Extracting metadata for {filename}", file_id=file_id
+        task_id, "extract_metadata_with_gpt", "in_progress", f"Extracting metadata for {os.path.basename(filename)}", file_id=file_id
     )
 
     # Get file_id from database if not provided
     if file_id is None:
         tmp_dir = os.path.join(settings.workdir, "tmp")
-        file_path = os.path.join(tmp_dir, filename)
+        # Handle both basename and full path
+        if os.path.isabs(filename):
+            file_path = filename
+        else:
+            file_path = os.path.join(tmp_dir, filename)
         if os.path.exists(file_path):
             with SessionLocal() as db:
                 file_record = db.query(FileRecord).filter_by(local_filename=file_path).first()
@@ -162,14 +173,14 @@ def extract_metadata_with_gpt(self, filename: str, cleaned_text: str, file_id: i
         )
 
         # Trigger the next step: embedding metadata into the PDF
-        # Pass the original filename (UUID-based) so embed_metadata_into_pdf can find the file on disk
+        # Pass the filename (can be basename or full path) so embed_metadata_into_pdf can find the file on disk
         logger.info(f"[{task_id}] Queueing metadata embedding task")
         log_task_progress(
             task_id, "extract_metadata_with_gpt", "success", "Metadata extracted, queuing embed task", file_id=file_id
         )
         embed_metadata_into_pdf.delay(filename, cleaned_text, metadata, file_id)
 
-        return {"s3_file": filename, "metadata": metadata}
+        return {"s3_file": os.path.basename(filename), "metadata": metadata}
 
     except Exception as e:
         logger.exception(f"[{task_id}] OpenAI classification failed for {filename}: {e}")

tests/test_file_detail_endpoints.py

@@ -2,6 +2,7 @@
 Tests for file detail view improvements including reprocessing and preview endpoints.
 """
 
+import shutil
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -257,6 +258,48 @@ class TestSubtaskRetry:
         assert response.status_code == 400
         assert "not found on disk" in response.json()["detail"].lower()
 
+    def test_retry_embed_metadata_with_processed_file(self, client: TestClient, db_session, sample_pdf_path, tmp_path):
+        """Test retrying embed_metadata_into_pdf when file is in processed directory."""
+        mock_task = MagicMock()
+        mock_task.id = "embed-processed-retry-task"
+
+        # Create processed directory and copy file there
+        processed_dir = tmp_path / "processed"
+        processed_dir.mkdir(exist_ok=True)
+        processed_file = processed_dir / "processed_doc.pdf"
+
+        # Copy the sample PDF to processed directory
+        shutil.copy(sample_pdf_path, processed_file)
+
+        # Create file record with non-existent local_filename but existing processed_file_path
+        file_record = FileRecord(
+            filehash="pipeline_retry6",
+            original_filename="processed_doc.pdf",
+            local_filename="/nonexistent/tmp/doc.pdf",  # File no longer in tmp
+            processed_file_path=str(processed_file),  # But exists in processed
+            file_size=1024,
+            mime_type="application/pdf",
+        )
+        db_session.add(file_record)
+        db_session.commit()
+        db_session.refresh(file_record)
+
+        with patch("app.tasks.extract_metadata_with_gpt.extract_metadata_with_gpt") as mock_extract:
+            mock_extract.delay.return_value = mock_task
+            response = client.post(f"/api/files/{file_record.id}/retry-subtask?subtask_name=embed_metadata_into_pdf")
+
+        # Should succeed because file exists in processed directory
+        assert response.status_code == 200
+        data = response.json()
+        assert data["status"] == "success"
+        assert data["subtask_name"] == "embed_metadata_into_pdf"
+        # Verify extract_metadata_with_gpt.delay was called with full path
+        mock_extract.delay.assert_called_once()
+        call_args = mock_extract.delay.call_args
+        # First argument should be the full path to the processed file
+        assert str(processed_file) in str(call_args[0][0])
+
+
 
 @pytest.mark.integration
 class TestFilePreview: