feat(04-05): implement Pipeline.run() method

- Add async run() method to Pipeline class
- Execute processors sequentially with state persistence
- Handle step-specific input types (Parse, Download, ASR, Analyze)
- Support skip_completed for resumable workflows
- Return PipelineOutput with results and content_dir

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

kb_pipeline/pipeline.py

@@ -102,3 +102,165 @@ class Pipeline:
         content_dir = self.output_dir / f"{parsed.platform}_{parsed.content_id}"
         content_dir.mkdir(parents=True, exist_ok=True)
         return content_dir
+
+    async def run(self, url: str) -> PipelineOutput:
+        """Run the pipeline for a given URL.
+
+        Executes all configured processors in order:
+        1. Parse URL to get platform and content_id
+        2. Create content directory
+        3. Run each processor sequentially
+        4. Handle errors and state persistence
+        5. Return results
+
+        Args:
+            url: URL to process
+
+        Returns:
+            PipelineOutput with success status, results, and content_dir
+        """
+        # 1. Parse URL
+        parsed = self._parse_url(url)
+        if not parsed:
+            return PipelineOutput(
+                success=False,
+                error_message=f"Failed to parse URL: {url}",
+                results={},
+                content_dir=None,
+            )
+
+        # 2. Create content directory
+        content_dir = self._get_content_dir(parsed)
+
+        # 3. Initialize state manager
+        from kb_pipeline.state import WorkflowStateManager
+        state_manager = WorkflowStateManager(content_dir)
+
+        # Get or create workflow state
+        state = state_manager.get_or_create(
+            content_id=parsed.content_id,
+            platform=parsed.platform,
+            source_url=url,
+        )
+
+        # 4. Run processors in order
+        results: Dict = {}
+
+        for step_name in self.config.steps:
+            if step_name not in self.processors:
+                logger.warning(f"Step {step_name} not found in processors, skipping")
+                continue
+
+            # Check if step should be skipped
+            if self.config.skip_completed and state.is_step_completed(step_name):
+                logger.info(f"Skipping completed step: {step_name}")
+                continue
+
+            processor = self.processors[step_name]
+
+            # Mark step as started
+            state.mark_step_started(step_name)
+            state_manager.save(state)
+
+            try:
+                # Create step input based on step type
+                from kb_pipeline.schemas import (
+                    ParseInput, DownloadInput, ASRInput, AnalyzeInput
+                )
+
+                if step_name == "parse":
+                    step_input = ParseInput(
+                        url=url,
+                        content_id=parsed.content_id,
+                        platform=parsed.platform,
+                    )
+                elif step_name == "download":
+                    step_input = DownloadInput(
+                        content_id=parsed.content_id,
+                        platform=parsed.platform,
+                        content_dir=str(content_dir),
+                        content_type=parsed.content_type,
+                    )
+                elif step_name == "asr":
+                    # ASR needs video path from download step
+                    download_result = results.get("download", {})
+                    video_path = download_result.get("content_file", "")
+                    step_input = ASRInput(
+                        content_id=parsed.content_id,
+                        platform=parsed.platform,
+                        video_path=video_path,
+                        content_type=parsed.content_type,
+                    )
+                elif step_name == "analyze":
+                    # Analyze needs metadata and content files
+                    download_result = results.get("download", {})
+                    asr_result = results.get("asr", {})
+                    metadata_file = download_result.get("metadata_file", "")
+                    # Use subtitle JSON if available, otherwise content file
+                    content_file = asr_result.get("output_files", {}).get("json", "") or download_result.get("content_file", "")
+
+                    step_input = AnalyzeInput(
+                        content_id=parsed.content_id,
+                        platform=parsed.platform,
+                        metadata_file=metadata_file,
+                        content_file=content_file,
+                        content_type=parsed.content_type,
+                        output_file=str(content_dir / "analysis.md"),
+                    )
+                else:
+                    # Generic step input
+                    from kb_pipeline.schemas import StepInput
+                    step_input = StepInput(
+                        content_id=parsed.content_id,
+                        platform=parsed.platform,
+                    )
+
+                # Run processor
+                step_output = await processor.process(step_input)
+
+                # Record result
+                results[step_name] = {
+                    "success": step_output.success,
+                    "error_message": step_output.error_message,
+                }
+
+                if step_output.success:
+                    # Add output fields to results
+                    for field_name in ["metadata_file", "content_file", "title", "author",
+                                        "analysis_file", "summary", "output_files"]:
+                        if hasattr(step_output, field_name):
+                            results[step_name][field_name] = getattr(step_output, field_name)
+
+                    state.mark_step_completed(step_name)
+                else:
+                    state.mark_step_failed(step_name, step_output.error_message or "Unknown error")
+                    # Stop pipeline on error
+                    state_manager.save(state)
+                    return PipelineOutput(
+                        success=False,
+                        error_message=f"Step {step_name} failed: {step_output.error_message}",
+                        results=results,
+                        content_dir=content_dir,
+                    )
+
+            except Exception as e:
+                logger.error(f"Error in step {step_name}: {e}")
+                state.mark_step_failed(step_name, str(e))
+                state_manager.save(state)
+                return PipelineOutput(
+                    success=False,
+                    error_message=f"Step {step_name} raised exception: {e}",
+                    results=results,
+                    content_dir=content_dir,
+                )
+
+            # Save state after each successful step
+            state_manager.save(state)
+
+        # 5. Return success
+        return PipelineOutput(
+            success=True,
+            error_message=None,
+            results=results,
+            content_dir=content_dir,
+        )