feat(06-02): move pipeline core files to kb_workflow.pipeline
- Move pipeline.py, schemas.py, config.py, state.py, errors.py - Move retry.py, idempotency.py, link_parser.py - Create pipeline/__init__.py with updated imports - Update all internal imports to use kb_workflow.pipeline prefix Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
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"""Pipeline engine - orchestrates multiple processors
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The Pipeline class coordinates the workflow:
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1. Parse URL to extract platform and content ID
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2. Run processors in order (parse, download, asr, analyze)
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3. Handle errors and state persistence
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4. Return results
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Key design decisions:
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- Sequential execution (simpler, easier to debug)
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- State persistence after each step (via WorkflowStateManager)
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- Simple progress logging
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"""
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from __future__ import annotations
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import logging
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from pathlib import Path
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from typing import Dict, List, Optional
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from kb_workflow.pipeline.config import get_settings
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from kb_workflow.pipeline.link_parser import LinkParser, ParsedLink
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from kb_workflow.pipeline.processors.base import BaseProcessor
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from kb_workflow.pipeline.schemas import PipelineConfig, PipelineInput, PipelineOutput
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logger = logging.getLogger(__name__)
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class Pipeline:
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"""Pipeline engine - orchestrates multiple processors
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Orchestrates the workflow:
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1. Parse URL to extract platform and content ID
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2. Run processors in order (parse, download, asr, analyze)
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3. Handle errors and state persistence
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4. Return results
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Key design decisions:
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- Sequential execution (simpler, easier to debug)
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- State persistence after each step (via WorkflowStateManager)
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- Simple progress logging
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"""
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def __init__(
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self,
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processors: List[BaseProcessor],
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config: Optional[PipelineConfig] = None,
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output_dir: Optional[Path] = None,
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):
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"""Initialize the Pipeline.
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Args:
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processors: List of processor instances to use
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config: Optional PipelineConfig for pipeline settings
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output_dir: Optional output directory for content
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Raises:
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ValueError: If a processor's STEP_NAME is not in config.steps
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"""
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self.processors: Dict[str, BaseProcessor] = {p.STEP_NAME: p for p in processors}
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self.config = config or PipelineConfig()
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self.settings = get_settings()
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self.output_dir = output_dir or Path(self.settings.intermediate_path)
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# Validate processors - each processor's STEP_NAME must be in config.steps
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for step_name in self.processors:
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if step_name not in self.config.steps:
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raise ValueError(f"Unknown processor: {step_name}")
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def _parse_url(self, url: str) -> Optional[ParsedLink]:
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"""Parse URL using LinkParser.
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Args:
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url: URL to parse
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Returns:
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ParsedLink with extracted information, or None if URL is not recognized
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"""
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if not url:
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return None
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parser = LinkParser()
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parsed = parser.parse(url)
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if not parsed:
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logger.warning(f"Failed to parse URL: {url}")
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return None
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return parsed
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def _get_content_dir(self, parsed: ParsedLink) -> Path:
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"""Get content directory from parsed URL.
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Creates the directory if it doesn't exist.
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Args:
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parsed: ParsedLink with platform and content_id
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Returns:
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Path to the content directory
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"""
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content_dir = self.output_dir / f"{parsed.platform}_{parsed.content_id}"
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content_dir.mkdir(parents=True, exist_ok=True)
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return content_dir
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async def run(self, url: str) -> PipelineOutput:
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"""Run the pipeline for a given URL.
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Executes all configured processors in order:
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1. Parse URL to get platform and content_id
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2. Create content directory
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3. Run each processor sequentially
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4. Handle errors and state persistence
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5. Return results
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Args:
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url: URL to process
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Returns:
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PipelineOutput with success status, results, and content_dir
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"""
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# 1. Parse URL
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parsed = self._parse_url(url)
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if not parsed:
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return PipelineOutput(
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success=False,
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error_message=f"Failed to parse URL: {url}",
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results={},
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content_dir=None,
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)
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# 2. Create content directory
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content_dir = self._get_content_dir(parsed)
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# 3. Initialize state manager
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from kb_workflow.pipeline.state import WorkflowStateManager
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state_manager = WorkflowStateManager(content_dir)
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# Get or create workflow state
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state = state_manager.get_or_create(
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content_id=parsed.content_id,
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platform=parsed.platform,
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source_url=url,
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)
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# 4. Run processors in order
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results: Dict = {}
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for step_name in self.config.steps:
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if step_name not in self.processors:
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logger.warning(f"Step {step_name} not found in processors, skipping")
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continue
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# Check if step should be skipped
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if self.config.skip_completed and state.is_step_completed(step_name):
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# Ask user whether to re-run the completed step
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should_rerun = self._ask_rerun_step(step_name, content_dir)
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if should_rerun:
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logger.info(f"Re-running completed step: {step_name}")
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# Clear the step's state record so it can be re-executed
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if step_name in state.steps:
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del state.steps[step_name]
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state_manager.save(state)
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else:
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logger.info(f"Skipping completed step: {step_name}")
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# Scan existing files and populate results for skipped steps
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self._populate_skipped_step_results(step_name, results, content_dir, parsed)
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continue
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# Skip ASR for text-based content types
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text_content_types = {"article", "answer", "zhuanlan", "question"}
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if step_name == "asr" and parsed.content_type in text_content_types:
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logger.info(f"Skipping ASR for text content type: {parsed.content_type}")
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continue
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processor = self.processors[step_name]
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# Mark step as started
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state.mark_step_started(step_name)
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state_manager.save(state)
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try:
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# Create step input based on step type
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from kb_workflow.pipeline.schemas import (
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ParseInput, DownloadInput, ASRInput, AnalyzeInput, ArchiveInput
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)
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if step_name == "parse":
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step_input = ParseInput(
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url=url,
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content_id=parsed.content_id,
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platform=parsed.platform,
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)
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elif step_name == "download":
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step_input = DownloadInput(
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content_id=parsed.content_id,
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platform=parsed.platform,
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content_dir=str(content_dir),
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content_type=parsed.content_type,
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source_url=url,
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)
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elif step_name == "asr":
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# ASR needs video path from download step
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download_result = results.get("download", {})
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video_path = download_result.get("content_file", "")
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step_input = ASRInput(
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content_id=parsed.content_id,
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platform=parsed.platform,
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video_path=video_path,
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content_type=parsed.content_type,
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content_dir=str(content_dir),
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)
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elif step_name == "analyze":
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# Analyze needs metadata and content files
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download_result = results.get("download", {})
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asr_result = results.get("asr", {})
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metadata_file = download_result.get("metadata_file", "")
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# Use subtitle JSON if available, otherwise content file
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content_file = asr_result.get("output_files", {}).get("json", "") or download_result.get("content_file", "")
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step_input = AnalyzeInput(
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content_id=parsed.content_id,
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platform=parsed.platform,
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metadata_file=metadata_file,
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content_file=content_file,
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content_type=parsed.content_type,
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output_file=str(content_dir / "analysis.md"),
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)
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elif step_name == "archive":
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# Archive needs analysis file from analyze step
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analyze_result = results.get("analyze", {})
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analysis_file = analyze_result.get("analysis_file", "")
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step_input = ArchiveInput(
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content_id=parsed.content_id,
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platform=parsed.platform,
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content_dir=str(content_dir),
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analysis_file=analysis_file,
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content_type=parsed.content_type,
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)
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else:
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# Generic step input
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from kb_workflow.pipeline.schemas import StepInput
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step_input = StepInput(
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content_id=parsed.content_id,
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platform=parsed.platform,
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)
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# Run processor
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step_output = await processor.process(step_input)
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# Record result
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results[step_name] = {
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"success": step_output.success,
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"error_message": step_output.error_message,
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}
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if step_output.success:
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# Add output fields to results
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for field_name in ["metadata_file", "content_file", "title", "author",
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"analysis_file", "summary", "output_files",
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"archive_file", "skipped"]:
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if hasattr(step_output, field_name):
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results[step_name][field_name] = getattr(step_output, field_name)
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state.mark_step_completed(step_name)
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else:
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state.mark_step_failed(step_name, step_output.error_message or "Unknown error")
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# Stop pipeline on error
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state_manager.save(state)
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return PipelineOutput(
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success=False,
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error_message=f"Step {step_name} failed: {step_output.error_message}",
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results=results,
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content_dir=content_dir,
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)
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except Exception as e:
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logger.error(f"Error in step {step_name}: {e}")
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state.mark_step_failed(step_name, str(e))
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state_manager.save(state)
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return PipelineOutput(
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success=False,
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error_message=f"Step {step_name} raised exception: {e}",
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results=results,
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content_dir=content_dir,
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)
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# Save state after each successful step
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state_manager.save(state)
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# 5. Return success
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return PipelineOutput(
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success=True,
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error_message=None,
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results=results,
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content_dir=content_dir,
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)
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def _ask_rerun_step(self, step_name: str, content_dir: Path) -> bool:
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"""Ask user whether to re-run a completed step.
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Args:
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step_name: Name of the step to potentially re-run
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content_dir: Content directory path
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Returns:
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True if user wants to re-run, False to skip
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"""
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try:
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from rich.console import Console
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from rich.prompt import Confirm
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console = Console()
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return Confirm.ask(
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f"Step '{step_name}' is already completed. Re-run it?",
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default=False
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)
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except ImportError:
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# Fallback to basic input if rich is not available
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response = input(f"Step '{step_name}' is already completed. Re-run it? [y/N] ")
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return response.lower() in ('y', 'yes')
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def _populate_skipped_step_results(
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self,
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step_name: str,
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results: Dict,
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content_dir: Path,
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parsed: ParsedLink
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) -> None:
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"""Populate results for skipped steps by scanning existing files.
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This ensures downstream steps can find the outputs they need,
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even when a step is skipped.
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Args:
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step_name: Name of the skipped step
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results: Results dictionary to populate
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content_dir: Content directory path
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parsed: Parsed link with platform and content_id
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"""
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if step_name == "asr":
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# Scan for subtitle files generated by ASR
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# Try both new naming convention (with _subtitle suffix) and old convention (without suffix)
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base_name_new = f"{parsed.platform}_{parsed.content_id}_subtitle"
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base_name_old = f"{parsed.platform}_{parsed.content_id}"
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json_path = content_dir / f"{base_name_new}.json"
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if not json_path.exists():
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json_path = content_dir / f"{base_name_old}.json"
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srt_path = content_dir / f"{base_name_new}.srt"
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if not srt_path.exists():
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srt_path = content_dir / f"{base_name_old}.srt"
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output_files = {}
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if json_path.exists():
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output_files["json"] = str(json_path)
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if srt_path.exists():
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output_files["srt"] = str(srt_path)
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if output_files:
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results["asr"] = {
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"success": True,
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"error_message": None,
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"output_files": output_files,
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}
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logger.info(f"Found existing ASR outputs: {output_files}")
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else:
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logger.warning(f"No existing ASR outputs found in {content_dir}")
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results["asr"] = {
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"success": False,
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"error_message": "No existing subtitle files found",
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"output_files": {},
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}
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elif step_name == "download":
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# Scan for metadata file
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metadata_path = content_dir / "metadata.json"
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if metadata_path.exists():
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results["download"] = {
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"success": True,
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"error_message": None,
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"metadata_file": str(metadata_path),
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}
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logger.info(f"Found existing download metadata: {metadata_path}")
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elif step_name == "analyze":
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# Scan for analysis output
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analysis_path = content_dir / "analysis.md"
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if analysis_path.exists():
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results["analyze"] = {
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"success": True,
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"error_message": None,
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"analysis_file": str(analysis_path),
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}
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logger.info(f"Found existing analysis: {analysis_path}")
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