starmen1265
ec2d12a05b
- 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>
267 lines
9.5 KiB
Python
267 lines
9.5 KiB
Python
"""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_pipeline.config import get_settings
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from kb_pipeline.link_parser import LinkParser, ParsedLink
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from kb_pipeline.processors.base import BaseProcessor
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from kb_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.output_dir = output_dir or Path.cwd()
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self.settings = get_settings()
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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_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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logger.info(f"Skipping completed step: {step_name}")
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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_pipeline.schemas import (
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ParseInput, DownloadInput, ASRInput, AnalyzeInput
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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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)
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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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)
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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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else:
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# Generic step input
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from kb_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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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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