12bc29ab36
- Fix extract_visual_data returning 2-tuple instead of 3 (ValueError crash) - Move pytesseract from core deps to [video-full] optional group - Add 30-min timeout + user feedback to video enhancement subprocess - Add scrape_video_impl to MCP server fallback import block - Detect auto-generated YouTube captions via is_generated property - Forward --vision-ocr and --video-playlist through create command - Fix filename collision for non-ASCII video titles (fallback to video_id) - Make _vision_used a proper dataclass field on FrameSubSection - Expose 6 visual params in MCP scrape_video tool - Add install instructions on missing video deps in unified scraper - Update MCP docstring tool counts (25→33, 7 categories) - Add video and word commands to main.py docstring - Document video-full exclusion from [all] deps in pyproject.toml - Update parser registry test count (22→23 for video parser) All 2437 tests passing, 0 failures. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
1318 lines
51 KiB
Python
1318 lines
51 KiB
Python
#!/usr/bin/env python3
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"""
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Unified Multi-Source Scraper
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Orchestrates scraping from multiple sources (documentation, GitHub, PDF),
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detects conflicts, merges intelligently, and builds unified skills.
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This is the main entry point for unified config workflow.
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Usage:
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skill-seekers unified --config configs/godot_unified.json
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skill-seekers unified --config configs/react_unified.json --merge-mode claude-enhanced
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"""
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import argparse
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import json
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import logging
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import os
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import shutil
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import subprocess
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import sys
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from pathlib import Path
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from typing import Any
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# Import validators and scrapers
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try:
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from skill_seekers.cli.config_validator import validate_config
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from skill_seekers.cli.conflict_detector import ConflictDetector
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from skill_seekers.cli.merge_sources import ClaudeEnhancedMerger, RuleBasedMerger
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from skill_seekers.cli.unified_skill_builder import UnifiedSkillBuilder
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from skill_seekers.cli.utils import setup_logging
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except ImportError as e:
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print(f"Error importing modules: {e}")
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print("Make sure you're running from the project root directory")
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sys.exit(1)
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logger = logging.getLogger(__name__)
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class UnifiedScraper:
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"""
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Orchestrates multi-source scraping and merging.
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Main workflow:
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1. Load and validate unified config
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2. Scrape all sources (docs, GitHub, PDF)
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3. Detect conflicts between sources
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4. Merge intelligently (rule-based or Claude-enhanced)
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5. Build unified skill
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"""
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def __init__(self, config_path: str, merge_mode: str | None = None):
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"""
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Initialize unified scraper.
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Args:
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config_path: Path to unified config JSON
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merge_mode: Override config merge_mode ('rule-based' or 'claude-enhanced')
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"""
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self.config_path = config_path
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# Validate and load config
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logger.info(f"Loading config: {config_path}")
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self.validator = validate_config(config_path)
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self.config = self.validator.config
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# Determine merge mode
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self.merge_mode = merge_mode or self.config.get("merge_mode", "rule-based")
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logger.info(f"Merge mode: {self.merge_mode}")
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# Storage for scraped data - use lists to support multiple sources of same type
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self.scraped_data = {
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"documentation": [], # List of doc sources
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"github": [], # List of github sources
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"pdf": [], # List of pdf sources
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"word": [], # List of word sources
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"video": [], # List of video sources
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"local": [], # List of local sources (docs or code)
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}
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# Track source index for unique naming (multi-source support)
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self._source_counters = {
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"documentation": 0,
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"github": 0,
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"pdf": 0,
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"word": 0,
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"video": 0,
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"local": 0,
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}
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# Output paths - cleaner organization
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self.name = self.config["name"]
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self.output_dir = f"output/{self.name}" # Final skill only
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# Use hidden cache directory for intermediate files
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self.cache_dir = f".skillseeker-cache/{self.name}"
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self.sources_dir = f"{self.cache_dir}/sources"
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self.data_dir = f"{self.cache_dir}/data"
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self.repos_dir = f"{self.cache_dir}/repos"
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self.logs_dir = f"{self.cache_dir}/logs"
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# Create directories
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os.makedirs(self.output_dir, exist_ok=True)
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os.makedirs(self.sources_dir, exist_ok=True)
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os.makedirs(self.data_dir, exist_ok=True)
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os.makedirs(self.repos_dir, exist_ok=True)
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os.makedirs(self.logs_dir, exist_ok=True)
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# Setup file logging
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self._setup_logging()
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def _setup_logging(self):
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"""Setup file logging for this scraping session."""
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from datetime import datetime
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# Create log filename with timestamp
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timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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log_file = f"{self.logs_dir}/unified_{timestamp}.log"
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# Add file handler to root logger
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file_handler = logging.FileHandler(log_file, encoding="utf-8")
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file_handler.setLevel(logging.DEBUG)
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# Create formatter
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formatter = logging.Formatter(
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"%(asctime)s - %(name)s - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
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)
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file_handler.setFormatter(formatter)
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# Add to root logger
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logging.getLogger().addHandler(file_handler)
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logger.info(f"📝 Logging to: {log_file}")
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logger.info(f"🗂️ Cache directory: {self.cache_dir}")
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def scrape_all_sources(self):
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"""
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Scrape all configured sources.
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Routes to appropriate scraper based on source type.
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"""
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logger.info("=" * 60)
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logger.info("PHASE 1: Scraping all sources")
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logger.info("=" * 60)
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if not self.validator.is_unified:
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logger.warning("Config is not unified format, converting...")
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self.config = self.validator.convert_legacy_to_unified()
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sources = self.config.get("sources", [])
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for i, source in enumerate(sources):
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source_type = source["type"]
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logger.info(f"\n[{i + 1}/{len(sources)}] Scraping {source_type} source...")
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try:
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if source_type == "documentation":
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self._scrape_documentation(source)
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elif source_type == "github":
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self._scrape_github(source)
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elif source_type == "pdf":
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self._scrape_pdf(source)
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elif source_type == "word":
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self._scrape_word(source)
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elif source_type == "video":
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self._scrape_video(source)
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elif source_type == "local":
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self._scrape_local(source)
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else:
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logger.warning(f"Unknown source type: {source_type}")
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except Exception as e:
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logger.error(f"Error scraping {source_type}: {e}")
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logger.info("Continuing with other sources...")
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logger.info(f"\n✅ Scraped {len(self.scraped_data)} sources successfully")
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def _scrape_documentation(self, source: dict[str, Any]):
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"""Scrape documentation website."""
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# Create temporary config for doc scraper
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doc_config = {
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"name": f"{self.name}_docs",
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"base_url": source["base_url"],
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"selectors": source.get("selectors", {}),
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"url_patterns": source.get("url_patterns", {}),
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"categories": source.get("categories", {}),
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"rate_limit": source.get("rate_limit", 0.5),
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"max_pages": source.get("max_pages", 100),
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}
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# Pass through llms.txt settings (so unified configs behave the same as doc_scraper configs)
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if "llms_txt_url" in source:
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doc_config["llms_txt_url"] = source.get("llms_txt_url")
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if "skip_llms_txt" in source:
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doc_config["skip_llms_txt"] = source.get("skip_llms_txt")
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# Optional: support overriding start URLs
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if "start_urls" in source:
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doc_config["start_urls"] = source.get("start_urls")
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# Write temporary config
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temp_config_path = os.path.join(self.data_dir, "temp_docs_config.json")
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with open(temp_config_path, "w", encoding="utf-8") as f:
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json.dump(doc_config, f, indent=2)
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# Run doc_scraper as subprocess
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logger.info(f"Scraping documentation from {source['base_url']}")
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doc_scraper_path = Path(__file__).parent / "doc_scraper.py"
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cmd = [sys.executable, str(doc_scraper_path), "--config", temp_config_path, "--fresh"]
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result = subprocess.run(cmd, capture_output=True, text=True, stdin=subprocess.DEVNULL)
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if result.returncode != 0:
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logger.error(f"Documentation scraping failed with return code {result.returncode}")
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logger.error(f"STDERR: {result.stderr}")
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logger.error(f"STDOUT: {result.stdout}")
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return
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# Log subprocess output for debugging
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if result.stdout:
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logger.info(f"Doc scraper output: {result.stdout[-500:]}") # Last 500 chars
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# Load scraped data
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docs_data_file = f"output/{doc_config['name']}_data/summary.json"
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if os.path.exists(docs_data_file):
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with open(docs_data_file, encoding="utf-8") as f:
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summary = json.load(f)
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# Append to documentation list (multi-source support)
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self.scraped_data["documentation"].append(
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{
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"source_id": doc_config["name"],
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"base_url": source["base_url"],
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"pages": summary.get("pages", []),
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"total_pages": summary.get("total_pages", 0),
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"data_file": docs_data_file,
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"refs_dir": "", # Will be set after moving to cache
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}
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)
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logger.info(f"✅ Documentation: {summary.get('total_pages', 0)} pages scraped")
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else:
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logger.warning("Documentation data file not found")
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# Clean up temp config
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if os.path.exists(temp_config_path):
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os.remove(temp_config_path)
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# Move intermediate files to cache to keep output/ clean
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docs_output_dir = f"output/{doc_config['name']}"
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docs_data_dir = f"output/{doc_config['name']}_data"
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if os.path.exists(docs_output_dir):
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cache_docs_dir = os.path.join(self.sources_dir, f"{doc_config['name']}")
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if os.path.exists(cache_docs_dir):
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shutil.rmtree(cache_docs_dir)
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shutil.move(docs_output_dir, cache_docs_dir)
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logger.info(f"📦 Moved docs output to cache: {cache_docs_dir}")
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# Update refs_dir in scraped_data with cache location
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refs_dir_path = os.path.join(cache_docs_dir, "references")
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if self.scraped_data["documentation"]:
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self.scraped_data["documentation"][-1]["refs_dir"] = refs_dir_path
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if os.path.exists(docs_data_dir):
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cache_data_dir = os.path.join(self.data_dir, f"{doc_config['name']}_data")
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if os.path.exists(cache_data_dir):
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shutil.rmtree(cache_data_dir)
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shutil.move(docs_data_dir, cache_data_dir)
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logger.info(f"📦 Moved docs data to cache: {cache_data_dir}")
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def _clone_github_repo(self, repo_name: str, idx: int = 0) -> str | None:
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"""
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Clone GitHub repository to cache directory for C3.x analysis.
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Reuses existing clone if already present.
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Args:
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repo_name: GitHub repo in format "owner/repo"
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idx: Source index for unique naming when multiple repos
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Returns:
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Path to cloned repo, or None if clone failed
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"""
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# Clone to cache repos folder for future reuse
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repo_dir_name = f"{idx}_{repo_name.replace('/', '_')}" # e.g., 0_encode_httpx
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clone_path = os.path.join(self.repos_dir, repo_dir_name)
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# Check if already cloned
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if os.path.exists(clone_path) and os.path.isdir(os.path.join(clone_path, ".git")):
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logger.info(f"♻️ Found existing repository clone: {clone_path}")
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logger.info(" Reusing for C3.x analysis (skip re-cloning)")
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return clone_path
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# repos_dir already created in __init__
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# Clone repo (full clone, not shallow - for complete analysis)
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repo_url = f"https://github.com/{repo_name}.git"
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logger.info(f"🔄 Cloning repository for C3.x analysis: {repo_url}")
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logger.info(f" → {clone_path}")
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logger.info(" 💾 Clone will be saved for future reuse")
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try:
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result = subprocess.run(
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["git", "clone", repo_url, clone_path],
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capture_output=True,
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text=True,
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timeout=600, # 10 minute timeout for full clone
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)
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if result.returncode == 0:
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logger.info("✅ Repository cloned successfully")
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logger.info(f" 📁 Saved to: {clone_path}")
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return clone_path
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else:
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logger.error(f"❌ Git clone failed: {result.stderr}")
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# Clean up failed clone
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if os.path.exists(clone_path):
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shutil.rmtree(clone_path)
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return None
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except subprocess.TimeoutExpired:
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logger.error("❌ Git clone timed out after 10 minutes")
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if os.path.exists(clone_path):
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shutil.rmtree(clone_path)
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return None
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except Exception as e:
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logger.error(f"❌ Git clone failed: {e}")
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if os.path.exists(clone_path):
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shutil.rmtree(clone_path)
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return None
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def _scrape_github(self, source: dict[str, Any]):
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"""Scrape GitHub repository."""
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try:
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from skill_seekers.cli.github_scraper import GitHubScraper
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except ImportError:
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logger.error("github_scraper.py not found")
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return
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# Multi-source support: Get unique index for this GitHub source
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idx = self._source_counters["github"]
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self._source_counters["github"] += 1
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# Extract repo identifier for unique naming
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repo = source["repo"]
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repo_id = repo.replace("/", "_")
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# Check if we need to clone for C3.x analysis
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enable_codebase_analysis = source.get("enable_codebase_analysis", True)
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local_repo_path = source.get("local_repo_path")
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cloned_repo_path = None
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# Auto-clone if C3.x analysis is enabled but no local path provided
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if enable_codebase_analysis and not local_repo_path:
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logger.info("🔬 C3.x codebase analysis enabled - cloning repository...")
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cloned_repo_path = self._clone_github_repo(repo, idx=idx)
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if cloned_repo_path:
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local_repo_path = cloned_repo_path
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logger.info(f"✅ Using cloned repo for C3.x analysis: {local_repo_path}")
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else:
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logger.warning("⚠️ Failed to clone repo - C3.x analysis will be skipped")
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enable_codebase_analysis = False
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|
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# Create config for GitHub scraper
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github_config = {
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"repo": repo,
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"name": f"{self.name}_github_{idx}_{repo_id}",
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"github_token": source.get("github_token"),
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"include_issues": source.get("include_issues", True),
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"max_issues": source.get("max_issues", 100),
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"include_changelog": source.get("include_changelog", True),
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"include_releases": source.get("include_releases", True),
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"include_code": source.get("include_code", True),
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"code_analysis_depth": source.get("code_analysis_depth", "surface"),
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"file_patterns": source.get("file_patterns", []),
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"local_repo_path": local_repo_path, # Use cloned path if available
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}
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# Pass directory exclusions if specified (optional)
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if "exclude_dirs" in source:
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github_config["exclude_dirs"] = source["exclude_dirs"]
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if "exclude_dirs_additional" in source:
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github_config["exclude_dirs_additional"] = source["exclude_dirs_additional"]
|
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# Scrape
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logger.info(f"Scraping GitHub repository: {source['repo']}")
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scraper = GitHubScraper(github_config)
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github_data = scraper.scrape()
|
|
|
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# Run C3.x codebase analysis if enabled and local_repo_path available
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if enable_codebase_analysis and local_repo_path:
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logger.info("🔬 Running C3.x codebase analysis...")
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try:
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c3_data = self._run_c3_analysis(local_repo_path, source)
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if c3_data:
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github_data["c3_analysis"] = c3_data
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logger.info("✅ C3.x analysis complete")
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else:
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logger.warning("⚠️ C3.x analysis returned no data")
|
|
except Exception as e:
|
|
logger.warning(f"⚠️ C3.x analysis failed: {e}")
|
|
import traceback
|
|
|
|
logger.debug(f"Traceback: {traceback.format_exc()}")
|
|
# Continue without C3.x data - graceful degradation
|
|
|
|
# Note: We keep the cloned repo in output/ for future reuse
|
|
if cloned_repo_path:
|
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logger.info(f"📁 Repository clone saved for future use: {cloned_repo_path}")
|
|
|
|
# Save data to unified location with unique filename
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github_data_file = os.path.join(self.data_dir, f"github_data_{idx}_{repo_id}.json")
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with open(github_data_file, "w", encoding="utf-8") as f:
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json.dump(github_data, f, indent=2, ensure_ascii=False)
|
|
|
|
# ALSO save to the location GitHubToSkillConverter expects (with C3.x data!)
|
|
converter_data_file = f"output/{github_config['name']}_github_data.json"
|
|
with open(converter_data_file, "w", encoding="utf-8") as f:
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json.dump(github_data, f, indent=2, ensure_ascii=False)
|
|
|
|
# Append to list instead of overwriting (multi-source support)
|
|
self.scraped_data["github"].append(
|
|
{
|
|
"repo": repo,
|
|
"repo_id": repo_id,
|
|
"idx": idx,
|
|
"data": github_data,
|
|
"data_file": github_data_file,
|
|
}
|
|
)
|
|
|
|
# Build standalone SKILL.md for synthesis using GitHubToSkillConverter
|
|
try:
|
|
from skill_seekers.cli.github_scraper import GitHubToSkillConverter
|
|
|
|
# Use github_config which has the correct name field
|
|
# Converter will load from output/{name}_github_data.json which now has C3.x data
|
|
converter = GitHubToSkillConverter(config=github_config)
|
|
converter.build_skill()
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|
logger.info("✅ GitHub: Standalone SKILL.md created")
|
|
except Exception as e:
|
|
logger.warning(f"⚠️ Failed to build standalone GitHub SKILL.md: {e}")
|
|
|
|
# Move intermediate files to cache to keep output/ clean
|
|
github_output_dir = f"output/{github_config['name']}"
|
|
github_data_file_path = f"output/{github_config['name']}_github_data.json"
|
|
|
|
if os.path.exists(github_output_dir):
|
|
cache_github_dir = os.path.join(self.sources_dir, github_config["name"])
|
|
if os.path.exists(cache_github_dir):
|
|
shutil.rmtree(cache_github_dir)
|
|
shutil.move(github_output_dir, cache_github_dir)
|
|
logger.info(f"📦 Moved GitHub output to cache: {cache_github_dir}")
|
|
|
|
if os.path.exists(github_data_file_path):
|
|
cache_github_data = os.path.join(
|
|
self.data_dir, f"{github_config['name']}_github_data.json"
|
|
)
|
|
if os.path.exists(cache_github_data):
|
|
os.remove(cache_github_data)
|
|
shutil.move(github_data_file_path, cache_github_data)
|
|
logger.info(f"📦 Moved GitHub data to cache: {cache_github_data}")
|
|
|
|
logger.info("✅ GitHub: Repository scraped successfully")
|
|
|
|
def _scrape_pdf(self, source: dict[str, Any]):
|
|
"""Scrape PDF document."""
|
|
try:
|
|
from skill_seekers.cli.pdf_scraper import PDFToSkillConverter
|
|
except ImportError:
|
|
logger.error("pdf_scraper.py not found")
|
|
return
|
|
|
|
# Multi-source support: Get unique index for this PDF source
|
|
idx = self._source_counters["pdf"]
|
|
self._source_counters["pdf"] += 1
|
|
|
|
# Extract PDF identifier for unique naming (filename without extension)
|
|
pdf_path = source["path"]
|
|
pdf_id = os.path.splitext(os.path.basename(pdf_path))[0]
|
|
|
|
# Create config for PDF scraper
|
|
pdf_config = {
|
|
"name": f"{self.name}_pdf_{idx}_{pdf_id}",
|
|
"pdf_path": source["path"], # Fixed: use pdf_path instead of pdf
|
|
"description": f"{source.get('name', pdf_id)} documentation",
|
|
"extract_tables": source.get("extract_tables", False),
|
|
"ocr": source.get("ocr", False),
|
|
"password": source.get("password"),
|
|
}
|
|
|
|
# Scrape
|
|
logger.info(f"Scraping PDF: {source['path']}")
|
|
converter = PDFToSkillConverter(pdf_config)
|
|
|
|
# Extract PDF content
|
|
converter.extract_pdf()
|
|
|
|
# Load extracted data from file
|
|
pdf_data_file = converter.data_file
|
|
with open(pdf_data_file, encoding="utf-8") as f:
|
|
pdf_data = json.load(f)
|
|
|
|
# Copy data file to cache
|
|
cache_pdf_data = os.path.join(self.data_dir, f"pdf_data_{idx}_{pdf_id}.json")
|
|
shutil.copy(pdf_data_file, cache_pdf_data)
|
|
|
|
# Append to list instead of overwriting
|
|
self.scraped_data["pdf"].append(
|
|
{
|
|
"pdf_path": pdf_path,
|
|
"pdf_id": pdf_id,
|
|
"idx": idx,
|
|
"data": pdf_data,
|
|
"data_file": cache_pdf_data,
|
|
}
|
|
)
|
|
|
|
# Build standalone SKILL.md for synthesis
|
|
try:
|
|
converter.build_skill()
|
|
logger.info("✅ PDF: Standalone SKILL.md created")
|
|
except Exception as e:
|
|
logger.warning(f"⚠️ Failed to build standalone PDF SKILL.md: {e}")
|
|
|
|
logger.info(f"✅ PDF: {len(pdf_data.get('pages', []))} pages extracted")
|
|
|
|
def _scrape_word(self, source: dict[str, Any]):
|
|
"""Scrape Word document (.docx)."""
|
|
try:
|
|
from skill_seekers.cli.word_scraper import WordToSkillConverter
|
|
except ImportError:
|
|
logger.error("word_scraper.py not found")
|
|
return
|
|
|
|
# Multi-source support: Get unique index for this Word source
|
|
idx = self._source_counters["word"]
|
|
self._source_counters["word"] += 1
|
|
|
|
# Extract Word identifier for unique naming (filename without extension)
|
|
docx_path = source["path"]
|
|
docx_id = os.path.splitext(os.path.basename(docx_path))[0]
|
|
|
|
# Create config for Word scraper
|
|
word_config = {
|
|
"name": f"{self.name}_word_{idx}_{docx_id}",
|
|
"docx_path": source["path"],
|
|
"description": f"{source.get('name', docx_id)} documentation",
|
|
}
|
|
|
|
# Scrape
|
|
logger.info(f"Scraping Word document: {source['path']}")
|
|
converter = WordToSkillConverter(word_config)
|
|
|
|
# Extract Word content
|
|
converter.extract_docx()
|
|
|
|
# Load extracted data from file
|
|
word_data_file = converter.data_file
|
|
with open(word_data_file, encoding="utf-8") as f:
|
|
word_data = json.load(f)
|
|
|
|
# Copy data file to cache
|
|
cache_word_data = os.path.join(self.data_dir, f"word_data_{idx}_{docx_id}.json")
|
|
shutil.copy(word_data_file, cache_word_data)
|
|
|
|
# Append to list
|
|
self.scraped_data["word"].append(
|
|
{
|
|
"docx_path": docx_path,
|
|
"docx_id": docx_id,
|
|
"idx": idx,
|
|
"data": word_data,
|
|
"data_file": cache_word_data,
|
|
}
|
|
)
|
|
|
|
# Build standalone SKILL.md for synthesis
|
|
try:
|
|
converter.build_skill()
|
|
logger.info("✅ Word: Standalone SKILL.md created")
|
|
except Exception as e:
|
|
logger.warning(f"⚠️ Failed to build standalone Word SKILL.md: {e}")
|
|
|
|
logger.info(f"✅ Word: {len(word_data.get('pages', []))} sections extracted")
|
|
|
|
def _scrape_video(self, source: dict[str, Any]):
|
|
"""Scrape video source (YouTube, local file, etc.)."""
|
|
try:
|
|
from skill_seekers.cli.video_scraper import VideoToSkillConverter
|
|
except ImportError as e:
|
|
logger.error(
|
|
f"Video scraper dependencies not installed: {e}\n"
|
|
" Install with: pip install skill-seekers[video]\n"
|
|
" For visual extraction (frame analysis, OCR): pip install skill-seekers[video-full]"
|
|
)
|
|
return
|
|
|
|
# Multi-source support: Get unique index for this video source
|
|
idx = self._source_counters["video"]
|
|
self._source_counters["video"] += 1
|
|
|
|
# Determine video identifier
|
|
video_url = source.get("url", "")
|
|
video_id = video_url or source.get("path", f"video_{idx}")
|
|
|
|
# Create config for video scraper
|
|
video_config = {
|
|
"name": f"{self.name}_video_{idx}",
|
|
"url": source.get("url"),
|
|
"video_file": source.get("path"),
|
|
"playlist": source.get("playlist"),
|
|
"description": source.get("description", ""),
|
|
"languages": ",".join(source.get("languages", ["en"])),
|
|
"visual": source.get("visual_extraction", False),
|
|
"whisper_model": source.get("whisper_model", "base"),
|
|
}
|
|
|
|
# Process video
|
|
logger.info(f"Scraping video: {video_id}")
|
|
converter = VideoToSkillConverter(video_config)
|
|
|
|
try:
|
|
result = converter.process()
|
|
converter.save_extracted_data()
|
|
|
|
# Append to list
|
|
self.scraped_data["video"].append(
|
|
{
|
|
"video_id": video_id,
|
|
"idx": idx,
|
|
"data": result.to_dict(),
|
|
"data_file": converter.data_file,
|
|
}
|
|
)
|
|
|
|
# Build standalone SKILL.md for synthesis
|
|
converter.build_skill()
|
|
logger.info("✅ Video: Standalone SKILL.md created")
|
|
|
|
logger.info(
|
|
f"✅ Video: {len(result.videos)} videos, {result.total_segments} segments extracted"
|
|
)
|
|
except Exception as e:
|
|
logger.error(f"Failed to process video source: {e}")
|
|
|
|
def _scrape_local(self, source: dict[str, Any]):
|
|
"""
|
|
Scrape local directory (documentation files or source code).
|
|
|
|
Handles both:
|
|
- Local documentation files (RST, Markdown, etc.)
|
|
- Local source code for C3.x analysis
|
|
"""
|
|
try:
|
|
from skill_seekers.cli.codebase_scraper import analyze_codebase
|
|
except ImportError:
|
|
logger.error("codebase_scraper.py not found")
|
|
return
|
|
|
|
# Multi-source support: Get unique index for this local source
|
|
idx = self._source_counters.get("local", 0)
|
|
self._source_counters["local"] = idx + 1
|
|
|
|
# Extract path and create identifier
|
|
local_path = source["path"]
|
|
path_id = os.path.basename(local_path.rstrip("/"))
|
|
source_name = source.get("name", path_id)
|
|
|
|
logger.info(f"Analyzing local directory: {local_path}")
|
|
|
|
# Create temp output dir for local source analysis
|
|
temp_output = Path(self.data_dir) / f"local_analysis_{idx}_{path_id}"
|
|
temp_output.mkdir(parents=True, exist_ok=True)
|
|
|
|
try:
|
|
# Map source config to analyze_codebase parameters
|
|
analysis_depth = source.get("analysis_depth", "deep")
|
|
languages = source.get("languages")
|
|
file_patterns = source.get("file_patterns")
|
|
# Note: skip_patterns is not supported by analyze_codebase()
|
|
# It's a config validator field but not used in codebase analysis
|
|
|
|
# Map feature flags (default all ON for unified configs)
|
|
build_api_reference = source.get("api_reference", True)
|
|
build_dependency_graph = source.get("dependency_graph", True)
|
|
detect_patterns = source.get("extract_patterns", True)
|
|
extract_test_examples = source.get("extract_tests", True)
|
|
build_how_to_guides = source.get("how_to_guides", True)
|
|
extract_config_patterns = source.get("extract_config", True)
|
|
extract_docs = source.get("extract_docs", True)
|
|
# Note: Signal flow analysis is automatic for Godot projects (C3.10)
|
|
|
|
# AI enhancement settings (CLI --enhance-level overrides per-source config)
|
|
cli_args = getattr(self, "_cli_args", None)
|
|
cli_enhance_level = (
|
|
getattr(cli_args, "enhance_level", None) if cli_args is not None else None
|
|
)
|
|
enhance_level = (
|
|
cli_enhance_level
|
|
if cli_enhance_level is not None
|
|
else source.get("enhance_level", 0)
|
|
)
|
|
|
|
# Run codebase analysis
|
|
logger.info(f" Analysis depth: {analysis_depth}")
|
|
if languages:
|
|
logger.info(f" Languages: {', '.join(languages)}")
|
|
if file_patterns:
|
|
logger.info(f" File patterns: {', '.join(file_patterns)}")
|
|
|
|
analyze_codebase(
|
|
directory=Path(local_path),
|
|
output_dir=temp_output,
|
|
depth=analysis_depth,
|
|
languages=languages,
|
|
file_patterns=file_patterns,
|
|
build_api_reference=build_api_reference,
|
|
extract_comments=False, # Not needed for unified configs
|
|
build_dependency_graph=build_dependency_graph,
|
|
detect_patterns=detect_patterns,
|
|
extract_test_examples=extract_test_examples,
|
|
build_how_to_guides=build_how_to_guides,
|
|
extract_config_patterns=extract_config_patterns,
|
|
extract_docs=extract_docs,
|
|
enhance_level=enhance_level,
|
|
)
|
|
|
|
# Load analysis outputs into memory
|
|
local_data = {
|
|
"source_id": f"{self.name}_local_{idx}_{path_id}",
|
|
"path": local_path,
|
|
"name": source_name,
|
|
"description": source.get("description", f"Local analysis of {path_id}"),
|
|
"weight": source.get("weight", 1.0),
|
|
"patterns": self._load_json(temp_output / "patterns" / "detected_patterns.json"),
|
|
"test_examples": self._load_json(
|
|
temp_output / "test_examples" / "test_examples.json"
|
|
),
|
|
"how_to_guides": self._load_guide_collection(temp_output / "tutorials"),
|
|
"config_patterns": self._load_json(
|
|
temp_output / "config_patterns" / "config_patterns.json"
|
|
),
|
|
"architecture": self._load_json(temp_output / "ARCHITECTURE.json"),
|
|
"api_reference": self._load_api_reference(temp_output / "api_reference"),
|
|
"dependency_graph": self._load_json(
|
|
temp_output / "dependencies" / "dependency_graph.json"
|
|
),
|
|
}
|
|
|
|
# Handle signal flow analysis for Godot projects (C3.10)
|
|
# Signal analysis is automatic for Godot files
|
|
signal_flow_file = temp_output / "signals" / "signal_flow.json"
|
|
if signal_flow_file.exists():
|
|
local_data["signal_flow"] = self._load_json(signal_flow_file)
|
|
logger.info("✅ Signal flow analysis included (Godot)")
|
|
|
|
# Load SKILL.md if it exists
|
|
skill_md_path = temp_output / "SKILL.md"
|
|
if skill_md_path.exists():
|
|
local_data["skill_md"] = skill_md_path.read_text(encoding="utf-8")
|
|
logger.info(f"✅ Local: SKILL.md loaded ({len(local_data['skill_md'])} chars)")
|
|
|
|
# Save local data to cache
|
|
local_data_file = os.path.join(self.data_dir, f"local_data_{idx}_{path_id}.json")
|
|
with open(local_data_file, "w", encoding="utf-8") as f:
|
|
# Don't save skill_md in JSON (too large), keep it in local_data dict
|
|
json_data = {k: v for k, v in local_data.items() if k != "skill_md"}
|
|
json.dump(json_data, f, indent=2, ensure_ascii=False)
|
|
|
|
# Move SKILL.md to cache if it exists
|
|
skill_cache_dir = os.path.join(self.sources_dir, f"local_{idx}_{path_id}")
|
|
os.makedirs(skill_cache_dir, exist_ok=True)
|
|
if skill_md_path.exists():
|
|
shutil.copy(skill_md_path, os.path.join(skill_cache_dir, "SKILL.md"))
|
|
|
|
# Append to local sources list
|
|
self.scraped_data["local"].append(local_data)
|
|
|
|
logger.info(f"✅ Local: Analysis complete for {path_id}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Local analysis failed: {e}")
|
|
import traceback
|
|
|
|
logger.debug(f"Traceback: {traceback.format_exc()}")
|
|
raise
|
|
|
|
def _load_json(self, file_path: Path) -> dict:
|
|
"""
|
|
Load JSON file safely.
|
|
|
|
Args:
|
|
file_path: Path to JSON file
|
|
|
|
Returns:
|
|
Dict with JSON data, or empty dict if file doesn't exist or is invalid
|
|
"""
|
|
if not file_path.exists():
|
|
logger.warning(f"JSON file not found: {file_path}")
|
|
return {}
|
|
|
|
try:
|
|
with open(file_path, encoding="utf-8") as f:
|
|
return json.load(f)
|
|
except (OSError, json.JSONDecodeError) as e:
|
|
logger.warning(f"Failed to load JSON {file_path}: {e}")
|
|
return {}
|
|
|
|
def _load_guide_collection(self, tutorials_dir: Path) -> dict:
|
|
"""
|
|
Load how-to guide collection from tutorials directory.
|
|
|
|
Args:
|
|
tutorials_dir: Path to tutorials directory
|
|
|
|
Returns:
|
|
Dict with guide collection data
|
|
"""
|
|
if not tutorials_dir.exists():
|
|
logger.warning(f"Tutorials directory not found: {tutorials_dir}")
|
|
return {"guides": []}
|
|
|
|
collection_file = tutorials_dir / "guide_collection.json"
|
|
if collection_file.exists():
|
|
return self._load_json(collection_file)
|
|
|
|
# Fallback: scan for individual guide JSON files
|
|
guides = []
|
|
for guide_file in tutorials_dir.glob("guide_*.json"):
|
|
guide_data = self._load_json(guide_file)
|
|
if guide_data:
|
|
guides.append(guide_data)
|
|
|
|
return {"guides": guides, "total_count": len(guides)}
|
|
|
|
def _load_api_reference(self, api_dir: Path) -> dict[str, Any]:
|
|
"""
|
|
Load API reference markdown files from api_reference directory.
|
|
|
|
Args:
|
|
api_dir: Path to api_reference directory
|
|
|
|
Returns:
|
|
Dict mapping module names to markdown content, or empty dict if not found
|
|
"""
|
|
if not api_dir.exists():
|
|
logger.debug(f"API reference directory not found: {api_dir}")
|
|
return {}
|
|
|
|
api_refs = {}
|
|
for md_file in api_dir.glob("*.md"):
|
|
try:
|
|
module_name = md_file.stem
|
|
api_refs[module_name] = md_file.read_text(encoding="utf-8")
|
|
except OSError as e:
|
|
logger.warning(f"Failed to read API reference {md_file}: {e}")
|
|
|
|
return api_refs
|
|
|
|
def _run_c3_analysis(self, local_repo_path: str, source: dict[str, Any]) -> dict[str, Any]:
|
|
"""
|
|
Run comprehensive C3.x codebase analysis.
|
|
|
|
Calls codebase_scraper.analyze_codebase() with all C3.x features enabled,
|
|
loads the results into memory, and cleans up temporary files.
|
|
|
|
Args:
|
|
local_repo_path: Path to local repository
|
|
source: GitHub source configuration dict
|
|
|
|
Returns:
|
|
Dict with keys: patterns, test_examples, how_to_guides,
|
|
config_patterns, architecture
|
|
"""
|
|
try:
|
|
from skill_seekers.cli.codebase_scraper import analyze_codebase
|
|
except ImportError:
|
|
logger.error("codebase_scraper.py not found")
|
|
return {}
|
|
|
|
# Create temp output dir for C3.x analysis
|
|
temp_output = Path(self.data_dir) / "c3_analysis_temp"
|
|
temp_output.mkdir(parents=True, exist_ok=True)
|
|
|
|
logger.info(f" Analyzing codebase: {local_repo_path}")
|
|
|
|
try:
|
|
# Run full C3.x analysis
|
|
_results = analyze_codebase(
|
|
directory=Path(local_repo_path),
|
|
output_dir=temp_output,
|
|
depth="deep",
|
|
languages=None, # Analyze all languages
|
|
file_patterns=source.get("file_patterns"),
|
|
build_api_reference=True, # C2.5: API Reference
|
|
extract_comments=False, # Not needed
|
|
build_dependency_graph=True, # C2.6: Dependency Graph
|
|
detect_patterns=True, # C3.1: Design patterns
|
|
extract_test_examples=True, # C3.2: Test examples
|
|
build_how_to_guides=True, # C3.3: How-to guides
|
|
extract_config_patterns=True, # C3.4: Config patterns
|
|
enhance_with_ai=source.get("ai_mode", "auto") != "none",
|
|
ai_mode=source.get("ai_mode", "auto"),
|
|
)
|
|
|
|
# Load C3.x outputs into memory
|
|
c3_data = {
|
|
"patterns": self._load_json(temp_output / "patterns" / "detected_patterns.json"),
|
|
"test_examples": self._load_json(
|
|
temp_output / "test_examples" / "test_examples.json"
|
|
),
|
|
"how_to_guides": self._load_guide_collection(temp_output / "tutorials"),
|
|
"config_patterns": self._load_json(
|
|
temp_output / "config_patterns" / "config_patterns.json"
|
|
),
|
|
"architecture": self._load_json(
|
|
temp_output / "architecture" / "architectural_patterns.json"
|
|
),
|
|
"api_reference": self._load_api_reference(temp_output / "api_reference"), # C2.5
|
|
"dependency_graph": self._load_json(
|
|
temp_output / "dependencies" / "dependency_graph.json"
|
|
), # C2.6
|
|
}
|
|
|
|
# Log summary
|
|
total_patterns = sum(len(f.get("patterns", [])) for f in c3_data.get("patterns", []))
|
|
total_examples = c3_data.get("test_examples", {}).get("total_examples", 0)
|
|
total_guides = len(c3_data.get("how_to_guides", {}).get("guides", []))
|
|
total_configs = len(c3_data.get("config_patterns", {}).get("config_files", []))
|
|
arch_patterns = len(c3_data.get("architecture", {}).get("patterns", []))
|
|
|
|
logger.info(f" ✓ Design Patterns: {total_patterns}")
|
|
logger.info(f" ✓ Test Examples: {total_examples}")
|
|
logger.info(f" ✓ How-To Guides: {total_guides}")
|
|
logger.info(f" ✓ Config Files: {total_configs}")
|
|
logger.info(f" ✓ Architecture Patterns: {arch_patterns}")
|
|
|
|
return c3_data
|
|
|
|
except Exception as e:
|
|
logger.error(f"C3.x analysis failed: {e}")
|
|
import traceback
|
|
|
|
traceback.print_exc()
|
|
return {}
|
|
|
|
finally:
|
|
# Clean up temp directory
|
|
if temp_output.exists():
|
|
try:
|
|
shutil.rmtree(temp_output)
|
|
except Exception as e:
|
|
logger.warning(f"Failed to clean up temp directory: {e}")
|
|
|
|
def detect_conflicts(self) -> list:
|
|
"""
|
|
Detect conflicts between documentation and code.
|
|
|
|
Only applicable if both documentation and GitHub sources exist.
|
|
|
|
Returns:
|
|
List of conflicts
|
|
"""
|
|
logger.info("\n" + "=" * 60)
|
|
logger.info("PHASE 2: Detecting conflicts")
|
|
logger.info("=" * 60)
|
|
|
|
if not self.validator.needs_api_merge():
|
|
logger.info("No API merge needed (only one API source)")
|
|
return []
|
|
|
|
# Get documentation and GitHub data
|
|
docs_data = self.scraped_data.get("documentation", {})
|
|
github_data = self.scraped_data.get("github", {})
|
|
|
|
if not docs_data or not github_data:
|
|
logger.warning("Missing documentation or GitHub data for conflict detection")
|
|
return []
|
|
|
|
# Load data files
|
|
with open(docs_data["data_file"], encoding="utf-8") as f:
|
|
docs_json = json.load(f)
|
|
|
|
with open(github_data["data_file"], encoding="utf-8") as f:
|
|
github_json = json.load(f)
|
|
|
|
# Detect conflicts
|
|
detector = ConflictDetector(docs_json, github_json)
|
|
conflicts = detector.detect_all_conflicts()
|
|
|
|
# Save conflicts
|
|
conflicts_file = os.path.join(self.data_dir, "conflicts.json")
|
|
detector.save_conflicts(conflicts, conflicts_file)
|
|
|
|
# Print summary
|
|
summary = detector.generate_summary(conflicts)
|
|
logger.info("\n📊 Conflict Summary:")
|
|
logger.info(f" Total: {summary['total']}")
|
|
logger.info(" By Type:")
|
|
for ctype, count in summary["by_type"].items():
|
|
if count > 0:
|
|
logger.info(f" - {ctype}: {count}")
|
|
logger.info(" By Severity:")
|
|
for severity, count in summary["by_severity"].items():
|
|
if count > 0:
|
|
emoji = "🔴" if severity == "high" else "🟡" if severity == "medium" else "🟢"
|
|
logger.info(f" {emoji} {severity}: {count}")
|
|
|
|
return conflicts
|
|
|
|
def merge_sources(self, conflicts: list):
|
|
"""
|
|
Merge data from multiple sources.
|
|
|
|
Args:
|
|
conflicts: List of detected conflicts
|
|
"""
|
|
logger.info("\n" + "=" * 60)
|
|
logger.info(f"PHASE 3: Merging sources ({self.merge_mode})")
|
|
logger.info("=" * 60)
|
|
|
|
if not conflicts:
|
|
logger.info("No conflicts to merge")
|
|
return None
|
|
|
|
# Get data files
|
|
docs_data = self.scraped_data.get("documentation", {})
|
|
github_data = self.scraped_data.get("github", {})
|
|
|
|
# Load data
|
|
with open(docs_data["data_file"], encoding="utf-8") as f:
|
|
docs_json = json.load(f)
|
|
|
|
with open(github_data["data_file"], encoding="utf-8") as f:
|
|
github_json = json.load(f)
|
|
|
|
# Choose merger
|
|
if self.merge_mode == "claude-enhanced":
|
|
merger = ClaudeEnhancedMerger(docs_json, github_json, conflicts)
|
|
else:
|
|
merger = RuleBasedMerger(docs_json, github_json, conflicts)
|
|
|
|
# Merge
|
|
merged_data = merger.merge_all()
|
|
|
|
# Save merged data
|
|
merged_file = os.path.join(self.data_dir, "merged_data.json")
|
|
with open(merged_file, "w", encoding="utf-8") as f:
|
|
json.dump(merged_data, f, indent=2, ensure_ascii=False)
|
|
|
|
logger.info(f"✅ Merged data saved: {merged_file}")
|
|
|
|
return merged_data
|
|
|
|
def build_skill(self, merged_data: dict | None = None):
|
|
"""
|
|
Build final unified skill.
|
|
|
|
Args:
|
|
merged_data: Merged API data (if conflicts were resolved)
|
|
"""
|
|
logger.info("\n" + "=" * 60)
|
|
logger.info("PHASE 4: Building unified skill")
|
|
logger.info("=" * 60)
|
|
|
|
# Load conflicts if they exist
|
|
conflicts = []
|
|
conflicts_file = os.path.join(self.data_dir, "conflicts.json")
|
|
if os.path.exists(conflicts_file):
|
|
with open(conflicts_file, encoding="utf-8") as f:
|
|
conflicts_data = json.load(f)
|
|
conflicts = conflicts_data.get("conflicts", [])
|
|
|
|
# Build skill
|
|
builder = UnifiedSkillBuilder(
|
|
self.config, self.scraped_data, merged_data, conflicts, cache_dir=self.cache_dir
|
|
)
|
|
|
|
builder.build()
|
|
|
|
logger.info(f"✅ Unified skill built: {self.output_dir}/")
|
|
|
|
def run(self, args=None):
|
|
"""
|
|
Execute complete unified scraping workflow.
|
|
|
|
Args:
|
|
args: Optional parsed CLI arguments for workflow integration.
|
|
When provided, enhancement workflows (--enhance-workflow,
|
|
--enhance-stage) are executed after the skill is built.
|
|
"""
|
|
# Store CLI args so _scrape_local() can access --enhance-level override
|
|
self._cli_args = args
|
|
|
|
logger.info("\n" + "🚀 " * 20)
|
|
logger.info(f"Unified Scraper: {self.config['name']}")
|
|
logger.info("🚀 " * 20 + "\n")
|
|
|
|
try:
|
|
# Phase 1: Scrape all sources
|
|
self.scrape_all_sources()
|
|
|
|
# Phase 2: Detect conflicts (if applicable)
|
|
conflicts = self.detect_conflicts()
|
|
|
|
# Phase 3: Merge sources (if conflicts exist)
|
|
merged_data = None
|
|
if conflicts:
|
|
merged_data = self.merge_sources(conflicts)
|
|
|
|
# Phase 4: Build skill
|
|
self.build_skill(merged_data)
|
|
|
|
# Phase 5: Enhancement Workflow Integration
|
|
# Support workflow fields in JSON config as well as CLI args.
|
|
# JSON fields: "workflows" (list), "workflow_stages" (list), "workflow_vars" (dict)
|
|
# CLI args always take precedence; JSON fields are appended after.
|
|
json_workflows = self.config.get("workflows", [])
|
|
json_stages = self.config.get("workflow_stages", [])
|
|
json_vars = self.config.get("workflow_vars", {})
|
|
has_json_workflows = bool(json_workflows or json_stages or json_vars)
|
|
|
|
if args is not None or has_json_workflows:
|
|
import argparse
|
|
|
|
from skill_seekers.cli.workflow_runner import run_workflows
|
|
|
|
# Build effective args: use CLI args when provided, otherwise empty namespace
|
|
effective_args = (
|
|
args
|
|
if args is not None
|
|
else argparse.Namespace(
|
|
enhance_workflow=None,
|
|
enhance_stage=None,
|
|
var=None,
|
|
workflow_dry_run=False,
|
|
)
|
|
)
|
|
|
|
# Merge JSON workflow config into effective_args (JSON appended after CLI)
|
|
if json_workflows:
|
|
effective_args.enhance_workflow = (
|
|
list(effective_args.enhance_workflow or []) + json_workflows
|
|
)
|
|
if json_stages:
|
|
effective_args.enhance_stage = (
|
|
list(effective_args.enhance_stage or []) + json_stages
|
|
)
|
|
if json_vars:
|
|
effective_args.var = list(effective_args.var or []) + [
|
|
f"{k}={v}" for k, v in json_vars.items()
|
|
]
|
|
|
|
unified_context = {
|
|
"name": self.config.get("name", ""),
|
|
"description": self.config.get("description", ""),
|
|
}
|
|
run_workflows(effective_args, context=unified_context)
|
|
|
|
logger.info("\n" + "✅ " * 20)
|
|
logger.info("Unified scraping complete!")
|
|
logger.info("✅ " * 20 + "\n")
|
|
|
|
logger.info(f"📁 Output: {self.output_dir}/")
|
|
logger.info(f"📁 Data: {self.data_dir}/")
|
|
|
|
except KeyboardInterrupt:
|
|
logger.info("\n\n⚠️ Scraping interrupted by user")
|
|
sys.exit(1)
|
|
except Exception as e:
|
|
logger.error(f"\n\n❌ Error during scraping: {e}")
|
|
import traceback
|
|
|
|
traceback.print_exc()
|
|
sys.exit(1)
|
|
|
|
|
|
def main():
|
|
"""Main entry point."""
|
|
parser = argparse.ArgumentParser(
|
|
description="Unified multi-source scraper",
|
|
formatter_class=argparse.RawDescriptionHelpFormatter,
|
|
epilog="""
|
|
Examples:
|
|
# Basic usage with unified config
|
|
skill-seekers unified --config configs/godot_unified.json
|
|
|
|
# Override merge mode
|
|
skill-seekers unified --config configs/react_unified.json --merge-mode claude-enhanced
|
|
|
|
# Backward compatible with legacy configs
|
|
skill-seekers unified --config configs/react.json
|
|
""",
|
|
)
|
|
|
|
parser.add_argument("--config", "-c", required=True, help="Path to unified config JSON file")
|
|
parser.add_argument(
|
|
"--merge-mode",
|
|
"-m",
|
|
choices=["rule-based", "claude-enhanced"],
|
|
help="Override config merge mode",
|
|
)
|
|
parser.add_argument(
|
|
"--skip-codebase-analysis",
|
|
action="store_true",
|
|
help="Skip C3.x codebase analysis for GitHub sources (default: enabled)",
|
|
)
|
|
parser.add_argument(
|
|
"--fresh",
|
|
action="store_true",
|
|
help="Clear any existing data and start fresh (ignore checkpoints)",
|
|
)
|
|
parser.add_argument(
|
|
"--dry-run",
|
|
action="store_true",
|
|
help="Preview what will be scraped without actually scraping",
|
|
)
|
|
# Enhancement Workflow arguments (mirrors scrape/github/pdf/codebase scrapers)
|
|
parser.add_argument(
|
|
"--enhance-workflow",
|
|
action="append",
|
|
dest="enhance_workflow",
|
|
help="Apply enhancement workflow (file path or preset). Can use multiple times to chain workflows.",
|
|
metavar="WORKFLOW",
|
|
)
|
|
parser.add_argument(
|
|
"--enhance-stage",
|
|
action="append",
|
|
dest="enhance_stage",
|
|
help="Add inline enhancement stage (format: 'name:prompt'). Can be used multiple times.",
|
|
metavar="STAGE",
|
|
)
|
|
parser.add_argument(
|
|
"--var",
|
|
action="append",
|
|
dest="var",
|
|
help="Override workflow variable (format: 'key=value'). Can be used multiple times.",
|
|
metavar="VAR",
|
|
)
|
|
parser.add_argument(
|
|
"--workflow-dry-run",
|
|
action="store_true",
|
|
dest="workflow_dry_run",
|
|
help="Preview workflow stages without executing (requires --enhance-workflow)",
|
|
)
|
|
parser.add_argument(
|
|
"--api-key",
|
|
type=str,
|
|
metavar="KEY",
|
|
help="Anthropic API key (or set ANTHROPIC_API_KEY env var)",
|
|
)
|
|
parser.add_argument(
|
|
"--enhance-level",
|
|
type=int,
|
|
choices=[0, 1, 2, 3],
|
|
default=None,
|
|
metavar="LEVEL",
|
|
help=(
|
|
"Global AI enhancement level override for all sources "
|
|
"(0=off, 1=SKILL.md, 2=+arch/config, 3=full). "
|
|
"Overrides per-source enhance_level in config."
|
|
),
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
setup_logging()
|
|
|
|
# Create scraper
|
|
scraper = UnifiedScraper(args.config, args.merge_mode)
|
|
|
|
# Disable codebase analysis if requested
|
|
if args.skip_codebase_analysis:
|
|
for source in scraper.config.get("sources", []):
|
|
if source["type"] == "github":
|
|
source["enable_codebase_analysis"] = False
|
|
logger.info(
|
|
f"⏭️ Skipping codebase analysis for GitHub source: {source.get('repo', 'unknown')}"
|
|
)
|
|
|
|
# Handle --fresh flag (clear cache)
|
|
if args.fresh:
|
|
import shutil
|
|
|
|
if os.path.exists(scraper.cache_dir):
|
|
logger.info(f"🧹 Clearing cache: {scraper.cache_dir}")
|
|
shutil.rmtree(scraper.cache_dir)
|
|
# Recreate directories
|
|
os.makedirs(scraper.sources_dir, exist_ok=True)
|
|
os.makedirs(scraper.data_dir, exist_ok=True)
|
|
os.makedirs(scraper.repos_dir, exist_ok=True)
|
|
os.makedirs(scraper.logs_dir, exist_ok=True)
|
|
|
|
# Handle --dry-run flag
|
|
if args.dry_run:
|
|
logger.info("🔍 DRY RUN MODE - Preview only, no scraping will occur")
|
|
logger.info(f"\nWould scrape {len(scraper.config.get('sources', []))} sources:")
|
|
for idx, source in enumerate(scraper.config.get("sources", []), 1):
|
|
source_type = source.get("type", "unknown")
|
|
if source_type == "documentation":
|
|
logger.info(f" {idx}. Documentation: {source.get('base_url', 'N/A')}")
|
|
elif source_type == "github":
|
|
logger.info(f" {idx}. GitHub: {source.get('repo', 'N/A')}")
|
|
elif source_type == "pdf":
|
|
logger.info(f" {idx}. PDF: {source.get('pdf_path', 'N/A')}")
|
|
logger.info(f"\nOutput directory: {scraper.output_dir}")
|
|
logger.info(f"Merge mode: {scraper.merge_mode}")
|
|
return
|
|
|
|
# Run scraper (pass args for workflow integration)
|
|
scraper.run(args=args)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|