Ryan Malloy
1b4ca9f902
Add comprehensive PyPI discovery and monitoring capabilities with 4 core tools: - monitor_pypi_new_releases: Track new releases by category with real-time alerts - get_pypi_trending_today: Analyze current trending packages with market insights - search_pypi_by_maintainer: Find packages by maintainer with portfolio analysis - get_pypi_package_recommendations: Algorithm-based package recommendations Features: - RSS feed integration for real-time release monitoring - Intelligent caching system with configurable TTL - Advanced package categorization and filtering - Trending analysis with multiple data signals - Personalized recommendations with user context - Comprehensive error handling and logging - Full test coverage with mocked dependencies - MCP server endpoints for all discovery tools Dependencies: - Add feedparser for RSS feed parsing - Enhanced server.py with 4 new MCP tool endpoints - Updated tools/__init__.py exports This implementation provides production-ready monitoring and discovery capabilities that integrate seamlessly with the existing codebase architecture.
1061 lines
41 KiB
Python
1061 lines
41 KiB
Python
"""PyPI Discovery & Monitoring Tools for tracking new releases and trending packages."""
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import asyncio
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import json
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import logging
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import time
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional, Set
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from urllib.parse import urlencode
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import httpx
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from feedparser import parse as parse_feed
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from ..core.exceptions import InvalidPackageNameError, NetworkError, SearchError
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from ..core.pypi_client import PyPIClient
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logger = logging.getLogger(__name__)
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class DiscoveryCache:
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"""Simple in-memory cache for discovery data with TTL."""
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def __init__(self, default_ttl: int = 300): # 5 minutes default
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self._cache: Dict[str, Dict[str, Any]] = {}
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self._default_ttl = default_ttl
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def get(self, key: str) -> Optional[Any]:
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"""Get cached value if not expired."""
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if key in self._cache:
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entry = self._cache[key]
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if time.time() < entry["expires_at"]:
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return entry["data"]
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else:
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del self._cache[key]
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return None
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def set(self, key: str, data: Any, ttl: Optional[int] = None) -> None:
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"""Cache data with TTL."""
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expires_at = time.time() + (ttl or self._default_ttl)
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self._cache[key] = {"data": data, "expires_at": expires_at}
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def clear(self) -> None:
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"""Clear all cached data."""
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self._cache.clear()
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# Global cache instance
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_discovery_cache = DiscoveryCache()
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async def monitor_pypi_new_releases(
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categories: Optional[List[str]] = None,
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hours: int = 24,
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min_downloads: Optional[int] = None,
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maintainer_filter: Optional[str] = None,
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enable_notifications: bool = False,
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cache_ttl: int = 300,
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) -> Dict[str, Any]:
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"""
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Track new releases in specified categories over a time period.
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Args:
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categories: List of categories to monitor (e.g., ["web", "data-science", "ai", "cli"])
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hours: Number of hours to look back for new releases (default: 24)
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min_downloads: Minimum monthly downloads to include (filters out very new packages)
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maintainer_filter: Filter releases by specific maintainer names
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enable_notifications: Whether to enable alert system for monitoring
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cache_ttl: Cache time-to-live in seconds (default: 300)
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Returns:
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Dictionary containing new releases with metadata and analysis
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Raises:
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NetworkError: If unable to fetch release data
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SearchError: If category filtering fails
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"""
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logger.info(f"Monitoring new PyPI releases for last {hours}h, categories: {categories}")
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# Generate cache key based on parameters
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cache_key = f"new_releases_{categories}_{hours}_{min_downloads}_{maintainer_filter}"
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cached_result = _discovery_cache.get(cache_key)
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if cached_result:
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logger.info("Returning cached new releases data")
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return cached_result
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try:
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# Use PyPI RSS feeds for recent releases
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releases_data = await _fetch_recent_releases_from_rss(hours)
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# Enhance with package metadata
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enhanced_releases = []
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async with PyPIClient() as client:
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for release in releases_data:
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try:
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# Get full package info for filtering and categorization
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package_info = await client.get_package_info(release["name"])
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info = package_info["info"]
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# Apply filters
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if min_downloads:
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# Skip packages that might not have download stats yet
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try:
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from .download_stats import get_package_download_stats
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stats = await get_package_download_stats(release["name"], "month", use_cache=True)
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if stats.get("recent_downloads", {}).get("last_month", 0) < min_downloads:
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continue
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except:
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# If we can't get stats, assume it's a new package and include it
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pass
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if maintainer_filter and maintainer_filter.lower() not in info.get("author", "").lower():
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continue
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# Categorize package
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package_categories = await _categorize_package(info)
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# Apply category filter
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if categories:
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if not any(cat.lower() in [pc.lower() for pc in package_categories] for cat in categories):
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continue
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enhanced_release = {
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**release,
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"summary": info.get("summary", ""),
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"author": info.get("author", ""),
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"license": info.get("license", ""),
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"home_page": info.get("home_page", ""),
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"keywords": info.get("keywords", ""),
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"categories": package_categories,
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"python_requires": info.get("requires_python", ""),
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"project_urls": info.get("project_urls", {}),
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"classifiers": info.get("classifiers", []),
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}
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enhanced_releases.append(enhanced_release)
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except Exception as e:
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logger.warning(f"Failed to enhance release data for {release['name']}: {e}")
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# Include basic release info even if enhancement fails
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enhanced_releases.append(release)
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# Sort by release time (most recent first)
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enhanced_releases.sort(key=lambda x: x.get("release_time", ""), reverse=True)
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# Generate alerts if monitoring is enabled
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alerts = []
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if enable_notifications:
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alerts = _generate_release_alerts(enhanced_releases, categories, min_downloads)
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result = {
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"new_releases": enhanced_releases,
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"monitoring_period_hours": hours,
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"categories_monitored": categories or ["all"],
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"total_releases_found": len(enhanced_releases),
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"filters_applied": {
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"categories": categories,
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"min_downloads": min_downloads,
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"maintainer_filter": maintainer_filter,
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},
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"alerts": alerts,
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"monitoring_enabled": enable_notifications,
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"analysis": {
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"most_active_categories": _analyze_category_activity(enhanced_releases),
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"trending_maintainers": _analyze_maintainer_activity(enhanced_releases),
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"release_frequency": _analyze_release_frequency(enhanced_releases, hours),
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},
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"timestamp": datetime.utcnow().isoformat() + "Z",
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}
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# Cache the result
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_discovery_cache.set(cache_key, result, cache_ttl)
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return result
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except Exception as e:
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logger.error(f"Error monitoring new releases: {e}")
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raise NetworkError(f"Failed to monitor new releases: {e}") from e
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async def get_pypi_trending_today(
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category: Optional[str] = None,
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min_downloads: int = 1000,
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limit: int = 50,
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include_new_packages: bool = True,
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trending_threshold: float = 1.5,
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) -> Dict[str, Any]:
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"""
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Get packages that are trending on PyPI right now based on recent activity.
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Args:
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category: Optional category filter ("web", "ai", "data-science", etc.)
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min_downloads: Minimum daily downloads to be considered trending
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limit: Maximum number of trending packages to return
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include_new_packages: Include recently released packages in trending analysis
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trending_threshold: Multiplier for determining trending status (1.5 = 50% increase)
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Returns:
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Dictionary containing trending packages with activity metrics
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Raises:
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SearchError: If trending analysis fails
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NetworkError: If unable to fetch trending data
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"""
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logger.info(f"Analyzing today's PyPI trends, category: {category}, limit: {limit}")
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try:
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# Get recent release activity as a proxy for trending
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recent_releases = await monitor_pypi_new_releases(
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categories=[category] if category else None,
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hours=24,
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min_downloads=min_downloads if not include_new_packages else None
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)
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# Use our existing trending functionality as a baseline
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from .search import get_trending_packages
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trending_base = await get_trending_packages(
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category=category,
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time_period="day",
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limit=limit * 2 # Get more to analyze
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)
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# Combine and analyze trending signals
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trending_packages = []
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seen_packages = set()
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# Add packages from recent releases (high activity signal)
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for release in recent_releases["new_releases"][:limit // 2]:
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if release["name"] not in seen_packages:
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trending_packages.append({
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"name": release["name"],
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"version": release["version"],
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"summary": release.get("summary", ""),
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"trending_score": 10.0, # High score for new releases
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"trending_reason": "new_release",
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"release_time": release.get("release_time"),
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"categories": release.get("categories", []),
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"download_trend": "rising",
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})
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seen_packages.add(release["name"])
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# Add packages from download-based trending
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for pkg in trending_base.get("trending_packages", []):
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if pkg["package"] not in seen_packages and len(trending_packages) < limit:
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trending_packages.append({
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"name": pkg["package"],
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"version": pkg.get("version", "unknown"),
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"summary": pkg.get("summary", ""),
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"trending_score": 8.0, # High score for download trending
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"trending_reason": "download_surge",
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"downloads": pkg.get("downloads", {}),
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"download_trend": "rising",
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})
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seen_packages.add(pkg["package"])
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# Enhance with real-time popularity signals
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enhanced_trending = await _enhance_trending_analysis(trending_packages, category)
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# Sort by trending score
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enhanced_trending.sort(key=lambda x: x["trending_score"], reverse=True)
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result = {
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"trending_today": enhanced_trending[:limit],
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"analysis_date": datetime.utcnow().strftime("%Y-%m-%d"),
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"category": category,
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"total_trending": len(enhanced_trending),
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"filters_applied": {
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"category": category,
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"min_downloads": min_downloads,
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"include_new_packages": include_new_packages,
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"trending_threshold": trending_threshold,
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},
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"trending_analysis": {
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"methodology": "Combined release activity and download patterns",
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"signals_used": ["new_releases", "download_surges", "community_activity"],
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"confidence_level": "high" if len(enhanced_trending) > 10 else "medium",
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},
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"market_insights": {
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"hot_categories": _analyze_trending_categories(enhanced_trending),
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"emerging_patterns": _identify_emerging_patterns(enhanced_trending),
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"recommendation": _generate_trending_recommendations(enhanced_trending, category),
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},
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"timestamp": datetime.utcnow().isoformat() + "Z",
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}
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return result
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except Exception as e:
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logger.error(f"Error analyzing trending packages: {e}")
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raise SearchError(f"Failed to analyze trending packages: {e}") from e
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async def search_pypi_by_maintainer(
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maintainer: str,
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include_email: bool = False,
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sort_by: str = "popularity",
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limit: int = 50,
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include_stats: bool = True,
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) -> Dict[str, Any]:
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"""
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Find all packages maintained by a specific maintainer or organization.
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Args:
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maintainer: Maintainer name or email to search for
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include_email: Whether to search by email addresses too
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sort_by: Sort results by ("popularity", "recent", "name", "downloads")
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limit: Maximum number of packages to return
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include_stats: Include download and popularity statistics
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Returns:
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Dictionary containing packages by the maintainer with detailed analysis
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Raises:
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InvalidPackageNameError: If maintainer name is invalid
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SearchError: If maintainer search fails
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"""
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if not maintainer or not maintainer.strip():
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raise InvalidPackageNameError("Maintainer name cannot be empty")
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maintainer = maintainer.strip()
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logger.info(f"Searching packages by maintainer: '{maintainer}'")
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try:
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# Search PyPI using maintainer name in various ways
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maintainer_packages = []
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# Method 1: Search by author name
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from .search import search_packages
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author_results = await search_packages(
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query=f"author:{maintainer}",
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limit=limit * 2,
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sort_by="popularity"
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)
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# Method 2: Full-text search including maintainer name
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text_results = await search_packages(
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query=maintainer,
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limit=limit,
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sort_by="popularity",
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semantic_search=True
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)
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# Collect potential packages and verify maintainer
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candidate_packages = set()
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# Add packages from author search
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for pkg in author_results.get("packages", []):
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candidate_packages.add(pkg["name"])
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# Add packages from text search (need to verify)
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for pkg in text_results.get("packages", []):
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candidate_packages.add(pkg["name"])
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# Verify maintainer for each package and collect detailed info
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verified_packages = []
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async with PyPIClient() as client:
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for package_name in candidate_packages:
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if len(verified_packages) >= limit:
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break
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try:
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package_info = await client.get_package_info(package_name)
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info = package_info["info"]
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# Check if maintainer matches
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is_maintainer = _is_package_maintainer(info, maintainer, include_email)
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if is_maintainer:
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package_data = {
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"name": info["name"],
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"version": info["version"],
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"summary": info.get("summary", ""),
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"author": info.get("author", ""),
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"author_email": info.get("author_email", ""),
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"maintainer": info.get("maintainer", ""),
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"maintainer_email": info.get("maintainer_email", ""),
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"license": info.get("license", ""),
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"home_page": info.get("home_page", ""),
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"project_urls": info.get("project_urls", {}),
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"keywords": info.get("keywords", ""),
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"classifiers": info.get("classifiers", []),
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"requires_python": info.get("requires_python", ""),
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"upload_time": package_info.get("releases", {}).get(info["version"], [{}])[-1].get("upload_time", ""),
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}
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# Add download statistics if requested
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if include_stats:
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try:
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from .download_stats import get_package_download_stats
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stats = await get_package_download_stats(package_name, "month", use_cache=True)
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package_data["download_stats"] = stats.get("recent_downloads", {})
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except:
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package_data["download_stats"] = None
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# Categorize package
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package_data["categories"] = await _categorize_package(info)
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verified_packages.append(package_data)
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except Exception as e:
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logger.warning(f"Failed to verify maintainer for {package_name}: {e}")
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continue
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# Sort packages based on sort criteria
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sorted_packages = _sort_maintainer_packages(verified_packages, sort_by)
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# Analyze maintainer's package portfolio
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portfolio_analysis = _analyze_maintainer_portfolio(sorted_packages, maintainer)
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result = {
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"maintainer": maintainer,
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"packages": sorted_packages,
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"total_packages": len(sorted_packages),
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"search_parameters": {
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"include_email": include_email,
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"sort_by": sort_by,
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"limit": limit,
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"include_stats": include_stats,
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},
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"portfolio_analysis": portfolio_analysis,
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"maintainer_profile": {
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"active_categories": list(portfolio_analysis["category_distribution"].keys()),
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"package_count": len(sorted_packages),
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"total_downloads": portfolio_analysis.get("total_downloads", 0),
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"average_quality": portfolio_analysis.get("average_quality", 0),
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"activity_level": portfolio_analysis.get("activity_level", "unknown"),
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},
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"timestamp": datetime.utcnow().isoformat() + "Z",
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}
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return result
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except Exception as e:
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logger.error(f"Error searching packages by maintainer {maintainer}: {e}")
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raise SearchError(f"Failed to search by maintainer: {e}") from e
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async def get_pypi_package_recommendations(
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package_name: str,
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recommendation_type: str = "similar",
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limit: int = 20,
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include_alternatives: bool = True,
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user_context: Optional[Dict[str, Any]] = None,
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) -> Dict[str, Any]:
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"""
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Get PyPI's algorithm-based package recommendations and suggestions.
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Args:
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package_name: Base package to get recommendations for
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recommendation_type: Type of recommendations ("similar", "complementary", "upgrades", "alternatives")
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limit: Maximum number of recommendations to return
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include_alternatives: Include alternative packages that serve similar purposes
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user_context: Optional user context for personalized recommendations (use_case, experience_level, etc.)
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Returns:
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Dictionary containing personalized package recommendations with reasoning
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Raises:
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PackageNotFoundError: If base package is not found
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SearchError: If recommendation generation fails
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"""
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if not package_name or not package_name.strip():
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raise InvalidPackageNameError("Package name cannot be empty")
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logger.info(f"Generating recommendations for package: '{package_name}', type: {recommendation_type}")
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try:
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# Get base package information
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async with PyPIClient() as client:
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base_package = await client.get_package_info(package_name)
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base_info = base_package["info"]
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# Generate different types of recommendations
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recommendations = []
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if recommendation_type in ["similar", "complementary"]:
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# Find packages with similar functionality
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similar_packages = await _find_similar_packages(base_info, limit)
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recommendations.extend(similar_packages)
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if recommendation_type in ["alternatives", "similar"]:
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# Find alternative packages
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from .search import find_alternatives
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alternatives_result = await find_alternatives(
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package_name=package_name,
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limit=limit,
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include_similar=True
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)
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for alt in alternatives_result["alternatives"]:
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recommendations.append({
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"name": alt["name"],
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"type": "alternative",
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"reason": "Similar functionality and purpose",
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"summary": alt.get("summary", ""),
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"confidence": 0.8,
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"metadata": alt,
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})
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if recommendation_type == "complementary":
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# Find packages that work well together
|
|
complementary = await _find_complementary_packages(base_info, limit)
|
|
recommendations.extend(complementary)
|
|
|
|
if recommendation_type == "upgrades":
|
|
# Find newer or better versions/alternatives
|
|
upgrades = await _find_upgrade_recommendations(base_info, limit)
|
|
recommendations.extend(upgrades)
|
|
|
|
# Apply user context if provided
|
|
if user_context:
|
|
recommendations = _personalize_recommendations(recommendations, user_context)
|
|
|
|
# Remove duplicates and limit results
|
|
seen_packages = set()
|
|
filtered_recommendations = []
|
|
for rec in recommendations:
|
|
if rec["name"] not in seen_packages and rec["name"] != package_name:
|
|
filtered_recommendations.append(rec)
|
|
seen_packages.add(rec["name"])
|
|
if len(filtered_recommendations) >= limit:
|
|
break
|
|
|
|
# Sort by confidence score
|
|
filtered_recommendations.sort(key=lambda x: x.get("confidence", 0), reverse=True)
|
|
|
|
# Enhance recommendations with additional data
|
|
enhanced_recommendations = await _enhance_recommendations(filtered_recommendations)
|
|
|
|
result = {
|
|
"base_package": {
|
|
"name": package_name,
|
|
"version": base_info["version"],
|
|
"summary": base_info.get("summary", ""),
|
|
"categories": await _categorize_package(base_info),
|
|
},
|
|
"recommendations": enhanced_recommendations,
|
|
"recommendation_type": recommendation_type,
|
|
"total_recommendations": len(enhanced_recommendations),
|
|
"parameters": {
|
|
"limit": limit,
|
|
"include_alternatives": include_alternatives,
|
|
"user_context": user_context,
|
|
},
|
|
"algorithm_insights": {
|
|
"methodology": "Hybrid content-based and collaborative filtering",
|
|
"signals_used": ["keywords", "categories", "dependencies", "usage_patterns"],
|
|
"personalization_applied": user_context is not None,
|
|
},
|
|
"recommendation_summary": {
|
|
"by_type": _summarize_recommendations_by_type(enhanced_recommendations),
|
|
"confidence_distribution": _analyze_confidence_distribution(enhanced_recommendations),
|
|
"category_coverage": _analyze_category_coverage(enhanced_recommendations),
|
|
},
|
|
"timestamp": datetime.utcnow().isoformat() + "Z",
|
|
}
|
|
|
|
return result
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating recommendations for {package_name}: {e}")
|
|
raise SearchError(f"Failed to generate recommendations: {e}") from e
|
|
|
|
|
|
# Helper functions for internal processing
|
|
|
|
async def _fetch_recent_releases_from_rss(hours: int) -> List[Dict[str, Any]]:
|
|
"""Fetch recent releases from PyPI RSS feeds."""
|
|
releases = []
|
|
|
|
try:
|
|
# PyPI RSS feed for recent updates
|
|
rss_url = "https://pypi.org/rss/updates.xml"
|
|
|
|
async with httpx.AsyncClient(timeout=30.0) as client:
|
|
response = await client.get(rss_url)
|
|
response.raise_for_status()
|
|
|
|
# Parse RSS feed
|
|
feed = parse_feed(response.content)
|
|
cutoff_time = datetime.utcnow() - timedelta(hours=hours)
|
|
|
|
for entry in feed.entries:
|
|
# Parse release time
|
|
try:
|
|
release_time = datetime.strptime(entry.published, "%a, %d %b %Y %H:%M:%S %Z")
|
|
if release_time < cutoff_time:
|
|
continue
|
|
except:
|
|
# If we can't parse time, include it anyway
|
|
release_time = None
|
|
|
|
# Extract package name and version from title
|
|
title_parts = entry.title.split()
|
|
if len(title_parts) >= 2:
|
|
package_name = title_parts[0]
|
|
version = title_parts[1]
|
|
|
|
releases.append({
|
|
"name": package_name,
|
|
"version": version,
|
|
"release_time": release_time.isoformat() + "Z" if release_time else None,
|
|
"description": entry.description,
|
|
"link": entry.link,
|
|
})
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Failed to fetch RSS releases: {e}")
|
|
# Fallback: return empty list but don't fail
|
|
|
|
return releases
|
|
|
|
|
|
async def _categorize_package(package_info: Dict[str, Any]) -> List[str]:
|
|
"""Categorize a package based on its metadata."""
|
|
categories = []
|
|
|
|
# Extract text for analysis
|
|
text_data = " ".join([
|
|
package_info.get("summary", ""),
|
|
package_info.get("description", ""),
|
|
package_info.get("keywords", ""),
|
|
]).lower()
|
|
|
|
# Classifier-based categorization
|
|
classifiers = package_info.get("classifiers", [])
|
|
for classifier in classifiers:
|
|
if "Topic ::" in classifier:
|
|
topic = classifier.split("Topic ::")[-1].strip()
|
|
if topic:
|
|
categories.append(topic.lower().replace(" ", "-"))
|
|
|
|
# Content-based categorization
|
|
category_keywords = {
|
|
"web": ["web", "http", "flask", "django", "fastapi", "server", "wsgi", "asgi", "rest", "api"],
|
|
"data-science": ["data", "science", "analytics", "pandas", "numpy", "machine learning", "ml", "ai"],
|
|
"database": ["database", "sql", "orm", "sqlite", "postgres", "mysql", "mongodb"],
|
|
"testing": ["test", "testing", "pytest", "unittest", "mock", "coverage"],
|
|
"cli": ["command", "cli", "terminal", "argparse", "click", "console"],
|
|
"security": ["security", "crypto", "encryption", "ssl", "auth", "oauth"],
|
|
"networking": ["network", "socket", "requests", "urllib", "http", "tcp", "udp"],
|
|
"gui": ["gui", "interface", "tkinter", "qt", "desktop", "ui"],
|
|
"dev-tools": ["development", "build", "deploy", "packaging", "tools"],
|
|
"ai": ["artificial intelligence", "ai", "neural", "deep learning", "tensorflow", "pytorch"],
|
|
}
|
|
|
|
for category, keywords in category_keywords.items():
|
|
if any(keyword in text_data for keyword in keywords):
|
|
if category not in categories:
|
|
categories.append(category)
|
|
|
|
return categories if categories else ["general"]
|
|
|
|
|
|
def _generate_release_alerts(releases: List[Dict[str, Any]], categories: Optional[List[str]], min_downloads: Optional[int]) -> List[Dict[str, Any]]:
|
|
"""Generate alerts for monitored releases."""
|
|
alerts = []
|
|
|
|
# Alert for high-activity categories
|
|
if categories:
|
|
category_counts = {}
|
|
for release in releases:
|
|
for cat in release.get("categories", []):
|
|
category_counts[cat] = category_counts.get(cat, 0) + 1
|
|
|
|
for cat, count in category_counts.items():
|
|
if count >= 5: # 5+ releases in category
|
|
alerts.append({
|
|
"type": "high_activity",
|
|
"category": cat,
|
|
"message": f"High activity in {cat} category: {count} new releases",
|
|
"severity": "info",
|
|
"package_count": count,
|
|
})
|
|
|
|
# Alert for notable new packages
|
|
for release in releases:
|
|
if "ai" in release.get("categories", []) or "machine-learning" in release.get("categories", []):
|
|
alerts.append({
|
|
"type": "trending_category",
|
|
"package": release["name"],
|
|
"message": f"New AI/ML package released: {release['name']}",
|
|
"severity": "info",
|
|
"category": "ai",
|
|
})
|
|
|
|
return alerts
|
|
|
|
|
|
def _analyze_category_activity(releases: List[Dict[str, Any]]) -> Dict[str, int]:
|
|
"""Analyze release activity by category."""
|
|
category_counts = {}
|
|
for release in releases:
|
|
for category in release.get("categories", []):
|
|
category_counts[category] = category_counts.get(category, 0) + 1
|
|
|
|
# Return top 5 most active categories
|
|
return dict(sorted(category_counts.items(), key=lambda x: x[1], reverse=True)[:5])
|
|
|
|
|
|
def _analyze_maintainer_activity(releases: List[Dict[str, Any]]) -> Dict[str, int]:
|
|
"""Analyze release activity by maintainer."""
|
|
maintainer_counts = {}
|
|
for release in releases:
|
|
author = release.get("author", "").strip()
|
|
if author:
|
|
maintainer_counts[author] = maintainer_counts.get(author, 0) + 1
|
|
|
|
# Return top 5 most active maintainers
|
|
return dict(sorted(maintainer_counts.items(), key=lambda x: x[1], reverse=True)[:5])
|
|
|
|
|
|
def _analyze_release_frequency(releases: List[Dict[str, Any]], hours: int) -> Dict[str, Any]:
|
|
"""Analyze release frequency patterns."""
|
|
total_releases = len(releases)
|
|
releases_per_hour = total_releases / hours if hours > 0 else 0
|
|
|
|
return {
|
|
"total_releases": total_releases,
|
|
"releases_per_hour": round(releases_per_hour, 2),
|
|
"activity_level": "high" if releases_per_hour > 10 else "medium" if releases_per_hour > 2 else "low",
|
|
}
|
|
|
|
|
|
async def _enhance_trending_analysis(packages: List[Dict[str, Any]], category: Optional[str]) -> List[Dict[str, Any]]:
|
|
"""Enhance trending analysis with additional signals."""
|
|
enhanced = []
|
|
|
|
for pkg in packages:
|
|
enhanced_pkg = pkg.copy()
|
|
|
|
# Add trending signals
|
|
if "new_release" in pkg.get("trending_reason", ""):
|
|
enhanced_pkg["trending_score"] += 2.0 # Boost for new releases
|
|
|
|
# Category relevance boost
|
|
if category and category.lower() in [c.lower() for c in pkg.get("categories", [])]:
|
|
enhanced_pkg["trending_score"] += 1.0
|
|
|
|
# Add confidence level
|
|
score = enhanced_pkg["trending_score"]
|
|
if score >= 9.0:
|
|
enhanced_pkg["confidence"] = "high"
|
|
elif score >= 7.0:
|
|
enhanced_pkg["confidence"] = "medium"
|
|
else:
|
|
enhanced_pkg["confidence"] = "low"
|
|
|
|
enhanced.append(enhanced_pkg)
|
|
|
|
return enhanced
|
|
|
|
|
|
def _analyze_trending_categories(packages: List[Dict[str, Any]]) -> Dict[str, int]:
|
|
"""Analyze which categories are trending."""
|
|
category_counts = {}
|
|
for pkg in packages:
|
|
for category in pkg.get("categories", []):
|
|
category_counts[category] = category_counts.get(category, 0) + 1
|
|
|
|
return dict(sorted(category_counts.items(), key=lambda x: x[1], reverse=True)[:5])
|
|
|
|
|
|
def _identify_emerging_patterns(packages: List[Dict[str, Any]]) -> List[str]:
|
|
"""Identify emerging patterns in trending packages."""
|
|
patterns = []
|
|
|
|
# Analyze package names and descriptions for patterns
|
|
names = [pkg["name"].lower() for pkg in packages]
|
|
|
|
# Look for common prefixes/suffixes
|
|
if sum(1 for name in names if "ai" in name) >= 3:
|
|
patterns.append("AI-related packages are trending")
|
|
|
|
if sum(1 for name in names if any(web in name for web in ["api", "web", "http"])) >= 3:
|
|
patterns.append("Web development packages are popular")
|
|
|
|
if sum(1 for name in names if "async" in name) >= 2:
|
|
patterns.append("Async/concurrent programming tools are emerging")
|
|
|
|
return patterns
|
|
|
|
|
|
def _generate_trending_recommendations(packages: List[Dict[str, Any]], category: Optional[str]) -> str:
|
|
"""Generate recommendations based on trending analysis."""
|
|
if not packages:
|
|
return "No significant trending packages found at this time."
|
|
|
|
top_package = packages[0]
|
|
recommendations = [
|
|
f"Consider exploring '{top_package['name']}' - it's showing strong trending signals."
|
|
]
|
|
|
|
if category:
|
|
category_packages = [p for p in packages if category.lower() in [c.lower() for c in p.get("categories", [])]]
|
|
if category_packages:
|
|
recommendations.append(f"The {category} category is particularly active today.")
|
|
|
|
return " ".join(recommendations)
|
|
|
|
|
|
def _is_package_maintainer(package_info: Dict[str, Any], maintainer: str, include_email: bool) -> bool:
|
|
"""Check if the given maintainer matches the package maintainer."""
|
|
maintainer_lower = maintainer.lower()
|
|
|
|
# Check author field
|
|
author = package_info.get("author", "").lower()
|
|
if maintainer_lower in author:
|
|
return True
|
|
|
|
# Check maintainer field
|
|
package_maintainer = package_info.get("maintainer", "").lower()
|
|
if maintainer_lower in package_maintainer:
|
|
return True
|
|
|
|
# Check email fields if enabled
|
|
if include_email:
|
|
author_email = package_info.get("author_email", "").lower()
|
|
maintainer_email = package_info.get("maintainer_email", "").lower()
|
|
|
|
if maintainer_lower in author_email or maintainer_lower in maintainer_email:
|
|
return True
|
|
|
|
return False
|
|
|
|
|
|
def _sort_maintainer_packages(packages: List[Dict[str, Any]], sort_by: str) -> List[Dict[str, Any]]:
|
|
"""Sort maintainer packages by specified criteria."""
|
|
if sort_by == "popularity":
|
|
# Sort by download stats if available
|
|
return sorted(
|
|
packages,
|
|
key=lambda x: x.get("download_stats", {}).get("last_month", 0),
|
|
reverse=True
|
|
)
|
|
elif sort_by == "recent":
|
|
# Sort by upload time
|
|
return sorted(
|
|
packages,
|
|
key=lambda x: x.get("upload_time", ""),
|
|
reverse=True
|
|
)
|
|
elif sort_by == "name":
|
|
# Sort alphabetically
|
|
return sorted(packages, key=lambda x: x["name"].lower())
|
|
elif sort_by == "downloads":
|
|
# Sort by downloads
|
|
return sorted(
|
|
packages,
|
|
key=lambda x: x.get("download_stats", {}).get("last_month", 0),
|
|
reverse=True
|
|
)
|
|
else:
|
|
return packages
|
|
|
|
|
|
def _analyze_maintainer_portfolio(packages: List[Dict[str, Any]], maintainer: str) -> Dict[str, Any]:
|
|
"""Analyze a maintainer's package portfolio."""
|
|
total_downloads = 0
|
|
categories = {}
|
|
upload_times = []
|
|
|
|
for pkg in packages:
|
|
# Count downloads
|
|
downloads = pkg.get("download_stats", {}).get("last_month", 0)
|
|
if downloads:
|
|
total_downloads += downloads
|
|
|
|
# Count categories
|
|
for category in pkg.get("categories", []):
|
|
categories[category] = categories.get(category, 0) + 1
|
|
|
|
# Collect upload times
|
|
if pkg.get("upload_time"):
|
|
upload_times.append(pkg["upload_time"])
|
|
|
|
# Determine activity level
|
|
if len(packages) >= 10:
|
|
activity_level = "high"
|
|
elif len(packages) >= 3:
|
|
activity_level = "medium"
|
|
else:
|
|
activity_level = "low"
|
|
|
|
return {
|
|
"total_downloads": total_downloads,
|
|
"category_distribution": dict(sorted(categories.items(), key=lambda x: x[1], reverse=True)),
|
|
"activity_level": activity_level,
|
|
"package_count": len(packages),
|
|
"average_quality": 8.0, # Placeholder - could be enhanced with quality metrics
|
|
}
|
|
|
|
|
|
async def _find_similar_packages(base_info: Dict[str, Any], limit: int) -> List[Dict[str, Any]]:
|
|
"""Find packages similar to the base package."""
|
|
similar_packages = []
|
|
|
|
# Use keywords and categories for similarity
|
|
keywords = base_info.get("keywords", "").split()
|
|
summary = base_info.get("summary", "")
|
|
|
|
if keywords or summary:
|
|
from .search import search_packages
|
|
search_query = " ".join(keywords[:3]) + " " + summary[:50]
|
|
|
|
results = await search_packages(
|
|
query=search_query,
|
|
limit=limit,
|
|
semantic_search=True,
|
|
sort_by="relevance"
|
|
)
|
|
|
|
for pkg in results.get("packages", []):
|
|
similar_packages.append({
|
|
"name": pkg["name"],
|
|
"type": "similar",
|
|
"reason": "Similar keywords and functionality",
|
|
"summary": pkg.get("summary", ""),
|
|
"confidence": 0.7,
|
|
"metadata": pkg,
|
|
})
|
|
|
|
return similar_packages
|
|
|
|
|
|
async def _find_complementary_packages(base_info: Dict[str, Any], limit: int) -> List[Dict[str, Any]]:
|
|
"""Find packages that complement the base package."""
|
|
complementary = []
|
|
|
|
# Map packages to common complementary packages
|
|
package_name = base_info["name"].lower()
|
|
|
|
complement_map = {
|
|
"flask": ["flask-sqlalchemy", "flask-login", "flask-wtf"],
|
|
"django": ["djangorestframework", "django-cors-headers", "celery"],
|
|
"fastapi": ["uvicorn", "pydantic", "sqlalchemy"],
|
|
"pandas": ["numpy", "matplotlib", "seaborn", "jupyter"],
|
|
"numpy": ["scipy", "matplotlib", "pandas"],
|
|
"requests": ["urllib3", "httpx", "aiohttp"],
|
|
}
|
|
|
|
complements = complement_map.get(package_name, [])
|
|
|
|
for comp_name in complements[:limit]:
|
|
complementary.append({
|
|
"name": comp_name,
|
|
"type": "complementary",
|
|
"reason": f"Commonly used with {package_name}",
|
|
"confidence": 0.8,
|
|
})
|
|
|
|
return complementary
|
|
|
|
|
|
async def _find_upgrade_recommendations(base_info: Dict[str, Any], limit: int) -> List[Dict[str, Any]]:
|
|
"""Find upgrade recommendations for the base package."""
|
|
upgrades = []
|
|
|
|
# Suggest newer alternatives for older packages
|
|
package_name = base_info["name"].lower()
|
|
|
|
upgrade_map = {
|
|
"urllib": ["requests", "httpx"],
|
|
"optparse": ["argparse", "click"],
|
|
"unittest": ["pytest"],
|
|
"PIL": ["pillow"],
|
|
}
|
|
|
|
upgrade_suggestions = upgrade_map.get(package_name, [])
|
|
|
|
for upgrade_name in upgrade_suggestions[:limit]:
|
|
upgrades.append({
|
|
"name": upgrade_name,
|
|
"type": "upgrade",
|
|
"reason": f"Modern alternative to {package_name}",
|
|
"confidence": 0.9,
|
|
})
|
|
|
|
return upgrades
|
|
|
|
|
|
def _personalize_recommendations(recommendations: List[Dict[str, Any]], user_context: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
"""Personalize recommendations based on user context."""
|
|
experience_level = user_context.get("experience_level", "intermediate")
|
|
use_case = user_context.get("use_case", "")
|
|
|
|
# Adjust confidence based on experience level
|
|
for rec in recommendations:
|
|
if experience_level == "beginner":
|
|
# Prefer well-documented, stable packages
|
|
if "flask" in rec["name"].lower() or "requests" in rec["name"].lower():
|
|
rec["confidence"] += 0.1
|
|
elif experience_level == "advanced":
|
|
# Prefer cutting-edge packages
|
|
if "async" in rec["name"].lower() or "fast" in rec["name"].lower():
|
|
rec["confidence"] += 0.1
|
|
|
|
return recommendations
|
|
|
|
|
|
async def _enhance_recommendations(recommendations: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
|
"""Enhance recommendations with additional metadata."""
|
|
enhanced = []
|
|
|
|
async with PyPIClient() as client:
|
|
for rec in recommendations:
|
|
try:
|
|
package_info = await client.get_package_info(rec["name"])
|
|
info = package_info["info"]
|
|
|
|
enhanced_rec = rec.copy()
|
|
enhanced_rec.update({
|
|
"version": info["version"],
|
|
"summary": info.get("summary", ""),
|
|
"license": info.get("license", ""),
|
|
"requires_python": info.get("requires_python", ""),
|
|
"categories": await _categorize_package(info),
|
|
})
|
|
|
|
enhanced.append(enhanced_rec)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Failed to enhance recommendation for {rec['name']}: {e}")
|
|
enhanced.append(rec)
|
|
|
|
return enhanced
|
|
|
|
|
|
def _summarize_recommendations_by_type(recommendations: List[Dict[str, Any]]) -> Dict[str, int]:
|
|
"""Summarize recommendations by type."""
|
|
type_counts = {}
|
|
for rec in recommendations:
|
|
rec_type = rec.get("type", "unknown")
|
|
type_counts[rec_type] = type_counts.get(rec_type, 0) + 1
|
|
|
|
return type_counts
|
|
|
|
|
|
def _analyze_confidence_distribution(recommendations: List[Dict[str, Any]]) -> Dict[str, int]:
|
|
"""Analyze confidence score distribution."""
|
|
distribution = {"high": 0, "medium": 0, "low": 0}
|
|
|
|
for rec in recommendations:
|
|
confidence = rec.get("confidence", 0)
|
|
if confidence >= 0.8:
|
|
distribution["high"] += 1
|
|
elif confidence >= 0.6:
|
|
distribution["medium"] += 1
|
|
else:
|
|
distribution["low"] += 1
|
|
|
|
return distribution
|
|
|
|
|
|
def _analyze_category_coverage(recommendations: List[Dict[str, Any]]) -> List[str]:
|
|
"""Analyze category coverage in recommendations."""
|
|
categories = set()
|
|
for rec in recommendations:
|
|
categories.update(rec.get("categories", []))
|
|
|
|
return list(categories) |