Enhance AmazonJobScraper to support flexible location matching and extract posted dates; refine LLMJobRefiner prompts for better data extraction.
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@ -1,4 +1,3 @@
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"Specifically for scraping job postings from Amazon Jobs."
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import asyncio
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import random
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from typing import Optional, Dict
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@ -7,7 +6,7 @@ from browserforge.injectors.playwright import AsyncNewContext
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from llm_agent import LLMJobRefiner
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import re
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from fetcher import StealthyFetcher
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from datetime import datetime
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from datetime import datetime, timedelta
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import json
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import redis
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@ -28,8 +27,28 @@ class AmazonJobScraper:
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self.llm_agent = LLMJobRefiner()
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self.redis_client = redis.Redis(host='localhost', port=6379, db=0, decode_responses=True)
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# Country alias map for flexible location matching
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self.country_aliases = {
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"united states": ["united states", "usa", "u.s.a", "u.s.", "us", "america", ", us", ", usa"],
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"united kingdom": ["united kingdom", "uk", "great britain", "england", "gb", ", uk", ", gb"],
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"canada": ["canada", "ca", ", ca"],
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"india": ["india", "in", ", in"],
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"germany": ["germany", "de", ", de"],
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"france": ["france", "fr", ", fr"],
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"australia": ["australia", "au", ", au"],
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# Add more as needed
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}
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def _init_db(self):
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pass # Handled by LLMJobRefiner
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pass
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async def _safe_inner_text(self, element):
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if not element:
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return "Unknown"
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try:
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return await element.text_content()
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except:
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return "Unknown"
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async def _human_click(self, page, element, wait_after: bool = True):
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if not element:
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@ -45,8 +64,6 @@ class AmazonJobScraper:
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return False
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async def _login(self, page, credentials: Dict) -> bool:
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# Amazon job pages do NOT require login.
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# Skip login unless we're scraping internal dashboards (not needed here).
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return True
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async def _extract_page_content_for_llm(self, page) -> str:
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@ -55,100 +72,138 @@ class AmazonJobScraper:
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await asyncio.sleep(2 * self.human_speed)
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return await page.content()
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def _calculate_keyword_match(self, title: str, keywords: str) -> float:
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if not title or not keywords:
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return 0.0
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title_lower = title.lower()
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keyword_list = [kw.strip().lower() for kw in keywords.split()]
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matches = sum(1 for kw in keyword_list if kw in title_lower)
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return matches / len(keyword_list) if keyword_list else 0.0
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def _extract_location_from_keywords(self, search_keywords: str) -> str:
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def _extract_keywords_and_location(self, search_keywords: str):
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location_match = re.search(r'location:\s*([^,]+)', search_keywords, re.IGNORECASE)
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return location_match.group(1).strip().lower() if location_match else ""
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location = location_match.group(1).strip() if location_match else ""
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clean_keywords = re.sub(r'location:\s*[^,]+', '', search_keywords, flags=re.IGNORECASE).strip()
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return clean_keywords, location
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def _normalize_text(self, text: str) -> str:
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return re.sub(r'[^a-z0-9\s]', ' ', text.lower()).strip()
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def _location_matches(self, job_location_text: str, target_location: str) -> bool:
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if not target_location:
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return True
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target = target_location.lower().strip()
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job_text = job_location_text.lower()
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# Direct substring match (e.g., "Berlin" in "Berlin, Germany")
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if target in job_text:
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return True
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# Check country aliases
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for canonical, aliases in self.country_aliases.items():
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if target in canonical or any(target == alias for alias in aliases if len(alias) <= 3):
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return any(alias in job_text for alias in aliases)
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return False
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def _parse_posted_date_from_card_text(self, card_text: str) -> str:
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date_match = re.search(r'Posted\s+([A-Za-z]+\s+\d{1,2},\s+\d{4})', card_text)
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if date_match:
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try:
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dt = datetime.strptime(date_match.group(1), "%B %d, %Y")
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return dt.strftime("%m/%d/%y")
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except ValueError:
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pass
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days_match = re.search(r'Posted\s+(\d+)\s+day[s]?\s+ago', card_text, re.IGNORECASE)
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if days_match:
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days = int(days_match.group(1))
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dt = datetime.now() - timedelta(days=days)
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return dt.strftime("%m/%d/%y")
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return datetime.now().strftime("%m/%d/%y")
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async def _scrape_jobs_from_current_page(self, page, search_keywords: str, seen_job_ids, all_job_links):
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current_links = await page.query_selector_all("a[href*='/jobs/']")
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await asyncio.sleep(1.5 * self.human_speed)
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job_cards = await page.query_selector_all("div[data-job-id]")
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new_jobs = 0
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location_from_keywords = self._extract_location_from_keywords(search_keywords)
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for link in current_links:
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href = await link.get_attribute("href")
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if not href or "page=" in href or "search?" in href:
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clean_kw, location_kw = self._extract_keywords_and_location(search_keywords)
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keyword_terms = [term.lower().strip() for term in clean_kw.split() if term.strip()]
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for card in job_cards:
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job_id = await card.get_attribute("data-job-id")
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if not job_id or not job_id.isdigit() or job_id in seen_job_ids:
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continue
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full_url = href if href.startswith("http") else f"https://www.amazon.jobs{href}"
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job_id = href.strip("/").split("/")[-1] if href else "unknown"
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link = await card.query_selector("a[href*='/jobs/']")
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if not link:
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continue
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if job_id and job_id not in seen_job_ids:
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title_element = await link.query_selector("h3") or await link.query_selector(".job-title")
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title = await title_element.inner_text() if title_element else "Unknown Title"
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href = await link.get_attribute("href")
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if not href or any(x in href for x in ["search?", "locations", "teams", "page=", "my.", "/account/"]):
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continue
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match_percentage = self._calculate_keyword_match(title, search_keywords)
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card_text = await self._safe_inner_text(card)
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normalized_card = self._normalize_text(card_text)
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# ✅ Check: ALL keyword terms must appear in card
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keywords_match = all(term in normalized_card for term in keyword_terms) if keyword_terms else True
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# ✅ Check location separately with alias support
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location_match = True
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if location_from_keywords:
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location_element = await link.query_selector(".location-and-id")
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if location_element:
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location_text = await location_element.inner_text()
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location_match = location_from_keywords in location_text.lower()
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if location_kw:
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loc_el = await card.query_selector(".location-and-id span")
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job_loc = (await self._safe_inner_text(loc_el)).strip() if loc_el else ""
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location_match = self._location_matches(job_loc, location_kw)
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if match_percentage >= 0.7 and location_match:
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if keywords_match and location_match:
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title_span = await card.query_selector("h2.job-title span, h2 span")
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title = (await self._safe_inner_text(title_span)).strip() if title_span else "Unknown"
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posted_date = self._parse_posted_date_from_card_text(card_text)
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seen_job_ids.add(job_id)
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all_job_links.append((href, title))
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all_job_links.append((href, title, posted_date))
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new_jobs += 1
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elif match_percentage < 0.7:
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print(f" ⚠️ Skipping job due to low keyword match: {title[:50]}... (match: {match_percentage:.2%})")
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elif not location_match:
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print(f" ⚠️ Skipping job due to location mismatch: {title[:50]}... (expected: {location_from_keywords})")
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print(f" ✅ Accepted: {title} (posted: {posted_date})")
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else:
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seen_job_ids.add(job_id)
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all_job_links.append((href, "Unknown Title"))
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new_jobs += 1
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reasons = []
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if not keywords_match:
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reasons.append("keyword mismatch")
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if not location_match:
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reasons.append("location mismatch")
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print(f" ⚠️ Skipping: {'; '.join(reasons)}")
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return new_jobs
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async def _handle_pagination(self, page, search_keywords: str, seen_job_ids, all_job_links):
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current_page = 1
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while current_page <= 10: # Amazon limits to ~10 pages publicly
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print(f"📄 Processing page {current_page}")
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current_page_num = 1
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max_pages = 400
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while current_page_num <= max_pages:
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print(f"📄 Processing page {current_page_num}")
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await asyncio.sleep(1.5 * self.human_speed)
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new_jobs = await self._scrape_jobs_from_current_page(page, search_keywords, seen_job_ids, all_job_links)
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print(f" ➕ Found {new_jobs} new job(s) on page {current_page} (total: {len(all_job_links)})")
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print(f" ➕ Found {new_jobs} new job(s) (total: {len(all_job_links)})")
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next_btn = await page.query_selector("a[aria-label='Next page']")
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if next_btn:
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next_url = await next_btn.get_attribute("href")
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if next_url:
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full_next_url = next_url if next_url.startswith("http") else f"https://www.amazon.jobs{next_url}"
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print(f" ➡️ Navigating to next page: {full_next_url}")
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await page.goto(full_next_url, timeout=120000)
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await asyncio.sleep(random.uniform(3.0, 5.0) * self.human_speed)
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current_page += 1
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else:
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break
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else:
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print("🔚 No 'Next' button found — stopping pagination.")
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break
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# Scroll to bottom to trigger lazy-loaded pagination (if any)
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await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
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await asyncio.sleep(2 * self.human_speed)
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async def _extract_job_posted_date(self, page) -> str:
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# Look for ANY link containing 'page=N'
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next_page_num = current_page_num + 1
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next_selector = f"a[href*='page={next_page_num}']"
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next_link = await page.query_selector(next_selector)
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if next_link:
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href = await next_link.get_attribute("href")
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if href:
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next_url = "https://www.amazon.jobs" + href if href.startswith("/") else href
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print(f" ➡️ Going to page {next_page_num}: {next_url}")
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await page.goto(next_url, timeout=120000)
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try:
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# Amazon often includes "Posted X days ago" in job description
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content = await page.content()
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match = re.search(r'Posted\s+(\d+)\s+day[s]?\s+ago', content, re.IGNORECASE)
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if match:
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days_ago = int(match.group(1))
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posted_date = datetime.now() - timedelta(days=days_ago)
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return posted_date.strftime("%m/%d/%y")
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await page.wait_for_selector("div[data-job-id]", timeout=30000)
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except PlaywrightTimeoutError:
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print(" ⚠️ No jobs loaded on next page.")
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break
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current_page_num = next_page_num
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else:
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break
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else:
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print(" 🔚 No next page link found.")
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break
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# Fallback: check for explicit date in page (rare)
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date_match = re.search(r'(\d{1,2})/(\d{1,2})/(\d{4})', content)
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if date_match:
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month, day, year = date_match.groups()
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return f"{month.zfill(2)}/{day.zfill(2)}/{year[-2:]}"
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# Default to today
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return datetime.now().strftime("%m/%d/%y")
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except Exception as e:
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print(f" ⚠️ Error extracting Amazon posted date: {str(e)}")
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return datetime.now().strftime("%m/%d/%y")
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print(f"✅ Finished pagination after {current_page_num} pages.")
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async def _add_job_to_redis_cache(self, job_url: str, job_id: str, error_type: str):
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try:
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@ -161,25 +216,20 @@ class AmazonJobScraper:
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self.redis_client.hset("failed_jobs", job_id, json.dumps(job_data))
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print(f" 📦 Added failed job to Redis cache: {job_id} (Error: {error_type})")
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except Exception as e:
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print(f" ❌ Failed to add job to Redis cache: {str(e)}")
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print(f" ❌ Failed to add to Redis: {str(e)}")
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async def scrape_jobs(
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self,
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search_keywords: Optional[str],
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max_pages: int = 400,
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max_pages: int = 10,
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credentials: Optional[Dict] = None
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):
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from datetime import timedelta # needed for date math
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location_match = re.search(r'location:\s*([^,]+)', search_keywords, re.IGNORECASE)
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location = location_match.group(1).strip() if location_match else ""
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clean_keywords = re.sub(r'location:\s*[^,]+', '', search_keywords, flags=re.IGNORECASE).strip()
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encoded_keywords = clean_keywords.replace(" ", "+") # Amazon uses + for spaces
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clean_kw, location_kw = self._extract_keywords_and_location(search_keywords)
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encoded_keywords = clean_kw.replace(" ", "+")
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# ✅ FIXED: removed extra spaces
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search_url = f"https://www.amazon.jobs/en/search?base_query={encoded_keywords}"
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if location:
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# Amazon uses location filter via `loc_query`
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search_url += f"&loc_query={location.replace(' ', '+')}"
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if location_kw:
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search_url += f"&loc_query={location_kw.replace(' ', '+')}"
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profile = self.engine._select_profile()
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renderer = random.choice(self.engine.common_renderers[self.engine.os])
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@ -204,14 +254,11 @@ class AmazonJobScraper:
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temp_fetcher = StealthyFetcher(self.engine, browser, context)
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print("✅ Bypassing login (Amazon jobs are public)...")
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login_successful = True
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await page.wait_for_load_state("load", timeout=120000)
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# Protection check (same as LinkedIn logic)
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protection_type = await temp_fetcher._detect_protection(page)
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if protection_type:
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print(f"🛡️ Protection detected on initial page: {protection_type}")
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print(f"🛡️ Protection detected: {protection_type}")
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content_accessible = await temp_fetcher._is_content_accessible(page)
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if not content_accessible:
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handled = await self.engine._handle_cloudflare(page) if protection_type == "cloudflare" else False
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@ -220,25 +267,18 @@ class AmazonJobScraper:
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self.engine.report_outcome("protection_block")
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return
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else:
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print("✅ Protection present but content accessible — proceeding.")
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print("✅ Protection present but content accessible.")
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print(f"🔍 Searching Amazon for: {search_keywords}")
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await page.goto(search_url, wait_until='load', timeout=120000)
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await asyncio.sleep(random.uniform(4.0, 6.0) * self.human_speed)
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await page.goto(search_url, timeout=120000)
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# Protection check on search page
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protection_type = await temp_fetcher._detect_protection(page)
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if protection_type:
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print(f"🛡️ Protection detected on search page: {protection_type}")
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content_accessible = await temp_fetcher._is_content_accessible(page)
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if not content_accessible:
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handled = await self.engine._handle_cloudflare(page) if protection_type == "cloudflare" else False
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if not handled:
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try:
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await page.wait_for_selector("div[data-job-id]", timeout=40000)
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print("✅ Job listings detected.")
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except PlaywrightTimeoutError:
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print("❌ No job cards found.")
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await browser.close()
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self.engine.report_outcome("protection_block")
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return
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else:
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print("✅ Protection present but content accessible — proceeding.")
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all_job_links = []
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seen_job_ids = set()
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@ -247,67 +287,28 @@ class AmazonJobScraper:
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initial_jobs = await self._scrape_jobs_from_current_page(page, search_keywords, seen_job_ids, all_job_links)
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print(f" ➕ Found {initial_jobs} initial job(s) (total: {len(all_job_links)})")
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# Amazon uses pagination (not infinite scroll)
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await self._handle_pagination(page, search_keywords, seen_job_ids, all_job_links)
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print(f"✅ Collected {len(all_job_links)} unique job links.")
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print(f"✅ Collected {len(all_job_links)} unique job listings.")
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scraped_count = 0
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for idx, (href, title) in enumerate(all_job_links):
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for idx, (href, title, posted_date) in enumerate(all_job_links):
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try:
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# ✅ FIXED: removed extra spaces
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full_url = href if href.startswith("http") else f"https://www.amazon.jobs{href}"
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print(f" → Opening job {idx+1}/{len(all_job_links)}: {full_url}")
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print(f" → Opening job {idx+1}/{len(all_job_links)}: {full_url} (posted: {posted_date})")
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fetcher = StealthyFetcher(self.engine, browser, context)
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job_page = await fetcher.fetch_url(full_url, wait_for_selector="h1[data-testid='job-title']")
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if not job_page:
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print(f" ❌ Failed to fetch job page {full_url} after retries.")
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job_id = href.strip("/").split("/")[-1] if href else "unknown"
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await self._add_job_to_redis_cache(full_url, job_id, "fetch_failure")
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self.engine.report_outcome("fetch_failure", url=full_url)
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continue
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posted_date = await self._extract_job_posted_date(job_page)
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print(f" 📅 Posted date extracted: {posted_date}")
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apply_btn = await job_page.query_selector("a:has-text('Apply now'), button:has-text('Apply now')")
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final_url = full_url
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external_url = None
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page_content = None
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if apply_btn:
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apply_href = await apply_btn.get_attribute("href")
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if apply_href and apply_href.startswith("http"):
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print(" 🌐 Detected external apply URL — capturing directly.")
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||||
external_url = apply_href
|
||||
final_url = external_url
|
||||
# We won't navigate; just pass Amazon job page to LLM
|
||||
page_content = await self._extract_page_content_for_llm(job_page)
|
||||
else:
|
||||
print(" → Clicking 'Apply now' (may open new tab)...")
|
||||
page_waiter = asyncio.create_task(context.wait_for_event("page"))
|
||||
await self._human_click(job_page, apply_btn, wait_after=False)
|
||||
|
||||
external_page = None
|
||||
try:
|
||||
external_page = await asyncio.wait_for(page_waiter, timeout=5.0)
|
||||
print(" 🌐 External job site opened in new tab.")
|
||||
await external_page.wait_for_load_state("load", timeout=120000)
|
||||
await asyncio.sleep(2 * self.human_speed)
|
||||
await self.engine._human_like_scroll(external_page)
|
||||
external_url = external_page.url
|
||||
final_url = external_url
|
||||
page_content = await self._extract_page_content_for_llm(external_page)
|
||||
if not external_page.is_closed():
|
||||
await external_page.close()
|
||||
except asyncio.TimeoutError:
|
||||
print(" 🖥️ No external tab — using Amazon job page.")
|
||||
page_content = await self._extract_page_content_for_llm(job_page)
|
||||
else:
|
||||
print(" ⚠️ No 'Apply now' button — scraping job page directly.")
|
||||
page_content = await self._extract_page_content_for_llm(job_page)
|
||||
|
||||
job_id = href.strip("/").split("/")[-1] if href else "unknown"
|
||||
|
||||
raw_data = {
|
||||
@ -332,22 +333,22 @@ class AmazonJobScraper:
|
||||
refined_data[field] = "Amazon"
|
||||
|
||||
refined_data['scraped_at'] = datetime.now().isoformat()
|
||||
refined_data['category'] = clean_keywords
|
||||
refined_data['category'] = clean_kw
|
||||
refined_data['posted_date'] = posted_date
|
||||
await self.llm_agent.save_job_data(refined_data, search_keywords)
|
||||
scraped_count += 1
|
||||
print(f" ✅ Scraped and refined: {refined_data['title'][:50]}...")
|
||||
print(f" ✅ Scraped: {refined_data['title'][:50]}...")
|
||||
self.engine.report_outcome("success", url=raw_data["url"])
|
||||
else:
|
||||
print(f" 🟡 Could not extract meaningful data from: {final_url}")
|
||||
await self._add_job_to_redis_cache(final_url, job_id, "llm_failure")
|
||||
print(f" 🟡 LLM failed to refine: {full_url}")
|
||||
await self._add_job_to_redis_cache(full_url, job_id, "llm_failure")
|
||||
self.engine.report_outcome("llm_failure", url=raw_data["url"])
|
||||
|
||||
await job_page.close()
|
||||
|
||||
except Exception as e:
|
||||
error_msg = str(e)[:100]
|
||||
print(f" ⚠️ Failed on job {idx+1}: {error_msg}")
|
||||
print(f" ⚠️ Exception on job {idx+1}: {error_msg}")
|
||||
job_id = (href.strip("/").split("/")[-1] if href else "unknown") if 'href' in locals() else "unknown"
|
||||
job_url = full_url if 'full_url' in locals() else "unknown"
|
||||
await self._add_job_to_redis_cache(job_url, job_id, f"exception: {error_msg}")
|
||||
@ -356,7 +357,7 @@ class AmazonJobScraper:
|
||||
continue
|
||||
|
||||
finally:
|
||||
print(" ↩️ Returning to Amazon search results...")
|
||||
if not page.is_closed():
|
||||
await page.goto(search_url, timeout=120000)
|
||||
await asyncio.sleep(4 * self.human_speed)
|
||||
|
||||
@ -364,7 +365,7 @@ class AmazonJobScraper:
|
||||
|
||||
if scraped_count > 0:
|
||||
self.engine.report_outcome("success")
|
||||
print(f"✅ Completed! Processed {scraped_count} jobs for '{search_keywords}' based on request '{self.user_request}'.")
|
||||
print(f"✅ Completed! Processed {scraped_count} jobs.")
|
||||
else:
|
||||
self.engine.report_outcome("captcha")
|
||||
print("⚠️ No jobs processed successfully.")
|
||||
22
llm_agent.py
22
llm_agent.py
@ -146,14 +146,21 @@ class LLMJobRefiner:
|
||||
posted_date = raw_data.get('posted_date', datetime.now().strftime("%m/%d/%y"))
|
||||
|
||||
prompt = f"""
|
||||
You are a job posting data extractor.
|
||||
You are an expert job posting parser. Extract information EXACTLY as it appears in the text. DO NOT summarize, paraphrase, or invent.
|
||||
|
||||
EXTRACT EXACT TEXT - DO NOT SUMMARIZE, PARAPHRASE, OR INVENT.
|
||||
CRITICAL INSTRUCTIONS:
|
||||
- The job is from AMAZON. Look for these exact section headings:
|
||||
- "## Basic Qualifications" → extract as "qualifications"
|
||||
- "## Preferred Qualifications" → include this in "qualifications" too
|
||||
- "## Description" or "About the Role" or "Key job responsibilities" → extract as "description"
|
||||
- "You Will:" or "Job responsibilities" → include in "description"
|
||||
- Requirements are often embedded in qualifications or description
|
||||
|
||||
For these critical fields, follow these rules:
|
||||
- description: Extract ALL job description text. If ANY job details exist (duties, responsibilities, overview), include them. Only use "Not provided" if absolutely no description exists.
|
||||
- requirements: Extract ALL requirements text. If ANY requirements exist (skills, experience, education needed), include them. Only use "Not provided" if none exist.
|
||||
- qualifications: Extract ALL qualifications text. If ANY qualifications exist, include them. Only use "Not provided" if none exist.
|
||||
FIELD RULES:
|
||||
- description: MUST include ALL role details, responsibilities, and overview. Never "Not provided" if any job description exists.
|
||||
- qualifications: MUST include ALL content from "Basic Qualifications" and "Preferred Qualifications" sections. Combine them.
|
||||
- requirements: If no separate "requirements" section, extract required skills/experience from qualifications/description.
|
||||
- For Amazon jobs, company_name = "Amazon".
|
||||
|
||||
REQUIRED FIELDS (must have valid values, never "N/A"):
|
||||
- title, company_name, job_id, url
|
||||
@ -170,7 +177,6 @@ class LLMJobRefiner:
|
||||
"company_name": "...",
|
||||
"location": "...",
|
||||
"description": "...",
|
||||
"requirements": "...",
|
||||
"qualifications": "...",
|
||||
"salary_range": "...",
|
||||
"nature_of_work": "...",
|
||||
@ -196,7 +202,7 @@ class LLMJobRefiner:
|
||||
return None
|
||||
|
||||
# CRITICAL: Validate content fields - check if they SHOULD exist
|
||||
content_fields = ['description', 'requirements', 'qualifications']
|
||||
content_fields = ['description', 'qualifications']
|
||||
cleaned_original = cleaned_content.lower()
|
||||
|
||||
# Simple heuristic: if page contains job-related keywords, content fields should NOT be "Not provided"
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user