pyr0ball
7d6ce555f0
- 3_Resume_Editor.py: replace "Alex's" in docstring and caption - user_profile.py: expose mission_preferences and candidate_accessibility_focus - user.yaml.example: add mission_preferences section + candidate_accessibility_focus flag - generate_cover_letter.py: build _MISSION_NOTES from user profile instead of hardcoded personal passion notes; falls back to generic defaults when not set - company_research.py: gate "Inclusion & Accessibility" section behind candidate_accessibility_focus flag; section count adjusts (7 or 8) accordingly
484 lines
19 KiB
Python
484 lines
19 KiB
Python
# scripts/company_research.py
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"""
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Pre-interview company research generator.
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Three-phase approach:
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1. If SearXNG is available, use companyScraper.py to fetch live
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data: CEO name, HQ address, LinkedIn, contact info.
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1b. Use Phase 1 data (company name + CEO if found) to query SearXNG for
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recent news snippets (funding, launches, leadership changes, etc.).
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2. Feed all real data into an LLM prompt to synthesise a structured brief
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covering company overview, leadership, recent developments, and talking
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points tailored to the candidate.
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Falls back to pure LLM knowledge when SearXNG is offline.
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Usage (standalone):
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conda run -n job-seeker python scripts/company_research.py --job-id 42
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conda run -n job-seeker python scripts/company_research.py --job-id 42 --no-scrape
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"""
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import re
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import sys
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from pathlib import Path
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from types import SimpleNamespace
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from scripts.user_profile import UserProfile
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_USER_YAML = Path(__file__).parent.parent / "config" / "user.yaml"
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_profile = UserProfile(_USER_YAML) if UserProfile.exists(_USER_YAML) else None
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# ── SearXNG scraper integration ───────────────────────────────────────────────
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# companyScraper is bundled into the Docker image at /app/scrapers/
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_SCRAPER_AVAILABLE = False
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for _scraper_candidate in [
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Path("/app/scrapers"), # Docker container path
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Path(__file__).parent.parent / "scrapers", # local dev fallback
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]:
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if _scraper_candidate.exists():
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sys.path.insert(0, str(_scraper_candidate))
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try:
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from companyScraper import EnhancedCompanyScraper, Config as _ScraperConfig
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_SCRAPER_AVAILABLE = True
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except (ImportError, SystemExit):
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pass
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break
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_SEARXNG_URL: str = _profile.searxng_url if _profile else "http://localhost:8888"
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def _searxng_running(searxng_url: str = "http://localhost:8888") -> bool:
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"""Quick check whether SearXNG is reachable."""
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try:
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import requests
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r = requests.get(f"{searxng_url}/", timeout=3)
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return r.status_code == 200
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except Exception:
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return False
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def _scrape_company(company: str) -> dict:
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"""
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Use companyScraper in minimal mode to pull live CEO / HQ data.
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Returns a dict with keys: ceo, headquarters, linkedin (may be 'Not found').
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"""
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mock_args = SimpleNamespace(
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mode="minimal",
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verbose=False,
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dry_run=False,
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debug=False,
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use_cache=True,
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save_raw=False,
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target_staff=None,
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include_types=None,
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exclude_types=None,
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include_contact=False,
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include_address=False,
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include_social=True, # grab LinkedIn while we're at it
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timeout=20,
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input_file=None,
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output_file="/dev/null",
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searxng_url=_SEARXNG_URL + "/",
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)
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# Override the singleton Config URL
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_ScraperConfig.SEARXNG_URL = _SEARXNG_URL + "/"
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scraper = EnhancedCompanyScraper(mock_args)
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scraper.companies = [company]
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result: dict = {"ceo": "Not found", "headquarters": "Not found", "linkedin": "Not found"}
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for search_type in ["ceo", "hq", "social"]:
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html = scraper.search_company(company, search_type)
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if search_type == "ceo":
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result["ceo"] = scraper.extract_ceo(html, company)
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elif search_type == "hq":
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result["headquarters"] = scraper.extract_address(html, company)
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elif search_type == "social":
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social = scraper.extract_social(html, company)
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# Pull out just the LinkedIn entry
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for part in (social or "").split(";"):
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if "linkedin" in part.lower():
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result["linkedin"] = part.strip()
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break
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return result
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_SEARCH_QUERIES = {
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"news": '"{company}" news 2025 2026',
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"funding": '"{company}" funding round investors Series valuation',
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"tech": '"{company}" tech stack engineering technology platform',
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"competitors": '"{company}" competitors alternatives vs market',
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"culture": '"{company}" glassdoor culture reviews employees',
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"accessibility": '"{company}" ADA accessibility disability inclusion accommodation ERG',
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"ceo_press": '"{ceo}" "{company}"', # only used if ceo is known
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}
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def _run_search_query(query: str, results: dict, key: str) -> None:
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"""Thread target: run one SearXNG JSON query, store up to 4 snippets in results[key]."""
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import requests
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snippets: list[str] = []
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seen: set[str] = set()
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try:
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resp = requests.get(
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f"{_SEARXNG_URL}/search",
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params={"q": query, "format": "json", "language": "en-US"},
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timeout=12,
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)
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if resp.status_code != 200:
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return
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for r in resp.json().get("results", [])[:4]:
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url = r.get("url", "")
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if url in seen:
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continue
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seen.add(url)
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title = r.get("title", "").strip()
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content = r.get("content", "").strip()
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if title or content:
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snippets.append(f"- **{title}**\n {content}\n <{url}>")
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except Exception:
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pass
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results[key] = "\n\n".join(snippets)
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def _fetch_search_data(company: str, ceo: str = "") -> dict[str, str]:
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"""
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Run all search queries in parallel threads.
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Returns dict keyed by search type (news, funding, tech, competitors, culture, ceo_press).
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Missing/failed queries produce empty strings.
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"""
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import threading
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results: dict[str, str] = {}
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threads = []
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keys: list[str] = []
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for key, pattern in _SEARCH_QUERIES.items():
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if key == "ceo_press" and not ceo or (ceo or "").lower() == "not found":
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continue
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# Use replace() not .format() — company names may contain curly braces
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query = pattern.replace("{company}", company).replace("{ceo}", ceo)
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t = threading.Thread(
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target=_run_search_query,
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args=(query, results, key),
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daemon=True,
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)
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threads.append(t)
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keys.append(key)
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t.start()
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for t, key in zip(threads, keys):
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t.join(timeout=15)
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# Thread may still be alive after timeout — pre-populate key so
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# the results dict contract ("missing queries → empty string") holds
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if t.is_alive():
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results.setdefault(key, "")
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return results
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def _parse_sections(text: str) -> dict[str, str]:
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"""Split LLM markdown output on ## headers into named sections."""
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sections: dict[str, str] = {}
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pattern = re.compile(r"^##\s+(.+)$", re.MULTILINE)
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matches = list(pattern.finditer(text))
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for i, match in enumerate(matches):
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name = match.group(1).strip()
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start = match.end()
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end = matches[i + 1].start() if i + 1 < len(matches) else len(text)
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sections[name] = text[start:end].strip()
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return sections
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_RESUME_YAML = Path(__file__).parent.parent / "aihawk" / "data_folder" / "plain_text_resume.yaml"
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_KEYWORDS_YAML = Path(__file__).parent.parent / "config" / "resume_keywords.yaml"
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def _company_label(exp: dict) -> str:
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company = exp.get("company", "")
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score = exp.get("score", 0)
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if _profile:
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return _profile.nda_label(company, score)
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return company
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def _score_experiences(experiences: list[dict], keywords: list[str], jd: str) -> list[dict]:
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"""Score each experience entry by keyword overlap with JD; return sorted descending."""
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jd_lower = jd.lower()
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scored = []
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for exp in experiences:
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text = " ".join([
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exp.get("position", ""),
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exp.get("company", ""),
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" ".join(
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v
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for resp in exp.get("key_responsibilities", [])
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for v in resp.values()
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),
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]).lower()
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score = sum(1 for kw in keywords if kw.lower() in text and kw.lower() in jd_lower)
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scored.append({**exp, "score": score})
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return sorted(scored, key=lambda x: x["score"], reverse=True)
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def _build_resume_context(resume: dict, keywords: list[str], jd: str) -> str:
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"""
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Build the resume section of the LLM context block.
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Top 2 scored experiences included in full detail; rest as one-liners.
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NDA companies are masked via UserProfile.nda_label() when score < threshold.
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"""
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experiences = resume.get("experience_details", [])
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if not experiences:
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return ""
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scored = _score_experiences(experiences, keywords, jd)
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top2 = scored[:2]
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rest = scored[2:]
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candidate = _profile.name if _profile else "the candidate"
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def _exp_header(exp: dict) -> str:
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return f"{exp.get('position', '')} @ {_company_label(exp)} ({exp.get('employment_period', '')})"
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def _exp_bullets(exp: dict) -> str:
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bullets = [v for resp in exp.get("key_responsibilities", []) for v in resp.values()]
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return "\n".join(f" - {b}" for b in bullets)
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lines = [f"## {candidate}'s Matched Experience"]
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for exp in top2:
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lines.append(f"\n**{_exp_header(exp)}** (match score: {exp['score']})")
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lines.append(_exp_bullets(exp))
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if rest:
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condensed = ", ".join(_exp_header(e) for e in rest)
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lines.append(f"\nAlso in {candidate}'s background: {condensed}")
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return "\n".join(lines)
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def _load_resume_and_keywords() -> tuple[dict, list[str]]:
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"""Load resume YAML and keywords config. Returns (resume_dict, all_keywords_list)."""
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import yaml as _yaml
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resume = {}
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if _RESUME_YAML.exists():
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resume = _yaml.safe_load(_RESUME_YAML.read_text()) or {}
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keywords: list[str] = []
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if _KEYWORDS_YAML.exists():
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kw_cfg = _yaml.safe_load(_KEYWORDS_YAML.read_text()) or {}
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for lst in kw_cfg.values():
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if isinstance(lst, list):
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keywords.extend(lst)
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return resume, keywords
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def research_company(job: dict, use_scraper: bool = True, on_stage=None) -> dict:
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"""
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Generate a pre-interview research brief for a job.
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Parameters
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----------
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job : dict
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Job row from the DB (needs at least 'company', 'title', 'description').
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use_scraper : bool
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Whether to attempt live data via SearXNG before falling back to LLM.
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Returns
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-------
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dict with keys: raw_output, company_brief, ceo_brief, tech_brief,
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funding_brief, competitors_brief, red_flags, talking_points
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"""
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from scripts.llm_router import LLMRouter
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router = LLMRouter()
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research_order = router.config.get("research_fallback_order") or router.config["fallback_order"]
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company = job.get("company") or "the company"
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title = job.get("title") or "this role"
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jd_excerpt = (job.get("description") or "")[:1500]
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resume, keywords = _load_resume_and_keywords()
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matched_keywords = [kw for kw in keywords if kw.lower() in jd_excerpt.lower()]
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resume_context = _build_resume_context(resume, keywords, jd_excerpt)
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keywords_note = (
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f"\n\n## Matched Skills & Keywords\nSkills matching this JD: {', '.join(matched_keywords)}"
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if matched_keywords else ""
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)
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def _stage(msg: str) -> None:
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if on_stage:
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try:
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on_stage(msg)
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except Exception:
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pass # never let stage callbacks break the task
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# ── Phase 1: live scrape (optional) ──────────────────────────────────────
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live_data: dict = {}
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scrape_note = ""
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_stage("Checking for live company data…")
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if use_scraper and _SCRAPER_AVAILABLE and _searxng_running(_SEARXNG_URL):
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_stage("Scraping CEO & HQ data…")
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try:
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live_data = _scrape_company(company)
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parts = []
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if live_data.get("ceo") not in (None, "Not found"):
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parts.append(f"CEO: {live_data['ceo']}")
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if live_data.get("headquarters") not in (None, "Not found"):
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parts.append(f"HQ: {live_data['headquarters']}")
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if live_data.get("linkedin") not in (None, "Not found"):
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parts.append(f"LinkedIn: {live_data['linkedin']}")
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if parts:
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scrape_note = (
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"\n\n**Live data retrieved via SearXNG:**\n"
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+ "\n".join(f"- {p}" for p in parts)
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+ "\n\nIncorporate these facts where relevant."
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)
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except BaseException as e:
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scrape_note = f"\n\n_(Live scrape attempted but failed: {e})_"
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# ── Phase 1b: parallel search queries ────────────────────────────────────
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search_data: dict[str, str] = {}
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_stage("Running web searches…")
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if use_scraper and _searxng_running(_SEARXNG_URL):
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_stage("Running web searches (news, funding, tech, culture)…")
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try:
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ceo_name = (live_data.get("ceo") or "") if live_data else ""
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search_data = _fetch_search_data(company, ceo=ceo_name)
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except BaseException:
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pass # best-effort; never fail the whole task
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# Track whether SearXNG actually contributed usable data to this brief.
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scrape_used = 1 if (live_data or any(v.strip() for v in search_data.values())) else 0
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def _section_note(key: str, label: str) -> str:
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text = search_data.get(key, "").strip()
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return f"\n\n## {label} (live web search)\n\n{text}" if text else ""
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news_note = _section_note("news", "News & Press")
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funding_note = _section_note("funding", "Funding & Investors")
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tech_note = _section_note("tech", "Tech Stack")
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competitors_note = _section_note("competitors", "Competitors")
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culture_note = _section_note("culture", "Culture & Employee Signals")
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accessibility_note = _section_note("accessibility", "Accessibility & Disability Inclusion")
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ceo_press_note = _section_note("ceo_press", "CEO in the News")
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# ── Phase 2: LLM synthesis ────────────────────────────────────────────────
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_stage("Generating brief with LLM… (30–90 seconds)")
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name = _profile.name if _profile else "the candidate"
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career_summary = _profile.career_summary if _profile else ""
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accessibility_focus = _profile.candidate_accessibility_focus if _profile else False
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_section_count = 8 if accessibility_focus else 7
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_accessibility_section = """
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## Inclusion & Accessibility
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Assess {company}'s commitment to disability inclusion and accessibility. Cover:
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- ADA accommodation language in job postings or company policy
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- Disability Employee Resource Group (ERG) or affinity group
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- Product or service accessibility (WCAG compliance, adaptive features, AT integrations)
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- Any public disability/accessibility advocacy, partnerships, or certifications
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- Glassdoor or press signals about how employees with disabilities experience the company
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If no specific signals are found, say so clearly — absence of public commitment is itself signal.
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This section is for the candidate's personal decision-making only and will not appear in any application.
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""".format(company=company) if accessibility_focus else ""
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prompt = f"""You are preparing {name} for a job interview.
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{f"Candidate background: {career_summary}" if career_summary else ""}
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Role: **{title}** at **{company}**
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## Job Description
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{jd_excerpt}
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{resume_context}{keywords_note}
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## Live Company Data
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{scrape_note.strip() or "_(scrape unavailable)_"}
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{news_note}{funding_note}{tech_note}{competitors_note}{culture_note}{accessibility_note}{ceo_press_note}
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---
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Produce a structured research brief using **exactly** these {_section_count} markdown section headers
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(include all {_section_count} even if a section has limited data — say so honestly):
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## Company Overview
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What {company} does, core product/service, business model, size/stage (startup / scale-up / enterprise), market positioning.
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## Leadership & Culture
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CEO background and leadership style, key execs, mission/values statements, Glassdoor themes.
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## Tech Stack & Product
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Technologies, platforms, and product direction relevant to the {title} role.
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## Funding & Market Position
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Funding stage, key investors, recent rounds, burn/growth signals, competitor landscape.
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## Recent Developments
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News, launches, acquisitions, exec moves, pivots, or press from the past 12–18 months.
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Draw on the live snippets above; if none available, note what is publicly known.
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## Red Flags & Watch-outs
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Culture issues, layoffs, exec departures, financial stress, or Glassdoor concerns worth knowing before the call.
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If nothing notable, write "No significant red flags identified."
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{_accessibility_section}
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## Talking Points for {name}
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Five specific talking points for the phone screen. Each must:
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- Reference a concrete experience from {name}'s matched background by name
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(NDA rule: use the masked label shown in the matched experience section for any NDA-protected employer)
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- Connect to a specific signal from the JD or company context above
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- Be 1–2 sentences, ready to speak aloud
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- Never give generic advice
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---
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⚠️ This brief combines live web data and LLM training knowledge. Verify key facts before the call.
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"""
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raw = router.complete(prompt, fallback_order=research_order)
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# Strip <think>…</think> blocks emitted by reasoning models (e.g. DeepSeek, Qwen-R)
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raw = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip()
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sections = _parse_sections(raw)
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return {
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"raw_output": raw,
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"company_brief": sections.get("Company Overview", ""),
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"ceo_brief": sections.get("Leadership & Culture", ""),
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"tech_brief": sections.get("Tech Stack & Product", ""),
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"funding_brief": sections.get("Funding & Market Position", ""),
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"competitors_brief": sections.get("Funding & Market Position", ""), # competitor landscape is in the funding section
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"red_flags": sections.get("Red Flags & Watch-outs", ""),
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"accessibility_brief": sections.get("Inclusion & Accessibility", ""),
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"talking_points": sections.get(f"Talking Points for {name}", ""),
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"scrape_used": scrape_used,
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}
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if __name__ == "__main__":
|
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import argparse
|
||
|
||
parser = argparse.ArgumentParser(description="Generate company research brief")
|
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parser.add_argument("--job-id", type=int, required=True, help="Job ID in staging.db")
|
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parser.add_argument("--no-scrape", action="store_true", help="Skip SearXNG live scrape")
|
||
args = parser.parse_args()
|
||
|
||
from scripts.db import DEFAULT_DB, init_db, save_research
|
||
import sqlite3
|
||
|
||
init_db(DEFAULT_DB)
|
||
conn = sqlite3.connect(DEFAULT_DB)
|
||
conn.row_factory = sqlite3.Row
|
||
row = conn.execute("SELECT * FROM jobs WHERE id = ?", (args.job_id,)).fetchone()
|
||
conn.close()
|
||
|
||
if not row:
|
||
sys.exit(f"Job {args.job_id} not found in {DEFAULT_DB}")
|
||
|
||
job = dict(row)
|
||
print(f"Researching: {job['title']} @ {job['company']} …\n")
|
||
if _SCRAPER_AVAILABLE and not args.no_scrape:
|
||
print(f"SearXNG available: {_searxng_running(_SEARXNG_URL)}")
|
||
|
||
result = research_company(job, use_scraper=not args.no_scrape)
|
||
save_research(DEFAULT_DB, job_id=args.job_id, **result)
|
||
print(result["raw_output"])
|
||
print(f"\n[Saved to company_research for job {args.job_id}]")
|