bolade
731 lines
26 KiB
Python
731 lines
26 KiB
Python
"""
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LinkedIn Profile Scraper for Investor Members
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This module uses crawl4ai to scrape team pages and find LinkedIn profiles.
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Strategies:
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1. Crawl the source_url (team pages) to extract LinkedIn profile links
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2. Use LLM-powered web search to find LinkedIn profiles by name
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Key advantages of crawl4ai:
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- Handles JavaScript-rendered pages
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- Better at extracting content from modern websites
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- More reliable than simple requests
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"""
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import asyncio
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import logging
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import os
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import re
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from typing import Dict, List, Optional
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from crawl4ai import AsyncWebCrawler
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from ddgs import DDGS
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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# Setup logging
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
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)
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logger = logging.getLogger("linkedin_scraper")
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load_dotenv()
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OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
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class LinkedInProfileScraper:
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"""
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LinkedIn profile finder using crawl4ai and LLM-powered web search.
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Strategies:
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1. Crawl source URLs (team pages) to extract LinkedIn links
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2. Use LLM-powered web search to find profiles by name
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"""
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def __init__(
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self,
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rate_limit_delay: float = 0.5,
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use_cache: bool = True,
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use_llm_search: bool = True,
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):
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"""
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Initialize the scraper
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Args:
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rate_limit_delay: Delay between requests in seconds
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use_cache: Whether to cache crawled pages
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use_llm_search: Whether to use LLM-powered web search as fallback
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"""
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self.rate_limit_delay = rate_limit_delay
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self.use_cache = use_cache
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self.use_llm_search = use_llm_search and OPENROUTER_API_KEY
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self.page_cache: Dict[str, str] = {} # Cache crawled pages by URL
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self.html_cache: Dict[str, str] = {} # Cache HTML separately
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self.profile_cache: Dict[str, Dict] = {} # Cache results by member
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# Initialize LLM agent if API key available
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if self.use_llm_search:
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self._init_llm_agent()
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else:
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self.llm = None
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self.agent = None
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self.ddg_search = None
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logger.info("LLM search disabled (no OPENROUTER_API_KEY)")
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def _init_llm_agent(self):
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"""Initialize LLM agent for web search"""
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try:
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self.llm = ChatOpenAI(
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api_key=OPENROUTER_API_KEY,
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base_url="https://openrouter.ai/api/v1",
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model="x-ai/grok-4.1-fast:free",
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temperature=0,
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)
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self.ddg_search = DDGS()
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logger.info("LLM search agent initialized")
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except Exception as e:
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logger.error(f"Failed to initialize LLM agent: {e}")
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self.llm = None
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self.ddg_search = None
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def web_search(self, query: str) -> List[Dict]:
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"""Tool to search the web using DuckDuckGo"""
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if not self.ddg_search:
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return []
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try:
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results = list(self.ddg_search.text(query, max_results=10))
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return results
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except Exception as e:
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logger.error(f"Web search error: {e}")
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return []
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async def crawl_page(self, url: str) -> Optional[str]:
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"""
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Crawl a webpage and return its content.
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Args:
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url: URL to crawl
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Returns:
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Page content as markdown/text, or None if failed
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"""
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if not url:
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return None
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# Check cache first
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if self.use_cache and url in self.page_cache:
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logger.debug(f"Using cached page for {url}")
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return self.page_cache[url]
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try:
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logger.info(f"Crawling: {url}")
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async with AsyncWebCrawler() as crawler:
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result = await crawler.arun(url)
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if result and result.markdown:
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content = result.markdown
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# Also get HTML for better link extraction
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html_content = result.html if hasattr(result, "html") else ""
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# Cache the results
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if self.use_cache:
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self.page_cache[url] = content
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self.html_cache[url] = html_content
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return content
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except Exception as e:
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logger.error(f"Error crawling {url}: {e}")
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return None
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def extract_linkedin_urls_from_content(self, content: str) -> List[Dict[str, str]]:
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"""
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Extract all LinkedIn profile URLs from content (HTML or markdown).
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Returns:
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List of dicts with 'url', 'context', and 'username'
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"""
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linkedin_links = []
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# Pattern for LinkedIn profile URLs (handles country-specific domains)
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linkedin_pattern = (
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r"https?://(?:www\.)?(?:[a-z]{2,3}\.)?linkedin\.com/in/([a-zA-Z0-9_-]+)/?"
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)
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# Find all LinkedIn URLs
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matches = list(re.finditer(linkedin_pattern, content, re.IGNORECASE))
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for match in matches:
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url = match.group(0).rstrip("/")
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# Normalize URL
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url = self._normalize_linkedin_url(url)
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# Get surrounding context (200 chars before and after)
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start = max(0, match.start() - 200)
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end = min(len(content), match.end() + 200)
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context = content[start:end]
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# Clean up context (remove HTML tags for readability)
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context = re.sub(r"<[^>]+>", " ", context)
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context = " ".join(context.split()) # Normalize whitespace
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linkedin_links.append(
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{"url": url, "context": context, "username": match.group(1)}
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)
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# Remove duplicates while preserving order
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seen_urls = set()
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unique_links = []
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for link in linkedin_links:
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if link["url"] not in seen_urls:
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seen_urls.add(link["url"])
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unique_links.append(link)
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return unique_links
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def _normalize_linkedin_url(self, url: str) -> str:
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"""Normalize LinkedIn URL to standard format"""
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# Remove trailing slashes
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url = url.rstrip("/")
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# Convert country-specific to www
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url = re.sub(
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r"https?://[a-z]{2,3}\.linkedin\.com", "https://www.linkedin.com", url
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)
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# Ensure https
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if url.startswith("http://"):
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url = url.replace("http://", "https://")
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return url
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def _name_matches_context(self, name: str, context: str) -> float:
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"""
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Check if a person's name appears in the context around a LinkedIn URL.
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Returns:
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Confidence score 0-100
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"""
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if not name or not context:
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return 0
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context_lower = context.lower()
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name_lower = name.lower()
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# Split name into parts (handle multiple spaces, titles like "Dr.", etc.)
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name_parts = [p for p in name_lower.replace(".", " ").split() if len(p) > 1]
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# Check for full name match
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if name_lower in context_lower:
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return 95
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# Check for name parts in context
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matches = sum(
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1 for part in name_parts if part in context_lower and len(part) > 2
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)
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if len(name_parts) > 0:
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if matches == len(name_parts):
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return 90 # All name parts found
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elif matches >= 2:
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return 75 # At least 2 parts found (first + last typically)
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elif matches == 1 and len(name_parts) <= 2:
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return 50 # Only one part found but name is short
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elif matches == 1:
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return 35 # Only one part found
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return 0
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def _name_matches_username(self, name: str, username: str) -> float:
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"""
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Check if LinkedIn username contains parts of the name.
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Returns:
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Confidence score 0-100
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"""
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if not name or not username:
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return 0
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name_lower = name.lower()
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username_lower = username.lower().replace("-", " ").replace("_", " ")
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name_parts = [p for p in name_lower.replace(".", " ").split() if len(p) > 2]
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matches = sum(1 for part in name_parts if part in username_lower)
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if len(name_parts) > 0:
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if matches == len(name_parts) and len(name_parts) >= 2:
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return 85 # Full name in username
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elif matches >= 2:
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return 70 # Multiple parts match
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elif matches == 1:
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return 35 # Only one part matches
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return 0
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async def find_linkedin_from_source(
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self, name: str, source_url: str, role: Optional[str] = None
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) -> Dict:
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"""
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Find LinkedIn profile by crawling the source URL (team page).
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Args:
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name: Person's name
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source_url: URL of the team/about page
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role: Person's role (for additional context matching)
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Returns:
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Dict with linkedin_url, confidence, method, notes
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"""
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if not source_url:
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return {
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"linkedin_url": None,
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"confidence": 0,
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"method": "source_crawl",
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"notes": "No source URL provided",
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}
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# Crawl the page
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content = await self.crawl_page(source_url)
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if not content:
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return {
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"linkedin_url": None,
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"confidence": 0,
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"method": "source_crawl",
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"notes": f"Failed to crawl {source_url}",
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}
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# Get HTML for better link extraction
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html = self.html_cache.get(source_url, content)
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# Extract all LinkedIn URLs from both HTML and markdown
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linkedin_links = self.extract_linkedin_urls_from_content(html)
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if not linkedin_links:
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linkedin_links = self.extract_linkedin_urls_from_content(content)
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if not linkedin_links:
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return {
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"linkedin_url": None,
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"confidence": 0,
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"method": "source_crawl",
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"notes": f"No LinkedIn URLs found on {source_url}",
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}
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# Score each LinkedIn URL based on name matching
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best_match = None
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best_score = 0
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for link in linkedin_links:
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# Score based on context matching
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context_score = self._name_matches_context(name, link["context"])
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# Score based on username matching
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username_score = self._name_matches_username(name, link["username"])
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# Also check if role appears in context
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role_bonus = 0
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if role and role.lower() in link["context"].lower():
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role_bonus = 10
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# Combined score (take best of context or username, plus role bonus)
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total_score = max(context_score, username_score) + role_bonus
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logger.debug(
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f" {name} -> {link['url']}: context={context_score}, username={username_score}, role={role_bonus}, total={total_score}"
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)
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if total_score > best_score:
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best_score = total_score
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best_match = link
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if best_match and best_score >= 30: # Minimum threshold
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return {
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"linkedin_url": best_match["url"],
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"confidence": min(best_score, 100),
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"method": "source_crawl",
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"notes": f"Found on {source_url}",
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}
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return {
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"linkedin_url": None,
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"confidence": 0,
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"method": "source_crawl",
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"notes": f'No matching LinkedIn profile found for "{name}" on {source_url}',
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}
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async def find_linkedin_via_search(
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self, name: str, company: str, role: Optional[str] = None
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) -> Dict:
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"""
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Find LinkedIn profile using web search.
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Args:
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name: Person's name
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company: Company/investor name
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role: Person's role (optional)
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Returns:
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Dict with linkedin_url, confidence, method, notes
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"""
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if not self.ddg_search:
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return {
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"linkedin_url": None,
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"confidence": 0,
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"method": "web_search",
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"notes": "Web search not available",
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}
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try:
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# Build search query - search for LinkedIn profile
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query = f"{name} {company} site:linkedin.com/in"
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if role:
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query = f"{name} {role} {company} site:linkedin.com/in"
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logger.debug(f"Searching: {query}")
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results = self.web_search(query)
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if results:
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# Look for LinkedIn profile URLs in results
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linkedin_pattern = r"https?://(?:www\.)?(?:[a-z]{2,3}\.)?linkedin\.com/in/([a-zA-Z0-9_-]+)"
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for result in results:
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url = result.get("href") or result.get("link") or ""
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title = result.get("title", "").lower()
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body = result.get("body", "").lower()
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match = re.search(linkedin_pattern, url, re.IGNORECASE)
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if match:
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linkedin_url = self._normalize_linkedin_url(match.group(0))
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username = match.group(1)
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# Score based on name matching in title/body and username
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context = f"{title} {body}"
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context_score = self._name_matches_context(name, context)
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username_score = self._name_matches_username(name, username)
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total_score = max(context_score, username_score)
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if total_score >= 30:
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return {
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"linkedin_url": linkedin_url,
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"confidence": min(
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total_score, 90
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), # Cap at 90 for search results
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"method": "web_search",
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"notes": "Found via web search",
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}
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return {
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"linkedin_url": None,
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"confidence": 0,
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"method": "web_search",
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"notes": "No matching profile found in search results",
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}
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except Exception as e:
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logger.error(f"Web search error for {name}: {e}")
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return {
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"linkedin_url": None,
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"confidence": 0,
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"method": "web_search",
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"notes": f"Search error: {str(e)}",
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}
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async def find_linkedin_profile(
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self,
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name: str,
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company: str,
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role: Optional[str] = None,
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source_url: Optional[str] = None,
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) -> Dict:
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"""
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Find LinkedIn profile for a person.
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Primary strategy: Crawl source URL to find LinkedIn links.
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Args:
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name: Person's name
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company: Company/investor name
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role: Person's role/title (optional)
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source_url: URL where person info was found (optional)
|
|
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|
Returns:
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Dict with:
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- linkedin_url: Found LinkedIn URL or None
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- confidence: Confidence score (0-100)
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- method: Method used to find the profile
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- notes: Additional information
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"""
|
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cache_key = f"{name}|{company}"
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# Check cache
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if self.use_cache and cache_key in self.profile_cache:
|
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logger.debug(f"Using cached result for {name}")
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return self.profile_cache[cache_key]
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result = {"linkedin_url": None, "confidence": 0, "method": "none", "notes": ""}
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# Primary strategy: Crawl source URL
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if source_url:
|
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result = await self.find_linkedin_from_source(name, source_url, role)
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if result["linkedin_url"]:
|
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if self.use_cache:
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self.profile_cache[cache_key] = result
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return result
|
|
|
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# Fallback strategy: Web search (if enabled and no result from source crawl)
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if self.use_llm_search and not result.get("linkedin_url"):
|
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search_result = await self.find_linkedin_via_search(name, company, role)
|
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if search_result["linkedin_url"]:
|
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if self.use_cache:
|
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self.profile_cache[cache_key] = search_result
|
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return search_result
|
|
|
|
# If no source URL or no match found
|
|
if not result["linkedin_url"]:
|
|
result = {
|
|
"linkedin_url": None,
|
|
"confidence": 0,
|
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"method": "none",
|
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"notes": "No source URL available"
|
|
if not source_url
|
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else result.get("notes", "Not found"),
|
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}
|
|
|
|
if self.use_cache:
|
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self.profile_cache[cache_key] = result
|
|
|
|
return result
|
|
|
|
async def batch_find_profiles(
|
|
self, members: List[Dict], progress_callback=None, db_callback=None
|
|
) -> List[Dict]:
|
|
"""
|
|
Find LinkedIn profiles for multiple members efficiently.
|
|
|
|
Groups members by source_url to minimize crawling the same page multiple times.
|
|
|
|
Args:
|
|
members: List of dicts with 'name', 'company', 'role', 'source_url', 'id'
|
|
progress_callback: Optional callback function(current, total, result)
|
|
db_callback: Optional callback to save to database immediately when profile found
|
|
Signature: db_callback(member_id, linkedin_url) -> bool
|
|
|
|
Returns:
|
|
List of results for each member
|
|
"""
|
|
results = []
|
|
total = len(members)
|
|
|
|
# Group members by source_url for efficient crawling
|
|
url_groups: Dict[str, List[Dict]] = {}
|
|
no_url_members = []
|
|
|
|
for member in members:
|
|
url = member.get("source_url")
|
|
if url:
|
|
if url not in url_groups:
|
|
url_groups[url] = []
|
|
url_groups[url].append(member)
|
|
else:
|
|
no_url_members.append(member)
|
|
|
|
logger.info(
|
|
f"Processing {len(url_groups)} unique source URLs for {total} members"
|
|
)
|
|
logger.info(f"Members with source URLs: {total - len(no_url_members)}")
|
|
logger.info(f"Members without source URLs: {len(no_url_members)}")
|
|
if self.use_llm_search:
|
|
logger.info("Web search fallback: ENABLED")
|
|
else:
|
|
logger.info("Web search fallback: DISABLED")
|
|
|
|
processed = 0
|
|
|
|
# Process members grouped by URL (efficient - one crawl per page)
|
|
for url, group_members in url_groups.items():
|
|
# Crawl the page once
|
|
content = await self.crawl_page(url)
|
|
html = self.html_cache.get(url, content or "")
|
|
|
|
# Extract all LinkedIn URLs from this page
|
|
linkedin_links = []
|
|
if content:
|
|
linkedin_links = self.extract_linkedin_urls_from_content(html)
|
|
if not linkedin_links:
|
|
linkedin_links = self.extract_linkedin_urls_from_content(content)
|
|
|
|
# Match each member in this group
|
|
for member in group_members:
|
|
processed += 1
|
|
result = None
|
|
found_linkedin = False
|
|
|
|
if linkedin_links:
|
|
# Find best matching LinkedIn for this member
|
|
best_match = None
|
|
best_score = 0
|
|
|
|
for link in linkedin_links:
|
|
context_score = self._name_matches_context(
|
|
member["name"], link["context"]
|
|
)
|
|
username_score = self._name_matches_username(
|
|
member["name"], link["username"]
|
|
)
|
|
role_bonus = (
|
|
10
|
|
if member.get("role")
|
|
and member["role"].lower() in link["context"].lower()
|
|
else 0
|
|
)
|
|
total_score = max(context_score, username_score) + role_bonus
|
|
|
|
if total_score > best_score:
|
|
best_score = total_score
|
|
best_match = link
|
|
|
|
if best_match and best_score >= 30:
|
|
result = {
|
|
"linkedin_url": best_match["url"],
|
|
"confidence": min(best_score, 100),
|
|
"method": "source_crawl",
|
|
"notes": f"Found on {url}",
|
|
"member_id": member.get("id"),
|
|
"member_name": member["name"],
|
|
}
|
|
found_linkedin = True
|
|
# Save to database immediately if callback provided
|
|
if db_callback and member.get("id"):
|
|
db_callback(member["id"], best_match["url"])
|
|
|
|
# If no result from source crawl, try web search IMMEDIATELY
|
|
if not found_linkedin and self.use_llm_search:
|
|
search_result = await self.find_linkedin_via_search(
|
|
member["name"], member["company"], member.get("role")
|
|
)
|
|
|
|
if search_result["linkedin_url"]:
|
|
result = {
|
|
"linkedin_url": search_result["linkedin_url"],
|
|
"confidence": search_result["confidence"],
|
|
"method": "web_search",
|
|
"notes": search_result.get("notes", "Found via web search"),
|
|
"member_id": member.get("id"),
|
|
"member_name": member["name"],
|
|
}
|
|
found_linkedin = True
|
|
# Save to database immediately
|
|
if db_callback and member.get("id"):
|
|
db_callback(member["id"], search_result["linkedin_url"])
|
|
|
|
# If still no result, record as not found
|
|
if not found_linkedin:
|
|
result = {
|
|
"linkedin_url": None,
|
|
"confidence": 0,
|
|
"method": "source_crawl" if content else "none",
|
|
"notes": f"No match on {url}"
|
|
if linkedin_links
|
|
else (
|
|
f"No LinkedIn URLs on {url}"
|
|
if content
|
|
else f"Failed to crawl {url}"
|
|
),
|
|
"member_id": member.get("id"),
|
|
"member_name": member["name"],
|
|
}
|
|
|
|
results.append(result)
|
|
|
|
if progress_callback:
|
|
progress_callback(processed, total, result)
|
|
|
|
# Small delay between different URLs
|
|
await asyncio.sleep(self.rate_limit_delay)
|
|
|
|
# Process members without source URLs - do web search immediately for each
|
|
for member in no_url_members:
|
|
processed += 1
|
|
result = None
|
|
|
|
# Try web search immediately
|
|
if self.use_llm_search:
|
|
search_result = await self.find_linkedin_via_search(
|
|
member["name"], member["company"], member.get("role")
|
|
)
|
|
|
|
if search_result["linkedin_url"]:
|
|
result = {
|
|
"linkedin_url": search_result["linkedin_url"],
|
|
"confidence": search_result["confidence"],
|
|
"method": "web_search",
|
|
"notes": search_result.get("notes", "Found via web search"),
|
|
"member_id": member.get("id"),
|
|
"member_name": member["name"],
|
|
}
|
|
# Save to database immediately
|
|
if db_callback and member.get("id"):
|
|
db_callback(member["id"], search_result["linkedin_url"])
|
|
|
|
# If no result from search
|
|
if not result:
|
|
result = {
|
|
"linkedin_url": None,
|
|
"confidence": 0,
|
|
"method": "web_search" if self.use_llm_search else "none",
|
|
"notes": "No LinkedIn profile found"
|
|
if self.use_llm_search
|
|
else "No source URL available",
|
|
"member_id": member.get("id"),
|
|
"member_name": member["name"],
|
|
}
|
|
|
|
results.append(result)
|
|
|
|
if progress_callback:
|
|
progress_callback(processed, total, result)
|
|
|
|
# Rate limit between searches
|
|
await asyncio.sleep(self.rate_limit_delay)
|
|
|
|
return results
|
|
|
|
|
|
def format_linkedin_url(url: str) -> str:
|
|
"""Normalize LinkedIn URL format"""
|
|
if not url:
|
|
return url
|
|
|
|
# Remove trailing slashes
|
|
url = url.rstrip("/")
|
|
|
|
# Ensure https and normalize to www
|
|
url = re.sub(r"https?://[a-z]{2,3}\.linkedin\.com", "https://www.linkedin.com", url)
|
|
if url.startswith("http://"):
|
|
url = url.replace("http://", "https://")
|
|
|
|
return url
|
|
|
|
|
|
# Async wrapper for sync contexts
|
|
def run_batch_scraper(
|
|
members: List[Dict], rate_limit: float = 0.5, progress_callback=None
|
|
) -> List[Dict]:
|
|
"""
|
|
Synchronous wrapper for batch_find_profiles.
|
|
|
|
Args:
|
|
members: List of member dicts
|
|
rate_limit: Delay between URL crawls
|
|
progress_callback: Optional progress callback
|
|
|
|
Returns:
|
|
List of results
|
|
"""
|
|
scraper = LinkedInProfileScraper(rate_limit_delay=rate_limit)
|
|
return asyncio.run(scraper.batch_find_profiles(members, progress_callback))
|