linkedin.py•4.54 kB
"""LinkedIn enrichment provider."""
import asyncio
import time
from typing import Dict, Any, Optional
import httpx
from opentelemetry import trace
from .base import EnrichmentProvider, EnrichmentResult
from ..config import get_settings
from ..metrics import (
ENRICHMENT_PROVIDER_SUCCESS,
ENRICHMENT_PROVIDER_ERROR,
ENRICHMENT_PROVIDER_DURATION,
)
settings = get_settings()
class LinkedInProvider(EnrichmentProvider):
"""Enrichment provider using LinkedIn API (mock implementation)."""
def __init__(self):
super().__init__("linkedin", enabled=settings.ENABLE_LINKEDIN)
self.api_key = settings.LINKEDIN_API_KEY
# Note: This is a mock implementation since LinkedIn's API
# has strict terms of service regarding automated access
def can_enrich(self, company_data: Dict[str, Any]) -> bool:
"""Check if we have enough data to search LinkedIn."""
if not self.enabled or not self.api_key:
return False
# Need company name or LinkedIn URL
return bool(
company_data.get("name") or
company_data.get("linkedin_url")
)
async def enrich(self, company_data: Dict[str, Any]) -> EnrichmentResult:
"""Mock LinkedIn enrichment."""
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("linkedin_enrich") as span:
start_time = time.time()
# Mock delay to simulate API call
await asyncio.sleep(0.5)
try:
# Mock enriched data
enriched_data = await self._mock_linkedin_data(company_data)
processing_time_ms = int((time.time() - start_time) * 1000)
confidence_score = self._calculate_confidence(company_data, enriched_data)
ENRICHMENT_PROVIDER_SUCCESS.labels(provider=self.name).inc()
ENRICHMENT_PROVIDER_DURATION.labels(provider=self.name).observe(
(time.time() - start_time)
)
span.set_attribute("success", True)
span.set_attribute("confidence_score", confidence_score)
self.logger.info(
"LinkedIn enrichment completed (mock)",
company_name=company_data.get("name"),
enriched_fields=len(enriched_data),
)
return EnrichmentResult(
provider_name=self.name,
success=True,
data=enriched_data,
confidence_score=confidence_score,
processing_time_ms=processing_time_ms,
)
except Exception as e:
processing_time_ms = int((time.time() - start_time) * 1000)
ENRICHMENT_PROVIDER_ERROR.labels(provider=self.name).inc()
span.set_attribute("success", False)
span.set_attribute("error", str(e))
return EnrichmentResult(
provider_name=self.name,
success=False,
data={},
error_message=str(e),
processing_time_ms=processing_time_ms,
)
async def _mock_linkedin_data(self, company_data: Dict[str, Any]) -> Dict[str, Any]:
"""Generate mock LinkedIn data for testing."""
# In a real implementation, this would make actual LinkedIn API calls
# following their terms of service and rate limits
mock_data = {}
# Mock some additional company details that LinkedIn might provide
if company_data.get("name"):
company_name = company_data["name"]
# Mock LinkedIn URL if not present
if not company_data.get("linkedin_url"):
mock_data["linkedin_url"] = f"https://linkedin.com/company/{company_name.lower().replace(' ', '-')}"
# Mock employee count if not present
if not company_data.get("employee_count"):
mock_data["employee_count_range"] = "201-500"
# Mock industry if not present
if not company_data.get("industry"):
mock_data["industry"] = "Technology"
return mock_data