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https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
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129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
"""
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Enrichment Agent — augments lead data with public info.
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وكيل الإثراء — يثري بيانات العميل من مصادر عامة.
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Note: production enrichment typically uses providers like Clearbit, Apollo,
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or company-domain lookups. This agent provides:
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1. Domain-based inference (guess company size / sector from email domain)
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2. LLM-based inference from company name (best effort)
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any
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from auto_client_acquisition.agents.intake import Lead
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from core.agents.base import BaseAgent
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from core.config.models import Task
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from core.llm.base import Message
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@dataclass
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class EnrichmentData:
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inferred_sector: str | None = None
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inferred_size: str | None = None
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inferred_region: str | None = None
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website: str | None = None
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linkedin_handle: str | None = None
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notes: list[str] = field(default_factory=list)
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confidence: float = 0.0 # 0-1
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def to_dict(self) -> dict[str, Any]:
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return {
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"inferred_sector": self.inferred_sector,
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"inferred_size": self.inferred_size,
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"inferred_region": self.inferred_region,
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"website": self.website,
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"linkedin_handle": self.linkedin_handle,
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"notes": self.notes,
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"confidence": round(self.confidence, 2),
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}
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# Domain → sector hints (extend as needed)
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DOMAIN_HINTS: dict[str, str] = {
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".edu.sa": "education",
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".gov.sa": "government",
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".med.sa": "healthcare",
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"aramco.com": "oil_gas",
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"sabic.com": "manufacturing",
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"stc.com.sa": "technology",
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"alrajhibank.com.sa": "finance",
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"saudiairlines.com": "tourism",
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}
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class EnrichmentAgent(BaseAgent):
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"""Enriches lead data using heuristics + LLM inference."""
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name = "enrichment"
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async def run(
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self,
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*,
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lead: Lead,
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use_llm: bool = True,
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**_: Any,
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) -> EnrichmentData:
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data = EnrichmentData()
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confidence = 0.0
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# 1. Email-domain hints
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if lead.contact_email and "@" in lead.contact_email:
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domain = lead.contact_email.split("@", 1)[1].lower()
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data.website = f"https://{domain}"
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for key, sector in DOMAIN_HINTS.items():
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if key in domain:
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data.inferred_sector = sector
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confidence = max(confidence, 0.8)
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data.notes.append(f"Sector from domain: {domain} → {sector}")
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break
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if domain.endswith(".sa") or domain.endswith(".ksa"):
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data.inferred_region = "Saudi Arabia"
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confidence = max(confidence, 0.7)
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# 2. Phone country hint
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if lead.contact_phone and lead.contact_phone.startswith("+966"):
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data.inferred_region = data.inferred_region or "Saudi Arabia"
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confidence = max(confidence, 0.7)
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elif lead.contact_phone and lead.contact_phone.startswith("+971"):
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data.inferred_region = data.inferred_region or "UAE"
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confidence = max(confidence, 0.7)
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# 3. LLM inference from company name
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if use_llm and lead.company_name and not data.inferred_sector:
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try:
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prompt = (
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f"Given the Saudi/GCC company name '{lead.company_name}', "
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f"infer the most likely sector from this list: "
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f"technology, real_estate, healthcare, education, logistics, "
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f"retail, finance, manufacturing, consulting, construction, "
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f"oil_gas, tourism, other. "
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f'Respond with JSON: {{"sector": str, "confidence": 0-1, "note": str}}. '
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f"If you don't know, say 'other' with low confidence."
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)
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response = await self.router.run(
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task=Task.CLASSIFICATION,
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messages=[Message(role="user", content=prompt)],
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max_tokens=200,
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temperature=0.1,
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)
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parsed = self.parse_json_response(response.content)
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if parsed.get("sector") and parsed["sector"] != "other":
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data.inferred_sector = parsed["sector"]
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confidence = max(confidence, float(parsed.get("confidence", 0.3)))
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data.notes.append(f"Sector from name: {lead.company_name} → {parsed['sector']}")
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except Exception as e:
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self.log.warning("enrichment_llm_failed", error=str(e))
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data.confidence = confidence
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self.log.info(
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"enriched",
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lead_id=lead.id,
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sector=data.inferred_sector,
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region=data.inferred_region,
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confidence=confidence,
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)
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return data
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