mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
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d7a5af9156
Revolutionary AI system for autonomous B2B partnerships, negotiations, and deals: Models (strategic_deal.py - 238 lines): - CompanyProfile: Rich Saudi company profiles with CR, capabilities, needs - StrategicDeal: Full deal lifecycle (discovery → negotiation → close) - DealMatch: AI-generated company matches with scoring Services (4 files, ~2,060 lines): - company_profiler.py: Profile creation, AI enrichment, needs/capability analysis - deal_matcher.py: 6-dimension scoring, semantic matching, barter chain discovery - deal_negotiator.py: Multi-round Arabic negotiation with cultural awareness - deal_agent.py: Autonomous outreach via WhatsApp/LinkedIn/Email API (strategic_deals.py - 681 lines, 16 endpoints): - Profile management + AI enrichment - Match discovery + approval - Deal lifecycle (create → negotiate → proposal → term sheet → close) - Barter chain scanning - Analytics dashboard Deal types: partnership, distribution, franchise, JV, referral, acquisition, barter Channels: WhatsApp (primary), LinkedIn, Email Languages: Arabic (Saudi dialect) + English Cultural: Saudi negotiation norms, relationship-first, face-saving https://claude.ai/code/session_01LsnvBa7HwF5hs99VZbgLGj
415 lines
17 KiB
Python
415 lines
17 KiB
Python
"""
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Company Profiler — Builds rich company profiles for B2B matching.
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محلل الشركات: يبني ملفات شركات غنية للمطابقة بين الشركات
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"""
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import json
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import logging
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from typing import Optional
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from datetime import datetime, timezone
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from sqlalchemy import select, func
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.strategic_deal import CompanyProfile
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from app.services.llm.provider import get_llm
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logger = logging.getLogger("dealix.strategic_deals.profiler")
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# ── ISIC industry mapping (common Saudi sectors) ─────────────────────────────
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SAUDI_INDUSTRIES = {
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"construction": {"ar": "مقاولات وبناء", "isic": "F"},
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"real_estate": {"ar": "عقارات", "isic": "L"},
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"retail": {"ar": "تجارة تجزئة", "isic": "G"},
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"wholesale": {"ar": "تجارة جملة", "isic": "G"},
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"technology": {"ar": "تقنية معلومات", "isic": "J"},
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"manufacturing": {"ar": "صناعة", "isic": "C"},
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"healthcare": {"ar": "رعاية صحية", "isic": "Q"},
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"education": {"ar": "تعليم وتدريب", "isic": "P"},
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"food_beverage": {"ar": "أغذية ومشروبات", "isic": "I"},
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"logistics": {"ar": "نقل ولوجستيات", "isic": "H"},
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"finance": {"ar": "خدمات مالية", "isic": "K"},
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"energy": {"ar": "طاقة", "isic": "D"},
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"tourism": {"ar": "سياحة وضيافة", "isic": "I"},
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"consulting": {"ar": "استشارات", "isic": "M"},
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"marketing": {"ar": "تسويق وإعلان", "isic": "M"},
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"agriculture": {"ar": "زراعة", "isic": "A"},
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"telecom": {"ar": "اتصالات", "isic": "J"},
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"media": {"ar": "إعلام وترفيه", "isic": "R"},
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"automotive": {"ar": "سيارات", "isic": "G"},
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"government": {"ar": "قطاع حكومي", "isic": "O"},
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}
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SAUDI_REGIONS = [
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"الرياض", "مكة المكرمة", "المنطقة الشرقية", "المدينة المنورة",
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"القصيم", "عسير", "تبوك", "حائل", "الحدود الشمالية",
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"جازان", "نجران", "الباحة", "الجوف",
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]
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class CompanyProfiler:
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"""
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Builds, enriches, and scores company profiles for strategic B2B matching.
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يبني ملفات الشركات ويثريها ويقيمها للمطابقة الاستراتيجية
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"""
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def __init__(self):
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self.llm = get_llm()
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# ── Create Profile ───────────────────────────────────────────────────────
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async def create_profile(
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self,
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company_data: dict,
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tenant_id,
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db: AsyncSession,
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) -> CompanyProfile:
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"""
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Create a company profile from user input.
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إنشاء ملف شركة من بيانات المستخدم
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"""
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profile = CompanyProfile(
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tenant_id=tenant_id,
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company_name=company_data["company_name"],
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company_name_ar=company_data.get("company_name_ar"),
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industry=company_data.get("industry"),
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sub_industry=company_data.get("sub_industry"),
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cr_number=company_data.get("cr_number"),
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city=company_data.get("city"),
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region=company_data.get("region"),
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employee_count=company_data.get("employee_count"),
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annual_revenue_sar=company_data.get("annual_revenue_sar"),
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capabilities=company_data.get("capabilities", []),
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needs=company_data.get("needs", []),
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deal_preferences=company_data.get("deal_preferences", {}),
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website=company_data.get("website"),
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linkedin_url=company_data.get("linkedin_url"),
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whatsapp_number=company_data.get("whatsapp_number"),
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trust_score=0.0,
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is_verified=False,
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)
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db.add(profile)
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await db.flush()
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await db.refresh(profile)
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logger.info("Created company profile %s for tenant %s", profile.id, tenant_id)
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return profile
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# ── Enrich Profile with AI ───────────────────────────────────────────────
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async def enrich_profile(
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self,
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profile_id,
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db: AsyncSession,
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) -> CompanyProfile:
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"""
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Use LLM to enrich a company profile: analyze website, detect industry,
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extract capabilities, identify needs, estimate company size.
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إثراء ملف الشركة بالذكاء الاصطناعي
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"""
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result = await db.execute(select(CompanyProfile).where(CompanyProfile.id == profile_id))
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profile = result.scalar_one_or_none()
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if not profile:
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raise ValueError(f"Profile {profile_id} not found")
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# Build context from available data
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context_parts = [
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f"Company: {profile.company_name}",
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]
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if profile.company_name_ar:
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context_parts.append(f"Arabic name: {profile.company_name_ar}")
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if profile.website:
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context_parts.append(f"Website: {profile.website}")
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if profile.industry:
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context_parts.append(f"Industry: {profile.industry}")
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if profile.city:
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context_parts.append(f"City: {profile.city}")
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if profile.cr_number:
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context_parts.append(f"CR Number: {profile.cr_number}")
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if profile.capabilities:
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context_parts.append(f"Known capabilities: {', '.join(profile.capabilities)}")
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company_context = "\n".join(context_parts)
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system_prompt = """أنت محلل شركات سعودي متخصص. حلل بيانات الشركة التالية وأعد تقريراً مفصلاً بصيغة JSON.
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You are a Saudi company analyst. Analyze the following company data and return a detailed JSON report.
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Return JSON with these fields:
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{
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"industry": "industry code from the list",
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"sub_industry": "specific sub-industry",
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"capabilities": ["list of what this company can offer to partners"],
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"needs": ["list of what this company likely needs from partners"],
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"estimated_employee_range": "micro/small/medium/large",
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"deal_preferences": {
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"partnership": 0.0-1.0,
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"distribution": 0.0-1.0,
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"franchise": 0.0-1.0,
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"jv": 0.0-1.0,
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"referral": 0.0-1.0,
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"acquisition": 0.0-1.0,
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"barter": 0.0-1.0
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},
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"enrichment_notes_ar": "ملاحظات الإثراء بالعربي"
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}
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Available industries: """ + ", ".join(SAUDI_INDUSTRIES.keys())
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llm_response = await self.llm.complete(
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system_prompt=system_prompt,
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user_message=company_context,
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json_mode=True,
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temperature=0.3,
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)
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enrichment = llm_response.parse_json()
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if enrichment:
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if enrichment.get("industry") and not profile.industry:
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profile.industry = enrichment["industry"]
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if enrichment.get("sub_industry") and not profile.sub_industry:
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profile.sub_industry = enrichment["sub_industry"]
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if enrichment.get("capabilities"):
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existing_caps = set(profile.capabilities or [])
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new_caps = [c for c in enrichment["capabilities"] if c not in existing_caps]
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profile.capabilities = list(existing_caps) + new_caps
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if enrichment.get("needs"):
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existing_needs = set(profile.needs or [])
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new_needs = [n for n in enrichment["needs"] if n not in existing_needs]
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profile.needs = list(existing_needs) + new_needs
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if enrichment.get("deal_preferences") and not profile.deal_preferences:
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profile.deal_preferences = enrichment["deal_preferences"]
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await db.flush()
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await db.refresh(profile)
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logger.info("Enriched profile %s with AI analysis", profile_id)
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return profile
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# ── Analyze Needs (Arabic Input) ─────────────────────────────────────────
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async def analyze_needs(
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self,
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profile_id,
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user_description: str,
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db: AsyncSession,
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) -> dict:
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"""
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User describes needs in Arabic free-text. AI extracts structured needs.
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المستخدم يصف احتياجاته بالعربي والذكاء الاصطناعي يستخرج البيانات المهيكلة
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"""
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result = await db.execute(select(CompanyProfile).where(CompanyProfile.id == profile_id))
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profile = result.scalar_one_or_none()
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if not profile:
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raise ValueError(f"Profile {profile_id} not found")
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system_prompt = """أنت مستشار أعمال سعودي. المستخدم يصف احتياجاته بالعربي.
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استخرج المعلومات التالية وأعدها بصيغة JSON:
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{
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"deal_type": "partnership/distribution/franchise/jv/referral/acquisition/barter",
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"specific_needs": ["قائمة الاحتياجات المحددة"],
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"budget_range_sar": {"min": 0, "max": 0},
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"timeline": "فوري/1-3 أشهر/3-6 أشهر/6-12 شهر/أكثر من سنة",
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"priorities": ["الأولوية الأولى", "الأولوية الثانية"],
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"ideal_partner_profile": "وصف الشريك المثالي",
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"deal_breakers": ["الأمور التي لا يمكن التنازل عنها"],
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"summary_ar": "ملخص الاحتياجات بالعربي"
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}
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Company context:
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- Name: """ + profile.company_name + """
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- Industry: """ + (profile.industry or "unknown") + """
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- City: """ + (profile.city or "unknown")
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llm_response = await self.llm.complete(
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system_prompt=system_prompt,
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user_message=user_description,
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json_mode=True,
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temperature=0.2,
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)
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analysis = llm_response.parse_json() or {}
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# Persist extracted needs onto the profile
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if analysis.get("specific_needs"):
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existing = set(profile.needs or [])
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for need in analysis["specific_needs"]:
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existing.add(need)
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profile.needs = list(existing)
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if analysis.get("deal_type") and not profile.deal_preferences:
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profile.deal_preferences = {analysis["deal_type"]: 1.0}
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await db.flush()
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logger.info("Analyzed needs for profile %s: %s", profile_id, analysis.get("summary_ar", ""))
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return analysis
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# ── Analyze Capabilities ─────────────────────────────────────────────────
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async def analyze_capabilities(
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self,
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profile_id,
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db: AsyncSession,
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) -> dict:
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"""
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Analyze what the company can offer to partners.
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تحليل ما يمكن للشركة تقديمه للشركاء
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"""
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result = await db.execute(select(CompanyProfile).where(CompanyProfile.id == profile_id))
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profile = result.scalar_one_or_none()
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if not profile:
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raise ValueError(f"Profile {profile_id} not found")
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context_parts = [
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f"Company: {profile.company_name}",
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f"Industry: {profile.industry or 'unknown'}",
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f"Sub-industry: {profile.sub_industry or 'unknown'}",
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f"City: {profile.city or 'unknown'}",
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f"Employees: {profile.employee_count or 'unknown'}",
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f"Revenue (SAR): {profile.annual_revenue_sar or 'unknown'}",
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]
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if profile.capabilities:
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context_parts.append(f"Known capabilities: {', '.join(profile.capabilities)}")
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system_prompt = """أنت محلل قدرات شركات سعودي. حلل الشركة وحدد قدراتها التي يمكن تقديمها لشركاء.
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You are a Saudi company capabilities analyst. Analyze the company and identify what it can offer to partners.
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Return JSON:
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{
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"core_capabilities": ["القدرات الأساسية"],
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"secondary_capabilities": ["القدرات الثانوية"],
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"unique_advantages": ["المزايا التنافسية الفريدة"],
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"capacity_utilization": "low/medium/high",
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"partnership_value_ar": "وصف القيمة التي يمكن تقديمها للشركاء بالعربي",
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"recommended_deal_types": ["أنواع الصفقات المقترحة"],
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"target_industries": ["القطاعات المستهدفة للشراكة"]
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}"""
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llm_response = await self.llm.complete(
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system_prompt=system_prompt,
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user_message="\n".join(context_parts),
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json_mode=True,
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temperature=0.3,
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)
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analysis = llm_response.parse_json() or {}
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# Merge discovered capabilities into the profile
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if analysis.get("core_capabilities"):
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existing = set(profile.capabilities or [])
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for cap in analysis["core_capabilities"]:
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existing.add(cap)
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if analysis.get("secondary_capabilities"):
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for cap in analysis["secondary_capabilities"]:
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existing.add(cap)
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profile.capabilities = list(existing)
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await db.flush()
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logger.info("Analyzed capabilities for profile %s", profile_id)
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return analysis
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# ── Deal Readiness Score ─────────────────────────────────────────────────
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async def get_deal_readiness(
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self,
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profile_id,
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db: AsyncSession,
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) -> dict:
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"""
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Score the company's readiness to engage in strategic deals.
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تقييم جاهزية الشركة للصفقات الاستراتيجية
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"""
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from app.models.strategic_deal import StrategicDeal, DealStatus
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result = await db.execute(select(CompanyProfile).where(CompanyProfile.id == profile_id))
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profile = result.scalar_one_or_none()
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if not profile:
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raise ValueError(f"Profile {profile_id} not found")
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score = 0.0
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breakdown = {}
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recommendations_ar = []
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# 1. Profile completeness (0-25)
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completeness = 0
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if profile.company_name:
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completeness += 3
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if profile.company_name_ar:
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completeness += 2
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if profile.industry:
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completeness += 3
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if profile.cr_number:
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completeness += 4
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if profile.city and profile.region:
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completeness += 2
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if profile.website:
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completeness += 2
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if profile.whatsapp_number:
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completeness += 2
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if profile.capabilities and len(profile.capabilities) >= 3:
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completeness += 4
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else:
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recommendations_ar.append("أضف 3 قدرات على الأقل لتحسين المطابقة")
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if profile.needs and len(profile.needs) >= 2:
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completeness += 3
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else:
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recommendations_ar.append("حدد احتياجاتك لنجد لك الشريك المناسب")
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breakdown["profile_completeness"] = completeness
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score += completeness
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# 2. Verification status (0-25)
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verification = 0
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if profile.is_verified:
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verification = 20
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if profile.cr_number:
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verification += 5
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else:
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recommendations_ar.append("أضف رقم السجل التجاري للتحقق من شركتك")
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breakdown["verification"] = verification
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score += verification
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# 3. Trust score (0-25)
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trust = int(profile.trust_score * 25)
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breakdown["trust"] = trust
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score += trust
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if profile.trust_score < 0.5:
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recommendations_ar.append("أكمل عملية التحقق لرفع درجة الثقة")
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# 4. Deal history (0-25)
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deal_count_q = select(func.count()).select_from(StrategicDeal).where(
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StrategicDeal.initiator_profile_id == profile_id,
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)
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total_deals = (await db.execute(deal_count_q)).scalar() or 0
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won_count_q = select(func.count()).select_from(StrategicDeal).where(
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StrategicDeal.initiator_profile_id == profile_id,
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StrategicDeal.status == DealStatus.CLOSED_WON.value,
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)
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won_deals = (await db.execute(won_count_q)).scalar() or 0
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history = min(25, total_deals * 3 + won_deals * 5)
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breakdown["deal_history"] = history
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score += history
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if total_deals == 0:
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recommendations_ar.append("ابدأ أول صفقة لبناء سجل تاريخي")
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readiness_level = "low"
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if score >= 70:
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readiness_level = "high"
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elif score >= 40:
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readiness_level = "medium"
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return {
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"score": round(score, 1),
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"max_score": 100,
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"readiness_level": readiness_level,
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"breakdown": breakdown,
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"recommendations_ar": recommendations_ar,
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"total_deals": total_deals,
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"won_deals": won_deals,
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"readiness_label_ar": {
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"high": "جاهز للصفقات",
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"medium": "يحتاج تحسين",
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"low": "يحتاج اهتمام",
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}[readiness_level],
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}
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