349 lines
15 KiB
Python
349 lines
15 KiB
Python
from sqlalchemy import create_engine, Column, Integer, String, Text, DateTime, ForeignKey, Float, Boolean, JSON
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from sqlalchemy.ext.declarative import declarative_base
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from sqlalchemy.orm import sessionmaker, relationship
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from datetime import datetime
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from .config import settings
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# Setup
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engine = create_engine(settings.DATABASE_URL, connect_args={"check_same_thread": False})
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SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
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Base = declarative_base()
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# ==============================================================================
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# MODELS
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# ==============================================================================
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class Company(Base):
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__tablename__ = "companies"
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id = Column(Integer, primary_key=True, index=True)
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# Core Identity (Golden Record - from Research)
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name = Column(String, index=True)
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website = Column(String, index=True) # Normalized Domain preferred
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crm_id = Column(String, unique=True, index=True, nullable=True) # Link to D365
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# CRM Original Data (Source of Truth for Import)
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crm_name = Column(String, nullable=True)
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crm_website = Column(String, nullable=True)
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crm_address = Column(String, nullable=True) # Full address string or JSON
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crm_vat = Column(String, nullable=True)
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# Classification
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industry_crm = Column(String, nullable=True) # The "allowed" industry
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industry_ai = Column(String, nullable=True) # The AI suggested industry
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# Location (Golden Record)
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city = Column(String, nullable=True)
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country = Column(String, default="DE")
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# Workflow Status
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status = Column(String, default="NEW", index=True) # NEW, TO_ENRICH, ENRICHED, QUALIFIED, DISQUALIFIED
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# Quality & Confidence
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confidence_score = Column(Float, default=0.0) # Overall confidence
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data_mismatch_score = Column(Float, default=0.0) # 0.0=Match, 1.0=Mismatch
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# Scraping Status Flags
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website_scrape_status = Column(String, default="PENDING") # PENDING, SUCCESS, FAILED, BLOCKED
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wiki_search_status = Column(String, default="PENDING") # PENDING, FOUND, NOT_FOUND
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# Granular Process Tracking (Timestamps)
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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last_scraped_at = Column(DateTime, nullable=True)
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last_wiki_search_at = Column(DateTime, nullable=True)
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last_classification_at = Column(DateTime, nullable=True)
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last_signal_check_at = Column(DateTime, nullable=True)
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# NEW: Quantitative Potential Metrics (v0.7.0)
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calculated_metric_name = Column(String, nullable=True) # e.g., "Anzahl Betten"
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calculated_metric_value = Column(Float, nullable=True) # e.g., 180.0
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calculated_metric_unit = Column(String, nullable=True) # e.g., "Betten"
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standardized_metric_value = Column(Float, nullable=True) # e.g., 4500.0
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standardized_metric_unit = Column(String, nullable=True) # e.g., "m²"
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metric_source = Column(String, nullable=True) # "website", "wikipedia", "serpapi"
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metric_proof_text = Column(Text, nullable=True) # Snippet showing the value (e.g. "2,0 Mio Besucher (2020)")
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metric_source_url = Column(Text, nullable=True) # URL where the proof was found
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metric_confidence = Column(Float, nullable=True) # 0.0 - 1.0
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metric_confidence_reason = Column(Text, nullable=True) # Why is it high/low?
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# Relationships
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signals = relationship("Signal", back_populates="company", cascade="all, delete-orphan")
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enrichment_data = relationship("EnrichmentData", back_populates="company", cascade="all, delete-orphan")
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reported_mistakes = relationship("ReportedMistake", back_populates="company", cascade="all, delete-orphan")
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contacts = relationship("Contact", back_populates="company", cascade="all, delete-orphan")
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class Contact(Base):
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"""
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Represents a person associated with a company.
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"""
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__tablename__ = "contacts"
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id = Column(Integer, primary_key=True, index=True)
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company_id = Column(Integer, ForeignKey("companies.id"), index=True)
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gender = Column(String) # "männlich", "weiblich"
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title = Column(String, default="") # "Dr.", "Prof."
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first_name = Column(String)
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last_name = Column(String)
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email = Column(String, index=True)
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job_title = Column(String) # Visitenkarten-Titel
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language = Column(String, default="De") # "De", "En"
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# SuperOffice Mapping
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so_contact_id = Column(Integer, nullable=True, index=True) # SuperOffice Contact ID (Company)
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so_person_id = Column(Integer, nullable=True, unique=True, index=True) # SuperOffice Person ID
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role = Column(String) # Operativer Entscheider, etc.
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status = Column(String, default="") # Marketing Status
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is_primary = Column(Boolean, default=False)
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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company = relationship("Company", back_populates="contacts")
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class Industry(Base):
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"""
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Represents a specific industry vertical (Branche).
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"""
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__tablename__ = "industries"
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id = Column(Integer, primary_key=True, index=True)
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notion_id = Column(String, unique=True, index=True, nullable=True) # Notion Page ID
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name = Column(String, unique=True, index=True)
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description = Column(Text, nullable=True) # Definition aus Notion
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# Notion Sync Fields (V3.0+)
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status_notion = Column(String, nullable=True) # e.g. "P1 Focus Industry"
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is_focus = Column(Boolean, default=False) # Derived from status_notion
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# Enhanced Fields (v3.1 - Pains/Gains/Priority)
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pains = Column(Text, nullable=True)
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gains = Column(Text, nullable=True)
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notes = Column(Text, nullable=True)
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priority = Column(String, nullable=True) # Replaces old status concept ("Freigegeben")
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ops_focus_secondary = Column(Boolean, default=False)
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# NEW SCHEMA FIELDS (from MIGRATION_PLAN)
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metric_type = Column(String, nullable=True) # Unit_Count, Area_in, Area_out
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min_requirement = Column(Float, nullable=True)
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whale_threshold = Column(Float, nullable=True)
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proxy_factor = Column(Float, nullable=True)
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scraper_search_term = Column(Text, nullable=True)
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scraper_keywords = Column(Text, nullable=True) # JSON-Array von Strings
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standardization_logic = Column(Text, nullable=True) # Formel, z.B. "wert * 25m²"
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# Optional link to a Robotics Category (the "product" relevant for this industry)
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primary_category_id = Column(Integer, ForeignKey("robotics_categories.id"), nullable=True)
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secondary_category_id = Column(Integer, ForeignKey("robotics_categories.id"), nullable=True)
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primary_category = relationship("RoboticsCategory", foreign_keys=[primary_category_id])
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secondary_category = relationship("RoboticsCategory", foreign_keys=[secondary_category_id])
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created_at = Column(DateTime, default=datetime.utcnow)
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class JobRoleMapping(Base):
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"""
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Maps job title patterns (regex or simple string) to Roles.
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"""
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__tablename__ = "job_role_mappings"
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id = Column(Integer, primary_key=True, index=True)
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pattern = Column(String, unique=True) # e.g. "%CTO%" or "Technischer Leiter"
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role = Column(String) # The target Role
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created_at = Column(DateTime, default=datetime.utcnow)
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class Persona(Base):
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"""
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Represents a generalized persona/role (e.g. 'Geschäftsführer', 'IT-Leiter')
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independent of the specific job title pattern.
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Stores the strategic messaging components.
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"""
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__tablename__ = "personas"
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id = Column(Integer, primary_key=True, index=True)
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name = Column(String, unique=True, index=True) # Matches the 'role' string in JobRoleMapping
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pains = Column(Text, nullable=True) # JSON list or multiline string
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gains = Column(Text, nullable=True) # JSON list or multiline string
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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class Signal(Base):
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"""
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Represents a specific sales signal or potential.
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Example: type='has_spa', value='true', proof='Wellnessbereich mit 2000qm'
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"""
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__tablename__ = "signals"
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id = Column(Integer, primary_key=True, index=True)
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company_id = Column(Integer, ForeignKey("companies.id"))
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signal_type = Column(String, index=True) # e.g. "robotics_cleaning_potential"
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confidence = Column(Float, default=0.0) # 0.0 to 1.0
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value = Column(String) # "High", "Medium", "Yes", "No"
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proof_text = Column(Text, nullable=True) # Snippet from website/source
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created_at = Column(DateTime, default=datetime.utcnow)
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company = relationship("Company", back_populates="signals")
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class EnrichmentData(Base):
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"""
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Stores raw data blobs (HTML, API responses) to allow re-processing.
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"""
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__tablename__ = "enrichment_data"
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id = Column(Integer, primary_key=True, index=True)
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company_id = Column(Integer, ForeignKey("companies.id"))
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source_type = Column(String) # "website_scrape", "wikipedia", "google_serp"
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content = Column(JSON) # The raw data
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is_locked = Column(Boolean, default=False) # Manual override flag
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wiki_verified_empty = Column(Boolean, default=False) # NEW: Mark Wikipedia as definitively empty
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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company = relationship("Company", back_populates="enrichment_data")
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class RoboticsCategory(Base):
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"""
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Stores definitions for robotics categories to allow user customization via UI.
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"""
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__tablename__ = "robotics_categories"
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id = Column(Integer, primary_key=True, index=True)
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notion_id = Column(String, unique=True, index=True, nullable=True) # Notion Page ID
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key = Column(String, unique=True, index=True) # e.g. "cleaning", "service"
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name = Column(String) # Display Name
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description = Column(Text) # The core definition used in LLM prompts
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reasoning_guide = Column(Text) # Instructions for the Chain-of-Thought
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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class ImportLog(Base):
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"""
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Logs bulk imports (e.g. from Excel lists).
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"""
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__tablename__ = "import_logs"
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id = Column(Integer, primary_key=True)
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filename = Column(String)
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import_type = Column(String) # "crm_dump" or "event_list"
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total_rows = Column(Integer)
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imported_rows = Column(Integer)
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duplicate_rows = Column(Integer)
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created_at = Column(DateTime, default=datetime.utcnow)
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class ReportedMistake(Base):
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__tablename__ = "reported_mistakes"
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id = Column(Integer, primary_key=True, index=True)
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company_id = Column(Integer, ForeignKey("companies.id"), index=True, nullable=False)
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field_name = Column(String, nullable=False)
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wrong_value = Column(Text, nullable=True)
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corrected_value = Column(Text, nullable=True)
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source_url = Column(String, nullable=True)
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quote = Column(Text, nullable=True)
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user_comment = Column(Text, nullable=True)
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status = Column(String, default="PENDING", nullable=False) # PENDING, APPROVED, REJECTED
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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company = relationship("Company", back_populates="reported_mistakes")
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class MarketingMatrix(Base):
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"""
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Stores the static marketing texts for Industry x Persona combinations.
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Source: Generated via AI.
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"""
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__tablename__ = "marketing_matrix"
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id = Column(Integer, primary_key=True, index=True)
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# The combination keys
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industry_id = Column(Integer, ForeignKey("industries.id"), nullable=False)
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persona_id = Column(Integer, ForeignKey("personas.id"), nullable=False)
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# The Content
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subject = Column(Text, nullable=True)
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intro = Column(Text, nullable=True)
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social_proof = Column(Text, nullable=True)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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industry = relationship("Industry")
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persona = relationship("Persona")
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# ==============================================================================
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# UTILS
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# ==============================================================================
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def init_db():
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Base.metadata.create_all(bind=engine)
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init_robotics_defaults()
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def init_robotics_defaults():
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"""Seeds the database with default robotics categories if empty."""
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db = SessionLocal()
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try:
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if db.query(RoboticsCategory).count() == 0:
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defaults = [
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{
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"key": "cleaning",
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"name": "Cleaning Robots",
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"description": "Does the company manage large floors, hospitals, hotels, or public spaces? (Keywords: Hygiene, Cleaning, SPA, Facility Management)",
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"reasoning_guide": "High (80-100): Large industrial floors, shopping malls, hospitals, airports. Medium (40-79): Mid-sized production, large offices, supermarkets. Low (0-39): Small offices, software consultancies."
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},
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{
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"key": "transport",
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"name": "Intralogistics / Transport",
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"description": "Do they move goods internally? (Keywords: Warehouse, Intralogistics, Production line, Hospital logistics)",
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"reasoning_guide": "High: Manufacturing, E-Commerce fulfillment, Hospitals. Low: Pure service providers, law firms."
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},
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{
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"key": "security",
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"name": "Security & Surveillance",
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"description": "Do they have large perimeters, solar parks, wind farms, or night patrols? (Keywords: Werkschutz, Security, Monitoring)",
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"reasoning_guide": "High: Critical infrastructure, large open-air storage, factories with valuable assets, 24/7 operations. Medium: Standard corporate HQs. Low: Offices in shared buildings."
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},
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{
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"key": "service",
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"name": "Service / Waiter Robots",
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"description": "Do they operate restaurants, nursing homes, or event venues where food/items need to be served to people?",
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"reasoning_guide": "High: Restaurants, Hotels (Room Service), Nursing Homes (Meal delivery). Low: B2B manufacturing, closed offices, pure installation services."
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}
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]
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for d in defaults:
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db.add(RoboticsCategory(**d))
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db.commit()
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print("Seeded Robotics Categories.")
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except Exception as e:
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print(f"Error seeding robotics defaults: {e}")
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finally:
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db.close()
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def get_db():
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db = SessionLocal()
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try:
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yield db
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finally:
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db.close() |