mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
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066ce32aa7
Complete outreach automation that generates drafts → Sami approves → system sends:
1. OutreachDraft model (models/outreach_draft.py):
DB-persisted draft queue. Every message starts as status='draft'.
Fields: company, channel, subject, body, followups, sector, scores,
status (draft→approved→sent→replied→opted_out→bounced), timestamps.
2. Daily Pipeline (automation.py → /daily-pipeline/run):
Generates N targets per sector/city, runs compliance check,
creates personalized emails with Arabic pain maps, stores as
draft rows in DB. Returns batch_id for approval.
3. Draft Queue API (drafts.py):
- GET /drafts — list by status/channel/batch
- GET /drafts/stats — counts per status
- GET /drafts/{id} — full draft with body + followups
- POST /drafts/{id}/approve — mark approved
- POST /drafts/approve-batch — approve entire batch
- POST /drafts/{id}/send — dispatch via email/whatsapp/sms
- POST /drafts/{id}/skip — archive draft
- PATCH /drafts/{id} — edit before approving
- POST /drafts/{id}/log-reply — paste reply → auto-classify →
generate suggested response → update status
4. Send dispatch uses existing integrations:
- Email: integrations/email_sender.py (SMTP)
- WhatsApp: integrations/whatsapp.py (Business API + mock)
- SMS: integrations/sms.py (Unifonic)
- LinkedIn: manual_required (copy from dashboard)
Safety:
- All drafts require approval (approval_required=True default)
- Unsubscribe reply → immediate opt_out status
- Compliance gate blocks: opt_out, bounced, high_risk, no_source
- Personal email → warning to use manual channel
- Rate limits enforced at send level
https://claude.ai/code/session_01W1rJthWDkasijTdXCfxVHs
78 lines
3.1 KiB
Python
78 lines
3.1 KiB
Python
"""OutreachDraft — DB-persisted draft queue for all outreach channels.
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Every generated message starts as status='draft'. Sami reviews and
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approves before any send. Approved drafts are dispatched via existing
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Celery send tasks.
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"""
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from __future__ import annotations
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import uuid
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from datetime import datetime, timezone
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from typing import Any, Dict, Optional
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from sqlalchemy import Column, String, Integer, Boolean, DateTime, Text, JSON
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from sqlalchemy.dialects.postgresql import UUID
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try:
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from app.database import Base
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except ImportError:
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from sqlalchemy.orm import DeclarativeBase
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class Base(DeclarativeBase):
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pass
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class OutreachDraft(Base):
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__tablename__ = "outreach_drafts"
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
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batch_id = Column(String(64), index=True)
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company = Column(String(255), nullable=False)
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contact_name = Column(String(255), default="")
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contact_email = Column(String(255), default="")
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contact_phone = Column(String(32), default="")
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channel = Column(String(20), nullable=False) # email | whatsapp | sms | linkedin
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subject = Column(String(500), default="")
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body = Column(Text, nullable=False)
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followup_2d = Column(Text, default="")
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followup_5d = Column(Text, default="")
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call_script = Column(Text, default="")
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sector = Column(String(100), default="")
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city = Column(String(100), default="")
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pain_hypothesis = Column(Text, default="")
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fit_score = Column(Integer, default=0)
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risk_score = Column(Integer, default=0)
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status = Column(String(20), default="draft", index=True)
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# draft | approved | sent | replied | opted_out | bounced | skipped
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approval_required = Column(Boolean, default=True)
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created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
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approved_at = Column(DateTime(timezone=True), nullable=True)
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sent_at = Column(DateTime(timezone=True), nullable=True)
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replied_at = Column(DateTime(timezone=True), nullable=True)
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reply_text = Column(Text, nullable=True)
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reply_category = Column(String(50), nullable=True)
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next_action = Column(String(100), nullable=True)
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source = Column(String(100), default="daily_pipeline")
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metadata_ = Column("metadata", JSON, default=dict)
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def to_dict(self) -> Dict[str, Any]:
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return {
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"id": str(self.id),
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"batch_id": self.batch_id,
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"company": self.company,
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"contact_name": self.contact_name,
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"contact_email": self.contact_email,
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"channel": self.channel,
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"subject": self.subject,
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"body": self.body[:200] + "..." if len(self.body or "") > 200 else self.body,
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"sector": self.sector,
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"city": self.city,
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"fit_score": self.fit_score,
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"risk_score": self.risk_score,
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"status": self.status,
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"created_at": self.created_at.isoformat() if self.created_at else None,
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"sent_at": self.sent_at.isoformat() if self.sent_at else None,
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"reply_category": self.reply_category,
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"next_action": self.next_action,
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}
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