mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-18 23:39:34 +00:00
400 lines
15 KiB
Python
400 lines
15 KiB
Python
"""Smoke tests for the Revenue Graph layer — graph, why-now, leaks,
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maturity, simulator, objections, proof pack, agent registry, playbooks."""
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from __future__ import annotations
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from datetime import datetime, timedelta, timezone
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import pytest
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from auto_client_acquisition.revenue_graph.agent_registry import (
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ALL_AGENTS,
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agents_summary,
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get_agent,
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list_agents_by_autonomy,
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)
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from auto_client_acquisition.revenue_graph.graph import (
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CompanyVector,
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aggregate_outcomes,
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cosine_similarity,
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find_similar_companies,
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graph_health_summary,
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predict_outcome_probabilities,
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recommend_next_action,
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)
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from auto_client_acquisition.revenue_graph.leak_detector import (
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detect_all_leaks,
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detect_lead_no_followup,
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detect_stalled_deals,
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)
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from auto_client_acquisition.revenue_graph.maturity_score import (
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DIMENSIONS,
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compute_benchmark_score,
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)
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from auto_client_acquisition.revenue_graph.objection_library import (
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OBJECTION_CATEGORIES,
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SAUDI_B2B_OBJECTIONS,
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category_summary,
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find_by_keyword,
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)
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from auto_client_acquisition.revenue_graph.proof_pack import (
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ProofPackInputs,
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generate_proof_pack,
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)
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from auto_client_acquisition.revenue_graph.sector_playbooks import (
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ALL_PLAYBOOKS,
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get_playbook,
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)
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from auto_client_acquisition.revenue_graph.simulator import (
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SECTOR_BENCHMARKS,
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SimulatorInputs,
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simulate,
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)
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from auto_client_acquisition.revenue_graph.why_now import (
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SIGNAL_WEIGHTS,
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WhyNowSignal,
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explain_why_now,
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freshness_factor,
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rank_todays_priorities,
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)
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# ── Graph ─────────────────────────────────────────────────────────
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def test_cosine_similarity_self_is_one():
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a = CompanyVector("c1", sector="real_estate", city="riyadh", arabic_first=True)
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# self-similarity for non-identical IDs but same vector should be high
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b = CompanyVector("c2", sector="real_estate", city="riyadh", arabic_first=True)
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assert cosine_similarity(a, b) >= 0.6
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def test_cosine_similarity_different_sector_lower():
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"""Same city + flags but different sector → score should be lower than full match."""
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same = CompanyVector("c1", sector="real_estate", city="riyadh")
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same2 = CompanyVector("c2", sector="real_estate", city="riyadh")
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diff = CompanyVector("c3", sector="construction", city="riyadh")
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assert cosine_similarity(same, diff) < cosine_similarity(same, same2)
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def test_aggregate_outcomes_below_min_returns_none():
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out = aggregate_outcomes([{"responded": True}, {"responded": False}], min_cohort=5)
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assert out is None
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def test_aggregate_outcomes_at_min_returns_stats():
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out = aggregate_outcomes(
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[
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{"responded": True, "booked": True, "won": True, "deal_size_sar": 100, "cycle_days": 30},
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{"responded": True, "booked": False, "won": False, "cycle_days": 25},
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{"responded": False, "booked": False, "won": False},
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{"responded": True, "booked": True, "won": False, "cycle_days": 40},
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{"responded": False, "booked": False, "won": False},
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],
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min_cohort=5,
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)
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assert out is not None
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assert out.cohort_size == 5
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assert 0 <= out.reply_rate <= 1
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assert 0 <= out.confidence <= 1
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def test_predict_outcome_probabilities():
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target = CompanyVector("new", sector="clinics", city="riyadh", has_whatsapp_business=True)
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historical = []
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for i in range(20):
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v = CompanyVector(f"c{i}", sector="clinics", city="riyadh", has_whatsapp_business=True)
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historical.append((v, {"responded": i % 3 == 0, "booked": i % 5 == 0, "won": i % 7 == 0, "cycle_days": 28}))
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pred = predict_outcome_probabilities(target=target, historical=historical, top_k=10, min_cohort=5)
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assert pred is not None
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assert 0 <= pred["reply_probability"] <= 1
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def test_recommend_next_action_no_history_with_whatsapp():
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target = CompanyVector("c1", has_whatsapp_business=True)
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nba = recommend_next_action(target=target, last_outcome=None, days_since_last_touch=0)
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assert nba.channel == "whatsapp"
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assert nba.expected_reply_lift > 1.0
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def test_recommend_next_action_positive_reply_pushes_demo():
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target = CompanyVector("c1")
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nba = recommend_next_action(target=target, last_outcome="positive_reply", days_since_last_touch=1)
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assert "24" in nba.action or "demo" in nba.action
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# ── Why-Now ───────────────────────────────────────────────────────
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def test_freshness_factor_today_is_one():
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now = datetime.now(timezone.utc).replace(tzinfo=None)
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assert freshness_factor(now, now=now) == 1.0
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def test_freshness_factor_decays():
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now = datetime.now(timezone.utc).replace(tzinfo=None)
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old = now - timedelta(days=14)
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assert 0.45 <= freshness_factor(old, now=now) <= 0.55 # half-life
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def test_explain_why_now_with_strong_signal():
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now = datetime.now(timezone.utc).replace(tzinfo=None)
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signals = [
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WhyNowSignal("hiring_sales_rep", now - timedelta(days=2), "linkedin"),
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WhyNowSignal("new_branch_opened", now - timedelta(days=5), "google_search"),
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]
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exp = explain_why_now(company_id="c1", signals=signals)
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assert exp is not None
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assert exp.score > 0
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assert "يوظفون" in exp.headline_ar or "افتتحوا" in exp.headline_ar
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def test_explain_why_now_no_signals_returns_none():
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assert explain_why_now(company_id="c1", signals=[]) is None
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def test_explain_why_now_ranks_todays_priorities():
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now = datetime.now(timezone.utc).replace(tzinfo=None)
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explanations = []
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for i, sig in enumerate(["hiring_sales_rep", "new_branch_opened", "tender_published"]):
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e = explain_why_now(
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company_id=f"c{i}",
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signals=[WhyNowSignal(sig, now - timedelta(days=1), "src")],
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)
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if e:
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explanations.append(e)
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top = rank_todays_priorities(explanations=explanations, top_n=2)
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assert len(top) <= 2
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# ── Leak Detector ────────────────────────────────────────────────
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def test_lead_no_followup_flags_old_untouched():
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now = datetime.now(timezone.utc).replace(tzinfo=None)
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leads = [
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{
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"id": "L1",
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"company_name": "Old Co.",
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"created_at": now - timedelta(days=10),
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"last_outreach_at": None,
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}
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]
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leaks = detect_lead_no_followup(leads=leads, now=now)
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assert len(leaks) == 1
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assert leaks[0].severity in ("medium", "high", "critical")
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def test_stalled_deals_detected():
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now = datetime.now(timezone.utc).replace(tzinfo=None)
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deals = [
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{
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"id": "D1",
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"company_name": "Stalled Co.",
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"status": "open",
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"value_sar": 100_000,
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"last_activity_at": now - timedelta(days=20),
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}
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]
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leaks = detect_stalled_deals(deals=deals, now=now)
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assert len(leaks) == 1
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def test_detect_all_leaks_sorts_by_impact():
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now = datetime.now(timezone.utc).replace(tzinfo=None)
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report = detect_all_leaks(
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leads=[{"id": "L1", "created_at": now - timedelta(days=8), "last_outreach_at": None}],
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deals=[{"id": "D1", "status": "open", "value_sar": 200_000, "last_activity_at": now - timedelta(days=22)}],
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now=now,
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)
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assert report.total_estimated_impact_sar > 0
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assert "critical" in report.by_severity or "high" in report.by_severity
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# ── Maturity Score ───────────────────────────────────────────────
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def test_benchmark_zero_returns_weak():
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r = compute_benchmark_score(customer_id="c1")
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assert r.bucket == "weak"
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assert r.overall < 50
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def test_benchmark_strong_returns_strong():
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r = compute_benchmark_score(
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customer_id="c1",
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has_playbook=True, has_quota=True, weekly_pipeline_review=True,
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median_response_minutes=30, followups_per_lead=4,
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reply_rate=0.12, positive_reply_rate=0.04,
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sectors_targeted=2, win_rate_top_sector=0.30,
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has_pricing_page=True, has_case_studies=True, avg_proposal_pages=3,
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lead_to_meeting=0.20, meeting_to_deal=0.45, deal_to_close=0.35,
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has_onboarding_flow=True, nps_collected=True, runs_qbr=True,
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)
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assert r.bucket in ("strong", "exceptional")
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assert r.overall >= 70
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def test_benchmark_returns_all_dimensions():
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r = compute_benchmark_score(customer_id="c")
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assert len(r.dimensions) == len(DIMENSIONS)
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names = {d.name for d in r.dimensions}
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assert names == set(DIMENSIONS)
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def test_benchmark_markdown_export():
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r = compute_benchmark_score(customer_id="c1", has_playbook=True)
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md = r.to_markdown()
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assert "Dealix Benchmark" in md
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assert "خريطة الطريق" in md
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# ── Simulator ────────────────────────────────────────────────────
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def test_simulate_real_estate_returns_funnel():
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inputs = SimulatorInputs(
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sector="real_estate",
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city="الرياض",
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avg_deal_value_sar=500_000,
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target_revenue_sar=2_000_000,
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target_period_days=90,
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)
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r = simulate(inputs=inputs)
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# With Dealix lift, you need FEWER leads to hit the same target
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assert r.with_dealix.leads_needed < r.baseline.leads_needed
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# And the ratio is meaningful — typically ~25-40% of baseline
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assert r.with_dealix.leads_needed > r.baseline.leads_needed * 0.1
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assert r.expected_roi_x > 0
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assert r.plan.plan_name in ("Starter", "Growth", "Scale")
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def test_simulate_warns_on_too_short_period():
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inputs = SimulatorInputs(
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sector="real_estate", city="الرياض",
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avg_deal_value_sar=10_000, target_revenue_sar=50_000,
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target_period_days=15,
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)
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r = simulate(inputs=inputs)
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assert any("قصيرة" in risk for risk in r.risks_ar)
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def test_simulate_unknown_sector_uses_default():
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inputs = SimulatorInputs(
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sector="unknown_sector",
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city="الرياض",
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avg_deal_value_sar=50_000,
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target_revenue_sar=500_000,
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)
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r = simulate(inputs=inputs)
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assert r.with_dealix.leads_needed > 0
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# ── Objection Library ────────────────────────────────────────────
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def test_library_has_objections_in_each_category():
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summary = category_summary()
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assert len(summary) > 0
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assert sum(summary.values()) == len(SAUDI_B2B_OBJECTIONS)
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def test_find_by_keyword_price():
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obj = find_by_keyword("السعر عالي")
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assert obj is not None
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assert obj.category == "price"
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def test_find_by_keyword_no_match():
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obj = find_by_keyword("XXXX_no_match_string_XXXX")
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assert obj is None
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def test_objection_categories_known():
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for o in SAUDI_B2B_OBJECTIONS:
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assert o.category in OBJECTION_CATEGORIES
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# ── Proof Pack ───────────────────────────────────────────────────
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def test_proof_pack_grades_a_for_high_multiple():
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inp = ProofPackInputs(
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customer_id="c1", customer_name="Test", sector="real_estate",
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month_label="إبريل 2026", plan="Growth", monthly_price_sar=2999,
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leads_discovered=500, leads_enriched=400, drafts_created=300,
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drafts_sent=280, whatsapp_sent=180, emails_sent=80, linkedin_sent=20,
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replies_received=42, positive_replies=18,
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meetings_booked=12, proposals_sent=6, deals_won=3,
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pipeline_added_sar=500_000, revenue_won_sar=180_000,
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avg_response_minutes=40, bounce_rate=0.04, opt_outs=2, compliance_blocks=0,
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sector_reply_rate_p50=0.07, sector_meeting_rate_p50=0.30, sector_win_rate_p50=0.18,
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)
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p = generate_proof_pack(inp)
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assert p.grade in ("A+", "A")
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assert "ROI" in p.to_markdown()
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def test_proof_pack_grades_d_for_no_pipeline():
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inp = ProofPackInputs(
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customer_id="c1", customer_name="Test", sector="real_estate",
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month_label="مارس 2026", plan="Growth", monthly_price_sar=2999,
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leads_discovered=10, leads_enriched=5, drafts_created=3,
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drafts_sent=3, whatsapp_sent=2, emails_sent=1, linkedin_sent=0,
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replies_received=0, positive_replies=0,
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meetings_booked=0, proposals_sent=0, deals_won=0,
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pipeline_added_sar=1000, revenue_won_sar=0,
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avg_response_minutes=180, bounce_rate=0.0, opt_outs=0, compliance_blocks=0,
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sector_reply_rate_p50=0.07, sector_meeting_rate_p50=0.30, sector_win_rate_p50=0.18,
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)
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p = generate_proof_pack(inp)
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assert p.grade in ("D", "C")
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# ── Agent Registry ───────────────────────────────────────────────
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def test_eleven_agents_registered():
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assert len(ALL_AGENTS) == 11
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def test_agent_registry_summary():
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s = agents_summary()
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assert s["total"] == 11
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assert s["safe_auto"] >= 5
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assert s["pdpl_gated"] >= 8
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def test_get_agent_returns_valid_spec():
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a = get_agent("prospecting")
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assert a is not None
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assert a.name_ar
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assert a.role_ar
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def test_get_agent_unknown_returns_none():
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assert get_agent("nonexistent") is None
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def test_list_by_autonomy():
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safe = list_agents_by_autonomy("safe_auto")
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assert all(a.autonomy_level == "safe_auto" for a in safe)
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# ── Sector Playbooks ─────────────────────────────────────────────
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def test_eight_playbooks_present():
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assert len(ALL_PLAYBOOKS) == 8
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def test_playbook_has_required_fields():
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p = get_playbook("real_estate")
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assert p is not None
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assert p.pain_points_ar
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assert p.opening_lines_ar
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assert p.benchmarks
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assert sum(p.recommended_channel_mix.values()) > 0.99
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def test_playbook_unknown_returns_none():
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assert get_playbook("xxx_unknown") is None
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# ── Graph health ─────────────────────────────────────────────────
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def test_graph_health_summary():
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s = graph_health_summary(
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n_companies=200, n_signals=500, n_messages=1500,
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n_outcomes=400, n_won_deals=20,
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)
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assert "moat_score" in s
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assert s["ready_for_predictions"] is True
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def test_graph_health_zero_safe():
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s = graph_health_summary(n_companies=0, n_signals=0, n_messages=0, n_outcomes=0, n_won_deals=0)
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assert s["moat_score"] == 0
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assert s["ready_for_predictions"] is False
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