A continuation of Janady13/AI_Brain / services/seeder_api/app.py. The complete source identity, every recorded occurrence, and linked equations are on its first page.
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def _competitive_signal_payload(signal: CompetitiveSignal) -> "CompetitiveLandscapeInsight":
return CompetitiveLandscapeInsight(
competitor=signal.competitor,
channel=signal.channel,
summary=signal.summary,
strengths=signal.strengths,
weaknesses=signal.weaknesses,
recommended_play=signal.recommended_play,
share_of_voice=signal.share_of_voice,
signal_intensity=signal.signal_intensity,
threat_level=signal.threat_level,
focus_alignment=signal.focus_alignment,
source=signal.source,
notes=signal.notes,
)
def _excellence_payload(summary: ExcellenceSummary) -> ExcellenceInsight:
return ExcellenceInsight(
excellence_index=summary.excellence_index,
maturity_level=summary.maturity_level,
headline=summary.headline,
strengths=summary.strengths,
watchouts=summary.watchouts,
process_coverage=summary.process_coverage,
notes=summary.notes,
)
def _directive_payload(directive: StrategicDirective) -> StrategicDirectiveInsight:
return StrategicDirectiveInsight(
name=directive.name,
priority=directive.priority,
objective=directive.objective,
primary_channel=directive.primary_channel,
supporting_channels=directive.supporting_channels,
investment_usd=directive.investment_usd,
expected_revenue_usd=directive.expected_revenue_usd,
confidence=directive.confidence,
time_horizon_hours=directive.time_horizon_hours,
north_star_metric=directive.north_star_metric,
supporting_metrics=directive.supporting_metrics,
action_steps=directive.action_steps,
dependencies=directive.dependencies,
risk_mitigations=directive.risk_mitigations,
inspiration=directive.inspiration,
source=directive.source,
notes=directive.notes,
)
def _playbook_payload(playbook: ExecutivePlaybook) -> ExecutivePlaybookInsight:
return ExecutivePlaybookInsight(
headline=playbook.headline,
focus=playbook.focus,
urgency=playbook.urgency,
north_star_metric=playbook.north_star_metric,
total_budget_usd=playbook.total_budget_usd,
expected_revenue_usd=playbook.expected_revenue_usd,
confidence=playbook.confidence,
directives=[_directive_payload(directive) for directive in playbook.directives],
summary=playbook.summary,
notes=playbook.notes,
)
def _scenario_projection_payload(projection: ScenarioProjection) -> ScenarioProjectionInsight:
return ScenarioProjectionInsight(
scenario=projection.scenario,
expected_revenue=projection.expected_revenue,
expected_roi=projection.expected_roi,
probability=projection.probability,
confidence=projection.confidence,
accelerators=projection.accelerators,
stabilizers=projection.stabilizers,
watchouts=projection.watchouts,
source=projection.source,
notes=projection.notes,
)
def _scenario_planning_payload(analysis: ScenarioAnalysis) -> ScenarioPlanningInsight:
return ScenarioPlanningInsight(
headline=analysis.headline,
summary=analysis.summary,
projections=[_scenario_projection_payload(proj) for proj in analysis.projections],
confidence=analysis.confidence,
accelerators=analysis.accelerators,
stabilizers=analysis.stabilizers,
source=analysis.source,
notes=analysis.notes,
)
@app.post("/intelligence/marketing/diagnostics", response_model=DiagnosticResponse)
def marketing_diagnostics(query: MarketingQuery) -> DiagnosticResponse:
engine = default_marketing_engine()
diagnostics = engine.diagnose(query.prompt)
return _diagnostics_payload(query.prompt, diagnostics)
@app.post("/intelligence/marketing/processes", response_model=MarketingProcessesResponse)
def marketing_processes(query: MarketingQuery) -> MarketingProcessesResponse:
engine = default_marketing_engine()
rag_campaigns = engine.recommend(query.prompt, query.limit)
rag_insights = [
_campaign_to_insight(engine, query.prompt, campaign) for campaign in rag_campaigns
]
rag_summary = engine.summarize(query.prompt, rag_campaigns)
ranked = engine.recommend_statistical(query.prompt, query.limit)
egg_experiments = engine.expansion_grid(query.prompt, ranked)
google_ads_data = engine.google_ads_enrichment(query.prompt, ranked, query.limit)
cj_affiliate = engine.cj_affiliate_enrichment(query.prompt, ranked, query.limit)
crypto_markets = engine.crypto_market_enrichment(query.prompt, query.limit)
stock_markets = engine.stock_market_enrichment(query.prompt, ranked, query.limit)
stripe_plans = engine.stripe_checkout_enrichment(query.prompt, ranked, query.limit)
paypal_plans = engine.paypal_checkout_enrichment(query.prompt, ranked, query.limit)
payment_integrations = engine.payment_integration_enrichment(
query.prompt, ranked, query.limit
)
investing_plans = engine.investing_enrichment(query.prompt, ranked, query.limit)
influencer_partners = engine.influencer_collaboration_enrichment(
query.prompt, ranked, query.limit
)
amazon_listings = engine.amazon_marketplace_enrichment(query.prompt, ranked, query.limit)
ebay_listings = engine.ebay_marketplace_enrichment(query.prompt, ranked, query.limit)
aws_programs = engine.aws_cloud_enrichment(query.prompt, ranked, query.limit)
budget_plan = engine.budget_optimizer(query.prompt, ranked, query.limit)
automation_run = engine.automation_sprint(query.prompt, ranked, query.limit)
learning_recommendations = engine.learning_loop(query.prompt, ranked, query.limit)
competitive_signals = engine.competitive_landscape(query.prompt, ranked, query.limit)
cognitive_architecture = engine.cognitive_architecture_enrichment(
query.prompt, ranked, query.limit
)
agentic_systems = engine.agentic_system_enrichment(query.prompt, ranked, query.limit)
knowledge_plans = engine.knowledge_workflow_enrichment(query.prompt, ranked, query.limit)
marketing_knowledge_plans = engine.marketing_knowledge_enrichment(
query.prompt, ranked, query.limit
)
language_capabilities = engine.language_capability_enrichment(
query.prompt, ranked, query.limit
)
code_automation_plans = engine.code_automation_enrichment(query.prompt, ranked, query.limit)
meta_learning_plans = engine.meta_learning_enrichment(query.prompt, ranked, query.limit)
rag_workflows = [plan for plan in knowledge_plans if plan.category == "rag"]
knowledge_graphs = [plan for plan in knowledge_plans if plan.category == "kag"]
moe_plans = engine.mixture_of_experts_activation(query.prompt, ranked, query.limit)
agentic_loops = engine.agentic_loop_blueprints(query.prompt, ranked, limit=1)
quantum_plans = engine.quantum_simulation_enrichment(
query.prompt, ranked, limit=query.limit
)
physics_economics_plans = engine.physics_economics_enrichment(
query.prompt, ranked, query.limit
)
statistical_summary = engine.statistical_reasoning_summary(
query.prompt,
ranked,
budget=budget_plan,
stock_markets=stock_markets,
crypto_markets=crypto_markets,
limit=query.limit,
)
excellence = engine.excellence_summary(
query.prompt,
ranked,
limit=query.limit,
rag=rag_campaigns,
egg=egg_experiments,
google_ads=google_ads_data,
cj_affiliate=cj_affiliate,
crypto_markets=crypto_markets,
stripe=stripe_plans,
paypal=paypal_plans,
payments=payment_integrations,
investing=investing_plans,
influencers=influencer_partners,
amazon=amazon_listings,
ebay=ebay_listings,
aws=aws_programs,
budget=budget_plan,
automation=automation_run,
learning_loop=learning_recommendations,
competitive=competitive_signals,
stocks=stock_markets,
statistics=statistical_summary,
cognitive=cognitive_architecture,
agentic=agentic_systems,
knowledge=knowledge_plans,
marketing_knowledge=marketing_knowledge_plans,
code_automation=code_automation_plans,
language=language_capabilities,
meta_learning=meta_learning_plans,
moe=moe_plans,
agentic_loops=agentic_loops,
quantum=quantum_plans,
physics_economics=physics_economics_plans,
)
scenario_analysis = engine.scenario_planning(
query.prompt,
ranked,
limit=query.limit,
budget=budget_plan,
google_ads=google_ads_data,
cj_affiliate=cj_affiliate,
amazon=amazon_listings,
ebay=ebay_listings,
aws=aws_programs,
stripe=stripe_plans,
paypal=paypal_plans,
payments=payment_integrations,
investing=investing_plans,
influencers=influencer_partners,
crypto=crypto_markets,
stocks=stock_markets,
statistics=statistical_summary,
cognitive=cognitive_architecture,
agentic=agentic_systems,
knowledge=knowledge_plans,
marketing_knowledge=marketing_knowledge_plans,
code_automation=code_automation_plans,
language=language_capabilities,
meta_learning=meta_learning_plans,
moe=moe_plans,
agentic_loops=agentic_loops,
quantum=quantum_plans,
physics_economics=physics_economics_plans,
)
playbook = engine.executive_playbook(
query.prompt,
ranked,
limit=query.limit,
rag=rag_campaigns,
egg=egg_experiments,
google_ads=google_ads_data,
cj_affiliate=cj_affiliate,
amazon=amazon_listings,
ebay=ebay_listings,
paypal=paypal_plans,
payments=payment_integrations,
investing=investing_plans,
influencers=influencer_partners,
stocks=stock_markets,
budget=budget_plan,
automation=automation_run,
learning_loop=learning_recommendations,
competitive=competitive_signals,
excellence=excellence,
statistics=statistical_summary,
cognitive=cognitive_architecture,
agentic=agentic_systems,
knowledge=knowledge_plans,
marketing_knowledge=marketing_knowledge_plans,
code_automation=code_automation_plans,
language=language_capabilities,
meta_learning=meta_learning_plans,
moe=moe_plans,
agentic_loops=agentic_loops,
quantum=quantum_plans,
physics_economics=physics_economics_plans,
)
return MarketingProcessesResponse(
query=query.prompt,
rag=ProcessRun(process="rag", recommendations=rag_insights, summary=rag_summary),
egg=[_experiment_payload(experiment) for experiment in egg_experiments],
google_ads=[_google_ads_payload(performance) for performance in google_ads_data],
cj_affiliate=[_cj_affiliate_payload(rec) for rec in cj_affiliate],
crypto_markets=[_crypto_market_payload(snapshot) for snapshot in crypto_markets],
stock_markets=[_stock_market_payload(forecast) for forecast in stock_markets],
stripe=[_stripe_plan_payload(plan) for plan in stripe_plans],
paypal=[_paypal_plan_payload(plan) for plan in paypal_plans],
payment_integrations=[
_payment_integration_payload(plan) for plan in payment_integrations
],
investing=[_investing_payload(plan) for plan in investing_plans],
cognitive_architecture=[
_cognitive_architecture_payload(plan) for plan in cognitive_architecture
],
agentic_systems=[_agentic_system_payload(plan) for plan in agentic_systems],
rag_workflows=[_knowledge_workflow_payload(plan) for plan in rag_workflows],
knowledge_graphs=[_knowledge_workflow_payload(plan) for plan in knowledge_graphs],
marketing_knowledge=[
_marketing_knowledge_payload(plan) for plan in marketing_knowledge_plans
],
language_intelligence=[
_language_capability_payload(plan) for plan in language_capabilities
],
code_automation=[_code_automation_payload(plan) for plan in code_automation_plans],
meta_learning=[_meta_learning_payload(plan) for plan in meta_learning_plans],
mixture_of_experts=[_mixture_of_experts_payload(plan) for plan in moe_plans],
agentic_loops=[_agentic_loop_payload(plan) for plan in agentic_loops],
quantum_simulation=[_quantum_simulation_payload(plan) for plan in quantum_plans],
physics_economics=[
_physics_economics_payload(plan) for plan in physics_economics_plans
],
influencer_collaborations=[
_influencer_payload(partner) for partner in influencer_partners
],
amazon_marketplace=[_amazon_listing_payload(rec) for rec in amazon_listings],
ebay_marketplace=[_ebay_listing_payload(rec) for rec in ebay_listings],
aws_cloud=[_aws_revenue_payload(rec) for rec in aws_programs],
budget_optimizer=_budget_plan_payload(budget_plan),
automation=_automation_sprint_payload(automation_run),
learning_loop=[_learning_loop_payload(rec) for rec in learning_recommendations],
competitive_landscape=[_competitive_signal_payload(signal) for signal in competitive_signals],
excellence=_excellence_payload(excellence),
executive_playbook=_playbook_payload(playbook),
scenario_planning=_scenario_planning_payload(scenario_analysis),
statistical_reasoning=_statistical_reasoning_payload(statistical_summary),
)