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source = "meta_learning_configured" if self.configured else "meta_learning_catalog"
notes = None
if not self.configured:
notes = (
"Simulated from meta-learning corpus; set RAY_ADDRESS/SAGEMAKER_ROLE/MLFLOW_TRACKING_URI to enable "
"live adaptive learning infrastructure."
)
plans.append(
(
score,
MetaLearningPlan(
capability=capability.name,
category=capability.category,
maturity=maturity,
description=capability.description,
reasoning=" ".join(reasoning_parts),
learning_modes=list(capability.learning_modes),
data_sources=list(capability.data_sources),
feedback_channels=list(capability.feedback_channels),
tooling=list(capability.tooling),
metrics=list(capability.metrics),
deployment_modes=list(capability.deployment_modes),
source=source,
notes=notes,
),
)
)
plans.sort(key=lambda item: item[0], reverse=True)
return [plan for _, plan in plans[: max(1, limit)]]
class PhysicsEconomicsProcess:
"""Blends physics, quantum, and economics readiness into actionable plans."""
OPTIONAL_ENV_VARS = {
"QISKIT_RUNTIME": "quantum",
"AWS_BRAKET_REGION": "quantum",
"GOOGLE_CIRQ_SERVICE_ACCOUNT": "quantum",
"ECONOMICS_DATA_WAREHOUSE": "economics",
"GPU_RESOURCE_POOL": "hardware",
"EDGE_ACCELERATOR_POOL": "hardware",
}
def __init__(
self,
capabilities: Sequence[PhysicsEconomicsCapability] | None = None,
dataset_path: str = PHYSICS_ECONOMICS_DATASET,
config: Dict[str, Any] | None = None,
) -> None:
self._capabilities = (
list(capabilities)
if capabilities is not None
else self._load(dataset_path)
)
self._config = config or self._config_from_env()
@classmethod
def _config_from_env(cls) -> Dict[str, List[str]]:
config: Dict[str, set[str]] = {}
for env_var, key in cls.OPTIONAL_ENV_VARS.items():
if os.getenv(env_var):
config.setdefault(key, set()).add(env_var.lower())
return {key: sorted(values) for key, values in config.items()}
@property
def configured(self) -> bool:
return bool(self._config)
@staticmethod
def _load(path: str) -> List[PhysicsEconomicsCapability]:
try:
with open(path, "r", encoding="utf-8") as handle:
payload = json.load(handle)
except (FileNotFoundError, json.JSONDecodeError):
payload = []
catalog: List[PhysicsEconomicsCapability] = []
if isinstance(payload, list):
for entry in payload:
if isinstance(entry, dict):
try:
catalog.append(PhysicsEconomicsCapability.from_dict(entry))
except (KeyError, TypeError, ValueError):
continue
if not catalog:
catalog.append(
PhysicsEconomicsCapability(
name="Foundational Physics & Economics Readiness",
domain="physics_economics",
description="Fallback capability that maps marketing prompts to basic physics and economic guardrails.",
trigger_keywords=["physics", "economics", "quantum", "pricing", "compression"],
marketing_algorithms=["Baseline ROI sensitivity analysis"],
physics_principles=["System dynamics balancing"],
quantum_strategies=["Monte Carlo variance reduction"],
economic_models=["Unit economics calculator"],
hardware_strategies=["CPU efficient inference"],
data_compression=["Vector sketching"],
risk_controls=["Stress test budgets weekly"],
metrics=["roi_guardrail"],
maturity_signals=["Fallback heuristics"],
base_maturity="emerging",
orchestration_play="Use fallback heuristics to align marketing spend with baseline physics/economics guardrails.",
docs=[],
)
)
return catalog
@staticmethod
def _tokenize(text: str) -> List[str]:
return [token for token in re.findall(r"[a-zA-Z0-9]+", text.lower()) if len(token) > 2]
@staticmethod
def _phrase_hits(collection: Sequence[str], tokens: Sequence[str]) -> List[str]:
hits: List[str] = []
token_set = set(tokens)
for phrase in collection:
normalized = phrase.lower()
if any(token in normalized for token in token_set):
hits.append(phrase)
return hits
def plans(
self,
prompt: str,
campaigns: Sequence["CampaignScore"],
limit: int = 3,
) -> List[PhysicsEconomicsPlan]:
if not campaigns:
raise ValueError("Physics economics plans require campaign scores")
prompt_tokens = set(self._tokenize(prompt))
campaign_tokens = set(
token
for campaign in campaigns
for token in self._tokenize(
" ".join(
[
campaign.campaign.channel,
campaign.campaign.audience,
campaign.campaign.offer,
campaign.campaign.objective,
]
)
)
)
avg_roi = mean(score.metrics.expected_roi for score in campaigns)
avg_confidence = mean(score.metrics.confidence for score in campaigns)
plans: List[Tuple[float, PhysicsEconomicsPlan]] = []
for capability in self._capabilities:
keyword_hits = [
kw
for kw in capability.trigger_keywords
if kw in prompt_tokens or kw in campaign_tokens
]
physics_hits = self._phrase_hits(capability.physics_principles, prompt_tokens)
quantum_hits = self._phrase_hits(capability.quantum_strategies, prompt_tokens)
economics_hits = self._phrase_hits(capability.economic_models, prompt_tokens)
hardware_hits = self._phrase_hits(capability.hardware_strategies, prompt_tokens)
compression_hits = self._phrase_hits(capability.data_compression, prompt_tokens)
base_score = 0.45 if capability.base_maturity in {"advanced", "elite"} else 0.35
score = base_score
score += 0.07 * len(keyword_hits)
score += 0.05 * len(physics_hits)
score += 0.05 * len(quantum_hits)
score += 0.06 * len(economics_hits)
score += 0.04 * len(hardware_hits)
score += 0.03 * len(compression_hits)
if avg_roi >= 2.5:
score += 0.05
if avg_confidence >= 0.65:
score += 0.04
if {"quantum", "physics", "economics", "pricing"} & prompt_tokens:
score += 0.05
if self.configured:
score += 0.08
maturity_levels = ["emerging", "advanced", "elite", "autonomous"]
base_index = maturity_levels.index(capability.base_maturity)
if score >= 1.15:
maturity_index = min(base_index + 2, len(maturity_levels) - 1)
elif score >= 0.85:
maturity_index = min(base_index + 1, len(maturity_levels) - 1)
else:
maturity_index = base_index
maturity = maturity_levels[maturity_index]
reasoning_parts: List[str] = []
if keyword_hits:
reasoning_parts.append(
f"Prompt references {', '.join(keyword_hits[:3])} indicating physics/economics urgency."
)
if economics_hits:
reasoning_parts.append(
f"Economic models {', '.join(economics_hits[:2])} align with revenue objectives."
)
if physics_hits or quantum_hits:
physics_blend = physics_hits[:2] + quantum_hits[:1]
if physics_blend:
reasoning_parts.append(
f"Physics stack engages {', '.join(physics_blend)} for stress stability."
)
if not reasoning_parts:
reasoning_parts.append(
f"Campaign ROI {avg_roi:.2f}x and confidence {avg_confidence:.0%} justify activating {capability.name}."
)
reasoning = " ".join(reasoning_parts)
orchestration_steps: List[str] = [capability.orchestration_play]
if capability.marketing_algorithms:
orchestration_steps.append(
"Algorithms: " + ", ".join(capability.marketing_algorithms[:3])
)
if capability.physics_principles:
orchestration_steps.append(
"Physics focus: " + ", ".join(capability.physics_principles[:2])
)
if capability.economic_models:
orchestration_steps.append(
"Economic models: " + ", ".join(capability.economic_models[:2])
)
if capability.hardware_strategies:
orchestration_steps.append(
"Hardware plan: " + ", ".join(capability.hardware_strategies[:2])
)
if capability.data_compression:
orchestration_steps.append(
"Compression: " + ", ".join(capability.data_compression[:2])
)
if capability.risk_controls:
orchestration_steps.append(
"Risk controls: " + ", ".join(capability.risk_controls[:2])
)
source = "physics_economics_live" if self.configured else "physics_economics_catalog"
notes: str | None = None
if not self.configured:
notes = (
"Simulated from physics/economics catalog; set QISKIT_RUNTIME / AWS_BRAKET_REGION / ECONOMICS_DATA_WAREHOUSE "
"to enable live telemetry."
)
plans.append(
(
score,
PhysicsEconomicsPlan(
capability=capability.name,
domain=capability.domain,
maturity=maturity,
description=capability.description,
rationale=reasoning,
marketing_algorithms=list(capability.marketing_algorithms),
physics_principles=list(capability.physics_principles),
quantum_strategies=list(capability.quantum_strategies),
economic_models=list(capability.economic_models),
hardware_strategies=list(capability.hardware_strategies),
data_compression=list(capability.data_compression),
risk_controls=list(capability.risk_controls),
metrics=list(capability.metrics),
orchestration_steps=orchestration_steps,
docs=list(capability.docs),
source=source,
notes=notes,
),
)
)
plans.sort(key=lambda item: item[0], reverse=True)
return [plan for _, plan in plans[: max(1, limit)]]
class PaymentIntegrationProcess:
"""Plans how the ANI orchestrates payment provider integrations."""
PROVIDER_ENV_HINTS: Dict[str, List[str]] = {
"stripe": ["STRIPE_API_KEY", "STRIPE_ACCOUNT_ID"],
"paypal": ["PAYPAL_CLIENT_ID", "PAYPAL_CLIENT_SECRET", "PAYPAL_MERCHANT_ID"],
"cash app": ["SQUARE_ACCESS_TOKEN", "SQUARE_APPLICATION_ID", "SQUARE_LOCATION_ID"],
"venmo": ["VENMO_CLIENT_ID", "VENMO_CLIENT_SECRET", "VENMO_ACCESS_TOKEN"],
}
def __init__(
self,
capabilities: Sequence[PaymentPlatformCapability] | None = None,
dataset_path: str = PAYMENT_PLATFORMS_DATASET,
config: Dict[str, List[str]] | None = None,
) -> None:
self._capabilities = (
list(capabilities)
if capabilities is not None
else self._load(dataset_path)
)
self._config = config or self._config_from_env()
@classmethod
def _config_from_env(cls) -> Dict[str, List[str]]:
config: Dict[str, List[str]] = {}
for provider_key, env_vars in cls.PROVIDER_ENV_HINTS.items():
available = [env for env in env_vars if os.getenv(env)]
if available:
config[provider_key] = sorted(set(env.lower() for env in available))
return config
@property
def configured_providers(self) -> set[str]:
return set(self._config.keys())
@staticmethod
def _load(path: str) -> List[PaymentPlatformCapability]:
try:
with open(path, "r", encoding="utf-8") as handle:
payload = json.load(handle)
except (FileNotFoundError, json.JSONDecodeError):
payload = []
catalog: List[PaymentPlatformCapability] = []
if isinstance(payload, list):
for entry in payload:
if isinstance(entry, dict):
try:
catalog.append(PaymentPlatformCapability.from_dict(entry))
except (KeyError, TypeError, ValueError):
continue
if not catalog:
catalog.append(
PaymentPlatformCapability(
provider="Stripe",
description="Fallback Stripe guidance for payment readiness.",
integration_focus=["Checkout"],
docs=["https://docs.stripe.com/"],
sdks=["stripe"],
setup=["Review Stripe docs"],
troubleshooting=["Verify API keys"],
codefix=["Regenerate secret keys"],
marketing_plays=["Launch payment link"],
keywords=["stripe"],
base_maturity="foundational",
)
)
return catalog
@staticmethod
def _tokenize(text: str) -> List[str]:
return [token for token in re.findall(r"[a-zA-Z0-9]+", text.lower()) if len(token) > 2]
def plans(
self,
prompt: str,
campaigns: Sequence["CampaignScore"],
limit: int = 4,
) -> List[PaymentIntegrationPlan]:
if not campaigns:
raise ValueError("Payment integration plans require campaign scores")
prompt_tokens = set(self._tokenize(prompt))
campaign_tokens = set(
token
for campaign in campaigns
for token in self._tokenize(
" ".join(
[
campaign.campaign.channel,
campaign.campaign.audience,
campaign.campaign.offer,
campaign.campaign.objective,
]
)
)
)
plans: List[Tuple[int, PaymentIntegrationPlan]] = []
for capability in self._capabilities:
keywords = set(capability.keywords)
prompt_hits = sorted(prompt_tokens & keywords)
campaign_hits = sorted(campaign_tokens & keywords)
score = len(prompt_hits) * 2 + len(campaign_hits)
if any(key in prompt.lower() for key in capability.keywords):
score += 1
provider_key = capability.provider.lower()
configured = False
for key, envs in self._config.items():
if provider_key.startswith(key):
configured = True
provider_key = key
break
if configured and score >= 3:
maturity = "autonomous"
elif score >= 3:
maturity = "advanced"
elif capability.base_maturity:
maturity = capability.base_maturity
else:
maturity = "foundational"
reasoning_parts = []
if prompt_hits:
reasoning_parts.append(
f"Prompt references {', '.join(prompt_hits[:3])} signals"
)
if campaign_hits:
reasoning_parts.append(
f"Campaign corpus echoes {', '.join(campaign_hits[:3])} demand"
)
if configured:
envs = ", ".join(self._config.get(provider_key, []))
reasoning_parts.append(f"Environment exposes {envs} credentials")
if not reasoning_parts:
reasoning_parts.append("Fallback payment integration guidance available")
notes = None
if not configured:
hint_envs = self.PROVIDER_ENV_HINTS.get(provider_key, [])
if hint_envs:
notes = "Export env vars: " + ", ".join(hint_envs)
plans.append(
(
score,
PaymentIntegrationPlan(
provider=capability.provider,
maturity=maturity,
description=capability.description,
reasoning="; ".join(reasoning_parts) + ".",
integration_focus=list(capability.integration_focus),
docs=list(capability.docs),
sdks=list(capability.sdks),
setup=list(capability.setup),
troubleshooting=list(capability.troubleshooting),
codefix=list(capability.codefix),
marketing_plays=list(capability.marketing_plays),
source=capability.source,
notes=notes,
),
)
)
plans.sort(key=lambda item: item[0], reverse=True)
return [plan for _, plan in plans[: max(1, limit)]]
class InvestingProcess:
"""Designs investing platform activation plans."""
def __init__(
self,
capabilities: Sequence[InvestingCapability] | None = None,
dataset_path: str = INVESTING_PLATFORMS_DATASET,
) -> None:
self._capabilities = (
list(capabilities)
if capabilities is not None
else self._load(dataset_path)
)
@staticmethod
def _load(path: str) -> List[InvestingCapability]:
try:
with open(path, "r", encoding="utf-8") as handle:
payload = json.load(handle)
except (FileNotFoundError, json.JSONDecodeError):
payload = []
catalog: List[InvestingCapability] = []
if isinstance(payload, list):
for entry in payload:
if isinstance(entry, dict):
try:
catalog.append(InvestingCapability.from_dict(entry))
except (KeyError, TypeError, ValueError):
continue
if not catalog:
catalog.append(
InvestingCapability(
provider="Fallback Investing",
category="investing",
description="Baseline investing plan to highlight brokerage monetization paths.",
asset_classes=["equities", "etfs"],
onboarding=["Publish investing landing page", "Launch nurture emails"],
monetization=["Promote brokerage affiliate", "Sell premium research"],
risk_controls=["Disclose investing risks", "Monitor compliance copy"],
tools=["Broker dashboard"],
keywords=["invest", "brokerage", "stock"],
revenue_programs=["Broker affiliate"],
kpis=["funded_accounts", "aum_contributed"],
base_maturity="foundational",
source="investing_platforms_fallback",
)
)
return catalog
@staticmethod
def _tokenize(text: str) -> List[str]:
return [token for token in re.findall(r"[a-zA-Z0-9]+", text.lower()) if len(token) > 2]
@staticmethod
def _phrase_hits(collection: Sequence[str], token_set: set[str]) -> List[str]:
hits: List[str] = []
for phrase in collection:
normalized = phrase.lower()
if any(token in normalized for token in token_set):
hits.append(phrase)
return hits
def plans(
self,
prompt: str,
campaigns: Sequence["CampaignScore"],
limit: int = 3,
) -> List[InvestingPlan]:
if not campaigns:
raise ValueError("Investing plans require campaign scores")
prompt_tokens = set(self._tokenize(prompt))
campaign_tokens = set(
token
for campaign in campaigns
for token in self._tokenize(
" ".join(
[
campaign.campaign.channel,
campaign.campaign.offer,
campaign.campaign.objective,
campaign.campaign.audience,
]
)
)
)
combined_tokens = prompt_tokens | campaign_tokens
avg_roi = mean(score.metrics.expected_roi for score in campaigns)
avg_confidence = mean(score.metrics.confidence for score in campaigns)
plans: List[Tuple[float, InvestingPlan]] = []
for capability in self._capabilities:
keyword_hits = sorted(set(capability.keywords) & combined_tokens)
asset_hits = self._phrase_hits(capability.asset_classes, combined_tokens)
monetization_hits = self._phrase_hits(capability.monetization, combined_tokens)
onboarding_hits = self._phrase_hits(capability.onboarding, prompt_tokens)
category_hit = capability.category.lower() in combined_tokens
score = 0.45
score += 0.1 * len(keyword_hits)
score += 0.07 * len(asset_hits)
score += 0.06 * len(monetization_hits)
score += 0.05 * len(onboarding_hits)
if category_hit:
score += 0.08
if "invest" in prompt_tokens or "trading" in prompt_tokens:
score += 0.05
if avg_roi >= 2.5:
score += 0.06
if avg_confidence >= 0.65:
score += 0.04
if score >= 1.05:
maturity = "autonomous"
elif score >= 0.8:
maturity = "advanced"
else:
maturity = "foundational"
base_order = ["foundational", "advanced", "autonomous"]
if base_order.index(maturity) < base_order.index(capability.base_maturity):
maturity = capability.base_maturity
reasoning_parts: List[str] = []
if keyword_hits:
reasoning_parts.append(
f"Prompt and campaigns reference {', '.join(keyword_hits[:3])} investing signals"
)
if asset_hits:
reasoning_parts.append(
f"Asset focus aligns with {', '.join(asset_hits[:3])} plays"
)
if monetization_hits:
reasoning_parts.append(
f"Monetization hooks mention {', '.join(monetization_hits[:2])}"
)
if onboarding_hits:
reasoning_parts.append(
f"Prompt language maps to onboarding step {onboarding_hits[0]}"
)
if not reasoning_parts:
reasoning_parts.append("Fallback investing monetization playbook ready")
plans.append(
(
score,
InvestingPlan(
provider=capability.provider,
category=capability.category,
maturity=maturity,
description=capability.description,
reasoning="; ".join(reasoning_parts) + ".",
asset_classes=list(capability.asset_classes),
monetization=list(capability.monetization),
onboarding=list(capability.onboarding),
tools=list(capability.tools),
risk_controls=list(capability.risk_controls),
revenue_programs=list(capability.revenue_programs),
kpis=list(capability.kpis),
source=capability.source,
notes=None,
),
)
)
plans.sort(key=lambda item: item[0], reverse=True)
return [plan for _, plan in plans[: max(1, limit)]]
class KnowledgeWorkflowProcess:
"""Designs retrieval and knowledge graph plans for the ANI."""
def __init__(
self,
assets: Sequence[KnowledgeWorkflowAsset] | None = None,
dataset_path: str = KNOWLEDGE_WORKFLOWS_DATASET,
) -> None:
self._assets = list(assets) if assets is not None else self._load(dataset_path)
@staticmethod
def _load(path: str) -> List[KnowledgeWorkflowAsset]:
try:
with open(path, "r", encoding="utf-8") as handle:
payload = json.load(handle)
except (FileNotFoundError, json.JSONDecodeError):
payload = []
catalog: List[KnowledgeWorkflowAsset] = []
if isinstance(payload, list):
for entry in payload:
if isinstance(entry, dict):
try:
catalog.append(KnowledgeWorkflowAsset.from_dict(entry))
except (KeyError, TypeError, ValueError):
continue
if not catalog:
catalog.append(
KnowledgeWorkflowAsset(
name="Foundational Retrieval Workflow",
category="rag",
description="Fallback retrieval workflow for campaign collateral.",
trigger_keywords=["campaign", "retrieval"],
indexing_steps=["Collect campaign documents", "Chunk and embed"],
retrieval_steps=["Similarity search", "Augment prompt"],
vector_store="faiss",
embedding_models=["sentence-transformers/all-MiniLM-L6-v2"],
tooling=["LangChain"],
evaluation=["context_precision"],
governance=["document versioning"],
caching=["prompt cache"],
source="knowledge_workflows_fallback",
)
)
return catalog
@staticmethod
def _tokenize(text: str) -> set[str]:
return {token for token in re.findall(r"[a-zA-Z0-9]+", text.lower()) if len(token) > 2}
def plans(
self,
prompt: str,
campaigns: Sequence["CampaignScore"],
limit: int = 3,
) -> List[KnowledgeWorkflowPlan]:
if not campaigns:
raise ValueError("Knowledge workflow plans require campaign scores")
prompt_tokens = self._tokenize(prompt)
campaign_corpus = " ".join(score.campaign.raw for score in campaigns)
campaign_tokens = self._tokenize(campaign_corpus)
avg_confidence = mean(score.metrics.confidence for score in campaigns)
avg_roi = mean(score.metrics.expected_roi for score in campaigns)
plans_by_category: Dict[str, List[Tuple[float, KnowledgeWorkflowPlan]]] = {}
for asset in self._assets:
keyword_hits = [kw for kw in asset.trigger_keywords if kw in prompt_tokens]
campaign_hits = [kw for kw in asset.trigger_keywords if kw in campaign_tokens]
tooling_hits = [tool for tool in asset.tooling if tool.lower() in prompt_tokens]
score = 0.45 + 0.12 * len(keyword_hits) + 0.08 * len(campaign_hits) + 0.05 * len(tooling_hits)
if asset.category == "rag" and {"rag", "retrieval", "cache"} & prompt_tokens:
score += 0.15
if asset.category == "kag" and {"kag", "graph", "knowledge"} & prompt_tokens:
score += 0.18
if asset.category == "kag" and avg_confidence >= 0.7:
score += 0.07
if asset.category == "rag" and avg_roi >= 2.5:
score += 0.07
maturity: str
if score >= 1.15:
maturity = "autonomous"
elif score >= 0.85:
maturity = "advanced"
else:
maturity = "foundational"
reasoning_parts: List[str] = []
if keyword_hits:
reasoning_parts.append(
f"Prompt signals {', '.join(keyword_hits[:3])} which align to {asset.name}."
)
if tooling_hits:
reasoning_parts.append(
f"Tooling cues {', '.join(tooling_hits[:2])} are already referenced in the brief."
)
if not reasoning_parts:
reasoning_parts.append(
f"Campaign ROI {avg_roi:.2f}x and confidence {avg_confidence:.0%} benefit from {asset.category.upper()} orchestration."
)
reasoning = " ".join(reasoning_parts)
indexing_plan = list(asset.indexing_steps)
retrieval_plan = list(asset.retrieval_steps)
primary_campaign = campaigns[0].campaign if campaigns else None
if primary_campaign:
indexing_plan.append(
f"Tag chunks with channel={primary_campaign.channel.lower()} and audience={primary_campaign.audience.lower()}"
)
retrieval_plan.append(
f"Prioritize offers related to {primary_campaign.offer.lower()} during similarity search"
)
plan = KnowledgeWorkflowPlan(
name=asset.name,
category=asset.category,
maturity=maturity,
description=asset.description,
reasoning=reasoning,
indexing_plan=indexing_plan,
retrieval_plan=retrieval_plan,
vector_store=asset.vector_store,
embedding_models=list(asset.embedding_models),
tooling=list(asset.tooling),
evaluation=list(asset.evaluation),
governance=list(asset.governance),
caching=list(asset.caching),
source=asset.source,
)
plans_by_category.setdefault(asset.category, []).append((score, plan))
plans: List[KnowledgeWorkflowPlan] = []
for category, items in plans_by_category.items():
items.sort(key=lambda item: item[0], reverse=True)
category_limit = max(1, min(limit, len(items)))
plans.extend(plan for _, plan in items[:category_limit])
return plans
class MarketingKnowledgeProcess:
"""Automates ingestion of marketing knowledge repositories."""
OPTIONAL_ENV_VARS: Dict[str, str] = {
"NOTION_API_KEY": "[credential removed]",
"AIRTABLE_API_KEY": "[credential removed]",
"CONFLUENCE_API_TOKEN": "confluence",
"SLACK_BOT_TOKEN": "slack",
"GOOGLE_APPLICATION_CREDENTIALS": "gdrive",
"DROPBOX_TOKEN": "dropbox",
}
def __init__(
self,
sources: Sequence[MarketingKnowledgeSource] | None = None,
dataset_path: str = MARKETING_KNOWLEDGE_DATASET,
connectors: Dict[str, List[str]] | None = None,
) -> None:
self._sources = list(sources) if sources is not None else self._load(dataset_path)
self._connectors = connectors or self._connectors_from_env()
@classmethod
def _connectors_from_env(cls) -> Dict[str, List[str]]:
connectors: Dict[str, set[str]] = {}
for env_var, key in cls.OPTIONAL_ENV_VARS.items():
if os.getenv(env_var):
connectors.setdefault(key, set()).add(env_var.lower())
return {key: sorted(values) for key, values in connectors.items()}
@property
def configured(self) -> bool:
return bool(self._connectors)
@staticmethod
def _load(path: str) -> List[MarketingKnowledgeSource]:
try:
with open(path, "r", encoding="utf-8") as handle:
payload = json.load(handle)
except (FileNotFoundError, json.JSONDecodeError):
payload = []
sources: List[MarketingKnowledgeSource] = []
if isinstance(payload, list):
for entry in payload:
if isinstance(entry, dict):
try:
sources.append(MarketingKnowledgeSource.from_dict(entry))
except (KeyError, TypeError, ValueError):
continue
if not sources:
sources.append(
MarketingKnowledgeSource(
name="Fallback Marketing Wiki",
category="documentation",
description="Baseline marketing wiki sync to keep prompts contextualized.",
topics=["marketing", "campaign", "playbook"],
ingestion_steps=["Export wiki pages", "Chunk and store"],
automation=["Nightly export", "Diff changes", "Notify owners"],
coverage_assets=["playbooks", "personas", "faqs"],
update_frequency="weekly",
tooling=["filesystem"],
signals=["wiki", "documentation"],
metrics=["refresh cadence"],
base_maturity="foundational",
source="marketing_knowledge_fallback",
)
)
return sources
@staticmethod
def _tokenize(text: str) -> set[str]:
return {token for token in re.findall(r"[a-zA-Z0-9]+", text.lower()) if len(token) > 2}
def plans(
self,
prompt: str,
campaigns: Sequence["CampaignScore"],
limit: int = 3,
) -> List[MarketingKnowledgePlan]:
if not campaigns:
raise ValueError("Marketing knowledge plans require campaign scores")
prompt_tokens = self._tokenize(prompt)
campaign_tokens = self._tokenize(" ".join(score.campaign.raw for score in campaigns))
channel_tokens = {
score.campaign.channel.lower().split()[0]
for score in campaigns
if score.campaign.channel
}
avg_roi = mean(score.metrics.expected_roi for score in campaigns)
avg_confidence = mean(score.metrics.confidence for score in campaigns)
connector_bonus = min(
0.15, 0.04 * sum(len(values) for values in self._connectors.values())
)
plans: List[Tuple[float, MarketingKnowledgePlan]] = []
for source in self._sources:
topic_hits = [topic for topic in source.topics if topic in prompt_tokens]
signal_hits = [signal for signal in source.signals if signal in prompt_tokens]
coverage_hits = [
asset
for asset in source.coverage_assets
if any(token in asset.lower() for token in prompt_tokens | campaign_tokens)
or any(asset.lower().startswith(token) for token in channel_tokens)
]
tooling_hits = [
tool
for tool in source.tooling
if tool.lower() in prompt_tokens or tool.lower().split()[0] in prompt_tokens
]
connector_hits = [
key
for key in self._connectors
if key in source.signals or any(key in topic for topic in source.topics)
]
base = 0.4 if source.base_maturity in {"advanced", "autonomous"} else 0.32
score = base
score += 0.08 * len(topic_hits)
score += 0.07 * len(signal_hits)
score += 0.05 * len(coverage_hits)
score += 0.04 * len(tooling_hits)
score += 0.05 * len(connector_hits)
if {"knowledge", "wiki", "documentation", "library"} & prompt_tokens:
score += 0.05
if avg_roi >= 2.4:
score += 0.04
if avg_confidence >= 0.65:
score += 0.03
score += connector_bonus
if score >= 1.1:
maturity = "autonomous"
elif score >= 0.82:
maturity = "advanced"
else:
maturity = "foundational"
levels = ["foundational", "advanced", "autonomous"]
base_index = levels.index(source.base_maturity)
maturity_index = levels.index(maturity)
if maturity_index < base_index:
maturity = source.base_maturity
reasoning_parts: List[str] = []
if topic_hits:
reasoning_parts.append(
f"Prompt references {', '.join(topic_hits[:3])} aligning to {source.name}."
)
if coverage_hits:
reasoning_parts.append(
f"Coverage includes {', '.join(coverage_hits[:3])} needed for campaign reuse."
)
if connector_hits:
reasoning_parts.append(
f"Live connectors detected for {', '.join(connector_hits[:2])} enable automation."
)
if not reasoning_parts:
reasoning_parts.append(
f"Baseline ingestion keeps marketing knowledge fresh for {source.category}."
)
reasoning = " ".join(reasoning_parts)
missing_envs = [
env
for env, key in self.OPTIONAL_ENV_VARS.items()
if key in source.signals and key not in self._connectors
]
notes = None
if missing_envs:
notes = (
"Set "
+ ", ".join(sorted(missing_envs))
+ " to enable real-time ingestion for this repository."
)
elif not self.configured:
notes = "Simulated from marketing knowledge corpus; configure connectors for live sync."
plans.append(
(
score,
MarketingKnowledgePlan(
name=source.name,
category=source.category,
maturity=maturity,
description=source.description,
reasoning=reasoning,
ingestion_steps=list(source.ingestion_steps),
automation=list(source.automation),
tooling=list(source.tooling),
metrics=list(source.metrics),
coverage_assets=list(source.coverage_assets),
update_frequency=source.update_frequency,
source=source.source,
notes=notes,
),
)
)
plans.sort(key=lambda item: item[0], reverse=True)
return [plan for _, plan in plans[: max(1, limit)]]
class MixtureOfExpertsProcess:
"""Routes prompts through marketing monetization experts."""
def __init__(
self,
experts: Sequence[ExpertProfile] | None = None,
dataset_path: str = MOE_EXPERTS_DATASET,
) -> None:
self._experts = list(experts) if experts is not None else self._load(dataset_path)
@staticmethod
def _load(path: str) -> List[ExpertProfile]:
try:
with open(path, "r", encoding="utf-8") as handle:
payload = json.load(handle)
except (FileNotFoundError, json.JSONDecodeError):
payload = []
profiles: List[ExpertProfile] = []
if isinstance(payload, list):
for entry in payload:
if isinstance(entry, dict):
try:
profiles.append(ExpertProfile.from_dict(entry))
except (KeyError, TypeError, ValueError):
continue
if not profiles:
profiles.append(
ExpertProfile(
name="Benchmark Revenue Expert",
provider="benchmark",
description="Fallback expert using benchmark datasets to inform routing.",
trigger_keywords=["revenue", "marketing"],
specialties=["revenue operations"],
routing_signals=["roi"],
outputs=["recommendations"],
fallback="Use benchmark dataset",
metrics=["roi", "ctr"],
source="moe_experts_fallback",
)
)
return profiles
@staticmethod
def _tokenize(text: str) -> set[str]:
return {token for token in re.findall(r"[a-zA-Z0-9]+", text.lower()) if len(token) > 2}
def activate(
self,
prompt: str,
campaigns: Sequence["CampaignScore"],
limit: int = 3,
) -> List[MixtureOfExpertsPlan]:
if not campaigns:
raise ValueError("Mixture-of-experts activation requires campaign scores")
prompt_tokens = self._tokenize(prompt)
campaign_corpus = " ".join(score.campaign.raw for score in campaigns)
campaign_tokens = self._tokenize(campaign_corpus)
avg_roi = mean(score.metrics.expected_roi for score in campaigns)
avg_confidence = mean(score.metrics.confidence for score in campaigns)
scored: List[Tuple[float, ExpertProfile, float]] = []
for expert in self._experts:
keyword_hits = [kw for kw in expert.trigger_keywords if kw in prompt_tokens or kw in campaign_tokens]
specialty_hits = [
specialty
for specialty in expert.specialties
if any(word in campaign_corpus.lower() for word in specialty.lower().split())
]
provider_bonus = 0.0
if expert.provider == "google" and {"google", "sem", "ads"} & prompt_tokens:
provider_bonus += 0.18
if expert.provider == "stripe" and {"stripe", "pricing", "checkout"} & prompt_tokens:
provider_bonus += 0.18
if expert.provider == "cj" and {"affiliate", "cj", "publisher"} & prompt_tokens:
provider_bonus += 0.16
if expert.provider == "amazon" and {"amazon", "asin", "prime"} & prompt_tokens:
provider_bonus += 0.16
score = 0.42 + 0.14 * len(keyword_hits) + 0.09 * len(specialty_hits) + provider_bonus
if avg_roi >= 2.8:
score += 0.06
if avg_confidence >= 0.7:
score += 0.05
confidence = round(min(0.95, max(0.4, score / 1.2)), 2)
scored.append((score, expert, confidence))
scored.sort(key=lambda item: item[0], reverse=True)
if not scored:
return []
top_k = min(max(2, limit), len(scored))
activations: List[ExpertActivation] = []
metrics: set[str] = set()
router_notes: List[str] = []
for score, expert, confidence in scored[:top_k]:
reason_parts: List[str] = []
matched = [kw for kw in expert.trigger_keywords if kw in prompt_tokens]
if matched:
reason_parts.append(f"Router matched tokens {', '.join(matched[:3])}.")
if expert.specialties:
reason_parts.append(f"Specialty focus: {expert.specialties[0]}.")
if not matched:
reason_parts.append("Activated for coverage balance across revenue levers.")
metrics.update(expert.metrics)
router_notes.append(f"{expert.name} confidence {confidence:.0%}")
activations.append(
ExpertActivation(
name=expert.name,
provider=expert.provider,
role=expert.specialties[0] if expert.specialties else "expert",
confidence=confidence,
reasoning=" ".join(reason_parts),
outputs=list(expert.outputs),
metrics=list(expert.metrics),
fallback=expert.fallback,
source=expert.source,
)
)
avg_conf = mean(act.confidence for act in activations)
if avg_conf >= 0.8 and len(activations) >= 3:
maturity = "autonomous"
elif avg_conf >= 0.65:
maturity = "advanced"
else:
maturity = "foundational"
summary_tokens = sorted((prompt_tokens & {"google", "stripe", "cj", "amazon", "pricing", "revenue"}))
router_summary = (
"Router matched tokens: " + ", ".join(summary_tokens) if summary_tokens else "Router balanced coverage across experts"
)
notes = " | ".join(router_notes)
plan = MixtureOfExpertsPlan(
router_summary=router_summary,
maturity=maturity,
activations=activations,
evaluation_metrics=sorted(metrics) if metrics else ["roi", "ctr"],
notes=notes,
source="moe_router",
)
return [plan]
class AgenticLoopProcess:
"""Transforms prompts into continuous agentic loop blueprints."""
def __init__(
self,
loops: Sequence[Dict[str, Any]] | None = None,
dataset_path: str = AGENTIC_LOOPS_DATASET,
) -> None:
self._raw_loops = list(loops) if loops is not None else self._load(dataset_path)
@staticmethod
def _load(path: str) -> List[Dict[str, Any]]:
try:
with open(path, "r", encoding="utf-8") as handle:
payload = json.load(handle)
except (FileNotFoundError, json.JSONDecodeError):
payload = []
if isinstance(payload, list):
return [entry for entry in payload if isinstance(entry, dict)]
return []
@staticmethod
def _tokenize(text: str) -> set[str]:
return {token for token in re.findall(r"[a-zA-Z0-9]+", text.lower()) if len(token) > 2}
def plans(
self,
prompt: str,
campaigns: Sequence["CampaignScore"],
limit: int = 1,
) -> List[AgenticLoopPlan]:
if not campaigns:
raise ValueError("Agentic loop plans require campaign scores")
if not self._raw_loops:
return []
prompt_tokens = self._tokenize(prompt)
avg_confidence = mean(score.metrics.confidence for score in campaigns)
plans: List[AgenticLoopPlan] = []
for loop in self._raw_loops[: max(1, limit)]:
trigger_keywords = {kw.lower() for kw in loop.get("trigger_keywords", [])}
matched = trigger_keywords & prompt_tokens
if avg_confidence >= 0.75 and matched:
maturity = "autonomous"
elif avg_confidence >= 0.65:
maturity = "advanced"
else:
maturity = "foundational"
reasoning_parts = []
if matched:
reasoning_parts.append(
f"Prompt references {', '.join(sorted(matched))} indicating readiness for agentic loop activation."
)
reasoning_parts.append(
f"Campaign confidence {avg_confidence:.0%} supports continuous execution without drift."
)
reasoning = " ".join(reasoning_parts)
stages: List[AgenticLoopStage] = []
for stage in loop.get("stages", []):
stages.append(
AgenticLoopStage(
stage=stage.get("stage", "stage"),
objective=stage.get("objective", ""),
actions=list(stage.get("actions", [])),
tools=list(stage.get("tools", [])),
signals=list(stage.get("signals", [])),
outputs=list(stage.get("outputs", [])),
metrics=list(stage.get("metrics", [])),
)
)
plans.append(
AgenticLoopPlan(
name=loop.get("name", "Agentic Loop"),
goal_focus=loop.get("goal_focus", "marketing"),
summary=loop.get("summary", ""),
maturity=maturity,
reasoning=reasoning,
stages=stages,
guardrails=list(loop.get("guardrails", [])),
source=loop.get("source", "agentic_loop_dataset"),
)
)
return plans
class MiniQuantumSimulator:
"""Minimal single-qubit simulator used for deterministic stress scoring.
The simulator now supports optional Aer/Cirq style execution when the
dependencies are available, allows callers to control shot counts, and can
perform lightweight Monte Carlo sampling when deterministic gates are not
sufficient for stress harnesses. All fallbacks remain deterministic so the
wider marketing ANI continues to operate without external services.
"""
def __init__(self, shots: int = 1024, backend: str | None = None) -> None:
self._sqrt_half = 1 / math.sqrt(2)
self._shots = max(1, shots)
self._last_shots = self._shots
self._rand = random.Random(1337)
self._qiskit_available = False
self._backend = None
self._QuantumCircuit = None
backend_name = backend or os.getenv("QUANTUM_SIM_BACKEND", "aer_simulator")
try: # pragma: no cover - optional dependency wiring
from qiskit import QuantumCircuit # type: ignore
from qiskit_aer import Aer # type: ignore
self._QuantumCircuit = QuantumCircuit
try:
self._backend = Aer.get_backend(backend_name)
except Exception: # pragma: no cover - backend lookup
self._backend = Aer.get_backend("aer_simulator")
self._qiskit_available = True
except Exception: # pragma: no cover - library optional
self._qiskit_available = False
@staticmethod
def _parse_angle(text: str) -> float:
value = text.strip().lower()
if not value:
return 0.0
multiplier = -1.0 if value.startswith("-") else 1.0
if value.startswith("-"):
value = value[1:]
if value == "pi":
return multiplier * math.pi
if value.startswith("pi/"):
try:
return multiplier * math.pi / float(value[3:])
except (TypeError, ValueError):
return 0.0
if "*" in value and "pi" in value:
left, right = value.split("*", 1)
try:
factor = float(left)
except ValueError:
factor = 1.0
if right == "pi":
return multiplier * factor * math.pi
if right.startswith("pi/"):
try:
return multiplier * factor * math.pi / float(right[3:])
except (TypeError, ValueError):
return 0.0
try:
return multiplier * float(value)
except ValueError:
return 0.0
def _matrix_for_gate(self, spec: str) -> Tuple[Tuple[complex, complex], Tuple[complex, complex]]:
gate = spec.strip().upper()
identity = ((1 + 0j, 0 + 0j), (0 + 0j, 1 + 0j))
if not gate:
return identity
if gate == "H":
s = self._sqrt_half
return ((s, s), (s, -s))
if gate == "X":
return ((0 + 0j, 1 + 0j), (1 + 0j, 0 + 0j))
if gate == "Y":
return ((0 + 0j, -1j), (1j, 0 + 0j))
if gate == "Z":
return ((1 + 0j, 0 + 0j), (0 + 0j, -1 + 0j))
if gate.startswith("RX(") and gate.endswith(")"):
angle = self._parse_angle(gate[3:-1])
c = math.cos(angle / 2.0)
s = math.sin(angle / 2.0)
return ((c, -1j * s), (-1j * s, c))
if gate.startswith("RY(") and gate.endswith(")"):
angle = self._parse_angle(gate[3:-1])
c = math.cos(angle / 2.0)
s = math.sin(angle / 2.0)
return ((c, -s), (s, c))
if gate.startswith("RZ(") and gate.endswith(")"):
angle = self._parse_angle(gate[3:-1])
half = angle / 2.0
return (
(complex(math.cos(-half), math.sin(-half)), 0 + 0j),
(0 + 0j, complex(math.cos(half), math.sin(half))),
)
if gate.startswith("PHASE(") and gate.endswith(")"):
angle = self._parse_angle(gate[6:-1])
return ((1 + 0j, 0 + 0j), (0 + 0j, complex(math.cos(angle), math.sin(angle))))
return identity
def _run_qiskit(self, gate_sequence: Sequence[str], shots: int) -> float:
if not self._qiskit_available or not self._QuantumCircuit or not self._backend:
return -1.0
circuit = self._QuantumCircuit(1, 1)
for gate in gate_sequence:
upper = gate.strip().upper()
if not upper:
continue
if upper == "H":
circuit.h(0)
elif upper == "X":
circuit.x(0)
elif upper == "Y":
circuit.y(0)
elif upper == "Z":
circuit.z(0)
elif upper.startswith("RX(") and upper.endswith(")"):
circuit.rx(self._parse_angle(upper[3:-1]), 0)
elif upper.startswith("RY(") and upper.endswith(")"):
circuit.ry(self._parse_angle(upper[3:-1]), 0)
elif upper.startswith("RZ(") and upper.endswith(")"):
circuit.rz(self._parse_angle(upper[3:-1]), 0)
elif upper.startswith("PHASE(") and upper.endswith(")"):
circuit.p(self._parse_angle(upper[6:-1]), 0)
# fall back to no-op for unknown gates to keep compatibility
circuit.measure(0, 0)
try: # pragma: no cover - optional dependency
from qiskit import execute # type: ignore
job = execute(circuit, backend=self._backend, shots=shots)
result = job.result()
counts = result.get_counts()
except Exception: # pragma: no cover - runtime fallback
return -1.0
ones = counts.get("1", 0)
return float(max(0.0, min(1.0, ones / shots)))
def run(
self,
gate_sequence: Sequence[str],
shots: int | None = None,
monte_carlo: bool = False,
) -> float:
active_shots = max(1, shots or self._shots)
if self._qiskit_available:
probability = self._run_qiskit(gate_sequence, active_shots)
if probability >= 0.0:
self._last_shots = active_shots
return probability
state = (1 + 0j, 0 + 0j)
for gate in gate_sequence:
matrix = self._matrix_for_gate(gate)
state = (
matrix[0][0] * state[0] + matrix[0][1] * state[1],
matrix[1][0] * state[0] + matrix[1][1] * state[1],
)
probability_one = abs(state[1]) ** 2
if monte_carlo:
successes = 0
for _ in range(active_shots):
if self._rand.random() < probability_one:
successes += 1
probability_one = successes / active_shots
self._last_shots = active_shots
return float(max(0.0, min(1.0, probability_one)))
@property
def available(self) -> bool: # pragma: no cover - simple property
return True
@property
def shots(self) -> int:
return self._shots
@property
def last_shots(self) -> int:
return self._last_shots
class QuantumSimulationProcess:
"""Generates quantum stress testing plans spanning revenue systems."""
REQUIRED_DOMAINS: set[str] = {
"google_ads",
"aws",
"marketing",
"bitcoin",
"stockmarket",
"stripe",
"cashapp",
"venmo",
"paypal",
"agentic",
}
ENV_HINTS: Dict[str, List[str]] = {
"qiskit": ["QISKIT_API_TOKEN", "QISKIT_RUNTIME_API_KEY"],
"aws_braket": ["AWS_BRAKET_REGION", "AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY"],
"google_cirq": ["GOOGLE_CIRQ_PROJECT", "GOOGLE_APPLICATION_CREDENTIALS"],
"azure_quantum": ["AZURE_QUANTUM_WORKSPACE", "AZURE_QUANTUM_RESOURCE_ID"],
}
DOMAIN_ACTION_HINTS: Dict[str, str] = {
"google_ads": "Sync quantum budget insights with Google Ads enrichment to guard ROAS.",
"aws": "Blend simulator outputs with AWS revenue playbooks for quota planning.",
"marketing": "Route funnel drift alerts into automation sprints and learning loops.",
"bitcoin": "Pair quantum volatility scores with crypto market fallbacks.",
"stockmarket": "Feed equity whipsaw metrics into scenario planning envelopes.",
"stripe": "Stage redundant checkouts when Stripe latency probability climbs.",
"cashapp": "Coordinate payout pacing with Cash App Pay readiness guides.",
"venmo": "Tune social commerce messaging using Venmo stress telemetry.",
"paypal": "Adjust PayPal reserve buffers based on quantum payout drag signals.",
"agentic": "Throttle agent workloads when loop saturation probabilities spike.",
}
def __init__(
self,
capabilities: Sequence[QuantumSimulatorCapability] | None = None,
dataset_path: str = QUANTUM_SIMULATIONS_DATASET,
simulator: MiniQuantumSimulator | None = None,
config: Dict[str, List[str]] | None = None,
) -> None:
self._capabilities = (
list(capabilities)
if capabilities is not None
else self._load(dataset_path)
)
self._simulator = simulator or MiniQuantumSimulator()
self._config = config or self._config_from_env()
@classmethod
def _config_from_env(cls) -> Dict[str, List[str]]:
config: Dict[str, List[str]] = {}
for name, env_vars in cls.ENV_HINTS.items():
available = [env for env in env_vars if os.getenv(env)]
if available:
config[name] = sorted({env.lower() for env in available})
return config
@property
def configured(self) -> bool:
return bool(self._config)
@staticmethod
def _tokenize(text: str) -> set[str]:
return {token for token in re.findall(r"[a-zA-Z0-9]+", text.lower()) if len(token) > 2}
@classmethod
def _load(cls, path: str) -> List[QuantumSimulatorCapability]:
try:
with open(path, "r", encoding="utf-8") as handle:
payload = json.load(handle)
except (FileNotFoundError, json.JSONDecodeError):
payload = []
capabilities: List[QuantumSimulatorCapability] = []
if isinstance(payload, list):
for entry in payload:
if not isinstance(entry, dict):
continue
scenarios: List[QuantumScenarioTemplate] = []
for raw in entry.get("scenarios", []):
if not isinstance(raw, dict):
continue