From e25d28195c4f0d1e19aa79855b33e4ae30370469 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Thu, 29 Jan 2026 12:03:53 -0500 Subject: [PATCH 01/24] Add AML fraud investigation agent --- .env.example | 8 +- .gitignore | 3 +- .../aieng/agent_evals/async_utils.py | 66 + .../aieng/agent_evals/configs.py | 157 +- .../fraud_investigation/__init__.py | 17 + .../agent_evals/fraud_investigation/policy.py | 77 + .../agent_evals/fraud_investigation/types.py | 56 + .../fraud_investigation/workflow.py | 254 + .../aieng/agent_evals/tools/__init__.py | 6 + .../aieng/agent_evals/tools/sql_database.py | 207 + aieng-eval-agents/pyproject.toml | 5 +- .../test_sql_database.py} | 0 implementations/fraud_investigation/README.md | 33 + implementations/fraud_investigation/agent.py | 258 + .../fraud_investigation/data/prepare_db.py | 376 ++ .../fraud_investigation/data/schema.ddl | 42 + pyproject.toml | 2 +- uv.lock | 5869 ++++++++++------- 18 files changed, 4808 insertions(+), 2628 deletions(-) create mode 100644 aieng-eval-agents/aieng/agent_evals/async_utils.py create mode 100644 aieng-eval-agents/aieng/agent_evals/fraud_investigation/__init__.py create mode 100644 aieng-eval-agents/aieng/agent_evals/fraud_investigation/policy.py create mode 100644 aieng-eval-agents/aieng/agent_evals/fraud_investigation/types.py create mode 100644 aieng-eval-agents/aieng/agent_evals/fraud_investigation/workflow.py create mode 100644 aieng-eval-agents/aieng/agent_evals/tools/__init__.py create mode 100644 aieng-eval-agents/aieng/agent_evals/tools/sql_database.py rename aieng-eval-agents/tests/aieng/agent_evals/{impl/test_example_impl.py => tools/test_sql_database.py} (100%) create mode 100644 implementations/fraud_investigation/README.md create mode 100644 implementations/fraud_investigation/agent.py create mode 100644 implementations/fraud_investigation/data/prepare_db.py create mode 100644 implementations/fraud_investigation/data/schema.ddl diff --git a/.env.example b/.env.example index d00bf26..c03ae0c 100644 --- a/.env.example +++ b/.env.example @@ -1,2 +1,6 @@ -ENV_VARIABLE_ONE= -ENV_VARIABLE_TWO= +GOOGLE_API_KEY="..." + +# AML Database Configuration +AML_DB__DRIVER="sqlite" +AML_DB__DATABASE="implementations/fraud_investigation/data/aml_prod.db" +AML_DB__QUERY__MODE="ro" diff --git a/.gitignore b/.gitignore index 7999612..9ceead8 100644 --- a/.gitignore +++ b/.gitignore @@ -25,5 +25,6 @@ wheels/ .env .gradio -implementations/report_generation/data/*.db +.adk +*.db implementations/report_generation/reports/* diff --git a/aieng-eval-agents/aieng/agent_evals/async_utils.py b/aieng-eval-agents/aieng/agent_evals/async_utils.py new file mode 100644 index 0000000..615fea6 --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/async_utils.py @@ -0,0 +1,66 @@ +"""Utils for async workflows.""" + +import asyncio +import types +from typing import Any, Awaitable, Callable, Coroutine, Sequence, TypeVar + +from rich.progress import ( + BarColumn, + Progress, + TextColumn, + TimeElapsedColumn, + TimeRemainingColumn, +) + + +T = TypeVar("T") + + +async def indexed(index: int, coro: Coroutine[None, None, T]) -> tuple[int, T]: + """Return (index, await coro).""" + return index, (await coro) + + +async def rate_limited(_fn: Callable[[], Awaitable[T]], semaphore: asyncio.Semaphore) -> T: + """Run _fn with semaphore rate limit.""" + async with semaphore: + return await _fn() + + +async def gather_with_progress( + coros: "list[types.CoroutineType[Any, Any, T]]", + description: str = "Running tasks", +) -> Sequence[T]: + """ + Run a list of coroutines concurrently, display a rich.Progress bar as each finishes. + + Returns the results in the same order as the input list. + + :param coros: List of coroutines to run. + :return: List of results, ordered to match the input coroutines. + """ + # Wrap each coroutine in a Task and remember its original index + tasks = [asyncio.create_task(indexed(index=index, coro=coro)) for index, coro in enumerate(coros)] + + # Pre‐allocate a results list; we'll fill in each slot as its Task completes + results: list[T | None] = [None] * len(tasks) + + # Create and start a Progress bar with a total equal to the number of tasks + with Progress( + TextColumn("[bold blue]{task.description}"), + BarColumn(), + TextColumn("{task.completed}/{task.total}"), + TimeRemainingColumn(), + TimeElapsedColumn(), + ) as progress: + progress_task = progress.add_task(description, total=len(tasks)) + + # as_completed yields each Task as soon as it finishes + for finished in asyncio.as_completed(tasks): + index, result = await finished + results[index] = result + progress.update(progress_task, advance=1) + + # At this point, every slot in `results` is guaranteed to be non‐None + # so we can safely cast it back to List[T] + return results # type: ignore diff --git a/aieng-eval-agents/aieng/agent_evals/configs.py b/aieng-eval-agents/aieng/agent_evals/configs.py index c3cc425..6e290f0 100644 --- a/aieng-eval-agents/aieng/agent_evals/configs.py +++ b/aieng-eval-agents/aieng/agent_evals/configs.py @@ -1,7 +1,44 @@ """Configuration settings for the agent evals.""" -from pydantic import AliasChoices, Field +from typing import Any + +from pydantic import AliasChoices, BaseModel, Field, SecretStr, field_validator from pydantic_settings import BaseSettings, SettingsConfigDict +from sqlalchemy.engine.url import URL + + +class DatabaseConfig(BaseModel): + """Database connection configuration.""" + + driver: str = Field(..., description="SQLAlchemy dialect (e.g., 'sqlite', 'postgresql', 'mysql+pymysql').") + """SQLAlchemy dialect (e.g., 'sqlite', 'postgresql', 'mysql+pymysql').""" + username: str | None = None + """Database username. For SQLite or integrated authentication, this can be None.""" + host: str | None = None + """Database host address or file path for SQLite.""" + password: SecretStr | None = None + """Database password. For SQLite or integrated authentication, this can be None.""" + port: int | None = None + """Database port number.""" + database: str | None = Field(None, description="Database name or file path for SQLite.") + """Database name or file path for SQLite.""" + query: dict[str, Any] = Field(default_factory=dict, description="URL query parameters (e.g. {'mode': 'ro'})") + """URL query parameters (e.g. {'mode': 'ro'} for read-only SQLite).""" + + def build_uri(self) -> str: + """Construct the SQLAlchemy connection URI safely using the official URL object. + + This handles special character escaping in passwords automatically. + """ + return URL.create( + drivername=self.driver, + username=self.username, + password=self.password.get_secret_value() if self.password else None, + host=self.host, + port=self.port, + database=self.database, + query=self.query, + ).render_as_string(hide_password=False) class Configs(BaseSettings): @@ -11,55 +48,6 @@ class Configs(BaseSettings): and a .env file, and provides type-safe access to all settings. It validates environment variables on instantiation. - Attributes - ---------- - openai_base_url : str - Base URL for OpenAI-compatible API (defaults to Gemini endpoint). - openai_api_key : str - API key for OpenAI-compatible API (accepts OPENAI_API_KEY, GEMINI_API_KEY, - or GOOGLE_API_KEY). - default_planner_model : str, default='gemini-2.5-pro' - Model name for planning tasks. This is typically a more capable and expensive - model. - default_worker_model : str, default='gemini-2.5-flash' - Model name for worker tasks. This is typically a less expensive model. - embedding_base_url : str - Base URL for embedding API service. - embedding_api_key : str - API key for embedding service. - embedding_model_name : str, default='@cf/baai/bge-m3' - Name of the embedding model. - weaviate_collection_name : str, default='enwiki_20250520' - Name of the Weaviate collection to use. - weaviate_api_key : str - API key for Weaviate cloud instance. - weaviate_http_host : str - Weaviate HTTP host (must end with .weaviate.cloud). - weaviate_grpc_host : str - Weaviate gRPC host (must start with grpc- and end with .weaviate.cloud). - weaviate_http_port : int, default=443 - Port for Weaviate HTTP connections. - weaviate_grpc_port : int, default=443 - Port for Weaviate gRPC connections. - weaviate_http_secure : bool, default=True - Use secure HTTP connection. - weaviate_grpc_secure : bool, default=True - Use secure gRPC connection. - langfuse_public_key : str - Langfuse public key (must start with pk-lf-). - langfuse_secret_key : str - Langfuse secret key (must start with sk-lf-). - langfuse_host : str, default='https://us.cloud.langfuse.com' - Langfuse host URL. - e2b_api_key : str or None - Optional E2B.dev API key for code interpreter (must start with e2b_). - default_code_interpreter_template : str or None - Optional default template name or ID for E2B.dev code interpreter. - web_search_base_url : str or None - Optional base URL for web search service. - web_search_api_key : str or None - Optional API key for web search service. - Examples -------- >>> from src.utils.env_vars import Configs @@ -73,37 +61,86 @@ class Configs(BaseSettings): variables. The class will automatically load and validate them. """ - model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", env_ignore_empty=True) + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", env_ignore_empty=True, env_nested_delimiter="__" + ) + + aml_db: DatabaseConfig | None = None + """Anti-Money Laundering database configuration. Used by the Fraud Investigation \ + Agent. + """ openai_base_url: str = "https://generativelanguage.googleapis.com/v1beta/openai/" - openai_api_key: str = Field(validation_alias=AliasChoices("OPENAI_API_KEY", "GEMINI_API_KEY", "GOOGLE_API_KEY")) + """Base URL for OpenAI-compatible API (defaults to Gemini endpoint).""" + openai_api_key: SecretStr = Field( + validation_alias=AliasChoices("OPENAI_API_KEY", "GEMINI_API_KEY", "GOOGLE_API_KEY") + ) + """API key for OpenAI-compatible API (accepts OPENAI_API_KEY, GEMINI_API_KEY, \ + or GOOGLE_API_KEY).""" default_planner_model: str = "gemini-2.5-pro" + """Model name for planning tasks. This is typically a more capable and expensive \ + model.""" default_worker_model: str = "gemini-2.5-flash" + """Model name for worker tasks. This is typically a less expensive model.""" embedding_base_url: str - embedding_api_key: str + """Base URL for embedding API service.""" + embedding_api_key: SecretStr + """API key for embedding service.""" embedding_model_name: str = "@cf/baai/bge-m3" + """Name of the embedding model.""" weaviate_collection_name: str = "enwiki_20250520" - weaviate_api_key: str | None = None - # ends with .weaviate.cloud, or it's "localhost" + """Name of the Weaviate collection to use.""" + weaviate_api_key: SecretStr | None = None + """API key for Weaviate cloud instance.""" weaviate_http_host: str = Field(pattern=r"^.*\.weaviate\.cloud$|localhost") - # starts with grpc- ends with .weaviate.cloud, or it's "localhost" + """HTTP host for Weaviate cloud instance. Must end with .weaviate.cloud or be \ + 'localhost'.""" weaviate_grpc_host: str = Field(pattern=r"^grpc-.*\.weaviate\.cloud$|localhost") + """gRPC host for Weaviate cloud instance. Must start with grpc- and end with \ + .weaviate.cloud or be 'localhost'.""" weaviate_http_port: int = 443 + """Port for Weaviate HTTP connections.""" weaviate_grpc_port: int = 443 + """Port for Weaviate gRPC connections.""" weaviate_http_secure: bool = True + """Use secure HTTP connection.""" weaviate_grpc_secure: bool = True + """Use secure gRPC connection.""" langfuse_public_key: str = Field(pattern=r"^pk-lf-.*$") - langfuse_secret_key: str = Field(pattern=r"^sk-lf-.*$") + """Langfuse public key (must start with pk-lf-).""" + langfuse_secret_key: SecretStr + """Langfuse secret key (must start with sk-lf-).""" langfuse_host: str = "https://us.cloud.langfuse.com" + """Langfuse host URL.""" - # Optional E2B.dev API key for Python Code Interpreter tool - e2b_api_key: str | None = Field(default=None, pattern=r"^e2b_.*$") + e2b_api_key: SecretStr | None = None + """Optional E2B.dev API key for code interpreter (must start with e2b_).""" default_code_interpreter_template: str | None = "9p6favrrqijhasgkq1tv" + """Optional default template name or ID for E2B.dev code interpreter.""" # Optional configs for web search tool web_search_base_url: str | None = None - web_search_api_key: str | None = None + """Optional base URL for web search service.""" + web_search_api_key: SecretStr | None = None + """Optional API key for web search service.""" + + # Add Validators for the SecretStr fields + @field_validator("langfuse_secret_key") + @classmethod + def validate_langfuse_secret(cls, v: SecretStr) -> SecretStr: + """Validate that the Langfuse secret key starts with 'sk-lf-'.""" + if not v.get_secret_value().startswith("sk-lf-"): + raise ValueError("Langfuse secret key must start with 'sk-lf-'") + return v + + @field_validator("e2b_api_key") + @classmethod + def validate_e2b_key(cls, v: SecretStr | None) -> SecretStr | None: + """Validate that the E2B API key starts with 'e2b_' if provided.""" + if v is not None and not v.get_secret_value().startswith("e2b_"): + raise ValueError("E2B API key must start with 'e2b_'") + return v diff --git a/aieng-eval-agents/aieng/agent_evals/fraud_investigation/__init__.py b/aieng-eval-agents/aieng/agent_evals/fraud_investigation/__init__.py new file mode 100644 index 0000000..7836e6e --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/fraud_investigation/__init__.py @@ -0,0 +1,17 @@ +"""Utilities for the Fraud Investigation agent.""" + +from .policy import RuleBasedTriage, TriageOutcome, TriagePolicyConfig, TriageStrategy +from .types import AnalystResult, AnalystVerdict, SuspiciousActivityType +from .workflow import AmlInvestigationWorkflow + + +__all__ = [ + "TriageOutcome", + "TriageStrategy", + "RuleBasedTriage", + "TriagePolicyConfig", + "AnalystResult", + "AnalystVerdict", + "SuspiciousActivityType", + "AmlInvestigationWorkflow", +] diff --git a/aieng-eval-agents/aieng/agent_evals/fraud_investigation/policy.py b/aieng-eval-agents/aieng/agent_evals/fraud_investigation/policy.py new file mode 100644 index 0000000..c694f9f --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/fraud_investigation/policy.py @@ -0,0 +1,77 @@ +"""Defines triage policies for fraud investigation alerts.""" + +from abc import ABC, abstractmethod +from enum import Enum + +from pydantic import BaseModel + + +class TriageOutcome(str, Enum): + """Possible triage outcomes for fraud investigation alerts.""" + + CLOSE = "CLOSE" + INVESTIGATE = "INVESTIGATE" + ESCALATE = "ESCALATE" + + +class TriageStrategy(ABC): + """Abstract base class for triage strategies.""" + + @abstractmethod + async def triage(self, alert: dict) -> TriageOutcome: + """Return the triage outcome for the given alert. + + Parameters + ---------- + alert : dict + The alert data to be triaged. + + Returns + ------- + TriageOutcome + The outcome of the triage process. + """ + pass + + +class TriagePolicyConfig(BaseModel): + """Configuration for rule-based triage policy.""" + + auto_close_threshold: int = 35 + auto_escalate_threshold: int = 95 + + +class RuleBasedTriage(TriageStrategy): + """A simple rule-based triage strategy based on risk score. + + This simply compares the alert's risk score against predefined thresholds + to determine the triage outcome. + + Parameters + ---------- + config : TriagePolicyConfig + Configuration parameters for the triage policy. + """ + + def __init__(self, config: TriagePolicyConfig) -> None: + self.config = config + + async def triage(self, alert: dict) -> TriageOutcome: + """Triage the alert based on its risk score. + + Parameters + ---------- + alert : dict + The alert data to be triaged. Should contain a 'risk_score' key. + + Returns + ------- + TriageOutcome + The outcome of the triage process. + """ + score = alert.get("risk_score", 0) + if score < self.config.auto_close_threshold: + return TriageOutcome.CLOSE + if score > self.config.auto_escalate_threshold: + return TriageOutcome.ESCALATE + return TriageOutcome.INVESTIGATE diff --git a/aieng-eval-agents/aieng/agent_evals/fraud_investigation/types.py b/aieng-eval-agents/aieng/agent_evals/fraud_investigation/types.py new file mode 100644 index 0000000..58f1260 --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/fraud_investigation/types.py @@ -0,0 +1,56 @@ +"""Types/Objects for the Fraud Investigation Agent.""" + +from enum import Enum + +from pydantic import BaseModel, Field + + +class AnalystVerdict(str, Enum): + """Investigation outcome classification.""" + + SUSPICIOUS = "SUSPICIOUS" # Strong evidence, SAR recommended + BENIGN = "BENIGN" # False positive, no SAR needed + NEEDS_REVIEW = "NEEDS_REVIEW" # Insufficient evidence, requires senior review + + +class SuspiciousActivityType(str, Enum): + """Standard FinCEN SAR Categories.""" + + # STRUCTURING: Breaking txns to avoid reporting thresholds (e.g. $9,900) + STRUCTURING = "structuring" + + # LAYERING: Complex movement to hide origin + LAYERING = "layering" + + # FUNDS_TRANSFERS: High velocity/volume + RAPID_MOVEMENT = "suspicious_eft_wire_transfers" + + # OTHERS + UNUSUAL_VOLUME = "unusual_volume" + TERRORIST_FINANCING = "terrorist_financing" + OTHER = "other" + + +class AnalystResult(BaseModel): + """The 'Case File' generated by the Analyst, passed through to the Reporter.""" + + # Narrative (Context for Reporter) + summary_narrative: str = Field(..., description="A 2-3 paragraph explanation of the fraud scheme.") + """2-3 paragraph explanation of the fraud scheme.""" + + # Logic + risk_score: int = Field(..., ge=0, le=100) + """Risk score assigned by the analyst, ranging from 0 to 100.""" + + verdict: AnalystVerdict + """Investigation outcome classification.""" + + # Classification + suspicious_activity_type: SuspiciousActivityType = Field( + ..., description="Dominant type of suspicious activity identified." + ) + """Dominant type of suspicious activity identified.""" + + # Hard Evidence + suspicious_transactions: str + """List of transaction identifiers deemed suspicious by the analyst.""" diff --git a/aieng-eval-agents/aieng/agent_evals/fraud_investigation/workflow.py b/aieng-eval-agents/aieng/agent_evals/fraud_investigation/workflow.py new file mode 100644 index 0000000..cfc4c3e --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/fraud_investigation/workflow.py @@ -0,0 +1,254 @@ +"""Anti-Money Laundering workflow.""" + +import json +import logging +from contextlib import aclosing +from enum import Enum +from typing import TYPE_CHECKING, Any, AsyncGenerator, Optional + +import google.genai.types +from aieng.agent_evals.fraud_investigation.policy import TriageOutcome, TriageStrategy +from aieng.agent_evals.fraud_investigation.types import AnalystResult, AnalystVerdict +from google.adk.agents.base_agent import BaseAgent, BaseAgentState +from google.adk.events.event import Event +from pydantic import Field +from pydantic.fields import PrivateAttr + + +if TYPE_CHECKING: + from google.adk.agents.invocation_context import InvocationContext + +logger = logging.getLogger(__name__) + + +class _WorkflowStep(str, Enum): + """Steps in the AML investigation workflow.""" + + START = "START" + ANALYZE = "ANALYZE" + REPORT = "REPORT" + DONE = "DONE" + + +class _WorkflowState(BaseAgentState): + """State for the AML investigation workflow.""" + + input_dict: dict[str, Any] + step: _WorkflowStep = _WorkflowStep.START + triage_decision: TriageOutcome | None = None + analysis: AnalystResult | None = None + final_report: Optional[str] = None + + +class AmlInvestigationWorkflow(BaseAgent): + """An AML investigation workflow coordinating triage, analysis, and reporting.""" + + name: str = Field(default="AMLWorkflowManager", description="Coordinates AML investigation and reporting agents.") + description: str = Field( + default="Oversees the end-to-end AML investigation process, delegating tasks to specialized agents." + ) + + # PrivateAttr are used here to hide complex sub-agents from Pydantic validation + _triage: TriageStrategy = PrivateAttr() + _analyst: BaseAgent = PrivateAttr() + _reporter: BaseAgent = PrivateAttr() + + def __init__(self, triage_strategy: TriageStrategy, analyst: BaseAgent, reporter: BaseAgent, **kwargs: Any) -> None: + """Initialize the AML investigation workflow. + + Parameters + ---------- + triage_strategy : TriageStrategy + The triage strategy to use for initial alert assessment. + analyst : BaseAgent + The agent responsible for in-depth fraud analysis. This agent must + produce an ``AnalystResult``, which will be passed to the reporter. + reporter : BaseAgent + The agent responsible for generating the final SAR report. + **kwargs : Any + Additional keyword arguments for the ``BaseAgent`` class. + """ + super().__init__(**kwargs) + self._triage = triage_strategy + self._analyst = analyst + self._reporter = reporter + self.sub_agents = [analyst, reporter] + + async def _initialize_state(self, ctx: "InvocationContext") -> _WorkflowState: + """Initialize workflow state from context.""" + state = self._load_agent_state(ctx, _WorkflowState) + + if state: + return state + + input_text = "" + if ctx.user_content and ctx.user_content.parts: + input_text = ctx.user_content.parts[0].text or "" + + try: + alert_dict = json.loads(input_text.strip()) + except json.JSONDecodeError: + logger.warning("Failed to parse alert payload as JSON. Using raw text.") + alert_dict = {"description": input_text} + + return _WorkflowState(input_dict=alert_dict) + + async def _update_step( + self, ctx: "InvocationContext", state: _WorkflowState, new_step: _WorkflowStep + ) -> AsyncGenerator["Event", None]: + """Update workflow step and persist state.""" + state.step = new_step + ctx.set_agent_state(self.name, agent_state=state) + logger.info(ctx) + yield self._create_agent_state_event(ctx) + + async def _handle_triage(self, ctx: "InvocationContext", state: _WorkflowState) -> AsyncGenerator["Event", None]: + """Handle triage step.""" + decision = await self._triage.triage(state.input_dict) + state.triage_decision = decision + + triage_event = _create_text_event(f"Triage decision: {decision.value}", ctx) + if decision == TriageOutcome.CLOSE: + state.final_report = "CASE CLOSED: No further action required after triage." + yield _create_text_event(state.final_report, ctx) + + async for event in self._update_step(ctx, state, _WorkflowStep.DONE): + yield event + elif decision == TriageOutcome.ESCALATE: + triage_event.actions.escalate = True + yield triage_event + async for event in self._update_step(ctx, state, _WorkflowStep.DONE): + yield event + else: + triage_event.actions.transfer_to_agent = self._analyst.name + yield triage_event + async for event in self._update_step(ctx, state, _WorkflowStep.ANALYZE): + yield event + + async def _handle_analysis(self, ctx: "InvocationContext", state: _WorkflowState) -> AsyncGenerator["Event", None]: + """Handle analysis step by delegating to analyst agent.""" + analyst_ctx = ctx.model_copy() + analyst_ctx.branch = f"{ctx.branch}.{self._analyst.name}" if ctx.branch else self._analyst.name + + async with aclosing(self._analyst.run_async(analyst_ctx)) as agen: + async for event in agen: + yield event + + if event.is_final_response() and event.content and event.content.parts: + analysis_text = event.content.parts[0].text or "" + try: + analysis_dict = json.loads(analysis_text.strip()) + state.analysis = AnalystResult.model_validate(analysis_dict) + except json.JSONDecodeError: + logger.error("Failed to parse analyst result as JSON.") + + if state.analysis or ctx.should_pause_invocation(event): + break + + if state.analysis: + async for e in self._route_analysis_result(ctx, state): + yield e + else: + async for e in self._update_step(ctx, state, _WorkflowStep.DONE): + yield e + + async def _route_analysis_result( + self, ctx: "InvocationContext", state: _WorkflowState + ) -> AsyncGenerator["Event", None]: + """Route based on analysis verdict.""" + if not state.analysis: + return + + verdict = state.analysis.verdict + + if verdict == AnalystVerdict.SUSPICIOUS: + reporter_msg = f"""\ + # Analyst Findings + **Analyst Risk Score**: {state.analysis.risk_score} + **Analyst Verdict**: {state.analysis.verdict.value} + ## Summary Narrative + {state.analysis.summary_narrative} + ## Key Findings + **Suspicious Activity Type**: {state.analysis.suspicious_activity_type.value} + ### Suspicious Transactions + {state.analysis.suspicious_transactions} + """ + analyst_event = _create_text_event(reporter_msg, ctx) + analyst_event.actions.transfer_to_agent = self._reporter.name + yield analyst_event + + async for e in self._update_step(ctx, state, _WorkflowStep.REPORT): + yield e + elif verdict == AnalystVerdict.NEEDS_REVIEW: + state.final_report = ( + f"ESCALATED TO HUMAN: Further review required.\n" + f"Reason: {state.analysis.summary_narrative}\n" + f"Evidence: Identified {len(state.analysis.suspicious_transactions)} suspicious transactions." + ) + analyst_event = _create_text_event(state.final_report, ctx) + analyst_event.actions.escalate = True + yield analyst_event + + async for e in self._update_step(ctx, state, _WorkflowStep.DONE): + yield e + else: + state.final_report = ( + f"CASE CLOSED (False Positive).\n" + f"Reason: {state.analysis.summary_narrative}\n" + f"Evidence: Investigated {len(state.analysis.suspicious_transactions)} transactions and found no illicit pattern." + ) + yield _create_text_event(state.final_report, ctx) + + async for e in self._update_step(ctx, state, _WorkflowStep.DONE): + yield e + + async def _handle_reporting(self, ctx: "InvocationContext", state: _WorkflowState) -> AsyncGenerator["Event", None]: + """Handle reporting step by delegating to reporter agent.""" + if not state.analysis: + return + + reporter_ctx = ctx.model_copy() + reporter_ctx.branch = f"{ctx.branch}.{self._reporter.name}" if ctx.branch else self._reporter.name + + async with aclosing(self._reporter.run_async(reporter_ctx)) as agen: + async for event in agen: + yield event + + if event.is_final_response() and event.content and event.content.parts: + state.final_report = event.content.parts[0].text + break + + if ctx.should_pause_invocation(event): + return + + async for e in self._update_step(ctx, state, _WorkflowStep.DONE): + yield e + + async def _run_async_impl(self, ctx: "InvocationContext") -> AsyncGenerator["Event", None]: + """Run the AML investigation workflow.""" + state = await self._initialize_state(ctx) + + if state.step == _WorkflowStep.START: + async for event in self._handle_triage(ctx, state): + yield event + + if state.step == _WorkflowStep.ANALYZE: + async for event in self._handle_analysis(ctx, state): + yield event + + if state.step == _WorkflowStep.REPORT and state.analysis: + async for event in self._handle_reporting(ctx, state): + yield event + + if state.step == _WorkflowStep.DONE: + ctx.set_agent_state(self.name, end_of_agent=True) + yield self._create_agent_state_event(ctx) + + +def _create_text_event(text: str, ctx: "InvocationContext") -> Event: + """Create a text event to be sent back to the user.""" + return Event( + invocation_id=ctx.invocation_id, + author=ctx.agent.name, + content=google.genai.types.Content(role="model", parts=[google.genai.types.Part(text=text)]), + ) diff --git a/aieng-eval-agents/aieng/agent_evals/tools/__init__.py b/aieng-eval-agents/aieng/agent_evals/tools/__init__.py new file mode 100644 index 0000000..604f953 --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/tools/__init__.py @@ -0,0 +1,6 @@ +"""Tools.""" + +from .sql_database import ReadOnlySqlDatabase + + +__all__ = ["ReadOnlySqlDatabase"] diff --git a/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py b/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py new file mode 100644 index 0000000..a9dcb3f --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py @@ -0,0 +1,207 @@ +"""SQL Database Tool with Read-Only Enforcement for Agents.""" + +import logging +from datetime import datetime +from typing import Optional + +import sqlglot +from sqlalchemy import create_engine, inspect, text +from sqlglot import exp + + +logger = logging.getLogger(__name__) + + +class ReadOnlySqlDatabase: + """A SQL database query tool for Agents. + + Features: + - AST-based Read-Only Enforcement (SQLGlot) + - Row Limits & Timeouts + - Schema Introspection + - Compliance Audit Logging + + Parameters + ---------- + connection_uri : str + SQLAlchemy connection string (e.g., 'sqlite:///data/prod.db?mode=ro' + or 'postgresql://reader:pass@host/db'). + max_rows : int, default=100_000 + Hard limit on number of rows returned to the agent. + query_timeout_sec : int, default=60 + Maximum execution time for queries in seconds. + agent_name : str, default="UnknownAgent" + Name of the agent using this tool (for audit logs). + **kwargs : Any + Additional keyword arguments passed to SQLAlchemy's ``create_engine`` function. + """ + + def __init__( + self, + connection_uri: str, + max_rows: int = 100_000, + query_timeout_sec: int = 60, + agent_name: str = "UnknownAgent", + **kwargs, + ) -> None: + """Initialize the database tool.""" + self.engine = create_engine(connection_uri, **kwargs) + self.agent_name = agent_name + self.max_rows = max_rows + self.timeout = query_timeout_sec + + def _is_safe_readonly_query(self, query: str) -> bool: + """Verify that query is semantically read-only using a SQL Parser (SQLGlot). + + Blocks: DELETE, UPDATE, INSERT, DROP, ALTER, etc. + Allows: SELECT, WITH (if read-only), EXPLAIN. + """ + try: + # Parse the query into an AST (Abstract Syntax Tree) + expressions = sqlglot.parse(query) + + if not expressions: + # Assume unsafe if we can't parse anything + logger.warning("Empty parse result - blocking query") + return False + + for expression in expressions: + # Check Root Expression Type + if not isinstance(expression, (exp.Select, exp.Union, exp.Paren)): + logger.warning("Blocked Unsafe Query Type: %s", type(expression)) + return False + + # Deep Search for Forbidden Nodes anywhere in the AST + # Catches hidden writes inside CTEs or Subqueries + if expression.find(exp.Delete, exp.Update, exp.Insert, exp.Drop, exp.Alter, exp.TruncateTable): + logger.warning("Blocked Query containing Write operation in AST") + return False + + return True + except Exception as e: + logger.error("SQL Parsing Error: %s", e) + # If we can't parse it, we don't run it. + return False + + def get_schema_info(self, table_names: Optional[list[str]] = None) -> str: + """Return schema for specific tables or all if None. + + Parameters + ---------- + table_names : Optional[list[str]], default=None + List of table names to retrieve schema for. If ``None``, retrieves all + tables. + + Returns + ------- + str + Formatted schema information. + """ + inspector = inspect(self.engine) + all_tables = inspector.get_table_names() + + # Filter logic + if table_names: + # Case-insensitive matching + targets = [table.lower() for table in table_names] + tables_to_scan = [table for table in all_tables if table.lower() in targets] + else: + tables_to_scan = all_tables + + schema_text = [] + for table_name in tables_to_scan: + try: + columns = inspector.get_columns(table_name) + # Compact Format for LLM: "TableName (col1: type, col2: type)" + col_strs = [f"{c['name']}: {str(c['type'])}" for c in columns] + schema_text.append(f"Table: {table_name}\n Columns: {', '.join(col_strs)}") + except Exception: + schema_text.append(f"Table: {table_name} (Error reading schema)") + + return "\n".join(schema_text) + + def execute(self, query: str) -> str: + """Execute a SQL query against the database with read-only enforcement. + + Parameters + ---------- + query : str + The SQL query string to execute. + + Returns + ------- + list[Any] + List of result rows (as tuples or dicts depending on config). + + Raises + ------ + PermissionError + If the query attempts to perform a write operation. + Exception + For any database execution errors. + + """ + start_time = datetime.now() + status = "FAILED" + error_msg = None + row_count = 0 + + try: + # AST Safety Check + if not self._is_safe_readonly_query(query): + raise PermissionError("Security Violation: Query contains prohibited WRITE operations.") + + # Connection & Execution + with self.engine.connect() as conn: + # Apply Timeout (Database specific options) + # Note: 'max_execution_time' syntax varies by DB (MySQL vs Postgres). + # This generic approach relies on the driver or SQLAlchemy 1.4+ + # execution_options + if self.engine.dialect.name == "sqlite": + # SQLite does not support `execution_options` for timeout directly + conn.execute(text(f"PRAGMA busy_timeout = {self.timeout * 1000}")) + + execution_options = {"timeout": self.timeout} + result = conn.execute(text(query).execution_options(**execution_options)) + + # Header Extraction + keys = list(result.keys()) + + # Fetch with Row Limit + # This protects against excessive memory use + rows = result.fetchmany(self.max_rows) + row_count = len(rows) + + # Formatting for LLM (String Table) + output = [f"| {' | '.join(keys)} |"] + output.append(f"| {'---' * len(keys)} |") + for row in rows: + output.append(f"| {' | '.join(map(str, row))} |") + + if row_count == self.max_rows: + output.append(f"\n... (Truncated at {self.max_rows} rows) ...") + + status = "SUCCESS" + return "\n".join(output) + + except Exception as e: + error_msg = str(e) + return f"Query Error: {error_msg}" + + finally: + # Compliance Audit Log + duration = (datetime.now() - start_time).total_seconds() + log_entry = { + "timestamp": start_time.isoformat(), + "agent": self.agent_name, + "query": query, + "status": status, + "rows_returned": row_count, + "duration_sec": duration, + "error": error_msg, + } + logger.info("AUDIT: %s", log_entry) + + def close(self) -> None: + """Dispose of the connection pool.""" + self.engine.dispose(close=True) diff --git a/aieng-eval-agents/pyproject.toml b/aieng-eval-agents/pyproject.toml index 1565996..266bce3 100644 --- a/aieng-eval-agents/pyproject.toml +++ b/aieng-eval-agents/pyproject.toml @@ -7,7 +7,10 @@ requires-python = ">=3.12,<4.0" readme = "README.md" license = "MIT" dependencies = [ - "kagglehub>=0.3.13", + "kagglehub>=0.4.1", + "sqlalchemy>=2.0.46", + "sqlglot>=28.6.0", + "weaviate-client>=4.18.3", ] [dependency-groups] diff --git a/aieng-eval-agents/tests/aieng/agent_evals/impl/test_example_impl.py b/aieng-eval-agents/tests/aieng/agent_evals/tools/test_sql_database.py similarity index 100% rename from aieng-eval-agents/tests/aieng/agent_evals/impl/test_example_impl.py rename to aieng-eval-agents/tests/aieng/agent_evals/tools/test_sql_database.py diff --git a/implementations/fraud_investigation/README.md b/implementations/fraud_investigation/README.md new file mode 100644 index 0000000..0472c65 --- /dev/null +++ b/implementations/fraud_investigation/README.md @@ -0,0 +1,33 @@ +# [Implementation Name] Overview + +## Introduction +Welcome to the [Implementation Name] of the [Bootcamp/Workshop/Lab/Project Name]. This folder contains a collection of notebooks and a README.md file to enhance your understanding of [specific aspect of the topic]. Here, we will explore [brief description of what will be covered]. + +## Prerequisites +Before you dive into the materials, ensure you have the following prerequisites: +- [Software/Tool/Library] version x.x or higher +- Basic knowledge of [relevant technology or field] +- [Any other requirement] + +## Notebooks +Here you will find the following Jupyter notebooks: +1. **[Subtopic 1]** - This notebook covers [brief description]. +2. **[Subtopic 2] Application** - This notebook covers [brief description]. + +## Package dependencies +This section describes the packages developed for this specific implementation. +- **package_a**: [Description of what the package offers and how it is used] +- **package_b**: [Description of what the package offers and how it is used] + +## Resources +For further reading and additional studies, consider the following resources: +- [Online course, book, or tutorial 1] - [brief description] ([link]) +- [Online course, book, or tutorial 2] - [brief description] ([link]) + +## Getting Started +To get started with the materials in this topic: +1. Ensure you have followed the [reference to the installation guide, environment creation, etc.] in `docs/`. +2. Complete any other steps that are required before moving to the notebooks or code. +3. Move to notebook [notebook name] to [a brief objective of the notebook]. +4. Run code [code name] with the command [command] to [a brief objective of the code]. +5. Add any extra steps if needed. diff --git a/implementations/fraud_investigation/agent.py b/implementations/fraud_investigation/agent.py new file mode 100644 index 0000000..08754ce --- /dev/null +++ b/implementations/fraud_investigation/agent.py @@ -0,0 +1,258 @@ +"""Fraud Investigation Agent Implementation. + +Run with: uv run --env-file .env implementations/fraud_investigation/agent.py +""" + +import asyncio +import getpass +import logging +import uuid +from pathlib import Path +from typing import Any + +import google.genai.types +import pandas as pd +from aieng.agent_evals.async_client_manager import AsyncClientManager +from aieng.agent_evals.async_utils import gather_with_progress, rate_limited +from aieng.agent_evals.fraud_investigation.policy import RuleBasedTriage, TriageOutcome, TriagePolicyConfig +from aieng.agent_evals.fraud_investigation.types import AnalystResult +from aieng.agent_evals.fraud_investigation.workflow import AmlInvestigationWorkflow +from aieng.agent_evals.tools import ReadOnlySqlDatabase +from dotenv import load_dotenv +from google.adk.agents import Agent +from google.adk.runners import Runner +from google.adk.sessions import InMemorySessionService + + +logger = logging.getLogger(__name__) + +ANALYST_PROMPT = """\ +You are a Financial Crimes Investigation Analyst. Your primary role is to conduct a focused investigation into \ +transactions flagged by our automated monitoring system. Your objective is to determine if the activity is suspicious \ +and requires reporting, or if it is a false positive that can be closed. + +## Core Principle: Falsification +Begin with the hypothesis that the automated alert is a false positive. \ +Your primary goal is to find positive evidence to prove the transaction is legitimate. + +## Standard Operating Procedure +### Initial Assessment +- Review the provided transaction data. Understand why it was flagged (e.g., high amount, unusual pattern). + +### Evidence Gathering +- **Customer Profile:** Examine the historical activity of the involved parties. Is this transaction consistent with their typical behavior? +- **Counterparty Analysis:** Investigate the transaction's source or destination. What is the relationship between the parties? +- **Pattern Analysis:** Search for broader patterns that could indicate illicit activity, such as: + - Structuring: multiple transactions just below reporting thresholds + - Layering: complex fund movements to obscure origins + - Rapid Movement: high volume or velocity of transfers + - Unusual Volume: sudden spikes in transaction amounts or frequency + - Terrorist Financing Indicators + - Other red flags as per FinCEN guidelines + +REMEMBER: The risk scores and triggered rules in the database are imperfect. Take them with a grain of salt. The actual \ +transaction patterns are the hard evidence. + +### Analysis and Verdict +Synthesize your findings and determine the appropriate verdict: +- BENIGN: Clear evidence supports legitimacy. Recommend closing the alert. +- SUSPICIOUS: Strong indicators of illicit activity. Recommend filing a SAR. +- NEEDS_REVIEW: Inconclusive evidence to confirm nor deny the suspicion after thorough investigation. Recommend escalation to a senior analyst. + +### Case Documentation +Document your findings comprehensively, citing specific evidence and rationale for your verdict. \ +The documentation must include the following: +- Summary Narrative: A concise explanation (2-3 paragraphs) of the fraud scheme. Include the reasoning behind your verdict and enough information for filing a SAR (the 5 "Ws") if needed. +- Risk Score: A value between 0-100 indicating the level of risk based on your findings. +- Verdict: One of BENIGN, SUSPICIOUS, or NEEDS_REVIEW. Applies to ONLY the transaction under investigation. +- Suspicious Activity Type: Classify the dominant type of suspicious activity identified. +- Suspicious Transactions: Tabulate the transactions deemed suspicious, including the transaction IDs, timestamp, amount, currencies, banks, entities involved. + +NOTE: For any given transaction, focus the investigation on transactions that happenned before or on the same day as the \ +transaction date. Do not consider transactions that occurred after the transaction date. +""" + +SAR_REPORT_PROMPT = """\ +You are a Compliance Officer responsible for drafting a formal Suspicious Activity Report (SAR) narrative for submission \ +to the Financial Crimes Enforcement Network (FinCEN). Your work is critical for law enforcement and regulatory oversight. + +You will receive a completed case file. This file contains the investigation summary, verdict, and key evidence. \ +Your task is to transform this information into a compliant and comprehensive SAR narrative. + +## SAR Narrative Structure +The narrative must be written in the third person, be clear, concise, and chronological. Adhere strictly to the following "Five Ws" structure: + +- **Who** is conducting the suspicious activity? Introduce the subject(s) and any associated entities. +- **What** instruments or mechanisms are being used? Describe the types of transactions (e.g., wire transfers, cash deposits) and accounts involved. +- **When** did the suspicious activity take place? Reference specific dates from `suspicious_transactions`. +- **Where** did the suspicious activity take place? +- **Why** do you deem the activity suspicious? + - Explain the "red flags" that led to the suspicion. + - Clearly state *why* the observed behavior deviates from the expected norm for the customer. + +## Output Format +Generate a single, cohesive narrative text. Do not use markdown or headers. \ +The text should be ready for direct insertion into Part V of the FinCEN SAR form. \ +Begin the report with an opening sentence that summarizes the filing, for example: "[Your Financial Institution] is filing \ +this report on [Subject Name] due to suspicious [activity type, e.g., wire transfers] intended to obscure the source of funds." +""" + + +client_manager = AsyncClientManager().get_instance() + +# Define Agents +if client_manager.configs.aml_db is None: + raise ValueError("AML database configuration is missing.") + +db = ReadOnlySqlDatabase( + connection_uri=client_manager.configs.aml_db.build_uri(), + agent_name="FraudInvestigationAnalyst", +) + +# NOTE: these are left here so that `adk web` command can pick up the `root_agent` for +# launching a web interface +analyst = Agent( + name="FraudInvestigationAnalyst", + description="Conducts detailed multi-step fraud investigations with database queries.", + tools=[db.execute, db.get_schema_info], + model="gemini-3-flash-preview", + instruction=ANALYST_PROMPT, + output_schema=AnalystResult, + generate_content_config=google.genai.types.GenerateContentConfig( + thinking_config=google.genai.types.ThinkingConfig(include_thoughts=True) + ), +) + +sar_report_agent = Agent( + name="SARReportGenerator", + description="Generates comprehensive Suspicious Activity Reports from investigation findings.", + model="gemini-2.5-flash", + instruction=SAR_REPORT_PROMPT, +) + +root_agent = AmlInvestigationWorkflow( + triage_strategy=RuleBasedTriage(config=TriagePolicyConfig()), analyst=analyst, reporter=sar_report_agent +) + + +async def _investigate_transaction(row: pd.Series) -> dict[str, Any]: + """Investigate a single transaction and return structured results.""" + # Initialize a session service to store conversation state + session_service = InMemorySessionService() + + analyst = Agent( + name="FraudInvestigationAnalyst", + description="Conducts detailed multi-step fraud investigations with database queries.", + tools=[db.execute, db.get_schema_info], + model="gemini-3-flash-preview", + instruction=ANALYST_PROMPT, + output_schema=AnalystResult, + ) + + sar_report_agent = Agent( + name="SARReportGenerator", + description="Generates comprehensive Suspicious Activity Reports from investigation findings.", + model="gemini-2.5-flash", + instruction=SAR_REPORT_PROMPT, + ) + + root_agent = AmlInvestigationWorkflow( + triage_strategy=RuleBasedTriage(config=TriagePolicyConfig()), analyst=analyst, reporter=sar_report_agent + ) + + # Create a runner to manage the execution loop + runner = Runner( + app_name="fraud_investigation", agent=root_agent, session_service=session_service, auto_create_session=True + ) + + transaction_id = row.get("transaction_id", "unknown") + + # Collect output in memory instead of printing + output_buffer: list[str] = [] + + message = google.genai.types.Content( + role="user", + parts=[google.genai.types.Part(text=row.drop(labels=["is_laundering", "triggered_rules"]).to_json())], + ) + events_async = runner.run_async(session_id=str(uuid.uuid4()), user_id=getpass.getuser(), new_message=message) + + # Extract structured results from the workflow state + triage_decision: TriageOutcome | None = None + analyst_findings: dict[str, Any] | None = None + final_report = None + + async for event in events_async: + # Capture text output + if event.content and event.content.parts: + for part in event.content.parts: + if part.text: + output_buffer.append(part.text) + + # Extract workflow state when investigation completes + if event.actions.agent_state: + state_data = event.actions.agent_state + triage_decision = state_data.get("triage_decision") + analyst_findings = state_data.get("analysis") + final_report = state_data.get("final_report") + + logger.info(f"✅ Investigation for transaction {transaction_id} completed.") + logger.debug("".join(output_buffer)) + + return { + "transaction_id": transaction_id, + "is_laundering": row.get("is_laundering"), + "system_risk_score": row.get("risk_score"), + "triggered_rules": row.get("triggered_rules"), + "triage_decision": triage_decision, + "analyst_verdict": analyst_findings.get("verdict") if analyst_findings else None, + "agent_risk_score": analyst_findings.get("risk_score") if analyst_findings else None, + "suspicious_activity_type": analyst_findings.get("suspicious_activity_type") if analyst_findings else None, + "summary_narrative": analyst_findings.get("summary_narrative") if analyst_findings else None, + "suspicious_transactions": analyst_findings.get("suspicious_transactions") if analyst_findings else None, + "suspicious_entities": analyst_findings.get("suspicious_entities") if analyst_findings else None, + "final_report": final_report, + } + + +async def main() -> None: + """Run fraud investigations on a batch of transactions.""" + # Prepare your input + path_to_transactions = Path("implementations/fraud_investigation/data/test_transactions.csv") + if not path_to_transactions.exists(): + raise FileNotFoundError(f"Transaction file not found at {path_to_transactions.resolve()}") + + transactions = pd.read_csv(path_to_transactions, dtype_backend="pyarrow") + + # Investigate each transaction with limited concurrency + semaphore = asyncio.Semaphore(10) # Limit to 10 concurrent investigations + results: list[dict[str, Any]] = [] + + # TODO: remove or change this line for full run + transactions = transactions.sample(n=100, random_state=42) + + # Gather all results + tasks = [ + rate_limited(lambda _row=_row: _investigate_transaction(_row), semaphore) for _, _row in transactions.iterrows() + ] + results = list(await gather_with_progress(tasks, description="Investigating transactions...")) + + # Convert results to DataFrame and save + results_df = pd.DataFrame(results) + results_path = path_to_transactions.parent / "investigation_results.csv" + results_df.to_csv(results_path, index=False) + print(f"\n✅ Completed {len(results)} investigations. Results saved to {results_path}") + + # Print summary statistics + print("\nSummary:") + print(f" Triage decisions: {results_df['triage_decision'].value_counts().to_dict()}") + print(f" Analyst verdicts: {results_df['analyst_verdict'].value_counts().to_dict()}") + print(f" Suspicious activity types: {results_df['suspicious_activity_type'].value_counts().to_dict()}") + print(f" Average agent risk score: {results_df['agent_risk_score'].dropna().mean():.2f}") + + db.close() + + +if __name__ == "__main__": + load_dotenv() + + asyncio.run(main()) diff --git a/implementations/fraud_investigation/data/prepare_db.py b/implementations/fraud_investigation/data/prepare_db.py new file mode 100644 index 0000000..2029963 --- /dev/null +++ b/implementations/fraud_investigation/data/prepare_db.py @@ -0,0 +1,376 @@ +"""Prepare and populate the fraud investigation database.""" + +import hashlib +import logging +import sqlite3 +from pathlib import Path +from typing import Literal + +import kagglehub +import numpy as np +import pandas as pd +from aieng.agent_evals.configs import Configs +from dotenv import load_dotenv + + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s") +logger = logging.getLogger(__name__) + +load_dotenv() + + +def _compute_fan_metrics(transactions: pd.DataFrame, window: str, direction: Literal["in", "out"]) -> pd.Series: + """Compute fan-in or fan-out degree over a rolling time window. + + Fan-in means many unique senders to one receiver. + Fan-out means one sender to many unique receivers. + """ + acct_col = "from_account" if direction == "out" else "to_account" + partner_col = "to_account" if direction == "out" else "from_account" + time_window = pd.Timedelta(window) + + # Sort once upfront + transactions_sorted = transactions.sort_values([acct_col, "timestamp"]).copy() + + # Vectorized: identify new counterparties + prev_ts = transactions_sorted.groupby([acct_col, partner_col], sort=False)["timestamp"].shift() + is_new = (prev_ts.isna() | ((transactions_sorted["timestamp"] - prev_ts) > time_window)).astype(int) + transactions_sorted["is_new"] = is_new + + # Vectorized rolling sum on sorted data + rolled = ( + transactions_sorted.groupby(acct_col, sort=False) + .rolling(time_window, on="timestamp", closed="left")["is_new"] + .sum() + .fillna(0) + .reset_index(level=0, drop=True) # Drop the account level from multi-index + ) + + # Map back to original index + result = pd.Series(0, index=transactions.index) + result.loc[transactions_sorted.index] = rolled.values + is_new.values + + return result.astype(int) + + +def _detect_cycles(transactions: pd.DataFrame, window: str = "10D") -> pd.Series: + """Detect 2-hop cycles within a time window. + + A cycle is defined as A->B followed by B->A within the specified time window. + """ + time_window = pd.Timedelta(window) + + # Create bidirectional edge lookup + edges = transactions[["from_account", "to_account", "timestamp"]].copy() + edges["pair_key"] = edges["from_account"] + "|" + edges["to_account"] + edges["reverse_key"] = edges["to_account"] + "|" + edges["from_account"] + + # Self-join on reverse pairs within time window + merged = pd.merge( + edges, + edges[["pair_key", "timestamp"]].rename(columns={"timestamp": "reverse_ts"}), + left_on="reverse_key", + right_on="pair_key", + how="left", + suffixes=("", "_reverse"), + ) + + # Vectorized time window check + has_cycle = ( + merged["reverse_ts"].notna() + & (merged["reverse_ts"] < merged["timestamp"]) + & (merged["reverse_ts"] > merged["timestamp"] - time_window) + ) + + # Group by original index and check if any reverse exists + result = has_cycle.groupby(merged.index).any() + return result.reindex(transactions.index, fill_value=False) + + +def _detect_stack(transactions: pd.DataFrame, window: str = "3D", similarity_threshold: float = 0.75) -> pd.Series: + """Detect stack/layering patterns. + + A stack pattern is defined as a transaction from A->B that is closely preceded + by an inbound transaction to A from another party within a time window. + In other words, A receives funds and quickly passes them on to B. + """ + inbound = transactions[["to_account", "timestamp", "amount_received", "receiving_currency"]].copy() + inbound = inbound.rename(columns={"to_account": "account"}) + + transactions_sorted = transactions.sort_values("timestamp").copy() + transactions_sorted["account"] = transactions_sorted["from_account"] + + merged = pd.merge_asof( + transactions_sorted, + inbound.sort_values("timestamp"), + left_on="timestamp", + right_on="timestamp", + by="account", + suffixes=("", "_in"), + direction="backward", + tolerance=pd.Timedelta(window), + ) + + # More forgiving similarity threshold to match ground truth + amount_diff = (merged["amount_paid"] - merged["amount_received"]).abs() + denom = merged["amount_received"].clip(lower=1) + similarity = (1 - amount_diff / denom).clip(0, 1) + + has_prior_inbound = merged["amount_received"].notna() + + result = pd.Series(False, index=transactions.index) + result.loc[merged.index] = (similarity > similarity_threshold) & has_prior_inbound + + return result + + +def _score_fan_pattern(fan_degree: pd.Series, prefix: str) -> tuple[pd.Series, pd.Series]: + """Score fan patterns using tiered thresholds. + + Lower degrees get some points, higher degrees get more points. + """ + tiers = [ + (10, 45, f"{prefix}: Max degree = 10+"), + (5, 30, f"{prefix}: Max degree = 5-9"), + (2, 15, f"{prefix}: Max degree = 2-4"), + ] + + scores = pd.Series(0, index=fan_degree.index) + evidence_strs = pd.Series("", index=fan_degree.index) + + for threshold, points, label in tiers: + mask = (fan_degree >= threshold) & (evidence_strs == "") # Only assign once + scores[mask] = points + evidence_strs[mask] = label + + return scores, evidence_strs + + +def _compute_risk_score(sorted_transactions: pd.DataFrame) -> tuple[pd.Series, pd.Series]: + """Compute risk scores using pattern detection.""" + # Ensure timestamps are real datetimes so rolling windows transactions correctly. + if not np.issubdtype(sorted_transactions["timestamp"].dtype, np.datetime64): + sorted_transactions["timestamp"] = pd.to_datetime(sorted_transactions["timestamp"]) + + score = pd.Series(0.0, index=sorted_transactions.index) + evidence = pd.Series([[] for _ in range(len(sorted_transactions))], index=sorted_transactions.index) + + # Compute all pattern detections + fan_out = _compute_fan_metrics(sorted_transactions, "3D", "out") + fan_in = _compute_fan_metrics(sorted_transactions, "3D", "in") + has_cycle = _detect_cycles(sorted_transactions, "10D") + has_stack = _detect_stack(sorted_transactions, "3D", similarity_threshold=0.8) + + # Fan-out scoring (ground truth shows 2-16 degree, most common 10-16) + fan_out_score, fan_out_evidence = _score_fan_pattern(fan_out, "FAN-OUT") + score += fan_out_score + for idx in fan_out_evidence[fan_out_evidence != ""].index: + evidence.at[idx] = evidence.at[idx] + [fan_out_evidence.at[idx]] + + # Fan-in scoring (ground truth shows 2-16 degree, most common 10-16) + fan_in_score, fan_in_evidence = _score_fan_pattern(fan_in, "FAN-IN") + score += fan_in_score + for idx in fan_in_evidence[fan_in_evidence != ""].index: + evidence.at[idx] = evidence.at[idx] + [fan_in_evidence.at[idx]] + + # Cycles (ground truth: 2-10 hops within 10 days) + score[has_cycle] += 25 + evidence[has_cycle] = evidence[has_cycle].apply(lambda x: x + ["CYCLE: Max hops >=2"]) + + # Stack/Layering (ground truth: A→B→C pass-through) + score[has_stack] += 20 + evidence[has_stack] = evidence[has_stack].apply(lambda x: x + ["STACK/LAYERING"]) + + # Gather-scatter (ground truth: hub behavior with both fan-in and fan-out) + gather_scatter = (fan_in >= 3) & (fan_out >= 3) + score[gather_scatter] += 15 + evidence[gather_scatter] = evidence[gather_scatter].apply(lambda x: x + ["GATHER-SCATTER"]) + + # Pattern combinations (multiple signals = higher confidence) + high_signal_count = ( + has_cycle.astype(int) + + has_stack.astype(int) + + (fan_in >= 5).astype(int) + + (fan_out >= 5).astype(int) + + gather_scatter.astype(int) + ) + + mask = high_signal_count == 2 + score[mask] += 20 + evidence[mask] = evidence[mask].apply(lambda x: x + ["MULTIPLE PATTERNS: 2 high-risk signals"]) + + mask = high_signal_count >= 3 + score[mask] += 35 + evidence[mask] = evidence[mask].apply(lambda x: x + ["MULTIPLE PATTERNS: 3+ high-risk signals"]) + + triggered_rules: pd.Series = evidence.apply(lambda x: ",".join(x) if len(x) > 0 else "") + return score.clip(0, 100), triggered_rules + + +def _process_transaction_data(file_path: str) -> pd.DataFrame: + """Process transaction data and populate the database.""" + transc_df = pd.read_csv(file_path, dtype_backend="pyarrow") + transc_df.rename( + columns={ + "Timestamp": "timestamp", + "From Bank": "from_bank", + "Account": "from_account", + "To Bank": "to_bank", + "Account.1": "to_account", + "Amount Received": "amount_received", + "Receiving Currency": "receiving_currency", + "Amount Paid": "amount_paid", + "Payment Currency": "payment_currency", + "Payment Format": "payment_format", + "Is Laundering": "is_laundering", + }, + inplace=True, + ) + transc_df.drop_duplicates(inplace=True) + + # Convert Timestamp column to ISO 8601 format + transc_df["timestamp"] = pd.to_datetime(transc_df["timestamp"]).dt.strftime("%Y-%m-%dT%H:%M:%S") + + # Add new columns: date, day_of_week, time_of_day + transc_df["date"] = pd.to_datetime(transc_df["timestamp"]).dt.date + transc_df["day_of_week"] = pd.to_datetime(transc_df["timestamp"]).dt.day_name() + transc_df["time_of_day"] = pd.to_datetime(transc_df["timestamp"]).dt.time + + # Create Transaction ID by hashing row content, excluding label + # Concatenate values of desired columns into a string + cols = [ + "timestamp", + "time_of_day", + "from_bank", + "from_account", + "to_bank", + "to_account", + "amount_received", + "receiving_currency", + "amount_paid", + "payment_currency", + "payment_format", + ] + + txn_str = transc_df[cols].astype("string").agg("|".join, axis=1) + transc_df["transaction_id"] = txn_str.map(lambda x: hashlib.sha256(x.encode()).hexdigest()[:16]) + + transc_df_sorted = transc_df.copy() + + transc_df_sorted["timestamp"] = pd.to_datetime(transc_df_sorted["timestamp"]) + transc_df_sorted = transc_df_sorted.sort_values("timestamp").reset_index(drop=True) + + pattern_scores, pattern_evidence = _compute_risk_score(transc_df_sorted) + transc_df_sorted.loc[: len(pattern_scores) - 1, "risk_score"] = pattern_scores + transc_df_sorted.loc[: len(pattern_scores) - 1, "triggered_rules"] = pattern_evidence + + # Set Transaction ID as index + transc_df_sorted.set_index("transaction_id", drop=True, inplace=True) + + return transc_df_sorted + + +def _curate_test_cases(df: pd.DataFrame) -> pd.DataFrame: + """Curate a test set with diverse fraud and legitimate cases.""" + test_cases = [] + + fraud = df[df["is_laundering"] == 1] + legit = df[df["is_laundering"] == 0] + + # All distinct fraud patterns (at least 2 examples each) + for evidence_pattern in fraud["triggered_rules"].value_counts().index: + cases = fraud[fraud["triggered_rules"] == evidence_pattern].sample( + min(2, len(fraud[fraud["triggered_rules"] == evidence_pattern])), random_state=42 + ) + test_cases.append(cases) + + # Use percentiles to define thresholds + high_risk_threshold = fraud["risk_score"].quantile(0.75) + low_risk_threshold = legit["risk_score"].quantile(0.25) + normal_threshold = legit["risk_score"].quantile(0.5) + + # High-score false positives + false_positives = df[(df["is_laundering"] == 0) & (df["risk_score"] >= high_risk_threshold)] + test_cases.append(false_positives.sample(min(50, len(false_positives)), random_state=42)) + + # Low-score true positives + false_negatives = df[(df["is_laundering"] == 1) & (df["risk_score"] < low_risk_threshold)] + if len(false_negatives) > 0: + test_cases.append(false_negatives) # Include all + + # 4. Representative sample of normal transactions + normal = df[(df["is_laundering"] == 0) & (df["risk_score"] < normal_threshold)] + test_cases.append(normal.sample(min(100, len(normal)), random_state=42)) + + # 5. Edge cases: single-transaction fraud attempts + single_txn_fraud = df[df["is_laundering"] == 1].groupby("from_account").filter(lambda x: len(x) == 1) + if len(single_txn_fraud) > 0: + test_cases.append(single_txn_fraud.sample(min(10, len(single_txn_fraud)), random_state=42)) + + return pd.concat(test_cases).drop_duplicates().sort_values("timestamp") + + +def main() -> None: + """Prepare and populate the fraud investigation database.""" + configs = Configs() + if configs.aml_db is None: + raise ValueError("AML database path is not configured.") + + db_path = Path(configs.aml_db.database or "./aml_prod.db").resolve() + ddl_file_path = Path("implementations/fraud_investigation/data/schema.ddl") + + # Download datasets from Kaggle + path_to_transc_csv = kagglehub.dataset_download( + handle="ealtman2019/ibm-transactions-for-anti-money-laundering-aml", + path="HI-Small_Trans.csv", + ) + path_to_accts_csv = kagglehub.dataset_download( + handle="ealtman2019/ibm-transactions-for-anti-money-laundering-aml", + path="HI-Small_accounts.csv", + ) + logger.info(f"Downloaded transaction data to {path_to_transc_csv}") + logger.info(f"Downloaded account data to {path_to_accts_csv}") + + conn = sqlite3.connect(db_path) + conn.execute("PRAGMA foreign_keys = ON;") + + with open(ddl_file_path, "r") as file: + conn.executescript(file.read()) + conn.commit() + + accts_df = pd.read_csv(path_to_accts_csv, dtype_backend="pyarrow") + + # Rename duplicate 'Account' columns to distinguish sender and receiver + accts_df.rename( + columns={ + "Bank Name": "bank_name", + "Bank ID": "bank_id", + "Account Number": "account_number", + "Entity ID": "entity_id", + "Entity Name": "entity_name", + }, + inplace=True, + ) + + accts_df.to_sql("accounts", conn, if_exists="append", index=False) + logger.info("✅ Populated accounts table.") + + # Create the transactions table + logger.info("Processing transaction data...") + transc_df = _process_transaction_data(path_to_transc_csv) + + # Drop the "is_laundering" column and insert into the database + transc_df.drop(columns=["is_laundering", "triggered_rules"]).to_sql("transactions", conn, if_exists="append") + logger.info("✅ Populated transactions table.") + logger.info(f"✅ Populated database at {db_path}") + + # Split out a test set with the last 3 days of transactions + test_df = _curate_test_cases(transc_df) + test_df.to_csv(ddl_file_path.parent / "test_transactions.csv") + logger.info(f"✅ Created curated test set with {len(test_df)} cases.") + + conn.close() + + +if __name__ == "__main__": + main() diff --git a/implementations/fraud_investigation/data/schema.ddl b/implementations/fraud_investigation/data/schema.ddl new file mode 100644 index 0000000..486e5d5 --- /dev/null +++ b/implementations/fraud_investigation/data/schema.ddl @@ -0,0 +1,42 @@ +DROP TABLE IF EXISTS "accounts"; +CREATE TABLE "accounts" ( + "bank_name" TEXT, + "bank_id" INTEGER, + "account_number" TEXT, + "entity_id" TEXT, + "entity_name" TEXT, + PRIMARY KEY ("bank_id", "account_number") +); + +DROP TABLE IF EXISTS "transactions"; +CREATE TABLE "transactions" ( + "transaction_id" TEXT PRIMARY KEY, + "timestamp" Text, + "date" TEXT, + "day_of_week" TEXT, + "time_of_day" TEXT, + "from_bank" INTEGER, + "from_account" TEXT, + "to_bank" INTEGER, + "to_account" TEXT, + "amount_received" REAL, + "receiving_currency" TEXT, + "amount_paid" REAL, + "payment_currency" TEXT, + "payment_format" TEXT, + "risk_score" REAL, + FOREIGN KEY ("from_bank", "from_account") + REFERENCES "accounts" ("bank_id", "account_number"), + FOREIGN KEY ("to_bank", "to_account") + REFERENCES "accounts" ("bank_id", "account_number") +); + +DROP VIEW IF EXISTS "v_unified_transactions"; +CREATE VIEW "v_unified_transactions" AS +SELECT + t.*, + -- Create a single unique ID for the sender + t.from_bank || '_' || t.from_account as from_uid, + -- Create a single unique ID for the receiver + t.to_bank || '_' || t.to_account as to_uid +FROM transactions t; diff --git a/pyproject.toml b/pyproject.toml index ef71b33..34db2e1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,13 +23,13 @@ dependencies = [ "pydantic>=2.12.4", "pydantic-ai-slim[logfire]>=1.26.0", "scikit-learn>=1.7.0", - "weaviate-client>=4.18.3", "urllib3>=2.6.3", "openpyxl>=3.1.5", "authlib>=1.6.6", "filelock>=3.20.3", "pyasn1>=0.6.2", "virtualenv>=20.36.1", + "google-adk>=1.23.0", ] [dependency-groups] diff --git a/uv.lock b/uv.lock index 81256e9..f9dc84e 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 3 requires-python = ">=3.12, <4.0" resolution-markers = [ "python_full_version >= '3.14'", @@ -25,6 +25,7 @@ dependencies = [ { name = "datasets" }, { name = "e2b-code-interpreter" }, { name = "filelock" }, + { name = "google-adk" }, { name = "gradio" }, { name = "langfuse" }, { name = "lxml" }, @@ -40,7 +41,6 @@ dependencies = [ { name = "scikit-learn" }, { name = "urllib3" }, { name = "virtualenv" }, - { name = "weaviate-client" }, ] [package.dev-dependencies] @@ -88,6 +88,7 @@ requires-dist = [ { name = "datasets", specifier = ">=3.6.0" }, { name = "e2b-code-interpreter", specifier = ">=2.4.1" }, { name = "filelock", specifier = ">=3.20.3" }, + { name = "google-adk", specifier = ">=1.23.0" }, { name = "gradio", specifier = ">=6.0.2" }, { name = "langfuse", specifier = ">=3.10.3" }, { name = "lxml", specifier = ">=6.0.0" }, @@ -103,7 +104,6 @@ requires-dist = [ { name = "scikit-learn", specifier = ">=1.7.0" }, { name = "urllib3", specifier = ">=2.6.3" }, { name = "virtualenv", specifier = ">=20.36.1" }, - { name = "weaviate-client", specifier = ">=4.18.3" }, ] [package.metadata.requires-dev] @@ -148,6 +148,9 @@ version = "0.1.0" source = { editable = "aieng-eval-agents" } dependencies = [ { name = "kagglehub" }, + { name = "sqlalchemy" }, + { name = "sqlglot" }, + { name = "weaviate-client" }, ] [package.dev-dependencies] @@ -156,7 +159,12 @@ dev = [ ] 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size = 10276, upload-time = "2025-06-08T17:06:38.034Z" }, ] From edc42d161635884d179e68bca0d0ab809da2e9b8 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 09:14:57 -0500 Subject: [PATCH 02/24] Add simpler AML investigation agent --- .env.example | 2 +- .../__init__.py | 2 +- .../aml_investigation/data/__init__.py | 16 + .../aml_investigation/data/cases.py | 469 ++++++++++++++++++ .../aml_investigation/data/utils.py | 245 +++++++++ .../policy.py | 0 .../types.py | 0 .../workflow.py | 4 +- .../aieng/agent_evals/impl/__init__.py | 6 - .../aieng/agent_evals/impl/example_impl.py | 6 - .../aieng/agent_evals/tools/sql_database.py | 54 +- .../README.md | 0 implementations/aml_investigation/agent.py | 276 +++++++++++ implementations/aml_investigation/data/cli.py | 295 +++++++++++ .../data/schema.ddl | 36 +- implementations/fraud_investigation/agent.py | 258 ---------- .../fraud_investigation/data/prepare_db.py | 376 -------------- pyproject.toml | 7 + 18 files changed, 1379 insertions(+), 673 deletions(-) rename aieng-eval-agents/aieng/agent_evals/{fraud_investigation => aml_investigation}/__init__.py (89%) create mode 100644 aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py create mode 100644 aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py create mode 100644 aieng-eval-agents/aieng/agent_evals/aml_investigation/data/utils.py rename aieng-eval-agents/aieng/agent_evals/{fraud_investigation => aml_investigation}/policy.py (100%) rename aieng-eval-agents/aieng/agent_evals/{fraud_investigation => aml_investigation}/types.py (100%) rename aieng-eval-agents/aieng/agent_evals/{fraud_investigation => aml_investigation}/workflow.py (98%) delete mode 100644 aieng-eval-agents/aieng/agent_evals/impl/__init__.py delete mode 100644 aieng-eval-agents/aieng/agent_evals/impl/example_impl.py rename implementations/{fraud_investigation => aml_investigation}/README.md (100%) create mode 100644 implementations/aml_investigation/agent.py create mode 100644 implementations/aml_investigation/data/cli.py rename implementations/{fraud_investigation => aml_investigation}/data/schema.ddl (58%) delete mode 100644 implementations/fraud_investigation/agent.py delete mode 100644 implementations/fraud_investigation/data/prepare_db.py diff --git a/.env.example b/.env.example index c03ae0c..f83f055 100644 --- a/.env.example +++ b/.env.example @@ -2,5 +2,5 @@ GOOGLE_API_KEY="..." # AML Database Configuration AML_DB__DRIVER="sqlite" -AML_DB__DATABASE="implementations/fraud_investigation/data/aml_prod.db" +AML_DB__DATABASE="implementations/aml_investigation/data/aml_transactions.db" AML_DB__QUERY__MODE="ro" diff --git a/aieng-eval-agents/aieng/agent_evals/fraud_investigation/__init__.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py similarity index 89% rename from aieng-eval-agents/aieng/agent_evals/fraud_investigation/__init__.py rename to aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py index 7836e6e..5addd92 100644 --- a/aieng-eval-agents/aieng/agent_evals/fraud_investigation/__init__.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py @@ -1,4 +1,4 @@ -"""Utilities for the Fraud Investigation agent.""" +"""Utilities for the AML Investigation agent.""" from .policy import RuleBasedTriage, TriageOutcome, TriagePolicyConfig, TriageStrategy from .types import AnalystResult, AnalystVerdict, SuspiciousActivityType diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py new file mode 100644 index 0000000..ded4965 --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py @@ -0,0 +1,16 @@ +from .cases import AnalystOutput, CaseFile, CaseRecord, GroundTruth, LaunderingPattern, build_cases, parse_patterns_file +from .utils import apply_lookback_window, download_dataset_file, normalize_transactions_data + + +__all__ = [ + "AnalystOutput", + "CaseFile", + "CaseRecord", + "LaunderingPattern", + "GroundTruth", + "apply_lookback_window", + "build_cases", + "parse_patterns_file", + "download_dataset_file", + "normalize_transactions_data", +] diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py new file mode 100644 index 0000000..2c3ff12 --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py @@ -0,0 +1,469 @@ +"""Case generation and parsing utilities for AML evaluation data. + +This module parses laundering patterns, builds case records, and provides +Pydantic models used by downstream evaluation and agent inputs. +""" + +import csv +import json +import logging +import random +from datetime import datetime +from enum import Enum +from pathlib import Path +from typing import Any + +import pandas as pd +from pydantic import BaseModel + +from .utils import ( + _canonicalize_numeric, + _canonicalize_text, + _canonicalize_timestamp, + _create_id, + _parse_timestamp, + apply_lookback_window, +) + + +logger = logging.getLogger(__name__) + +__all__ = [ + "LaunderingPattern", + "GroundTruth", + "CaseFile", + "CaseRecord", + "parse_patterns_file", + "build_cases", +] + +_TRANSACTION_ID_COLUMNS = [ + "timestamp", + "from_bank", + "from_account", + "to_bank", + "to_account", + "amount_received", + "receiving_currency", + "amount_paid", + "payment_currency", + "payment_format", +] +_REQUIRED_TRANSACTION_COLUMNS = {"timestamp", "transaction_id", "is_laundering", "from_account"} + + +class LaunderingPattern(str, Enum): + """Enumeration of laundering pattern types.""" + + FAN_IN = "FAN-IN" + FAN_OUT = "FAN-OUT" + CYCLE = "CYCLE" + GATHER_SCATTER = "GATHER-SCATTER" + SCATTER_GATHER = "SCATTER-GATHER" + STACK = "STACK" + RANDOM = "RANDOM" + BIPARTITE = "BIPARTITE" + NONE = "NONE" + + +_RANDOM_PATTERN_TYPES = [pattern.value for pattern in LaunderingPattern if pattern not in {LaunderingPattern.NONE}] + + +class CaseFile(BaseModel): + """Metadata for a laundering case file.""" + + case_id: str + """Unique identifier for the case.""" + seed_transaction_id: str + """The transaction ID that seeded the laundering attempt. This is typically the last + transaction in the pattern.""" + seed_timestamp: str + """The timestamp of the seed transaction.""" + window_start: str + """The start timestamp of the case window.""" + suspected_pattern_type: str + """The type of laundering pattern suspected in the case.""" + + +class GroundTruth(BaseModel): + """Ground truth information for a laundering case.""" + + is_laundering: bool + """Whether the case involves money laundering.""" + pattern_type: LaunderingPattern + """The type of laundering pattern in the case.""" + pattern_description: str + """A short description of the laundering pattern.""" + attempt_transaction_ids: str + """Comma-separated list of transaction IDs involved in the laundering attempt.""" + + +class AnalystOutput(BaseModel): + """Analyst notes for a laundering case.""" + + summary_narrative: str + """Analyst's reasoning/evidence summary.""" + is_laundering: bool + """Whether the case involves money laundering.""" + pattern_type: LaunderingPattern + """The type of laundering pattern in the case.""" + pattern_description: str + """A short description of the laundering pattern.""" + flagged_transaction_ids: list[str] + """A comma-separeted list of transaction IDs flagged by the analyst as suspicious. + """ + + +class CaseRecord(BaseModel): + """Combined case file and ground truth record.""" + + case: CaseFile + """Metadata for the laundering case.""" + groundtruth: GroundTruth + """Ground truth information for the laundering case.""" + analysis: AnalystOutput | None = None + """Optional analyst output for the laundering case. Typically populated after \ + investigation.""" + + +def parse_patterns_file(path: str | Path, lookback_days: int = 0, min_timestamp: str | None = None) -> list[CaseRecord]: + """Parse laundering pattern attempts from a patterns file. + + Parameters + ---------- + path : str | Path + Path to the patterns file. + lookback_days : int, default=0 + Number of days to look back from the first transaction timestamp to + compute the case window start. + min_timestamp : str | None, default=None + Minimum timestamp boundary (ISO 8601 or dataset format). If provided, + the window start will not precede this timestamp. + + Returns + ------- + list[CaseRecord] + Parsed laundering cases with seed metadata and ground truth. + + Raises + ------ + FileNotFoundError + If the patterns file does not exist. + ValueError + If the file contains malformed transaction rows or an unterminated block. + """ + if lookback_days < 0: + raise ValueError("lookback_days must be >= 0") + + patterns_path = Path(path) + if not patterns_path.exists(): + raise FileNotFoundError(f"Patterns file not found: {patterns_path}") + if min_timestamp: + _parse_timestamp(min_timestamp) + + cases: list[CaseRecord] = [] + current: dict[str, Any] | None = None + begin_prefix = "BEGIN LAUNDERING ATTEMPT - " + end_prefix = "END LAUNDERING ATTEMPT" + + with patterns_path.open(newline="") as file: + for line_number, raw_line in enumerate(file, start=1): + line = raw_line.strip() + if not line: + continue + + if line.startswith(begin_prefix): + header = line[len(begin_prefix) :] + pattern_type, pattern_description = _parse_pattern_header(header) + current = { + "pattern_type": pattern_type, + "pattern_description": pattern_description, + "transactions": [], + } + continue + + if line.startswith(end_prefix): + if current and current["transactions"]: + txns: list[dict[str, str]] = current["transactions"] + txns_sorted = sorted(txns, key=lambda t: _parse_timestamp(t["timestamp"])) + seed_txn = txns_sorted[-1] + window_start = apply_lookback_window( + txns_sorted[0]["timestamp"], lookback_days, min_timestamp=min_timestamp + ) + attempt_ids = _serialize_attempt_ids([txn["transaction_id"] for txn in txns_sorted]) + case_file = CaseFile( + case_id=_create_id(json.dumps(txns_sorted)), + seed_transaction_id=seed_txn["transaction_id"], + seed_timestamp=seed_txn["timestamp"], + window_start=window_start, + suspected_pattern_type=current["pattern_type"], + ) + groundtruth = GroundTruth( + is_laundering=True, + pattern_type=current["pattern_type"], + pattern_description=current["pattern_description"], + attempt_transaction_ids=attempt_ids, + ) + cases.append(CaseRecord(case=case_file, groundtruth=groundtruth)) + current = None + continue + + if current: + row = next(csv.reader([line])) + if len(row) < 11: + raise ValueError(f"Malformed transaction row at line {line_number}: {line}") + + txn = { + "timestamp": _canonicalize_timestamp(row[0]), + "from_bank": _canonicalize_numeric(row[1]), + "from_account": _canonicalize_text(row[2]), + "to_bank": _canonicalize_numeric(row[3]), + "to_account": _canonicalize_text(row[4]), + "amount_received": _canonicalize_numeric(row[5]), + "receiving_currency": _canonicalize_text(row[6]), + "amount_paid": _canonicalize_numeric(row[7]), + "payment_currency": _canonicalize_text(row[8]), + "payment_format": _canonicalize_text(row[9]), + } + txn_str = "|".join(txn[col] for col in _TRANSACTION_ID_COLUMNS) + txn["transaction_id"] = _create_id(txn_str) + current["transactions"].append(txn) + + if current is not None: + raise ValueError("Unterminated laundering attempt block in patterns file.") + + return cases + + +def build_cases( + patterns_filepath: str | Path, + transactions: pd.DataFrame, + num_laundering_cases: int, + num_false_positive_cases: int, + num_false_negative_cases: int, + num_normal_cases: int, + lookback_days: int, +) -> list[CaseRecord]: + """Build case files from laundering patterns and a transactions dataset. + + Parameters + ---------- + patterns_filepath : str | Path + Path to the patterns file. + transactions : pandas.DataFrame + Normalized transactions dataframe. Must include the following columns: + ``timestamp``, ``transaction_id``, ``is_laundering``, and ``from_account``. + num_laundering_cases : int + Number of laundering cases to sample from known pattern attempts. + num_false_positive_cases : int + Number of false positive cases to generate from benign activity. + num_false_negative_cases : int + Number of false negative cases to generate from laundering activity. + num_normal_cases : int + Number of normal (benign) cases to sample. + lookback_days : int + Number of days to look back for case window start. + + Returns + ------- + list[CaseRecord] + Combined list of case records. + + Raises + ------ + ValueError + If required columns are missing or numeric arguments are negative. + TypeError + If ``transactions`` is not a pandas DataFrame. + FileNotFoundError + If ``patterns_filepath`` does not exist. + """ + if not isinstance(transactions, pd.DataFrame): + raise TypeError("transactions must be a pandas DataFrame") + if lookback_days < 0: + raise ValueError("lookback_days must be >= 0") + for name, value in [ + ("num_laundering_cases", num_laundering_cases), + ("num_false_positive_cases", num_false_positive_cases), + ("num_false_negative_cases", num_false_negative_cases), + ("num_normal_cases", num_normal_cases), + ]: + if value < 0: + raise ValueError(f"{name} must be >= 0") + + missing_columns = _REQUIRED_TRANSACTION_COLUMNS - set(transactions.columns) + if missing_columns: + raise ValueError(f"transactions is missing required columns: {sorted(missing_columns)}") + + min_timestamp = transactions["timestamp"].min() if not transactions.empty else None + rng = random.Random(42) + + all_attempts = parse_patterns_file(patterns_filepath, lookback_days=lookback_days, min_timestamp=min_timestamp) + rng.shuffle(all_attempts) + laundering_cases = all_attempts[:num_laundering_cases] + remaining_attempts = all_attempts[num_laundering_cases:] + + laundering_attempt_txn_ids: set[str] = set() + for case in laundering_cases: + laundering_attempt_txn_ids.update(_deserialize_attempt_ids(case.groundtruth.attempt_transaction_ids)) + + false_negative_cases = _build_false_negative_cases( + remaining_attempts, num_false_negative_cases, laundering_attempt_txn_ids + ) + + false_positive_cases = _build_false_positive_cases(transactions, num_false_positive_cases) + + fp_seed_ids = {case.case.seed_transaction_id for case in false_positive_cases} + normal_cases = _build_normal_cases(transactions, num_normal_cases, fp_seed_ids, lookback_days, min_timestamp) + + return laundering_cases + false_negative_cases + false_positive_cases + normal_cases + + +def _build_false_negative_cases( + remaining_attempts: list[CaseRecord], num_false_negative_cases: int, laundering_attempt_txn_ids: set[str] +) -> list[CaseRecord]: + """Create false negative cases from remaining attempts or sampled transactions.""" + false_negative_cases: list[CaseRecord] = [] + fn_attempts = remaining_attempts[:num_false_negative_cases] + for case in fn_attempts: + if case.case.seed_transaction_id in laundering_attempt_txn_ids: + continue + case_file = CaseFile( + case_id=_create_id(f"fn:{case.case.case_id}"), + seed_transaction_id=case.case.seed_transaction_id, + seed_timestamp=case.case.seed_timestamp, + window_start=case.case.window_start, + suspected_pattern_type="UNKNOWN", + ) + groundtruth = GroundTruth( + is_laundering=case.groundtruth.is_laundering, + pattern_type=case.groundtruth.pattern_type, + pattern_description=case.groundtruth.pattern_description, + attempt_transaction_ids=case.groundtruth.attempt_transaction_ids, + ) + false_negative_cases.append(CaseRecord(case=case_file, groundtruth=groundtruth)) + + if len(false_negative_cases) < num_false_negative_cases: + logger.warning( + "Only %d false negative cases could be built; requested %d.", + len(false_negative_cases), + num_false_negative_cases, + ) + + return false_negative_cases + + +def _build_false_positive_cases(transc_df: pd.DataFrame, num_false_positive_cases: int) -> list[CaseRecord]: + """Create false positive cases from benign transaction bursts.""" + false_positive_cases: list[CaseRecord] = [] + if num_false_positive_cases <= 0: + return false_positive_cases + + benign_df = transc_df[transc_df["is_laundering"] == 0].copy() + if benign_df.empty: + return false_positive_cases + + benign_df["date"] = pd.to_datetime(benign_df["timestamp"]).dt.date + window_stats = ( + benign_df.groupby(["from_account", "date"], as_index=False) + .size() + .rename(columns={"size": "txn_count"}) + .sort_values(["txn_count", "date"], ascending=[False, True]) + ) + top_windows = window_stats.head(num_false_positive_cases) + for _, window in top_windows.iterrows(): + window_txns = benign_df[ + (benign_df["from_account"] == window["from_account"]) & (benign_df["date"] == window["date"]) + ].sort_values("timestamp") + if window_txns.empty: + continue + seed_row = window_txns.iloc[-1] + case_file = CaseFile( + case_id=_create_id(f"{seed_row['transaction_id']}|false_positive"), + seed_transaction_id=seed_row["transaction_id"], + seed_timestamp=seed_row["timestamp"], + window_start=_date_window_start(window["date"]), + suspected_pattern_type=random.choice(_RANDOM_PATTERN_TYPES), + ) + groundtruth = GroundTruth( + is_laundering=False, + pattern_type=LaunderingPattern.NONE, + pattern_description="Normal transaction", + attempt_transaction_ids="", + ) + false_positive_cases.append(CaseRecord(case=case_file, groundtruth=groundtruth)) + + return false_positive_cases + + +def _build_normal_cases( + transc_df: pd.DataFrame, + num_normal_cases: int, + fp_seed_ids: set[str], + lookback_days: int, + min_timestamp: str | None, +) -> list[CaseRecord]: + """Create normal (benign) cases from the transaction pool.""" + normal_cases: list[CaseRecord] = [] + if num_normal_cases <= 0: + return normal_cases + + normal_pool = transc_df[(transc_df["is_laundering"] == 0) & (~transc_df["transaction_id"].isin(fp_seed_ids))] + if normal_pool.empty: + return normal_cases + + sample_count = min(num_normal_cases, len(normal_pool)) + normal_txns = normal_pool.sample(n=sample_count, random_state=42) + for _, row in normal_txns.iterrows(): + window_start = apply_lookback_window(row["timestamp"], lookback_days, min_timestamp=min_timestamp) + case_file = CaseFile( + case_id=_create_id(row.drop(labels=["transaction_id", "is_laundering"]).to_json()), + seed_transaction_id=row["transaction_id"], + seed_timestamp=row["timestamp"], + window_start=window_start, + suspected_pattern_type=LaunderingPattern.NONE.value, + ) + groundtruth = GroundTruth( + is_laundering=False, + pattern_type=LaunderingPattern.NONE, + pattern_description="Normal transaction", + attempt_transaction_ids="", + ) + normal_cases.append(CaseRecord(case=case_file, groundtruth=groundtruth)) + + return normal_cases + + +def _parse_pattern_header(header: str) -> tuple[str, str]: + """Parse a pattern header line into type and description.""" + cleaned = header.strip() + if ":" in cleaned: + pattern_type, description = cleaned.split(":", 1) + pattern_type = pattern_type.strip() + description = description.strip() or "N/A" + else: + pattern_type = cleaned + description = "N/A" + return pattern_type, description + + +def _date_window_start(date_value: Any) -> str: + """Return an ISO 8601 window start string for a given date value.""" + if isinstance(date_value, datetime): + date_value = date_value.date() + if hasattr(date_value, "isoformat"): + return f"{date_value.isoformat()}T00:00:00" + parsed = _parse_timestamp(str(date_value)) + return parsed.strftime("%Y-%m-%dT00:00:00") + + +def _serialize_attempt_ids(ids: list[str]) -> str: + """Serialize attempt transaction IDs as a comma-separated string.""" + return ",".join(ids) + + +def _deserialize_attempt_ids(value: str) -> list[str]: + """Parse a comma-separated attempt transaction ID string.""" + if not value: + return [] + return [item.strip() for item in value.split(",") if item.strip()] diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/utils.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/utils.py new file mode 100644 index 0000000..dbe8212 --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/utils.py @@ -0,0 +1,245 @@ +"""Shared utilities for AML dataset preparation and normalization.""" + +import hashlib +import math +from datetime import datetime, timedelta +from decimal import Decimal, InvalidOperation +from typing import Any, Literal + +import kagglehub +import pandas as pd + + +__all__ = ["download_dataset_file", "normalize_transactions_data", "apply_lookback_window"] + +_TRANSACTION_ID_TEXT_COLUMNS = [ + "from_account", + "to_account", + "receiving_currency", + "payment_currency", + "payment_format", +] + + +def download_dataset_file( + illicit_ratio: Literal["HI", "LI"] = "HI", + transactions_size: Literal["Small", "Medium", "Large"] = "Small", + filename: Literal["Trans.csv", "accounts.csv", "Patterns.txt"] = "Trans.csv", +) -> str: + """Download a specific file from the Kaggle AML dataset. + + Parameters + ---------- + illicit_ratio : {"HI", "LI"}, default="HI" + The illicit transaction ratio. + transactions_size : {"Small", "Medium", "Large"}, default="Small" + The size of the transactions dataset. + filename : {"Trans.csv", "accounts.csv", "Patterns.txt"}, default="Trans.csv" + The name of the file to download. + + Returns + ------- + str + Local path to the downloaded file (from Kaggle cache). + + Raises + ------ + ValueError + If any option is not a supported value. + """ + valid_ratio = {"HI", "LI"} + valid_size = {"Small", "Medium", "Large"} + valid_filename = {"Trans.csv", "accounts.csv", "Patterns.txt"} + if illicit_ratio not in valid_ratio: + raise ValueError(f"illicit_ratio must be one of {sorted(valid_ratio)}") + if transactions_size not in valid_size: + raise ValueError(f"transactions_size must be one of {sorted(valid_size)}") + if filename not in valid_filename: + raise ValueError(f"filename must be one of {sorted(valid_filename)}") + + dataset_path = f"{illicit_ratio}-{transactions_size}_{filename}" + return kagglehub.dataset_download( + handle="ealtman2019/ibm-transactions-for-anti-money-laundering-aml", path=dataset_path + ) + + +def normalize_transactions_data(transc_df: pd.DataFrame) -> pd.DataFrame: + """Normalize and enrich the raw transactions dataframe. + + This function mutates the input dataframe in place. + + Parameters + ---------- + transc_df : pandas.DataFrame + Raw transactions dataframe with the original Kaggle column names. + + Returns + ------- + pandas.DataFrame + Normalized dataframe with canonical column names, a generated + ``transaction_id``, and sorted by timestamp. + + Raises + ------ + ValueError + If required columns are missing. + """ + required_columns = { + "Timestamp", + "From Bank", + "Account", + "To Bank", + "Account.1", + "Amount Received", + "Receiving Currency", + "Amount Paid", + "Payment Currency", + "Payment Format", + "Is Laundering", + } + missing = required_columns - set(transc_df.columns) + if missing: + raise ValueError(f"transc_df is missing required columns: {sorted(missing)}") + + transc_df.rename( + columns={ + "Timestamp": "timestamp", + "From Bank": "from_bank", + "Account": "from_account", + "To Bank": "to_bank", + "Account.1": "to_account", + "Amount Received": "amount_received", + "Receiving Currency": "receiving_currency", + "Amount Paid": "amount_paid", + "Payment Currency": "payment_currency", + "Payment Format": "payment_format", + "Is Laundering": "is_laundering", + }, + inplace=True, + ) + # Canonicalize fields used for transaction_id hashing so all sources match + transc_df["timestamp"] = pd.to_datetime(transc_df["timestamp"]).dt.strftime("%Y-%m-%dT%H:%M:%S") + for col in _TRANSACTION_ID_TEXT_COLUMNS: + transc_df[col] = transc_df[col].map(_canonicalize_text) + + txn_str = ( + transc_df["timestamp"] + + "|" + + transc_df["from_bank"].map(_canonicalize_numeric) + + "|" + + transc_df["from_account"] + + "|" + + transc_df["to_bank"].map(_canonicalize_numeric) + + "|" + + transc_df["to_account"] + + "|" + + transc_df["amount_received"].map(_canonicalize_numeric) + + "|" + + transc_df["receiving_currency"] + + "|" + + transc_df["amount_paid"].map(_canonicalize_numeric) + + "|" + + transc_df["payment_currency"] + + "|" + + transc_df["payment_format"] + ) + transc_df["transaction_id"] = txn_str.map(_create_id) + transc_df.drop_duplicates(subset=["transaction_id"], inplace=True) + + # Sort by timestamp + return transc_df.sort_values("timestamp").reset_index(drop=True) + + +def apply_lookback_window(base_timestamp: str, lookback_days: int, min_timestamp: str | None = None) -> str: + """Compute the case window start timestamp. + + Parameters + ---------- + base_timestamp : str + Seed transaction timestamp (ISO 8601 or dataset format). + lookback_days : int + Number of days to look back from the base timestamp. + min_timestamp : str | None, default=None + Minimum timestamp boundary (ISO 8601 or dataset format). + + Returns + ------- + str + Window start timestamp in ISO 8601 format. + + Raises + ------ + ValueError + If lookback_days is negative or timestamps are invalid. + """ + if lookback_days < 0: + raise ValueError("lookback_days must be >= 0") + if not base_timestamp: + raise ValueError("base_timestamp must be a non-empty string") + + base_dt = _parse_timestamp(base_timestamp) + window_start = base_dt - timedelta(days=lookback_days) if lookback_days > 0 else base_dt + + if min_timestamp: + min_dt = _parse_timestamp(min_timestamp) + window_start = max(window_start, min_dt) + + return window_start.strftime("%Y-%m-%dT%H:%M:%S") + + +def _create_id(serialized_data: str) -> str: + """Create a unique ID by hashing the serialized data.""" + return hashlib.sha256(serialized_data.encode()).hexdigest()[:16] + + +def _canonicalize_text(value: Any) -> str: + """Normalize text fields for stable hashing.""" + if pd.isna(value): + return "" + return str(value).strip() + + +def _canonicalize_numeric(value: Any) -> str: + """Normalize numeric fields into a consistent string form.""" + if value is None or (isinstance(value, float) and math.isnan(value)) or pd.isna(value): + return "" + + if isinstance(value, str): + cleaned = value.strip() + if not cleaned: + return "" + try: + dec = Decimal(cleaned) + except InvalidOperation: + return cleaned + else: + try: + dec = Decimal(str(value)) + except InvalidOperation: + return str(value) + + if dec.is_zero(): + return "0" + normalized = dec.normalize() + formatted = format(normalized, "f") + if "." in formatted: + formatted = formatted.rstrip("0").rstrip(".") + return formatted + + +def _canonicalize_timestamp(value: Any) -> str: + """Normalize timestamps into ISO 8601 strings.""" + if pd.isna(value): + return "" + parsed = value if isinstance(value, datetime) else _parse_timestamp(str(value).strip()) + return parsed.strftime("%Y-%m-%dT%H:%M:%S") + + +def _parse_timestamp(value: str) -> datetime: + """Parse a timestamp from known dataset formats.""" + for fmt in ("%Y/%m/%d %H:%M", "%Y/%m/%d %H:%M:%S", "%Y-%m-%d %H:%M:%S", "%Y-%m-%dT%H:%M:%S"): + try: + return datetime.strptime(value, fmt) + except ValueError: + continue + return datetime.fromisoformat(value) diff --git a/aieng-eval-agents/aieng/agent_evals/fraud_investigation/policy.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/policy.py similarity index 100% rename from aieng-eval-agents/aieng/agent_evals/fraud_investigation/policy.py rename to aieng-eval-agents/aieng/agent_evals/aml_investigation/policy.py diff --git a/aieng-eval-agents/aieng/agent_evals/fraud_investigation/types.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/types.py similarity index 100% rename from aieng-eval-agents/aieng/agent_evals/fraud_investigation/types.py rename to aieng-eval-agents/aieng/agent_evals/aml_investigation/types.py diff --git a/aieng-eval-agents/aieng/agent_evals/fraud_investigation/workflow.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/workflow.py similarity index 98% rename from aieng-eval-agents/aieng/agent_evals/fraud_investigation/workflow.py rename to aieng-eval-agents/aieng/agent_evals/aml_investigation/workflow.py index cfc4c3e..27c2888 100644 --- a/aieng-eval-agents/aieng/agent_evals/fraud_investigation/workflow.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/workflow.py @@ -7,8 +7,8 @@ from typing import TYPE_CHECKING, Any, AsyncGenerator, Optional import google.genai.types -from aieng.agent_evals.fraud_investigation.policy import TriageOutcome, TriageStrategy -from aieng.agent_evals.fraud_investigation.types import AnalystResult, AnalystVerdict +from aieng.agent_evals.aml_investigation.policy import TriageOutcome, TriageStrategy +from aieng.agent_evals.aml_investigation.types import AnalystResult, AnalystVerdict from google.adk.agents.base_agent import BaseAgent, BaseAgentState from google.adk.events.event import Event from pydantic import Field diff --git a/aieng-eval-agents/aieng/agent_evals/impl/__init__.py b/aieng-eval-agents/aieng/agent_evals/impl/__init__.py deleted file mode 100644 index 9afa7ff..0000000 --- a/aieng-eval-agents/aieng/agent_evals/impl/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Example implementation for an example topic.""" - -from aieng.agent_evals.impl.example_impl import example_impl - - -__all__ = ["example_impl"] diff --git a/aieng-eval-agents/aieng/agent_evals/impl/example_impl.py b/aieng-eval-agents/aieng/agent_evals/impl/example_impl.py deleted file mode 100644 index fd41107..0000000 --- a/aieng-eval-agents/aieng/agent_evals/impl/example_impl.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Example implementation module.""" - - -def example_impl() -> str: - """Show an example implementation function.""" - return "example_impl" diff --git a/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py b/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py index a9dcb3f..6be9bf8 100644 --- a/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py +++ b/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py @@ -26,7 +26,7 @@ class ReadOnlySqlDatabase: connection_uri : str SQLAlchemy connection string (e.g., 'sqlite:///data/prod.db?mode=ro' or 'postgresql://reader:pass@host/db'). - max_rows : int, default=100_000 + max_rows : int, default=100 Hard limit on number of rows returned to the agent. query_timeout_sec : int, default=60 Maximum execution time for queries in seconds. @@ -39,7 +39,7 @@ class ReadOnlySqlDatabase: def __init__( self, connection_uri: str, - max_rows: int = 100_000, + max_rows: int = 100, query_timeout_sec: int = 60, agent_name: str = "UnknownAgent", **kwargs, @@ -84,13 +84,13 @@ def _is_safe_readonly_query(self, query: str) -> bool: return False def get_schema_info(self, table_names: Optional[list[str]] = None) -> str: - """Return schema for specific tables or all if None. + """Return schema for specific tables/views or all if None. Parameters ---------- table_names : Optional[list[str]], default=None - List of table names to retrieve schema for. If ``None``, retrieves all - tables. + List of table or view names to retrieve schema for. If ``None``, + retrieves all tables and views. Returns ------- @@ -99,24 +99,45 @@ def get_schema_info(self, table_names: Optional[list[str]] = None) -> str: """ inspector = inspect(self.engine) all_tables = inspector.get_table_names() + try: + all_views = inspector.get_view_names() + except Exception: + all_views = [] + + # Normalize (name, kind) so we can report both tables and views. + all_relations: list[tuple[str, str]] = [(t, "table") for t in all_tables] + [(v, "view") for v in all_views] - # Filter logic + # Filter logic (case-insensitive) if table_names: - # Case-insensitive matching - targets = [table.lower() for table in table_names] - tables_to_scan = [table for table in all_tables if table.lower() in targets] + targets = {name.lower() for name in table_names} + relations_to_scan = [(name, kind) for name, kind in all_relations if name.lower() in targets] else: - tables_to_scan = all_tables + relations_to_scan = all_relations schema_text = [] - for table_name in tables_to_scan: + for relation_name, relation_kind in relations_to_scan: + label = "View" if relation_kind == "view" else "Table" try: - columns = inspector.get_columns(table_name) + columns = inspector.get_columns(relation_name) # Compact Format for LLM: "TableName (col1: type, col2: type)" col_strs = [f"{c['name']}: {str(c['type'])}" for c in columns] - schema_text.append(f"Table: {table_name}\n Columns: {', '.join(col_strs)}") + schema_text.append(f"{label}: {relation_name}\n Columns: {', '.join(col_strs)}") except Exception: - schema_text.append(f"Table: {table_name} (Error reading schema)") + if self.engine.dialect.name == "sqlite": + try: + # SQLite supports PRAGMA table_info for both tables and views. + safe_relation_name = relation_name.replace('"', '""') + with self.engine.connect() as conn: + pragma = conn.execute(text(f'PRAGMA table_info("{safe_relation_name}")')) + pragma_rows = pragma.fetchall() + col_names = [row[1] for row in pragma_rows] + if col_names: + schema_text.append(f"{label}: {relation_name}\n Columns: {', '.join(col_names)}") + continue + except Exception: + pass + + schema_text.append(f"{label}: {relation_name} (Error reading schema)") return "\n".join(schema_text) @@ -140,6 +161,11 @@ def execute(self, query: str) -> str: Exception For any database execution errors. + Notes + ----- + This method enforces a limit on the number of rows returned and logs + all query attempts for compliance auditing. + """ start_time = datetime.now() status = "FAILED" diff --git a/implementations/fraud_investigation/README.md b/implementations/aml_investigation/README.md similarity index 100% rename from implementations/fraud_investigation/README.md rename to implementations/aml_investigation/README.md diff --git a/implementations/aml_investigation/agent.py b/implementations/aml_investigation/agent.py new file mode 100644 index 0000000..7d4b561 --- /dev/null +++ b/implementations/aml_investigation/agent.py @@ -0,0 +1,276 @@ +"""AML Investigation Agent Implementation. + +Run with: uv run --env-file .env implementations/aml_investigation/agent.py +""" + +import asyncio +import getpass +import json +import logging +import os +import uuid +from pathlib import Path + +import google.genai.types +from aieng.agent_evals.aml_investigation.data import AnalystOutput, CaseRecord +from aieng.agent_evals.async_client_manager import AsyncClientManager +from aieng.agent_evals.async_utils import rate_limited +from aieng.agent_evals.tools import ReadOnlySqlDatabase +from dotenv import load_dotenv +from google.adk.agents import Agent +from google.adk.runners import Runner +from google.adk.sessions import InMemorySessionService +from rich.progress import BarColumn, Progress, TextColumn, TimeElapsedColumn, TimeRemainingColumn + + +logger = logging.getLogger(__name__) + +ANALYST_PROMPT = """\ +You are a newly hired Anti‑Money Laundering (AML) Investigation Analyst at a financial institution. +Your job is to investigate a single AML case by reviewing transaction activity in our database and documenting +whether the activity is consistent with money laundering or a benign explanation. + +You have access to database query tools. Use them. Do not guess or invent transactions. + +## Core Principle: Falsification (Default to Benign) +Start with the hypothesis that the case is benign (a false positive). Your primary goal is to find positive, +transaction-level evidence that supports a legitimate explanation. Only conclude laundering when the transaction +pattern supports it. + +## Your Input (CaseFile JSON) +You will be given a JSON object with these fields: +- `case_id`: unique case identifier. +- `seed_transaction_id`: the primary transaction that triggered the case. +- `seed_timestamp`: timestamp of the seed transaction (end of the investigation window). +- `window_start`: timestamp of the beginning of the investigation window. +- `suspected_pattern_type`: a preliminary label/hint (may be wrong or intentionally misleading). + +### Time Scope Rule (Strict) +Only analyze transactions with `timestamp` between `window_start` and `seed_timestamp` (inclusive). +Do not use transactions after `seed_timestamp`. + +## What to Investigate (Standard Workflow) +1) **Seed review** + - Pull the seed transaction details and identify parties (from/to accounts and banks), currencies, amounts, and format. +2) **Account history within the window** + - Pull inbound/outbound activity for the involved accounts within the window. +3) **Pattern detection** + Look for transaction patterns consistent with laundering typologies, such as: + - **FAN-IN**: many sources → one account (aggregation). + - **FAN-OUT**: one account → many destinations (dispersion). + - **GATHER-SCATTER / SCATTER-GATHER**: aggregation then dispersion (or vice‑versa), often over short time windows. + - **STACK / LAYERING**: sequential hops meant to obscure origin. + - **RANDOM / CYCLE**: complex paths or circular movement. + - **BIPARTITE**: two groups of accounts with structured flows between them. +4) **Legitimate explanations** + - If activity is consistent with normal behavior (e.g., payroll, vendor payments, routine transfers), + state what makes it consistent and what would have been suspicious but is not present. + +## Your Output (AnalystOutput JSON) +Return a single JSON object that matches the `AnalystOutput` schema exactly. Fill every field. + +- `summary_narrative` (str): + 2–3 paragraphs in plain text summarizing what you checked and what you found. + Cite specific evidence (transaction IDs, dates/times, amounts, counterparties) and explain your reasoning. +- `is_laundering` (bool): + `true` only when the observed pattern within the window supports laundering; otherwise `false`. +- `pattern_type` (LaunderingPattern): + Your best classification of the dominant pattern observed. Use: + - `NONE` if you find no laundering pattern, + - `UNKNOWN` if you believe laundering is present but does not match a known pattern well. +- `pattern_description` (str): + A short, human-readable description of the observed pattern (or `"N/A"` when `pattern_type` is `NONE`). +- `flagged_transaction_ids` (list[str]): + The transaction IDs you consider most relevant/suspicious for this case. + If `is_laundering` is `false`, this can be an empty list. +""" + +client_manager = AsyncClientManager().get_instance() + +if client_manager.configs.aml_db is None: + raise ValueError("AML database configuration is missing.") + +db = ReadOnlySqlDatabase( + connection_uri=client_manager.configs.aml_db.build_uri(), + agent_name="FraudInvestigationAnalyst", +) + +root_agent = Agent( + name="AmlInvestigationAnalyst", + description="Conducts detailed multi-step AML investigations with database queries.", + tools=[db.execute, db.get_schema_info], + model="gemini-3-flash-preview", + instruction=ANALYST_PROMPT, + output_schema=AnalystOutput, +) + + +def _load_records(path: Path) -> list[CaseRecord]: + if not path.exists(): + return [] + + records: list[CaseRecord] = [] + with path.open("r", encoding="utf-8") as file: + for line_number, line in enumerate(file, start=1): + stripped_line = line.strip() + if not stripped_line: + continue + try: + records.append(CaseRecord.model_validate_json(stripped_line)) + except Exception as exc: + logger.warning("Skipping invalid JSONL record at %s:%d (%s)", path, line_number, exc) + return records + + +def _extract_json(text: str) -> dict: + try: + return json.loads(text) + except json.JSONDecodeError: + start = text.find("{") + end = text.rfind("}") + if start == -1 or end == -1 or end <= start: + raise + return json.loads(text[start : end + 1]) + + +def _write_results(output_path: Path, input_records: list[CaseRecord], results_by_id: dict[str, CaseRecord]) -> int: + tmp_path = output_path.with_suffix(output_path.suffix + ".tmp") + written: set[str] = set() + analyzed = 0 + + with tmp_path.open("w", encoding="utf-8") as outfile: + for record in input_records: + case_id = record.case.case_id + if case_id in written: + continue + written.add(case_id) + out_record = results_by_id.get(case_id, record) + analyzed += int(out_record.analysis is not None) + outfile.write(out_record.model_dump_json() + "\n") + + tmp_path.replace(output_path) + return analyzed + + +async def _analyze_case(runner: Runner, record: CaseRecord) -> CaseRecord: + message = google.genai.types.Content( + role="user", parts=[google.genai.types.Part(text=record.case.model_dump_json())] + ) + events_async = runner.run_async(session_id=str(uuid.uuid4()), user_id=getpass.getuser(), new_message=message) + + final_text: str | None = None + async for event in events_async: + if event.is_final_response() and event.content and event.content.parts: + final_text = "".join(part.text or "" for part in event.content.parts if part.text) + + if not final_text: + logger.warning("No analyst output produced for case_id=%s", record.case.case_id) + return record + + record.analysis = AnalystOutput.model_validate(_extract_json(final_text.strip())) + return record + + +async def _safe_analyze_case(runner: Runner, record: CaseRecord) -> CaseRecord: + try: + return await _analyze_case(runner, record) + except Exception as exc: + logger.exception("Case failed (case_id=%s): %s", record.case.case_id, exc) + return record + + +async def _analyze_cases_to_jsonl( + runner: Runner, + cases: list[CaseRecord], + semaphore: asyncio.Semaphore, + output_path: Path, +) -> dict[str, CaseRecord]: + if not cases: + return {} + + output_path.parent.mkdir(parents=True, exist_ok=True) + tasks = [ + asyncio.create_task(rate_limited(lambda r=record: _safe_analyze_case(runner, r), semaphore)) for record in cases + ] + + analyzed_by_id: dict[str, CaseRecord] = {} + with ( + output_path.open("a", encoding="utf-8") as outfile, + Progress( + TextColumn("[bold blue]{task.description}"), + BarColumn(), + TextColumn("{task.completed}/{task.total}"), + TimeRemainingColumn(), + TimeElapsedColumn(), + ) as progress, + ): + progress_task = progress.add_task("Analyzing AML cases", total=len(tasks)) + + for finished in asyncio.as_completed(tasks): + record = await finished + analyzed_by_id[record.case.case_id] = record + outfile.write(record.model_dump_json() + "\n") + outfile.flush() + os.fsync(outfile.fileno()) + progress.update(progress_task, advance=1) + + return analyzed_by_id + + +async def main() -> None: + """Run the AML investigation agent on cases from JSONL.""" + input_path = Path("implementations/aml_investigation/data/aml_cases.jsonl") + if not input_path.exists(): + raise FileNotFoundError(f"Case JSONL not found at {input_path.resolve()}") + + output_path = input_path.with_name("aml_cases_with_analysis.jsonl") + output_path.parent.mkdir(parents=True, exist_ok=True) + + input_records = _load_records(input_path) + existing_results = {record.case.case_id: record for record in _load_records(output_path)} + to_run = [r for r in input_records if existing_results.get(r.case.case_id, r).analysis is None] + + logger.info("Resume: %d/%d done; %d remaining.", len(input_records) - len(to_run), len(input_records), len(to_run)) + + runner = Runner( + app_name="aml_investigation", + agent=root_agent, + session_service=InMemorySessionService(), + auto_create_session=True, + ) + + try: + analyzed_by_id = await _analyze_cases_to_jsonl(runner, to_run, asyncio.Semaphore(5), output_path) + existing_results.update(analyzed_by_id) + analyzed_count = _write_results(output_path, input_records, existing_results) + logger.info("Wrote %d analyzed cases to %s", analyzed_count, output_path) + + final_records = [existing_results.get(r.case.case_id, r) for r in input_records] + scored = [r for r in final_records if r.analysis is not None] + if not scored: + logger.info("Metrics: N/A (no analyzed cases)") + else: + tp = fp = fn = tn = 0 + for r in scored: + gt = r.groundtruth.is_laundering + pred = r.analysis.is_laundering + if gt and pred: + tp += 1 + elif (not gt) and pred: + fp += 1 + elif gt and (not pred): + fn += 1 + else: + tn += 1 + logger.info("is_laundering confusion matrix:") + logger.info(" TP=%d FP=%d", tp, fp) + logger.info(" FN=%d TN=%d", fn, tn) + finally: + db.close() + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO, format="%(message)s") + load_dotenv() + + asyncio.run(main()) diff --git a/implementations/aml_investigation/data/cli.py b/implementations/aml_investigation/data/cli.py new file mode 100644 index 0000000..2770c60 --- /dev/null +++ b/implementations/aml_investigation/data/cli.py @@ -0,0 +1,295 @@ +"""CLI for building the AML database and case files. + +This module provides Click commands to: +- download Kaggle AML datasets, +- create and populate the SQLite database, and +- generate case files for downstream evaluation. + +Typical usage +------------- +Create the database: + python implementations/aml_investigation/data/cli.py create-db \ + --illicit-ratio HI --transactions-size Small + +Generate case files: + python implementations/aml_investigation/data/cli.py create-cases \ + --illicit-ratio HI --transactions-size Small --output-dir ./data +""" + +import functools +import logging +import sqlite3 +from pathlib import Path +from typing import Any, Callable + +import click +import pandas as pd +from aieng.agent_evals.aml_investigation.data import ( + CaseRecord, + build_cases, + download_dataset_file, + normalize_transactions_data, +) + + +logger = logging.getLogger(__name__) + + +def _create_accounts_table(csv_filepath: str, conn: sqlite3.Connection) -> None: + """Create and populate the accounts table from a CSV file.""" + accts_df = pd.read_csv(csv_filepath, dtype_backend="pyarrow") + + # Rename duplicate 'Account' columns to distinguish sender and receiver + accts_df.rename( + columns={ + "Bank Name": "bank_name", + "Bank ID": "bank_id", + "Account Number": "account_number", + "Entity ID": "entity_id", + "Entity Name": "entity_name", + }, + inplace=True, + ) + + accts_df.to_sql("accounts", conn, if_exists="append", index=False) + + +def _create_transactions_table(csv_filepath: str, conn: sqlite3.Connection) -> None: + """Create and populate the transactions table from a CSV file.""" + transc_df = pd.read_csv(csv_filepath, dtype_backend="pyarrow") + transc_df = normalize_transactions_data(transc_df) + + # Add new columns: date, day_of_week, time_of_day + transc_df["date"] = pd.to_datetime(transc_df["timestamp"]).dt.date + transc_df["day_of_week"] = pd.to_datetime(transc_df["timestamp"]).dt.day_name() + transc_df["time_of_day"] = pd.to_datetime(transc_df["timestamp"]).dt.time + + # Set Transaction ID as index + transc_df.set_index("transaction_id", drop=True, inplace=True) + + transc_df.drop(columns=["is_laundering"]).to_sql("transactions", conn, if_exists="append") + + +def _write_jsonl(path: Path, records: list[CaseRecord]) -> None: + """Write CaseRecord entries to a JSONL file.""" + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as file: + for record in records: + file.write(record.model_dump_json() + "\n") + + +def _dataset_options(fn: Callable[..., Any]) -> Callable[..., Any]: + """Attach shared dataset options to a Click command.""" + + @click.option( + "--illicit-ratio", + type=click.Choice(["HI", "LI"], case_sensitive=False), + default="HI", + show_default=True, + help="Illicit transaction ratio.", + ) + @click.option( + "--transactions-size", + type=click.Choice(["Small", "Medium", "Large"], case_sensitive=False), + default="Small", + show_default=True, + help="Size of the transactions dataset.", + ) + @functools.wraps(fn) + def wrapper(*args, **kwargs): + return fn(*args, **kwargs) + + return wrapper + + +@click.group() +def cli() -> None: + """Entry point for CLI commands.""" + pass + + +@cli.command() +@_dataset_options +@click.option( + "--ddl-file-path", + type=click.Path(exists=True, dir_okay=False, resolve_path=True, path_type=Path), + default="implementations/aml_investigation/data/schema.ddl", + show_default=True, + help="Path to the SQL DDL file for creating database schema.", +) +@click.option( + "--db-path", + type=click.Path(dir_okay=False, writable=True, resolve_path=True, path_type=Path), + envvar="AML_DB__DATABASE", + default=None, + show_default=True, + help="Optional path to the SQLite database file to create. If not provided, " + "the AML_DB__DATABASE environment variable will be checked. If neither is provided, " + "the database will be created in the same directory as the DDL file.", +) +def create_db(illicit_ratio: str, transactions_size: str, ddl_file_path: Path, db_path: Path | None = None) -> None: + """Create and populate the AML Fraud Investigation SQLite database. + + Parameters + ---------- + illicit_ratio : {"HI", "LI"} + Illicit transaction ratio to select the dataset variant. + transactions_size : {"Small", "Medium", "Large"} + Size of the transactions dataset. + ddl_file_path : pathlib.Path + Path to the SQL DDL file for creating database schema. + db_path : pathlib.Path | None, default=None + Optional path to the SQLite database file to create. If ``None``, the + database will be created in the same directory as the DDL file. + + Raises + ------ + ValueError + If any option value is invalid. + FileNotFoundError + If the DDL file does not exist. + """ + valid_ratio = {"HI", "LI"} + valid_size = {"Small", "Medium", "Large"} + if illicit_ratio not in valid_ratio: + raise ValueError(f"illicit_ratio must be one of {sorted(valid_ratio)}") + if transactions_size not in valid_size: + raise ValueError(f"transactions_size must be one of {sorted(valid_size)}") + if not ddl_file_path.exists(): + raise FileNotFoundError(f"DDL file not found: {ddl_file_path}") + + if db_path is None: + db_path = ddl_file_path.parent / "aml_transactions.db" + + # Download datasets from Kaggle + click.echo("Downloading dataset files...") + path_to_transc_csv = download_dataset_file(illicit_ratio, transactions_size, "Trans.csv") + path_to_accts_csv = download_dataset_file(illicit_ratio, transactions_size, "accounts.csv") + click.echo("✅ Download complete.") + + with sqlite3.connect(db_path) as conn: + conn.execute("PRAGMA foreign_keys = ON;") + + with open(ddl_file_path, "r") as file: + conn.executescript(file.read()) + conn.commit() + click.echo("✅ Schema applied.") + + _create_accounts_table(path_to_accts_csv, conn) + click.echo("✅ Accounts loaded.") + _create_transactions_table(path_to_transc_csv, conn) + click.echo("✅ Transactions loaded.") + + +@cli.command() +@_dataset_options +@click.option( + "--num-laundering-cases", type=int, default=10, show_default=True, help="Number of laundering cases to create." +) +@click.option("--num-normal-cases", type=int, default=10, show_default=True, help="Number of normal cases to create.") +@click.option( + "--num-false-negative-cases", + type=int, + default=10, + show_default=True, + help="Number of false negative cases to create.", +) +@click.option( + "--num-false-positive-cases", + type=int, + default=10, + show_default=True, + help="Number of false positive cases to create.", +) +@click.option( + "--lookback-days", + type=int, + default=0, + show_default=True, + help="Number of days to look back for the case window start (clamped to available data).", +) +@click.option( + "--output-dir", + type=click.Path(file_okay=False, writable=True, resolve_path=True, path_type=Path), + default=Path("implementations/aml_investigation/data"), + show_default=True, + help="Directory to write case JSONL files.", +) +def create_cases( + illicit_ratio: str, + transactions_size: str, + num_laundering_cases: int, + num_normal_cases: int, + num_false_negative_cases: int, + num_false_positive_cases: int, + lookback_days: int, + output_dir: Path, +) -> None: + """Create AML case files as JSONL. + + Parameters + ---------- + illicit_ratio : {"HI", "LI"} + Illicit transaction ratio to select the dataset variant. + transactions_size : {"Small", "Medium", "Large"} + Size of the transactions dataset. + num_laundering_cases : int + Number of laundering cases to sample. + num_normal_cases : int + Number of normal cases to sample. + num_false_negative_cases : int + Number of false negative cases to generate. + num_false_positive_cases : int + Number of false positive cases to generate. + lookback_days : int + Number of days to look back for the case window start. + output_dir : pathlib.Path + Directory to write case JSONL files. + + Raises + ------ + ValueError + If any numeric argument is negative or option values are invalid. + """ + valid_ratio = {"HI", "LI"} + valid_size = {"Small", "Medium", "Large"} + if illicit_ratio not in valid_ratio: + raise ValueError(f"illicit_ratio must be one of {sorted(valid_ratio)}") + if transactions_size not in valid_size: + raise ValueError(f"transactions_size must be one of {sorted(valid_size)}") + for name, value in [ + ("num_laundering_cases", num_laundering_cases), + ("num_normal_cases", num_normal_cases), + ("num_false_negative_cases", num_false_negative_cases), + ("num_false_positive_cases", num_false_positive_cases), + ("lookback_days", lookback_days), + ]: + if value < 0: + raise ValueError(f"{name} must be >= 0") + + path_to_transc_csv = download_dataset_file(illicit_ratio, transactions_size, "Trans.csv") + path_to_patterns_txt = download_dataset_file(illicit_ratio, transactions_size, "Patterns.txt") + click.echo("✅ Downloaded dataset files.") + + transc_df = pd.read_csv(path_to_transc_csv, dtype_backend="pyarrow") + transc_df = normalize_transactions_data(transc_df) + click.echo("✅ Transactions normalized.") + + cases = build_cases( + path_to_patterns_txt, + transc_df, + num_laundering_cases=num_laundering_cases, + num_false_negative_cases=num_false_negative_cases, + num_false_positive_cases=num_false_positive_cases, + num_normal_cases=num_normal_cases, + lookback_days=lookback_days, + ) + click.echo(f"✅ Built {len(cases)} cases.") + + output_dir.mkdir(parents=True, exist_ok=True) + _write_jsonl(output_dir / "aml_cases.jsonl", cases) + click.echo(f"✅ Wrote JSONL to {output_dir / 'aml_cases.jsonl'}") + + +if __name__ == "__main__": + cli() diff --git a/implementations/fraud_investigation/data/schema.ddl b/implementations/aml_investigation/data/schema.ddl similarity index 58% rename from implementations/fraud_investigation/data/schema.ddl rename to implementations/aml_investigation/data/schema.ddl index 486e5d5..0b667a6 100644 --- a/implementations/fraud_investigation/data/schema.ddl +++ b/implementations/aml_investigation/data/schema.ddl @@ -1,3 +1,5 @@ +DROP VIEW IF EXISTS "account_transactions"; + DROP TABLE IF EXISTS "accounts"; CREATE TABLE "accounts" ( "bank_name" TEXT, @@ -24,19 +26,35 @@ CREATE TABLE "transactions" ( "amount_paid" REAL, "payment_currency" TEXT, "payment_format" TEXT, - "risk_score" REAL, FOREIGN KEY ("from_bank", "from_account") REFERENCES "accounts" ("bank_id", "account_number"), FOREIGN KEY ("to_bank", "to_account") REFERENCES "accounts" ("bank_id", "account_number") ); -DROP VIEW IF EXISTS "v_unified_transactions"; -CREATE VIEW "v_unified_transactions" AS +CREATE VIEW account_transactions AS +SELECT + transaction_id, + timestamp, + from_account AS account, + 'OUT' AS direction, + to_account AS counterparty, + from_bank AS bank, + to_bank AS counterparty_bank, + amount_paid AS amount, + payment_currency AS currency, + payment_format +FROM transactions +UNION ALL SELECT - t.*, - -- Create a single unique ID for the sender - t.from_bank || '_' || t.from_account as from_uid, - -- Create a single unique ID for the receiver - t.to_bank || '_' || t.to_account as to_uid -FROM transactions t; + transaction_id, + timestamp, + to_account AS account, + 'IN' AS direction, + from_account AS counterparty, + to_bank AS bank, + from_bank AS counterparty_bank, + amount_received AS amount, + receiving_currency AS currency, + payment_format +FROM transactions; diff --git a/implementations/fraud_investigation/agent.py b/implementations/fraud_investigation/agent.py deleted file mode 100644 index 08754ce..0000000 --- a/implementations/fraud_investigation/agent.py +++ /dev/null @@ -1,258 +0,0 @@ -"""Fraud Investigation Agent Implementation. - -Run with: uv run --env-file .env implementations/fraud_investigation/agent.py -""" - -import asyncio -import getpass -import logging -import uuid -from pathlib import Path -from typing import Any - -import google.genai.types -import pandas as pd -from aieng.agent_evals.async_client_manager import AsyncClientManager -from aieng.agent_evals.async_utils import gather_with_progress, rate_limited -from aieng.agent_evals.fraud_investigation.policy import RuleBasedTriage, TriageOutcome, TriagePolicyConfig -from aieng.agent_evals.fraud_investigation.types import AnalystResult -from aieng.agent_evals.fraud_investigation.workflow import AmlInvestigationWorkflow -from aieng.agent_evals.tools import ReadOnlySqlDatabase -from dotenv import load_dotenv -from google.adk.agents import Agent -from google.adk.runners import Runner -from google.adk.sessions import InMemorySessionService - - -logger = logging.getLogger(__name__) - -ANALYST_PROMPT = """\ -You are a Financial Crimes Investigation Analyst. Your primary role is to conduct a focused investigation into \ -transactions flagged by our automated monitoring system. Your objective is to determine if the activity is suspicious \ -and requires reporting, or if it is a false positive that can be closed. - -## Core Principle: Falsification -Begin with the hypothesis that the automated alert is a false positive. \ -Your primary goal is to find positive evidence to prove the transaction is legitimate. - -## Standard Operating Procedure -### Initial Assessment -- Review the provided transaction data. Understand why it was flagged (e.g., high amount, unusual pattern). - -### Evidence Gathering -- **Customer Profile:** Examine the historical activity of the involved parties. Is this transaction consistent with their typical behavior? -- **Counterparty Analysis:** Investigate the transaction's source or destination. What is the relationship between the parties? -- **Pattern Analysis:** Search for broader patterns that could indicate illicit activity, such as: - - Structuring: multiple transactions just below reporting thresholds - - Layering: complex fund movements to obscure origins - - Rapid Movement: high volume or velocity of transfers - - Unusual Volume: sudden spikes in transaction amounts or frequency - - Terrorist Financing Indicators - - Other red flags as per FinCEN guidelines - -REMEMBER: The risk scores and triggered rules in the database are imperfect. Take them with a grain of salt. The actual \ -transaction patterns are the hard evidence. - -### Analysis and Verdict -Synthesize your findings and determine the appropriate verdict: -- BENIGN: Clear evidence supports legitimacy. Recommend closing the alert. -- SUSPICIOUS: Strong indicators of illicit activity. Recommend filing a SAR. -- NEEDS_REVIEW: Inconclusive evidence to confirm nor deny the suspicion after thorough investigation. Recommend escalation to a senior analyst. - -### Case Documentation -Document your findings comprehensively, citing specific evidence and rationale for your verdict. \ -The documentation must include the following: -- Summary Narrative: A concise explanation (2-3 paragraphs) of the fraud scheme. Include the reasoning behind your verdict and enough information for filing a SAR (the 5 "Ws") if needed. -- Risk Score: A value between 0-100 indicating the level of risk based on your findings. -- Verdict: One of BENIGN, SUSPICIOUS, or NEEDS_REVIEW. Applies to ONLY the transaction under investigation. -- Suspicious Activity Type: Classify the dominant type of suspicious activity identified. -- Suspicious Transactions: Tabulate the transactions deemed suspicious, including the transaction IDs, timestamp, amount, currencies, banks, entities involved. - -NOTE: For any given transaction, focus the investigation on transactions that happenned before or on the same day as the \ -transaction date. Do not consider transactions that occurred after the transaction date. -""" - -SAR_REPORT_PROMPT = """\ -You are a Compliance Officer responsible for drafting a formal Suspicious Activity Report (SAR) narrative for submission \ -to the Financial Crimes Enforcement Network (FinCEN). Your work is critical for law enforcement and regulatory oversight. - -You will receive a completed case file. This file contains the investigation summary, verdict, and key evidence. \ -Your task is to transform this information into a compliant and comprehensive SAR narrative. - -## SAR Narrative Structure -The narrative must be written in the third person, be clear, concise, and chronological. Adhere strictly to the following "Five Ws" structure: - -- **Who** is conducting the suspicious activity? Introduce the subject(s) and any associated entities. -- **What** instruments or mechanisms are being used? Describe the types of transactions (e.g., wire transfers, cash deposits) and accounts involved. -- **When** did the suspicious activity take place? Reference specific dates from `suspicious_transactions`. -- **Where** did the suspicious activity take place? -- **Why** do you deem the activity suspicious? - - Explain the "red flags" that led to the suspicion. - - Clearly state *why* the observed behavior deviates from the expected norm for the customer. - -## Output Format -Generate a single, cohesive narrative text. Do not use markdown or headers. \ -The text should be ready for direct insertion into Part V of the FinCEN SAR form. \ -Begin the report with an opening sentence that summarizes the filing, for example: "[Your Financial Institution] is filing \ -this report on [Subject Name] due to suspicious [activity type, e.g., wire transfers] intended to obscure the source of funds." -""" - - -client_manager = AsyncClientManager().get_instance() - -# Define Agents -if client_manager.configs.aml_db is None: - raise ValueError("AML database configuration is missing.") - -db = ReadOnlySqlDatabase( - connection_uri=client_manager.configs.aml_db.build_uri(), - agent_name="FraudInvestigationAnalyst", -) - -# NOTE: these are left here so that `adk web` command can pick up the `root_agent` for -# launching a web interface -analyst = Agent( - name="FraudInvestigationAnalyst", - description="Conducts detailed multi-step fraud investigations with database queries.", - tools=[db.execute, db.get_schema_info], - model="gemini-3-flash-preview", - instruction=ANALYST_PROMPT, - output_schema=AnalystResult, - generate_content_config=google.genai.types.GenerateContentConfig( - thinking_config=google.genai.types.ThinkingConfig(include_thoughts=True) - ), -) - -sar_report_agent = Agent( - name="SARReportGenerator", - description="Generates comprehensive Suspicious Activity Reports from investigation findings.", - model="gemini-2.5-flash", - instruction=SAR_REPORT_PROMPT, -) - -root_agent = AmlInvestigationWorkflow( - triage_strategy=RuleBasedTriage(config=TriagePolicyConfig()), analyst=analyst, reporter=sar_report_agent -) - - -async def _investigate_transaction(row: pd.Series) -> dict[str, Any]: - """Investigate a single transaction and return structured results.""" - # Initialize a session service to store conversation state - session_service = InMemorySessionService() - - analyst = Agent( - name="FraudInvestigationAnalyst", - description="Conducts detailed multi-step fraud investigations with database queries.", - tools=[db.execute, db.get_schema_info], - model="gemini-3-flash-preview", - instruction=ANALYST_PROMPT, - output_schema=AnalystResult, - ) - - sar_report_agent = Agent( - name="SARReportGenerator", - description="Generates comprehensive Suspicious Activity Reports from investigation findings.", - model="gemini-2.5-flash", - instruction=SAR_REPORT_PROMPT, - ) - - root_agent = AmlInvestigationWorkflow( - triage_strategy=RuleBasedTriage(config=TriagePolicyConfig()), analyst=analyst, reporter=sar_report_agent - ) - - # Create a runner to manage the execution loop - runner = Runner( - app_name="fraud_investigation", agent=root_agent, session_service=session_service, auto_create_session=True - ) - - transaction_id = row.get("transaction_id", "unknown") - - # Collect output in memory instead of printing - output_buffer: list[str] = [] - - message = google.genai.types.Content( - role="user", - parts=[google.genai.types.Part(text=row.drop(labels=["is_laundering", "triggered_rules"]).to_json())], - ) - events_async = runner.run_async(session_id=str(uuid.uuid4()), user_id=getpass.getuser(), new_message=message) - - # Extract structured results from the workflow state - triage_decision: TriageOutcome | None = None - analyst_findings: dict[str, Any] | None = None - final_report = None - - async for event in events_async: - # Capture text output - if event.content and event.content.parts: - for part in event.content.parts: - if part.text: - output_buffer.append(part.text) - - # Extract workflow state when investigation completes - if event.actions.agent_state: - state_data = event.actions.agent_state - triage_decision = state_data.get("triage_decision") - analyst_findings = state_data.get("analysis") - final_report = state_data.get("final_report") - - logger.info(f"✅ Investigation for transaction {transaction_id} completed.") - logger.debug("".join(output_buffer)) - - return { - "transaction_id": transaction_id, - "is_laundering": row.get("is_laundering"), - "system_risk_score": row.get("risk_score"), - "triggered_rules": row.get("triggered_rules"), - "triage_decision": triage_decision, - "analyst_verdict": analyst_findings.get("verdict") if analyst_findings else None, - "agent_risk_score": analyst_findings.get("risk_score") if analyst_findings else None, - "suspicious_activity_type": analyst_findings.get("suspicious_activity_type") if analyst_findings else None, - "summary_narrative": analyst_findings.get("summary_narrative") if analyst_findings else None, - "suspicious_transactions": analyst_findings.get("suspicious_transactions") if analyst_findings else None, - "suspicious_entities": analyst_findings.get("suspicious_entities") if analyst_findings else None, - "final_report": final_report, - } - - -async def main() -> None: - """Run fraud investigations on a batch of transactions.""" - # Prepare your input - path_to_transactions = Path("implementations/fraud_investigation/data/test_transactions.csv") - if not path_to_transactions.exists(): - raise FileNotFoundError(f"Transaction file not found at {path_to_transactions.resolve()}") - - transactions = pd.read_csv(path_to_transactions, dtype_backend="pyarrow") - - # Investigate each transaction with limited concurrency - semaphore = asyncio.Semaphore(10) # Limit to 10 concurrent investigations - results: list[dict[str, Any]] = [] - - # TODO: remove or change this line for full run - transactions = transactions.sample(n=100, random_state=42) - - # Gather all results - tasks = [ - rate_limited(lambda _row=_row: _investigate_transaction(_row), semaphore) for _, _row in transactions.iterrows() - ] - results = list(await gather_with_progress(tasks, description="Investigating transactions...")) - - # Convert results to DataFrame and save - results_df = pd.DataFrame(results) - results_path = path_to_transactions.parent / "investigation_results.csv" - results_df.to_csv(results_path, index=False) - print(f"\n✅ Completed {len(results)} investigations. Results saved to {results_path}") - - # Print summary statistics - print("\nSummary:") - print(f" Triage decisions: {results_df['triage_decision'].value_counts().to_dict()}") - print(f" Analyst verdicts: {results_df['analyst_verdict'].value_counts().to_dict()}") - print(f" Suspicious activity types: {results_df['suspicious_activity_type'].value_counts().to_dict()}") - print(f" Average agent risk score: {results_df['agent_risk_score'].dropna().mean():.2f}") - - db.close() - - -if __name__ == "__main__": - load_dotenv() - - asyncio.run(main()) diff --git a/implementations/fraud_investigation/data/prepare_db.py b/implementations/fraud_investigation/data/prepare_db.py deleted file mode 100644 index 2029963..0000000 --- a/implementations/fraud_investigation/data/prepare_db.py +++ /dev/null @@ -1,376 +0,0 @@ -"""Prepare and populate the fraud investigation database.""" - -import hashlib -import logging -import sqlite3 -from pathlib import Path -from typing import Literal - -import kagglehub -import numpy as np -import pandas as pd -from aieng.agent_evals.configs import Configs -from dotenv import load_dotenv - - -logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s") -logger = logging.getLogger(__name__) - -load_dotenv() - - -def _compute_fan_metrics(transactions: pd.DataFrame, window: str, direction: Literal["in", "out"]) -> pd.Series: - """Compute fan-in or fan-out degree over a rolling time window. - - Fan-in means many unique senders to one receiver. - Fan-out means one sender to many unique receivers. - """ - acct_col = "from_account" if direction == "out" else "to_account" - partner_col = "to_account" if direction == "out" else "from_account" - time_window = pd.Timedelta(window) - - # Sort once upfront - transactions_sorted = transactions.sort_values([acct_col, "timestamp"]).copy() - - # Vectorized: identify new counterparties - prev_ts = transactions_sorted.groupby([acct_col, partner_col], sort=False)["timestamp"].shift() - is_new = (prev_ts.isna() | ((transactions_sorted["timestamp"] - prev_ts) > time_window)).astype(int) - transactions_sorted["is_new"] = is_new - - # Vectorized rolling sum on sorted data - rolled = ( - transactions_sorted.groupby(acct_col, sort=False) - .rolling(time_window, on="timestamp", closed="left")["is_new"] - .sum() - .fillna(0) - .reset_index(level=0, drop=True) # Drop the account level from multi-index - ) - - # Map back to original index - result = pd.Series(0, index=transactions.index) - result.loc[transactions_sorted.index] = rolled.values + is_new.values - - return result.astype(int) - - -def _detect_cycles(transactions: pd.DataFrame, window: str = "10D") -> pd.Series: - """Detect 2-hop cycles within a time window. - - A cycle is defined as A->B followed by B->A within the specified time window. - """ - time_window = pd.Timedelta(window) - - # Create bidirectional edge lookup - edges = transactions[["from_account", "to_account", "timestamp"]].copy() - edges["pair_key"] = edges["from_account"] + "|" + edges["to_account"] - edges["reverse_key"] = edges["to_account"] + "|" + edges["from_account"] - - # Self-join on reverse pairs within time window - merged = pd.merge( - edges, - edges[["pair_key", "timestamp"]].rename(columns={"timestamp": "reverse_ts"}), - left_on="reverse_key", - right_on="pair_key", - how="left", - suffixes=("", "_reverse"), - ) - - # Vectorized time window check - has_cycle = ( - merged["reverse_ts"].notna() - & (merged["reverse_ts"] < merged["timestamp"]) - & (merged["reverse_ts"] > merged["timestamp"] - time_window) - ) - - # Group by original index and check if any reverse exists - result = has_cycle.groupby(merged.index).any() - return result.reindex(transactions.index, fill_value=False) - - -def _detect_stack(transactions: pd.DataFrame, window: str = "3D", similarity_threshold: float = 0.75) -> pd.Series: - """Detect stack/layering patterns. - - A stack pattern is defined as a transaction from A->B that is closely preceded - by an inbound transaction to A from another party within a time window. - In other words, A receives funds and quickly passes them on to B. - """ - inbound = transactions[["to_account", "timestamp", "amount_received", "receiving_currency"]].copy() - inbound = inbound.rename(columns={"to_account": "account"}) - - transactions_sorted = transactions.sort_values("timestamp").copy() - transactions_sorted["account"] = transactions_sorted["from_account"] - - merged = pd.merge_asof( - transactions_sorted, - inbound.sort_values("timestamp"), - left_on="timestamp", - right_on="timestamp", - by="account", - suffixes=("", "_in"), - direction="backward", - tolerance=pd.Timedelta(window), - ) - - # More forgiving similarity threshold to match ground truth - amount_diff = (merged["amount_paid"] - merged["amount_received"]).abs() - denom = merged["amount_received"].clip(lower=1) - similarity = (1 - amount_diff / denom).clip(0, 1) - - has_prior_inbound = merged["amount_received"].notna() - - result = pd.Series(False, index=transactions.index) - result.loc[merged.index] = (similarity > similarity_threshold) & has_prior_inbound - - return result - - -def _score_fan_pattern(fan_degree: pd.Series, prefix: str) -> tuple[pd.Series, pd.Series]: - """Score fan patterns using tiered thresholds. - - Lower degrees get some points, higher degrees get more points. - """ - tiers = [ - (10, 45, f"{prefix}: Max degree = 10+"), - (5, 30, f"{prefix}: Max degree = 5-9"), - (2, 15, f"{prefix}: Max degree = 2-4"), - ] - - scores = pd.Series(0, index=fan_degree.index) - evidence_strs = pd.Series("", index=fan_degree.index) - - for threshold, points, label in tiers: - mask = (fan_degree >= threshold) & (evidence_strs == "") # Only assign once - scores[mask] = points - evidence_strs[mask] = label - - return scores, evidence_strs - - -def _compute_risk_score(sorted_transactions: pd.DataFrame) -> tuple[pd.Series, pd.Series]: - """Compute risk scores using pattern detection.""" - # Ensure timestamps are real datetimes so rolling windows transactions correctly. - if not np.issubdtype(sorted_transactions["timestamp"].dtype, np.datetime64): - sorted_transactions["timestamp"] = pd.to_datetime(sorted_transactions["timestamp"]) - - score = pd.Series(0.0, index=sorted_transactions.index) - evidence = pd.Series([[] for _ in range(len(sorted_transactions))], index=sorted_transactions.index) - - # Compute all pattern detections - fan_out = _compute_fan_metrics(sorted_transactions, "3D", "out") - fan_in = _compute_fan_metrics(sorted_transactions, "3D", "in") - has_cycle = _detect_cycles(sorted_transactions, "10D") - has_stack = _detect_stack(sorted_transactions, "3D", similarity_threshold=0.8) - - # Fan-out scoring (ground truth shows 2-16 degree, most common 10-16) - fan_out_score, fan_out_evidence = _score_fan_pattern(fan_out, "FAN-OUT") - score += fan_out_score - for idx in fan_out_evidence[fan_out_evidence != ""].index: - evidence.at[idx] = evidence.at[idx] + [fan_out_evidence.at[idx]] - - # Fan-in scoring (ground truth shows 2-16 degree, most common 10-16) - fan_in_score, fan_in_evidence = _score_fan_pattern(fan_in, "FAN-IN") - score += fan_in_score - for idx in fan_in_evidence[fan_in_evidence != ""].index: - evidence.at[idx] = evidence.at[idx] + [fan_in_evidence.at[idx]] - - # Cycles (ground truth: 2-10 hops within 10 days) - score[has_cycle] += 25 - evidence[has_cycle] = evidence[has_cycle].apply(lambda x: x + ["CYCLE: Max hops >=2"]) - - # Stack/Layering (ground truth: A→B→C pass-through) - score[has_stack] += 20 - evidence[has_stack] = evidence[has_stack].apply(lambda x: x + ["STACK/LAYERING"]) - - # Gather-scatter (ground truth: hub behavior with both fan-in and fan-out) - gather_scatter = (fan_in >= 3) & (fan_out >= 3) - score[gather_scatter] += 15 - evidence[gather_scatter] = evidence[gather_scatter].apply(lambda x: x + ["GATHER-SCATTER"]) - - # Pattern combinations (multiple signals = higher confidence) - high_signal_count = ( - has_cycle.astype(int) - + has_stack.astype(int) - + (fan_in >= 5).astype(int) - + (fan_out >= 5).astype(int) - + gather_scatter.astype(int) - ) - - mask = high_signal_count == 2 - score[mask] += 20 - evidence[mask] = evidence[mask].apply(lambda x: x + ["MULTIPLE PATTERNS: 2 high-risk signals"]) - - mask = high_signal_count >= 3 - score[mask] += 35 - evidence[mask] = evidence[mask].apply(lambda x: x + ["MULTIPLE PATTERNS: 3+ high-risk signals"]) - - triggered_rules: pd.Series = evidence.apply(lambda x: ",".join(x) if len(x) > 0 else "") - return score.clip(0, 100), triggered_rules - - -def _process_transaction_data(file_path: str) -> pd.DataFrame: - """Process transaction data and populate the database.""" - transc_df = pd.read_csv(file_path, dtype_backend="pyarrow") - transc_df.rename( - columns={ - "Timestamp": "timestamp", - "From Bank": "from_bank", - "Account": "from_account", - "To Bank": "to_bank", - "Account.1": "to_account", - "Amount Received": "amount_received", - "Receiving Currency": "receiving_currency", - "Amount Paid": "amount_paid", - "Payment Currency": "payment_currency", - "Payment Format": "payment_format", - "Is Laundering": "is_laundering", - }, - inplace=True, - ) - transc_df.drop_duplicates(inplace=True) - - # Convert Timestamp column to ISO 8601 format - transc_df["timestamp"] = pd.to_datetime(transc_df["timestamp"]).dt.strftime("%Y-%m-%dT%H:%M:%S") - - # Add new columns: date, day_of_week, time_of_day - transc_df["date"] = pd.to_datetime(transc_df["timestamp"]).dt.date - transc_df["day_of_week"] = pd.to_datetime(transc_df["timestamp"]).dt.day_name() - transc_df["time_of_day"] = pd.to_datetime(transc_df["timestamp"]).dt.time - - # Create Transaction ID by hashing row content, excluding label - # Concatenate values of desired columns into a string - cols = [ - "timestamp", - "time_of_day", - "from_bank", - "from_account", - "to_bank", - "to_account", - "amount_received", - "receiving_currency", - "amount_paid", - "payment_currency", - "payment_format", - ] - - txn_str = transc_df[cols].astype("string").agg("|".join, axis=1) - transc_df["transaction_id"] = txn_str.map(lambda x: hashlib.sha256(x.encode()).hexdigest()[:16]) - - transc_df_sorted = transc_df.copy() - - transc_df_sorted["timestamp"] = pd.to_datetime(transc_df_sorted["timestamp"]) - transc_df_sorted = transc_df_sorted.sort_values("timestamp").reset_index(drop=True) - - pattern_scores, pattern_evidence = _compute_risk_score(transc_df_sorted) - transc_df_sorted.loc[: len(pattern_scores) - 1, "risk_score"] = pattern_scores - transc_df_sorted.loc[: len(pattern_scores) - 1, "triggered_rules"] = pattern_evidence - - # Set Transaction ID as index - transc_df_sorted.set_index("transaction_id", drop=True, inplace=True) - - return transc_df_sorted - - -def _curate_test_cases(df: pd.DataFrame) -> pd.DataFrame: - """Curate a test set with diverse fraud and legitimate cases.""" - test_cases = [] - - fraud = df[df["is_laundering"] == 1] - legit = df[df["is_laundering"] == 0] - - # All distinct fraud patterns (at least 2 examples each) - for evidence_pattern in fraud["triggered_rules"].value_counts().index: - cases = fraud[fraud["triggered_rules"] == evidence_pattern].sample( - min(2, len(fraud[fraud["triggered_rules"] == evidence_pattern])), random_state=42 - ) - test_cases.append(cases) - - # Use percentiles to define thresholds - high_risk_threshold = fraud["risk_score"].quantile(0.75) - low_risk_threshold = legit["risk_score"].quantile(0.25) - normal_threshold = legit["risk_score"].quantile(0.5) - - # High-score false positives - false_positives = df[(df["is_laundering"] == 0) & (df["risk_score"] >= high_risk_threshold)] - test_cases.append(false_positives.sample(min(50, len(false_positives)), random_state=42)) - - # Low-score true positives - false_negatives = df[(df["is_laundering"] == 1) & (df["risk_score"] < low_risk_threshold)] - if len(false_negatives) > 0: - test_cases.append(false_negatives) # Include all - - # 4. Representative sample of normal transactions - normal = df[(df["is_laundering"] == 0) & (df["risk_score"] < normal_threshold)] - test_cases.append(normal.sample(min(100, len(normal)), random_state=42)) - - # 5. Edge cases: single-transaction fraud attempts - single_txn_fraud = df[df["is_laundering"] == 1].groupby("from_account").filter(lambda x: len(x) == 1) - if len(single_txn_fraud) > 0: - test_cases.append(single_txn_fraud.sample(min(10, len(single_txn_fraud)), random_state=42)) - - return pd.concat(test_cases).drop_duplicates().sort_values("timestamp") - - -def main() -> None: - """Prepare and populate the fraud investigation database.""" - configs = Configs() - if configs.aml_db is None: - raise ValueError("AML database path is not configured.") - - db_path = Path(configs.aml_db.database or "./aml_prod.db").resolve() - ddl_file_path = Path("implementations/fraud_investigation/data/schema.ddl") - - # Download datasets from Kaggle - path_to_transc_csv = kagglehub.dataset_download( - handle="ealtman2019/ibm-transactions-for-anti-money-laundering-aml", - path="HI-Small_Trans.csv", - ) - path_to_accts_csv = kagglehub.dataset_download( - handle="ealtman2019/ibm-transactions-for-anti-money-laundering-aml", - path="HI-Small_accounts.csv", - ) - logger.info(f"Downloaded transaction data to {path_to_transc_csv}") - logger.info(f"Downloaded account data to {path_to_accts_csv}") - - conn = sqlite3.connect(db_path) - conn.execute("PRAGMA foreign_keys = ON;") - - with open(ddl_file_path, "r") as file: - conn.executescript(file.read()) - conn.commit() - - accts_df = pd.read_csv(path_to_accts_csv, dtype_backend="pyarrow") - - # Rename duplicate 'Account' columns to distinguish sender and receiver - accts_df.rename( - columns={ - "Bank Name": "bank_name", - "Bank ID": "bank_id", - "Account Number": "account_number", - "Entity ID": "entity_id", - "Entity Name": "entity_name", - }, - inplace=True, - ) - - accts_df.to_sql("accounts", conn, if_exists="append", index=False) - logger.info("✅ Populated accounts table.") - - # Create the transactions table - logger.info("Processing transaction data...") - transc_df = _process_transaction_data(path_to_transc_csv) - - # Drop the "is_laundering" column and insert into the database - transc_df.drop(columns=["is_laundering", "triggered_rules"]).to_sql("transactions", conn, if_exists="append") - logger.info("✅ Populated transactions table.") - logger.info(f"✅ Populated database at {db_path}") - - # Split out a test set with the last 3 days of transactions - test_df = _curate_test_cases(transc_df) - test_df.to_csv(ddl_file_path.parent / "test_transactions.csv") - logger.info(f"✅ Created curated test set with {len(test_df)} cases.") - - conn.close() - - -if __name__ == "__main__": - main() diff --git a/pyproject.toml b/pyproject.toml index 34db2e1..d14e85d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -146,6 +146,13 @@ markers = [ "integration_test: marks tests as integration tests", ] +filterwarnings = [ + # Ignore deprecation warnings from google-adk/google-genai internal code + "ignore:deprecated:DeprecationWarning:google.adk.runners", + "ignore:Inheritance class AiohttpClientSession:DeprecationWarning:google.genai", + "ignore:[EXPERIMENTAL] BaseAgentState:UserWarning:aieng.agent_evals.aml_investigation.workflow", +] + [tool.coverage] [tool.coverage.run] source=["aieng-eval-agents/aieng"] From 13d59ee391050badeac13633c0ce4ed905e5a089 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 15:21:32 -0500 Subject: [PATCH 03/24] Remove AML investigation policy and workflow implementations --- .../agent_evals/aml_investigation/__init__.py | 18 +- .../agent_evals/aml_investigation/policy.py | 77 ------ .../agent_evals/aml_investigation/types.py | 56 ---- .../agent_evals/aml_investigation/workflow.py | 254 ------------------ 4 files changed, 1 insertion(+), 404 deletions(-) delete mode 100644 aieng-eval-agents/aieng/agent_evals/aml_investigation/policy.py delete mode 100644 aieng-eval-agents/aieng/agent_evals/aml_investigation/types.py delete mode 100644 aieng-eval-agents/aieng/agent_evals/aml_investigation/workflow.py diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py index 5addd92..78903f5 100644 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py @@ -1,17 +1 @@ -"""Utilities for the AML Investigation agent.""" - -from .policy import RuleBasedTriage, TriageOutcome, TriagePolicyConfig, TriageStrategy -from .types import AnalystResult, AnalystVerdict, SuspiciousActivityType -from .workflow import AmlInvestigationWorkflow - - -__all__ = [ - "TriageOutcome", - "TriageStrategy", - "RuleBasedTriage", - "TriagePolicyConfig", - "AnalystResult", - "AnalystVerdict", - "SuspiciousActivityType", - "AmlInvestigationWorkflow", -] +"""Utilities for AML Investigation agent.""" diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/policy.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/policy.py deleted file mode 100644 index c694f9f..0000000 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/policy.py +++ /dev/null @@ -1,77 +0,0 @@ -"""Defines triage policies for fraud investigation alerts.""" - -from abc import ABC, abstractmethod -from enum import Enum - -from pydantic import BaseModel - - -class TriageOutcome(str, Enum): - """Possible triage outcomes for fraud investigation alerts.""" - - CLOSE = "CLOSE" - INVESTIGATE = "INVESTIGATE" - ESCALATE = "ESCALATE" - - -class TriageStrategy(ABC): - """Abstract base class for triage strategies.""" - - @abstractmethod - async def triage(self, alert: dict) -> TriageOutcome: - """Return the triage outcome for the given alert. - - Parameters - ---------- - alert : dict - The alert data to be triaged. - - Returns - ------- - TriageOutcome - The outcome of the triage process. - """ - pass - - -class TriagePolicyConfig(BaseModel): - """Configuration for rule-based triage policy.""" - - auto_close_threshold: int = 35 - auto_escalate_threshold: int = 95 - - -class RuleBasedTriage(TriageStrategy): - """A simple rule-based triage strategy based on risk score. - - This simply compares the alert's risk score against predefined thresholds - to determine the triage outcome. - - Parameters - ---------- - config : TriagePolicyConfig - Configuration parameters for the triage policy. - """ - - def __init__(self, config: TriagePolicyConfig) -> None: - self.config = config - - async def triage(self, alert: dict) -> TriageOutcome: - """Triage the alert based on its risk score. - - Parameters - ---------- - alert : dict - The alert data to be triaged. Should contain a 'risk_score' key. - - Returns - ------- - TriageOutcome - The outcome of the triage process. - """ - score = alert.get("risk_score", 0) - if score < self.config.auto_close_threshold: - return TriageOutcome.CLOSE - if score > self.config.auto_escalate_threshold: - return TriageOutcome.ESCALATE - return TriageOutcome.INVESTIGATE diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/types.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/types.py deleted file mode 100644 index 58f1260..0000000 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/types.py +++ /dev/null @@ -1,56 +0,0 @@ -"""Types/Objects for the Fraud Investigation Agent.""" - -from enum import Enum - -from pydantic import BaseModel, Field - - -class AnalystVerdict(str, Enum): - """Investigation outcome classification.""" - - SUSPICIOUS = "SUSPICIOUS" # Strong evidence, SAR recommended - BENIGN = "BENIGN" # False positive, no SAR needed - NEEDS_REVIEW = "NEEDS_REVIEW" # Insufficient evidence, requires senior review - - -class SuspiciousActivityType(str, Enum): - """Standard FinCEN SAR Categories.""" - - # STRUCTURING: Breaking txns to avoid reporting thresholds (e.g. $9,900) - STRUCTURING = "structuring" - - # LAYERING: Complex movement to hide origin - LAYERING = "layering" - - # FUNDS_TRANSFERS: High velocity/volume - RAPID_MOVEMENT = "suspicious_eft_wire_transfers" - - # OTHERS - UNUSUAL_VOLUME = "unusual_volume" - TERRORIST_FINANCING = "terrorist_financing" - OTHER = "other" - - -class AnalystResult(BaseModel): - """The 'Case File' generated by the Analyst, passed through to the Reporter.""" - - # Narrative (Context for Reporter) - summary_narrative: str = Field(..., description="A 2-3 paragraph explanation of the fraud scheme.") - """2-3 paragraph explanation of the fraud scheme.""" - - # Logic - risk_score: int = Field(..., ge=0, le=100) - """Risk score assigned by the analyst, ranging from 0 to 100.""" - - verdict: AnalystVerdict - """Investigation outcome classification.""" - - # Classification - suspicious_activity_type: SuspiciousActivityType = Field( - ..., description="Dominant type of suspicious activity identified." - ) - """Dominant type of suspicious activity identified.""" - - # Hard Evidence - suspicious_transactions: str - """List of transaction identifiers deemed suspicious by the analyst.""" diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/workflow.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/workflow.py deleted file mode 100644 index 27c2888..0000000 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/workflow.py +++ /dev/null @@ -1,254 +0,0 @@ -"""Anti-Money Laundering workflow.""" - -import json -import logging -from contextlib import aclosing -from enum import Enum -from typing import TYPE_CHECKING, Any, AsyncGenerator, Optional - -import google.genai.types -from aieng.agent_evals.aml_investigation.policy import TriageOutcome, TriageStrategy -from aieng.agent_evals.aml_investigation.types import AnalystResult, AnalystVerdict -from google.adk.agents.base_agent import BaseAgent, BaseAgentState -from google.adk.events.event import Event -from pydantic import Field -from pydantic.fields import PrivateAttr - - -if TYPE_CHECKING: - from google.adk.agents.invocation_context import InvocationContext - -logger = logging.getLogger(__name__) - - -class _WorkflowStep(str, Enum): - """Steps in the AML investigation workflow.""" - - START = "START" - ANALYZE = "ANALYZE" - REPORT = "REPORT" - DONE = "DONE" - - -class _WorkflowState(BaseAgentState): - """State for the AML investigation workflow.""" - - input_dict: dict[str, Any] - step: _WorkflowStep = _WorkflowStep.START - triage_decision: TriageOutcome | None = None - analysis: AnalystResult | None = None - final_report: Optional[str] = None - - -class AmlInvestigationWorkflow(BaseAgent): - """An AML investigation workflow coordinating triage, analysis, and reporting.""" - - name: str = Field(default="AMLWorkflowManager", description="Coordinates AML investigation and reporting agents.") - description: str = Field( - default="Oversees the end-to-end AML investigation process, delegating tasks to specialized agents." - ) - - # PrivateAttr are used here to hide complex sub-agents from Pydantic validation - _triage: TriageStrategy = PrivateAttr() - _analyst: BaseAgent = PrivateAttr() - _reporter: BaseAgent = PrivateAttr() - - def __init__(self, triage_strategy: TriageStrategy, analyst: BaseAgent, reporter: BaseAgent, **kwargs: Any) -> None: - """Initialize the AML investigation workflow. - - Parameters - ---------- - triage_strategy : TriageStrategy - The triage strategy to use for initial alert assessment. - analyst : BaseAgent - The agent responsible for in-depth fraud analysis. This agent must - produce an ``AnalystResult``, which will be passed to the reporter. - reporter : BaseAgent - The agent responsible for generating the final SAR report. - **kwargs : Any - Additional keyword arguments for the ``BaseAgent`` class. - """ - super().__init__(**kwargs) - self._triage = triage_strategy - self._analyst = analyst - self._reporter = reporter - self.sub_agents = [analyst, reporter] - - async def _initialize_state(self, ctx: "InvocationContext") -> _WorkflowState: - """Initialize workflow state from context.""" - state = self._load_agent_state(ctx, _WorkflowState) - - if state: - return state - - input_text = "" - if ctx.user_content and ctx.user_content.parts: - input_text = ctx.user_content.parts[0].text or "" - - try: - alert_dict = json.loads(input_text.strip()) - except json.JSONDecodeError: - logger.warning("Failed to parse alert payload as JSON. Using raw text.") - alert_dict = {"description": input_text} - - return _WorkflowState(input_dict=alert_dict) - - async def _update_step( - self, ctx: "InvocationContext", state: _WorkflowState, new_step: _WorkflowStep - ) -> AsyncGenerator["Event", None]: - """Update workflow step and persist state.""" - state.step = new_step - ctx.set_agent_state(self.name, agent_state=state) - logger.info(ctx) - yield self._create_agent_state_event(ctx) - - async def _handle_triage(self, ctx: "InvocationContext", state: _WorkflowState) -> AsyncGenerator["Event", None]: - """Handle triage step.""" - decision = await self._triage.triage(state.input_dict) - state.triage_decision = decision - - triage_event = _create_text_event(f"Triage decision: {decision.value}", ctx) - if decision == TriageOutcome.CLOSE: - state.final_report = "CASE CLOSED: No further action required after triage." - yield _create_text_event(state.final_report, ctx) - - async for event in self._update_step(ctx, state, _WorkflowStep.DONE): - yield event - elif decision == TriageOutcome.ESCALATE: - triage_event.actions.escalate = True - yield triage_event - async for event in self._update_step(ctx, state, _WorkflowStep.DONE): - yield event - else: - triage_event.actions.transfer_to_agent = self._analyst.name - yield triage_event - async for event in self._update_step(ctx, state, _WorkflowStep.ANALYZE): - yield event - - async def _handle_analysis(self, ctx: "InvocationContext", state: _WorkflowState) -> AsyncGenerator["Event", None]: - """Handle analysis step by delegating to analyst agent.""" - analyst_ctx = ctx.model_copy() - analyst_ctx.branch = f"{ctx.branch}.{self._analyst.name}" if ctx.branch else self._analyst.name - - async with aclosing(self._analyst.run_async(analyst_ctx)) as agen: - async for event in agen: - yield event - - if event.is_final_response() and event.content and event.content.parts: - analysis_text = event.content.parts[0].text or "" - try: - analysis_dict = json.loads(analysis_text.strip()) - state.analysis = AnalystResult.model_validate(analysis_dict) - except json.JSONDecodeError: - logger.error("Failed to parse analyst result as JSON.") - - if state.analysis or ctx.should_pause_invocation(event): - break - - if state.analysis: - async for e in self._route_analysis_result(ctx, state): - yield e - else: - async for e in self._update_step(ctx, state, _WorkflowStep.DONE): - yield e - - async def _route_analysis_result( - self, ctx: "InvocationContext", state: _WorkflowState - ) -> AsyncGenerator["Event", None]: - """Route based on analysis verdict.""" - if not state.analysis: - return - - verdict = state.analysis.verdict - - if verdict == AnalystVerdict.SUSPICIOUS: - reporter_msg = f"""\ - # Analyst Findings - **Analyst Risk Score**: {state.analysis.risk_score} - **Analyst Verdict**: {state.analysis.verdict.value} - ## Summary Narrative - {state.analysis.summary_narrative} - ## Key Findings - **Suspicious Activity Type**: {state.analysis.suspicious_activity_type.value} - ### Suspicious Transactions - {state.analysis.suspicious_transactions} - """ - analyst_event = _create_text_event(reporter_msg, ctx) - analyst_event.actions.transfer_to_agent = self._reporter.name - yield analyst_event - - async for e in self._update_step(ctx, state, _WorkflowStep.REPORT): - yield e - elif verdict == AnalystVerdict.NEEDS_REVIEW: - state.final_report = ( - f"ESCALATED TO HUMAN: Further review required.\n" - f"Reason: {state.analysis.summary_narrative}\n" - f"Evidence: Identified {len(state.analysis.suspicious_transactions)} suspicious transactions." - ) - analyst_event = _create_text_event(state.final_report, ctx) - analyst_event.actions.escalate = True - yield analyst_event - - async for e in self._update_step(ctx, state, _WorkflowStep.DONE): - yield e - else: - state.final_report = ( - f"CASE CLOSED (False Positive).\n" - f"Reason: {state.analysis.summary_narrative}\n" - f"Evidence: Investigated {len(state.analysis.suspicious_transactions)} transactions and found no illicit pattern." - ) - yield _create_text_event(state.final_report, ctx) - - async for e in self._update_step(ctx, state, _WorkflowStep.DONE): - yield e - - async def _handle_reporting(self, ctx: "InvocationContext", state: _WorkflowState) -> AsyncGenerator["Event", None]: - """Handle reporting step by delegating to reporter agent.""" - if not state.analysis: - return - - reporter_ctx = ctx.model_copy() - reporter_ctx.branch = f"{ctx.branch}.{self._reporter.name}" if ctx.branch else self._reporter.name - - async with aclosing(self._reporter.run_async(reporter_ctx)) as agen: - async for event in agen: - yield event - - if event.is_final_response() and event.content and event.content.parts: - state.final_report = event.content.parts[0].text - break - - if ctx.should_pause_invocation(event): - return - - async for e in self._update_step(ctx, state, _WorkflowStep.DONE): - yield e - - async def _run_async_impl(self, ctx: "InvocationContext") -> AsyncGenerator["Event", None]: - """Run the AML investigation workflow.""" - state = await self._initialize_state(ctx) - - if state.step == _WorkflowStep.START: - async for event in self._handle_triage(ctx, state): - yield event - - if state.step == _WorkflowStep.ANALYZE: - async for event in self._handle_analysis(ctx, state): - yield event - - if state.step == _WorkflowStep.REPORT and state.analysis: - async for event in self._handle_reporting(ctx, state): - yield event - - if state.step == _WorkflowStep.DONE: - ctx.set_agent_state(self.name, end_of_agent=True) - yield self._create_agent_state_event(ctx) - - -def _create_text_event(text: str, ctx: "InvocationContext") -> Event: - """Create a text event to be sent back to the user.""" - return Event( - invocation_id=ctx.invocation_id, - author=ctx.agent.name, - content=google.genai.types.Content(role="model", parts=[google.genai.types.Part(text=text)]), - ) From 8ae76f6fb9b224b2907785a8e00cf4c641935518 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 15:21:52 -0500 Subject: [PATCH 04/24] Add ReadOnlySqlPolicy to enforce SQL statement restrictions --- .../aieng/agent_evals/tools/__init__.py | 4 +- .../aieng/agent_evals/tools/sql_database.py | 153 ++++++++++++++++-- 2 files changed, 143 insertions(+), 14 deletions(-) diff --git a/aieng-eval-agents/aieng/agent_evals/tools/__init__.py b/aieng-eval-agents/aieng/agent_evals/tools/__init__.py index 604f953..0c452d6 100644 --- a/aieng-eval-agents/aieng/agent_evals/tools/__init__.py +++ b/aieng-eval-agents/aieng/agent_evals/tools/__init__.py @@ -1,6 +1,6 @@ """Tools.""" -from .sql_database import ReadOnlySqlDatabase +from .sql_database import ReadOnlySqlDatabase, ReadOnlySqlPolicy -__all__ = ["ReadOnlySqlDatabase"] +__all__ = ["ReadOnlySqlDatabase", "ReadOnlySqlPolicy"] diff --git a/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py b/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py index 6be9bf8..92f3f89 100644 --- a/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py +++ b/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py @@ -1,6 +1,7 @@ """SQL Database Tool with Read-Only Enforcement for Agents.""" import logging +from dataclasses import dataclass from datetime import datetime from typing import Optional @@ -11,6 +12,31 @@ logger = logging.getLogger(__name__) +__all__ = ["ReadOnlySqlDatabase", "ReadOnlySqlPolicy"] + + +@dataclass(frozen=True) +class ReadOnlySqlPolicy: + """Policy controlling which SQL statements can execute.""" + + allowed_roots: tuple[str, ...] = ("select", "union", "paren") + forbidden_nodes: tuple[str, ...] = ( + "create", + "insert", + "update", + "delete", + "drop", + "alter", + "truncate_table", + "merge", + "command", + "pragma", + "attach", + "detach", + "set", + ) + allow_multiple_statements: bool = False + class ReadOnlySqlDatabase: """A SQL database query tool for Agents. @@ -32,8 +58,20 @@ class ReadOnlySqlDatabase: Maximum execution time for queries in seconds. agent_name : str, default="UnknownAgent" Name of the agent using this tool (for audit logs). + policy : Optional[ReadOnlySqlPolicy], default=None + AST policy controlling what statements are permitted. If ``None``, + uses ``ReadOnlySqlPolicy()`` defaults. **kwargs : Any Additional keyword arguments passed to SQLAlchemy's ``create_engine`` function. + + Raises + ------ + ValueError + If any of the parameters are invalid. + PermissionError + If a query attempts to perform a write operation. + Exception + For any database execution errors. """ def __init__( @@ -42,42 +80,91 @@ def __init__( max_rows: int = 100, query_timeout_sec: int = 60, agent_name: str = "UnknownAgent", + policy: ReadOnlySqlPolicy | None = None, **kwargs, ) -> None: - """Initialize the database tool.""" + """Initialize the database tool. + + Note + ---- + ``policy`` is validated at runtime (to fail fast on misconfiguration), + even though the public type signature constrains it. + """ + if not connection_uri or not connection_uri.strip(): + raise ValueError("connection_uri must be a non-empty string.") + if max_rows <= 0: + raise ValueError("max_rows must be a positive integer.") + if query_timeout_sec <= 0: + raise ValueError("query_timeout_sec must be a positive integer.") + if not agent_name or not agent_name.strip(): + raise ValueError("agent_name must be a non-empty string.") + if policy is not None and not isinstance(policy, ReadOnlySqlPolicy): + raise TypeError("policy must be a ReadOnlySqlPolicy or None.") + self.engine = create_engine(connection_uri, **kwargs) self.agent_name = agent_name self.max_rows = max_rows self.timeout = query_timeout_sec + self.policy = policy or ReadOnlySqlPolicy() + if not self.policy.allowed_roots: + raise ValueError("policy.allowed_roots must not be empty.") + self._allowed_root_types = _resolve_sqlglot_expression_types(self.policy.allowed_roots) + self._forbidden_node_types = _resolve_sqlglot_expression_types(self.policy.forbidden_nodes) def _is_safe_readonly_query(self, query: str) -> bool: """Verify that query is semantically read-only using a SQL Parser (SQLGlot). - Blocks: DELETE, UPDATE, INSERT, DROP, ALTER, etc. - Allows: SELECT, WITH (if read-only), EXPLAIN. + Blocks: Based on ``self.policy.forbidden_nodes``. + Allows: Based on ``self.policy.allowed_roots``. + + Notes + ----- + - SQLGlot may parse ``WITH ... SELECT ...`` as a ``Select`` root expression, + so CTE usage is gated by scanning for CTE/With nodes unless ``"with"`` is + present in ``allowed_roots``. + - If parsing fails, this method fails closed and returns ``False``. """ try: # Parse the query into an AST (Abstract Syntax Tree) expressions = sqlglot.parse(query) + is_safe = True if not expressions: # Assume unsafe if we can't parse anything logger.warning("Empty parse result - blocking query") - return False + is_safe = False + + if is_safe and not self.policy.allow_multiple_statements and len(expressions) > 1: + logger.warning("Multiple statements blocked by policy") + is_safe = False + + allowed_root_names = {name.lower() for name in self.policy.allowed_roots} for expression in expressions: + if not is_safe: + break + # Check Root Expression Type - if not isinstance(expression, (exp.Select, exp.Union, exp.Paren)): + if not isinstance(expression, self._allowed_root_types): logger.warning("Blocked Unsafe Query Type: %s", type(expression)) - return False + is_safe = False + break + + # SQLGlot may parse CTEs as part of a SELECT/UNION tree, so we + # explicitly gate CTE usage on the presence of "with" in the policy. + if "with" not in allowed_root_names and expression.find(exp.With, exp.CTE): + logger.warning("CTE usage blocked by policy") + is_safe = False + break # Deep Search for Forbidden Nodes anywhere in the AST # Catches hidden writes inside CTEs or Subqueries - if expression.find(exp.Delete, exp.Update, exp.Insert, exp.Drop, exp.Alter, exp.TruncateTable): + if self._forbidden_node_types and expression.find(*self._forbidden_node_types): logger.warning("Blocked Query containing Write operation in AST") - return False + is_safe = False + break - return True + return is_safe except Exception as e: logger.error("SQL Parsing Error: %s", e) # If we can't parse it, we don't run it. @@ -151,8 +238,9 @@ def execute(self, query: str) -> str: Returns ------- - list[Any] - List of result rows (as tuples or dicts depending on config). + str + A markdown-formatted table with a header and up to ``max_rows`` rows, + or a ``"Query Error: ..."`` string on failure. Raises ------ @@ -165,7 +253,6 @@ def execute(self, query: str) -> str: ----- This method enforces a limit on the number of rows returned and logs all query attempts for compliance auditing. - """ start_time = datetime.now() status = "FAILED" @@ -231,3 +318,45 @@ def execute(self, query: str) -> str: def close(self) -> None: """Dispose of the connection pool.""" self.engine.dispose(close=True) + + +def _resolve_sqlglot_expression_type(name: str) -> type[exp.Expression]: + """Resolve a sqlglot expression name (e.g. ``"select"``) to an Expression class. + + Accepts a few common spellings: + - ``"Select"`` or ``"select"`` (camel-cased automatically) + - ``"truncate_table"`` or ``"TruncateTable"`` + - ``"exp.Select"`` (module prefix is ignored) + + Raises ``ValueError`` if the name cannot be resolved to a subclass of + ``sqlglot.exp.Expression``. + """ + cleaned = name.strip() + if not cleaned: + raise ValueError("Expression type name cannot be empty.") + + if cleaned.startswith("exp."): + cleaned = cleaned[4:] + + candidate = cleaned.replace("-", "_") + camel = "".join(part.capitalize() for part in candidate.split("_")) + + found_non_expression = False + for attr in dict.fromkeys((cleaned, candidate, camel)): # preserve order + de-dupe + if not attr: + continue + resolved = getattr(exp, attr, None) + if resolved is None: + continue + if isinstance(resolved, type) and issubclass(resolved, exp.Expression): + return resolved + found_non_expression = True + + if found_non_expression: + raise ValueError(f"sqlglot expression name {name!r} did not resolve to an Expression type.") + raise ValueError(f"Unknown sqlglot expression type: {name!r}") + + +def _resolve_sqlglot_expression_types(names: tuple[str, ...]) -> tuple[type[exp.Expression], ...]: + """Resolve many sqlglot expression names into Expression classes.""" + return tuple(_resolve_sqlglot_expression_type(name) for name in names) From 743159e52a3805e441624b7ae25367d089035d76 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 15:22:06 -0500 Subject: [PATCH 05/24] Refactor AML case model to replace suspected_pattern_type with trigger_label and add new review labels --- .../agent_evals/aml_investigation/data/cases.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py index 2c3ff12..6d389b3 100644 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py @@ -67,6 +67,8 @@ class LaunderingPattern(str, Enum): _RANDOM_PATTERN_TYPES = [pattern.value for pattern in LaunderingPattern if pattern not in {LaunderingPattern.NONE}] +_LOW_SIGNAL_REVIEW_LABELS = ["QA_SAMPLE", "RANDOM_REVIEW", "RETROSPECTIVE_REVIEW", "MODEL_MONITORING_SAMPLE"] +_FALSE_POSITIVE_TRIGGER_LABELS = ["ANOMALOUS_BEHAVIOR_ALERT", "LAW_ENFORCEMENT_REFERRAL", "EXTERNAL_TIP"] class CaseFile(BaseModel): @@ -81,8 +83,8 @@ class CaseFile(BaseModel): """The timestamp of the seed transaction.""" window_start: str """The start timestamp of the case window.""" - suspected_pattern_type: str - """The type of laundering pattern suspected in the case.""" + trigger_label: str + """Upstream alert/review trigger label or heuristic hint for this case.""" class GroundTruth(BaseModel): @@ -196,7 +198,7 @@ def parse_patterns_file(path: str | Path, lookback_days: int = 0, min_timestamp: seed_transaction_id=seed_txn["transaction_id"], seed_timestamp=seed_txn["timestamp"], window_start=window_start, - suspected_pattern_type=current["pattern_type"], + trigger_label=current["pattern_type"], ) groundtruth = GroundTruth( is_laundering=True, @@ -333,7 +335,7 @@ def _build_false_negative_cases( seed_transaction_id=case.case.seed_transaction_id, seed_timestamp=case.case.seed_timestamp, window_start=case.case.window_start, - suspected_pattern_type="UNKNOWN", + trigger_label=random.choice(_LOW_SIGNAL_REVIEW_LABELS), ) groundtruth = GroundTruth( is_laundering=case.groundtruth.is_laundering, @@ -383,7 +385,7 @@ def _build_false_positive_cases(transc_df: pd.DataFrame, num_false_positive_case seed_transaction_id=seed_row["transaction_id"], seed_timestamp=seed_row["timestamp"], window_start=_date_window_start(window["date"]), - suspected_pattern_type=random.choice(_RANDOM_PATTERN_TYPES), + trigger_label=random.choice(_FALSE_POSITIVE_TRIGGER_LABELS + _RANDOM_PATTERN_TYPES), ) groundtruth = GroundTruth( is_laundering=False, @@ -421,7 +423,7 @@ def _build_normal_cases( seed_transaction_id=row["transaction_id"], seed_timestamp=row["timestamp"], window_start=window_start, - suspected_pattern_type=LaunderingPattern.NONE.value, + trigger_label=random.choice(_LOW_SIGNAL_REVIEW_LABELS), ) groundtruth = GroundTruth( is_laundering=False, From ab0693f710bcacc89dbc2ff5561b6f65322bb79a Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 15:22:16 -0500 Subject: [PATCH 06/24] Add unit tests for AML case generation and parsing utilities --- .../aml_investigation/data/test_cases.py | 257 ++++++++++++++++++ .../aml_investigation/data/test_utils.py | 119 ++++++++ .../agent_evals/tools/test_sql_database.py | 239 +++++++++++++++- 3 files changed, 610 insertions(+), 5 deletions(-) create mode 100644 aieng-eval-agents/tests/aieng/agent_evals/aml_investigation/data/test_cases.py create mode 100644 aieng-eval-agents/tests/aieng/agent_evals/aml_investigation/data/test_utils.py diff --git a/aieng-eval-agents/tests/aieng/agent_evals/aml_investigation/data/test_cases.py b/aieng-eval-agents/tests/aieng/agent_evals/aml_investigation/data/test_cases.py new file mode 100644 index 0000000..de95a50 --- /dev/null +++ b/aieng-eval-agents/tests/aieng/agent_evals/aml_investigation/data/test_cases.py @@ -0,0 +1,257 @@ +"""Tests for AML case generation/parsing utilities.""" + +from pathlib import Path + +import pandas as pd +import pytest +from aieng.agent_evals.aml_investigation.data.cases import LaunderingPattern, build_cases, parse_patterns_file + + +@pytest.fixture() +def patterns_file(tmp_path: Path) -> Path: + """Write a minimal Patterns.txt fixture and return its path.""" + patterns_path = tmp_path / "Patterns.txt" + patterns_path.write_text( + "\n".join( + [ + "BEGIN LAUNDERING ATTEMPT - CYCLE: Max 5 hops", + "2022/08/01 08:35,011,800D7AE80,008777,8034B4510,101883.33,Yuan,101883.33,Yuan,ACH,1", + "2022/08/02 12:33,008777,8034B4510,021514,801089BD0,12575.99,Euro,12575.99,Euro,ACH,1", + "2022/08/22 16:07,021514,801089BD0,001148,801AC0540,11763.23,Euro,11763.23,Euro,ACH,1", + "2022/08/26 08:24,001148,801AC0540,011081,8056E3D30,13436.63,US Dollar,13436.63,US Dollar,ACH,1", + "2022/08/31 18:45,011081,8056E3D30,011,800D7AE80,12442.10,US Dollar,12442.10,US Dollar,ACH,1", + "END LAUNDERING ATTEMPT - CYCLE", + "", + "BEGIN LAUNDERING ATTEMPT - STACK", + "2022/08/09 12:30,005,80C1F0980,023,811AF7CE0,362288.79,Yen,362288.79,Yen,ACH,1", + "2022/08/13 23:55,023,811AF7CE0,005,80824CF90,353026.47,Yen,353026.47,Yen,ACH,1", + "2022/08/03 18:12,005,80824CF90,013,80008C270,257267.97,Yen,257267.97,Yen,ACH,1", + "2022/08/16 02:12,013,80008C270,023,811AF7C40,263730.95,Yen,263730.95,Yen,ACH,1", + "2022/08/13 18:01,023,811AF7C40,013,80824FD40,142445.81,Yen,142445.81,Yen,ACH,1", + "2022/08/15 10:53,013,80824FD40,005,80C1F0980,141030.04,Yen,141030.04,Yen,ACH,1", + "2022/08/05 13:32,0022177,80824F9D0,005,80008D800,682100.98,Yen,682100.98,Yen,ACH,1", + "2022/08/30 16:15,005,80008D800,0022177,80824F9D0,660159.30,Yen,660159.30,Yen,ACH,1", + "END LAUNDERING ATTEMPT - STACK", + "", + "BEGIN LAUNDERING ATTEMPT - GATHER-SCATTER: Max 2-degree Fan-In", + "2022/08/02 00:04,0074695,81C54C0B0,0282,81C54C100,79123.59,Ruble,79123.59,Ruble,ACH,1", + "2022/08/13 00:19,0173410,81C54C150,0282,81C54C100,748241.32,Ruble,748241.32,Ruble,ACH,1", + "2022/08/13 13:38,0282,81C54C100,0282,81C54C100,714784.82,Ruble,714784.82,Ruble,ACH,1", + "2022/09/11 13:00,0282,81C54C100,0074695,81C54C0B0,1503619.96,Ruble,1503619.96,Ruble,ACH,1", + "END LAUNDERING ATTEMPT - GATHER-SCATTER", + "", + ] + ), + encoding="utf-8", + ) + return patterns_path + + +@pytest.fixture() +def transactions_df() -> pd.DataFrame: + """Return a tiny transactions DataFrame suitable for build_cases().""" + return pd.DataFrame( + [ + { + "timestamp": "2022-08-01T08:00:00", + "transaction_id": "txn-001", + "is_laundering": 0, + "from_account": "acct-A", + }, + { + "timestamp": "2022-08-01T09:00:00", + "transaction_id": "txn-002", + "is_laundering": 0, + "from_account": "acct-A", + }, + { + "timestamp": "2022-08-02T10:00:00", + "transaction_id": "txn-003", + "is_laundering": 1, + "from_account": "acct-B", + }, + { + "timestamp": "2022-08-03T11:00:00", + "transaction_id": "txn-004", + "is_laundering": 0, + "from_account": "acct-C", + }, + { + "timestamp": "2022-08-03T12:00:00", + "transaction_id": "txn-005", + "is_laundering": 0, + "from_account": "acct-D", + }, + ] + ) + + +def test_parse_patterns_file_parses_attempts_and_sets_seed_and_window(patterns_file: Path) -> None: + """Parse multiple attempts and build CaseRecord objects.""" + min_timestamp = "2022-08-01T00:00:00" + cases = parse_patterns_file(patterns_file, lookback_days=10, min_timestamp=min_timestamp) + assert len(cases) == 3 + + expected_by_pattern = { + LaunderingPattern.CYCLE: ("2022-08-31T18:45:00", 5), + LaunderingPattern.STACK: ("2022-08-30T16:15:00", 8), + LaunderingPattern.GATHER_SCATTER: ("2022-09-11T13:00:00", 4), + } + + for record in cases: + assert record.groundtruth.is_laundering is True + assert record.case.seed_transaction_id + assert record.case.case_id + assert record.case.window_start == min_timestamp + + seed_timestamp, expected_count = expected_by_pattern[record.groundtruth.pattern_type] + assert record.case.seed_timestamp == seed_timestamp + + attempt_ids = [item.strip() for item in record.groundtruth.attempt_transaction_ids.split(",") if item.strip()] + assert len(attempt_ids) == expected_count + assert attempt_ids[-1] == record.case.seed_transaction_id + + +def test_parse_patterns_file_rejects_negative_lookback(patterns_file: Path) -> None: + """Reject negative lookback windows.""" + with pytest.raises(ValueError): + parse_patterns_file(patterns_file, lookback_days=-1) + + +def test_parse_patterns_file_raises_for_missing_file(tmp_path: Path) -> None: + """Raise FileNotFoundError when the patterns file does not exist.""" + with pytest.raises(FileNotFoundError): + parse_patterns_file(tmp_path / "missing.txt") + + +@pytest.mark.parametrize( + "contents", + [ + # Unterminated block. + "\n".join( + [ + "BEGIN LAUNDERING ATTEMPT - CYCLE", + "2022/08/01 08:35,011,800D7AE80,008777,8034B4510,101883.33,Yuan,101883.33,Yuan,ACH,1", + ] + ), + # Malformed transaction row (<11 columns). + "\n".join( + [ + "BEGIN LAUNDERING ATTEMPT - CYCLE", + "2022/08/01 08:35,011", + "END LAUNDERING ATTEMPT - CYCLE", + ] + ), + ], +) +def test_parse_patterns_file_rejects_malformed_patterns(tmp_path: Path, contents: str) -> None: + """Reject malformed patterns files.""" + patterns_path = tmp_path / "Patterns.txt" + patterns_path.write_text(contents, encoding="utf-8") + with pytest.raises(ValueError): + parse_patterns_file(patterns_path) + + +def test_parse_patterns_file_rejects_invalid_min_timestamp(patterns_file: Path) -> None: + """Reject invalid min_timestamp values.""" + with pytest.raises(ValueError): + parse_patterns_file(patterns_file, min_timestamp="not-a-timestamp") + + +def test_build_cases_builds_each_case_type(patterns_file: Path, transactions_df: pd.DataFrame) -> None: + """Build a mix of laundering/false-negative/false-positive/normal cases.""" + cases = build_cases( + patterns_filepath=patterns_file, + transactions=transactions_df, + num_laundering_cases=1, + num_false_positive_cases=1, + num_false_negative_cases=1, + num_normal_cases=1, + lookback_days=10, + ) + assert len(cases) == 4 + + low_signal_labels = {"QA_SAMPLE", "RANDOM_REVIEW", "RETROSPECTIVE_REVIEW", "MODEL_MONITORING_SAMPLE"} + laundering = [ + case + for case in cases + if case.groundtruth.is_laundering and case.case.trigger_label == case.groundtruth.pattern_type.value + ] + false_negatives = [ + case for case in cases if case.groundtruth.is_laundering and case.case.trigger_label in low_signal_labels + ] + false_positives = [ + case + for case in cases + if (not case.groundtruth.is_laundering) and case.case.trigger_label not in low_signal_labels + ] + normals = [ + case + for case in cases + if (not case.groundtruth.is_laundering) and case.case.trigger_label in low_signal_labels + ] + + assert len(laundering) == 1 + assert laundering[0].groundtruth.pattern_type in { + LaunderingPattern.CYCLE, + LaunderingPattern.STACK, + LaunderingPattern.GATHER_SCATTER, + } + + assert len(false_negatives) == 1 + assert false_negatives[0].groundtruth.is_laundering is True + assert false_negatives[0].groundtruth.pattern_type != LaunderingPattern.NONE + + assert len(false_positives) == 1 + assert false_positives[0].groundtruth.pattern_type == LaunderingPattern.NONE + + assert len(normals) == 1 + assert normals[0].groundtruth.pattern_type == LaunderingPattern.NONE + + +@pytest.mark.parametrize( + ("transactions", "kwargs", "expected_error"), + [ + (object(), {"lookback_days": 0}, TypeError), + (pd.DataFrame(), {"lookback_days": -1}, ValueError), + (pd.DataFrame(), {"lookback_days": 0, "num_laundering_cases": -1}, ValueError), + (pd.DataFrame(), {"lookback_days": 0, "num_false_positive_cases": -1}, ValueError), + (pd.DataFrame(), {"lookback_days": 0, "num_false_negative_cases": -1}, ValueError), + (pd.DataFrame(), {"lookback_days": 0, "num_normal_cases": -1}, ValueError), + ( + pd.DataFrame([{"timestamp": "2022-08-01T08:00:00", "transaction_id": "x", "is_laundering": 0}]), + {"lookback_days": 0}, + ValueError, + ), + ], +) +def test_build_cases_validates_inputs( + patterns_file: Path, transactions: object, kwargs: dict, expected_error: type[Exception] +) -> None: + """Validate input types/values and required transaction columns.""" + base_kwargs = dict( + patterns_filepath=patterns_file, + transactions=transactions, + num_laundering_cases=0, + num_false_positive_cases=0, + num_false_negative_cases=0, + num_normal_cases=0, + lookback_days=0, + ) + base_kwargs.update(kwargs) + with pytest.raises(expected_error): + build_cases(**base_kwargs) + + +def test_build_cases_raises_for_missing_patterns_file(transactions_df: pd.DataFrame, tmp_path: Path) -> None: + """Propagate FileNotFoundError when patterns_filepath does not exist.""" + with pytest.raises(FileNotFoundError): + build_cases( + patterns_filepath=tmp_path / "missing.txt", + transactions=transactions_df, + num_laundering_cases=0, + num_false_positive_cases=0, + num_false_negative_cases=0, + num_normal_cases=0, + lookback_days=0, + ) diff --git a/aieng-eval-agents/tests/aieng/agent_evals/aml_investigation/data/test_utils.py b/aieng-eval-agents/tests/aieng/agent_evals/aml_investigation/data/test_utils.py new file mode 100644 index 0000000..ebfb148 --- /dev/null +++ b/aieng-eval-agents/tests/aieng/agent_evals/aml_investigation/data/test_utils.py @@ -0,0 +1,119 @@ +"""Tests for AML dataset utilities.""" + +from __future__ import annotations + +import pandas as pd +import pytest +from aieng.agent_evals.aml_investigation.data import utils + + +def test_download_dataset_file_validates_inputs() -> None: + """Reject unsupported download option values.""" + with pytest.raises(ValueError): + utils.download_dataset_file(illicit_ratio="NOPE") # type: ignore[arg-type] + with pytest.raises(ValueError): + utils.download_dataset_file(transactions_size="Huge") # type: ignore[arg-type] + with pytest.raises(ValueError): + utils.download_dataset_file(filename="Nope.csv") # type: ignore[arg-type] + + +def test_download_dataset_file_calls_kagglehub(monkeypatch: pytest.MonkeyPatch) -> None: + """Call kagglehub with the expected handle/path and return its result.""" + calls: list[tuple[str, str]] = [] + + def fake_download(*, handle: str, path: str) -> str: + calls.append((handle, path)) + return "/tmp/fake" + + monkeypatch.setattr(utils.kagglehub, "dataset_download", fake_download) + result = utils.download_dataset_file(illicit_ratio="HI", transactions_size="Small", filename="Trans.csv") + + assert result == "/tmp/fake" + assert calls == [("ealtman2019/ibm-transactions-for-anti-money-laundering-aml", "HI-Small_Trans.csv")] + + +def test_normalize_transactions_data_requires_expected_columns() -> None: + """Raise when required Kaggle columns are missing.""" + with pytest.raises(ValueError): + utils.normalize_transactions_data(pd.DataFrame([{"Timestamp": "2022/01/01 00:00"}])) + + +def test_normalize_transactions_data_normalizes_and_dedupes() -> None: + """Normalize columns, compute transaction_id, drop duplicates, sort by timestamp.""" + df = pd.DataFrame( + [ + { + "Timestamp": "2022/08/01 09:00", + "From Bank": 1, + "Account": " acct-A ", + "To Bank": 2, + "Account.1": " acct-B ", + "Amount Received": "10.00", + "Receiving Currency": " USD ", + "Amount Paid": "10.00", + "Payment Currency": " USD ", + "Payment Format": " ACH ", + "Is Laundering": 0, + }, + # Earlier timestamp to confirm sorting. + { + "Timestamp": "2022/08/01 08:00", + "From Bank": 1, + "Account": "acct-A", + "To Bank": 2, + "Account.1": "acct-B", + "Amount Received": "10", + "Receiving Currency": "USD", + "Amount Paid": "10", + "Payment Currency": "USD", + "Payment Format": "ACH", + "Is Laundering": 0, + }, + # Duplicate of the first row to confirm dedupe on transaction_id. + { + "Timestamp": "2022/08/01 09:00", + "From Bank": 1, + "Account": "acct-A", + "To Bank": 2, + "Account.1": "acct-B", + "Amount Received": "10.00", + "Receiving Currency": "USD", + "Amount Paid": "10.00", + "Payment Currency": "USD", + "Payment Format": "ACH", + "Is Laundering": 0, + }, + ] + ) + + normalized = utils.normalize_transactions_data(df) + assert {"timestamp", "transaction_id", "from_account", "to_account", "is_laundering"}.issubset(normalized.columns) + assert normalized["timestamp"].tolist() == ["2022-08-01T08:00:00", "2022-08-01T09:00:00"] + assert normalized["from_account"].tolist() == ["acct-A", "acct-A"] + assert normalized["to_account"].tolist() == ["acct-B", "acct-B"] + assert normalized["transaction_id"].notna().all() + assert normalized["transaction_id"].nunique() == len(normalized) + + +@pytest.mark.parametrize( + ("base_timestamp", "lookback_days", "min_timestamp", "expected"), + [ + ("2022/08/03 12:00", 0, None, "2022-08-03T12:00:00"), + ("2022-08-03 12:00:00", 1, None, "2022-08-02T12:00:00"), + ("2022-08-03T12:00:00", 10, "2022-08-01T00:00:00", "2022-08-01T00:00:00"), + ("2022-08-03T12:00:00", 1, "2022-08-01T00:00:00", "2022-08-02T12:00:00"), + ], +) +def test_apply_lookback_window_computes_expected_start( + base_timestamp: str, lookback_days: int, min_timestamp: str | None, expected: str +) -> None: + """Compute window start and apply optional minimum timestamp clamp.""" + assert utils.apply_lookback_window(base_timestamp, lookback_days, min_timestamp=min_timestamp) == expected + + +def test_apply_lookback_window_validates_inputs() -> None: + """Reject invalid lookback window inputs.""" + with pytest.raises(ValueError): + utils.apply_lookback_window("2022-08-01T00:00:00", -1) + with pytest.raises(ValueError): + utils.apply_lookback_window("", 0) diff --git a/aieng-eval-agents/tests/aieng/agent_evals/tools/test_sql_database.py b/aieng-eval-agents/tests/aieng/agent_evals/tools/test_sql_database.py index 52d17b5..5ef21ce 100644 --- a/aieng-eval-agents/tests/aieng/agent_evals/tools/test_sql_database.py +++ b/aieng-eval-agents/tests/aieng/agent_evals/tools/test_sql_database.py @@ -1,8 +1,237 @@ -"""Test example implementation.""" +"""Tests for the ReadOnlySqlDatabase tool.""" -from aieng.agent_evals.impl import example_impl +from __future__ import annotations +import pytest +from aieng.agent_evals.tools import ReadOnlySqlDatabase, ReadOnlySqlPolicy, sql_database +from sqlalchemy import create_engine -def test_example_impl(): - """Test example implementation.""" - assert example_impl() == "example_impl" + +@pytest.fixture() +def sqlite_db_uri(tmp_path) -> str: + """Create a tiny SQLite database on disk and return its SQLAlchemy URI.""" + db_path = tmp_path / "test_readonly_sql_database.db" + engine = create_engine(f"sqlite:///{db_path}") + + try: + with engine.begin() as conn: + conn.exec_driver_sql( + """ + CREATE TABLE customers ( + customer_id INTEGER PRIMARY KEY, + name TEXT NOT NULL, + risk_score INTEGER NOT NULL + ) + """ + ) + conn.exec_driver_sql( + """ + CREATE TABLE transactions ( + transaction_id INTEGER PRIMARY KEY, + customer_id INTEGER NOT NULL, + amount REAL NOT NULL, + created_at TEXT NOT NULL, + FOREIGN KEY(customer_id) REFERENCES customers(customer_id) + ) + """ + ) + conn.exec_driver_sql( + """ + INSERT INTO customers (customer_id, name, risk_score) VALUES + (1, 'Alice', 10), + (2, 'Bob', 80) + """ + ) + conn.exec_driver_sql( + """ + INSERT INTO transactions (transaction_id, customer_id, amount, created_at) VALUES + (100, 1, 12.34, '2024-01-01T00:00:00Z'), + (101, 1, 2500.00, '2024-01-02T00:00:00Z'), + (102, 2, 99.99, '2024-01-03T00:00:00Z') + """ + ) + conn.exec_driver_sql( + """ + CREATE VIEW customer_totals AS + SELECT + c.customer_id, + c.name, + SUM(t.amount) AS total_amount + FROM customers c + JOIN transactions t ON t.customer_id = c.customer_id + GROUP BY c.customer_id, c.name + """ + ) + + return f"sqlite:///{db_path}" + finally: + engine.dispose() + + +@pytest.fixture() +def default_db(sqlite_db_uri: str) -> ReadOnlySqlDatabase: + """Return a ReadOnlySqlDatabase instance using default settings.""" + return ReadOnlySqlDatabase(connection_uri=sqlite_db_uri) + + +def test_readonly_sql_database_initializes_with_defaults(sqlite_db_uri: str) -> None: + """Initialize with defaults and store settings.""" + db = ReadOnlySqlDatabase(connection_uri=sqlite_db_uri) + assert db.engine.dialect.name == "sqlite" + assert db.agent_name == "UnknownAgent" + assert db.max_rows == 100 + assert db.timeout == 60 + assert isinstance(db.policy, ReadOnlySqlPolicy) + + +def test_readonly_sql_database_initializes_with_custom_policy(sqlite_db_uri: str) -> None: + """Initialize with a custom policy and custom settings.""" + policy = ReadOnlySqlPolicy(allowed_roots=("select", "with", "union", "paren")) + db = ReadOnlySqlDatabase(connection_uri=sqlite_db_uri, policy=policy, agent_name="test-agent", max_rows=5) + assert db.agent_name == "test-agent" + assert db.max_rows == 5 + assert db.policy.allowed_roots == ("select", "with", "union", "paren") + + +@pytest.mark.parametrize( + ("kwargs", "expected_error"), + [ + ({"connection_uri": ""}, ValueError), + ({"connection_uri": " "}, ValueError), + ({"connection_uri": "sqlite:///:memory:", "max_rows": 0}, ValueError), + ({"connection_uri": "sqlite:///:memory:", "query_timeout_sec": 0}, ValueError), + ({"connection_uri": "sqlite:///:memory:", "agent_name": ""}, ValueError), + ({"connection_uri": "sqlite:///:memory:", "policy": object()}, TypeError), + ], +) +def test_readonly_sql_database_rejects_bad_init_args(kwargs: dict, expected_error: type[Exception]) -> None: + """Reject invalid initialization arguments.""" + with pytest.raises(expected_error): + ReadOnlySqlDatabase(**kwargs) + + +def test_readonly_sql_database_rejects_empty_allowed_roots(sqlite_db_uri: str) -> None: + """Reject policies with an empty allowlist.""" + policy = ReadOnlySqlPolicy(allowed_roots=()) + with pytest.raises(ValueError): + ReadOnlySqlDatabase(connection_uri=sqlite_db_uri, policy=policy) + + +def test_readonly_sql_database_rejects_unknown_sqlglot_expression_types(sqlite_db_uri: str) -> None: + """Reject policies that reference unknown sqlglot expression names.""" + policy = ReadOnlySqlPolicy(allowed_roots=("select", "no_such_expression")) + with pytest.raises(ValueError): + ReadOnlySqlDatabase(connection_uri=sqlite_db_uri, policy=policy) + + +def test_is_safe_readonly_query_allows_select_by_default(default_db: ReadOnlySqlDatabase) -> None: + """Allow a simple SELECT by default.""" + assert default_db._is_safe_readonly_query("SELECT customer_id, name FROM customers") is True + + +def test_is_safe_readonly_query_blocks_with_by_default(default_db: ReadOnlySqlDatabase) -> None: + """Block CTE usage unless explicitly allowed by policy.""" + assert default_db._is_safe_readonly_query("WITH x AS (SELECT 1) SELECT * FROM x") is False + + +@pytest.mark.parametrize("query", ["", " ", "PRAGMA table_info(customers)"]) +def test_is_safe_readonly_query_blocks_unsafe_or_empty_roots(default_db: ReadOnlySqlDatabase, query: str) -> None: + """Block empty queries and unsafe root statement types.""" + assert default_db._is_safe_readonly_query(query) is False + + +def test_is_safe_readonly_query_blocks_multiple_statements_when_disabled(sqlite_db_uri: str) -> None: + """Block multi-statement queries when policy forbids them.""" + policy = ReadOnlySqlPolicy(allowed_roots=("select",), allow_multiple_statements=False) + db = ReadOnlySqlDatabase(connection_uri=sqlite_db_uri, policy=policy) + assert db._is_safe_readonly_query("SELECT 1; SELECT 2") is False + + +def test_is_safe_readonly_query_blocks_forbidden_nodes_even_if_root_is_allowed(sqlite_db_uri: str) -> None: + """Block forbidden nodes even if their root type is allowed.""" + policy = ReadOnlySqlPolicy(allowed_roots=("select", "delete"), forbidden_nodes=("delete",)) + db = ReadOnlySqlDatabase(connection_uri=sqlite_db_uri, policy=policy) + assert db._is_safe_readonly_query("DELETE FROM customers") is False + + +def test_is_safe_readonly_query_returns_false_on_parse_errors( + sqlite_db_uri: str, monkeypatch: pytest.MonkeyPatch +) -> None: + """Return False (fail closed) if parsing raises.""" + db = ReadOnlySqlDatabase(connection_uri=sqlite_db_uri) + monkeypatch.setattr("aieng.agent_evals.tools.sql_database.sqlglot.parse", lambda _query: 1 / 0) + assert db._is_safe_readonly_query("SELECT 1") is False + + +def test_get_schema_info_includes_tables_and_views(default_db: ReadOnlySqlDatabase) -> None: + """Include tables/views and at least one expected column name.""" + schema = default_db.get_schema_info() + assert "Table: customers" in schema + assert "Table: transactions" in schema + assert "View: customer_totals" in schema + assert "customer_id" in schema + + +def test_get_schema_info_filters_relations_case_insensitively(default_db: ReadOnlySqlDatabase) -> None: + """Filter requested relations in a case-insensitive way.""" + schema = default_db.get_schema_info(["CUSTOMERS"]) + assert "Table: customers" in schema + assert "Table: transactions" not in schema + + +def test_get_schema_info_uses_sqlite_pragma_fallback(sqlite_db_uri: str, monkeypatch: pytest.MonkeyPatch) -> None: + """Fall back to PRAGMA table_info when SQLAlchemy inspection fails (SQLite).""" + db = ReadOnlySqlDatabase(connection_uri=sqlite_db_uri) + + real_inspector = sql_database.inspect(db.engine) + + class FailingColumnsInspector: + def get_table_names(self): + """Return table names from the real inspector.""" + return real_inspector.get_table_names() + + def get_view_names(self): + """Return view names from the real inspector.""" + return real_inspector.get_view_names() + + def get_columns(self, _name): + """Raise to force the PRAGMA fallback path.""" + raise RuntimeError("boom") + + monkeypatch.setattr(sql_database, "inspect", lambda _engine: FailingColumnsInspector()) + schema = db.get_schema_info() + assert "Table: customers" in schema + assert "View: customer_totals" in schema + assert "Columns: customer_id" in schema + + +def test_execute_formats_select_results_as_markdown(default_db: ReadOnlySqlDatabase) -> None: + """Format SELECT results as a markdown table.""" + output = default_db.execute("SELECT customer_id, name FROM customers ORDER BY customer_id") + assert output.splitlines()[0] == "| customer_id | name |" + assert "| 1 | Alice |" in output + assert "| 2 | Bob |" in output + + +def test_execute_truncates_to_max_rows(sqlite_db_uri: str) -> None: + """Truncate output when max_rows is reached.""" + db = ReadOnlySqlDatabase(connection_uri=sqlite_db_uri, max_rows=2) + output = db.execute("SELECT transaction_id FROM transactions ORDER BY transaction_id") + assert "| transaction_id |" in output + assert "| 100 |" in output + assert "| 101 |" in output + assert "| 102 |" not in output + assert "... (Truncated at 2 rows) ..." in output + + +def test_execute_blocks_write_queries(default_db: ReadOnlySqlDatabase) -> None: + """Return an error when a write query is attempted.""" + output = default_db.execute("INSERT INTO customers (customer_id, name, risk_score) VALUES (3, 'Mallory', 0)") + assert output.startswith("Query Error:") + assert "Security Violation" in output + + +def test_execute_returns_query_error_on_sql_execution_failure(default_db: ReadOnlySqlDatabase) -> None: + """Return a Query Error string when SQL execution fails.""" + output = default_db.execute("SELECT definitely_not_a_column FROM customers") + assert output.startswith("Query Error:") From 41c68355587f0ae0672e0add8e77e9f439931c88 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 15:22:27 -0500 Subject: [PATCH 07/24] Refactor AML investigation agent: improve database handling, update analyst prompt, and enhance case analysis workflow --- implementations/aml_investigation/agent.py | 153 ++++++++++++--------- 1 file changed, 85 insertions(+), 68 deletions(-) diff --git a/implementations/aml_investigation/agent.py b/implementations/aml_investigation/agent.py index 7d4b561..30e7c33 100644 --- a/implementations/aml_investigation/agent.py +++ b/implementations/aml_investigation/agent.py @@ -9,6 +9,7 @@ import logging import os import uuid +from functools import lru_cache from pathlib import Path import google.genai.types @@ -20,92 +21,106 @@ from google.adk.agents import Agent from google.adk.runners import Runner from google.adk.sessions import InMemorySessionService +from google.adk.tools import FunctionTool from rich.progress import BarColumn, Progress, TextColumn, TimeElapsedColumn, TimeRemainingColumn logger = logging.getLogger(__name__) +load_dotenv() + + ANALYST_PROMPT = """\ -You are a newly hired Anti‑Money Laundering (AML) Investigation Analyst at a financial institution. -Your job is to investigate a single AML case by reviewing transaction activity in our database and documenting -whether the activity is consistent with money laundering or a benign explanation. +You are an Anti‑Money Laundering (AML) Investigation Analyst at a financial institution. +Your job is to investigate one case by reviewing activity in the available database and explaining whether the +observed behavior within the case window is consistent with money laundering or a benign explanation. You have access to database query tools. Use them. Do not guess or invent transactions. -## Core Principle: Falsification (Default to Benign) -Start with the hypothesis that the case is benign (a false positive). Your primary goal is to find positive, -transaction-level evidence that supports a legitimate explanation. Only conclude laundering when the transaction -pattern supports it. +## Core Principle: Falsification +Start with the hypothesis that the case is benign. Prefer legitimate explanations unless the transaction-level evidence +supports laundering. -## Your Input (CaseFile JSON) +## Input You will be given a JSON object with these fields: - `case_id`: unique case identifier. -- `seed_transaction_id`: the primary transaction that triggered the case. +- `seed_transaction_id`: identifier for the primary transaction that triggered the case. - `seed_timestamp`: timestamp of the seed transaction (end of the investigation window). - `window_start`: timestamp of the beginning of the investigation window. -- `suspected_pattern_type`: a preliminary label/hint (may be wrong or intentionally misleading). +- `trigger_label`: upstream alert/review label or heuristic hint (may be wrong). ### Time Scope Rule (Strict) -Only analyze transactions with `timestamp` between `window_start` and `seed_timestamp` (inclusive). -Do not use transactions after `seed_timestamp`. - -## What to Investigate (Standard Workflow) -1) **Seed review** - - Pull the seed transaction details and identify parties (from/to accounts and banks), currencies, amounts, and format. -2) **Account history within the window** - - Pull inbound/outbound activity for the involved accounts within the window. -3) **Pattern detection** - Look for transaction patterns consistent with laundering typologies, such as: - - **FAN-IN**: many sources → one account (aggregation). - - **FAN-OUT**: one account → many destinations (dispersion). - - **GATHER-SCATTER / SCATTER-GATHER**: aggregation then dispersion (or vice‑versa), often over short time windows. - - **STACK / LAYERING**: sequential hops meant to obscure origin. - - **RANDOM / CYCLE**: complex paths or circular movement. - - **BIPARTITE**: two groups of accounts with structured flows between them. -4) **Legitimate explanations** - - If activity is consistent with normal behavior (e.g., payroll, vendor payments, routine transfers), - state what makes it consistent and what would have been suspicious but is not present. - -## Your Output (AnalystOutput JSON) -Return a single JSON object that matches the `AnalystOutput` schema exactly. Fill every field. - -- `summary_narrative` (str): - 2–3 paragraphs in plain text summarizing what you checked and what you found. - Cite specific evidence (transaction IDs, dates/times, amounts, counterparties) and explain your reasoning. -- `is_laundering` (bool): - `true` only when the observed pattern within the window supports laundering; otherwise `false`. -- `pattern_type` (LaunderingPattern): - Your best classification of the dominant pattern observed. Use: - - `NONE` if you find no laundering pattern, - - `UNKNOWN` if you believe laundering is present but does not match a known pattern well. -- `pattern_description` (str): - A short, human-readable description of the observed pattern (or `"N/A"` when `pattern_type` is `NONE`). -- `flagged_transaction_ids` (list[str]): - The transaction IDs you consider most relevant/suspicious for this case. - If `is_laundering` is `false`, this can be an empty list. +Only analyze events with `timestamp` between `window_start` and `seed_timestamp` (inclusive). +Do not use events after `seed_timestamp`. + +## Investigation Workflow +1) **Orient** + - Treat `trigger_label` as context only. Do not assume it is correct. +2) **Seed review** + - Query the seed event/transaction using `seed_transaction_id`. + - Extract key attributes available in this database (e.g., involved parties, amounts, payment channel/instrument). +3) **Scope and collect** + - Pull related activity for involved entities between `window_start` and `seed_timestamp` (inclusive). +4) **Assess benign explanations (default)** + - Try to explain the observed activity as legitimate first. + - State what evidence supports the benign hypothesis and what data would be needed to strengthen it. +5) **Test laundering hypotheses (only if needed)** + - Only if benign explanations are insufficient, test whether the evidence supports laundering typologies or other + suspicious behavior. + - Cite the concrete indicators that rule out benign explanations. + +## Typologies / Heuristics +Look for transaction patterns consistent with laundering typologies, such as: +- FAN-IN (aggregation): many sources to one destination +- FAN-OUT (dispersion): one source to many destinations +- GATHER-SCATTER / SCATTER-GATHER: aggregation then dispersion (or vice‑versa), often over short time windows. +- STACK / LAYERING: multiple hops meant to obscure origin +- CYCLE: circular movement +- RANDOM: complext pattern +- BIPARTITE: structured flows between two groups + +## Output Format +Return a single JSON object that matches the configured output schema exactly. Fill every field. +Use `pattern_type = "NONE"` when no laundering pattern is supported by evidence in the investigation window. """ -client_manager = AsyncClientManager().get_instance() -if client_manager.configs.aml_db is None: - raise ValueError("AML database configuration is missing.") +@lru_cache(maxsize=1) +def _get_db() -> ReadOnlySqlDatabase: + """Lazily construct the read-only database tool from environment configuration.""" + client_manager = AsyncClientManager().get_instance() + if client_manager.configs.aml_db is None: + raise ValueError("AML database configuration is missing.") + + return ReadOnlySqlDatabase( + connection_uri=client_manager.configs.aml_db.build_uri(), + agent_name="FraudInvestigationAnalyst", + ) -db = ReadOnlySqlDatabase( - connection_uri=client_manager.configs.aml_db.build_uri(), - agent_name="FraudInvestigationAnalyst", -) +def _try_close_db() -> None: + """Close the lazily initialized database tool if it was created.""" + if _get_db.cache_info().currsize: + _get_db().close() + _get_db.cache_clear() + + +# ADK discovery expects a module-level `root_agent` root_agent = Agent( name="AmlInvestigationAnalyst", - description="Conducts detailed multi-step AML investigations with database queries.", - tools=[db.execute, db.get_schema_info], + description="Conducts multi-step financial crime investigations using database queries.", + tools=[FunctionTool(_get_db().get_schema_info), FunctionTool(_get_db().execute)], model="gemini-3-flash-preview", instruction=ANALYST_PROMPT, output_schema=AnalystOutput, + generate_content_config=google.genai.types.GenerateContentConfig( + thinking_config=google.genai.types.ThinkingConfig(include_thoughts=True) + ), ) def _load_records(path: Path) -> list[CaseRecord]: + """Load CaseRecord rows from a JSONL file, skipping invalid lines.""" if not path.exists(): return [] @@ -123,6 +138,7 @@ def _load_records(path: Path) -> list[CaseRecord]: def _extract_json(text: str) -> dict: + """Parse JSON from model output, falling back to the first JSON object substring.""" try: return json.loads(text) except json.JSONDecodeError: @@ -134,6 +150,7 @@ def _extract_json(text: str) -> dict: def _write_results(output_path: Path, input_records: list[CaseRecord], results_by_id: dict[str, CaseRecord]) -> int: + """Rewrite the output JSONL with updated analyses, preserving input order.""" tmp_path = output_path.with_suffix(output_path.suffix + ".tmp") written: set[str] = set() analyzed = 0 @@ -153,6 +170,7 @@ def _write_results(output_path: Path, input_records: list[CaseRecord], results_b async def _analyze_case(runner: Runner, record: CaseRecord) -> CaseRecord: + """Run the agent on one case and attach the validated AnalystOutput.""" message = google.genai.types.Content( role="user", parts=[google.genai.types.Part(text=record.case.model_dump_json())] ) @@ -172,6 +190,7 @@ async def _analyze_case(runner: Runner, record: CaseRecord) -> CaseRecord: async def _safe_analyze_case(runner: Runner, record: CaseRecord) -> CaseRecord: + """Analyze a case and swallow exceptions so batch runs continue.""" try: return await _analyze_case(runner, record) except Exception as exc: @@ -185,6 +204,7 @@ async def _analyze_cases_to_jsonl( semaphore: asyncio.Semaphore, output_path: Path, ) -> dict[str, CaseRecord]: + """Analyze cases concurrently and append each result to a JSONL output file.""" if not cases: return {} @@ -217,7 +237,7 @@ async def _analyze_cases_to_jsonl( return analyzed_by_id -async def main() -> None: +async def _main() -> None: """Run the AML investigation agent on cases from JSONL.""" input_path = Path("implementations/aml_investigation/data/aml_cases.jsonl") if not input_path.exists(): @@ -232,14 +252,13 @@ async def main() -> None: logger.info("Resume: %d/%d done; %d remaining.", len(input_records) - len(to_run), len(input_records), len(to_run)) - runner = Runner( - app_name="aml_investigation", - agent=root_agent, - session_service=InMemorySessionService(), - auto_create_session=True, - ) - try: + runner = Runner( + app_name="aml_investigation", + agent=root_agent, + session_service=InMemorySessionService(), + auto_create_session=True, + ) analyzed_by_id = await _analyze_cases_to_jsonl(runner, to_run, asyncio.Semaphore(5), output_path) existing_results.update(analyzed_by_id) analyzed_count = _write_results(output_path, input_records, existing_results) @@ -266,11 +285,9 @@ async def main() -> None: logger.info(" TP=%d FP=%d", tp, fp) logger.info(" FN=%d TN=%d", fn, tn) finally: - db.close() + _try_close_db() if __name__ == "__main__": logging.basicConfig(level=logging.INFO, format="%(message)s") - load_dotenv() - - asyncio.run(main()) + asyncio.run(_main()) From f458e9274e50f848c859c929eb5b4fa688391abc Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 16:25:34 -0500 Subject: [PATCH 08/24] Fix Langfuse client initialization to handle optional secret key --- aieng-eval-agents/aieng/agent_evals/async_client_manager.py | 4 +++- .../aieng/agent_evals/knowledge_agent/tracing.py | 4 ++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/aieng-eval-agents/aieng/agent_evals/async_client_manager.py b/aieng-eval-agents/aieng/agent_evals/async_client_manager.py index da792fe..7e2623c 100644 --- a/aieng-eval-agents/aieng/agent_evals/async_client_manager.py +++ b/aieng-eval-agents/aieng/agent_evals/async_client_manager.py @@ -158,7 +158,9 @@ def langfuse_client(self) -> Langfuse: if self._langfuse_client is None: self._langfuse_client = Langfuse( public_key=self.configs.langfuse_public_key, - secret_key=self.configs.langfuse_secret_key, + secret_key=self.configs.langfuse_secret_key.get_secret_value() + if self.configs.langfuse_secret_key + else None, host=self.configs.langfuse_host, ) self._initialized = True diff --git a/aieng-eval-agents/aieng/agent_evals/knowledge_agent/tracing.py b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/tracing.py index a22fa23..b5dfaef 100644 --- a/aieng-eval-agents/aieng/agent_evals/knowledge_agent/tracing.py +++ b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/tracing.py @@ -71,8 +71,8 @@ def init_tracing() -> bool: # Get credentials from configs configs = manager.configs - public_key = configs.langfuse_public_key - secret_key = configs.langfuse_secret_key + public_key = configs.langfuse_public_key or "" + secret_key = configs.langfuse_secret_key.get_secret_value() if configs.langfuse_secret_key else "" langfuse_host = configs.langfuse_host # Set up OpenTelemetry OTLP exporter to send traces to Langfuse From 2a922d88913f0a9f279bf0cf4144d9f3c2e73bac Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 16:27:08 -0500 Subject: [PATCH 09/24] Refactor DatabaseConfig and Configs classes: enhance field descriptions and improve code readability --- .../aieng/agent_evals/configs.py | 42 +++++++++++-------- 1 file changed, 24 insertions(+), 18 deletions(-) diff --git a/aieng-eval-agents/aieng/agent_evals/configs.py b/aieng-eval-agents/aieng/agent_evals/configs.py index 6a538d2..116823b 100644 --- a/aieng-eval-agents/aieng/agent_evals/configs.py +++ b/aieng-eval-agents/aieng/agent_evals/configs.py @@ -18,24 +18,28 @@ class DatabaseConfig(BaseModel): ..., description="SQLAlchemy dialect (e.g., 'sqlite', 'postgresql', 'mysql+pymysql').", ) - """SQLAlchemy dialect (e.g., 'sqlite', 'postgresql', 'mysql+pymysql').""" - username: str | None = None - """Database username. For SQLite or integrated authentication, this can be None.""" - host: str | None = None - """Database host address or file path for SQLite.""" - password: SecretStr | None = None - """Database password. For SQLite or integrated authentication, this can be None.""" - port: int | None = None - """Database port number.""" - database: str | None = Field(None, description="Database name or file path for SQLite.") - """Database name or file path for SQLite.""" - query: dict[str, Any] = Field(default_factory=dict, description="URL query parameters (e.g. {'mode': 'ro'})") - """URL query parameters (e.g. {'mode': 'ro'} for read-only SQLite).""" + username: str | None = Field( + default=None, description="Database username. For SQLite or integrated authentication, this can be None." + ) + host: str | None = Field(default=None, description="Database host address or file path for SQLite.") + password: SecretStr | None = Field( + default=None, description="Database password. For SQLite or integrated authentication, this can be None." + ) + port: int | None = Field(default=None, description="Database port number.") + database: str | None = Field(default=None, description="Database name or file path for SQLite.") + query: dict[str, Any] = Field( + default_factory=dict, description="URL query parameters (e.g. {'mode': 'ro'} for read-only SQLite)." + ) def build_uri(self) -> str: """Construct the SQLAlchemy connection URI safely using the official URL object. This handles special character escaping in passwords automatically. + + Returns + ------- + str + The full database connection URI. """ return URL.create( drivername=self.driver, @@ -73,10 +77,10 @@ class Configs(BaseSettings): env_nested_delimiter="__", ) - aml_db: DatabaseConfig | None = None - """Anti-Money Laundering database configuration. Used by the Fraud Investigation \ - Agent. - """ + aml_db: DatabaseConfig | None = Field( + default=None, + description="Anti-Money Laundering database configuration. Used by the Fraud Investigation Agent.", + ) # === Core LLM Settings === openai_base_url: str = Field( @@ -106,7 +110,9 @@ class Configs(BaseSettings): default=None, description="Langfuse secret key for tracing (must start with 'sk-lf-').", ) - langfuse_host: str = Field(default="https://us.cloud.langfuse.com", description="Langfuse host URL.") + langfuse_host: str = Field( + default="https://us.cloud.langfuse.com", validation_alias="LANGFUSE_BASE_URL", description="Langfuse base URL." + ) # === Embedding Service === embedding_base_url: str | None = Field(default=None, description="Base URL for embedding API service.") From 9eeca4fbb61c17bd301ef073b00f7d01eaf41609 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 16:27:24 -0500 Subject: [PATCH 10/24] Reorganize .env.example: move AML database configuration to the end and standardize LangFuse base URL variable --- .env.example | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/.env.example b/.env.example index a5fdeb8..94fae03 100644 --- a/.env.example +++ b/.env.example @@ -1,9 +1,3 @@ -GOOGLE_API_KEY="..." - -# AML Database Configuration -AML_DB__DRIVER="sqlite" -AML_DB__DATABASE="implementations/aml_investigation/data/aml_transactions.db" -AML_DB__QUERY__MODE="ro" # OpenAI-compatible LLM (using Gemini via OpenAI API) OPENAI_BASE_URL="https://generativelanguage.googleapis.com/v1beta/openai/" OPENAI_API_KEY="..." # Or use GEMINI_API_KEY or GOOGLE_API_KEY @@ -17,4 +11,9 @@ DEFAULT_WORKER_MODEL="gemini-2.5-flash" # LangFuse (optional, for tracing) LANGFUSE_SECRET_KEY="sk-lf-..." LANGFUSE_PUBLIC_KEY="pk-lf-..." -LANGFUSE_HOST="https://us.cloud.langfuse.com" +LANGFUSE_BASE_URL="https://us.cloud.langfuse.com" + +# AML Database Configuration +AML_DB__DRIVER="sqlite" +AML_DB__DATABASE="implementations/aml_investigation/data/aml_transactions.db" +AML_DB__QUERY__MODE="ro" From 1eb6eb1360181d39e12bd720e16cb0afcbf82634 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 16:39:27 -0500 Subject: [PATCH 11/24] Update lookback_days default to 30 and add warning for zero value in create_cases --- implementations/aml_investigation/data/cli.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/implementations/aml_investigation/data/cli.py b/implementations/aml_investigation/data/cli.py index 2770c60..ed478d1 100644 --- a/implementations/aml_investigation/data/cli.py +++ b/implementations/aml_investigation/data/cli.py @@ -204,7 +204,7 @@ def create_db(illicit_ratio: str, transactions_size: str, ddl_file_path: Path, d @click.option( "--lookback-days", type=int, - default=0, + default=30, show_default=True, help="Number of days to look back for the case window start (clamped to available data).", ) @@ -266,6 +266,8 @@ def create_cases( ]: if value < 0: raise ValueError(f"{name} must be >= 0") + if lookback_days == 0: + logger.warning("lookback_days=0 creates very narrow windows (can be seed-timestamp-only); consider >= 1.") path_to_transc_csv = download_dataset_file(illicit_ratio, transactions_size, "Trans.csv") path_to_patterns_txt = download_dataset_file(illicit_ratio, transactions_size, "Patterns.txt") From 719c57548451777c53e7c65fd04e6f441a3584c1 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 16:39:34 -0500 Subject: [PATCH 12/24] Change audit logging level from info to debug in ReadOnlySqlDatabase --- aieng-eval-agents/aieng/agent_evals/tools/sql_database.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py b/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py index 92f3f89..9d5c420 100644 --- a/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py +++ b/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py @@ -313,7 +313,7 @@ def execute(self, query: str) -> str: "duration_sec": duration, "error": error_msg, } - logger.info("AUDIT: %s", log_entry) + logger.debug("AUDIT: %s", log_entry) def close(self) -> None: """Dispose of the connection pool.""" From 39bba83d61eb3f318eca61d94a2911d26e7bb329 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 16:39:53 -0500 Subject: [PATCH 13/24] Enhance case generation logic: adjust window start handling for zero lookback days and improve false negative case construction --- .../aml_investigation/data/cases.py | 23 +++++++++++-------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py index 6d389b3..3860985 100644 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py @@ -28,14 +28,7 @@ logger = logging.getLogger(__name__) -__all__ = [ - "LaunderingPattern", - "GroundTruth", - "CaseFile", - "CaseRecord", - "parse_patterns_file", - "build_cases", -] +__all__ = ["LaunderingPattern", "GroundTruth", "CaseFile", "CaseRecord", "parse_patterns_file", "build_cases"] _TRANSACTION_ID_COLUMNS = [ "timestamp", @@ -192,6 +185,14 @@ def parse_patterns_file(path: str | Path, lookback_days: int = 0, min_timestamp: window_start = apply_lookback_window( txns_sorted[0]["timestamp"], lookback_days, min_timestamp=min_timestamp ) + if lookback_days == 0 and window_start == seed_txn["timestamp"]: + candidate_start = _date_window_start(seed_txn["timestamp"]) + if min_timestamp: + candidate_dt = _parse_timestamp(candidate_start) + min_dt = _parse_timestamp(min_timestamp) + if candidate_dt < min_dt: + candidate_start = min_dt.strftime("%Y-%m-%dT%H:%M:%S") + window_start = candidate_start attempt_ids = _serialize_attempt_ids([txn["transaction_id"] for txn in txns_sorted]) case_file = CaseFile( case_id=_create_id(json.dumps(txns_sorted)), @@ -334,7 +335,9 @@ def _build_false_negative_cases( case_id=_create_id(f"fn:{case.case.case_id}"), seed_transaction_id=case.case.seed_transaction_id, seed_timestamp=case.case.seed_timestamp, - window_start=case.case.window_start, + window_start=_date_window_start(case.case.seed_timestamp) + if case.case.window_start == case.case.seed_timestamp + else case.case.window_start, trigger_label=random.choice(_LOW_SIGNAL_REVIEW_LABELS), ) groundtruth = GroundTruth( @@ -418,6 +421,8 @@ def _build_normal_cases( normal_txns = normal_pool.sample(n=sample_count, random_state=42) for _, row in normal_txns.iterrows(): window_start = apply_lookback_window(row["timestamp"], lookback_days, min_timestamp=min_timestamp) + if lookback_days == 0 and window_start == row["timestamp"]: + window_start = _date_window_start(row["timestamp"]) case_file = CaseFile( case_id=_create_id(row.drop(labels=["transaction_id", "is_laundering"]).to_json()), seed_transaction_id=row["transaction_id"], From 3711313d32670d969e33ded02f48661b573f3516 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 16:42:40 -0500 Subject: [PATCH 14/24] Refactor concurrency handling: replace hardcoded semaphore value with MAX_CONCURRENT_CASES constant --- implementations/aml_investigation/agent.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/implementations/aml_investigation/agent.py b/implementations/aml_investigation/agent.py index 30e7c33..7667864 100644 --- a/implementations/aml_investigation/agent.py +++ b/implementations/aml_investigation/agent.py @@ -29,6 +29,7 @@ load_dotenv() +MAX_CONCURRENT_CASES = 5 # Limit for concurrent case analyses ANALYST_PROMPT = """\ You are an Anti‑Money Laundering (AML) Investigation Analyst at a financial institution. @@ -259,7 +260,9 @@ async def _main() -> None: session_service=InMemorySessionService(), auto_create_session=True, ) - analyzed_by_id = await _analyze_cases_to_jsonl(runner, to_run, asyncio.Semaphore(5), output_path) + analyzed_by_id = await _analyze_cases_to_jsonl( + runner, to_run, asyncio.Semaphore(MAX_CONCURRENT_CASES), output_path + ) existing_results.update(analyzed_by_id) analyzed_count = _write_results(output_path, input_records, existing_results) logger.info("Wrote %d analyzed cases to %s", analyzed_count, output_path) From 0b8d43fcb2ce3c06a14165fab2af9459e96484ec Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 17:04:42 -0500 Subject: [PATCH 15/24] Add pytest-asyncio as a development dependency --- aieng-eval-agents/pyproject.toml | 1 + uv.lock | 8 ++++++-- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/aieng-eval-agents/pyproject.toml b/aieng-eval-agents/pyproject.toml index 08077c8..05cee32 100644 --- a/aieng-eval-agents/pyproject.toml +++ b/aieng-eval-agents/pyproject.toml @@ -29,6 +29,7 @@ dependencies = [ [dependency-groups] dev = [ "pytest>=8.3.5", + "pytest-asyncio>=1.3.0", ] [build-system] diff --git a/uv.lock b/uv.lock index 97b4f56..a9e59fc 100644 --- a/uv.lock +++ b/uv.lock @@ -169,6 +169,7 @@ dependencies = [ [package.dev-dependencies] dev = [ { name = "pytest" }, + { name = "pytest-asyncio" }, ] [package.metadata] @@ -193,7 +194,10 @@ requires-dist = [ ] [package.metadata.requires-dev] -dev = [{ name = "pytest", specifier = ">=8.3.5" }] +dev = [ + { name = "pytest", specifier = ">=8.3.5" }, + { name = "pytest-asyncio", specifier = ">=1.3.0" }, +] [[package]] name = "aieng-platform-onboard" @@ -4321,7 +4325,7 @@ name = "pexpect" version = "4.9.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "ptyprocess" }, + { name = "ptyprocess", marker = "(python_full_version < '3.13' and sys_platform == 'emscripten') or (python_full_version < '3.13' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" }, ] sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450, upload-time = "2023-11-25T09:07:26.339Z" } wheels = [ From abe8650bb218b89eb3ec64e2daeb224ec33e993e Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Fri, 30 Jan 2026 17:25:07 -0500 Subject: [PATCH 16/24] Address PR comments --- .../aml_investigation/data/cases.py | 179 +++++++++++------- .../aieng/agent_evals/configs.py | 7 +- .../aieng/agent_evals/tools/sql_database.py | 2 +- implementations/aml_investigation/README.md | 138 ++++++++++---- implementations/aml_investigation/data/cli.py | 2 +- 5 files changed, 224 insertions(+), 104 deletions(-) diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py index 3860985..9412a3b 100644 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py @@ -105,7 +105,7 @@ class AnalystOutput(BaseModel): pattern_description: str """A short description of the laundering pattern.""" flagged_transaction_ids: list[str] - """A comma-separeted list of transaction IDs flagged by the analyst as suspicious. + """A comma-separated list of transaction IDs flagged by the analyst as suspicious. """ @@ -121,6 +121,113 @@ class CaseRecord(BaseModel): investigation.""" +def _validate_patterns_parse_inputs(path: str | Path, lookback_days: int, min_timestamp: str | None) -> Path: + """Validate parse_patterns_file inputs and return the normalized patterns path.""" + if lookback_days < 0: + raise ValueError("lookback_days must be >= 0") + + patterns_path = Path(path) + if not patterns_path.exists(): + raise FileNotFoundError(f"Patterns file not found: {patterns_path}") + + if min_timestamp: + _parse_timestamp(min_timestamp) + + return patterns_path + + +def _start_attempt_block(header: str) -> dict[str, Any]: + """Create a new in-memory attempt block state from a BEGIN header line.""" + pattern_type, pattern_description = _parse_pattern_header(header) + return { + "pattern_type": pattern_type, + "pattern_description": pattern_description, + "transactions": [], + } + + +def _parse_attempt_transaction_line(line: str, line_number: int) -> dict[str, str]: + """Parse a single transaction line into a canonicalized transaction dict.""" + row = next(csv.reader([line])) + if len(row) < 11: + raise ValueError(f"Malformed transaction row at line {line_number}: {line}") + + txn = { + "timestamp": _canonicalize_timestamp(row[0]), + "from_bank": _canonicalize_numeric(row[1]), + "from_account": _canonicalize_text(row[2]), + "to_bank": _canonicalize_numeric(row[3]), + "to_account": _canonicalize_text(row[4]), + "amount_received": _canonicalize_numeric(row[5]), + "receiving_currency": _canonicalize_text(row[6]), + "amount_paid": _canonicalize_numeric(row[7]), + "payment_currency": _canonicalize_text(row[8]), + "payment_format": _canonicalize_text(row[9]), + } + txn_str = "|".join(txn[col] for col in _TRANSACTION_ID_COLUMNS) + txn["transaction_id"] = _create_id(txn_str) + return txn + + +def _compute_attempt_window_start( + *, + txns_sorted: list[dict[str, str]], + seed_timestamp: str, + lookback_days: int, + min_timestamp: str | None, +) -> str: + """Compute an attempt window start and avoid zero-width windows when possible.""" + window_start = apply_lookback_window(txns_sorted[0]["timestamp"], lookback_days, min_timestamp=min_timestamp) + + if lookback_days != 0 or window_start != seed_timestamp: + return window_start + + candidate_start = _date_window_start(seed_timestamp) + if not min_timestamp: + return candidate_start + + candidate_dt = _parse_timestamp(candidate_start) + min_dt = _parse_timestamp(min_timestamp) + if candidate_dt < min_dt: + return min_dt.strftime("%Y-%m-%dT%H:%M:%S") + return candidate_start + + +def _finalize_attempt_block( + current: dict[str, Any], lookback_days: int, min_timestamp: str | None +) -> CaseRecord | None: + """Convert an in-progress attempt block into a CaseRecord if it has transactions.""" + txns: list[dict[str, str]] = current.get("transactions") or [] + if not txns: + return None + + txns_sorted = sorted(txns, key=lambda t: _parse_timestamp(t["timestamp"])) + seed_txn = txns_sorted[-1] + seed_timestamp = seed_txn["timestamp"] + window_start = _compute_attempt_window_start( + txns_sorted=txns_sorted, + seed_timestamp=seed_timestamp, + lookback_days=lookback_days, + min_timestamp=min_timestamp, + ) + attempt_ids = _serialize_attempt_ids([txn["transaction_id"] for txn in txns_sorted]) + + case_file = CaseFile( + case_id=_create_id(json.dumps(txns_sorted)), + seed_transaction_id=seed_txn["transaction_id"], + seed_timestamp=seed_timestamp, + window_start=window_start, + trigger_label=current["pattern_type"], + ) + groundtruth = GroundTruth( + is_laundering=True, + pattern_type=current["pattern_type"], + pattern_description=current["pattern_description"], + attempt_transaction_ids=attempt_ids, + ) + return CaseRecord(case=case_file, groundtruth=groundtruth) + + def parse_patterns_file(path: str | Path, lookback_days: int = 0, min_timestamp: str | None = None) -> list[CaseRecord]: """Parse laundering pattern attempts from a patterns file. @@ -147,14 +254,7 @@ def parse_patterns_file(path: str | Path, lookback_days: int = 0, min_timestamp: ValueError If the file contains malformed transaction rows or an unterminated block. """ - if lookback_days < 0: - raise ValueError("lookback_days must be >= 0") - - patterns_path = Path(path) - if not patterns_path.exists(): - raise FileNotFoundError(f"Patterns file not found: {patterns_path}") - if min_timestamp: - _parse_timestamp(min_timestamp) + patterns_path = _validate_patterns_parse_inputs(path, lookback_days, min_timestamp) cases: list[CaseRecord] = [] current: dict[str, Any] | None = None @@ -169,68 +269,19 @@ def parse_patterns_file(path: str | Path, lookback_days: int = 0, min_timestamp: if line.startswith(begin_prefix): header = line[len(begin_prefix) :] - pattern_type, pattern_description = _parse_pattern_header(header) - current = { - "pattern_type": pattern_type, - "pattern_description": pattern_description, - "transactions": [], - } + current = _start_attempt_block(header) continue if line.startswith(end_prefix): - if current and current["transactions"]: - txns: list[dict[str, str]] = current["transactions"] - txns_sorted = sorted(txns, key=lambda t: _parse_timestamp(t["timestamp"])) - seed_txn = txns_sorted[-1] - window_start = apply_lookback_window( - txns_sorted[0]["timestamp"], lookback_days, min_timestamp=min_timestamp - ) - if lookback_days == 0 and window_start == seed_txn["timestamp"]: - candidate_start = _date_window_start(seed_txn["timestamp"]) - if min_timestamp: - candidate_dt = _parse_timestamp(candidate_start) - min_dt = _parse_timestamp(min_timestamp) - if candidate_dt < min_dt: - candidate_start = min_dt.strftime("%Y-%m-%dT%H:%M:%S") - window_start = candidate_start - attempt_ids = _serialize_attempt_ids([txn["transaction_id"] for txn in txns_sorted]) - case_file = CaseFile( - case_id=_create_id(json.dumps(txns_sorted)), - seed_transaction_id=seed_txn["transaction_id"], - seed_timestamp=seed_txn["timestamp"], - window_start=window_start, - trigger_label=current["pattern_type"], - ) - groundtruth = GroundTruth( - is_laundering=True, - pattern_type=current["pattern_type"], - pattern_description=current["pattern_description"], - attempt_transaction_ids=attempt_ids, - ) - cases.append(CaseRecord(case=case_file, groundtruth=groundtruth)) + if current: + case_record = _finalize_attempt_block(current, lookback_days, min_timestamp) + if case_record: + cases.append(case_record) current = None continue if current: - row = next(csv.reader([line])) - if len(row) < 11: - raise ValueError(f"Malformed transaction row at line {line_number}: {line}") - - txn = { - "timestamp": _canonicalize_timestamp(row[0]), - "from_bank": _canonicalize_numeric(row[1]), - "from_account": _canonicalize_text(row[2]), - "to_bank": _canonicalize_numeric(row[3]), - "to_account": _canonicalize_text(row[4]), - "amount_received": _canonicalize_numeric(row[5]), - "receiving_currency": _canonicalize_text(row[6]), - "amount_paid": _canonicalize_numeric(row[7]), - "payment_currency": _canonicalize_text(row[8]), - "payment_format": _canonicalize_text(row[9]), - } - txn_str = "|".join(txn[col] for col in _TRANSACTION_ID_COLUMNS) - txn["transaction_id"] = _create_id(txn_str) - current["transactions"].append(txn) + current["transactions"].append(_parse_attempt_transaction_line(line, line_number)) if current is not None: raise ValueError("Unterminated laundering attempt block in patterns file.") diff --git a/aieng-eval-agents/aieng/agent_evals/configs.py b/aieng-eval-agents/aieng/agent_evals/configs.py index 116823b..42524c4 100644 --- a/aieng-eval-agents/aieng/agent_evals/configs.py +++ b/aieng-eval-agents/aieng/agent_evals/configs.py @@ -59,9 +59,6 @@ class Configs(BaseSettings): and a .env file. Service-specific fields are optional - agents validate required fields at initialization. - and a .env file, and provides type-safe access to all settings. It validates - environment variables on instantiation. - Examples -------- >>> from aieng.agent_evals.configs import Configs @@ -178,9 +175,9 @@ class Configs(BaseSettings): # Validators for the SecretStr fields @field_validator("langfuse_secret_key") @classmethod - def validate_langfuse_secret(cls, v: SecretStr) -> SecretStr: + def validate_langfuse_secret(cls, v: SecretStr | None) -> SecretStr | None: """Validate that the Langfuse secret key starts with 'sk-lf-'.""" - if not v.get_secret_value().startswith("sk-lf-"): + if v is not None and not v.get_secret_value().startswith("sk-lf-"): raise ValueError("Langfuse secret key must start with 'sk-lf-'") return v diff --git a/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py b/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py index 9d5c420..2d7210d 100644 --- a/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py +++ b/aieng-eval-agents/aieng/agent_evals/tools/sql_database.py @@ -287,7 +287,7 @@ def execute(self, query: str) -> str: # Formatting for LLM (String Table) output = [f"| {' | '.join(keys)} |"] - output.append(f"| {'---' * len(keys)} |") + output.append("| " + " | ".join(["---"] * len(keys)) + " |") for row in rows: output.append(f"| {' | '.join(map(str, row))} |") diff --git a/implementations/aml_investigation/README.md b/implementations/aml_investigation/README.md index 0472c65..13b76f1 100644 --- a/implementations/aml_investigation/README.md +++ b/implementations/aml_investigation/README.md @@ -1,33 +1,105 @@ -# [Implementation Name] Overview - -## Introduction -Welcome to the [Implementation Name] of the [Bootcamp/Workshop/Lab/Project Name]. This folder contains a collection of notebooks and a README.md file to enhance your understanding of [specific aspect of the topic]. Here, we will explore [brief description of what will be covered]. - -## Prerequisites -Before you dive into the materials, ensure you have the following prerequisites: -- [Software/Tool/Library] version x.x or higher -- Basic knowledge of [relevant technology or field] -- [Any other requirement] - -## Notebooks -Here you will find the following Jupyter notebooks: -1. **[Subtopic 1]** - This notebook covers [brief description]. -2. **[Subtopic 2] Application** - This notebook covers [brief description]. - -## Package dependencies -This section describes the packages developed for this specific implementation. -- **package_a**: [Description of what the package offers and how it is used] -- **package_b**: [Description of what the package offers and how it is used] - -## Resources -For further reading and additional studies, consider the following resources: -- [Online course, book, or tutorial 1] - [brief description] ([link]) -- [Online course, book, or tutorial 2] - [brief description] ([link]) - -## Getting Started -To get started with the materials in this topic: -1. Ensure you have followed the [reference to the installation guide, environment creation, etc.] in `docs/`. -2. Complete any other steps that are required before moving to the notebooks or code. -3. Move to notebook [notebook name] to [a brief objective of the notebook]. -4. Run code [code name] with the command [command] to [a brief objective of the code]. -5. Add any extra steps if needed. +# Anti-Money Laundering Investigation Agent + +This folder contains a teaching-focused implementation of an AML (Anti‑Money Laundering) investigation workflow: + +- a small SQLite database built from a transactions dataset, +- a case generator that produces investigation “case files” (JSONL), +- an ADK agent that uses a **read-only SQL tool** to investigate each case and write an analysis. + +The goal is to mirror a real analyst workflow: start from an alert/seed, pull relevant activity in a time window, and decide whether the evidence supports laundering or a benign explanation. + +## Setup + +1. If not already present, create a `.env` file (copy from `.env.example`): + +```bash +cp .env.example .env +``` + +1. Set the required API key (Gemini via OpenAI-compatible endpoint is used in this repo): + +```bash +GOOGLE_API_KEY="your-api-key" +``` + +1. Configure the AML database connection (SQLite by default): + +```bash +AML_DB__DRIVER="sqlite" +AML_DB__DATABASE="implementations/aml_investigation/data/aml_transactions.db" +AML_DB__QUERY__MODE="ro" +``` + +1. Install dependencies: + +```bash +uv sync +``` + +## Create the Database + +The CLI downloads a dataset and builds a local SQLite database using the schema in `implementations/aml_investigation/data/schema.ddl`. + +```bash +uv run --env-file .env python -m implementations.aml_investigation.data.cli create-db \ + --illicit-ratio HI \ + --transactions-size Small +``` + +This writes the SQLite DB at `implementations/aml_investigation/data/aml_transactions.db` by default. + +## Generate Case Files (JSONL) + +Cases are generated as `CaseRecord` objects (case metadata + ground truth, and later optional model analysis). + +```bash +uv run --env-file .env python -m implementations.aml_investigation.data.cli create-cases \ + --illicit-ratio HI \ + --transactions-size Small \ + --output-dir implementations/aml_investigation/data +``` + +Output: + +- `implementations/aml_investigation/data/aml_cases.jsonl` + +Each case contains: + +- `seed_transaction_id`: where the investigation starts, +- `window_start` and `seed_timestamp`: the time window the analyst must stay within, +- `trigger_label`: a free-form label describing why the case exists (rule alert, review sample, retrospective review, etc.). + +## Run the Agent (Batch) + +This reads `aml_cases.jsonl`, runs the agent over any cases missing `analysis`, and writes: + +- `implementations/aml_investigation/data/aml_cases_with_analysis.jsonl` + +```bash +uv run --env-file .env implementations/aml_investigation/agent.py +``` + +The script prints a simple confusion matrix for `is_laundering` based on the cases that have `analysis`. + +## Run with ADK Web UI + +If you want to inspect the agent interactively, the module exposes a top-level `root_agent` for ADK discovery. + +From `implementations/aml_investigation/`: + +```bash +uv run adk web --port 8000 --reload --reload_agents implementations/ +``` + +The DB tool is initialized lazily when a tool call happens (so importing the module doesn’t keep a DB connection open). + +## Safety Notes (Why Read‑Only SQL?) + +Agents should not be allowed to write to operational databases. This repo’s SQL tool is designed to be read-only and defensive: + +- it allows only a small set of statement roots (e.g., `SELECT`), +- it blocks write/DDL nodes anywhere in the parsed SQL AST, +- it limits rows returned and applies a timeout, +- it formats results as a small markdown table for LLM consumption. + +See `aieng-eval-agents/aieng/agent_evals/tools/sql_database.py`. diff --git a/implementations/aml_investigation/data/cli.py b/implementations/aml_investigation/data/cli.py index ed478d1..e2213e4 100644 --- a/implementations/aml_investigation/data/cli.py +++ b/implementations/aml_investigation/data/cli.py @@ -267,7 +267,7 @@ def create_cases( if value < 0: raise ValueError(f"{name} must be >= 0") if lookback_days == 0: - logger.warning("lookback_days=0 creates very narrow windows (can be seed-timestamp-only); consider >= 1.") + logger.warning("lookback_days=0 creates very narrow windows (can be seed timestamp only); consider >= 1.") path_to_transc_csv = download_dataset_file(illicit_ratio, transactions_size, "Trans.csv") path_to_patterns_txt = download_dataset_file(illicit_ratio, transactions_size, "Patterns.txt") From 186546c8adbabbfd9a4b8d5947c1225fbcc33cd6 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 2 Feb 2026 11:11:48 -0500 Subject: [PATCH 17/24] Add imports and define __all__ for AML Investigation utilities --- .../agent_evals/aml_investigation/__init__.py | 21 +++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py index 78903f5..0d8b739 100644 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py @@ -1 +1,22 @@ """Utilities for AML Investigation agent.""" + +from .data.cases import ( + AnalystOutput, + CaseFile, + CaseRecord, + GroundTruth, + LaunderingPattern, + build_cases, + parse_patterns_file, +) + + +__all__ = [ + "AnalystOutput", + "CaseFile", + "CaseRecord", + "LaunderingPattern", + "GroundTruth", + "build_cases", + "parse_patterns_file", +] From f84b897feaaecb8e41b0756c609aaf12ddb5f894 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 2 Feb 2026 11:11:57 -0500 Subject: [PATCH 18/24] Add docstring to __init__.py for clarity on AML investigation data models and utilities --- .../aieng/agent_evals/aml_investigation/data/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py index ded4965..530d477 100644 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py @@ -1,3 +1,5 @@ +"""Data models and utilities for AML investigation agent.""" + from .cases import AnalystOutput, CaseFile, CaseRecord, GroundTruth, LaunderingPattern, build_cases, parse_patterns_file from .utils import apply_lookback_window, download_dataset_file, normalize_transactions_data From 4bc45750f5d53438100ca9180aa333385b322088 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 2 Feb 2026 11:12:14 -0500 Subject: [PATCH 19/24] Refactor CaseFile, GroundTruth, and AnalystOutput models to use Pydantic Field descriptions for improved clarity --- .../aml_investigation/data/cases.py | 281 +++++++++--------- 1 file changed, 137 insertions(+), 144 deletions(-) diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py index 9412a3b..1187b5a 100644 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py @@ -14,7 +14,7 @@ from typing import Any import pandas as pd -from pydantic import BaseModel +from pydantic import BaseModel, Field from .utils import ( _canonicalize_numeric, @@ -67,165 +67,51 @@ class LaunderingPattern(str, Enum): class CaseFile(BaseModel): """Metadata for a laundering case file.""" - case_id: str - """Unique identifier for the case.""" - seed_transaction_id: str - """The transaction ID that seeded the laundering attempt. This is typically the last - transaction in the pattern.""" - seed_timestamp: str - """The timestamp of the seed transaction.""" - window_start: str - """The start timestamp of the case window.""" - trigger_label: str - """Upstream alert/review trigger label or heuristic hint for this case.""" + case_id: str = Field(..., description="Unique identifier for the case.") + seed_transaction_id: str = Field( + ..., + description=( + "The transaction ID that seeded the laundering attempt. This is typically the last " + "transaction in the pattern." + ), + ) + seed_timestamp: str = Field(..., description="The timestamp of the seed transaction.") + window_start: str = Field(..., description="The start timestamp of the case window.") + trigger_label: str = Field(..., description="Upstream alert/review trigger label or heuristic hint for this case.") class GroundTruth(BaseModel): """Ground truth information for a laundering case.""" - is_laundering: bool - """Whether the case involves money laundering.""" - pattern_type: LaunderingPattern - """The type of laundering pattern in the case.""" - pattern_description: str - """A short description of the laundering pattern.""" - attempt_transaction_ids: str - """Comma-separated list of transaction IDs involved in the laundering attempt.""" + is_laundering: bool = Field(..., description="Whether the case involves money laundering.") + pattern_type: LaunderingPattern = Field(..., description="The type of laundering pattern in the case.") + pattern_description: str = Field(..., description="A short description of the laundering pattern.") + attempt_transaction_ids: str = Field( + ..., description="Comma-separated list of transaction IDs involved in the laundering attempt." + ) class AnalystOutput(BaseModel): """Analyst notes for a laundering case.""" - summary_narrative: str - """Analyst's reasoning/evidence summary.""" - is_laundering: bool - """Whether the case involves money laundering.""" - pattern_type: LaunderingPattern - """The type of laundering pattern in the case.""" - pattern_description: str - """A short description of the laundering pattern.""" - flagged_transaction_ids: list[str] - """A comma-separated list of transaction IDs flagged by the analyst as suspicious. - """ + summary_narrative: str = Field(..., description="Analyst's reasoning/evidence summary.") + is_laundering: bool = Field(..., description="Whether the case involves money laundering.") + pattern_type: LaunderingPattern = Field(..., description="The type of laundering pattern in the case.") + pattern_description: str = Field(..., description="A short description of the laundering pattern.") + flagged_transaction_ids: str = Field( + ..., description="A comma-separated list of transaction IDs flagged by the analyst as suspicious." + ) class CaseRecord(BaseModel): """Combined case file and ground truth record.""" - case: CaseFile - """Metadata for the laundering case.""" - groundtruth: GroundTruth - """Ground truth information for the laundering case.""" - analysis: AnalystOutput | None = None - """Optional analyst output for the laundering case. Typically populated after \ - investigation.""" - - -def _validate_patterns_parse_inputs(path: str | Path, lookback_days: int, min_timestamp: str | None) -> Path: - """Validate parse_patterns_file inputs and return the normalized patterns path.""" - if lookback_days < 0: - raise ValueError("lookback_days must be >= 0") - - patterns_path = Path(path) - if not patterns_path.exists(): - raise FileNotFoundError(f"Patterns file not found: {patterns_path}") - - if min_timestamp: - _parse_timestamp(min_timestamp) - - return patterns_path - - -def _start_attempt_block(header: str) -> dict[str, Any]: - """Create a new in-memory attempt block state from a BEGIN header line.""" - pattern_type, pattern_description = _parse_pattern_header(header) - return { - "pattern_type": pattern_type, - "pattern_description": pattern_description, - "transactions": [], - } - - -def _parse_attempt_transaction_line(line: str, line_number: int) -> dict[str, str]: - """Parse a single transaction line into a canonicalized transaction dict.""" - row = next(csv.reader([line])) - if len(row) < 11: - raise ValueError(f"Malformed transaction row at line {line_number}: {line}") - - txn = { - "timestamp": _canonicalize_timestamp(row[0]), - "from_bank": _canonicalize_numeric(row[1]), - "from_account": _canonicalize_text(row[2]), - "to_bank": _canonicalize_numeric(row[3]), - "to_account": _canonicalize_text(row[4]), - "amount_received": _canonicalize_numeric(row[5]), - "receiving_currency": _canonicalize_text(row[6]), - "amount_paid": _canonicalize_numeric(row[7]), - "payment_currency": _canonicalize_text(row[8]), - "payment_format": _canonicalize_text(row[9]), - } - txn_str = "|".join(txn[col] for col in _TRANSACTION_ID_COLUMNS) - txn["transaction_id"] = _create_id(txn_str) - return txn - - -def _compute_attempt_window_start( - *, - txns_sorted: list[dict[str, str]], - seed_timestamp: str, - lookback_days: int, - min_timestamp: str | None, -) -> str: - """Compute an attempt window start and avoid zero-width windows when possible.""" - window_start = apply_lookback_window(txns_sorted[0]["timestamp"], lookback_days, min_timestamp=min_timestamp) - - if lookback_days != 0 or window_start != seed_timestamp: - return window_start - - candidate_start = _date_window_start(seed_timestamp) - if not min_timestamp: - return candidate_start - - candidate_dt = _parse_timestamp(candidate_start) - min_dt = _parse_timestamp(min_timestamp) - if candidate_dt < min_dt: - return min_dt.strftime("%Y-%m-%dT%H:%M:%S") - return candidate_start - - -def _finalize_attempt_block( - current: dict[str, Any], lookback_days: int, min_timestamp: str | None -) -> CaseRecord | None: - """Convert an in-progress attempt block into a CaseRecord if it has transactions.""" - txns: list[dict[str, str]] = current.get("transactions") or [] - if not txns: - return None - - txns_sorted = sorted(txns, key=lambda t: _parse_timestamp(t["timestamp"])) - seed_txn = txns_sorted[-1] - seed_timestamp = seed_txn["timestamp"] - window_start = _compute_attempt_window_start( - txns_sorted=txns_sorted, - seed_timestamp=seed_timestamp, - lookback_days=lookback_days, - min_timestamp=min_timestamp, + case: CaseFile = Field(..., description="Metadata for the laundering case.") + groundtruth: GroundTruth = Field(..., description="Ground truth information for the laundering case.") + analysis: AnalystOutput | None = Field( + default=None, + description="Optional analyst output for the laundering case. Typically populated after investigation.", ) - attempt_ids = _serialize_attempt_ids([txn["transaction_id"] for txn in txns_sorted]) - - case_file = CaseFile( - case_id=_create_id(json.dumps(txns_sorted)), - seed_transaction_id=seed_txn["transaction_id"], - seed_timestamp=seed_timestamp, - window_start=window_start, - trigger_label=current["pattern_type"], - ) - groundtruth = GroundTruth( - is_laundering=True, - pattern_type=current["pattern_type"], - pattern_description=current["pattern_description"], - attempt_transaction_ids=attempt_ids, - ) - return CaseRecord(case=case_file, groundtruth=groundtruth) def parse_patterns_file(path: str | Path, lookback_days: int = 0, min_timestamp: str | None = None) -> list[CaseRecord]: @@ -373,6 +259,113 @@ def build_cases( return laundering_cases + false_negative_cases + false_positive_cases + normal_cases +def _validate_patterns_parse_inputs(path: str | Path, lookback_days: int, min_timestamp: str | None) -> Path: + """Validate parse_patterns_file inputs and return the normalized patterns path.""" + if lookback_days < 0: + raise ValueError("lookback_days must be >= 0") + + patterns_path = Path(path) + if not patterns_path.exists(): + raise FileNotFoundError(f"Patterns file not found: {patterns_path}") + + if min_timestamp: + _parse_timestamp(min_timestamp) + + return patterns_path + + +def _start_attempt_block(header: str) -> dict[str, Any]: + """Create a new in-memory attempt block state from a BEGIN header line.""" + pattern_type, pattern_description = _parse_pattern_header(header) + return { + "pattern_type": pattern_type, + "pattern_description": pattern_description, + "transactions": [], + } + + +def _parse_attempt_transaction_line(line: str, line_number: int) -> dict[str, str]: + """Parse a single transaction line into a canonicalized transaction dict.""" + row = next(csv.reader([line])) + if len(row) < 11: + raise ValueError(f"Malformed transaction row at line {line_number}: {line}") + + txn = { + "timestamp": _canonicalize_timestamp(row[0]), + "from_bank": _canonicalize_numeric(row[1]), + "from_account": _canonicalize_text(row[2]), + "to_bank": _canonicalize_numeric(row[3]), + "to_account": _canonicalize_text(row[4]), + "amount_received": _canonicalize_numeric(row[5]), + "receiving_currency": _canonicalize_text(row[6]), + "amount_paid": _canonicalize_numeric(row[7]), + "payment_currency": _canonicalize_text(row[8]), + "payment_format": _canonicalize_text(row[9]), + } + txn_str = "|".join(txn[col] for col in _TRANSACTION_ID_COLUMNS) + txn["transaction_id"] = _create_id(txn_str) + return txn + + +def _compute_attempt_window_start( + *, + txns_sorted: list[dict[str, str]], + seed_timestamp: str, + lookback_days: int, + min_timestamp: str | None, +) -> str: + """Compute an attempt window start and avoid zero-width windows when possible.""" + window_start = apply_lookback_window(txns_sorted[0]["timestamp"], lookback_days, min_timestamp=min_timestamp) + + if lookback_days != 0 or window_start != seed_timestamp: + return window_start + + candidate_start = _date_window_start(seed_timestamp) + if not min_timestamp: + return candidate_start + + candidate_dt = _parse_timestamp(candidate_start) + min_dt = _parse_timestamp(min_timestamp) + if candidate_dt < min_dt: + return min_dt.strftime("%Y-%m-%dT%H:%M:%S") + return candidate_start + + +def _finalize_attempt_block( + current: dict[str, Any], lookback_days: int, min_timestamp: str | None +) -> CaseRecord | None: + """Convert an in-progress attempt block into a CaseRecord if it has transactions.""" + txns: list[dict[str, str]] = current.get("transactions") or [] + if not txns: + return None + + txns_sorted = sorted(txns, key=lambda t: _parse_timestamp(t["timestamp"])) + seed_txn = txns_sorted[-1] + seed_timestamp = seed_txn["timestamp"] + window_start = _compute_attempt_window_start( + txns_sorted=txns_sorted, + seed_timestamp=seed_timestamp, + lookback_days=lookback_days, + min_timestamp=min_timestamp, + ) + attempt_ids = _serialize_attempt_ids([txn["transaction_id"] for txn in txns_sorted]) + + case_file = CaseFile( + case_id=_create_id(json.dumps(txns_sorted)), + seed_transaction_id=seed_txn["transaction_id"], + seed_timestamp=seed_timestamp, + window_start=window_start, + trigger_label=current["pattern_type"], + ) + groundtruth = GroundTruth( + is_laundering=True, + pattern_type=current["pattern_type"], + pattern_description=current["pattern_description"], + attempt_transaction_ids=attempt_ids, + ) + return CaseRecord(case=case_file, groundtruth=groundtruth) + + def _build_false_negative_cases( remaining_attempts: list[CaseRecord], num_false_negative_cases: int, laundering_attempt_txn_ids: set[str] ) -> list[CaseRecord]: From 6c80a66e40efb541e100d4ebc42cd0f792ce9f2a Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 2 Feb 2026 11:12:25 -0500 Subject: [PATCH 20/24] Enhance documentation in agent.py with detailed usage examples for AML investigation agent --- implementations/aml_investigation/agent.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/implementations/aml_investigation/agent.py b/implementations/aml_investigation/agent.py index 7667864..4d48e61 100644 --- a/implementations/aml_investigation/agent.py +++ b/implementations/aml_investigation/agent.py @@ -1,6 +1,11 @@ """AML Investigation Agent Implementation. -Run with: uv run --env-file .env implementations/aml_investigation/agent.py +Examples +-------- +Run the agent on AML cases from JSONL file: + uv run --env-file .env implementations/aml_investigation/agent.py +Run with adk web: + uv run adk web --port 8000 --reload --reload_agents implementations/ """ import asyncio From 1d7ee69bca0f0ae8508e0d67fa676f7f846e2c6a Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 2 Feb 2026 11:12:34 -0500 Subject: [PATCH 21/24] Update CLI documentation section header from 'Typical usage' to 'Examples' --- implementations/aml_investigation/data/cli.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/implementations/aml_investigation/data/cli.py b/implementations/aml_investigation/data/cli.py index e2213e4..6ee18bd 100644 --- a/implementations/aml_investigation/data/cli.py +++ b/implementations/aml_investigation/data/cli.py @@ -5,8 +5,8 @@ - create and populate the SQLite database, and - generate case files for downstream evaluation. -Typical usage -------------- +Examples +-------- Create the database: python implementations/aml_investigation/data/cli.py create-db \ --illicit-ratio HI --transactions-size Small From eabafa5aca0b0605e5f2b0475fc42566a0ac7de9 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 2 Feb 2026 11:28:12 -0500 Subject: [PATCH 22/24] Enhance safety notes in README to clarify read-only SQL tool usage and access control measures --- implementations/aml_investigation/README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/implementations/aml_investigation/README.md b/implementations/aml_investigation/README.md index 13b76f1..c1b0807 100644 --- a/implementations/aml_investigation/README.md +++ b/implementations/aml_investigation/README.md @@ -95,11 +95,14 @@ The DB tool is initialized lazily when a tool call happens (so importing the mod ## Safety Notes (Why Read‑Only SQL?) -Agents should not be allowed to write to operational databases. This repo’s SQL tool is designed to be read-only and defensive: +Agents' access to operational databases should be limited to prevent accidental or malicious data modification. +This repo’s SQL tool is designed to be read-only and defensive: - it allows only a small set of statement roots (e.g., `SELECT`), - it blocks write/DDL nodes anywhere in the parsed SQL AST, - it limits rows returned and applies a timeout, - it formats results as a small markdown table for LLM consumption. +For an extra layer of security, access control can be enforced at the database user/role level. + See `aieng-eval-agents/aieng/agent_evals/tools/sql_database.py`. From 3cbb664d5033f7dd245bfdd14fb822ed5d05e75b Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 2 Feb 2026 14:44:50 -0500 Subject: [PATCH 23/24] Use type literals --- .../aml_investigation/data/__init__.py | 12 +++++++- .../aml_investigation/data/utils.py | 27 +++++++++-------- implementations/aml_investigation/data/cli.py | 30 +++++++++---------- 3 files changed, 40 insertions(+), 29 deletions(-) diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py index 530d477..18e457a 100644 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/__init__.py @@ -1,14 +1,24 @@ """Data models and utilities for AML investigation agent.""" from .cases import AnalystOutput, CaseFile, CaseRecord, GroundTruth, LaunderingPattern, build_cases, parse_patterns_file -from .utils import apply_lookback_window, download_dataset_file, normalize_transactions_data +from .utils import ( + Filenames, + IllicitRatios, + TransactionsSizes, + apply_lookback_window, + download_dataset_file, + normalize_transactions_data, +) __all__ = [ "AnalystOutput", "CaseFile", "CaseRecord", + "Filenames", + "IllicitRatios", "LaunderingPattern", + "TransactionsSizes", "GroundTruth", "apply_lookback_window", "build_cases", diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/utils.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/utils.py index dbe8212..0cd527f 100644 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/utils.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/utils.py @@ -4,7 +4,7 @@ import math from datetime import datetime, timedelta from decimal import Decimal, InvalidOperation -from typing import Any, Literal +from typing import Any, Literal, get_args import kagglehub import pandas as pd @@ -20,11 +20,15 @@ "payment_format", ] +IllicitRatios = Literal["HI", "LI"] +TransactionsSizes = Literal["Small", "Medium", "Large"] +Filenames = Literal["Trans.csv", "accounts.csv", "Patterns.txt"] + def download_dataset_file( - illicit_ratio: Literal["HI", "LI"] = "HI", - transactions_size: Literal["Small", "Medium", "Large"] = "Small", - filename: Literal["Trans.csv", "accounts.csv", "Patterns.txt"] = "Trans.csv", + illicit_ratio: IllicitRatios = "HI", + transactions_size: TransactionsSizes = "Small", + filename: Filenames = "Trans.csv", ) -> str: """Download a specific file from the Kaggle AML dataset. @@ -47,15 +51,12 @@ def download_dataset_file( ValueError If any option is not a supported value. """ - valid_ratio = {"HI", "LI"} - valid_size = {"Small", "Medium", "Large"} - valid_filename = {"Trans.csv", "accounts.csv", "Patterns.txt"} - if illicit_ratio not in valid_ratio: - raise ValueError(f"illicit_ratio must be one of {sorted(valid_ratio)}") - if transactions_size not in valid_size: - raise ValueError(f"transactions_size must be one of {sorted(valid_size)}") - if filename not in valid_filename: - raise ValueError(f"filename must be one of {sorted(valid_filename)}") + if illicit_ratio not in get_args(IllicitRatios): + raise ValueError(f"illicit_ratio must be one of {sorted(get_args(IllicitRatios))}") + if transactions_size not in get_args(TransactionsSizes): + raise ValueError(f"transactions_size must be one of {sorted(get_args(TransactionsSizes))}") + if filename not in get_args(Filenames): + raise ValueError(f"filename must be one of {sorted(get_args(Filenames))}") dataset_path = f"{illicit_ratio}-{transactions_size}_{filename}" return kagglehub.dataset_download( diff --git a/implementations/aml_investigation/data/cli.py b/implementations/aml_investigation/data/cli.py index 6ee18bd..dd9635d 100644 --- a/implementations/aml_investigation/data/cli.py +++ b/implementations/aml_investigation/data/cli.py @@ -20,12 +20,14 @@ import logging import sqlite3 from pathlib import Path -from typing import Any, Callable +from typing import Any, Callable, get_args import click import pandas as pd from aieng.agent_evals.aml_investigation.data import ( CaseRecord, + IllicitRatios, + TransactionsSizes, build_cases, download_dataset_file, normalize_transactions_data, @@ -83,14 +85,14 @@ def _dataset_options(fn: Callable[..., Any]) -> Callable[..., Any]: @click.option( "--illicit-ratio", - type=click.Choice(["HI", "LI"], case_sensitive=False), + type=click.Choice(get_args(IllicitRatios), case_sensitive=False), default="HI", show_default=True, help="Illicit transaction ratio.", ) @click.option( "--transactions-size", - type=click.Choice(["Small", "Medium", "Large"], case_sensitive=False), + type=click.Choice(get_args(TransactionsSizes), case_sensitive=False), default="Small", show_default=True, help="Size of the transactions dataset.", @@ -102,6 +104,14 @@ def wrapper(*args, **kwargs): return wrapper +def _validate_dataset_options(illicit_ratio: str, transactions_size: str) -> None: + """Validate dataset option values.""" + if illicit_ratio not in get_args(IllicitRatios): + raise ValueError(f"illicit_ratio must be one of {sorted(get_args(IllicitRatios))}") + if transactions_size not in get_args(TransactionsSizes): + raise ValueError(f"transactions_size must be one of {sorted(get_args(TransactionsSizes))}") + + @click.group() def cli() -> None: """Entry point for CLI commands.""" @@ -149,12 +159,7 @@ def create_db(illicit_ratio: str, transactions_size: str, ddl_file_path: Path, d FileNotFoundError If the DDL file does not exist. """ - valid_ratio = {"HI", "LI"} - valid_size = {"Small", "Medium", "Large"} - if illicit_ratio not in valid_ratio: - raise ValueError(f"illicit_ratio must be one of {sorted(valid_ratio)}") - if transactions_size not in valid_size: - raise ValueError(f"transactions_size must be one of {sorted(valid_size)}") + _validate_dataset_options(illicit_ratio, transactions_size) if not ddl_file_path.exists(): raise FileNotFoundError(f"DDL file not found: {ddl_file_path}") @@ -251,12 +256,7 @@ def create_cases( ValueError If any numeric argument is negative or option values are invalid. """ - valid_ratio = {"HI", "LI"} - valid_size = {"Small", "Medium", "Large"} - if illicit_ratio not in valid_ratio: - raise ValueError(f"illicit_ratio must be one of {sorted(valid_ratio)}") - if transactions_size not in valid_size: - raise ValueError(f"transactions_size must be one of {sorted(valid_size)}") + _validate_dataset_options(illicit_ratio, transactions_size) for name, value in [ ("num_laundering_cases", num_laundering_cases), ("num_normal_cases", num_normal_cases), From 6666e4ac07617d3e51c3ba9e949d58042c2ff1eb Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 2 Feb 2026 14:45:47 -0500 Subject: [PATCH 24/24] Simplify attempt ID serialization and deserialization --- .../aml_investigation/data/cases.py | 20 ++++++------------- 1 file changed, 6 insertions(+), 14 deletions(-) diff --git a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py index 1187b5a..9b883fb 100644 --- a/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py +++ b/aieng-eval-agents/aieng/agent_evals/aml_investigation/data/cases.py @@ -245,7 +245,11 @@ def build_cases( laundering_attempt_txn_ids: set[str] = set() for case in laundering_cases: - laundering_attempt_txn_ids.update(_deserialize_attempt_ids(case.groundtruth.attempt_transaction_ids)) + attempt_ids_list = [] + attempt_ids_str = case.groundtruth.attempt_transaction_ids + if attempt_ids_str: + attempt_ids_list = [item.strip() for item in attempt_ids_str.split(",") if item.strip()] + laundering_attempt_txn_ids.update(attempt_ids_list) false_negative_cases = _build_false_negative_cases( remaining_attempts, num_false_negative_cases, laundering_attempt_txn_ids @@ -348,7 +352,7 @@ def _finalize_attempt_block( lookback_days=lookback_days, min_timestamp=min_timestamp, ) - attempt_ids = _serialize_attempt_ids([txn["transaction_id"] for txn in txns_sorted]) + attempt_ids = ",".join([txn["transaction_id"] for txn in txns_sorted]) case_file = CaseFile( case_id=_create_id(json.dumps(txns_sorted)), @@ -506,15 +510,3 @@ def _date_window_start(date_value: Any) -> str: return f"{date_value.isoformat()}T00:00:00" parsed = _parse_timestamp(str(date_value)) return parsed.strftime("%Y-%m-%dT00:00:00") - - -def _serialize_attempt_ids(ids: list[str]) -> str: - """Serialize attempt transaction IDs as a comma-separated string.""" - return ",".join(ids) - - -def _deserialize_attempt_ids(value: str) -> list[str]: - """Parse a comma-separated attempt transaction ID string.""" - if not value: - return [] - return [item.strip() for item in value.split(",") if item.strip()]