Introduction

The iLEAPP AI API provides a comprehensive set of tools for integrating AI-powered forensic analysis capabilities into your applications. This reference documents all the available modules, classes, and methods that you can use to interact with iLEAPP AI programmatically.

Getting Started with the API

To use the iLEAPP AI API in your Python code, you first need to import the necessary modules:

from ileapp_ai import Analyzer, LLMClient
from ileapp_ai.analyzers import MessageAnalyzer, AppUsageAnalyzer
from ileapp_ai.security import ChainOfCustody
from ileapp_ai.visualization import TimelineGenerator

A basic example of using the API to analyze iOS messages:

from ileapp_ai import Analyzer, LLMClient

# Initialize the LLM client with your API key
llm_client = LLMClient(provider="openrouter", api_key="your_api_key")

# Create an analyzer instance
analyzer = Analyzer(llm_client=llm_client)

# Analyze iOS messages
results = analyzer.analyze_artifact(
    artifact_path="/path/to/sms.db",
    artifact_type="messages",
    analysis_depth="comprehensive"
)

# Print the results
print(results.summary)
print(results.insights)

# Generate a report
report = analyzer.generate_report(results, output_format="html")
print(f"Report generated at: {report.path}")

Core Modules

Analyzer

The Analyzer class is the main entry point for the iLEAPP AI API. It provides methods for analyzing iOS artifacts and generating reports.

from ileapp_ai import Analyzer, LLMClient

llm_client = LLMClient(provider="openrouter", api_key="your_api_key")
analyzer = Analyzer(llm_client=llm_client)

results = analyzer.analyze_artifact(
    artifact_path="/path/to/sms.db",
    artifact_type="messages",
    analysis_depth="comprehensive"
)

case_results = analyzer.analyze_case(
    case_path="/path/to/case_directory",
    artifacts=["messages", "app_usage", "browser_history"],
    analysis_depth="comprehensive"
)

report = analyzer.generate_report(
    analysis_result=results,
    output_format="pdf",
    output_path="/path/to/output/report.pdf"
)

LLM Client

The LLMClient class provides an interface for communicating with various AI models through different providers.

from ileapp_ai import LLMClient

# Using OpenRouter
client = LLMClient(
    provider="openrouter",
    api_key="your_openrouter_api_key",
    model="anthropic/claude-3-opus-20240229"
)

# Using a local model
local_client = LLMClient(
    provider="local",
    model="llama-3-70b"
)

result = client.analyze_text(
    text="Hello, how are you doing today?",
    prompt="Analyze the sentiment and tone of this message."
)

app_usage_data = [
    {"app": "Messages", "duration": 3600, "timestamp": "2025-04-01T12:00:00"},
    {"app": "Safari", "duration": 1800, "timestamp": "2025-04-01T13:00:00"},
    {"app": "Camera", "duration": 300, "timestamp": "2025-04-01T15:00:00"}
]

result = client.analyze_structured_data(
    data=app_usage_data,
    prompt="Identify patterns in this app usage data."
)

Artifact Processors

The ArtifactProcessor classes handle the extraction and preprocessing of different types of iOS artifacts before they are analyzed by AI models.

from ileapp_ai.processors import get_processor

processor = get_processor("messages")
processed_data = processor.process("/path/to/sms.db")

Specialized Analyzers

iLEAPP AI includes specialized analyzers for different types of iOS artifacts. These analyzers extend the base Analyzer class with artifact-specific functionality.

Message Analyzer

The MessageAnalyzer class provides specialized analysis for iOS messages and conversations.

from ileapp_ai import LLMClient
from ileapp_ai.analyzers import MessageAnalyzer

llm_client = LLMClient(provider="openrouter", api_key="your_api_key")
message_analyzer = MessageAnalyzer(llm_client=llm_client)

from ileapp_ai.processors import get_processor

processor = get_processor("messages")
messages_data = processor.process("/path/to/sms.db")

conversation_results = message_analyzer.analyze_conversations(messages_data)

Visualization

The visualization module provides tools for creating visual representations of forensic data and analysis results.

from ileapp_ai.visualization import TimelineGenerator

timeline_generator = TimelineGenerator()

events = [
    {
        "timestamp": "2025-04-01T12:00:00",
        "description": "Message sent to John",
        "type": "message",
        "details": {"recipient": "John", "content": "Hello"}
    },
    {
        "timestamp": "2025-04-01T12:05:00",
        "description": "Opened Safari",
        "type": "app_usage",
        "details": {"app": "Safari", "duration": 300}
    }
]

timeline = timeline_generator.generate_timeline(
    events=events,
    output_path="/path/to/output/timeline.html"
)

Report Generation

The report generation module provides tools for creating comprehensive forensic reports from analysis results.

from ileapp_ai.reporting import ReportGenerator

report_generator = ReportGenerator()

from ileapp_ai import Analyzer, LLMClient
from ileapp_ai.reporting import ReportGenerator

llm_client = LLMClient(provider="openrouter", api_key="your_api_key")
analyzer = Analyzer(llm_client=llm_client)
results = analyzer.analyze_artifact("/path/to/sms.db", "messages")

report_generator = ReportGenerator()
report = report_generator.generate_report(
    analysis_results=results,
    output_format="pdf",
    output_path="/path/to/output/report.pdf"
)

Security

The security module provides tools for maintaining forensic integrity and chain of custody.

from ileapp_ai.security import ChainOfCustody

chain_of_custody = ChainOfCustody(
    case_id="CASE-2025-001",
    investigator="John Doe"
)

action_id = chain_of_custody.log_action(
    action="Analyzed messages",
    artifact="/path/to/sms.db",
    details={"analysis_depth": "comprehensive", "model": "claude-3-opus"}
)

Extending iLEAPP AI

iLEAPP AI is designed to be extensible. You can create custom analyzers, processors, and report templates to suit your specific needs.

For detailed information on extending iLEAPP AI, please refer to the Integration Guide.