Code Catalog

Welcome to the Code Catalog section of the memories-dev documentation. This comprehensive catalog provides an organized view of the codebase, allowing developers to navigate the framework’s components with ease.

Note

The Code Catalog is designed to help developers understand the structure and organization of the memories-dev codebase. It is particularly useful for those looking to extend or modify the framework.

Package Structure

The memories-dev framework is organized into several key packages and modules:

memories/
├── __init__.py                  # Package initialization and version info
├── config.py                    # Global configuration
├── cli.py                       # Command-line interface
├── agents/                      # AI agents for domain-specific tasks
├── core/                        # Core functionality and base classes
├── data/                        # Data storage and management
├── data_acquisition/            # APIs and tools for acquiring Earth data
├── deployments/                 # Deployment configurations and utilities
├── models/                      # AI model integrations and abstractions
├── scripts/                     # Utility scripts
├── synthetic/                   # Synthetic data generation
└── utils/                       # Utility functions and helpers

Core Components

Component	Description
Memory Store	Central storage system implementing the tiered memory architecture
Earth Analyzers	Specialized modules for analyzing Earth observation data
Model Integrations	Connectors for various AI models and providers
Data Acquisition	Tools for acquiring and processing Earth observation data
Memory Manager	Manages the memory system, including tiering and retrieval

Core Modules

memory_store

from memories import MemoryStore, Config

# Configure the memory store
config = Config(
    storage_path="./earth_memory",
    hot_memory_size=50,  # GB
    warm_memory_size=200,  # GB
    cold_memory_size=1000,  # GB
)

# Initialize the memory store
memory_store = MemoryStore(config)

# Store data in memory
memory_store.store(
    key="location_123",
    data={
        "satellite_imagery": imagery_data,
        "environmental_metrics": metrics_data,
        "temporal_series": time_series_data
    },
    metadata={
        "location": {"lat": 37.7749, "lon": -122.4194},
        "timestamp": "2023-06-15T14:30:00Z",
        "source": "sentinel-2"
    }
)

earth_analyzers

from memories.core.analyzers import TerrainAnalyzer, ClimateAnalyzer, WaterResourceAnalyzer

# Initialize analyzers
terrain = TerrainAnalyzer()
climate = ClimateAnalyzer()
water = WaterResourceAnalyzer()

# Analyze location
terrain_analysis = await terrain.analyze(
    location={"lat": 37.7749, "lon": -122.4194},
    resolution="high"
)

climate_analysis = await climate.analyze(
    location={"lat": 37.7749, "lon": -122.4194},
    time_range={"start": "2020-01-01", "end": "2023-01-01"}
)

water_analysis = await water.analyze(
    location={"lat": 37.7749, "lon": -122.4194},
    include_forecast=True
)

model_integration

from memories.models.load_model import LoadModel
from memories.models.multi_model import MultiModelInference

# Initialize multiple models
models = {
    "openai": LoadModel(model_provider="openai", model_name="gpt-4"),
    "anthropic": LoadModel(model_provider="anthropic", model_name="claude-3-opus"),
    "local": LoadModel(model_provider="local", model_name="llama-3-8b")
}

# Create multi-model inference engine
multi_model = MultiModelInference(models=models)

# Get responses with Earth memory integration
responses = await multi_model.get_responses_with_earth_memory(
    query="Analyze this location for climate resilience",
    location={"lat": 37.7749, "lon": -122.4194},
    earth_memory_analyzers=["terrain", "climate", "water"]
)

Directory Catalog

Module Index

This section provides direct links to the API documentation for key modules:

For a complete list of all modules, see the ‘modindex’.

File Types

The memories-dev codebase consists of various file types:

Extension	Description
.py	Python source code files
.pyx	Cython source files for performance-critical components
.json	Configuration files and data schemas
.md	Markdown documentation files
.rst	reStructuredText documentation files
.yaml/.yml	YAML configuration files
.sh	Shell scripts for various utilities
.ipynb	Jupyter notebook examples and tutorials

Development Tools

The memories-dev project uses several development tools:

Black: Code formatting
isort: Import sorting
mypy: Static type checking
pytest: Testing framework
sphinx: Documentation generation
pre-commit: Pre-commit hooks for code quality

Code Statistics

Metric	Value
Total Python Files	200+
Lines of Code	50,000+
Test Coverage	85%+
Documentation Coverage	90%+