Rakam System Core

The core package of Rakam Systems providing foundational interfaces, base components, and utilities.

Overview

rakam-systems-core is the foundation of the Rakam Systems framework. It provides:

• Base Component: Abstract base class with lifecycle management
• Interfaces: Standard interfaces for agents, tools, vector stores, embeddings, and loaders
• Configuration System: YAML/JSON configuration loading and validation
• Tracking System: Input/output tracking for debugging and evaluation
• Logging Utilities: Structured logging with color support

This package is required by both rakam-systems-agent and rakam-systems-vectorstore.

Installation

pip install rakam-systems-core

Key Components

BaseComponent

All components extend BaseComponent which provides:

• Lifecycle management with setup() and shutdown() methods
• Auto-initialization via __call__
• Context manager support
• Built-in evaluation harness

from rakam_systems_core.ai_core.base import BaseComponent

class MyComponent(BaseComponent):
    def setup(self):
        super().setup()
        # Initialize resources

    def shutdown(self):
        # Clean up resources
        super().shutdown()

    def run(self, *args, **kwargs):
        # Main logic
        pass

Interfaces

Standard interfaces for building AI systems:

• AgentComponent: AI agents with sync/async support
• ToolComponent: Callable tools for agents
• LLMGateway: LLM provider abstraction
• VectorStore: Vector storage interface
• EmbeddingModel: Text embedding interface
• Loader: Document loading interface
• Chunker: Text chunking interface

from rakam_systems_core.ai_core.interfaces.agent import AgentComponent
from rakam_systems_core.ai_core.interfaces.tool import ToolComponent
from rakam_systems_core.ai_core.interfaces.vectorstore import VectorStore

Configuration System

Load and validate configurations from YAML files:

from rakam_systems_core.ai_core.config_loader import ConfigurationLoader

loader = ConfigurationLoader()
config = loader.load_from_yaml("agent_config.yaml")
agent = loader.create_agent("my_agent", config)

Tracking System

Track inputs and outputs for debugging:

from rakam_systems_core.ai_core.tracking import TrackingMixin

class MyAgent(TrackingMixin, BaseAgent):
    pass

agent.enable_tracking(output_dir="./tracking")
# Use agent...
agent.export_tracking_data(format='csv')

Package Structure

rakam-systems-core/
├── src/rakam_systems_core/
│   ├── ai_core/
│   │   ├── base.py              # BaseComponent
│   │   ├── interfaces/          # Standard interfaces
│   │   ├── config_loader.py     # Configuration system
│   │   ├── tracking.py          # I/O tracking
│   │   └── mcp/                 # MCP server support
│   └── ai_utils/
│       └── logging.py           # Logging utilities
└── pyproject.toml

Usage in Other Packages

Agent Package

# rakam-systems-agent uses core interfaces
from rakam_systems_core.ai_core.interfaces.agent import AgentComponent
from rakam_systems_agent import BaseAgent

agent = BaseAgent(name="my_agent", model="openai:gpt-4o")

Vectorstore Package

# rakam-systems-vectorstore uses core interfaces
from rakam_systems_core.ai_core.interfaces.vectorstore import VectorStore
from rakam_systems_vectorstore import ConfigurablePgVectorStore

store = ConfigurablePgVectorStore(config=config)

Development

This package contains only interfaces and utilities. To contribute:

1. Fork Rakam Systems repo
2. Make changes to interfaces or utilities
3. Ensure backward compatibility with agent and vectorstore packages
4. Update version in pyproject.toml
5. Open PR

License

Apache 2.0