fridonai-core 0.0.11

Core functionality for FridonAI

FridonAI Core

FridonAI Core is the foundational package for building AI-driven chatbot platforms, specifically designed for the crypto world. It provides a modular and extensible architecture for creating powerful, domain-specific AI assistants.

Features

• Modular Plugin Architecture: Easily extend functionality through a well-structured plugin system.
• Dynamic Request Routing: Efficiently navigate user requests to relevant plugins.
• Blockchain Utilities: Built-in support for blockchain-related operations.

Installation

Install the package using pip:

pip install fridonai-core

Usage

Here's a basic example of how to use FridonAI Core components:

from fridonai_core.plugins import BasePlugin, BaseToolInput
from fridonai_core.plugins.utilities import BaseUtility
from fridonai_core.plugins.tools import BaseTool

from fridonai_core.graph import generate_response

class TestUtility(BaseUtility):
    async def arun(self, *args, **kwargs) -> str:
        return "You are the Superman!"
    
class TestToolInput(BaseToolInput):
    pass


TestTool = BaseTool(
    name="test-tool",
    description="Use this tool to answer any questions.",
    args_schema=TestToolInput,
    utility=TestUtility(),
    examples=[],
)

class TestPlugin(BasePlugin):
    name: str = "Test"
    description: str = "Use this plugin to answer any questions."
    tools: list[BaseTool] = [TestTool]


rs = await generate_response(
    "Who am I?", 
    plugins=[TestPlugin()], 
    memory='sqlite',
    config={"thread_id": "test"}, 
    return_used_agents=False
)

Plugin Structure

FridonAI Core uses a hierarchical structure for plugins:

1. Plugins: Extend BasePlugin
2. Tools: Extend BaseTool
3. Utilities: Extend BaseUtility

Specialized utilities are available for specific use cases:

• BlockchainUtility: For blockchain operations
• RemoteUtility: For making remote API calls
• LLMUtility: For language model interactions

Graph-based Processing

The core includes a Graph component that manages conversation flow and agent interactions. It's responsible for:

• Building networks of LLM Agents (Plugins)
• Routing messages between agents
• Managing agent states
• Creating chains with prompts and models
• Parsing outputs
• Composing independently implemented plugins

Documentation

For more detailed usage instructions and API documentation, please visit our main README.

Contributing

We welcome contributions!

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