black-langcube 0.2.2

A LangGraph-based extension framework for complex workflow applications, enabling the integration of various AI models and tools into a cohesive system.

Black LangCube

A LangGraph-based extension framework designed to facilitate the development of complex applications by providing a structured way to define and manage workflows.

🚀 Features

• BaseGraph Framework: Foundational interface for constructing, compiling, and executing stateful workflow graphs
• Data Structures: Pydantic models for scientific article metadata, search strategies, outlines, and more
• LLM Nodes: Pre-built nodes for common language model operations
• Helper Utilities: Token counting, result processing, file management, and workflow utilities
• Subgraph System: Modular subworkflows for translation, output generation, and specialized tasks
• Extensible Architecture: Easy to extend with custom nodes and workflows

📦 Installation

From PyPI (when published):

pip install black_langcube

Development Installation:

git clone https://github.com/cerna-kostka/black-langcube.git
cd black-langcube
pip install -e .

With optional dependencies:

pip install black_langcube[dev,examples]

🏗️ Core Components

BaseGraph

The foundation for building stateful workflow graphs using LangGraph:

from black_langcube.graf.graph_base import BaseGraph, GraphState

class MyCustomGraph(BaseGraph):
    def __init__(self, user_message, folder_name, language):
        super().__init__(MyGraphState, user_message, folder_name, language)
        self.build_graph()
    
    def build_graph(self):
        # Add nodes and edges to your workflow
        self.add_node("my_node", my_node_function)
        self.add_edge(START, "my_node")
        self.add_edge("my_node", END)
    
    @property
    def workflow_name(self):
        return "my_custom_graph"

LLMNode

A base class for defining nodes that interact with language models:

from black_langcube.llm_modules.LLMNodes.LLMNode import LLMNode

class MyCustomNode(LLMNode):
    def generate_messages(self):
        return [
            ("system", "You are a helpful assistant"),
            ("human", self.state.get("user_input", ""))
        ]

    def execute(self, extra_input=None):
        result, tokens = self.run_chain(extra_input)
        return {"output": result, "tokens": tokens}

Data Structures

Pydantic models for structured data handling:

from black_langcube.data_structures.data_structures import Article, Strategies, Outline

# Use pre-defined data structures
article = Article(topic="AI Research", language="English")
strategies = Strategies(strategy1="Search academic papers", strategy2="Analyze trends")

LLM Nodes

Pre-built nodes for language model operations:

from black_langcube.llm_modules.LLMNodes.LLMNode import LLMNode

class MyCustomNode(LLMNode):
    def generate_messages(self):
        return [
            ("system", "You are a helpful assistant"),
            ("human", self.state.get("user_input", ""))
        ]
    
    def execute(self, extra_input=None):
        result, tokens = self.run_chain(extra_input)
        return {"output": result, "tokens": tokens}

📚 Architecture

The library is organized into several key modules:

• graf/: Core graph classes and workflow definitions
• data_structures/: Pydantic models for data validation
• llm_modules/: Language model integration and node definitions
• helper_modules/: Utility functions and helper classes
• messages/: Message formatting and composition utilities
• prompts/: Prompt templates and configurations
• format_instructions/: Output formatting utilities

🛠️ Usage Examples

Basic Workflow

from black_langcube.graf.graph_base import BaseGraph, GraphState
from langgraph.graph import START, END

class SimpleWorkflow(BaseGraph):
    def __init__(self, message, folder, language):
        super().__init__(GraphState, message, folder, language)
        self.build_graph()
    
    def build_graph(self):
        def process_message(state):
            return {"result": f"Processed: {state['messages'][-1].content}"}
        
        self.add_node("process", process_message)
        self.add_edge(START, "process")
        self.add_edge("process", END)
    
    @property
    def workflow_name(self):
        return "simple_workflow"

# Usage
workflow = SimpleWorkflow("Hello, world!", "output", "English")
result = workflow.run()

Using Subgraphs

from black_langcube.graf.subgrafs.translator_en_subgraf import TranslatorEnSubgraf

# Translation subgraph
translator = TranslatorEnSubgraf(config, subfolder="translations")
result = translator.run(extra_input={
    "translation_input": "Bonjour le monde",
    "language": "French"
})

🔧 Configuration

The library uses environment variables for configuration. Create a .env file:

OPENAI_API_KEY=your_openai_api_key_here

# optional: LangChain configuration
LANGCHAIN_API_KEY=your_langchain_api_key_here
LANGCHAIN_TRACING_V2=true

📖 Examples

See the examples/ directory for complete working examples:

• Basic Graph: Simple workflow with custom nodes
• Translation Pipeline: Multi-language processing workflow
• Scientific Article Processing: Complex multi-step analysis pipeline
• Custom Data Structures: Extending the framework with your own models

🧪 Development

Setting up development environment:

git clone https://github.com/cerna-kostka/black-langcube.git
cd black-langcube
pip install -e .[dev]

Running tests:

pytest

Code formatting:

black .
isort .

📋 Requirements

• Python 3.9+
• LangChain >= 0.3.24
• LangGraph >= 0.3.7
• Pydantic >= 2.0.0
• OpenAI API access

🤝 Contributing

This is a work in progress and contributions are welcome! Please feel free to:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

MIT License (MIT)

⚠️ Note

This library is intended to be used within a larger application context. The code is provided as-is and is actively being improved. Take it with a grain of salt and feel free to contribute improvements!

🔗 Links

• LangGraph Documentation
• LangChain Documentation
• Examples and Tutorials