planai 0.1.0

A simple framework for coordinating classical compute and LLM-based tasks.

PlanAI

PlanAI is an innovative system designed for complex task automation through a sophisticated graph-based architecture. It integrates traditional computations and cutting-edge AI technologies to enable versatile and efficient workflow management.

Table of Contents

• Key Features
• Requirements
• Installation
• Usage
• Advanced Features
• Documentation
• Contributing
• License
• Roadmap

Key Features

• Graph-Based Architecture: Construct dynamic workflows comprising interconnected TaskWorkers for highly customizable automation.
• Hybrid TaskWorkers: Combine conventional computations (e.g., API calls) with powerful LLM-driven operations, leveraging Retrieval-Augmented Generation (RAG) capabilities.
• Type Safety with Pydantic: Ensure data integrity and type consistency across workflows with Pydantic-validated input and output.
• Intelligent Data Routing: Utilize type-aware routing to efficiently manage data flow between nodes, adapting to multiple downstream consumers.

Requirements

• Python 3.10+
• Poetry (for development)

Installation

You can install PlanAI using pip:

pip install planai

For development, clone the repository and install dependencies:

git clone https://github.com/provos/planai.git
cd planai
poetry install

Usage

PlanAI allows you to create complex, AI-enhanced workflows using a graph-based architecture. Here's a basic example:

from planai import Graph, TaskWorker, LLMTaskWorker, llm_from_config
from planai.task import TaskWorkItem
from pydantic import Field

# Define custom TaskWorkers
class CustomDataProcessor(TaskWorker):
    output_types = [ProcessedData]
    
    def consume_work(self, task: RawData):
        processed_data = self.process(task.data)
        self.publish_work(ProcessedData(data=processed_data))

# Define an LLM-powered task
class AIAnalyzer(LLMTaskWorker):
    output_types = [AnalysisResult]
    
    def consume_work(self, task: ProcessedData):
        super().consume_work(task)

# Create and run the workflow
graph = Graph(name="Data Analysis Workflow")
data_processor = CustomDataProcessor()
ai_analyzer = AIAnalyzer(
   llm=llm_from_config(provider="openai", model_name="gpt-4"),
   prompt="Analyze the provided data and derive insights")

graph.add_workers(data_processor, ai_analyzer)
graph.set_dependency(data_processor, ai_analyzer)

initial_data = RawData(data="Some raw data")
graph.run(initial_tasks=[(data_processor, initial_data)])

Advanced Features

PlanAI supports advanced features like:

• Caching results with CachedTaskWorker
• Joining multiple task results with JoinedTaskWorker
• Integrating with various LLM providers (OpenAI, Ollama, etc.)

For more detailed examples and advanced usage, please refer to the examples/ directory in the repository.

Documentation

Full documentation for PlanAI is available at https://planai.readthedocs.io/

Contributing

We welcome contributions to PlanAI! Please see our Contributing Guide for more details on how to get started.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

For any questions or support, please open an issue on our GitHub issue tracker.