ControlFlow下載 - ControlFlow流源代碼下載

ControlFlow

其他源碼

v0.11.3: Exception-al Service

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控制流橫幅

控制流

ControlFlow是用於構建代理AI工作流程的Python框架。

ControlFlow提供了一個結構化的，以開發人員為中心的框架，用於定義工作流並將工作委派給LLM，而無需犧牲控製或透明度：

創建離散的，可觀察到的任務，供AI進行工作。
為每個任務分配一個或多個專業的AI代理。
將任務結合在一起，以協調更複雜的行為。

例子

最簡單的ControlFlow Workflow具有一個任務，一個默認代理和自動線程管理：

 import controlflow as cf

result = cf . run ( "Write a short poem about artificial intelligence" )

print ( result )

結果：

 In circuits and code, a mind does bloom,
With algorithms weaving through the gloom.
A spark of thought in silicon's embrace,
Artificial intelligence finds its place.

為什麼要控制流？

ControlFlow解決了構建功能強大且可預測的AI驅動應用程序的挑戰：

？以任務為中心的體系結構：將復雜的AI工作流擴散到可管理的，可觀察的步驟中。
結構化結果：橋接AI和傳統軟件之間的差距，並具有類型的安全性，經過驗證的輸出。
？專業代理：部署特定於任務的AI代理，以有效解決問題。
？靈活的控制：連續調整工作流中控制和自主權的平衡。
？️多機構編排：在單個工作流或任務中協調多個AI代理。
？本機可觀察性：以完整的3.0支持，監視和調試您的AI工作流程。
？生態系統集成：與您現有的代碼，工具和更廣泛的AI生態系統無縫合作。

安裝

使用pip安裝控制流：

pip install controlflow

接下來，配置您的LLM提供商。 ControlFlow的默認提供商是OpenAI，它需要OPENAI_API_KEY環境變量：

 export OPENAI_API_KEY=your-api-key

要使用其他LLM提供商，請參見LLM配置文檔。

工作流示例

這是一個更涉及的示例，可以展示用戶交互，多步工作流和結構化輸出：

 import controlflow as cf
from pydantic import BaseModel


class ResearchProposal ( BaseModel ):
    title : str
    abstract : str
    key_points : list [ str ]


@ cf . flow
def research_proposal_flow ():

    # Task 1: Get the research topic from the user
    user_input = cf . Task (
        "Work with the user to choose a research topic" ,
        interactive = True ,
    )
    
    # Task 2: Generate a structured research proposal
    proposal = cf . run (
        "Generate a structured research proposal" ,
        result_type = ResearchProposal ,
        depends_on = [ user_input ]
    )
    
    return proposal


result = research_proposal_flow ()

print ( result . model_dump_json ( indent = 2 ))

點擊查看結果

對話：

 Agent: Hello! I'm here to help you choose a research topic. Do you have 
any particular area of interest or field you would like to explore? 
If you have any specific ideas or requirements, please share them as well.

User: Yes, I'm interested in LLM agentic workflows

提議：

{
    "title" : " AI Agentic Workflows: Enhancing Efficiency and Automation " ,
    "abstract" : " This research proposal aims to explore the development and implementation of AI agentic workflows to enhance efficiency and automation in various domains. AI agents, equipped with advanced capabilities, can perform complex tasks, make decisions, and interact with other agents or humans to achieve specific goals. This research will investigate the underlying technologies, methodologies, and applications of AI agentic workflows, evaluate their effectiveness, and propose improvements to optimize their performance. " ,
    "key_points" : [
        " Introduction: Definition and significance of AI agentic workflows, Historical context and evolution of AI in workflows " ,
        " Technological Foundations: AI technologies enabling agentic workflows (e.g., machine learning, natural language processing), Software and hardware requirements for implementing AI workflows " ,
        " Methodologies: Design principles for creating effective AI agents, Workflow orchestration and management techniques, Interaction protocols between AI agents and human operators " ,
        " Applications: Case studies of AI agentic workflows in various industries (e.g., healthcare, finance, manufacturing), Benefits and challenges observed in real-world implementations " ,
        " Evaluation and Metrics: Criteria for assessing the performance of AI agentic workflows, Metrics for measuring efficiency, accuracy, and user satisfaction " ,
        " Proposed Improvements: Innovations to enhance the capabilities of AI agents, Strategies for addressing limitations and overcoming challenges " ,
        " Conclusion: Summary of key findings, Future research directions and potential impact on industry and society "
    ]
}