ControlFlow
v0.11.3: Exception-al Service
تدفق التحكم هو إطار بيثون لبناء مهام سير عمل AI Agency.
يوفر Controllow إطارًا منظمًا يركز على المطورين لتحديد مهام سير العمل وتفويض العمل إلى LLMs ، دون التضحية بالسيطرة أو الشفافية:
إن أبسط سير العمل في تدفق التحكم لديه مهمة واحدة ، وكيل افتراضي ، وإدارة مؤشرات الترابط التلقائي:
import controlflow as cf
result = cf . run ( "Write a short poem about artificial intelligence" )
print ( result )نتيجة:
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.
يعالج تدفق التحكم تحديات بناء التطبيقات التي تعمل بالنيابة القوية ويمكن التنبؤ بها على حد سواء:
تثبيت تدفق التحكم مع pip :
pip install controlflow بعد ذلك ، قم بتكوين مزود LLM الخاص بك. مزود ControlFlow الافتراضي هو Openai ، والذي يتطلب متغير بيئة OPENAI_API_KEY :
export OPENAI_API_KEY=your-api-key
لاستخدام مزود LLM مختلف ، راجع مستندات تكوين LLM.
فيما يلي مثال أكثر إشراكًا يعرض تفاعل المستخدم ، وسير عمل متعدد الخطوات ، والمخرجات المهيكلة:
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 ))محادثة:
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 workflowsعرض:
{ "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 " ] }
في هذا المثال ، تقوم Controlfowlow بإدارة flow تلقائيًا ، أو سياق مشترك لسلسلة من المهام. يمكنك التبديل بين وظائف Python القياسية والمهام الوكيل في أي وقت ، مما يجعل من السهل بناء مهام سير عمل معقدة بشكل متزايد.
الغوص أعمق في تدفق التحكم:
