add: main.py — ai-fluency

main.py

@@ -0,0 +1,95 @@
+import os
+import asyncio
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langchain.tools import tool
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+from pydantic import BaseModel, Field
+from langchain_core.output_parsers import PydanticOutputParser
+
+# Настройка LLM через OpenRouter
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
+
+# Бэкенд для хранения файлов и выполнения команд
+backend = CompositeBackend(
+    [
+        LocalShellBackend(workspace_dir="./workspace"),
+        FilesystemBackend(),
+    ]
+)
+
+# Инструмент для проверки корректности вывода
+@tool
+def validate_output(output: str) -> str:
+    """Проверяет, что вывод содержит все необходимые поля."""
+    issues = []
+    if "goal:" not in output.lower():
+        issues.append("Missing goal")
+    if "timeline:" not in output.lower():
+        issues.append("Missing timeline")
+    if "resources:" not in output.lower():
+        issues.append("Missing resources")
+    if "milestones:" not in output.lower():
+        issues.append("Missing milestones")
+    if "evaluation:" not in output.lower():
+        issues.append("Missing evaluation")
+    return "OK" if not issues else f"Issues: {', '.join(issues)}"
+
+# Модель структуры плана
+class PlanOutput(BaseModel):
+    goal: str = Field(description="Краткое описание цели обучения")
+    timeline: str = Field(description="План по времени (месяцы/недели)")
+    resources: list[str] = Field(description="Список ресурсов и курсов")
+    milestones: list[str] = Field(description="Ключевые контрольные точки")
+    evaluation: str = Field(description="Методы оценки прогресса")
+
+parser = PydanticOutputParser(pydantic_object=PlanOutput)
+
+# Создание агента
+agent = create_deep_agent(
+    model=llm,
+    tools=[validate_output],
+    backend=backend,
+    system_prompt=(
+        "You are a helpful agent that creates a structured AI fluency plan. "
+        "Return the plan in a format that matches the PlanOutput schema. "
+        "After generating the plan, use the validate_output tool to ensure all fields are present."
+    ),
+)
+
+async def main():
+    # Запрос к агенту
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content="Build a personal AI fluency plan for me.")]},
+        {"configurable": {"thread_id": "ai-fluency-plan"}},
+    )
+    # Получаем последний вывод агента
+    plan_text = result["messages"][-1].content
+    # Парсим в структуру
+    try:
+        plan = parser.parse(plan_text)
+    except Exception as e:
+        print("Failed to parse plan:", e)
+        print("Raw output:", plan_text)
+        return
+    # Выводим план
+    print("\nPersonal AI Fluency Plan")
+    print("------------------------")
+    print(f"Goal: {plan.goal}\n")
+    print(f"Timeline:\n{plan.timeline}\n")
+    print("Resources:")
+    for r in plan.resources:
+        print(f"- {r}")
+    print("\nMilestones:")
+    for m in plan.milestones:
+        print(f"- {m}")
+    print(f"\nEvaluation:\n{plan.evaluation}")
+
+if __name__ == "__main__":
+    asyncio.run(main())