add: main.py — Экзамен: Структурированный вывод (Pydantic)

main.py

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+import os
+import sys
+import asyncio
+from dotenv import load_dotenv
+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
+from langchain_core.prompts import PromptTemplate
+
+load_dotenv()
+
+# Pydantic models
+class PersonInfo(BaseModel):
+    name: str = Field(description="Full name of the person")
+    age: int | None = Field(description="Age in years, optional", default=None)
+    profession: str = Field(description="Current profession")
+    skills: list[str] = Field(description="List of skills")
+
+class MeetingNotes(BaseModel):
+    date: str = Field(description="Date of the meeting")
+    participants: list[str] = Field(description="List of participants")
+    topics: list[str] = Field(description="Discussion topics")
+    decisions: list[str] = Field(description="Decisions made")
+    next_steps: list[str] = Field(description="Next steps to be taken")
+
+# LLM configuration
+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,
+)
+
+# Backend for deepagents
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+# Parsers
+person_parser = PydanticOutputParser(pydantic_object=PersonInfo)
+meeting_parser = PydanticOutputParser(pydantic_object=MeetingNotes)
+
+# Prompt template
+prompt_template = PromptTemplate(
+    input_variables=["format_instructions", "text"],
+    template=(
+        "Extract structured data from the following text. "
+        "Follow the format:\n{format_instructions}\n\nText:\n{text}\n\nOutput:"
+    ),
+)
+
+# Helper to decide which parser to use
+def choose_parser(text: str):
+    lower = text.lower()
+    if any(word in lower for word in ["age", "years", "profession", "skills", "skill"]):
+        return person_parser
+    if any(word in lower for word in ["meeting", "participants", "topics", "decisions", "next steps", "next_step"]):
+        return meeting_parser
+    # Default to person_parser
+    return person_parser
+
+# Tool for extraction
+@tool
+def extract_structured(text: str) -> str:
+    """
+    Extract structured data from the given text and return a JSON string.
+    """
+    parser = choose_parser(text)
+    format_instructions = parser.get_format_instructions()
+    prompt = prompt_template.format(format_instructions=format_instructions, text=text)
+    raw_output = llm.invoke(prompt).content
+    try:
+        parsed_obj = parser.parse(raw_output)
+        return parsed_obj.model_dump_json()
+    except Exception as e:
+        return f"Error parsing output: {e}"
+
+# Create the agent
+agent = create_deep_agent(
+    model=llm,
+    tools=[extract_structured],
+    backend=backend,
+    system_prompt="You are a helpful agent that extracts structured data from text.",
+)
+
+# CLI logic
+async def run_agent(text: str):
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content=text)]},
+        {"configurable": {"thread_id": "session-1"}},
+    )
+    output = result["messages"][-1].content
+    try:
+        data = PersonInfo.model_validate_json(output)
+        obj_type = "PersonInfo"
+    except Exception:
+        try:
+            data = MeetingNotes.model_validate_json(output)
+            obj_type = "MeetingNotes"
+        except Exception:
+            print("Failed to parse JSON output.")
+            return
+    print("\nParsed object:")
+    print(data.model_dump())
+    print("\nSummary:")
+    if obj_type == "PersonInfo":
+        print(f"{obj_type}: {data.name}, age={data.age}, profession={data.profession}, skills={data.skills}")
+    else:
+        print(f"{obj_type}: date={data.date}, participants={data.participants}, topics={data.topics}, decisions={data.decisions}, next_steps={data.next_steps}")
+
+def main():
+    if len(sys.argv) > 1:
+        input_text = " ".join(sys.argv[1:])
+        asyncio.run(run_agent(input_text))
+    else:
+        examples = [
+            (
+                "Анна, 28 лет, Python-разработчик. Навыки: FastAPI, Docker.",
+                "PersonInfo example",
+            ),
+            (
+                "Встреча 12.09.2026. Участники: Иван, Мария. Темы: проект X, бюджет. Решения: увеличить бюджет. Следующие шаги: подготовить отчёт.",
+                "MeetingNotes example",
+            ),
+        ]
+        for text, title in examples:
+            print(f"\n=== {title} ===")
+            asyncio.run(run_agent(text))
+
+if __name__ == "__main__":
+    main()