add: main.py

main.py

@@ -0,0 +1,112 @@
+import os
+import asyncio
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import PydanticOutputParser
+from pydantic import BaseModel, Field
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+
+# ---------- LLM ----------
+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,
+)
+
+# ---------- Pydantic models ----------
+class PersonInfo(BaseModel):
+    name: str = Field(description="Full name of the person")
+    age: int | None = Field(default=None, description="Age in years, optional")
+    profession: str = Field(description="Current profession or job title")
+    skills: list[str] = Field(description="List of professional skills")
+
+class MeetingNotes(BaseModel):
+    date: str = Field(description="Meeting date in ISO format (YYYY-MM-DD)")
+    participants: list[str] = Field(description="Names of participants")
+    topics: list[str] = Field(description="Main discussion topics")
+    decisions: list[str] = Field(description="Decisions made during the meeting")
+    next_steps: list[str] = Field(description="Action items for next steps")
+
+# ---------- Prompt templates ----------
+person_prompt = PromptTemplate(
+    input_variables=["text"],
+    template="""
+You are a data extraction assistant. Extract the following information from the given text and output a JSON object that matches the PersonInfo schema.
+
+Text: {text}
+
+Output must be a valid JSON object with fields: name, age, profession, skills.
+"""
+)
+
+meeting_prompt = PromptTemplate(
+    input_variables=["text"],
+    template="""
+You are a data extraction assistant. Extract the following information from the given text and output a JSON object that matches the MeetingNotes schema.
+
+Text: {text}
+
+Output must be a valid JSON object with fields: date, participants, topics, decisions, next_steps.
+"""
+)
+
+# ---------- Parsers ----------
+person_parser = PydanticOutputParser(pydantic_object=PersonInfo)
+meeting_parser = PydanticOutputParser(pydantic_object=MeetingNotes)
+
+# ---------- Chains ----------
+person_chain = person_prompt | llm | person_parser
+meeting_chain = meeting_prompt | llm | meeting_parser
+
+# ---------- Backend ----------
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+# ---------- Agent ----------
+agent = create_deep_agent(
+    model=llm,
+    tools=[],
+    backend=backend,
+    system_prompt="You are a structured data extraction agent. Decide whether the input text is about a person or a meeting and return the parsed JSON accordingly.",
+)
+
+# ---------- Helper for routing ----------
+async def route_and_parse(text: str):
+    # Simple heuristic: if the word "meeting" or "встреча" appears, treat as meeting
+    if "meeting" in text.lower() or "встреча" in text.lower():
+        result = await meeting_chain.ainvoke({"text": text})
+        return MeetingNotes(**result)
+    else:
+        result = await person_chain.ainvoke({"text": text})
+        return PersonInfo(**result)
+
+# ---------- CLI ----------
+async def main():
+    examples = {
+        "person": "Анна, 28 лет, Python-разработчик. Навыки: FastAPI, Docker.",
+        "meeting": "Встреча 2026-06-10. Участники: Иван, Мария. Темы: проект X, бюджет. Решения: утвердить план. Next steps: подготовить презентацию.",
+    }
+    print("Выберите пример: 1 - человек, 2 - встреча, 3 - ввод с клавиатуры")
+    choice = input("> ")
+    if choice == "1":
+        text = examples["person"]
+    elif choice == "2":
+        text = examples["meeting"]
+    else:
+        text = input("Введите текст: ")
+    parsed = await route_and_parse(text)
+    print("\nРезультат (model_dump):")
+    print(parsed.model_dump(indent=2))
+    print("\nКраткая сводка:")
+    if isinstance(parsed, PersonInfo):
+        print(f"{parsed.name}, {parsed.age or 'неизвестно'} лет, {parsed.profession}. Навыки: {', '.join(parsed.skills)}")
+    else:
+        print(f"Дата: {parsed.date}\nУчастники: {', '.join(parsed.participants)}\nТемы: {', '.join(parsed.topics)}\nРешения: {', '.join(parsed.decisions)}\nСледующие шаги: {', '.join(parsed.next_steps)}")
+
+if __name__ == "__main__":
+    asyncio.run(main())