feat: solution for unknown

2026-05-28 16:29:47 +00:00
parent 9001ea3fa8
commit fe47b79129
1 changed files with 78 additions and 14 deletions
@@ -1,18 +1,82 @@
-def main():
-    if len(sys.argv) < 2:
-        print("Usage: python script.py --task-text 'текст задания'")
-        return
-    # Find the flag and its value
-    try:
-        idx = sys.argv.index("--task-text")
-        task_text = sys.argv[idx + 1]
-    except (ValueError, IndexError):
-        print("Error: '--task-text' flag not found or missing value.")
-        return
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field, SecretStr
+from langchain.agents import create_agent
+import sys

-    # Here you would normally process the task text with your orchestrator logic.
-    # For demonstration, we simply echo it back.
-    print(f"Received task text: {task_text}")
+# LLM placeholder configuration
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b",
+    base_url='https://platform.brojs.ru/jrnl-bh/api/inference/v1',
+    api_key=SecretStr("jrnl_30283ab953615cbb6846ff9940a1eedce0b76d7b2f59a2394f29e74643e6a90d"),
+    temperature=0.7,
+)
+
+# ---------- Pydantic models ----------
+class PersonInfo(BaseModel):
+    """Information about a person."""
+    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 occupation or job title")
+    skills: list[str] = Field(description="List of professional skills")
+
+class MeetingNotes(BaseModel):
+    """Summary of a meeting."""
+    date: str = Field(description="Date of the meeting (ISO format)")
+    participants: list[str] = Field(description="Names of attendees")
+    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 follow‑up")
+
+# ---------- Agent creation ----------
+agent_person = create_agent(
+    model=llm,
+    response_format=PersonInfo,
+    system_prompt="You are an assistant that extracts structured person information from a single sentence.",
+)
+
+agent_meeting = create_agent(
+    model=llm,
+    response_format=MeetingNotes,
+    system_prompt="You are an assistant that extracts structured meeting notes from a paragraph of text.",
+)
+
+# ---------- Simple heuristic to choose schema ----------
+def detect_schema(text: str) -> str:
+    """Return 'person' or 'meeting' based on simple keyword heuristics."""
+    lower = text.lower()
+    if any(word in lower for word in ("profession", "skills", "age")):
+        return "person"
+    if any(word in lower for word in ("meeting", "participants", "decisions", "next steps")):
+        return "meeting"
+    # Default to person if ambiguous
+    return "person"
+
+# ---------- CLI ----------
+def main():
+    examples = [
+        "Анна, 28 лет, Python-разработчик. Навыки: FastAPI, Docker.",
+        ("Встреча с командой по проекту X прошла 2024-05-20.\n"
+         "Участники: Иван, Мария, Алексей.\n"
+         "Темы: планирование спринта, распределение задач.\n"
+         "Решения: назначить ответственных за каждый модуль.\n"
+         "Next steps: подготовить спецификации к 2024-05-27."),
+    ]
+
+    if len(sys.argv) > 1:
+        texts = [" ".join(sys.argv[1:])]
+    else:
+        texts = examples
+
+    for txt in texts:
+        print("\n=== Input ===")
+        print(txt)
+        schema_type = detect_schema(txt)
+        agent = agent_person if schema_type == "person" else agent_meeting
+        result = agent.invoke({"messages": [{"role": "user", "content": txt}]})
+        structured = result["structured_response"]
+        print("\n=== Output ===")
+        print(structured.model_dump(indent=2))
+        print("\n---")

 if __name__ == "__main__":
    main()