add main

main.py

@@ -0,0 +1,98 @@
+"""
+Task: Convert raw assignment text into a flat card using LangChain.
+
+The script defines a Pydantic model `TaskCard` and uses LangChain to prompt an LLM
+to output the fields in a JSON format that can be parsed by
+`PydanticOutputParser`.  The result is printed as a validated object and a short
+summary.
+"""
+
+from __future__ import annotations
+
+import json
+from typing import Any, Dict
+
+from langchain_core.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI
+from langchain.output_parsers import PydanticOutputParser
+from pydantic import BaseModel, Field
+
+# ---------------------------------------------------------------------------
+# 1. Define the data model for a task card.
+# ---------------------------------------------------------------------------
+class TaskCard(BaseModel):
+    title: str = Field(..., description="Task title")
+    subject: str | None = Field(None, description="Subject or topic of the task")
+    deadline_hint: str | None = Field(
+        None,
+        description="Human‑readable hint about when the task should be finished",
+    )
+    deliverable_type: str | None = Field(
+        None,
+        description="What kind of output is expected (e.g., code, report)",
+    )
+    grading_hints: str | None = Field(
+        None,
+        description="Hints for how the task will be graded",
+    )
+
+# ---------------------------------------------------------------------------
+# 2. Prompt template – we ask the model to return a JSON object that matches
+#    TaskCard.
+# ---------------------------------------------------------------------------
+prompt_template = (
+    "You are an assistant that extracts structured information from a raw text.
+    Return only a JSON object with the following keys: title, subject,
+    deadline_hint, deliverable_type, grading_hints.  Do not add any
+    surrounding text or comments.
+
+    Raw text:
+    {raw_text}
+"
+)
+prompt = PromptTemplate.from_template(prompt_template)
+
+# ---------------------------------------------------------------------------
+# 3. LLM chain – use OpenAI chat model via LangChain.
+# ---------------------------------------------------------------------------
+llm = ChatOpenAI(temperature=0, model_name="gpt-4o-mini")
+parser = PydanticOutputParser(pydantic_object=TaskCard)
+
+# The chain: prompt -> LLM -> parser
+from langchain.chains import LLMChain
+chain = LLMChain(llm=llm, prompt=prompt, output_parser=parser)
+
+# ---------------------------------------------------------------------------
+# 4. Example usage – replace RAW_TEXT with the assignment description.
+# ---------------------------------------------------------------------------
+RAW_TEXT = """
+## Цель
+Научиться из одного пользовательского текста получить проверяемый набор полей (title, subject, deadline_hint, deliverable_type, grading_hints) без диалога и без «ручного» разбора строки в Python.
+
+## Стек
+- Python 3.10+
+- langchain-core, langchain-openai, pydantic
+- PydanticOutputParser для структурированного вывода
+
+## Что нужно сделать
+1. Описать Pydantic-модель карточки задания
+2. Собрать цепочку: шаблон промпта → вызов LLM → парсер в BaseModel
+3. В промпте попросить модель вернуть данные в формате для парсера
+4. Вывести валидированный объект и краткую сводку
+"""
+
+if __name__ == "__main__":
+    # Run the chain
+    result: TaskCard = chain.run(raw_text=RAW_TEXT)
+    print("Validated TaskCard:\n", result.json(indent=2))
+    # Simple summary
+    summary = (
+        f"Title: {result.title}\n"
+        f"Subject: {result.subject or 'N/A'}\n"
+        f"Deadline hint: {result.deadline_hint or 'N/A'}\n"
+        f"Deliverable type: {result.deliverable_type or 'N/A'}\n"
+        f"Grading hints: {result.grading_hints or 'N/A'}"
+    )
+    print("\nSummary:\n", summary)
+
+# End of file