feat: solution for 'сырой текст задания → плоская карточка'

2026-06-28 12:42:48 +03:00
commit 7dd5a51872
23 changed files with 630 additions and 0 deletions
@@ -0,0 +1,116 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Assignment Card Extraction
+
+This script demonstrates how to extract structured assignment details from a
+natural language description using LangChain and Pydantic.
+"""
+
+import os
+from typing import List
+
+from dotenv import load_dotenv
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
+
+# Load environment variables (expects OPENAI_API_KEY)
+load_dotenv()
+
+# --------------------------------------------------------------------------- #
+# Pydantic model definition
+# --------------------------------------------------------------------------- #
+class AssignmentCard(BaseModel):
+    """
+    Structured representation of an assignment description.
+    """
+
+    title: str = Field(
+        ...,
+        description="Short title of the assignment (e.g., 'Mini-report on LangChain').",
+    )
+    subject: str = Field(
+        ...,
+        description="Subject or topic of the assignment (e.g., 'LangChain').",
+    )
+    deadline_hint: str = Field(
+        ...,
+        description="A short phrase indicating the deadline (e.g., 'by Friday').",
+    )
+    deliverable_type: str = Field(
+        ...,
+        description="What to submit: report, code, presentation, etc.",
+    )
+    grading_hints: List[str] = Field(
+        ...,
+        description="List of key grading criteria mentioned in the description.",
+    )
+
+# --------------------------------------------------------------------------- #
+# LangChain components
+# --------------------------------------------------------------------------- #
+# Parser that will convert the LLM output into an AssignmentCard instance
+parser = PydanticOutputParser(pydantic_object=AssignmentCard)
+
+# Prompt template that instructs the LLM to output JSON matching the model
+prompt = PromptTemplate(
+    template=(
+        "You are an assignment extraction assistant. "
+        "Given the following assignment description, extract the following fields:\n\n"
+        "- title: short title of the assignment\n"
+        "- subject: subject or topic\n"
+        "- deadline_hint: a short phrase indicating the deadline\n"
+        "- deliverable_type: what to submit (e.g., report, code, presentation)\n"
+        "- grading_hints: list of key grading criteria mentioned\n\n"
+        "Return a JSON object with exactly these keys. Do not include any additional keys or text.\n\n"
+        "Description: {description}\n\n"
+        "{format_instructions}"
+    ),
+    input_variables=["description"],
+    partial_variables={"format_instructions": parser.get_format_instructions()},
+)
+
+# LLM configuration
+llm = ChatOpenAI(
+    temperature=0,
+    model="gpt-3.5-turbo",
+)
+
+# Chain: prompt -> LLM -> parser
+chain = prompt | llm | parser
+
+# --------------------------------------------------------------------------- #
+# Main execution
+# --------------------------------------------------------------------------- #
+def main() -> None:
+    # Sample assignment description
+    sample_description = (
+        "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. "
+        "Оценка: за полноту и за пример кода."
+    )
+
+    # Run the chain
+    try:
+        result = chain.invoke({"description": sample_description})
+    except Exception as e:
+        print(f"Error during chain execution: {e}")
+        return
+
+    # The result is already a validated AssignmentCard instance
+    print("\n=== Parsed Assignment Card ===")
+    print(result.model_dump(indent=2))
+
+    # Human-readable summary
+    print("\n=== Human-readable Summary ===")
+    print(f"Title: {result.title}")
+    print(f"Subject: {result.subject}")
+    print(f"Deadline: {result.deadline_hint}")
+    print(f"Deliverable: {result.deliverable_type}")
+    print(f"Grading Hints: {', '.join(result.grading_hints)}")
+
+
+if __name__ == "__main__":
+    main()