Add task.py

task.py

@@ -0,0 +1,106 @@
+"""
+Task: Parse a raw assignment description into a flat card.
+
+This script demonstrates a LangChain pipeline that:
+1. Defines a Pydantic model for the assignment card.
+2. Builds a prompt that asks the model to output JSON conforming to that model.
+3. Uses the `PydanticOutputParser` to enforce the structure.
+4. Runs the chain on a sample input and prints the validated object and a human‑readable summary.
+
+Requirements:
+- langchain-core
+- langchain-openai
+- pydantic
+- python-dotenv (optional, for loading API keys)
+"""
+
+import os
+from dotenv import load_dotenv
+
+from pydantic import BaseModel, Field
+from langchain_core.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI
+from langchain_core.output_parsers import PydanticOutputParser
+
+# Load environment variables (e.g. OPENAI_API_KEY)
+load_dotenv()
+
+# 1. Define the assignment card model
+class AssignmentCard(BaseModel):
+    """Flat representation of an assignment description.
+
+    Attributes
+    ----------
+    title : str
+        The main title or name of the assignment.
+    subject : str
+        The academic subject or topic.
+    deadline_hint : str
+        Free‑form hint about the deadline (e.g. "к пятнице").
+    deliverable_type : str
+        What the student should submit (e.g. "отчёт", "код").
+    grading_hints : list[str]
+        List of phrases or criteria mentioned for grading.
+    """
+
+    title: str = Field(..., description="Title of the assignment")
+    subject: str = Field(..., description="Subject or topic of the assignment")
+    deadline_hint: str = Field(..., description="Hint about the deadline")
+    deliverable_type: str = Field(..., description="What the student should submit")
+    grading_hints: list[str] = Field(..., description="List of grading criteria mentioned")
+
+# 2. Create the parser that will enforce the model
+parser = PydanticOutputParser(pydantic_object=AssignmentCard)
+
+# 3. Build the prompt template
+prompt_template = PromptTemplate(
+    template="""
+You are an assistant that extracts structured information from a single sentence or short paragraph describing an assignment.
+
+Given the following description, output a JSON object that conforms exactly to the following schema:
+{schema}
+
+The JSON should contain the keys: title, subject, deadline_hint, deliverable_type, grading_hints.
+
+Description:
+{description}
+
+Output:
+{format_instructions}
+""",
+    input_variables=["description"],
+    partial_variables={
+        "schema": parser.get_format_instructions(),
+        "format_instructions": parser.get_format_instructions(),
+    },
+)
+
+# 4. Set up the LLM (ChatOpenAI).  The model name can be overridden via env var.
+llm = ChatOpenAI(
+    model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
+    temperature=0,
+)
+
+# 5. Build the chain
+chain = prompt_template | llm | parser
+
+# 6. Example usage
+if __name__ == "__main__":
+    example_description = (
+        "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
+    )
+    result = chain.invoke({"description": example_description})
+    print("\n--- Parsed Assignment Card ---")
+    print(result.model_dump(indent=4))
+
+    # Human‑readable summary
+    summary = (
+        f"Title: {result.title}\n"
+        f"Subject: {result.subject}\n"
+        f"Deadline hint: {result.deadline_hint}\n"
+        f"Deliverable: {result.deliverable_type}\n"
+        f"Grading hints: {', '.join(result.grading_hints)}"
+    )
+    print("\n--- Summary ---")
+    print(summary)
+""