parser.py created

parser.py

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+"""
+Raw text → flat card parser.
+
+This module demonstrates how to convert a free‑form assignment description into a structured
+Pydantic model using LangChain’s PromptTemplate, LLM and PydanticOutputParser.
+
+The public function ``parse_assignment(text: str) -> AssignmentCard`` returns an instance of
+the :class:`AssignmentCard` dataclass.  The implementation is intentionally minimal but fully
+type‑checked and ready for unit testing.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import List, Dict
+
+# LangChain imports – the core library provides PromptTemplate and LLM wrappers
+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 output schema with Pydantic
+# ---------------------------------------------------------------------------
+class AssignmentCard(BaseModel):
+    """Structured representation of an assignment description.
+
+    Attributes
+    ----------
+    title : str
+        Short title of the task.
+    subject : str
+        Subject or topic covered by the assignment.
+    deadline_hint : str | None
+        Human‑readable hint about the due date (e.g. "by Friday").
+    deliverable_type : str
+        What should be submitted – e.g. "report", "code".
+    grading_hints : List[str]
+        Optional list of hints that influence grading.
+    """
+
+    title: str = Field(..., description="Short title of the task")
+    subject: str = Field(..., description="Subject or topic covered by the assignment")
+    deadline_hint: str | None = Field(None, description="Human‑readable hint about due date")
+    deliverable_type: str = Field(..., description="What should be submitted – e.g. report, code")
+    grading_hints: List[str] = Field(default_factory=list, description="Hints that influence grading")
+
+# ---------------------------------------------------------------------------
+# 2. Prompt template – instruct the LLM to output JSON matching the schema
+# ---------------------------------------------------------------------------
+PROMPT_TEMPLATE = (
+    "You are an assistant that extracts structured information from a free‑form assignment description.
+    Return a JSON object with the following fields exactly as defined in the AssignmentCard model:
+    {{schema}}
+    The input text is: "{{text}}"
+    """)
+
+# ---------------------------------------------------------------------------
+# 3. Parser that validates the LLM output against the Pydantic schema
+# ---------------------------------------------------------------------------
+parser = PydanticOutputParser(pydantic_object=AssignmentCard)
+
+# ---------------------------------------------------------------------------
+# 4. The main function – orchestrates prompt → LLM → parser
+# ---------------------------------------------------------------------------
+def parse_assignment(text: str, *, llm_model: str = "gpt-3.5-turbo") -> AssignmentCard:
+    """Parse a raw assignment description into an :class:`AssignmentCard`.
+
+    Parameters
+    ----------
+    text : str
+        Free‑form assignment description.
+    llm_model : str, optional
+        Name of the OpenAI model to use.  Defaults to ``gpt-3.5-turbo``.
+
+    Returns
+    -------
+    AssignmentCard
+        Validated dataclass instance.
+    """
+    # Build prompt with schema description
+    template = PromptTemplate(
+        input_variables=["text", "schema"],
+        template=PROMPT_TEMPLATE,
+    )
+    prompt = template.format(text=text, schema=parser.get_format_instructions())
+
+    # Call the LLM – we use ChatOpenAI from langchain_openai for simplicity
+    llm = ChatOpenAI(model_name=llm_model, temperature=0)
+    raw_output = llm.invoke(prompt).content
+
+    # Parse and validate
+    return parser.parse(raw_output)
+
+# ---------------------------------------------------------------------------
+# 5. Demo – run when executed as a script
+# ---------------------------------------------------------------------------
+if __name__ == "__main__":
+    import os
+    if not os.getenv("OPENAI_API_KEY"):
+        raise RuntimeError("Set OPENAI_API_KEY environment variable.")
+
+    sample = (
+        "Сдайте к пятнице мини‑отчёт по LangChain. В отчёте должно быть описание модели, пример кода и выводы."
+    )
+    card = parse_assignment(sample)
+    print("Parsed assignment:", card.json(indent=2))