OpenEdu MCP Server

""" Research Paper data model for OpenEdu MCP Server. This module defines the ResearchPaper model for representing academic papers from sources like arXiv. """ from dataclasses import dataclass, field from datetime import datetime, date from typing import Dict, Any, Optional, List import sys from pathlib import Path sys.path.insert(0, str(Path(__file__).parent.parent)) from models.base import BaseModel, EducationalMetadata class ResearchPaper(BaseModel): """Model representing an academic research paper.""" def __init__( self, arxiv_id: str, title: str, authors: List[str], abstract: str, subjects: List[str], publication_date: date, pdf_url: str, doi: Optional[str] = None, journal: Optional[str] = None, methodology: Optional[str] = None, source: str = "arxiv", source_url: Optional[str] = None, educational_metadata: Optional[EducationalMetadata] = None, key_findings: Optional[List[str]] = None, educational_applications: Optional[List[str]] = None, target_audience: Optional[List[str]] = None, created_at: Optional[datetime] = None, updated_at: Optional[datetime] = None ): """Initialize ResearchPaper instance.""" # Initialize BaseModel fields manually since it's a dataclass self.created_at = created_at or datetime.now() self.updated_at = updated_at or datetime.now() self.arxiv_id = arxiv_id self.title = title self.authors = authors self.abstract = abstract self.subjects = subjects self.publication_date = publication_date self.pdf_url = pdf_url self.doi = doi self.journal = journal self.methodology = methodology self.source = source self.source_url = source_url self.educational_metadata = educational_metadata or EducationalMetadata() self.key_findings = key_findings or [] self.educational_applications = educational_applications or [] self.target_audience = target_audience or [] def to_dict(self) -> Dict[str, Any]: """Convert to dictionary for serialization.""" return { "arxiv_id": self.arxiv_id, "title": self.title, "authors": self.authors, "abstract": self.abstract, "subjects": self.subjects, "publication_date": self.publication_date.isoformat(), "pdf_url": self.pdf_url, "doi": self.doi, "journal": self.journal, "educational_metadata": self.educational_metadata.to_dict(), "methodology": self.methodology, "key_findings": self.key_findings, "educational_applications": self.educational_applications, "target_audience": self.target_audience, "source": self.source, "source_url": self.source_url, "created_at": self.created_at.isoformat(), "updated_at": self.updated_at.isoformat() } @classmethod def from_dict(cls, data: Dict[str, Any]) -> 'ResearchPaper': """Create from dictionary.""" educational_metadata = EducationalMetadata.from_dict( data.get("educational_metadata", {}) ) return cls( arxiv_id=data["arxiv_id"], title=data["title"], authors=data.get("authors", []), abstract=data["abstract"], subjects=data.get("subjects", []), publication_date=date.fromisoformat(data["publication_date"]), pdf_url=data["pdf_url"], doi=data.get("doi"), journal=data.get("journal"), educational_metadata=educational_metadata, methodology=data.get("methodology"), key_findings=data.get("key_findings", []), educational_applications=data.get("educational_applications", []), target_audience=data.get("target_audience", []), source=data.get("source", "arxiv"), source_url=data.get("source_url"), created_at=datetime.fromisoformat(data.get("created_at", datetime.now().isoformat())), updated_at=datetime.fromisoformat(data.get("updated_at", datetime.now().isoformat())) ) @classmethod def from_arxiv(cls, arxiv_data: Dict[str, Any]) -> 'ResearchPaper': """Create ResearchPaper from arXiv API response.""" # Extract arXiv ID arxiv_id = arxiv_data.get("id", "").split("/")[-1] # Extract basic information title = arxiv_data.get("title", "").strip() abstract = arxiv_data.get("summary", "").strip() # Extract authors authors = [] if "authors" in arxiv_data: if isinstance(arxiv_data["authors"], list): authors = [author.get("name", "") for author in arxiv_data["authors"]] else: authors = [arxiv_data["authors"].get("name", "")] elif "author" in arxiv_data: authors = [arxiv_data["author"]] # Extract publication date publication_date = date.today() if "published" in arxiv_data: try: pub_datetime = datetime.fromisoformat(arxiv_data["published"].replace("Z", "+00:00")) publication_date = pub_datetime.date() except (ValueError, TypeError): pass # Extract subjects/categories subjects = [] if "categories" in arxiv_data: if isinstance(arxiv_data["categories"], list): subjects = arxiv_data["categories"] else: subjects = [arxiv_data["categories"]] elif "category" in arxiv_data: subjects = [arxiv_data["category"]] # Extract URLs pdf_url = "" source_url = "" if "links" in arxiv_data: for link in arxiv_data["links"]: if link.get("type") == "application/pdf": pdf_url = link.get("href", "") elif link.get("rel") == "alternate": source_url = link.get("href", "") if not pdf_url and arxiv_id: pdf_url = f"https://arxiv.org/pdf/{arxiv_id}.pdf" if not source_url and arxiv_id: source_url = f"https://arxiv.org/abs/{arxiv_id}" # Extract DOI if available doi = arxiv_data.get("doi") # Create educational metadata educational_metadata = EducationalMetadata() # Determine educational relevance based on subjects and abstract educational_subjects = [] abstract_lower = abstract.lower() # Map arXiv categories to educational subjects subject_mapping = { "math": "Mathematics", "physics": "Science", "cs": "Technology", "stat": "Mathematics", "bio": "Science", "chem": "Science", "econ": "Social Studies", "q-fin": "Social Studies" } for subject in subjects: subject_prefix = subject.split(".")[0].lower() if subject_prefix in subject_mapping: educational_subjects.append(subject_mapping[subject_prefix]) educational_metadata.educational_subjects = list(set(educational_subjects)) # Check for educational keywords in abstract educational_keywords = [ "education", "teaching", "learning", "pedagogy", "curriculum", "student", "classroom", "instruction", "assessment", "educational" ] relevance_score = 0.0 for keyword in educational_keywords: if keyword in abstract_lower: relevance_score += 0.2 # Boost score for certain subjects if any(subj in ["Mathematics", "Science", "Technology"] for subj in educational_subjects): relevance_score += 0.3 educational_metadata.educational_relevance_score = min(relevance_score, 1.0) # Determine target audience based on complexity target_audience = ["Researchers", "Graduate Students"] if any(term in abstract_lower for term in ["undergraduate", "introductory", "basic"]): target_audience.append("Undergraduate Students") if any(term in abstract_lower for term in ["high school", "secondary"]): target_audience.append("High School Teachers") return cls( arxiv_id=arxiv_id, title=title, authors=authors, abstract=abstract, subjects=subjects, publication_date=publication_date, pdf_url=pdf_url, doi=doi, journal=arxiv_data.get("journal"), educational_metadata=educational_metadata, methodology=None, # Can be extracted from full text later key_findings=[], # Can be extracted from full text later educational_applications=[], # Can be populated based on analysis target_audience=target_audience, source="arxiv", source_url=source_url ) def get_primary_subject(self) -> str: """Get the primary subject category.""" return self.subjects[0] if self.subjects else "" def is_recent(self, days: int = 365) -> bool: """Check if paper was published recently.""" days_since_publication = (date.today() - self.publication_date).days return days_since_publication <= days def has_educational_focus(self) -> bool: """Check if paper has explicit educational focus.""" educational_terms = [ "education", "teaching", "learning", "pedagogy", "curriculum", "instruction", "classroom", "student", "educational" ] text_to_check = (self.title + " " + self.abstract).lower() return any(term in text_to_check for term in educational_terms) def get_complexity_level(self) -> str: """Determine the complexity level of the paper.""" abstract_length = len(self.abstract.split()) technical_terms = [ "algorithm", "theorem", "proof", "analysis", "methodology", "framework", "model", "optimization", "statistical" ] technical_count = sum(1 for term in technical_terms if term in self.abstract.lower()) if abstract_length > 200 and technical_count > 5: return "Advanced" elif abstract_length > 150 and technical_count > 3: return "Intermediate" else: return "Introductory" def is_suitable_for_educators(self) -> bool: """Check if paper is suitable for educators.""" return ( self.has_educational_focus() or self.educational_metadata.educational_relevance_score > 0.5 or "High School Teachers" in self.target_audience or "Undergraduate Students" in self.target_audience )