bamboo-1 / src /paper_db.py

Claude Opus 4.6

Consolidate project: merge scripts/, bamboo1/ into src/, optimize training

24ec440 9 months ago

25.3 kB

# /// script
# requires-python = ">=3.9"
# dependencies = ["requests>=2.28.0", "pyyaml>=6.0", "arxiv>=2.0.0", "pymupdf4llm>=0.0.10"]
# ///
"""
Paper Database Manager for semantic paper discovery.
Usage:
    uv run references/paper_db.py index          # Index all papers in references/
    uv run references/paper_db.py search "query" # Search papers
    uv run references/paper_db.py cite <paper>   # Find citations/references
    uv run references/paper_db.py refs <paper>   # Find references from paper
    uv run references/paper_db.py related <paper> # Find related papers
    uv run references/paper_db.py discover       # Discover new papers via citations
    uv run references/paper_db.py fetch <arxiv_id> # Fetch paper from arXiv
    uv run references/paper_db.py graph          # Generate citation graph
    uv run references/paper_db.py stats          # Show database statistics
"""

import json
import os
import re
import sys
import time
from pathlib import Path
from dataclasses import dataclass, field, asdict
from typing import Optional
import yaml
import requests

# Semantic Scholar API
S2_API = "https://api.semanticscholar.org/graph/v1"
S2_FIELDS = "title,authors,year,venue,citationCount,abstract,externalIds,references,citations,tldr"

@dataclass
class Paper:
    """Paper metadata."""
    id: str  # Local folder name
    title: str
    authors: list[str]
    year: int
    venue: str
    url: str
    arxiv_id: Optional[str] = None
    s2_id: Optional[str] = None  # Semantic Scholar ID
    doi: Optional[str] = None
    citation_count: int = 0
    abstract: str = ""
    tldr: str = ""
    keywords: list[str] = field(default_factory=list)
    references: list[str] = field(default_factory=list)  # Paper IDs this cites
    cited_by: list[str] = field(default_factory=list)    # Paper IDs that cite this
    local_path: str = ""
    fetched: bool = False

    def to_dict(self):
        return asdict(self)

    @classmethod
    def from_dict(cls, d):
        return cls(**d)


class PaperDB:
    """Paper database with semantic search and citation discovery."""

    def __init__(self, base_dir: str = "references"):
        self.base_dir = Path(base_dir)
        self.db_path = self.base_dir / "paper_db.json"
        self.papers: dict[str, Paper] = {}
        self.s2_cache: dict[str, dict] = {}  # Cache S2 API responses
        self.load()

    def load(self):
        """Load database from disk."""
        if self.db_path.exists():
            data = json.loads(self.db_path.read_text())
            self.papers = {k: Paper.from_dict(v) for k, v in data.get("papers", {}).items()}
            self.s2_cache = data.get("s2_cache", {})
            print(f"Loaded {len(self.papers)} papers from database")

    def save(self):
        """Save database to disk."""
        data = {
            "papers": {k: v.to_dict() for k, v in self.papers.items()},
            "s2_cache": self.s2_cache
        }
        self.db_path.write_text(json.dumps(data, indent=2, ensure_ascii=False))
        print(f"Saved {len(self.papers)} papers to database")

    def index_local_papers(self):
        """Index all papers from local folders."""
        for folder in self.base_dir.iterdir():
            if not folder.is_dir():
                continue
            if folder.name.startswith("research_") or folder.name.startswith("."):
                continue

            md_path = folder / "paper.md"
            if not md_path.exists():
                continue

            paper_id = folder.name
            if paper_id in self.papers and self.papers[paper_id].fetched:
                continue

            # Parse YAML front matter
            content = md_path.read_text(encoding='utf-8', errors='ignore')
            metadata = self._parse_front_matter(content)

            if not metadata:
                print(f"  Skipping {paper_id}: no metadata")
                continue

            paper = Paper(
                id=paper_id,
                title=metadata.get("title", ""),
                authors=metadata.get("authors", []),
                year=metadata.get("year", 0),
                venue=metadata.get("venue", ""),
                url=metadata.get("url", ""),
                arxiv_id=metadata.get("arxiv"),
                local_path=str(folder),
                fetched=True
            )

            # Extract keywords from content
            paper.keywords = self._extract_keywords(content)

            self.papers[paper_id] = paper
            print(f"  Indexed: {paper_id} - {paper.title[:50]}...")

    def _parse_front_matter(self, content: str) -> dict:
        """Parse YAML front matter from markdown."""
        match = re.match(r'^---\n(.*?)\n---', content, re.DOTALL)
        if match:
            try:
                return yaml.safe_load(match.group(1))
            except:
                pass
        return {}

    def _extract_keywords(self, content: str) -> list[str]:
        """Extract keywords from paper content."""
        keywords = set()
        # Common NLP/parsing terms
        terms = [
            "dependency parsing", "biaffine", "transformer", "BERT", "PhoBERT",
            "Vietnamese", "treebank", "POS tagging", "NER", "word segmentation",
            "BiLSTM", "attention", "neural", "deep learning", "pre-trained",
            "Universal Dependencies", "CoNLL", "UAS", "LAS", "multi-task"
        ]
        content_lower = content.lower()
        for term in terms:
            if term.lower() in content_lower:
                keywords.add(term)
        return list(keywords)

    def enrich_with_s2(self, paper_id: str, force: bool = False):
        """Enrich paper with Semantic Scholar data."""
        if paper_id not in self.papers:
            print(f"Paper not found: {paper_id}")
            return

        paper = self.papers[paper_id]

        # Skip if already enriched
        if paper.s2_id and not force:
            return

        # Search by title or arxiv ID
        s2_data = None
        if paper.arxiv_id:
            s2_data = self._fetch_s2_paper(f"arXiv:{paper.arxiv_id}")
        if not s2_data and paper.title:
            s2_data = self._search_s2_paper(paper.title)

        if not s2_data:
            print(f"  Could not find S2 data for: {paper.title[:50]}")
            return

        # Update paper metadata
        paper.s2_id = s2_data.get("paperId")
        paper.citation_count = s2_data.get("citationCount", 0)
        paper.abstract = s2_data.get("abstract", "")
        if s2_data.get("tldr"):
            paper.tldr = s2_data["tldr"].get("text", "")

        # Get external IDs
        ext_ids = s2_data.get("externalIds", {})
        if not paper.arxiv_id and ext_ids.get("ArXiv"):
            paper.arxiv_id = ext_ids["ArXiv"]
        if not paper.doi and ext_ids.get("DOI"):
            paper.doi = ext_ids["DOI"]

        print(f"  Enriched: {paper_id} (citations: {paper.citation_count})")

    def _fetch_s2_paper(self, paper_id: str) -> Optional[dict]:
        """Fetch paper from Semantic Scholar by ID."""
        if paper_id in self.s2_cache:
            return self.s2_cache[paper_id]

        try:
            url = f"{S2_API}/paper/{paper_id}"
            params = {"fields": S2_FIELDS}
            response = requests.get(url, params=params, timeout=10)
            if response.status_code == 200:
                data = response.json()
                self.s2_cache[paper_id] = data
                return data
            elif response.status_code == 429:
                print("  Rate limited, waiting...")
                time.sleep(2)
                return self._fetch_s2_paper(paper_id)
        except Exception as e:
            print(f"  S2 fetch error: {e}")
        return None

    def _search_s2_paper(self, title: str) -> Optional[dict]:
        """Search Semantic Scholar by title."""
        cache_key = f"search:{title[:100]}"
        if cache_key in self.s2_cache:
            return self.s2_cache[cache_key]

        try:
            url = f"{S2_API}/paper/search"
            params = {"query": title, "limit": 1, "fields": S2_FIELDS}
            response = requests.get(url, params=params, timeout=10)
            if response.status_code == 200:
                data = response.json()
                if data.get("data"):
                    result = data["data"][0]
                    self.s2_cache[cache_key] = result
                    return result
            elif response.status_code == 429:
                print("  Rate limited, waiting...")
                time.sleep(2)
                return self._search_s2_paper(title)
        except Exception as e:
            print(f"  S2 search error: {e}")
        return None

    def get_citations(self, paper_id: str, limit: int = 20) -> list[dict]:
        """Get papers that cite this paper."""
        paper = self.papers.get(paper_id)
        if not paper or not paper.s2_id:
            self.enrich_with_s2(paper_id)
            paper = self.papers.get(paper_id)
            if not paper or not paper.s2_id:
                return []

        try:
            url = f"{S2_API}/paper/{paper.s2_id}/citations"
            params = {"fields": "title,authors,year,venue,citationCount", "limit": limit}
            response = requests.get(url, params=params, timeout=10)
            if response.status_code == 200:
                data = response.json()
                return [c["citingPaper"] for c in data.get("data", []) if c.get("citingPaper")]
        except Exception as e:
            print(f"  Error getting citations: {e}")
        return []

    def get_references(self, paper_id: str, limit: int = 20) -> list[dict]:
        """Get papers that this paper cites."""
        paper = self.papers.get(paper_id)
        if not paper or not paper.s2_id:
            self.enrich_with_s2(paper_id)
            paper = self.papers.get(paper_id)
            if not paper or not paper.s2_id:
                return []

        try:
            url = f"{S2_API}/paper/{paper.s2_id}/references"
            params = {"fields": "title,authors,year,venue,citationCount", "limit": limit}
            response = requests.get(url, params=params, timeout=10)
            if response.status_code == 200:
                data = response.json()
                return [r["citedPaper"] for r in data.get("data", []) if r.get("citedPaper")]
        except Exception as e:
            print(f"  Error getting references: {e}")
        return []

    def search(self, query: str) -> list[Paper]:
        """Search papers by keyword."""
        query_lower = query.lower()
        results = []
        for paper in self.papers.values():
            score = 0
            # Title match
            if query_lower in paper.title.lower():
                score += 10
            # Author match
            for author in paper.authors:
                if query_lower in author.lower():
                    score += 5
            # Keyword match
            for kw in paper.keywords:
                if query_lower in kw.lower():
                    score += 3
            # Abstract match
            if paper.abstract and query_lower in paper.abstract.lower():
                score += 2
            # TLDR match
            if paper.tldr and query_lower in paper.tldr.lower():
                score += 2

            if score > 0:
                results.append((score, paper))

        results.sort(key=lambda x: (-x[0], -x[1].citation_count))
        return [p for _, p in results]

    def discover_related(self, topic: str = "Vietnamese dependency parsing", limit: int = 20) -> list[dict]:
        """Discover new papers via Semantic Scholar search."""
        try:
            url = f"{S2_API}/paper/search"
            params = {
                "query": topic,
                "limit": limit,
                "fields": "title,authors,year,venue,citationCount,abstract,externalIds"
            }
            response = requests.get(url, params=params, timeout=10)
            if response.status_code == 200:
                data = response.json()
                papers = data.get("data", [])

                # Filter out papers we already have
                existing_titles = {p.title.lower() for p in self.papers.values()}
                new_papers = [
                    p for p in papers
                    if p.get("title", "").lower() not in existing_titles
                ]
                return new_papers
        except Exception as e:
            print(f"  Error discovering papers: {e}")
        return []

    def discover_via_citations(self, min_citations: int = 5) -> list[dict]:
        """Discover new papers by following citation networks."""
        discovered = []
        seen_ids = set()

        # Get S2 IDs of local papers
        local_s2_ids = {p.s2_id for p in self.papers.values() if p.s2_id}

        for paper_id, paper in self.papers.items():
            if not paper.s2_id:
                continue

            # Get citations (papers citing this one)
            citations = self.get_citations(paper_id, limit=10)
            time.sleep(0.5)  # Rate limiting

            for cite in citations:
                s2_id = cite.get("paperId")
                if not s2_id or s2_id in seen_ids or s2_id in local_s2_ids:
                    continue
                seen_ids.add(s2_id)

                citation_count = cite.get("citationCount", 0)
                if citation_count >= min_citations:
                    cite["_discovered_via"] = f"cites {paper_id}"
                    discovered.append(cite)

            # Get references (papers this one cites)
            refs = self.get_references(paper_id, limit=10)
            time.sleep(0.5)

            for ref in refs:
                s2_id = ref.get("paperId")
                if not s2_id or s2_id in seen_ids or s2_id in local_s2_ids:
                    continue
                seen_ids.add(s2_id)

                citation_count = ref.get("citationCount", 0)
                if citation_count >= min_citations:
                    ref["_discovered_via"] = f"cited by {paper_id}"
                    discovered.append(ref)

        # Sort by citation count
        discovered.sort(key=lambda x: x.get("citationCount", 0), reverse=True)
        return discovered

    def generate_graph(self) -> str:
        """Generate citation graph in Mermaid format."""
        lines = ["graph TD"]

        # Add nodes
        for paper_id, paper in self.papers.items():
            label = f"{paper.year}: {paper.title[:30]}..."
            lines.append(f'    {paper_id.replace(".", "_").replace("-", "_")}["{label}"]')

        # TODO: Add edges based on citation relationships
        # This would require enriching all papers first

        return "\n".join(lines)

    def print_stats(self):
        """Print database statistics."""
        print(f"\n=== Paper Database Statistics ===")
        print(f"Total papers: {len(self.papers)}")
        print(f"With S2 data: {sum(1 for p in self.papers.values() if p.s2_id)}")
        print(f"With abstracts: {sum(1 for p in self.papers.values() if p.abstract)}")

        # By year
        by_year = {}
        for p in self.papers.values():
            by_year[p.year] = by_year.get(p.year, 0) + 1
        print(f"\nBy year:")
        for year in sorted(by_year.keys()):
            print(f"  {year}: {by_year[year]} papers")

        # By venue
        by_venue = {}
        for p in self.papers.values():
            venue = p.venue.split()[0] if p.venue else "Unknown"
            by_venue[venue] = by_venue.get(venue, 0) + 1
        print(f"\nBy venue:")
        for venue, count in sorted(by_venue.items(), key=lambda x: -x[1])[:10]:
            print(f"  {venue}: {count} papers")

        # Top cited
        print(f"\nTop cited papers:")
        for p in sorted(self.papers.values(), key=lambda x: -x.citation_count)[:5]:
            if p.citation_count > 0:
                print(f"  [{p.citation_count}] {p.title[:60]}...")


def fetch_arxiv_paper(arxiv_id: str, db: PaperDB):
    """Fetch a paper from arXiv and add to database."""
    import arxiv
    import pymupdf4llm
    import unicodedata
    import tarfile
    import gzip
    from io import BytesIO

    arxiv_id = re.sub(r'^(arxiv:|https?://arxiv\.org/(abs|pdf)/)', '', arxiv_id)
    arxiv_id = arxiv_id.rstrip('.pdf').rstrip('/')

    # Get paper metadata
    print(f"  Fetching metadata for arXiv:{arxiv_id}...")
    client = arxiv.Client()
    try:
        paper = next(client.results(arxiv.Search(id_list=[arxiv_id])))
    except StopIteration:
        print(f"  Paper not found: {arxiv_id}")
        return None

    # Generate folder name
    year = paper.published.year
    first_author = paper.authors[0].name if paper.authors else "unknown"
    # Normalize author name
    lastname = first_author.split()[-1] if first_author.split() else first_author
    normalized = unicodedata.normalize('NFD', lastname)
    author = ''.join(c for c in normalized if unicodedata.category(c) != 'Mn').lower()

    folder_name = f"{year}.arxiv.{author}"
    folder = db.base_dir / folder_name
    folder.mkdir(exist_ok=True)
    print(f"  Title: {paper.title[:60]}...")
    print(f"  Folder: {folder}")

    # Build front matter
    authors_yaml = '\n'.join(f'  - "{a.name}"' for a in paper.authors)
    front_matter = f'''---
title: "{paper.title}"
authors:
{authors_yaml}
year: {year}
venue: "arXiv"
url: "{paper.entry_id}"
arxiv: "{arxiv_id}"
---
'''

    # Try to download LaTeX source
    tex_content = None
    source_url = f"https://arxiv.org/e-print/{arxiv_id}"
    try:
        response = requests.get(source_url, allow_redirects=True, timeout=30)
        response.raise_for_status()
        content = response.content

        try:
            with tarfile.open(fileobj=BytesIO(content), mode='r:gz') as tar:
                tex_files = [m.name for m in tar.getmembers() if m.name.endswith('.tex')]
                source_dir = folder / "source"
                source_dir.mkdir(exist_ok=True)
                tar.extractall(path=source_dir)
                print(f"  Extracted {len(tar.getmembers())} source files")

                main_tex = None
                for name in tex_files:
                    if 'main' in name.lower():
                        main_tex = name
                        break
                if not main_tex and tex_files:
                    main_tex = tex_files[0]

                if main_tex:
                    with open(source_dir / main_tex, 'r', encoding='utf-8', errors='ignore') as f:
                        tex_content = f.read()
        except tarfile.TarError:
            try:
                tex_content = gzip.decompress(content).decode('utf-8', errors='ignore')
                if '\\documentclass' not in tex_content:
                    tex_content = None
            except:
                pass
    except Exception as e:
        print(f"  Could not fetch source: {e}")

    if tex_content:
        (folder / "paper.tex").write_text(tex_content, encoding='utf-8')
        print(f"  Saved: paper.tex")

        # Convert LaTeX to Markdown
        md = tex_content
        doc_match = re.search(r'\\begin\{document\}', md)
        if doc_match:
            md = md[doc_match.end():]
        md = re.sub(r'\\end\{document\}.*', '', md, flags=re.DOTALL)
        md = re.sub(r'%.*$', '', md, flags=re.MULTILINE)
        md = re.sub(r'\\section\*?\{([^}]+)\}', r'# \1', md)
        md = re.sub(r'\\subsection\*?\{([^}]+)\}', r'## \1', md)
        md = re.sub(r'\\textbf\{([^}]+)\}', r'**\1**', md)
        md = re.sub(r'\\textit\{([^}]+)\}', r'*\1*', md)
        md = re.sub(r'\\cite\w*\{([^}]+)\}', r'[\1]', md)

        (folder / "paper.md").write_text(front_matter + md.strip(), encoding='utf-8')
        print(f"  Generated: paper.md")
        has_source = True
    else:
        has_source = False

    # Download PDF
    pdf_path = folder / "paper.pdf"
    paper.download_pdf(filename=str(pdf_path))
    print(f"  Downloaded: paper.pdf")

    # If no LaTeX, extract from PDF
    if not has_source:
        md_text = pymupdf4llm.to_markdown(str(pdf_path))
        (folder / "paper.md").write_text(front_matter + md_text, encoding='utf-8')
        print(f"  Extracted: paper.md (from PDF)")

    # Add to database
    new_paper = Paper(
        id=folder_name,
        title=paper.title,
        authors=[a.name for a in paper.authors],
        year=year,
        venue="arXiv",
        url=paper.entry_id,
        arxiv_id=arxiv_id,
        local_path=str(folder),
        fetched=True
    )
    db.papers[folder_name] = new_paper
    db.enrich_with_s2(folder_name)

    return folder_name


def main():
    if len(sys.argv) < 2:
        print(__doc__)
        return

    # Change to references directory
    script_dir = Path(__file__).parent
    os.chdir(script_dir.parent)

    db = PaperDB("references")
    cmd = sys.argv[1]

    if cmd == "index":
        print("Indexing local papers...")
        db.index_local_papers()
        print("\nEnriching with Semantic Scholar data...")
        for paper_id in list(db.papers.keys()):
            db.enrich_with_s2(paper_id)
            time.sleep(0.5)  # Rate limiting
        db.save()
        db.print_stats()

    elif cmd == "search":
        query = " ".join(sys.argv[2:]) if len(sys.argv) > 2 else "Vietnamese"
        print(f"Searching for: {query}\n")
        results = db.search(query)
        for paper in results[:10]:
            print(f"  [{paper.year}] {paper.title[:60]}...")
            print(f"         Authors: {', '.join(paper.authors[:3])}")
            print(f"         Citations: {paper.citation_count}, Venue: {paper.venue}")
            if paper.tldr:
                print(f"         TLDR: {paper.tldr[:100]}...")
            print()

    elif cmd == "cite":
        paper_id = sys.argv[2] if len(sys.argv) > 2 else ""
        if not paper_id:
            print("Usage: paper_db.py cite <paper_id>")
            return
        print(f"Citations for: {paper_id}\n")
        citations = db.get_citations(paper_id)
        for cite in citations[:15]:
            authors = [a["name"] for a in cite.get("authors", [])[:2]]
            print(f"  [{cite.get('year', '?')}] {cite.get('title', '?')[:60]}...")
            print(f"         Authors: {', '.join(authors)}")
            print(f"         Citations: {cite.get('citationCount', 0)}")
            print()

    elif cmd == "refs":
        paper_id = sys.argv[2] if len(sys.argv) > 2 else ""
        if not paper_id:
            print("Usage: paper_db.py refs <paper_id>")
            return
        print(f"References from: {paper_id}\n")
        refs = db.get_references(paper_id)
        for ref in refs[:15]:
            authors = [a["name"] for a in ref.get("authors", [])[:2]]
            print(f"  [{ref.get('year', '?')}] {ref.get('title', '?')[:60]}...")
            print(f"         Authors: {', '.join(authors)}")
            print(f"         Citations: {ref.get('citationCount', 0)}")
            print()

    elif cmd == "related":
        paper_id = sys.argv[2] if len(sys.argv) > 2 else ""
        if paper_id and paper_id in db.papers:
            paper = db.papers[paper_id]
            query = paper.title
        else:
            query = " ".join(sys.argv[2:]) if len(sys.argv) > 2 else "Vietnamese dependency parsing"
        print(f"Finding related papers for: {query[:50]}...\n")
        related = db.discover_related(query, limit=15)
        for p in related:
            authors = [a["name"] for a in p.get("authors", [])[:2]]
            print(f"  [{p.get('year', '?')}] {p.get('title', '?')[:60]}...")
            print(f"         Authors: {', '.join(authors)}")
            print(f"         Citations: {p.get('citationCount', 0)}, Venue: {p.get('venue', '?')}")
            ext_ids = p.get("externalIds", {})
            if ext_ids.get("ArXiv"):
                print(f"         arXiv: {ext_ids['ArXiv']}")
            print()

    elif cmd == "discover":
        print("Discovering new papers via citation network...\n")
        discovered = db.discover_via_citations(min_citations=10)
        print(f"Found {len(discovered)} new papers:\n")
        for p in discovered[:20]:
            authors = [a["name"] for a in p.get("authors", [])[:2]]
            print(f"  [{p.get('year', '?')}] {p.get('title', '?')[:60]}...")
            print(f"         Authors: {', '.join(authors)}")
            print(f"         Citations: {p.get('citationCount', 0)}")
            print(f"         Discovered via: {p.get('_discovered_via', '?')}")
            ext_ids = p.get("externalIds", {})
            if ext_ids.get("ArXiv"):
                print(f"         arXiv: {ext_ids['ArXiv']}")
            print()

    elif cmd == "graph":
        print("Generating citation graph...\n")
        graph = db.generate_graph()
        print(graph)

    elif cmd == "fetch":
        arxiv_id = sys.argv[2] if len(sys.argv) > 2 else ""
        if not arxiv_id:
            print("Usage: paper_db.py fetch <arxiv_id>")
            return
        print(f"Fetching paper: {arxiv_id}")
        fetch_arxiv_paper(arxiv_id, db)
        db.save()

    elif cmd == "stats":
        db.print_stats()

    else:
        print(f"Unknown command: {cmd}")
        print(__doc__)


if __name__ == "__main__":
    main()

	# /// script
	# requires-python = ">=3.9"
	# dependencies = ["requests>=2.28.0", "pyyaml>=6.0", "arxiv>=2.0.0", "pymupdf4llm>=0.0.10"]
	# ///
	"""
	Paper Database Manager for semantic paper discovery.

	Usage:
	uv run references/paper_db.py index # Index all papers in references/
	uv run references/paper_db.py search "query" # Search papers
	uv run references/paper_db.py cite <paper> # Find citations/references
	uv run references/paper_db.py refs <paper> # Find references from paper
	uv run references/paper_db.py related <paper> # Find related papers
	uv run references/paper_db.py discover # Discover new papers via citations
	uv run references/paper_db.py fetch <arxiv_id> # Fetch paper from arXiv
	uv run references/paper_db.py graph # Generate citation graph
	uv run references/paper_db.py stats # Show database statistics
	"""

	import json
	import os
	import re
	import sys
	import time
	from pathlib import Path
	from dataclasses import dataclass, field, asdict
	from typing import Optional
	import yaml
	import requests

	# Semantic Scholar API
	S2_API = "https://api.semanticscholar.org/graph/v1"
	S2_FIELDS = "title,authors,year,venue,citationCount,abstract,externalIds,references,citations,tldr"

	@dataclass
	class Paper:
	"""Paper metadata."""
	id: str # Local folder name
	title: str
	authors: list[str]
	year: int
	venue: str
	url: str
	arxiv_id: Optional[str] = None
	s2_id: Optional[str] = None # Semantic Scholar ID
	doi: Optional[str] = None
	citation_count: int = 0
	abstract: str = ""
	tldr: str = ""
	keywords: list[str] = field(default_factory=list)
	references: list[str] = field(default_factory=list) # Paper IDs this cites
	cited_by: list[str] = field(default_factory=list) # Paper IDs that cite this
	local_path: str = ""
	fetched: bool = False

	def to_dict(self):
	return asdict(self)

	@classmethod
	def from_dict(cls, d):
	return cls(**d)


	class PaperDB:
	"""Paper database with semantic search and citation discovery."""

	def __init__(self, base_dir: str = "references"):
	self.base_dir = Path(base_dir)
	self.db_path = self.base_dir / "paper_db.json"
	self.papers: dict[str, Paper] = {}
	self.s2_cache: dict[str, dict] = {} # Cache S2 API responses
	self.load()

	def load(self):
	"""Load database from disk."""
	if self.db_path.exists():
	data = json.loads(self.db_path.read_text())
	self.papers = {k: Paper.from_dict(v) for k, v in data.get("papers", {}).items()}
	self.s2_cache = data.get("s2_cache", {})
	print(f"Loaded {len(self.papers)} papers from database")

	def save(self):
	"""Save database to disk."""
	data = {
	"papers": {k: v.to_dict() for k, v in self.papers.items()},
	"s2_cache": self.s2_cache
	}
	self.db_path.write_text(json.dumps(data, indent=2, ensure_ascii=False))
	print(f"Saved {len(self.papers)} papers to database")

	def index_local_papers(self):
	"""Index all papers from local folders."""
	for folder in self.base_dir.iterdir():
	if not folder.is_dir():
	continue
	if folder.name.startswith("research_") or folder.name.startswith("."):
	continue

	md_path = folder / "paper.md"
	if not md_path.exists():
	continue

	paper_id = folder.name
	if paper_id in self.papers and self.papers[paper_id].fetched:
	continue

	# Parse YAML front matter
	content = md_path.read_text(encoding='utf-8', errors='ignore')
	metadata = self._parse_front_matter(content)

	if not metadata:
	print(f" Skipping {paper_id}: no metadata")
	continue

	paper = Paper(
	id=paper_id,
	title=metadata.get("title", ""),
	authors=metadata.get("authors", []),
	year=metadata.get("year", 0),
	venue=metadata.get("venue", ""),
	url=metadata.get("url", ""),
	arxiv_id=metadata.get("arxiv"),
	local_path=str(folder),
	fetched=True
	)

	# Extract keywords from content
	paper.keywords = self._extract_keywords(content)

	self.papers[paper_id] = paper
	print(f" Indexed: {paper_id} - {paper.title[:50]}...")

	def _parse_front_matter(self, content: str) -> dict:
	"""Parse YAML front matter from markdown."""
	match = re.match(r'^---\n(.*?)\n---', content, re.DOTALL)
	if match:
	try:
	return yaml.safe_load(match.group(1))
	except:
	pass
	return {}

	def _extract_keywords(self, content: str) -> list[str]:
	"""Extract keywords from paper content."""
	keywords = set()
	# Common NLP/parsing terms
	terms = [
	"dependency parsing", "biaffine", "transformer", "BERT", "PhoBERT",
	"Vietnamese", "treebank", "POS tagging", "NER", "word segmentation",
	"BiLSTM", "attention", "neural", "deep learning", "pre-trained",
	"Universal Dependencies", "CoNLL", "UAS", "LAS", "multi-task"
	]
	content_lower = content.lower()
	for term in terms:
	if term.lower() in content_lower:
	keywords.add(term)
	return list(keywords)

	def enrich_with_s2(self, paper_id: str, force: bool = False):
	"""Enrich paper with Semantic Scholar data."""
	if paper_id not in self.papers:
	print(f"Paper not found: {paper_id}")
	return

	paper = self.papers[paper_id]

	# Skip if already enriched
	if paper.s2_id and not force:
	return

	# Search by title or arxiv ID
	s2_data = None
	if paper.arxiv_id:
	s2_data = self._fetch_s2_paper(f"arXiv:{paper.arxiv_id}")
	if not s2_data and paper.title:
	s2_data = self._search_s2_paper(paper.title)

	if not s2_data:
	print(f" Could not find S2 data for: {paper.title[:50]}")
	return

	# Update paper metadata
	paper.s2_id = s2_data.get("paperId")
	paper.citation_count = s2_data.get("citationCount", 0)
	paper.abstract = s2_data.get("abstract", "")
	if s2_data.get("tldr"):
	paper.tldr = s2_data["tldr"].get("text", "")

	# Get external IDs
	ext_ids = s2_data.get("externalIds", {})
	if not paper.arxiv_id and ext_ids.get("ArXiv"):
	paper.arxiv_id = ext_ids["ArXiv"]
	if not paper.doi and ext_ids.get("DOI"):
	paper.doi = ext_ids["DOI"]

	print(f" Enriched: {paper_id} (citations: {paper.citation_count})")

	def _fetch_s2_paper(self, paper_id: str) -> Optional[dict]:
	"""Fetch paper from Semantic Scholar by ID."""
	if paper_id in self.s2_cache:
	return self.s2_cache[paper_id]

	try:
	url = f"{S2_API}/paper/{paper_id}"
	params = {"fields": S2_FIELDS}
	response = requests.get(url, params=params, timeout=10)
	if response.status_code == 200:
	data = response.json()
	self.s2_cache[paper_id] = data
	return data
	elif response.status_code == 429:
	print(" Rate limited, waiting...")
	time.sleep(2)
	return self._fetch_s2_paper(paper_id)
	except Exception as e:
	print(f" S2 fetch error: {e}")
	return None

	def _search_s2_paper(self, title: str) -> Optional[dict]:
	"""Search Semantic Scholar by title."""
	cache_key = f"search:{title[:100]}"
	if cache_key in self.s2_cache:
	return self.s2_cache[cache_key]

	try:
	url = f"{S2_API}/paper/search"
	params = {"query": title, "limit": 1, "fields": S2_FIELDS}
	response = requests.get(url, params=params, timeout=10)
	if response.status_code == 200:
	data = response.json()
	if data.get("data"):
	result = data["data"][0]
	self.s2_cache[cache_key] = result
	return result
	elif response.status_code == 429:
	print(" Rate limited, waiting...")
	time.sleep(2)
	return self._search_s2_paper(title)
	except Exception as e:
	print(f" S2 search error: {e}")
	return None

	def get_citations(self, paper_id: str, limit: int = 20) -> list[dict]:
	"""Get papers that cite this paper."""
	paper = self.papers.get(paper_id)
	if not paper or not paper.s2_id:
	self.enrich_with_s2(paper_id)
	paper = self.papers.get(paper_id)
	if not paper or not paper.s2_id:
	return []

	try:
	url = f"{S2_API}/paper/{paper.s2_id}/citations"
	params = {"fields": "title,authors,year,venue,citationCount", "limit": limit}
	response = requests.get(url, params=params, timeout=10)
	if response.status_code == 200:
	data = response.json()
	return [c["citingPaper"] for c in data.get("data", []) if c.get("citingPaper")]
	except Exception as e:
	print(f" Error getting citations: {e}")
	return []

	def get_references(self, paper_id: str, limit: int = 20) -> list[dict]:
	"""Get papers that this paper cites."""
	paper = self.papers.get(paper_id)
	if not paper or not paper.s2_id:
	self.enrich_with_s2(paper_id)
	paper = self.papers.get(paper_id)
	if not paper or not paper.s2_id:
	return []

	try:
	url = f"{S2_API}/paper/{paper.s2_id}/references"
	params = {"fields": "title,authors,year,venue,citationCount", "limit": limit}
	response = requests.get(url, params=params, timeout=10)
	if response.status_code == 200:
	data = response.json()
	return [r["citedPaper"] for r in data.get("data", []) if r.get("citedPaper")]
	except Exception as e:
	print(f" Error getting references: {e}")
	return []

	def search(self, query: str) -> list[Paper]:
	"""Search papers by keyword."""
	query_lower = query.lower()
	results = []
	for paper in self.papers.values():
	score = 0
	# Title match
	if query_lower in paper.title.lower():
	score += 10
	# Author match
	for author in paper.authors:
	if query_lower in author.lower():
	score += 5
	# Keyword match
	for kw in paper.keywords:
	if query_lower in kw.lower():
	score += 3
	# Abstract match
	if paper.abstract and query_lower in paper.abstract.lower():
	score += 2
	# TLDR match
	if paper.tldr and query_lower in paper.tldr.lower():
	score += 2

	if score > 0:
	results.append((score, paper))

	results.sort(key=lambda x: (-x[0], -x[1].citation_count))
	return [p for _, p in results]

	def discover_related(self, topic: str = "Vietnamese dependency parsing", limit: int = 20) -> list[dict]:
	"""Discover new papers via Semantic Scholar search."""
	try:
	url = f"{S2_API}/paper/search"
	params = {
	"query": topic,
	"limit": limit,
	"fields": "title,authors,year,venue,citationCount,abstract,externalIds"
	}
	response = requests.get(url, params=params, timeout=10)
	if response.status_code == 200:
	data = response.json()
	papers = data.get("data", [])

	# Filter out papers we already have
	existing_titles = {p.title.lower() for p in self.papers.values()}
	new_papers = [
	p for p in papers
	if p.get("title", "").lower() not in existing_titles
	]
	return new_papers
	except Exception as e:
	print(f" Error discovering papers: {e}")
	return []

	def discover_via_citations(self, min_citations: int = 5) -> list[dict]:
	"""Discover new papers by following citation networks."""
	discovered = []
	seen_ids = set()

	# Get S2 IDs of local papers
	local_s2_ids = {p.s2_id for p in self.papers.values() if p.s2_id}

	for paper_id, paper in self.papers.items():
	if not paper.s2_id:
	continue

	# Get citations (papers citing this one)
	citations = self.get_citations(paper_id, limit=10)
	time.sleep(0.5) # Rate limiting

	for cite in citations:
	s2_id = cite.get("paperId")
	if not s2_id or s2_id in seen_ids or s2_id in local_s2_ids:
	continue
	seen_ids.add(s2_id)

	citation_count = cite.get("citationCount", 0)
	if citation_count >= min_citations:
	cite["_discovered_via"] = f"cites {paper_id}"
	discovered.append(cite)

	# Get references (papers this one cites)
	refs = self.get_references(paper_id, limit=10)
	time.sleep(0.5)

	for ref in refs:
	s2_id = ref.get("paperId")
	if not s2_id or s2_id in seen_ids or s2_id in local_s2_ids:
	continue
	seen_ids.add(s2_id)

	citation_count = ref.get("citationCount", 0)
	if citation_count >= min_citations:
	ref["_discovered_via"] = f"cited by {paper_id}"
	discovered.append(ref)

	# Sort by citation count
	discovered.sort(key=lambda x: x.get("citationCount", 0), reverse=True)
	return discovered

	def generate_graph(self) -> str:
	"""Generate citation graph in Mermaid format."""
	lines = ["graph TD"]

	# Add nodes
	for paper_id, paper in self.papers.items():
	label = f"{paper.year}: {paper.title[:30]}..."
	lines.append(f' {paper_id.replace(".", "_").replace("-", "_")}["{label}"]')

	# TODO: Add edges based on citation relationships
	# This would require enriching all papers first

	return "\n".join(lines)

	def print_stats(self):
	"""Print database statistics."""
	print(f"\n=== Paper Database Statistics ===")
	print(f"Total papers: {len(self.papers)}")
	print(f"With S2 data: {sum(1 for p in self.papers.values() if p.s2_id)}")
	print(f"With abstracts: {sum(1 for p in self.papers.values() if p.abstract)}")

	# By year
	by_year = {}
	for p in self.papers.values():
	by_year[p.year] = by_year.get(p.year, 0) + 1
	print(f"\nBy year:")
	for year in sorted(by_year.keys()):
	print(f" {year}: {by_year[year]} papers")

	# By venue
	by_venue = {}
	for p in self.papers.values():
	venue = p.venue.split()[0] if p.venue else "Unknown"
	by_venue[venue] = by_venue.get(venue, 0) + 1
	print(f"\nBy venue:")
	for venue, count in sorted(by_venue.items(), key=lambda x: -x[1])[:10]:
	print(f" {venue}: {count} papers")

	# Top cited
	print(f"\nTop cited papers:")
	for p in sorted(self.papers.values(), key=lambda x: -x.citation_count)[:5]:
	if p.citation_count > 0:
	print(f" [{p.citation_count}] {p.title[:60]}...")


	def fetch_arxiv_paper(arxiv_id: str, db: PaperDB):
	"""Fetch a paper from arXiv and add to database."""
	import arxiv
	import pymupdf4llm
	import unicodedata
	import tarfile
	import gzip
	from io import BytesIO

	arxiv_id = re.sub(r'^(arxiv:\|https?://arxiv\.org/(abs\|pdf)/)', '', arxiv_id)
	arxiv_id = arxiv_id.rstrip('.pdf').rstrip('/')

	# Get paper metadata
	print(f" Fetching metadata for arXiv:{arxiv_id}...")
	client = arxiv.Client()
	try:
	paper = next(client.results(arxiv.Search(id_list=[arxiv_id])))
	except StopIteration:
	print(f" Paper not found: {arxiv_id}")
	return None

	# Generate folder name
	year = paper.published.year
	first_author = paper.authors[0].name if paper.authors else "unknown"
	# Normalize author name
	lastname = first_author.split()[-1] if first_author.split() else first_author
	normalized = unicodedata.normalize('NFD', lastname)
	author = ''.join(c for c in normalized if unicodedata.category(c) != 'Mn').lower()

	folder_name = f"{year}.arxiv.{author}"
	folder = db.base_dir / folder_name
	folder.mkdir(exist_ok=True)
	print(f" Title: {paper.title[:60]}...")
	print(f" Folder: {folder}")

	# Build front matter
	authors_yaml = '\n'.join(f' - "{a.name}"' for a in paper.authors)
	front_matter = f'''---
	title: "{paper.title}"
	authors:
	{authors_yaml}
	year: {year}
	venue: "arXiv"
	url: "{paper.entry_id}"
	arxiv: "{arxiv_id}"
	---

	'''

	# Try to download LaTeX source
	tex_content = None
	source_url = f"https://arxiv.org/e-print/{arxiv_id}"
	try:
	response = requests.get(source_url, allow_redirects=True, timeout=30)
	response.raise_for_status()
	content = response.content

	try:
	with tarfile.open(fileobj=BytesIO(content), mode='r:gz') as tar:
	tex_files = [m.name for m in tar.getmembers() if m.name.endswith('.tex')]
	source_dir = folder / "source"
	source_dir.mkdir(exist_ok=True)
	tar.extractall(path=source_dir)
	print(f" Extracted {len(tar.getmembers())} source files")

	main_tex = None
	for name in tex_files:
	if 'main' in name.lower():
	main_tex = name
	break
	if not main_tex and tex_files:
	main_tex = tex_files[0]

	if main_tex:
	with open(source_dir / main_tex, 'r', encoding='utf-8', errors='ignore') as f:
	tex_content = f.read()
	except tarfile.TarError:
	try:
	tex_content = gzip.decompress(content).decode('utf-8', errors='ignore')
	if '\\documentclass' not in tex_content:
	tex_content = None
	except:
	pass
	except Exception as e:
	print(f" Could not fetch source: {e}")

	if tex_content:
	(folder / "paper.tex").write_text(tex_content, encoding='utf-8')
	print(f" Saved: paper.tex")

	# Convert LaTeX to Markdown
	md = tex_content
	doc_match = re.search(r'\\begin\{document\}', md)
	if doc_match:
	md = md[doc_match.end():]
	md = re.sub(r'\\end\{document\}.*', '', md, flags=re.DOTALL)
	md = re.sub(r'%.*$', '', md, flags=re.MULTILINE)
	md = re.sub(r'\\section\*?\{([^}]+)\}', r'# \1', md)
	md = re.sub(r'\\subsection\*?\{([^}]+)\}', r'## \1', md)
	md = re.sub(r'\\textbf\{([^}]+)\}', r'\1', md)
	md = re.sub(r'\\textit\{([^}]+)\}', r'\1', md)
	md = re.sub(r'\\cite\w*\{([^}]+)\}', r'[\1]', md)

	(folder / "paper.md").write_text(front_matter + md.strip(), encoding='utf-8')
	print(f" Generated: paper.md")
	has_source = True
	else:
	has_source = False

	# Download PDF
	pdf_path = folder / "paper.pdf"
	paper.download_pdf(filename=str(pdf_path))
	print(f" Downloaded: paper.pdf")

	# If no LaTeX, extract from PDF
	if not has_source:
	md_text = pymupdf4llm.to_markdown(str(pdf_path))
	(folder / "paper.md").write_text(front_matter + md_text, encoding='utf-8')
	print(f" Extracted: paper.md (from PDF)")

	# Add to database
	new_paper = Paper(
	id=folder_name,
	title=paper.title,
	authors=[a.name for a in paper.authors],
	year=year,
	venue="arXiv",
	url=paper.entry_id,
	arxiv_id=arxiv_id,
	local_path=str(folder),
	fetched=True
	)
	db.papers[folder_name] = new_paper
	db.enrich_with_s2(folder_name)

	return folder_name


	def main():
	if len(sys.argv) < 2:
	print(__doc__)
	return

	# Change to references directory
	script_dir = Path(__file__).parent
	os.chdir(script_dir.parent)

	db = PaperDB("references")
	cmd = sys.argv[1]

	if cmd == "index":
	print("Indexing local papers...")
	db.index_local_papers()
	print("\nEnriching with Semantic Scholar data...")
	for paper_id in list(db.papers.keys()):
	db.enrich_with_s2(paper_id)
	time.sleep(0.5) # Rate limiting
	db.save()
	db.print_stats()

	elif cmd == "search":
	query = " ".join(sys.argv[2:]) if len(sys.argv) > 2 else "Vietnamese"
	print(f"Searching for: {query}\n")
	results = db.search(query)
	for paper in results[:10]:
	print(f" [{paper.year}] {paper.title[:60]}...")
	print(f" Authors: {', '.join(paper.authors[:3])}")
	print(f" Citations: {paper.citation_count}, Venue: {paper.venue}")
	if paper.tldr:
	print(f" TLDR: {paper.tldr[:100]}...")
	print()

	elif cmd == "cite":
	paper_id = sys.argv[2] if len(sys.argv) > 2 else ""
	if not paper_id:
	print("Usage: paper_db.py cite <paper_id>")
	return
	print(f"Citations for: {paper_id}\n")
	citations = db.get_citations(paper_id)
	for cite in citations[:15]:
	authors = [a["name"] for a in cite.get("authors", [])[:2]]
	print(f" [{cite.get('year', '?')}] {cite.get('title', '?')[:60]}...")
	print(f" Authors: {', '.join(authors)}")
	print(f" Citations: {cite.get('citationCount', 0)}")
	print()

	elif cmd == "refs":
	paper_id = sys.argv[2] if len(sys.argv) > 2 else ""
	if not paper_id:
	print("Usage: paper_db.py refs <paper_id>")
	return
	print(f"References from: {paper_id}\n")
	refs = db.get_references(paper_id)
	for ref in refs[:15]:
	authors = [a["name"] for a in ref.get("authors", [])[:2]]
	print(f" [{ref.get('year', '?')}] {ref.get('title', '?')[:60]}...")
	print(f" Authors: {', '.join(authors)}")
	print(f" Citations: {ref.get('citationCount', 0)}")
	print()

	elif cmd == "related":
	paper_id = sys.argv[2] if len(sys.argv) > 2 else ""
	if paper_id and paper_id in db.papers:
	paper = db.papers[paper_id]
	query = paper.title
	else:
	query = " ".join(sys.argv[2:]) if len(sys.argv) > 2 else "Vietnamese dependency parsing"
	print(f"Finding related papers for: {query[:50]}...\n")
	related = db.discover_related(query, limit=15)
	for p in related:
	authors = [a["name"] for a in p.get("authors", [])[:2]]
	print(f" [{p.get('year', '?')}] {p.get('title', '?')[:60]}...")
	print(f" Authors: {', '.join(authors)}")
	print(f" Citations: {p.get('citationCount', 0)}, Venue: {p.get('venue', '?')}")
	ext_ids = p.get("externalIds", {})
	if ext_ids.get("ArXiv"):
	print(f" arXiv: {ext_ids['ArXiv']}")
	print()

	elif cmd == "discover":
	print("Discovering new papers via citation network...\n")
	discovered = db.discover_via_citations(min_citations=10)
	print(f"Found {len(discovered)} new papers:\n")
	for p in discovered[:20]:
	authors = [a["name"] for a in p.get("authors", [])[:2]]
	print(f" [{p.get('year', '?')}] {p.get('title', '?')[:60]}...")
	print(f" Authors: {', '.join(authors)}")
	print(f" Citations: {p.get('citationCount', 0)}")
	print(f" Discovered via: {p.get('_discovered_via', '?')}")
	ext_ids = p.get("externalIds", {})
	if ext_ids.get("ArXiv"):
	print(f" arXiv: {ext_ids['ArXiv']}")
	print()

	elif cmd == "graph":
	print("Generating citation graph...\n")
	graph = db.generate_graph()
	print(graph)

	elif cmd == "fetch":
	arxiv_id = sys.argv[2] if len(sys.argv) > 2 else ""
	if not arxiv_id:
	print("Usage: paper_db.py fetch <arxiv_id>")
	return
	print(f"Fetching paper: {arxiv_id}")
	fetch_arxiv_paper(arxiv_id, db)
	db.save()

	elif cmd == "stats":
	db.print_stats()

	else:
	print(f"Unknown command: {cmd}")
	print(__doc__)


	if __name__ == "__main__":
	main()