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Live research-context lookup tools.
"""
import re
import xml.etree.ElementTree as ET
import requests
SEMANTIC_SCHOLAR_SEARCH_URL = "https://api.semanticscholar.org/graph/v1/paper/search"
ARXIV_SEARCH_URL = "https://export.arxiv.org/api/query"
PAPER_FIELDS = "title,abstract,year,authors,url,citationCount,venue,fieldsOfStudy"
def _clean_text(value: str | None, max_length: int | None = None) -> str:
text = re.sub(r"\s+", " ", value or "").strip()
if max_length and len(text) > max_length:
return f"{text[: max_length - 3].rstrip()}..."
return text
def _build_research_query(concept: str, paper_context: str, domain: str) -> str:
query_parts = [
_clean_text(concept, 120),
_clean_text(paper_context, 220),
_clean_text(domain, 80),
]
return " ".join(part for part in query_parts if part)
def _normalize_max_results(max_results: int) -> int:
return max(1, min(int(max_results or 5), 10))
def _semantic_scholar_papers(query: str, max_results: int) -> list[dict]:
response = requests.get(
SEMANTIC_SCHOLAR_SEARCH_URL,
params={
"query": query,
"limit": max_results,
"fields": PAPER_FIELDS,
},
timeout=10,
)
response.raise_for_status()
data = response.json()
papers: list[dict] = []
for paper in data.get("data", []):
title = _clean_text(paper.get("title"))
if not title:
continue
papers.append(
{
"title": title,
"year": paper.get("year"),
"authors": [
_clean_text(author.get("name"))
for author in paper.get("authors", [])[:5]
if author.get("name")
],
"venue": _clean_text(paper.get("venue")),
"url": paper.get("url"),
"citation_count": paper.get("citationCount"),
"fields_of_study": paper.get("fieldsOfStudy") or [],
"abstract": _clean_text(paper.get("abstract"), 700),
"source": "Semantic Scholar",
}
)
return papers
def _arxiv_papers(query: str, max_results: int) -> list[dict]:
response = requests.get(
ARXIV_SEARCH_URL,
params={
"search_query": f"all:{query}",
"start": 0,
"max_results": max_results,
"sortBy": "relevance",
"sortOrder": "descending",
},
timeout=10,
)
response.raise_for_status()
root = ET.fromstring(response.text)
namespace = {"atom": "http://www.w3.org/2005/Atom"}
papers: list[dict] = []
for entry in root.findall("atom:entry", namespace):
title = _clean_text(
entry.findtext("atom:title", default="", namespaces=namespace)
)
if not title:
continue
authors = [
_clean_text(author.findtext("atom:name", default="", namespaces=namespace))
for author in entry.findall("atom:author", namespace)[:5]
]
papers.append(
{
"title": title,
"year": (
entry.findtext("atom:published", default="", namespaces=namespace)
or ""
)[:4],
"authors": [author for author in authors if author],
"venue": "arXiv",
"url": entry.findtext("atom:id", default="", namespaces=namespace),
"citation_count": None,
"fields_of_study": [],
"abstract": _clean_text(
entry.findtext("atom:summary", default="", namespaces=namespace),
700,
),
"source": "arXiv",
}
)
return papers
def _suggest_research_directions(concept: str, papers: list[dict]) -> list[str]:
title_and_abstract = " ".join(
f"{paper.get('title', '')} {paper.get('abstract', '')}" for paper in papers
).lower()
directions: list[str] = []
keyword_directions = [
(
("efficient", "linear", "sparse", "compression"),
f"More efficient versions of {concept}",
),
(
("scaling", "large-scale", "foundation", "pretraining"),
f"Scaling {concept} to larger models or datasets",
),
(
("vision", "image", "multimodal", "video"),
f"Using {concept} in vision or multimodal systems",
),
(
("retrieval", "knowledge", "rag", "memory"),
f"Combining {concept} with retrieval or external knowledge",
),
(
("robust", "safety", "bias", "privacy"),
f"Studying robustness, safety, or privacy around {concept}",
),
]
for keywords, direction in keyword_directions:
if any(keyword in title_and_abstract for keyword in keywords):
directions.append(direction)
if not directions:
directions = [
f"Foundational papers that introduced or popularized {concept}",
f"Recent applications that adapt {concept} to new tasks",
f"Limitations and follow-up methods that improve on {concept}",
]
return directions[:5]
async def find_research_context(
concept: str,
paper_context: str,
domain: str = "machine learning",
max_results: int = 5,
) -> dict:
"""
Finds external research context for a concept discussed in the uploaded paper.
Use this when the user asks where a concept leads, what uses it, related work,
follow-up reading, or how the idea connects to broader research.
Args:
concept (str): The concept or method to investigate.
paper_context (str): Paper-specific context that makes the search precise.
domain (str): The broader research domain, such as machine learning.
max_results (int): Maximum number of papers to return, capped at 10.
Returns:
dict: Related papers, suggested directions, source metadata, or error details.
"""
concept = _clean_text(concept, 120)
paper_context = _clean_text(paper_context, 500)
domain = _clean_text(domain, 80) or "machine learning"
max_results = _normalize_max_results(max_results)
if not concept:
return {
"status": "failed",
"detail": "Provide a non-empty concept to search for research context.",
}
query = _build_research_query(concept, paper_context, domain)
errors: list[str] = []
try:
papers = _semantic_scholar_papers(query, max_results)
source = "Semantic Scholar"
except Exception as exc:
papers = []
source = "arXiv"
errors.append(f"Semantic Scholar search failed: {exc}")
if not papers:
try:
papers = _arxiv_papers(query, max_results)
source = "arXiv"
except Exception as exc:
errors.append(f"arXiv search failed: {exc}")
if not papers:
return {
"status": "failed",
"query": query,
"detail": "No related papers found from Semantic Scholar or arXiv.",
"errors": errors,
}
return {
"status": "success",
"concept": concept,
"query": query,
"source": source,
"suggested_directions": _suggest_research_directions(concept, papers),
"papers": papers[:max_results],
"errors": errors,
}
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