Add paper browser web app
This commit is contained in:
wuyang
+573
@@ -0,0 +1,573 @@
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#!/usr/bin/env python3
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"""Serve a local paper browser with lightweight Ollama-backed actions."""
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from __future__ import annotations
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import argparse
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import hashlib
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import html
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import json
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import os
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import re
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import threading
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import time
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import urllib.error
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import urllib.parse
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import urllib.request
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import xml.etree.ElementTree as ET
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from http import HTTPStatus
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from http.server import SimpleHTTPRequestHandler, ThreadingHTTPServer
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from pathlib import Path
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from typing import Any
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ROOT = Path(__file__).resolve().parents[1]
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WEB_ROOT = ROOT / "web"
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STATIC_ROOT = WEB_ROOT / "static"
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INDEX_PATH = ROOT / "data" / "index.json"
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CACHE_ROOT = WEB_ROOT / "cache"
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ARXIV_CACHE = CACHE_ROOT / "arxiv"
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AI_CACHE = CACHE_ROOT / "ai"
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OLLAMA_URL = os.environ.get("OLLAMA_URL", "http://192.168.1.10:11434").rstrip("/")
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DEFAULT_MODELS = {
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"translate": "ChatGPT-5.6:light",
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"summary": "ChatGPT-5.6:fast",
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"deep": "ChatGPT-5.6:large",
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}
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NS = {
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"atom": "http://www.w3.org/2005/Atom",
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"arxiv": "http://arxiv.org/schemas/atom",
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}
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INDEX_LOCK = threading.Lock()
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OLLAMA_LOCK = threading.Lock()
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INDEX_CACHE: dict[str, Any] = {"mtime": 0.0, "papers": [], "by_id": {}}
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def ensure_cache_dirs() -> None:
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ARXIV_CACHE.mkdir(parents=True, exist_ok=True)
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AI_CACHE.mkdir(parents=True, exist_ok=True)
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def clean_title(value: Any) -> str:
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title = str(value or "").strip()
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if len(title) >= 2 and title[0] == title[-1] == '"':
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return title[1:-1]
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return title
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def item_id(path: str) -> str:
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return hashlib.sha1(path.encode("utf-8")).hexdigest()[:14]
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def as_list(value: Any) -> list[str]:
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if value is None:
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return []
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if isinstance(value, list):
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return [str(item) for item in value if str(item).strip()]
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text = str(value).strip()
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return [text] if text else []
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def score_value(value: Any) -> int:
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try:
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return int(str(value or "0"))
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except ValueError:
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return 0
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def normalize_paper(item: dict[str, Any]) -> dict[str, Any]:
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meta = item.get("meta") or {}
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path = str(item.get("path") or "")
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title = clean_title(meta.get("title") or item.get("title") or Path(path).stem)
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topics = as_list(meta.get("topics"))
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datasets = as_list(meta.get("datasets"))
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authors = str(meta.get("authors") or "").strip()
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paper = {
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"id": item_id(path),
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"path": path,
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"title": title,
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"authors": authors,
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"year": str(meta.get("year") or ""),
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"venue": str(meta.get("venue") or ""),
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"url": str(meta.get("url") or ""),
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"code_url": as_list(meta.get("code_url")),
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"source": str(meta.get("source") or ""),
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"published_at": str(meta.get("published_at") or ""),
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"updated_at": str(meta.get("updated_at") or ""),
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"status": str(meta.get("status") or "unknown"),
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"relevance": str(meta.get("relevance") or ""),
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"topics": topics,
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"datasets": datasets,
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"collection_score": score_value(meta.get("collection_score")),
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"collection_queries": str(meta.get("collection_queries") or ""),
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"meta": meta,
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}
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search_text = " ".join(
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[
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title,
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authors,
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paper["year"],
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paper["venue"],
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paper["url"],
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paper["status"],
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paper["relevance"],
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paper["collection_queries"],
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" ".join(topics),
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" ".join(datasets),
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]
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)
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paper["_search"] = search_text.lower()
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return paper
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def load_papers() -> tuple[list[dict[str, Any]], dict[str, dict[str, Any]]]:
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mtime = INDEX_PATH.stat().st_mtime
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with INDEX_LOCK:
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if INDEX_CACHE["mtime"] == mtime:
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return INDEX_CACHE["papers"], INDEX_CACHE["by_id"]
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raw_items = json.loads(INDEX_PATH.read_text(encoding="utf-8"))
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papers = [
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normalize_paper(item)
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for item in raw_items
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if item.get("collection") == "papers" and item.get("path")
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]
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by_id = {paper["id"]: paper for paper in papers}
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INDEX_CACHE.update({"mtime": mtime, "papers": papers, "by_id": by_id})
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return papers, by_id
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def read_json_body(handler: SimpleHTTPRequestHandler) -> dict[str, Any]:
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length = int(handler.headers.get("content-length") or "0")
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if length <= 0:
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return {}
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raw = handler.rfile.read(length)
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return json.loads(raw.decode("utf-8"))
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def write_json(handler: SimpleHTTPRequestHandler, payload: Any, status: int = 200) -> None:
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data = json.dumps(payload, ensure_ascii=False, indent=2).encode("utf-8")
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handler.send_response(status)
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handler.send_header("content-type", "application/json; charset=utf-8")
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handler.send_header("cache-control", "no-store")
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handler.send_header("content-length", str(len(data)))
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handler.end_headers()
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handler.wfile.write(data)
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def write_error(handler: SimpleHTTPRequestHandler, status: int, message: str, **extra: Any) -> None:
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payload = {"error": message, **extra}
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write_json(handler, payload, status)
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def paper_public(paper: dict[str, Any]) -> dict[str, Any]:
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return {key: value for key, value in paper.items() if not key.startswith("_") and key != "meta"}
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def topic_counts(papers: list[dict[str, Any]]) -> list[dict[str, Any]]:
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counts: dict[str, int] = {}
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for paper in papers:
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for topic in paper["topics"]:
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counts[topic] = counts.get(topic, 0) + 1
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return [
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{"topic": topic, "count": count}
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for topic, count in sorted(counts.items(), key=lambda item: (-item[1], item[0]))
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]
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def arxiv_id_from_paper(paper: dict[str, Any]) -> str:
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url = str(paper.get("url") or "")
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match = re.search(r"arxiv\.org/(?:abs|html|pdf)/([0-9]{4}\.[0-9]+)", url)
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return match.group(1) if match else ""
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def normalize_space(value: str) -> str:
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return re.sub(r"\s+", " ", value).strip()
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def fetch_arxiv(arxiv_id: str) -> dict[str, Any]:
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ensure_cache_dirs()
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cache_path = ARXIV_CACHE / f"{arxiv_id}.json"
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if cache_path.exists():
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return json.loads(cache_path.read_text(encoding="utf-8"))
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query = urllib.parse.urlencode({"id_list": arxiv_id})
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request = urllib.request.Request(
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f"https://export.arxiv.org/api/query?{query}",
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headers={"User-Agent": "agent-kb-paper-browser/0.1"},
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)
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with urllib.request.urlopen(request, timeout=20) as response:
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xml_text = response.read().decode("utf-8", "replace")
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root = ET.fromstring(xml_text)
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entry = root.find("atom:entry", NS)
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if entry is None:
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raise ValueError(f"arXiv entry not found: {arxiv_id}")
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authors = [
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normalize_space(author.findtext("atom:name", default="", namespaces=NS))
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for author in entry.findall("atom:author", NS)
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]
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categories = [
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category.attrib.get("term", "")
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for category in entry.findall("atom:category", NS)
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if category.attrib.get("term")
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]
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payload = {
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"arxiv_id": arxiv_id,
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"title": normalize_space(entry.findtext("atom:title", default="", namespaces=NS)),
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"abstract": normalize_space(entry.findtext("atom:summary", default="", namespaces=NS)),
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"authors": authors,
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"published": entry.findtext("atom:published", default="", namespaces=NS)[:10],
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"updated": entry.findtext("atom:updated", default="", namespaces=NS)[:10],
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"categories": categories,
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"url": f"https://arxiv.org/abs/{arxiv_id}",
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}
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cache_path.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
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return payload
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def paper_context(paper: dict[str, Any], abstract: dict[str, Any] | None) -> str:
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meta = {
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"title": paper["title"],
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"authors": paper["authors"],
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"year": paper["year"],
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"venue": paper["venue"],
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"url": paper["url"],
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"topics": paper["topics"],
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"collection_score": paper["collection_score"],
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"collection_queries": paper["collection_queries"],
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}
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parts = [f"Metadata:\n{json.dumps(meta, ensure_ascii=False, indent=2)}"]
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if abstract and abstract.get("abstract"):
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parts.append(f"Abstract:\n{abstract['abstract']}")
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else:
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content = (ROOT / paper["path"]).read_text(encoding="utf-8")
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parts.append(f"Local note:\n{content[:4000]}")
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return "\n\n".join(parts)[:7000]
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def prompt_for(mode: str, context: str) -> str:
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no_think = "不要输出思考过程，不要输出 <think> 标签，只输出最终结果。"
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if mode == "translate":
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return (
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"你是论文摘要翻译助手。把下面论文摘要准确翻译成中文，保留技术术语，"
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f"不要扩写，不要编造。如果没有摘要，只翻译已有材料。{no_think}\n\n"
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f"{context}"
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)
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if mode == "deep":
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return (
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"你是 Agent 研究员。请基于给定论文材料做中文深度研读笔记，输出：\n"
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"1. 一句话定位\n2. 研究问题\n3. 方法/系统设计\n4. 评估方式\n"
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"5. 对 Agent 工程实践的启发\n6. 可疑点或待验证问题\n7. 推荐标签。\n"
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f"只根据材料回答，信息不足就明确写“不足”。{no_think}\n\n"
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f"{context}"
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)
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return (
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"你是 Agent 论文知识库助手。请基于给定论文材料输出中文摘要，结构如下：\n"
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"1. 一句话结论\n2. 解决的问题\n3. 核心方法\n4. 评估/实验\n"
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"5. 和 Agent 知识库的关系\n6. 推荐优先级：P0/P1/P2。\n"
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f"保持精炼，只根据材料回答，禁止编造。{no_think}\n\n"
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f"{context}"
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)
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def clean_model_response(value: str) -> str:
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text = re.sub(r"<think>[\s\S]*?</think>", "", value, flags=re.IGNORECASE).strip()
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return text or value.strip()
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def call_ollama(model: str, prompt: str, mode: str) -> dict[str, Any]:
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options = {
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"temperature": 0.2,
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"num_ctx": 4096,
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"num_predict": 520,
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}
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if mode == "translate":
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options.update({"num_ctx": 3072, "num_predict": 700, "temperature": 0.1})
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elif mode == "deep":
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options.update({"num_ctx": 8192, "num_predict": 900, "temperature": 0.25})
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payload = {
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"model": model,
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"prompt": prompt,
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"stream": False,
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"keep_alive": "2m",
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"options": options,
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}
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data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
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request = urllib.request.Request(
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f"{OLLAMA_URL}/api/generate",
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data=data,
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headers={"content-type": "application/json"},
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method="POST",
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)
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started = time.time()
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with urllib.request.urlopen(request, timeout=240) as response:
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result = json.loads(response.read().decode("utf-8"))
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return {
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"model": model,
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"mode": mode,
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"response": clean_model_response(str(result.get("response") or "")),
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"duration_seconds": round(time.time() - started, 2),
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"eval_count": result.get("eval_count"),
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"prompt_eval_count": result.get("prompt_eval_count"),
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}
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def ai_cache_path(paper_id: str, mode: str, model: str) -> Path:
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digest = hashlib.sha1(f"{paper_id}:{mode}:{model}".encode("utf-8")).hexdigest()[:18]
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return AI_CACHE / f"{digest}.json"
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class PaperBrowserHandler(SimpleHTTPRequestHandler):
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server_version = "AgentPaperBrowser/0.1"
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def log_message(self, format: str, *args: Any) -> None:
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print(f"[paper-web] {self.address_string()} - {format % args}")
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def do_GET(self) -> None:
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parsed = urllib.parse.urlparse(self.path)
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path = parsed.path
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query = urllib.parse.parse_qs(parsed.query)
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if path == "/":
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self.serve_file(STATIC_ROOT / "index.html", "text/html; charset=utf-8")
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return
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if path.startswith("/static/"):
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target = STATIC_ROOT / path.removeprefix("/static/")
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content_type = "text/plain; charset=utf-8"
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if target.suffix == ".css":
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content_type = "text/css; charset=utf-8"
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elif target.suffix == ".js":
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content_type = "application/javascript; charset=utf-8"
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self.serve_file(target, content_type)
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return
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if path == "/api/health":
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self.handle_health()
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return
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if path == "/api/models":
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self.handle_models()
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return
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if path == "/api/topics":
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papers, _ = load_papers()
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write_json(self, {"topics": topic_counts(papers)})
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return
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if path == "/api/papers":
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self.handle_paper_search(query)
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return
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if path == "/api/paper":
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self.handle_paper_detail(query)
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return
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if path == "/api/paper/abstract":
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self.handle_abstract(query)
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return
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write_error(self, HTTPStatus.NOT_FOUND, "not found")
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def do_POST(self) -> None:
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parsed = urllib.parse.urlparse(self.path)
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if parsed.path == "/api/ai":
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self.handle_ai()
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return
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write_error(self, HTTPStatus.NOT_FOUND, "not found")
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def serve_file(self, target: Path, content_type: str) -> None:
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try:
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resolved = target.resolve()
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if not str(resolved).startswith(str(STATIC_ROOT.resolve())):
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write_error(self, HTTPStatus.FORBIDDEN, "forbidden")
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return
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data = resolved.read_bytes()
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except FileNotFoundError:
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write_error(self, HTTPStatus.NOT_FOUND, "file not found")
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return
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self.send_response(HTTPStatus.OK)
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self.send_header("content-type", content_type)
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self.send_header("content-length", str(len(data)))
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self.end_headers()
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self.wfile.write(data)
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def handle_health(self) -> None:
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papers, _ = load_papers()
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ollama_ok = False
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try:
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request = urllib.request.Request(f"{OLLAMA_URL}/api/tags")
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with urllib.request.urlopen(request, timeout=3) as response:
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ollama_ok = response.status == 200
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except (urllib.error.URLError, TimeoutError):
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ollama_ok = False
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write_json(
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self,
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{
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"ok": True,
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"paper_count": len(papers),
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"ollama_url": OLLAMA_URL,
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"ollama_ok": ollama_ok,
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"default_models": DEFAULT_MODELS,
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},
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)
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def handle_models(self) -> None:
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try:
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request = urllib.request.Request(f"{OLLAMA_URL}/api/tags")
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with urllib.request.urlopen(request, timeout=5) as response:
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payload = json.loads(response.read().decode("utf-8"))
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models = [
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{
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"name": item.get("name"),
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"parameter_size": (item.get("details") or {}).get("parameter_size"),
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"context_length": (item.get("details") or {}).get("context_length"),
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}
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for item in payload.get("models", [])
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]
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write_json(self, {"models": models, "default_models": DEFAULT_MODELS})
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except Exception as exc: # noqa: BLE001
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write_error(self, HTTPStatus.BAD_GATEWAY, f"ollama unavailable: {exc}")
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def handle_paper_search(self, query: dict[str, list[str]]) -> None:
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papers, _ = load_papers()
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||||
q = (query.get("q") or [""])[0].strip().lower()
|
||||
topic = (query.get("topic") or [""])[0].strip()
|
||||
year = (query.get("year") or [""])[0].strip()
|
||||
status = (query.get("status") or [""])[0].strip()
|
||||
relevance = (query.get("relevance") or [""])[0].strip()
|
||||
sort = (query.get("sort") or ["score"])[0].strip()
|
||||
limit = max(1, min(100, int((query.get("limit") or ["40"])[0])))
|
||||
offset = max(0, int((query.get("offset") or ["0"])[0]))
|
||||
|
||||
filtered = []
|
||||
for paper in papers:
|
||||
if q and q not in paper["_search"]:
|
||||
continue
|
||||
if topic and topic not in paper["topics"]:
|
||||
continue
|
||||
if year and paper["year"] != year:
|
||||
continue
|
||||
if status and paper["status"] != status:
|
||||
continue
|
||||
if relevance and paper["relevance"] != relevance:
|
||||
continue
|
||||
filtered.append(paper)
|
||||
|
||||
if sort == "date":
|
||||
filtered.sort(key=lambda item: (item["published_at"], item["collection_score"]), reverse=True)
|
||||
elif sort == "title":
|
||||
filtered.sort(key=lambda item: item["title"].lower())
|
||||
else:
|
||||
filtered.sort(key=lambda item: (item["collection_score"], item["published_at"]), reverse=True)
|
||||
|
||||
page = filtered[offset : offset + limit]
|
||||
write_json(
|
||||
self,
|
||||
{
|
||||
"total": len(filtered),
|
||||
"offset": offset,
|
||||
"limit": limit,
|
||||
"items": [paper_public(paper) for paper in page],
|
||||
},
|
||||
)
|
||||
|
||||
def handle_paper_detail(self, query: dict[str, list[str]]) -> None:
|
||||
paper_id = (query.get("id") or [""])[0]
|
||||
_, by_id = load_papers()
|
||||
paper = by_id.get(paper_id)
|
||||
if not paper:
|
||||
write_error(self, HTTPStatus.NOT_FOUND, "paper not found")
|
||||
return
|
||||
text = (ROOT / paper["path"]).read_text(encoding="utf-8")
|
||||
write_json(self, {"paper": paper_public(paper), "markdown": text})
|
||||
|
||||
def handle_abstract(self, query: dict[str, list[str]]) -> None:
|
||||
paper_id = (query.get("id") or [""])[0]
|
||||
_, by_id = load_papers()
|
||||
paper = by_id.get(paper_id)
|
||||
if not paper:
|
||||
write_error(self, HTTPStatus.NOT_FOUND, "paper not found")
|
||||
return
|
||||
arxiv_id = arxiv_id_from_paper(paper)
|
||||
if not arxiv_id:
|
||||
write_json(self, {"abstract": None, "message": "not an arXiv paper"})
|
||||
return
|
||||
try:
|
||||
write_json(self, {"abstract": fetch_arxiv(arxiv_id)})
|
||||
except Exception as exc: # noqa: BLE001
|
||||
write_error(self, HTTPStatus.BAD_GATEWAY, f"arXiv fetch failed: {exc}")
|
||||
|
||||
def handle_ai(self) -> None:
|
||||
try:
|
||||
payload = read_json_body(self)
|
||||
paper_id = str(payload.get("id") or "")
|
||||
mode = str(payload.get("mode") or "summary")
|
||||
refresh = bool(payload.get("refresh"))
|
||||
model = str(payload.get("model") or DEFAULT_MODELS.get(mode) or DEFAULT_MODELS["summary"])
|
||||
if mode not in {"summary", "translate", "deep"}:
|
||||
write_error(self, HTTPStatus.BAD_REQUEST, "invalid ai mode")
|
||||
return
|
||||
|
||||
_, by_id = load_papers()
|
||||
paper = by_id.get(paper_id)
|
||||
if not paper:
|
||||
write_error(self, HTTPStatus.NOT_FOUND, "paper not found")
|
||||
return
|
||||
|
||||
ensure_cache_dirs()
|
||||
cache_path = ai_cache_path(paper_id, mode, model)
|
||||
if cache_path.exists() and not refresh:
|
||||
result = json.loads(cache_path.read_text(encoding="utf-8"))
|
||||
result["cached"] = True
|
||||
write_json(self, result)
|
||||
return
|
||||
|
||||
if not OLLAMA_LOCK.acquire(blocking=False):
|
||||
write_error(self, HTTPStatus.TOO_MANY_REQUESTS, "ollama is busy; try again later")
|
||||
return
|
||||
try:
|
||||
abstract = None
|
||||
arxiv_id = arxiv_id_from_paper(paper)
|
||||
if arxiv_id:
|
||||
abstract = fetch_arxiv(arxiv_id)
|
||||
context = paper_context(paper, abstract)
|
||||
result = call_ollama(model, prompt_for(mode, context), mode)
|
||||
result.update(
|
||||
{
|
||||
"id": paper_id,
|
||||
"title": paper["title"],
|
||||
"cached": False,
|
||||
"created_at": time.strftime("%Y-%m-%dT%H:%M:%S%z"),
|
||||
}
|
||||
)
|
||||
cache_path.write_text(json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
|
||||
write_json(self, result)
|
||||
finally:
|
||||
OLLAMA_LOCK.release()
|
||||
except Exception as exc: # noqa: BLE001
|
||||
write_error(self, HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--host", default=os.environ.get("HOST", "0.0.0.0"))
|
||||
parser.add_argument("--port", type=int, default=int(os.environ.get("PORT", "18080")))
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> int:
|
||||
args = parse_args()
|
||||
ensure_cache_dirs()
|
||||
load_papers()
|
||||
server = ThreadingHTTPServer((args.host, args.port), PaperBrowserHandler)
|
||||
print(f"Paper browser: http://{args.host}:{args.port}")
|
||||
print(f"Ollama: {OLLAMA_URL}")
|
||||
try:
|
||||
server.serve_forever()
|
||||
except KeyboardInterrupt:
|
||||
print("\nStopping paper browser")
|
||||
finally:
|
||||
server.server_close()
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
Reference in New Issue
Block a user