Add agentic paper search and chat
This commit is contained in:
wuyang
@@ -35,6 +35,8 @@ https://lab.k1412.top/
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- 深度分析模型：`ChatGPT-5.6:large`，只在前端手动选择时使用
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- Atlas 解释：`ChatGPT-5.6:fast`
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- 主题导读、阅读路径、主题比较：`ChatGPT-5.6:large`，只在前端手动确认后调用
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- Search Agent 查询理解：`ChatGPT-5.6:light`
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- Search Agent 结果简报、搜索追问、论文对话：`ChatGPT-5.6:fast`
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- Ollama 请求单并发执行，并缓存结果到 `web/cache/ai/`
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- arXiv 摘要缓存到 `web/cache/arxiv/`
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@@ -56,3 +58,6 @@ https://lab.k1412.top/
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- 生成阅读路径
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- 比较技术路线
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- 单篇论文摘要、翻译、深度研读
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- 搜索意图理解、关键词扩展、结果简报
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- 基于搜索结果继续追问
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- 基于单篇论文继续对话式研读
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+675
@@ -39,6 +39,10 @@ DEFAULT_MODELS = {
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"topic": "ChatGPT-5.6:large",
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"path": "ChatGPT-5.6:large",
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"compare": "ChatGPT-5.6:large",
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"search_plan": "ChatGPT-5.6:light",
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"search_summary": "ChatGPT-5.6:fast",
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"search_followup": "ChatGPT-5.6:fast",
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"paper_chat": "ChatGPT-5.6:fast",
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}
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NS = {
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@@ -48,8 +52,11 @@ NS = {
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INDEX_LOCK = threading.Lock()
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OLLAMA_LOCK = threading.Lock()
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SEARCH_LOCK = threading.Lock()
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INDEX_CACHE: dict[str, Any] = {"mtime": 0.0, "papers": [], "by_id": {}}
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ATLAS_CACHE: dict[str, Any] = {"mtime": 0.0, "atlas": None}
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NOTE_CACHE: dict[str, dict[str, Any]] = {}
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SEARCH_JOBS: dict[str, dict[str, Any]] = {}
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TOPIC_PROFILES = {
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"agent-evaluation": {
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@@ -305,6 +312,556 @@ def paper_card(paper: dict[str, Any]) -> dict[str, Any]:
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}
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def strip_note_frontmatter(markdown: str) -> str:
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text = str(markdown or "")
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match = re.match(r"^# .+?\n\n---[\s\S]*?\n---\n\n?", text)
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return text[match.end() :] if match else text
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def paper_note_text(paper: dict[str, Any]) -> str:
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path = ROOT / paper["path"]
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try:
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mtime = path.stat().st_mtime
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except FileNotFoundError:
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return ""
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cache_key = str(path)
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cached = NOTE_CACHE.get(cache_key)
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if cached and cached.get("mtime") == mtime:
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return str(cached.get("text") or "")
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text = strip_note_frontmatter(path.read_text(encoding="utf-8", errors="replace"))
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text = normalize_space(text)
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NOTE_CACHE[cache_key] = {"mtime": mtime, "text": text}
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return text
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def topic_label_map() -> dict[str, str]:
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return {topic: profile_for(topic)["label"] for topic in TOPIC_PROFILES}
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def tokenize_query(value: str) -> list[str]:
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text = str(value or "").lower()
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tokens = re.findall(r"[a-z0-9][a-z0-9.+#/-]{1,}|[\u4e00-\u9fff]{2,}", text)
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return [token.strip(".,;:()[]{}") for token in tokens if token.strip(".,;:()[]{}")]
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def unique_strings(items: list[Any], limit: int = 20) -> list[str]:
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seen: set[str] = set()
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output: list[str] = []
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for item in items:
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text = str(item or "").strip()
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key = text.lower()
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if not text or key in seen:
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continue
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seen.add(key)
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output.append(text)
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if len(output) >= limit:
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break
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return output
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def extract_json_object(text: str) -> dict[str, Any]:
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raw = clean_model_response(text)
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start = raw.find("{")
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end = raw.rfind("}")
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if start < 0 or end <= start:
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raise ValueError("model did not return a JSON object")
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return json.loads(raw[start : end + 1])
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QUERY_EXPANSIONS = {
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"强化学习": {
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"keywords_en": [
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"reinforcement learning",
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"RL",
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"policy optimization",
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"reward",
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"rollout",
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"self-feedback",
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"agent reinforcement learning",
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"credit assignment",
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"trajectory optimization",
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],
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"topics": ["planning", "reasoning", "agent-evaluation", "multi-agent"],
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},
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"评估": {
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"keywords_en": ["evaluation", "benchmark", "trajectory", "judge", "rubric", "agent benchmark"],
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"topics": ["agent-evaluation"],
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},
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"记忆": {
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"keywords_en": ["memory", "long-horizon", "state", "retention", "retrieval memory"],
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"topics": ["memory", "rag"],
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},
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"工具": {
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"keywords_en": ["tool use", "tool calling", "function calling", "tool failure", "tool selection"],
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"topics": ["tool-use"],
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},
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"安全": {
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"keywords_en": ["safety", "security", "privacy", "prompt injection", "guardrail", "permission"],
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"topics": ["agent-safety"],
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},
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}
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SEARCH_STOP_TERMS = {
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"agent",
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"agents",
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"ai",
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"llm",
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"model",
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"models",
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"system",
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"systems",
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"method",
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"methods",
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"approach",
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"approaches",
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"algorithm",
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"algorithms",
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"application",
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"applications",
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"research",
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"study",
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"paper",
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"learning",
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}
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def filter_search_keywords(items: list[Any], limit: int = 24) -> list[str]:
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output: list[str] = []
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for item in unique_strings(items, limit * 3):
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key = item.lower().strip()
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if key in SEARCH_STOP_TERMS:
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continue
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if len(key) < 2:
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continue
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output.append(item)
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if len(output) >= limit:
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break
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return output
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def heuristic_search_plan(query: str) -> dict[str, Any]:
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tokens = tokenize_query(query)
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keywords_en: list[str] = []
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topics: list[str] = []
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for key, expansion in QUERY_EXPANSIONS.items():
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if key in query:
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keywords_en.extend(expansion["keywords_en"])
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topics.extend(expansion["topics"])
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for topic, label in topic_label_map().items():
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haystack = f"{topic} {label}".lower()
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if any(token.lower() in haystack for token in tokens):
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topics.append(topic)
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keywords_en.append(label)
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keywords_en.append(topic.replace("-", " "))
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return {
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"intent": "topic_search",
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"interpreted_query": query,
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"keywords_zh": [token for token in tokens if re.search(r"[\u4e00-\u9fff]", token)],
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"keywords_en": filter_search_keywords(keywords_en + [token for token in tokens if re.search(r"[a-z0-9]", token)], 18),
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"topics": unique_strings(topics, 8),
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"filters": {},
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"search_rationale": "基于本地规则扩展中文/英文关键词，并映射到已有 Agent 主题。",
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}
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def search_plan_prompt(query: str, scope_topic: str = "") -> str:
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topics = [
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{"id": topic, "label": profile_for(topic)["label"], "question": profile_for(topic)["question"]}
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for topic in TOPIC_PROFILES
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]
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no_think = "不要输出思考过程，不要输出 <think> 标签。"
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return (
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"你是 Agent 论文库的搜索规划器。请理解用户输入，生成用于本地检索的 JSON。\n"
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"只返回 JSON 对象，不要 Markdown。字段：\n"
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"intent: topic_search|paper_lookup|question_answer|trend_scan|method_search|benchmark_search\n"
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"interpreted_query: 中文解释\nkeywords_zh: 中文关键词数组\nkeywords_en: 英文关键词数组\n"
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"topics: 从给定 topic id 中选择相关项数组\nfilters: 可为空对象，支持 recent_months 整数\n"
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"search_rationale: 一句话说明为什么这样搜。\n"
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f"当前 topic scope: {scope_topic or 'none'}\n"
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f"可选 topics:\n{json.dumps(topics, ensure_ascii=False)}\n"
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f"{no_think}\n\n用户输入：{query}"
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)
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def plan_search_query(query: str, scope_topic: str = "") -> tuple[dict[str, Any], dict[str, Any] | None]:
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fallback = heuristic_search_plan(query)
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model = DEFAULT_MODELS["search_plan"]
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result: dict[str, Any] | None = None
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try:
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with OLLAMA_LOCK:
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result = call_ollama(model, search_plan_prompt(query, scope_topic), "search_plan")
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plan = extract_json_object(result["response"])
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merged = {
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**fallback,
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**{key: value for key, value in plan.items() if value not in (None, "", [])},
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}
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merged["keywords_zh"] = unique_strings(as_list(merged.get("keywords_zh")) + fallback["keywords_zh"], 16)
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merged["keywords_en"] = filter_search_keywords(as_list(merged.get("keywords_en")) + fallback["keywords_en"], 24)
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merged["topics"] = [topic for topic in unique_strings(as_list(merged.get("topics")) + fallback["topics"], 10) if topic in TOPIC_PROFILES]
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if not isinstance(merged.get("filters"), dict):
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merged["filters"] = {}
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return merged, result
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except Exception as exc: # noqa: BLE001
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fallback["planner_error"] = str(exc)
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return fallback, result
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def term_score(term: str, title: str, meta: str, note: str) -> float:
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if not term:
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return 0.0
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term_lower = term.lower()
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score = 0.0
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if term_lower in title:
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score += 50
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if term_lower in meta:
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score += 18
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if term_lower in note:
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score += min(22, 5 + note.count(term_lower) * 2)
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for token in tokenize_query(term_lower):
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if len(token) < 2:
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continue
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if token in title:
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score += 14
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elif token in meta:
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score += 8
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elif token in note:
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score += 3
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return score
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def score_search_matches(
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papers: list[dict[str, Any]],
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query: str,
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plan: dict[str, Any],
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scope_topic: str = "",
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limit: int = 80,
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) -> list[dict[str, Any]]:
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raw_terms = [query, *as_list(plan.get("keywords_zh")), *as_list(plan.get("keywords_en"))]
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terms = unique_strings(raw_terms, 32)
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plan_topics = [topic for topic in as_list(plan.get("topics")) if topic in TOPIC_PROFILES]
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scored: list[tuple[float, dict[str, Any], list[str]]] = []
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for paper in papers:
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if scope_topic and scope_topic not in paper["topics"]:
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continue
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title = paper["title"].lower()
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meta = " ".join(
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[
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paper["_search"],
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" ".join(profile_for(topic)["label"] for topic in paper["topics"]),
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" ".join(topic.replace("-", " ") for topic in paper["topics"]),
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]
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).lower()
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note = paper_note_text(paper).lower()
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evidence_score = 0.0
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matched_terms: list[str] = []
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for term in terms:
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value = term_score(term, title, meta, note)
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if value > 0:
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evidence_score += value
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matched_terms.append(term)
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topic_overlap = sorted(set(plan_topics) & set(paper["topics"]))
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if topic_overlap:
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evidence_score += 34 + 9 * len(topic_overlap)
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matched_terms.extend(profile_for(topic)["label"] for topic in topic_overlap)
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if query.lower() in title:
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evidence_score += 70
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if evidence_score <= 0:
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continue
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score = evidence_score + float(paper["collection_score"]) * 0.35
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item = paper_card(paper)
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item["match_score"] = round(score, 2)
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item["matched_terms"] = unique_strings(matched_terms, 8)
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scored.append((score, item, matched_terms))
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scored.sort(key=lambda row: (row[0], row[1].get("published_at") or "", row[1]["collection_score"]), reverse=True)
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return [item for _, item, _ in scored[:limit]]
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def aggregate_search_results(items: list[dict[str, Any]]) -> dict[str, Any]:
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topic_counts: Counter = Counter()
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month_counts: Counter = Counter()
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year_counts: Counter = Counter()
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for item in items:
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month = month_key(str(item.get("published_at") or ""))
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if month != "unknown":
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month_counts[month] += 1
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year_counts[month[:4]] += 1
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for topic in item.get("topics") or []:
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topic_counts[topic] += 1
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topic_rows = [
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{"topic": topic, "label": profile_for(topic)["label"], "count": count}
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for topic, count in topic_counts.most_common(10)
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]
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months = [{"month": month, "count": count} for month, count in sorted(month_counts.items())]
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recent_months = months[-6:]
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date_values = [str(item.get("published_at") or "") for item in items if re.match(r"^\d{4}-\d{2}-\d{2}", str(item.get("published_at") or ""))]
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return {
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"total": len(items),
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"topics": topic_rows,
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"months": months,
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"recent_months": recent_months,
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"years": [{"year": year, "count": count} for year, count in sorted(year_counts.items())],
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"date_range": {
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"start": min(date_values) if date_values else "",
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"end": max(date_values) if date_values else "",
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},
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}
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def search_summary_prompt(query: str, plan: dict[str, Any], items: list[dict[str, Any]], aggregation: dict[str, Any]) -> str:
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compact_items = [
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{
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"title": item["title"],
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"published_at": item["published_at"],
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"topics": item["topics"],
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"score": item["collection_score"],
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"matched_terms": item.get("matched_terms", []),
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}
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for item in items[:18]
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]
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context = {
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"query": query,
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"plan": plan,
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"aggregation": aggregation,
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"top_papers": compact_items,
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}
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no_think = "不要输出思考过程，不要输出 <think> 标签。"
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return (
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"你是 Agent 研究助理。请基于本地论文检索结果生成中文搜索简报。\n"
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"只基于给定结果，不要编造论文细节。输出要短，包含：\n"
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"1. 搜索理解\n2. 主要发现\n3. 时间/趋势\n4. 建议先读\n5. 可以继续追问的方向。\n"
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f"{no_think}\n\n{json.dumps(context, ensure_ascii=False, indent=2)[:14000]}"
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)
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def fallback_search_summary(query: str, plan: dict[str, Any], aggregation: dict[str, Any], items: list[dict[str, Any]]) -> str:
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topics = "、".join(item["label"] for item in aggregation["topics"][:5]) or "暂无明显主题"
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papers = "；".join(item["title"] for item in items[:3]) or "暂无命中论文"
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keywords = ", ".join(as_list(plan.get("keywords_en"))[:8] + as_list(plan.get("keywords_zh"))[:4])
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return (
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f"搜索理解：你在查「{query}」，系统扩展了 {keywords or '原始关键词'}。\n\n"
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f"主要发现：命中 {aggregation['total']} 篇候选，集中在 {topics}。\n\n"
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f"时间/趋势：日期范围 {aggregation['date_range']['start'] or 'unknown'} 到 {aggregation['date_range']['end'] or 'unknown'}。\n\n"
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f"建议先读：{papers}。\n\n"
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"可以继续追问：只看最近论文、按主题拆分、生成阅读路径、比较工程/理论方向。"
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)
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def generate_search_summary(query: str, plan: dict[str, Any], items: list[dict[str, Any]], aggregation: dict[str, Any]) -> dict[str, Any]:
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model = DEFAULT_MODELS["search_summary"]
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try:
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with OLLAMA_LOCK:
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result = call_ollama(model, search_summary_prompt(query, plan, items, aggregation), "search_summary")
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return {
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"text": result["response"],
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"model": result["model"],
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"duration_seconds": result["duration_seconds"],
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"cached": False,
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}
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except Exception as exc: # noqa: BLE001
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return {
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"text": fallback_search_summary(query, plan, aggregation, items),
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"model": "fallback",
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"error": str(exc),
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"cached": False,
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}
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def new_search_job(query: str, scope_topic: str = "") -> dict[str, Any]:
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job_id = hashlib.sha1(f"{query}:{scope_topic}:{time.time()}".encode("utf-8")).hexdigest()[:14]
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now = time.strftime("%Y-%m-%dT%H:%M:%S%z")
|
||||
job = {
|
||||
"id": job_id,
|
||||
"query": query,
|
||||
"scope_topic": scope_topic,
|
||||
"status": "queued",
|
||||
"created_at": now,
|
||||
"updated_at": now,
|
||||
"steps": [
|
||||
{"id": "understand", "label": "理解问题", "status": "pending", "detail": ""},
|
||||
{"id": "expand", "label": "扩展关键词", "status": "pending", "detail": ""},
|
||||
{"id": "retrieve", "label": "检索论文库", "status": "pending", "detail": ""},
|
||||
{"id": "aggregate", "label": "统计主题/趋势", "status": "pending", "detail": ""},
|
||||
{"id": "summarize", "label": "生成搜索结论", "status": "pending", "detail": ""},
|
||||
],
|
||||
"result": None,
|
||||
"error": "",
|
||||
}
|
||||
with SEARCH_LOCK:
|
||||
SEARCH_JOBS[job_id] = job
|
||||
prune_search_jobs_locked()
|
||||
thread = threading.Thread(target=run_search_job, args=(job_id,), daemon=True)
|
||||
thread.start()
|
||||
return public_search_job(job)
|
||||
|
||||
|
||||
def public_search_job(job: dict[str, Any]) -> dict[str, Any]:
|
||||
return {
|
||||
"id": job["id"],
|
||||
"query": job["query"],
|
||||
"scope_topic": job.get("scope_topic", ""),
|
||||
"status": job["status"],
|
||||
"created_at": job["created_at"],
|
||||
"updated_at": job["updated_at"],
|
||||
"steps": job["steps"],
|
||||
"result": job.get("result"),
|
||||
"error": job.get("error", ""),
|
||||
}
|
||||
|
||||
|
||||
def get_search_job(job_id: str) -> dict[str, Any] | None:
|
||||
with SEARCH_LOCK:
|
||||
job = SEARCH_JOBS.get(job_id)
|
||||
return public_search_job(job) if job else None
|
||||
|
||||
|
||||
def update_search_job(job_id: str, *, status: str | None = None, result: Any = None, error: str = "") -> None:
|
||||
with SEARCH_LOCK:
|
||||
job = SEARCH_JOBS.get(job_id)
|
||||
if not job:
|
||||
return
|
||||
if status:
|
||||
job["status"] = status
|
||||
if result is not None:
|
||||
job["result"] = result
|
||||
if error:
|
||||
job["error"] = error
|
||||
job["updated_at"] = time.strftime("%Y-%m-%dT%H:%M:%S%z")
|
||||
|
||||
|
||||
def update_search_step(job_id: str, step_id: str, status: str, detail: str = "") -> None:
|
||||
with SEARCH_LOCK:
|
||||
job = SEARCH_JOBS.get(job_id)
|
||||
if not job:
|
||||
return
|
||||
for step in job["steps"]:
|
||||
if step["id"] == step_id:
|
||||
step["status"] = status
|
||||
if detail:
|
||||
step["detail"] = detail
|
||||
step["updated_at"] = time.strftime("%H:%M:%S")
|
||||
break
|
||||
job["updated_at"] = time.strftime("%Y-%m-%dT%H:%M:%S%z")
|
||||
|
||||
|
||||
def prune_search_jobs_locked() -> None:
|
||||
if len(SEARCH_JOBS) <= 24:
|
||||
return
|
||||
ordered = sorted(SEARCH_JOBS.values(), key=lambda item: item.get("created_at", ""))
|
||||
for job in ordered[: len(SEARCH_JOBS) - 24]:
|
||||
SEARCH_JOBS.pop(job["id"], None)
|
||||
|
||||
|
||||
def run_search_job(job_id: str) -> None:
|
||||
job = get_search_job(job_id)
|
||||
if not job:
|
||||
return
|
||||
query = job["query"]
|
||||
scope_topic = job.get("scope_topic", "")
|
||||
try:
|
||||
update_search_job(job_id, status="running")
|
||||
update_search_step(job_id, "understand", "running", "调用轻量模型理解搜索意图")
|
||||
plan, planner_result = plan_search_query(query, scope_topic)
|
||||
update_search_step(job_id, "understand", "done", str(plan.get("interpreted_query") or query))
|
||||
|
||||
keywords = unique_strings(as_list(plan.get("keywords_en")) + as_list(plan.get("keywords_zh")), 12)
|
||||
update_search_step(job_id, "expand", "done", " / ".join(keywords[:8]) or "使用原始输入")
|
||||
|
||||
update_search_step(job_id, "retrieve", "running", "扫描标题、主题、collection query 和本地卡片")
|
||||
papers, _ = load_papers()
|
||||
items = score_search_matches(papers, query, plan, scope_topic, limit=80)
|
||||
update_search_step(job_id, "retrieve", "done", f"找到 {len(items)} 篇候选论文")
|
||||
|
||||
update_search_step(job_id, "aggregate", "running", "统计日期、主题和最近趋势")
|
||||
aggregation = aggregate_search_results(items)
|
||||
update_search_step(
|
||||
job_id,
|
||||
"aggregate",
|
||||
"done",
|
||||
f"{len(aggregation['topics'])} 个相关主题，日期到 {aggregation['date_range']['end'] or 'unknown'}",
|
||||
)
|
||||
|
||||
update_search_step(job_id, "summarize", "running", "调用 fast 模型生成搜索简报")
|
||||
summary = generate_search_summary(query, plan, items, aggregation)
|
||||
update_search_step(job_id, "summarize", "done", summary.get("model", "fallback"))
|
||||
|
||||
result = {
|
||||
"query": query,
|
||||
"scope_topic": scope_topic,
|
||||
"plan": plan,
|
||||
"planner": {
|
||||
"model": planner_result.get("model") if planner_result else "fallback",
|
||||
"duration_seconds": planner_result.get("duration_seconds") if planner_result else None,
|
||||
},
|
||||
"summary": summary,
|
||||
"aggregation": aggregation,
|
||||
"items": items[:60],
|
||||
"total": len(items),
|
||||
}
|
||||
update_search_job(job_id, status="done", result=result)
|
||||
except Exception as exc: # noqa: BLE001
|
||||
update_search_step(job_id, "summarize", "error", str(exc))
|
||||
update_search_job(job_id, status="error", error=str(exc))
|
||||
|
||||
|
||||
def search_followup_prompt(job: dict[str, Any], question: str) -> str:
|
||||
result = job.get("result") or {}
|
||||
compact = {
|
||||
"query": result.get("query"),
|
||||
"plan": result.get("plan"),
|
||||
"summary": (result.get("summary") or {}).get("text"),
|
||||
"aggregation": result.get("aggregation"),
|
||||
"top_papers": [
|
||||
{
|
||||
"title": item["title"],
|
||||
"published_at": item["published_at"],
|
||||
"topics": item["topics"],
|
||||
"score": item["collection_score"],
|
||||
"matched_terms": item.get("matched_terms", []),
|
||||
}
|
||||
for item in (result.get("items") or [])[:18]
|
||||
],
|
||||
}
|
||||
return (
|
||||
"你是 Agent 论文库搜索助手。用户正在基于一次搜索结果继续追问。\n"
|
||||
"请只基于给定搜索上下文回答，中文，简洁但有判断。不要编造论文细节。\n"
|
||||
"不要输出思考过程，不要输出 <think> 标签。\n\n"
|
||||
f"搜索上下文：\n{json.dumps(compact, ensure_ascii=False, indent=2)[:14000]}\n\n"
|
||||
f"用户追问：{question}"
|
||||
)
|
||||
|
||||
|
||||
def paper_chat_prompt(paper: dict[str, Any], abstract: dict[str, Any] | None, question: str, history: list[dict[str, str]]) -> str:
|
||||
recent_history = [
|
||||
{"role": str(item.get("role") or ""), "content": str(item.get("content") or "")[:1200]}
|
||||
for item in history[-6:]
|
||||
if item.get("content")
|
||||
]
|
||||
context = {
|
||||
"paper": {
|
||||
"title": paper["title"],
|
||||
"authors": paper["authors"],
|
||||
"published_at": paper["published_at"],
|
||||
"venue": paper["venue"],
|
||||
"topics": paper["topics"],
|
||||
"collection_score": paper["collection_score"],
|
||||
"url": paper["url"],
|
||||
},
|
||||
"abstract": abstract,
|
||||
"local_note": paper_note_text(paper)[:5000],
|
||||
"history": recent_history,
|
||||
"question": question,
|
||||
}
|
||||
return (
|
||||
"你是 Agent 论文研读助手。请基于论文上下文和对话历史回答用户问题。\n"
|
||||
"中文回答，优先给出判断和证据；信息不足就明确说不足。不要编造论文细节。\n"
|
||||
"不要输出思考过程，不要输出 <think> 标签。\n\n"
|
||||
f"{json.dumps(context, ensure_ascii=False, indent=2)[:15000]}"
|
||||
)
|
||||
|
||||
|
||||
def topic_counts(papers: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
counts: dict[str, int] = {}
|
||||
for paper in papers:
|
||||
@@ -671,6 +1228,10 @@ def call_ollama(model: str, prompt: str, mode: str) -> dict[str, Any]:
|
||||
options.update({"num_ctx": 8192, "num_predict": 900, "temperature": 0.25})
|
||||
elif mode in {"atlas", "topic", "path", "compare"}:
|
||||
options.update({"num_ctx": 8192, "num_predict": 950, "temperature": 0.22})
|
||||
elif mode == "search_plan":
|
||||
options.update({"num_ctx": 4096, "num_predict": 420, "temperature": 0.1})
|
||||
elif mode in {"search_summary", "search_followup", "paper_chat"}:
|
||||
options.update({"num_ctx": 8192, "num_predict": 780, "temperature": 0.18})
|
||||
|
||||
payload = {
|
||||
"model": model,
|
||||
@@ -746,6 +1307,9 @@ class PaperBrowserHandler(SimpleHTTPRequestHandler):
|
||||
if path == "/api/papers":
|
||||
self.handle_paper_search(query)
|
||||
return
|
||||
if path == "/api/search/agent":
|
||||
self.handle_search_job(query)
|
||||
return
|
||||
if path == "/api/paper":
|
||||
self.handle_paper_detail(query)
|
||||
return
|
||||
@@ -763,6 +1327,15 @@ class PaperBrowserHandler(SimpleHTTPRequestHandler):
|
||||
if parsed.path == "/api/atlas/ai":
|
||||
self.handle_atlas_ai()
|
||||
return
|
||||
if parsed.path == "/api/search/agent":
|
||||
self.handle_search_agent_start()
|
||||
return
|
||||
if parsed.path == "/api/search/followup":
|
||||
self.handle_search_followup()
|
||||
return
|
||||
if parsed.path == "/api/paper/chat":
|
||||
self.handle_paper_chat()
|
||||
return
|
||||
write_error(self, HTTPStatus.NOT_FOUND, "not found")
|
||||
|
||||
def serve_file(self, target: Path, content_type: str) -> None:
|
||||
@@ -862,6 +1435,64 @@ class PaperBrowserHandler(SimpleHTTPRequestHandler):
|
||||
},
|
||||
)
|
||||
|
||||
def handle_search_agent_start(self) -> None:
|
||||
try:
|
||||
payload = read_json_body(self)
|
||||
query = str(payload.get("query") or "").strip()
|
||||
scope_topic = str(payload.get("topic") or "").strip()
|
||||
if not query:
|
||||
write_error(self, HTTPStatus.BAD_REQUEST, "query is required")
|
||||
return
|
||||
if scope_topic and scope_topic not in TOPIC_PROFILES:
|
||||
scope_topic = ""
|
||||
write_json(self, {"job": new_search_job(query, scope_topic)})
|
||||
except Exception as exc: # noqa: BLE001
|
||||
write_error(self, HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))
|
||||
|
||||
def handle_search_job(self, query: dict[str, list[str]]) -> None:
|
||||
job_id = (query.get("id") or [""])[0].strip()
|
||||
if not job_id:
|
||||
write_error(self, HTTPStatus.BAD_REQUEST, "id is required")
|
||||
return
|
||||
job = get_search_job(job_id)
|
||||
if not job:
|
||||
write_error(self, HTTPStatus.NOT_FOUND, "search job not found")
|
||||
return
|
||||
write_json(self, {"job": job})
|
||||
|
||||
def handle_search_followup(self) -> None:
|
||||
try:
|
||||
payload = read_json_body(self)
|
||||
job_id = str(payload.get("job_id") or "").strip()
|
||||
question = str(payload.get("question") or "").strip()
|
||||
model = str(payload.get("model") or DEFAULT_MODELS["search_followup"])
|
||||
if not job_id or not question:
|
||||
write_error(self, HTTPStatus.BAD_REQUEST, "job_id and question are required")
|
||||
return
|
||||
job = get_search_job(job_id)
|
||||
if not job or job.get("status") != "done":
|
||||
write_error(self, HTTPStatus.NOT_FOUND, "completed search job not found")
|
||||
return
|
||||
if not OLLAMA_LOCK.acquire(blocking=False):
|
||||
write_error(self, HTTPStatus.TOO_MANY_REQUESTS, "ollama is busy; try again later")
|
||||
return
|
||||
try:
|
||||
result = call_ollama(model, search_followup_prompt(job, question), "search_followup")
|
||||
write_json(
|
||||
self,
|
||||
{
|
||||
"job_id": job_id,
|
||||
"question": question,
|
||||
"answer": result["response"],
|
||||
"model": result["model"],
|
||||
"duration_seconds": result["duration_seconds"],
|
||||
},
|
||||
)
|
||||
finally:
|
||||
OLLAMA_LOCK.release()
|
||||
except Exception as exc: # noqa: BLE001
|
||||
write_error(self, HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))
|
||||
|
||||
def handle_paper_detail(self, query: dict[str, list[str]]) -> None:
|
||||
paper_id = (query.get("id") or [""])[0]
|
||||
_, by_id = load_papers()
|
||||
@@ -898,6 +1529,50 @@ class PaperBrowserHandler(SimpleHTTPRequestHandler):
|
||||
except Exception as exc: # noqa: BLE001
|
||||
write_error(self, HTTPStatus.BAD_GATEWAY, f"arXiv fetch failed: {exc}")
|
||||
|
||||
def handle_paper_chat(self) -> None:
|
||||
try:
|
||||
payload = read_json_body(self)
|
||||
paper_id = str(payload.get("id") or "").strip()
|
||||
question = str(payload.get("question") or "").strip()
|
||||
history = payload.get("history") or []
|
||||
model = str(payload.get("model") or DEFAULT_MODELS["paper_chat"])
|
||||
if not paper_id or not question:
|
||||
write_error(self, HTTPStatus.BAD_REQUEST, "id and question are required")
|
||||
return
|
||||
if not isinstance(history, list):
|
||||
history = []
|
||||
_, by_id = load_papers()
|
||||
paper = by_id.get(paper_id)
|
||||
if not paper:
|
||||
write_error(self, HTTPStatus.NOT_FOUND, "paper not found")
|
||||
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:
|
||||
try:
|
||||
abstract = fetch_arxiv(arxiv_id)
|
||||
except Exception:
|
||||
abstract = None
|
||||
result = call_ollama(model, paper_chat_prompt(paper, abstract, question, history), "paper_chat")
|
||||
write_json(
|
||||
self,
|
||||
{
|
||||
"id": paper_id,
|
||||
"question": question,
|
||||
"answer": result["response"],
|
||||
"model": result["model"],
|
||||
"duration_seconds": result["duration_seconds"],
|
||||
},
|
||||
)
|
||||
finally:
|
||||
OLLAMA_LOCK.release()
|
||||
except Exception as exc: # noqa: BLE001
|
||||
write_error(self, HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))
|
||||
|
||||
def handle_ai(self) -> None:
|
||||
try:
|
||||
payload = read_json_body(self)
|
||||
|
||||
+321
-27
@@ -8,8 +8,13 @@ const state = {
|
||||
selectedMarkdown: "",
|
||||
searchItems: [],
|
||||
searchTotal: 0,
|
||||
searchJobId: "",
|
||||
searchResult: null,
|
||||
searchPollTimer: null,
|
||||
searchFollowups: [],
|
||||
aiBusy: false,
|
||||
atlasChart: null,
|
||||
paperChatHistory: {},
|
||||
};
|
||||
|
||||
const $ = (selector) => document.querySelector(selector);
|
||||
@@ -498,6 +503,10 @@ function paperList(items, limit = items.length) {
|
||||
`;
|
||||
}
|
||||
|
||||
function compactPaperList(items, limit = items.length) {
|
||||
return paperList(items, limit);
|
||||
}
|
||||
|
||||
function routeButtons(routes) {
|
||||
return `
|
||||
<div class="chip-row">
|
||||
@@ -512,6 +521,169 @@ function routeButtons(routes) {
|
||||
`;
|
||||
}
|
||||
|
||||
function searchSteps(steps = []) {
|
||||
return `
|
||||
<div class="agent-steps">
|
||||
${steps
|
||||
.map(
|
||||
(step) => `
|
||||
<div class="agent-step ${escapeHtml(step.status)}">
|
||||
<span class="step-dot"></span>
|
||||
<div>
|
||||
<strong>${escapeHtml(step.label)}</strong>
|
||||
<span>${escapeHtml(step.detail || step.status)}</span>
|
||||
</div>
|
||||
</div>
|
||||
`,
|
||||
)
|
||||
.join("")}
|
||||
</div>
|
||||
`;
|
||||
}
|
||||
|
||||
function searchAggregationView(aggregation) {
|
||||
if (!aggregation) return "";
|
||||
const topTopics = aggregation.topics || [];
|
||||
const recentMonths = aggregation.recent_months || [];
|
||||
return `
|
||||
<section class="section">
|
||||
${statGrid([
|
||||
{ value: aggregation.total || 0, label: "matches" },
|
||||
{ value: topTopics.length, label: "topics" },
|
||||
{ value: aggregation.date_range?.end || "unknown", label: "latest" },
|
||||
])}
|
||||
<div class="mini-chart-row">
|
||||
${recentMonths
|
||||
.map((row) => `<span class="month-chip">${escapeHtml(row.month)} · ${escapeHtml(row.count)}</span>`)
|
||||
.join("")}
|
||||
</div>
|
||||
<div class="chip-row search-topic-row">
|
||||
${topTopics
|
||||
.slice(0, 8)
|
||||
.map(
|
||||
(topic) => `
|
||||
<button class="pill-button" type="button" data-topic="${escapeHtml(topic.topic)}">
|
||||
${escapeHtml(topic.label)} · ${topic.count}
|
||||
</button>
|
||||
`,
|
||||
)
|
||||
.join("")}
|
||||
</div>
|
||||
</section>
|
||||
`;
|
||||
}
|
||||
|
||||
function searchPlanView(plan) {
|
||||
if (!plan) return "";
|
||||
const keywords = [...(plan.keywords_en || []), ...(plan.keywords_zh || [])].slice(0, 14);
|
||||
return `
|
||||
<section class="section compact-section">
|
||||
<div class="eyebrow">Search Plan</div>
|
||||
<p class="statement">${escapeHtml(plan.interpreted_query || "")}</p>
|
||||
<p class="microcopy">${escapeHtml(plan.search_rationale || "")}</p>
|
||||
<div class="chip-row">
|
||||
${keywords.map((keyword) => `<span class="mini-badge">${escapeHtml(keyword)}</span>`).join("")}
|
||||
</div>
|
||||
</section>
|
||||
`;
|
||||
}
|
||||
|
||||
function renderSearchRunning(job) {
|
||||
setReaderMode(false);
|
||||
state.view = "search";
|
||||
setModeButtons("search");
|
||||
elements.lensEyebrow.textContent = "Search Agent";
|
||||
elements.lensTitle.textContent = job?.query || elements.searchInput.value.trim() || "搜索";
|
||||
elements.lensBody.innerHTML = `
|
||||
<section class="section">
|
||||
<p class="statement">Search Agent 正在理解问题、扩展关键词并检索本地论文库。</p>
|
||||
</section>
|
||||
<section class="section">
|
||||
${searchSteps(job?.steps || [])}
|
||||
</section>
|
||||
`;
|
||||
}
|
||||
|
||||
function renderSearchHome() {
|
||||
setReaderMode(false);
|
||||
state.view = "search";
|
||||
setModeButtons("search");
|
||||
elements.lensEyebrow.textContent = "Search Agent";
|
||||
elements.lensTitle.textContent = "智能搜索";
|
||||
elements.lensBody.innerHTML = `
|
||||
<section class="section">
|
||||
<p class="statement">输入主题、论文线索或研究问题。Search Agent 会理解意图、扩展中英文关键词、检索本地论文库，并生成一份简短结论。</p>
|
||||
</section>
|
||||
<section class="section">
|
||||
<h3>可以这样搜</h3>
|
||||
<div class="chip-row">
|
||||
<button class="pill-button" type="button" data-search-example="强化学习">强化学习</button>
|
||||
<button class="pill-button" type="button" data-search-example="最近的 agent evaluation benchmark">最近的 agent evaluation benchmark</button>
|
||||
<button class="pill-button" type="button" data-search-example="长期记忆和 RAG 有什么关系">长期记忆和 RAG 有什么关系</button>
|
||||
</div>
|
||||
</section>
|
||||
`;
|
||||
}
|
||||
|
||||
function renderSearchError(jobOrError) {
|
||||
const message = typeof jobOrError === "string" ? jobOrError : jobOrError?.error || "search failed";
|
||||
elements.lensBody.innerHTML = `
|
||||
<section class="section">
|
||||
<div class="empty-state">${escapeHtml(message)}</div>
|
||||
</section>
|
||||
`;
|
||||
}
|
||||
|
||||
function followupList() {
|
||||
const rows = state.searchFollowups || [];
|
||||
if (!rows.length) return "";
|
||||
return `
|
||||
<div class="dialogue-list">
|
||||
${rows
|
||||
.map(
|
||||
(row) => `
|
||||
<div class="dialogue-row ${escapeHtml(row.role)}">
|
||||
<strong>${row.role === "user" ? "你" : "Search Agent"}</strong>
|
||||
<div>${escapeHtml(row.content)}</div>
|
||||
</div>
|
||||
`,
|
||||
)
|
||||
.join("")}
|
||||
</div>
|
||||
`;
|
||||
}
|
||||
|
||||
function renderSearchResult(result) {
|
||||
setReaderMode(false);
|
||||
state.view = "search";
|
||||
setModeButtons("search");
|
||||
elements.lensEyebrow.textContent = "Search Agent";
|
||||
elements.lensTitle.textContent = result.query;
|
||||
const summary = result.summary || {};
|
||||
elements.lensBody.innerHTML = `
|
||||
<section class="section">
|
||||
<div class="ai-box search-brief">
|
||||
<h3>${escapeHtml(summary.model || "Search Agent")}</h3>
|
||||
<div class="ai-output">${escapeHtml(summary.text || "")}</div>
|
||||
</div>
|
||||
</section>
|
||||
${searchAggregationView(result.aggregation)}
|
||||
${searchPlanView(result.plan)}
|
||||
<section class="section">
|
||||
<h3>继续追问</h3>
|
||||
${followupList()}
|
||||
<div class="followup-row">
|
||||
<input id="searchFollowupInput" type="text" placeholder="基于当前结果继续问，比如：只看最近的 / 给阅读顺序" />
|
||||
<button class="tool-button primary" type="button" data-search-followup>Ask</button>
|
||||
</div>
|
||||
</section>
|
||||
<section class="section">
|
||||
<h3>相关论文</h3>
|
||||
${compactPaperList(result.items || [], 60)}
|
||||
</section>
|
||||
`;
|
||||
}
|
||||
|
||||
function renderOverview(aiHtml = "") {
|
||||
setReaderMode(false);
|
||||
state.view = "atlas";
|
||||
@@ -681,32 +853,85 @@ function renderRoute(routeId) {
|
||||
async function runSearch() {
|
||||
const q = elements.searchInput.value.trim();
|
||||
if (!q) {
|
||||
renderOverview();
|
||||
renderSearchHome();
|
||||
return;
|
||||
}
|
||||
const scopeTopic = state.topicFocus && state.view === "atlas" ? state.activeTopic : "";
|
||||
setReaderMode(false);
|
||||
state.view = "search";
|
||||
setModeButtons("search");
|
||||
elements.lensEyebrow.textContent = "Search";
|
||||
elements.lensTitle.textContent = q;
|
||||
elements.lensBody.innerHTML = `<div class="empty-state">searching</div>`;
|
||||
const params = new URLSearchParams({ q, limit: "60", sort: "score" });
|
||||
if (state.activeTopic) params.set("topic", state.activeTopic);
|
||||
const payload = await api(`/api/papers?${params.toString()}`);
|
||||
state.searchItems = payload.items;
|
||||
state.searchTotal = payload.total;
|
||||
elements.lensBody.innerHTML = `
|
||||
<section class="section">
|
||||
${statGrid([
|
||||
{ value: payload.total, label: "matches" },
|
||||
{ value: state.activeTopic ? topicLabel(state.activeTopic) : "all", label: "scope" },
|
||||
{ value: "score", label: "sort" },
|
||||
])}
|
||||
</section>
|
||||
<section class="section">
|
||||
${paperList(payload.items, payload.items.length)}
|
||||
</section>
|
||||
`;
|
||||
state.searchFollowups = [];
|
||||
state.searchResult = null;
|
||||
const starter = {
|
||||
query: q,
|
||||
steps: [
|
||||
{ label: "理解问题", status: "running", detail: "准备启动 Search Agent" },
|
||||
{ label: "扩展关键词", status: "pending", detail: "" },
|
||||
{ label: "检索论文库", status: "pending", detail: "" },
|
||||
{ label: "统计主题/趋势", status: "pending", detail: "" },
|
||||
{ label: "生成搜索结论", status: "pending", detail: "" },
|
||||
],
|
||||
};
|
||||
renderSearchRunning(starter);
|
||||
try {
|
||||
const payload = await api("/api/search/agent", {
|
||||
method: "POST",
|
||||
body: JSON.stringify({ query: q, topic: scopeTopic }),
|
||||
});
|
||||
state.searchJobId = payload.job.id;
|
||||
renderSearchRunning(payload.job);
|
||||
pollSearchJob(payload.job.id);
|
||||
} catch (error) {
|
||||
renderSearchError(error.message);
|
||||
}
|
||||
}
|
||||
|
||||
async function pollSearchJob(jobId) {
|
||||
window.clearTimeout(state.searchPollTimer);
|
||||
try {
|
||||
const payload = await api(`/api/search/agent?id=${encodeURIComponent(jobId)}`);
|
||||
const job = payload.job;
|
||||
if (job.status === "done") {
|
||||
state.searchResult = job.result;
|
||||
state.searchItems = job.result.items || [];
|
||||
state.searchTotal = job.result.total || 0;
|
||||
renderSearchResult(job.result);
|
||||
return;
|
||||
}
|
||||
if (job.status === "error") {
|
||||
renderSearchError(job);
|
||||
return;
|
||||
}
|
||||
renderSearchRunning(job);
|
||||
state.searchPollTimer = window.setTimeout(() => pollSearchJob(jobId), 900);
|
||||
} catch (error) {
|
||||
renderSearchError(error.message);
|
||||
}
|
||||
}
|
||||
|
||||
async function runSearchFollowup() {
|
||||
const input = $("#searchFollowupInput");
|
||||
const question = input?.value.trim();
|
||||
if (!question || !state.searchJobId) return;
|
||||
state.searchFollowups.push({ role: "user", content: question });
|
||||
state.searchFollowups.push({ role: "assistant", content: "thinking..." });
|
||||
renderSearchResult(state.searchResult);
|
||||
try {
|
||||
const payload = await api("/api/search/followup", {
|
||||
method: "POST",
|
||||
body: JSON.stringify({ job_id: state.searchJobId, question }),
|
||||
});
|
||||
state.searchFollowups[state.searchFollowups.length - 1] = {
|
||||
role: "assistant",
|
||||
content: `${payload.answer}\n\n${payload.model} · ${payload.duration_seconds}s`,
|
||||
};
|
||||
} catch (error) {
|
||||
state.searchFollowups[state.searchFollowups.length - 1] = {
|
||||
role: "assistant",
|
||||
content: error.message,
|
||||
};
|
||||
}
|
||||
renderSearchResult(state.searchResult);
|
||||
}
|
||||
|
||||
function renderReading() {
|
||||
@@ -755,6 +980,7 @@ function renderPaperReader(aiHtml = "", abstractHtml = "") {
|
||||
const paper = state.selectedPaper;
|
||||
if (!paper) return;
|
||||
setReaderMode(true);
|
||||
const chatRows = state.paperChatHistory[paper.id] || [];
|
||||
elements.lensEyebrow.textContent = "Paper";
|
||||
elements.lensTitle.textContent = "研读";
|
||||
elements.lensBody.innerHTML = `
|
||||
@@ -784,6 +1010,26 @@ function renderPaperReader(aiHtml = "", abstractHtml = "") {
|
||||
<section class="section" id="paperAbstract" style="${abstractHtml ? "" : "display:none"}">${abstractHtml}</section>
|
||||
<section class="section" id="paperAi" style="${aiHtml ? "" : "display:none"}">${aiHtml}</section>
|
||||
|
||||
<section class="section">
|
||||
<h3>继续研读</h3>
|
||||
<div class="dialogue-list paper-chat-list">
|
||||
${chatRows
|
||||
.map(
|
||||
(row) => `
|
||||
<div class="dialogue-row ${escapeHtml(row.role)}">
|
||||
<strong>${row.role === "user" ? "你" : "Paper Agent"}</strong>
|
||||
<div>${escapeHtml(row.content)}</div>
|
||||
</div>
|
||||
`,
|
||||
)
|
||||
.join("")}
|
||||
</div>
|
||||
<div class="followup-row">
|
||||
<input id="paperChatInput" type="text" placeholder="继续问这篇论文，比如：实验设计有什么问题？和工程实践有什么关系？" />
|
||||
<button class="tool-button primary" type="button" data-paper-chat>Ask</button>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section class="section">
|
||||
<h3>本地卡片</h3>
|
||||
<div class="reader-box markdown-preview">${renderMarkdown(state.selectedMarkdown)}</div>
|
||||
@@ -808,6 +1054,39 @@ async function loadAbstract() {
|
||||
}
|
||||
}
|
||||
|
||||
async function runPaperChat() {
|
||||
if (!state.selectedPaper) return;
|
||||
const input = $("#paperChatInput");
|
||||
const question = input?.value.trim();
|
||||
if (!question) return;
|
||||
const paperId = state.selectedPaper.id;
|
||||
const history = state.paperChatHistory[paperId] || [];
|
||||
history.push({ role: "user", content: question });
|
||||
history.push({ role: "assistant", content: "thinking..." });
|
||||
state.paperChatHistory[paperId] = history;
|
||||
const aiHtml = $("#paperAi")?.innerHTML || "";
|
||||
const abstractHtml = $("#paperAbstract")?.innerHTML || "";
|
||||
renderPaperReader(aiHtml, abstractHtml);
|
||||
try {
|
||||
const payload = await api("/api/paper/chat", {
|
||||
method: "POST",
|
||||
body: JSON.stringify({
|
||||
id: paperId,
|
||||
question,
|
||||
history: history.slice(0, -1),
|
||||
}),
|
||||
});
|
||||
history[history.length - 1] = {
|
||||
role: "assistant",
|
||||
content: `${payload.answer}\n\n${payload.model} · ${payload.duration_seconds}s`,
|
||||
};
|
||||
} catch (error) {
|
||||
history[history.length - 1] = { role: "assistant", content: error.message };
|
||||
}
|
||||
state.paperChatHistory[paperId] = history;
|
||||
renderPaperReader($("#paperAi")?.innerHTML || aiHtml, $("#paperAbstract")?.innerHTML || abstractHtml);
|
||||
}
|
||||
|
||||
function setAiBusy(isBusy, label = "") {
|
||||
state.aiBusy = isBusy;
|
||||
document.querySelectorAll(".tool-button, .quiet-button").forEach((button) => {
|
||||
@@ -904,6 +1183,9 @@ function bindEvents() {
|
||||
const compareButton = event.target.closest("[data-compare-topic]");
|
||||
const paperAction = event.target.closest("[data-paper-action]");
|
||||
const readerAction = event.target.closest("[data-reader-action]");
|
||||
const searchFollowup = event.target.closest("[data-search-followup]");
|
||||
const paperChat = event.target.closest("[data-paper-chat]");
|
||||
const searchExample = event.target.closest("[data-search-example]");
|
||||
|
||||
if (topicButton) {
|
||||
await selectTopic(topicButton.dataset.topic);
|
||||
@@ -926,16 +1208,28 @@ function bindEvents() {
|
||||
} else if (readerAction) {
|
||||
if (state.topicFocus) renderTopic(state.topicFocus);
|
||||
else renderOverview();
|
||||
} else if (searchFollowup) {
|
||||
await runSearchFollowup();
|
||||
} else if (paperChat) {
|
||||
await runPaperChat();
|
||||
} else if (searchExample) {
|
||||
elements.searchInput.value = searchExample.dataset.searchExample || "";
|
||||
await runSearch();
|
||||
}
|
||||
});
|
||||
elements.lensBody.addEventListener("keydown", async (event) => {
|
||||
if (event.key === "Enter" && event.target.closest("#searchFollowupInput")) {
|
||||
await runSearchFollowup();
|
||||
}
|
||||
if (event.key === "Enter" && event.target.closest("#paperChatInput")) {
|
||||
await runPaperChat();
|
||||
}
|
||||
});
|
||||
|
||||
elements.searchBtn.addEventListener("click", runSearch);
|
||||
elements.searchInput.addEventListener(
|
||||
"input",
|
||||
debounce(() => {
|
||||
if (elements.searchInput.value.trim()) runSearch();
|
||||
}),
|
||||
);
|
||||
elements.searchInput.addEventListener("keydown", (event) => {
|
||||
if (event.key === "Enter") runSearch();
|
||||
});
|
||||
|
||||
elements.explainAtlasBtn.addEventListener("click", () => runAtlasAi("atlas", state.activeTopic));
|
||||
elements.readingPathBtn.addEventListener("click", () => runAtlasAi("path", state.activeTopic));
|
||||
|
||||
@@ -5,7 +5,7 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
<title>Agent Knowledge Atlas</title>
|
||||
<link rel="icon" href="data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 64 64'%3E%3Crect width='64' height='64' rx='12' fill='%23cfe8e3'/%3E%3Ctext x='32' y='40' text-anchor='middle' font-size='24' font-family='Arial' font-weight='700' fill='%23064c47'%3EAK%3C/text%3E%3C/svg%3E" />
|
||||
<link rel="stylesheet" href="/static/styles.css?v=atlas-20260708-8" />
|
||||
<link rel="stylesheet" href="/static/styles.css?v=atlas-20260708-10" />
|
||||
</head>
|
||||
<body>
|
||||
<main class="atlas-app">
|
||||
@@ -79,6 +79,6 @@
|
||||
</footer>
|
||||
</main>
|
||||
|
||||
<script src="/static/app.js?v=atlas-20260708-8"></script>
|
||||
<script src="/static/app.js?v=atlas-20260708-10"></script>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@@ -521,6 +521,132 @@ button {
|
||||
padding: 3px 6px;
|
||||
}
|
||||
|
||||
.agent-steps {
|
||||
display: grid;
|
||||
gap: 10px;
|
||||
}
|
||||
|
||||
.agent-step {
|
||||
display: grid;
|
||||
grid-template-columns: 13px 1fr;
|
||||
gap: 10px;
|
||||
align-items: start;
|
||||
color: var(--muted);
|
||||
}
|
||||
|
||||
.step-dot {
|
||||
width: 9px;
|
||||
height: 9px;
|
||||
margin-top: 6px;
|
||||
border: 1px solid #aab3ad;
|
||||
border-radius: 50%;
|
||||
background: #ffffff;
|
||||
}
|
||||
|
||||
.agent-step strong {
|
||||
display: block;
|
||||
color: #17201d;
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
.agent-step span:last-child {
|
||||
display: block;
|
||||
margin-top: 2px;
|
||||
font-size: 12px;
|
||||
line-height: 1.42;
|
||||
}
|
||||
|
||||
.agent-step.running .step-dot {
|
||||
border-color: var(--teal);
|
||||
background: var(--teal);
|
||||
box-shadow: 0 0 0 5px rgba(13, 118, 111, 0.12);
|
||||
}
|
||||
|
||||
.agent-step.done .step-dot {
|
||||
border-color: var(--green);
|
||||
background: var(--green);
|
||||
}
|
||||
|
||||
.agent-step.error .step-dot {
|
||||
border-color: var(--coral);
|
||||
background: var(--coral);
|
||||
}
|
||||
|
||||
.search-brief h3 {
|
||||
margin: 0 0 8px;
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
.compact-section {
|
||||
display: grid;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.mini-chart-row,
|
||||
.search-topic-row {
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
.month-chip {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
min-height: 24px;
|
||||
margin: 0 6px 6px 0;
|
||||
border: 1px solid var(--line);
|
||||
border-radius: 5px;
|
||||
background: #ffffff;
|
||||
padding: 0 7px;
|
||||
color: #4c5550;
|
||||
font-size: 11px;
|
||||
font-weight: 720;
|
||||
}
|
||||
|
||||
.followup-row {
|
||||
display: grid;
|
||||
grid-template-columns: minmax(0, 1fr) auto;
|
||||
gap: 8px;
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
.followup-row input {
|
||||
min-width: 0;
|
||||
min-height: 36px;
|
||||
border: 1px solid var(--line);
|
||||
border-radius: 7px;
|
||||
background: #ffffff;
|
||||
padding: 0 10px;
|
||||
outline: 0;
|
||||
}
|
||||
|
||||
.dialogue-list {
|
||||
display: grid;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.dialogue-row {
|
||||
border: 1px solid var(--line);
|
||||
border-radius: 8px;
|
||||
background: #ffffff;
|
||||
padding: 10px;
|
||||
}
|
||||
|
||||
.dialogue-row.user {
|
||||
background: #f4f8f6;
|
||||
}
|
||||
|
||||
.dialogue-row strong {
|
||||
display: block;
|
||||
margin-bottom: 4px;
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.dialogue-row div {
|
||||
white-space: pre-wrap;
|
||||
color: #27312d;
|
||||
font-size: 13px;
|
||||
line-height: 1.58;
|
||||
}
|
||||
|
||||
.ai-box,
|
||||
.reader-box {
|
||||
border: 1px solid var(--line);
|
||||
|
||||
Reference in New Issue
Block a user