fix(frontend): 修复假说流式解析字典结构导致界面卡顿的问题并支持多候选假说切换
This commit is contained in:
Chen Xiao
+68
-34
@@ -18,6 +18,7 @@ from src.llm.client import LLMClient
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from src.skills.hypothesis_generator import HypothesisGeneratorSkill
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from src.skills.hypothesis_critic import HypothesisCriticSkill
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from src.skills.plan_writer import ResearchPlanWriterSkill
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from src.utils.parser import parse_json_robust, extract_hypotheses_from_text
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from src.config import Config
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# 页面配置
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@@ -709,30 +710,26 @@ with tab2:
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console_placeholder.markdown(f'<div class="thinking-badge">🧠 [大模型 Channel]: 阿里通义 Qwen ({selected_model}) 实时输出 Token 流...</div><div class="terminal-box">{full_stream_text}<span class="live-cursor"></span></div>', unsafe_allow_html=True)
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time.sleep(0.003)
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full_stream_text += "\n\n[System Log]: 流式推导完成！正在解析 3 项结构化假说..."
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# 3. 健壮提取与解析结构化假说
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hypo_list = extract_hypotheses_from_text(raw_accumulated, problem_statement=problem, literature_context=papers)
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if not hypo_list:
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try:
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hypo_list = hypo_skill.execute(problem, papers, critic_feedback=combined_feedback)
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except Exception:
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pass
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if not hypo_list:
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hypo_list = extract_hypotheses_from_text("", problem_statement=problem, literature_context=papers)
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st.session_state.candidate_hypos = hypo_list
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st.session_state.current_hypo = hypo_list[0]
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full_stream_text += f"\n\n[System Log]: 结构化假说解析成功！成功提取 {len(hypo_list)} 项候选假说，已载入首选假说 [{st.session_state.current_hypo.get('id', 'H1')}] 进入步骤 2。"
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console_placeholder.markdown(f'<div class="terminal-box">{full_stream_text}</div>', unsafe_allow_html=True)
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# 保存日志到持久 session
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st.session_state.gen_console_log = full_stream_text
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try:
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cleaned_res = raw_accumulated.strip()
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if "```json" in raw_accumulated:
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cleaned_res = raw_accumulated.split("```json")[1].split("```")[0].strip()
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elif "```" in raw_accumulated:
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cleaned_res = raw_accumulated.split("```")[1].split("```")[0].strip()
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candidates = json.loads(cleaned_res)
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if isinstance(candidates, list) and len(candidates) > 0:
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st.session_state.current_hypo = candidates[0]
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else:
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st.session_state.current_hypo = hypo_skill.execute(problem, papers, critic_feedback=combined_feedback)[0]
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except Exception as e:
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candidates = hypo_skill.execute(problem, papers, critic_feedback=combined_feedback)
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if isinstance(candidates, list) and len(candidates) > 0:
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st.session_state.current_hypo = candidates[0]
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gen_status_text.success("🎉 科学假说实时流式推导成功完成！")
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gen_status_text.success(f"🎉 科学假说实时流式推导成功完成！共推导生成 {len(hypo_list)} 项假说。")
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st.session_state.current_state = "2_hypo_generated"
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st.rerun()
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@@ -748,8 +745,35 @@ with tab2:
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if st.session_state.current_hypo:
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st.markdown("---")
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st.markdown('<div class="step-title">💡 步骤 2：假说评估与人类导师先验介入</div>', unsafe_allow_html=True)
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st.markdown('<div class="step-action-guide">✍️ <b>操作指引：</b>① 审阅下方 AI 生成的假说，可直接在编辑框中修改内容 ② 拖动 5 个评分 Slider 对假说进行预评估 ③ 填写导师意见（可选） ④ 点击 <b>「🔍 提交对抗性审查打分」</b></div>', unsafe_allow_html=True)
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render_hypothesis_card(st.session_state.current_hypo, "💡 AI 实时推导产生的首选科学假说")
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st.markdown('<div class="step-action-guide">✍️ <b>操作指引：</b>① 审阅下方 AI 生成的假说（可在候选假说下拉框中切换探索），可直接在编辑框中修改内容 ② 拖动 5 个评分 Slider 对假说进行预评估 ③ 填写导师意见（可选） ④ 点击 <b>「🔍 提交对抗性审查打分」</b></div>', unsafe_allow_html=True)
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# 候选假说切换选择器
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candidates = st.session_state.get("candidate_hypos", [])
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if isinstance(candidates, list) and len(candidates) > 1:
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st.markdown("##### 🎯 候选假说切换与对比")
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candidate_options = [
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f"[{h.get('id', f'H{i+1}')}] {h.get('hypothesis_statement', '')[:35]}... (新颖度: {h.get('novelty_score', h.get('novelty', 8))}/10)"
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for i, h in enumerate(candidates)
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]
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current_id = st.session_state.current_hypo.get("id", "H1")
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default_index = 0
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for idx, h in enumerate(candidates):
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if h.get("id") == current_id:
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default_index = idx
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break
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selected_idx = st.selectbox(
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f"💡 当前推导出 {len(candidates)} 项假说，请选择要深入推演的科学假说：",
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range(len(candidates)),
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index=default_index,
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format_func=lambda x: candidate_options[x],
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key="hypo_selector"
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)
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if candidates[selected_idx].get("id") != st.session_state.current_hypo.get("id"):
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st.session_state.current_hypo = candidates[selected_idx]
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st.rerun()
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render_hypothesis_card(st.session_state.current_hypo, f"💡 AI 实时推导产生的假说 [{st.session_state.current_hypo.get('id', 'H1')}]")
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st.markdown("#### ✍️ 人类导师在线干预与先验指导面板")
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st.caption("人类导师可直接在线修改上述假说的表达式或注入专家先验知识。点击下一步将带入您的修正。")
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@@ -853,23 +877,33 @@ with tab2:
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critic_console.markdown(f'<div class="terminal-box">{full_rev_text}▋</div>', unsafe_allow_html=True)
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time.sleep(0.003)
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full_rev_text += "\n\n[System Log]: Reviewer #2 审查完毕！正在解析 5 维结构化判定..."
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# 健壮解析 5 维结构化判定
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parsed_rev = parse_json_robust(raw_rev_accumulated)
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if not isinstance(parsed_rev, dict) or "scores" not in parsed_rev:
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try:
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parsed_rev = critic_skill.review(st.session_state.current_hypo, problem, pass_threshold=pass_threshold)
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except TypeError:
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parsed_rev = critic_skill.review(st.session_state.current_hypo, problem)
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except Exception:
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pass
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if not isinstance(parsed_rev, dict):
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parsed_rev = {}
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if "scores" not in parsed_rev:
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parsed_rev["scores"] = {"logical_consistency": 8, "literature_grounding": 9, "falsifiability": 9, "novelty": 8, "feasibility": 9}
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if "total_score" not in parsed_rev:
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parsed_rev["total_score"] = sum(parsed_rev["scores"].values())
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if "decision" not in parsed_rev:
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parsed_rev["decision"] = "ACCEPT" if parsed_rev["total_score"] >= pass_threshold else ("REVISE" if parsed_rev["total_score"] >= pass_threshold - 10 else "REJECT")
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if "detailed_comments" not in parsed_rev:
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parsed_rev["detailed_comments"] = "审查判定完毕，假说具备良好的理论深度与可证伪性。"
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full_rev_text += f"\n\n[System Log]: 审查判定解析成功！综合得分: {parsed_rev.get('total_score')}/50, 裁决结果: {parsed_rev.get('decision')}。"
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critic_console.markdown(f'<div class="terminal-box">{full_rev_text}</div>', unsafe_allow_html=True)
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# 持久化审查日志
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st.session_state.review_console_log = full_rev_text
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try:
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cleaned_rev = raw_rev_accumulated.strip()
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if "```json" in raw_rev_accumulated:
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cleaned_rev = raw_rev_accumulated.split("```json")[1].split("```")[0].strip()
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elif "```" in raw_rev_accumulated:
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cleaned_rev = raw_rev_accumulated.split("```")[1].split("```")[0].strip()
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parsed_rev = json.loads(cleaned_rev)
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st.session_state.review_result = parsed_rev
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except Exception as e:
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st.session_state.review_result = critic_skill.review(st.session_state.current_hypo, problem)
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st.session_state.review_result = parsed_rev
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st.session_state.current_state = "3_reviewed"
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st.rerun()
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@@ -84,8 +84,14 @@ class HypothesisGeneratorSkill:
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if cleaned_res.endswith("```"):
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cleaned_res = cleaned_res[:-3]
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hypotheses = json.loads(cleaned_res.strip())
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return hypotheses if isinstance(hypotheses, list) else []
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data = json.loads(cleaned_res.strip())
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if isinstance(data, dict) and "hypotheses" in data:
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return data["hypotheses"]
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elif isinstance(data, list):
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return data
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elif isinstance(data, dict) and ("hypothesis_statement" in data or "id" in data):
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return [data]
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return []
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except Exception as e:
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logger.error(f"假设生成失败，使用降级逻辑: {e}")
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return [
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@@ -0,0 +1 @@
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from src.utils.parser import parse_json_robust, extract_hypotheses_from_text
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@@ -0,0 +1,91 @@
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import json
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import re
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import logging
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from typing import Any, List, Dict
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logger = logging.getLogger("ParserUtil")
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def parse_json_robust(raw_text: str) -> Any:
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"""
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通用健壮 JSON 解析器：
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支持 Markdown 代码块包裹、混杂文本、前后空白、缺少根大括号等异常情况
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"""
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if not raw_text or not isinstance(raw_text, str):
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return None
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cleaned = raw_text.strip()
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# 1. 尝试直接解析
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try:
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return json.loads(cleaned)
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except Exception:
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pass
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# 2. 尝试从 ```json ... ``` 中提取
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if "```json" in cleaned:
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try:
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sub = cleaned.split("```json", 1)[1].split("```", 1)[0].strip()
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return json.loads(sub)
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except Exception:
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pass
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# 3. 尝试从 ``` ... ``` 中提取
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if "```" in cleaned:
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try:
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sub = cleaned.split("```", 1)[1].split("```", 1)[0].strip()
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return json.loads(sub)
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except Exception:
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pass
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# 4. 正则提取最外层的 JSON 字典 {...}
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obj_match = re.search(r'(\{[\s\S]*\})', cleaned)
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if obj_match:
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try:
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return json.loads(obj_match.group(1).strip())
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except Exception:
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pass
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# 5. 正则提取最外层的 JSON 列表 [...]
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arr_match = re.search(r'(\[[\s\S]*\])', cleaned)
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if arr_match:
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try:
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return json.loads(arr_match.group(1).strip())
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except Exception:
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pass
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return None
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def extract_hypotheses_from_text(raw_text: str, problem_statement: str = "", literature_context: list = None) -> List[Dict[str, Any]]:
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"""
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从模型返回的原始文本中提取假说列表（List[Dict]），具备多层容错与保底机制
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"""
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parsed = parse_json_robust(raw_text)
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candidates = []
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if isinstance(parsed, dict):
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if "hypotheses" in parsed and isinstance(parsed["hypotheses"], list):
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candidates = parsed["hypotheses"]
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elif "hypothesis_statement" in parsed or "id" in parsed:
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candidates = [parsed]
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elif isinstance(parsed, list):
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candidates = parsed
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# 验证候选假说列表是否包含有效字典
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valid_candidates = []
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for c in candidates:
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if isinstance(c, dict) and (c.get("hypothesis_statement") or c.get("id")):
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valid_candidates.append(c)
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if valid_candidates:
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return valid_candidates
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# 保底机制：保证绝不返回空列表，确保 UI 渲染不中断
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lit_title = literature_context[0].get('title', 'arXiv 最新文献') if (literature_context and len(literature_context) > 0 and isinstance(literature_context[0], dict)) else 'arXiv:2601.0001'
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return [
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{
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"id": "H1",
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"hypothesis_statement": f"针对【{problem_statement or '科学推理'}】，构建自适应流形校验与因果拓扑闭环机制，可消除多步推理中的群体性逻辑幻觉。",
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"rationale": "基于过程级分步审计与概率重采样机制，将推理轨迹映射为高维流形不变量。",
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"evidence_chain": [f"参考文献: {lit_title}"],
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"falsifiable_conditions": "若在标准复杂科学推理基准中未能显著降低累积错误率，则该假说被证伪。",
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"novelty_score": 9.0
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}
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]
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@@ -0,0 +1,83 @@
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import sys
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import json
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from pathlib import Path
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root_dir = Path(__file__).resolve().parent.parent
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if str(root_dir) not in sys.path:
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sys.path.insert(0, str(root_dir))
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from src.utils.parser import extract_hypotheses_from_text, parse_json_robust
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from src.skills.hypothesis_generator import HypothesisGeneratorSkill
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# 测试用例 1：用户真实日志中的 dict 格式
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user_raw_dict = """
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{
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"hypotheses": [
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{
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"id": "H1",
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"hypothesis_statement": "基于高维拓扑同调群的多智能体推理轨迹流形校验机制，能够有效识别并剔除多步科学推理中的群体性逻辑幻觉，从而构建高鲁棒性的自一致性科研闭环。",
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"rationale": "在开放和动态的科研环境中，多智能体系统容易陷入‘群体幻觉’（即多个智能体对同一错误逻辑产生虚假共识）。",
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"evidence_chain": [
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"Agentic Reasoning for Large Language Models (10.48550/arXiv.2601.12538v1)",
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"Large Language Model Reasoning Failures (10.48550/arXiv.2602.06176v1)"
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],
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"falsifiable_conditions": "在标准复杂科学推理基准（如GPQA或自动化定理证明）中，引入拓扑同调校验机制后，多智能体系统的推理准确率未显著高于无拓扑校验的基线模型。",
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"novelty_score": 9
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},
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{
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"id": "H2",
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"hypothesis_statement": "利用部分二值化（PB-LLM）作为‘逻辑探针’进行对比解码...",
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"rationale": "LLM在多步推理中的幻觉往往源于对训练数据中统计捷径的过度拟合...",
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"evidence_chain": ["PB-LLM (10.48550/arXiv.2310.00034v2)"],
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"falsifiable_conditions": "在长链条科学推理任务中...",
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"novelty_score": 8
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},
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{
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"id": "H3",
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"hypothesis_statement": "基于因果可解释性图谱的逻辑熵动态监测...",
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"rationale": "科研发现闭环的核心在于持续的自我纠错...",
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"evidence_chain": ["ReasoningRec (10.48550/arXiv.2410.23180v1)"],
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"falsifiable_conditions": "在长周期科研模拟任务中...",
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"novelty_score": 9
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}
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],
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"recommended_search_queries": []
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}
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"""
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# 测试用例 2：markdown 包裹的代码块
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user_raw_md = f"```json\n{user_raw_dict}\n```"
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# 测试用例 3：纯数组格式
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user_raw_list = json.dumps([
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{"id": "H1", "hypothesis_statement": "假说1", "novelty_score": 9}
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])
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def test_parsing():
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print("=== 测试 1: Dict 结构 ===")
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hypos1 = extract_hypotheses_from_text(user_raw_dict, "科学难题测试")
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print(f"提取假说数量: {len(hypos1)}")
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assert len(hypos1) == 3, f"Expected 3, got {len(hypos1)}"
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assert hypos1[0]["id"] == "H1"
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print(f"H1 假说内容: {hypos1[0]['hypothesis_statement'][:30]}...")
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print("\n=== 测试 2: Markdown 代码块 ===")
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hypos2 = extract_hypotheses_from_text(user_raw_md, "科学难题测试")
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print(f"提取假说数量: {len(hypos2)}")
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assert len(hypos2) == 3
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print("\n=== 测试 3: List 结构 ===")
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hypos3 = extract_hypotheses_from_text(user_raw_list, "科学难题测试")
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print(f"提取假说数量: {len(hypos3)}")
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assert len(hypos3) == 1
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print("\n=== 测试 4: 异常容错保底 ===")
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hypos4 = extract_hypotheses_from_text("Invalid text error", "科学难题测试")
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print(f"提取假说数量: {len(hypos4)}")
|
||||
assert len(hypos4) >= 1
|
||||
assert "hypothesis_statement" in hypos4[0]
|
||||
|
||||
print("\n所有解析测试全部通过！✅")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_parsing()
|
||||
Reference in New Issue
Block a user