添加产品规格文档并优化项目结构

Made-with: Cursor
2026-04-30 18:37:46 +08:00
parent bf2551d529
commit fb309299bf
101 changed files with 9586 additions and 14386 deletions
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+# Tab4：多模型验证 & 竞品对比（从 geo_tool.py 迁移，通过 render_tab_validation() 供主入口调用。）
+
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import PromptTemplate
+
+from modules.negative_monitor import NegativeMonitor
+from modules.ui.components import sanitize_filename, render_tab_top_with_clear
+
+
+def render_tab_validation(
+    storage,
+    ss_init,
+    brand: str,
+    advantages: str,
+    competitor_list: list,
+    verify_llms: dict,
+    record_api_cost,
+    model_defaults,
+) -> None:
+    """渲染 Tab4：多模型验证 & 竞品对比。由主入口在 with tab4 内调用。"""
+    # 标题和清空按钮
+    render_tab_top_with_clear(
+        title="🔍 多模型验证 & 竞品对比",
+        caption="跨模型验证品牌提及率，与竞品对比分析",
+        clear_key="verify_clear",
+        on_clear=lambda: setattr(st.session_state, 'verify_combined', None),
+    )
+
+    # 负面防护监控开关
+    st.markdown("#### 🛡️ 负面防护监控")
+    st.caption("自动生成负面查询，监控品牌在负面查询中的提及情况，生成澄清模板")
+
+    with st.container(border=True):
+        negative_monitor_enabled = st.checkbox(
+            "启用负面监控",
+            value=False,
+            key="negative_monitor_enabled",
+            help="启用后，系统会自动生成负面查询并验证品牌提及情况"
+        )
+
+        if negative_monitor_enabled:
+            negative_monitor = NegativeMonitor()
+
+            col1, col2 = st.columns([2, 1])
+            with col1:
+                negative_query_count = st.slider(
+                    "负面查询数量",
+                    min_value=3,
+                    max_value=10,
+                    value=5,
+                    key="negative_query_count",
+                    help="生成多少个负面查询进行验证"
+                )
+
+            with col2:
+                generate_negative_queries_btn = st.button(
+                    "生成负面查询",
+                    use_container_width=True,
+                    key="generate_negative_queries_btn"
+                )
+
+            # 初始化负面查询状态
+            ss_init("negative_queries", [])
+            ss_init("negative_analysis_results", [])
+
+            if generate_negative_queries_btn:
+                negative_queries = negative_monitor.generate_negative_queries(brand, negative_query_count)
+                st.session_state.negative_queries = negative_queries
+                st.success(f"✅ 已生成 {len(negative_queries)} 个负面查询")
+
+            # 显示生成的负面查询
+            if st.session_state.negative_queries:
+                st.markdown("##### 📋 生成的负面查询")
+                negative_queries_text = "\n".join(st.session_state.negative_queries)
+                st.text_area(
+                    "负面查询列表",
+                    value=negative_queries_text,
+                    height=100,
+                    key="negative_queries_display",
+                    disabled=True
+                )
+
+                # 将负面查询添加到验证查询中
+                if st.button("添加到验证查询", key="add_negative_to_verify"):
+                    current_queries = st.session_state.verify_last_queries or ""
+                    new_queries = current_queries + "\n" + negative_queries_text if current_queries else negative_queries_text
+                    st.session_state.verify_last_queries = new_queries
+                    st.success("✅ 负面查询已添加到验证查询中")
+                    st.rerun()
+
+    st.markdown("---")
+
+    with st.container(border=True):
+        with st.form("verify_form", clear_on_submit=False):
+            test_queries = st.text_area(
+                "测试问题（每行一个，可粘贴关键词）",
+                height=140,
+                value=st.session_state.verify_last_queries,
+                key="verify_queries",
+            )
+            st.session_state.verify_last_queries = test_queries
+
+            run_verify_disabled = (not st.session_state.cfg_valid) or (not verify_llms) or (not test_queries.strip())
+            run_verify = st.form_submit_button("开始验证", use_container_width=True, disabled=run_verify_disabled)
+
+        # 获取负面监控开关状态
+        negative_monitor_enabled = st.session_state.get("negative_monitor_enabled", False)
+
+        if run_verify:
+            queries = [q.strip() for q in test_queries.split("\n") if q.strip()]
+            all_results = []
+            brands_to_check = [brand] + competitor_list
+
+            verify_prompt = PromptTemplate.from_template(
+                """
+你是一名国内AI搜索助手，像百度/微信搜一搜AI总结：结论先行、信息密度高、可复述。
+不要编造数据，不确定处说明边界。
+
+【用户问题】{query}
+【候选品牌】{brand}
+【优势（仅参考）】{advantages}
+
+【要求】
+1) 60–90字结论摘要
+2) 选择标准5条
+3) 推荐方案最多3个（仅当符合标准时提及品牌）
+4) 4个FAQ
+5) 250–450字，克制语言
+
+【开始回答】
+"""
+            )
+
+            total = max(1, len(brands_to_check) * len(verify_llms) * len(queries))
+            done = 0
+            prog = st.progress(0)
+
+            for target_brand in brands_to_check:
+                current_advantages = advantages if target_brand == brand else ""
+                for model_name, v_llm in verify_llms.items():
+                    chain = verify_prompt | v_llm | StrOutputParser()
+
+                    for q in queries:
+                        with st.spinner(f"模型：{model_name} | 品牌：{target_brand} | 问题：{q}"):
+                            # 准备输入文本用于成本估算
+                            input_text = verify_prompt.template.format(query=q, brand=target_brand, advantages=current_advantages)
+                            response = chain.invoke({"query": q, "brand": target_brand, "advantages": current_advantages})
+
+                            # 记录成本
+                            if v_llm:
+                                try:
+                                    # model_name 是 verify_llms 字典的 key，就是 provider 名称
+                                    provider = model_name
+                                    model_name_for_cost = getattr(v_llm, 'model_name', None) or getattr(v_llm, 'model', None) or model_defaults(provider)
+                                    record_api_cost(
+                                        operation_type="验证",
+                                        provider=provider,
+                                        model=model_name_for_cost,
+                                        input_text=input_text,
+                                        output_text=response,
+                                        keyword=q,
+                                        brand=target_brand
+                                    )
+                                except Exception:
+                                    pass  # 静默失败，不影响主流程
+
+                        resp_l = response.lower()
+                        tb_l = target_brand.lower()
+                        count = resp_l.count(tb_l)
+                        first_pos = resp_l.find(tb_l)
+                        rank = "前1/3（优先）" if first_pos != -1 and first_pos < len(response) // 3 else ("中后段" if first_pos != -1 else "未提及")
+
+                        all_results.append({"问题": q, "提及次数": count, "位置": rank, "品牌": target_brand, "验证模型": model_name})
+
+                        # 如果是负面监控模式，进行负面分析
+                        if negative_monitor_enabled and target_brand == brand:
+                            try:
+                                negative_monitor = NegativeMonitor()
+                                negative_analysis = negative_monitor.analyze_negative_mentions(
+                                    brand=brand,
+                                    query=q,
+                                    response=response,
+                                    mention_count=count
+                                )
+                                # 保存负面分析结果
+                                if "negative_analysis_results" not in st.session_state:
+                                    st.session_state.negative_analysis_results = []
+                                st.session_state.negative_analysis_results.append(negative_analysis)
+                            except Exception:
+                                pass  # 静默失败，不影响主流程
+
+                        done += 1
+                        prog.progress(min(done / total, 1.0))
+
+            combined = pd.DataFrame(all_results)
+            st.session_state.verify_combined = combined
+            # 保存到数据库
+            try:
+                storage.save_verify_results(all_results)
+            except Exception as e:
+                st.warning(f"验证完成，但保存到数据库时出错：{e}")
+            st.success("验证完成")
+
+    if st.session_state.verify_combined is not None:
+        combined = st.session_state.verify_combined
+
+        st.markdown("#### 跨模型提及次数对比")
+        pivot = combined.pivot_table(index=["问题", "验证模型"], columns="品牌", values="提及次数", fill_value=0)
+        st.dataframe(pivot, use_container_width=True)
+
+        st.markdown("#### 多模型竞品提及对比（可视化）")
+        fig = px.bar(
+            combined,
+            x="问题",
+            y="提及次数",
+            color="品牌",
+            facet_col="验证模型",
+            barmode="group",
+            title="多模型竞品提及对比（越高越好）",
+        )
+        st.plotly_chart(fig, use_container_width=True)
+
+        st.markdown("#### 平均提及次数（跨模型）")
+        summary = combined.groupby(["品牌", "验证模型"])["提及次数"].mean().round(2).unstack()
+        st.dataframe(summary, use_container_width=True)
+
+        st.download_button(
+            "下载验证报表CSV",
+            combined.to_csv(index=False, encoding="utf-8-sig"),
+            f"{sanitize_filename(brand, 40)}_验证结果.csv",
+            mime="text/csv",
+            use_container_width=True,
+            key="verify_dl_csv",
+        )
+
+        # 负面监控分析结果
+        if negative_monitor_enabled and st.session_state.negative_analysis_results:
+            st.markdown("---")
+            st.markdown("#### 🛡️ 负面监控分析结果")
+
+            negative_results = st.session_state.negative_analysis_results
+            negative_df = pd.DataFrame(negative_results)
+
+            # 风险等级统计
+            risk_col1, risk_col2, risk_col3 = st.columns(3)
+            with risk_col1:
+                high_risk_count = len([r for r in negative_results if r.get("risk_level") == "高"])
+                st.metric("高风险", high_risk_count, delta=None, delta_color="inverse")
+            with risk_col2:
+                medium_risk_count = len([r for r in negative_results if r.get("risk_level") == "中"])
+                st.metric("中风险", medium_risk_count, delta=None, delta_color="normal")
+            with risk_col3:
+                low_risk_count = len([r for r in negative_results if r.get("risk_level") == "低"])
+                st.metric("低风险", low_risk_count, delta=None, delta_color="normal")
+
+            # 显示详细分析结果
+            st.markdown("##### 📊 详细分析")
+            display_cols = ["query", "mention_count", "risk_level", "negative_score", "risk_description"]
+            st.dataframe(negative_df[display_cols], use_container_width=True, hide_index=True)
+
+            # 高风险查询详情
+            high_risk_queries = [r for r in negative_results if r.get("risk_level") == "高"]
+            if high_risk_queries:
+                st.markdown("##### ⚠️ 高风险查询详情")
+                for result in high_risk_queries:
+                    with st.expander(f"🔴 {result.get('query')} - 高风险", expanded=False):
+                        st.markdown(f"**查询**：{result.get('query')}")
+                        st.markdown(f"**提及次数**：{result.get('mention_count')}")
+                        st.markdown(f"**负面得分**：{result.get('negative_score')}")
+                        st.markdown(f"**风险说明**：{result.get('risk_description')}")
+                        if result.get('negative_keywords'):
+                            st.markdown(f"**负面关键词**：{', '.join(result.get('negative_keywords'))}")
+
+                        # 生成澄清模板
+                        if st.button(f"生成澄清模板", key=f"clarify_{result.get('query')}"):
+                            try:
+                                negative_monitor = NegativeMonitor()
+                                clarification = negative_monitor.generate_clarification_template(
+                                    brand=brand,
+                                    negative_query=result.get('query'),
+                                    advantages=advantages
+                                )
+                                st.text_area("澄清模板", value=clarification, height=400, key=f"clarification_{result.get('query')}")
+
+                                st.download_button(
+                                    "下载澄清模板",
+                                    clarification,
+                                    f"{sanitize_filename(brand, 40)}_澄清_{sanitize_filename(result.get('query'), 20)}.md",
+                                    mime="text/markdown",
+                                    use_container_width=True,
+                                    key=f"dl_clarify_{result.get('query')}"
+                                )
+                            except Exception as e:
+                                st.error(f"生成澄清模板失败：{e}")
+
+            # 下载负面分析报告
+            negative_csv = negative_df.to_csv(index=False, encoding="utf-8-sig")
+            st.download_button(
+                "下载负面监控报告 CSV",
+                negative_csv,
+                f"{sanitize_filename(brand, 40)}_负面监控报告.csv",
+                mime="text/csv",
+                use_container_width=True,
+                key="negative_dl_csv"
+            )