添加产品规格文档并优化项目结构
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刘国栋
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"""
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知识库管理 Tab
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支持上传文档、查看文档列表、搜索测试
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"""
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import streamlit as st
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from modules.knowledge_base import KnowledgeBase, SourceVerifier
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def render_tab_knowledge(kb: KnowledgeBase):
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"""
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渲染知识库管理 Tab
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Args:
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kb: 知识库实例
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"""
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st.markdown("### 📚 品牌知识库")
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st.caption("上传品牌文档、产品手册、案例库，AI 生成内容时将自动检索引用")
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# 统计信息
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stats = kb.get_stats()
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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric("📄 文档数量", stats["total_documents"])
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with col2:
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st.metric("📦 分块数量", stats["total_chunks"])
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with col3:
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doc_types = stats.get("document_types", {})
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st.metric("📋 文档类型", len(doc_types))
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# 主要功能区域
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kb_tab1, kb_tab2, kb_tab3, kb_tab4 = st.tabs([
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"📤 上传文档", "📋 文档列表", "🔍 搜索测试", "📊 来源验证"
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])
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with kb_tab1:
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_render_upload_section(kb)
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with kb_tab2:
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_render_document_list(kb)
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with kb_tab3:
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_render_search_test(kb)
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with kb_tab4:
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_render_source_verifier()
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def _render_upload_section(kb: KnowledgeBase):
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"""渲染上传文档区域"""
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st.markdown("#### 上传新文档")
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# 文档类型选择
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doc_type = st.selectbox(
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"文档类型",
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["text", "faq", "product", "case", "markdown"],
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format_func=lambda x: {
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"text": "📝 通用文本",
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"faq": "❓ FAQ 问答",
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"product": "📦 产品文档",
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"case": "💼 客户案例",
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"markdown": "📑 Markdown 文档"
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}.get(x, x),
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help="选择文档类型有助于更精准的分块和检索"
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)
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# 上传方式选择
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upload_method = st.radio(
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"上传方式",
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["📁 上传文件", "📝 粘贴文本"],
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horizontal=True
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)
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if upload_method == "📁 上传文件":
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uploaded_file = st.file_uploader(
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"选择文件",
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type=["txt", "md", "csv"],
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help="支持 TXT、Markdown、CSV 格式"
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)
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if uploaded_file:
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content = uploaded_file.read().decode("utf-8")
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st.text_area("文件预览", content[:1000] + "..." if len(content) > 1000 else content,
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height=150, disabled=True)
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if st.button("📥 导入知识库", use_container_width=True, type="primary"):
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with st.spinner("正在处理文档..."):
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result = kb.add_document(
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filename=uploaded_file.name,
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content=content,
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doc_type=doc_type
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)
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st.success(f"✅ 文档 '{result['filename']}' 已导入，分为 {result['chunk_count']} 个分块")
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st.rerun()
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else:
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filename = st.text_input("文档名称", placeholder="例如：产品功能说明")
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content = st.text_area("粘贴文档内容", height=300,
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placeholder="粘贴品牌介绍、产品说明、FAQ 等内容...")
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if st.button("📥 导入知识库", use_container_width=True, type="primary"):
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if not filename:
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st.warning("请输入文档名称")
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elif not content.strip():
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st.warning("请输入文档内容")
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else:
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with st.spinner("正在处理文档..."):
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result = kb.add_document(
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filename=filename,
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content=content,
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doc_type=doc_type
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)
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st.success(f"✅ 文档 '{result['filename']}' 已导入，分为 {result['chunk_count']} 个分块")
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st.rerun()
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# 批量导入示例
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with st.expander("💡 快速导入示例数据"):
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st.markdown("""
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**FAQ 示例格式：**
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```
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Q：你们的产品有什么优势？
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A：我们的产品具有以下核心优势：1）AI深度赋能...；2）全流程覆盖...；3）数据驱动决策...
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Q：如何开始使用？
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A：只需三步：1）注册账号；2）配置基础信息；3）开始使用核心功能。
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```
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**产品文档示例格式：**
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```
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# 产品概述
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产品简介...
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# 核心功能
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功能说明...
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# 技术架构
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架构说明...
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```
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""")
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def _render_document_list(kb: KnowledgeBase):
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"""渲染文档列表区域"""
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st.markdown("#### 已导入文档")
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documents = kb.list_documents()
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if not documents:
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st.info("📭 知识库为空，请先上传文档")
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return
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for doc in documents:
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with st.expander(f"📄 {doc['filename']} ({doc['doc_type']})"):
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col1, col2, col3 = st.columns(3)
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with col1:
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st.write(f"**类型：** {doc['doc_type']}")
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with col2:
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st.write(f"**分块数：** {doc['chunk_count']}")
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with col3:
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st.write(f"**导入时间：** {doc['created_at'][:10]}")
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if st.button(f"🗑️ 删除", key=f"delete_{doc['doc_id']}"):
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kb.delete_document(doc['doc_id'])
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st.success(f"已删除文档 '{doc['filename']}'")
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st.rerun()
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def _render_search_test(kb: KnowledgeBase):
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"""渲染搜索测试区域"""
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st.markdown("#### 搜索测试")
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st.caption("测试知识库检索效果，验证文档是否被正确索引")
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query = st.text_input("输入测试查询", placeholder="例如：产品有什么优势？")
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col1, col2 = st.columns(2)
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with col1:
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top_k = st.slider("返回结果数", 1, 10, 3)
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with col2:
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doc_type_filter = st.selectbox(
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"过滤文档类型",
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["全部"] + ["text", "faq", "product", "case", "markdown"],
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index=0
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)
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if query:
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doc_type = None if doc_type_filter == "全部" else doc_type_filter
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results = kb.search(query, top_k=top_k, doc_type=doc_type)
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if results:
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st.markdown(f"**找到 {len(results)} 条相关结果：**")
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for i, result in enumerate(results, 1):
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with st.expander(f"结果 {i} (相关度: {result['score']:.2f})"):
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st.markdown(f"**来源：** {result['metadata'].get('filename', '未知')}")
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st.markdown(f"**类型：** {result['metadata'].get('type', '未知')}")
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st.text_area("内容", result['content'], height=150,
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key=f"result_{i}", disabled=True)
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else:
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st.warning("未找到相关结果，请尝试其他查询或添加更多文档")
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def _render_source_verifier():
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"""渲染来源验证区域"""
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st.markdown("#### 📊 来源质量验证")
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st.caption("检查内容中的来源声明是否真实可信")
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verifier = SourceVerifier()
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content = st.text_area(
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"粘贴待验证内容",
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height=200,
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placeholder="粘贴 AI 生成的内容，检查其中的来源引用是否真实..."
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)
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if st.button("🔍 开始验证", use_container_width=True, type="primary"):
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if not content.strip():
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st.warning("请输入待验证内容")
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else:
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with st.spinner("正在分析来源质量..."):
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result = verifier.assess_source_quality(content)
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# 显示结果
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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric("📝 来源声明数", result["claim_count"])
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with col2:
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if result["has_sources"]:
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st.metric("✅ 具体来源", result.get("specific_count", 0))
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else:
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st.metric("✅ 具体来源", 0)
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with col3:
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st.metric("📊 质量评分", f"{result['quality_score']:.0f}/100")
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# 详细建议
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if result["suggestions"]:
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st.markdown("**💡 改进建议：**")
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for suggestion in result["suggestions"]:
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st.markdown(f"- {suggestion}")
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# 显示检测到的来源声明
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if result.get("claims"):
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st.markdown("**🔍 检测到的来源声明：**")
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for i, claim in enumerate(result["claims"], 1):
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st.markdown(f"{i}. {claim['text']}")
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