update
This commit is contained in:
mozzie
parent
8feca18d2f
commit
b75f5fd477
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@ -197,7 +197,7 @@
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<span>RAG知识库召回率</span>
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||||
</div>
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||||
<p>通用的RAG方案的召回率不一定召回率越高越好，对于准确性也需要考虑，通用的召回率大概在70%，不论是dify还是fastgpt，对于生产场景，要求起码到90%的水平
|
||||
如何提升召回率数据预处理针对LLM...</p>
|
||||
如何提升召回率首先是针对搜索que...</p>
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||||
</div>
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||||
</a>
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||||
</li>
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||||
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|
@ -347,55 +347,6 @@ typedef struct&#123;
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|||
</a>
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||||
</li>
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||||
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||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
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||||
<div class="timeline-info">
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||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
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||||
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||||
<span>03-15 14:44:57</span>
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||||
</div>
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||||
<div class="timeline-marker"></div>
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||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
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||||
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||||
<span>短视频账号起号逻辑</span>
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||||
</div>
|
||||
<p>抖音流量池
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||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
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||||
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||||
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||||
初级流量池
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冷启动
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||||
0~500
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||||
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||||
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||||
二次曝光
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3K~5K
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||||
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||||
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||||
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||||
三次曝光
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||||
1W~2W
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||||
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||||
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||||
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||||
四次曝光（人工复审）
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10W~15W
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||||
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中级流量池
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五...</p>
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</div>
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||||
</a>
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||||
</li>
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||||
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||||
<li>
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||||
<a class="timeline-item" href="/posts/8323/">
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||||
<div class="timeline-info">
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||||
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@ -516,6 +467,55 @@ typedef struct&#123;
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|||
</a>
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</li>
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||||
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||||
<li>
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||||
<a class="timeline-item" href="/posts/58551/">
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||||
<div class="timeline-info">
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||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
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||||
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<span>03-15 14:44:57</span>
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||||
</div>
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||||
<div class="timeline-marker"></div>
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||||
<div class="timeline-content">
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||||
<div class="timeline-title ellipsis">
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||||
|
||||
<span>短视频账号起号逻辑</span>
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</div>
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<p>抖音流量池
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||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
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初级流量池
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冷启动
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0~500
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二次曝光
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3K~5K
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三次曝光
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1W~2W
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四次曝光（人工复审）
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10W~15W
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中级流量池
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五...</p>
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</div>
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</a>
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</li>
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||||
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||||
<li>
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||||
<a class="timeline-item" href="/posts/31204/">
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||||
<div class="timeline-info">
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||||
|
|
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|||
|
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@ -197,7 +197,7 @@
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<span>RAG知识库召回率</span>
|
||||
</div>
|
||||
<p>通用的RAG方案的召回率不一定召回率越高越好，对于准确性也需要考虑，通用的召回率大概在70%，不论是dify还是fastgpt，对于生产场景，要求起码到90%的水平
|
||||
如何提升召回率数据预处理针对LLM...</p>
|
||||
如何提升召回率首先是针对搜索que...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
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||||
|
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@ -347,55 +347,6 @@ typedef struct&#123;
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</a>
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||||
</li>
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||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
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||||
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<span>03-15 14:44:57</span>
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||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
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3K~5K
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|
||||
|
||||
|
||||
三次曝光
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1W~2W
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|
||||
|
||||
四次曝光（人工复审）
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10W~15W
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||||
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||||
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中级流量池
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五...</p>
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</div>
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</a>
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</li>
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||||
<li>
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||||
<a class="timeline-item" href="/posts/8323/">
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||||
<div class="timeline-info">
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|
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@ -516,6 +467,55 @@ typedef struct&#123;
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|||
</a>
|
||||
</li>
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||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
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<span>03-15 14:44:57</span>
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</div>
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||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
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||||
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||||
|
||||
中级流量池
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五...</p>
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</div>
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</a>
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</li>
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||||
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||||
<li>
|
||||
<a class="timeline-item" href="/posts/31204/">
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||||
<div class="timeline-info">
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||||
|
|
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|||
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@ -197,7 +197,7 @@
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|||
<span>RAG知识库召回率</span>
|
||||
</div>
|
||||
<p>通用的RAG方案的召回率不一定召回率越高越好，对于准确性也需要考虑，通用的召回率大概在70%，不论是dify还是fastgpt，对于生产场景，要求起码到90%的水平
|
||||
如何提升召回率数据预处理针对LLM...</p>
|
||||
如何提升召回率首先是针对搜索que...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
|
@ -347,55 +347,6 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
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||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
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</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/8323/">
|
||||
<div class="timeline-info">
|
||||
|
|
@ -516,6 +467,55 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
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五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/31204/">
|
||||
<div class="timeline-info">
|
||||
|
|
|
|||
|
|
@ -197,7 +197,7 @@
|
|||
<span>RAG知识库召回率</span>
|
||||
</div>
|
||||
<p>通用的RAG方案的召回率不一定召回率越高越好，对于准确性也需要考虑，通用的召回率大概在70%，不论是dify还是fastgpt，对于生产场景，要求起码到90%的水平
|
||||
如何提升召回率数据预处理针对LLM...</p>
|
||||
如何提升召回率首先是针对搜索que...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
|
@ -347,55 +347,6 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/8323/">
|
||||
<div class="timeline-info">
|
||||
|
|
@ -516,6 +467,55 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
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||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/31204/">
|
||||
<div class="timeline-info">
|
||||
|
|
|
|||
|
|
@ -197,7 +197,7 @@
|
|||
<span>RAG知识库召回率</span>
|
||||
</div>
|
||||
<p>通用的RAG方案的召回率不一定召回率越高越好，对于准确性也需要考虑，通用的召回率大概在70%，不论是dify还是fastgpt，对于生产场景，要求起码到90%的水平
|
||||
如何提升召回率数据预处理针对LLM...</p>
|
||||
如何提升召回率首先是针对搜索que...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
|
@ -347,55 +347,6 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/8323/">
|
||||
<div class="timeline-info">
|
||||
|
|
@ -516,6 +467,55 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/31204/">
|
||||
<div class="timeline-info">
|
||||
|
|
|
|||
|
|
@ -197,7 +197,7 @@
|
|||
<span>RAG知识库召回率</span>
|
||||
</div>
|
||||
<p>通用的RAG方案的召回率不一定召回率越高越好，对于准确性也需要考虑，通用的召回率大概在70%，不论是dify还是fastgpt，对于生产场景，要求起码到90%的水平
|
||||
如何提升召回率数据预处理针对LLM...</p>
|
||||
如何提升召回率首先是针对搜索que...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
|
@ -347,55 +347,6 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/8323/">
|
||||
<div class="timeline-info">
|
||||
|
|
@ -516,6 +467,55 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/31204/">
|
||||
<div class="timeline-info">
|
||||
|
|
|
|||
|
|
@ -197,7 +197,7 @@
|
|||
<span>RAG知识库召回率</span>
|
||||
</div>
|
||||
<p>通用的RAG方案的召回率不一定召回率越高越好，对于准确性也需要考虑，通用的召回率大概在70%，不论是dify还是fastgpt，对于生产场景，要求起码到90%的水平
|
||||
如何提升召回率数据预处理针对LLM...</p>
|
||||
如何提升召回率首先是针对搜索que...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
|
@ -347,55 +347,6 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/8323/">
|
||||
<div class="timeline-info">
|
||||
|
|
@ -516,6 +467,55 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/31204/">
|
||||
<div class="timeline-info">
|
||||
|
|
|
|||
|
|
@ -197,7 +197,7 @@
|
|||
<span>RAG知识库召回率</span>
|
||||
</div>
|
||||
<p>通用的RAG方案的召回率不一定召回率越高越好，对于准确性也需要考虑，通用的召回率大概在70%，不论是dify还是fastgpt，对于生产场景，要求起码到90%的水平
|
||||
如何提升召回率数据预处理针对LLM...</p>
|
||||
如何提升召回率首先是针对搜索que...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
|
@ -347,55 +347,6 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/8323/">
|
||||
<div class="timeline-info">
|
||||
|
|
@ -516,6 +467,55 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/31204/">
|
||||
<div class="timeline-info">
|
||||
|
|
|
|||
|
|
@ -197,7 +197,7 @@
|
|||
<span>RAG知识库召回率</span>
|
||||
</div>
|
||||
<p>通用的RAG方案的召回率不一定召回率越高越好，对于准确性也需要考虑，通用的召回率大概在70%，不论是dify还是fastgpt，对于生产场景，要求起码到90%的水平
|
||||
如何提升召回率数据预处理针对LLM...</p>
|
||||
如何提升召回率首先是针对搜索que...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
|
@ -347,55 +347,6 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/8323/">
|
||||
<div class="timeline-info">
|
||||
|
|
@ -516,6 +467,55 @@ typedef struct&#123;
|
|||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/58551/">
|
||||
<div class="timeline-info">
|
||||
|
||||
<cosy-tooltip><span slot="content">完成</span><cosy-icon size="sm"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 24 24"><g fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="9"></circle><path d="M9 12l2 2l4-4"></path></g></svg></cosy-icon></cosy-tooltip>
|
||||
|
||||
<span>03-15 14:44:57</span>
|
||||
</div>
|
||||
<div class="timeline-marker"></div>
|
||||
<div class="timeline-content">
|
||||
<div class="timeline-title ellipsis">
|
||||
|
||||
<span>短视频账号起号逻辑</span>
|
||||
</div>
|
||||
<p>抖音流量池
|
||||
|
||||
|
||||
级别
|
||||
曝光次数
|
||||
播放量范围
|
||||
|
||||
|
||||
|
||||
初级流量池
|
||||
冷启动
|
||||
0~500
|
||||
|
||||
|
||||
|
||||
二次曝光
|
||||
3K~5K
|
||||
|
||||
|
||||
|
||||
三次曝光
|
||||
1W~2W
|
||||
|
||||
|
||||
|
||||
四次曝光（人工复审）
|
||||
10W~15W
|
||||
|
||||
|
||||
中级流量池
|
||||
五...</p>
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a class="timeline-item" href="/posts/31204/">
|
||||
<div class="timeline-info">
|
||||
|
|
|
|||
|
|
@ -228,7 +228,7 @@
|
|||
<!-- 文章标题 -->
|
||||
<h1 class="post-title"></h1>
|
||||
<div class="last-updated">
|
||||
上次更新: 2024-03-28 12:32:07
|
||||
上次更新: 2024-03-28 12:59:17
|
||||
</div>
|
||||
<!-- 文章 -->
|
||||
<h1 id="stdio-h"><a href="#stdio-h" class="headerlink" title="stdio.h"></a>stdio.h</h1><table>
|
||||
|
|
|
|||
|
|
@ -248,11 +248,13 @@
|
|||
<!-- 文章标题 -->
|
||||
<h1 class="post-title">RAG知识库召回率</h1>
|
||||
<div class="last-updated">
|
||||
上次更新: 2024-03-28 12:58:29
|
||||
上次更新: 2024-03-28 13:11:54
|
||||
</div>
|
||||
<!-- 文章 -->
|
||||
<h1 id="通用的RAG方案的召回率"><a href="#通用的RAG方案的召回率" class="headerlink" title="通用的RAG方案的召回率"></a>通用的RAG方案的召回率</h1><p>不一定召回率越高越好，对于准确性也需要考虑，通用的召回率大概在<code>70%</code>，不论是<code>dify</code>还是<code>fastgpt</code>，对于生产场景，要求起码到<code>90%</code>的水平</p>
|
||||
<h1 id="如何提升召回率"><a href="#如何提升召回率" class="headerlink" title="如何提升召回率"></a>如何提升召回率</h1><h2 id="数据预处理"><a href="#数据预处理" class="headerlink" title="数据预处理"></a>数据预处理</h2><p>针对<code>LLM</code>模型来说，数据治理变的很重要，在数据切分的过程中，需要符合人的逻辑去<code>分词</code>。按照人的逻辑，例如：表格、章节、目录……</p>
|
||||
<h1 id="如何提升召回率"><a href="#如何提升召回率" class="headerlink" title="如何提升召回率"></a>如何提升召回率</h1><p>首先是针对搜索query来说，通过文本框的<code>打字输入</code>的角度，人类一定是倾向于偷懒，只输入关键词，这就是给表达真实意图带来的难度。</p>
|
||||
<p>语音交互，才有可能让大家使用起来更舒服。</p>
|
||||
<h2 id="数据预处理"><a href="#数据预处理" class="headerlink" title="数据预处理"></a>数据预处理</h2><p>针对<code>LLM</code>模型来说，数据治理变的很重要，在数据切分的过程中，需要符合人的逻辑去<code>分词</code>。按照人的逻辑，例如：表格、章节、目录……</p>
|
||||
<h3 id="分词-chunk"><a href="#分词-chunk" class="headerlink" title="分词 chunk"></a>分词 chunk</h3><p><code>chunk</code>的<code>size</code>越大，召回越少。但是分的越精细，同样会损失上下文连贯性</p>
|
||||
<h1 id="意图分类"><a href="#意图分类" class="headerlink" title="意图分类"></a>意图分类</h1><p>用户提问的内容，很短、缩写的情况，如何命中用户的<code>真实意图</code>。</p>
|
||||
<h2 id="缓存库"><a href="#缓存库" class="headerlink" title="缓存库"></a>缓存库</h2><p>可以做一个缓存库，直接命中返回结构就行，不需要走<code>LLM</code></p>
|
||||
|
|
@ -266,7 +268,9 @@
|
|||
<h1 id="embedding哪个算法好"><a href="#embedding哪个算法好" class="headerlink" title="embedding哪个算法好"></a>embedding哪个算法好</h1><p>目前openai的最好，能够输入1536个输入，输出结果能切分到700多个维度。针对模型来说，重要性并不是很高。可以考虑替代方案。差距不会非常大，从实际角度来说，并不是很重要。</p>
|
||||
<h1 id="国内知识库方案哪些底座比较好"><a href="#国内知识库方案哪些底座比较好" class="headerlink" title="国内知识库方案哪些底座比较好"></a>国内知识库方案哪些底座比较好</h1><p><code>qWen</code> 和 gpt 做过对齐，然后 <code>14B</code>、<code>72B int4</code> 效果就比较好，虽然比较慢，针对中文语料还是比较好的。</p>
|
||||
<p>另一个就是 <code>yi-34b</code> 也还行。</p>
|
||||
<h1 id="RAG的发展方向"><a href="#RAG的发展方向" class="headerlink" title="RAG的发展方向"></a>RAG的发展方向</h1>
|
||||
<h1 id="如何设计大模型测试样本"><a href="#如何设计大模型测试样本" class="headerlink" title="如何设计大模型测试样本"></a>如何设计大模型测试样本</h1><p>从用户的提问当中，选取50-100个场景，作为<code>大模型</code>选择、<code>知识库</code>效果的测评的数据集，例如需要测<code>text2sql</code>的场景的模型效果，首先就是要找到哪些查询的频次最高。</p>
|
||||
<h1 id="RAG的发展方向"><a href="#RAG的发展方向" class="headerlink" title="RAG的发展方向"></a>RAG的发展方向</h1><p>1、知识库作为专业领域的基座，大模型一定是无法覆盖的<br>2、从用户的角度来说，大厂商可能会说服开发者去接入自家agent，贡献领域知识</p>
|
||||
|
||||
<div class="post-tags">
|
||||
<!-- 文章tags -->
|
||||
|
||||
|
|
@ -293,7 +297,7 @@
|
|||
<span>目录</span>
|
||||
</p>
|
||||
<!-- 文章toc -->
|
||||
<ol class="toc"><li class="toc-item toc-level-1"><a class="toc-link" href="#%E9%80%9A%E7%94%A8%E7%9A%84RAG%E6%96%B9%E6%A1%88%E7%9A%84%E5%8F%AC%E5%9B%9E%E7%8E%87"><span class="toc-number">1.</span> <span class="toc-text">通用的RAG方案的召回率</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E5%A6%82%E4%BD%95%E6%8F%90%E5%8D%87%E5%8F%AC%E5%9B%9E%E7%8E%87"><span class="toc-number">2.</span> <span class="toc-text">如何提升召回率</span></a><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86"><span class="toc-number">2.1.</span> <span class="toc-text">数据预处理</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%88%86%E8%AF%8D-chunk"><span class="toc-number">2.1.1.</span> <span class="toc-text">分词 chunk</span></a></li></ol></li></ol></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E6%84%8F%E5%9B%BE%E5%88%86%E7%B1%BB"><span class="toc-number">3.</span> <span class="toc-text">意图分类</span></a><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#%E7%BC%93%E5%AD%98%E5%BA%93"><span class="toc-number">3.1.</span> <span class="toc-text">缓存库</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#QA"><span class="toc-number">3.2.</span> <span class="toc-text">QA</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E5%85%B3%E9%94%AE%E8%AF%8D-%E8%AF%8D%E5%85%B8-%E8%BF%AD%E4%BB%A3"><span class="toc-number">3.3.</span> <span class="toc-text">关键词+词典+迭代</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1"><span class="toc-number">3.4.</span> <span class="toc-text">知识图谱</span></a></li></ol></li><li class="toc-item toc-level-1"><a class="toc-link" href="#embedding%E5%93%AA%E4%B8%AA%E7%AE%97%E6%B3%95%E5%A5%BD"><span class="toc-number">4.</span> <span class="toc-text">embedding哪个算法好</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E5%9B%BD%E5%86%85%E7%9F%A5%E8%AF%86%E5%BA%93%E6%96%B9%E6%A1%88%E5%93%AA%E4%BA%9B%E5%BA%95%E5%BA%A7%E6%AF%94%E8%BE%83%E5%A5%BD"><span class="toc-number">5.</span> <span class="toc-text">国内知识库方案哪些底座比较好</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#RAG%E7%9A%84%E5%8F%91%E5%B1%95%E6%96%B9%E5%90%91"><span class="toc-number">6.</span> <span class="toc-text">RAG的发展方向</span></a></li></ol>
|
||||
<ol class="toc"><li class="toc-item toc-level-1"><a class="toc-link" href="#%E9%80%9A%E7%94%A8%E7%9A%84RAG%E6%96%B9%E6%A1%88%E7%9A%84%E5%8F%AC%E5%9B%9E%E7%8E%87"><span class="toc-number">1.</span> <span class="toc-text">通用的RAG方案的召回率</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E5%A6%82%E4%BD%95%E6%8F%90%E5%8D%87%E5%8F%AC%E5%9B%9E%E7%8E%87"><span class="toc-number">2.</span> <span class="toc-text">如何提升召回率</span></a><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86"><span class="toc-number">2.1.</span> <span class="toc-text">数据预处理</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%88%86%E8%AF%8D-chunk"><span class="toc-number">2.1.1.</span> <span class="toc-text">分词 chunk</span></a></li></ol></li></ol></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E6%84%8F%E5%9B%BE%E5%88%86%E7%B1%BB"><span class="toc-number">3.</span> <span class="toc-text">意图分类</span></a><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#%E7%BC%93%E5%AD%98%E5%BA%93"><span class="toc-number">3.1.</span> <span class="toc-text">缓存库</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#QA"><span class="toc-number">3.2.</span> <span class="toc-text">QA</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E5%85%B3%E9%94%AE%E8%AF%8D-%E8%AF%8D%E5%85%B8-%E8%BF%AD%E4%BB%A3"><span class="toc-number">3.3.</span> <span class="toc-text">关键词+词典+迭代</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1"><span class="toc-number">3.4.</span> <span class="toc-text">知识图谱</span></a></li></ol></li><li class="toc-item toc-level-1"><a class="toc-link" href="#embedding%E5%93%AA%E4%B8%AA%E7%AE%97%E6%B3%95%E5%A5%BD"><span class="toc-number">4.</span> <span class="toc-text">embedding哪个算法好</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E5%9B%BD%E5%86%85%E7%9F%A5%E8%AF%86%E5%BA%93%E6%96%B9%E6%A1%88%E5%93%AA%E4%BA%9B%E5%BA%95%E5%BA%A7%E6%AF%94%E8%BE%83%E5%A5%BD"><span class="toc-number">5.</span> <span class="toc-text">国内知识库方案哪些底座比较好</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E5%A6%82%E4%BD%95%E8%AE%BE%E8%AE%A1%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%B5%8B%E8%AF%95%E6%A0%B7%E6%9C%AC"><span class="toc-number">6.</span> <span class="toc-text">如何设计大模型测试样本</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#RAG%E7%9A%84%E5%8F%91%E5%B1%95%E6%96%B9%E5%90%91"><span class="toc-number">7.</span> <span class="toc-text">RAG的发展方向</span></a></li></ol>
|
||||
</div>
|
||||
</div>
|
||||
</cosy-drag-box>
|
||||
|
|
|
|||
|
|
@ -273,7 +273,7 @@
|
|||
<p><a target="_blank" rel="noopener" href="https://baidu.com/">https://baidu.com</a></p>
|
||||
<p><a target="_blank" rel="noopener" href="http://www.this-anchor-link.com/">锚点链接</a> </p>
|
||||
<p><a href="mailto:test.test@gmail.com">mailto:test.test@gmail.com</a></p>
|
||||
<p>GFM a-tail link <a target="_blank" rel="noopener" href="https://my.oschina.net/u/3691274">@pandao</a> 邮箱地址自动链接 <a href="mailto:test.test@gmail.com">test.test@gmail.com</a> <a href="mailto:www@vip.qq.com">www@vip.qq.com</a></p>
|
||||
<p>GFM a-tail link <a target="_blank" rel="noopener" href="https://my.oschina.net/u/3691274">@pandao</a> 邮箱地址自动链接 <a href="mailto:test.test@gmail.com">test.test@gmail.com</a> <a href="mailto:www@vip.qq.com">www@vip.qq.com</a></p>
|
||||
<blockquote>
|
||||
<p>@pandao</p>
|
||||
</blockquote>
|
||||
|
|
|
|||
|
|
@ -13,6 +13,10 @@ abbrlink: 21037
|
|||
|
||||
# 如何提升召回率
|
||||
|
||||
首先是针对搜索query来说，通过文本框的`打字输入`的角度，人类一定是倾向于偷懒，只输入关键词，这就是给表达真实意图带来的难度。
|
||||
|
||||
语音交互，才有可能让大家使用起来更舒服。
|
||||
|
||||
## 数据预处理
|
||||
|
||||
针对`LLM`模型来说，数据治理变的很重要，在数据切分的过程中，需要符合人的逻辑去`分词`。按照人的逻辑，例如：表格、章节、目录……
|
||||
|
|
@ -61,4 +65,11 @@ abbrlink: 21037
|
|||
|
||||
另一个就是 `yi-34b` 也还行。
|
||||
|
||||
# RAG的发展方向
|
||||
# 如何设计大模型测试样本
|
||||
|
||||
从用户的提问当中，选取50-100个场景，作为`大模型`选择、`知识库`效果的测评的数据集，例如需要测`text2sql`的场景的模型效果，首先就是要找到哪些查询的频次最高。
|
||||
|
||||
# RAG的发展方向
|
||||
|
||||
1、知识库作为专业领域的基座，大模型一定是无法覆盖的
|
||||
2、从用户的角度来说，大厂商可能会说服开发者去接入自家agent，贡献领域知识
|
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Reference in New Issue
Block a user