<table name="logo_release" border="0" cellspacing="10" cellpadding="5" align="right">   <tbody>    <tr>     <td><img src="https://mma.prnasia.com/media2/2713871/zilliz_milvus_Logo.jpg?p=medium600" border="0" alt="" title="logo" hspace="0" vspace="0" width="118" /></td>    </tr>   </tbody>  </table>  <p><span class="legendSpanClass"><span class="xn-location">REDWOOD CITY, Calif.</span>, Jan. 31, 2026 /PRNewswire/ -- <u><a href="https://zilliz.com/cloud?utm_source=vendor&amp;utm_medium=referral&amp;utm_campaign=seonews-Bilingual-semantic-highlighting-model" target="_blank" rel="nofollow" style="color: #0000FF">Zilliz</a></u>, the company behind the leading open-source vector database <u><a href="https://milvus.io/?utm_source=vendor&amp;utm_medium=referral&amp;utm_campaign=seonews-Bilingual-semantic-highlighting-model" target="_blank" rel="nofollow" style="color: #0000FF">Milvus</a></u>, today announced the open-source release of its <u><a href="https://huggingface.co/zilliz/semantic-highlight-bilingual-v1" target="_blank" rel="nofollow" style="color: #0000FF">Bilingual Semantic Highlighting Model</a></u>, an industry-first AI model designed to dramatically reduce <span>token</span> usage and improve answer quality in production RAG-powered AI applications.</span></p>  <p>This highlighting model introduces sentence-level relevance filtering, enabling AI developers to remove low-signal context before sending prompts to large language models. This approach directly addresses rising inference costs and accuracy issues caused by oversized context windows in enterprise RAG and RAG-powered AI deployments.</p>  <p>&quot;As RAG systems move into production, teams are running into very real cost and quality limits,&quot; said <span class="xn-person">James Luan</span>, VP of Engineering at Zilliz. &quot;This model gives developers a practical way to reduce prompt size and improve answer accuracy without reworking their existing pipelines.&quot;</p>  <div class="PRN_ImbeddedAssetReference" id="DivAssetPlaceHolder5705">   <p style="TEXT-ALIGN: center; WIDTH: 100%"><a href="https://mma.prnasia.com/media2/2873657/Traditional_Highlight_VS_Semantic_Highlight.html" target="_blank" rel="nofollow" style="color: #0000FF"><img src="https://mma.prnasia.com/media2/2873657/Traditional_Highlight_VS_Semantic_Highlight.jpg?p=medium600" title="Traditional Highlight VS Semantic Highlight" alt="Traditional Highlight VS Semantic Highlight" /></a><br /><span>Traditional Highlight VS Semantic Highlight</span></p>  </div>  <p><b>Key Innovations and Technical Breakthroughs</b></p>  <ul type="disc">   <li><b>Bilingual relevance by design:</b> Optimized for both English and Chinese, the model addresses cross-lingual relevance challenges common in global RAG deployments. It is built on the MiniCPM-2B architecture, enabling low-latency, production-ready performance.<br /><br /></li>   <li><b>Sentence-level context filtering: </b>Rather than scoring entire document chunks, the model evaluates relevance at the sentence level and retains only content that directly supports a user query before sending it to the LLM.<br /><br /></li>   <li><b>Lower <span>token</span> usage, higher answer quality: </b>Zilliz reports that sentence-level filtering significantly compresses prompt size while improving downstream response quality, helping teams reduce inference costs and improve generation speed in production environments.</li>  </ul>  <p><b>Availability</b></p>  <p>The Bilingual Semantic Highlighting Model is available today as an open-source release. To learn more about the training methodology and performance benchmarks, visit the <u><a href="https://milvus.io/blog/semantic-highlighting-model-for-rag-context-pruning-and-token-saving.md?utm_source=vendor&amp;utm_medium=referral&amp;utm_campaign=seonews-Bilingual-semantic-highlighting-model" target="_blank" rel="nofollow" style="color: #0000FF">Zilliz Technical Blog</a></u>.</p>  <p>Download: : <u><a href="https://huggingface.co/zilliz/semantic-highlight-bilingual-v1" target="_blank" rel="nofollow" style="color: #0000FF">zilliz/semantic-highlight-bilingual-v1</a></u></p>  <p><b>About Zilliz</b></p>  <p>Zilliz is the company behind <u><a href="https://milvus.io/?utm_source=vendor&amp;utm_medium=referral&amp;utm_campaign=seonews-Bilingual-semantic-highlighting-model" target="_blank" rel="nofollow" style="color: #0000FF">Milvus</a></u>, the world's most widely adopted open-source vector database. <u><a href="https://zilliz.com/cloud?utm_source=vendor&amp;utm_medium=referral&amp;utm_campaign=seonews-Bilingual-semantic-highlighting-model" target="_blank" rel="nofollow" style="color: #0000FF">Zilliz Cloud</a></u> brings that performance to production with a fully managed, cloud-native platform built for scalable, low-latency vector search and hybrid retrieval. It supports billion-scale workloads with sub-10ms latency, auto-scaling, and optimized indexes for GenAI use cases like semantic search and RAG.</p>  <p>Zilliz is built to make AI not just possible?봟ut practical. With a focus on performance and cost-efficiency, it helps engineering teams move from prototype to production without overprovisioning or complex infrastructure. Over 10,000 organizations worldwide rely on Zilliz to build intelligent applications at scale.</p>  <p>Headquartered in Redwood Shores, <span class="xn-location">California</span>, Zilliz is backed by leading investors, including Aramco's Prosperity 7 Ventures, Temasek's Pavilion Capital, Hillhouse Capital, 5Y Capital, Yunqi Partners, Trustbridge Partners, and others. Learn more at&nbsp; <u><a href="https://zilliz.com/?utm_source=vendor&amp;utm_medium=referral&amp;utm_campaign=seonews-Bilingual-semantic-highlighting-model" target="_blank" rel="nofollow" style="color: #0000FF">Zilliz.com</a></u>.</p>  <p>&nbsp;</p>  <div class="PRN_ImbeddedAssetReference" id="DivAssetPlaceHolder0">   <p> </p>  </div>