法律嵌入概念的应用与风险分析
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Gemini Embedding 2State-of-the-art multimodal embedding model Read publication Your browser does not support the video tag. Your browser does not support the video tag.Maps text, images, videos, audio, and documents into a single, unified embedding space to capture the semantic relationships across data Read blog Capabilities Performance Hands-on Showcase Model information CapabilitiesGemini Embedding 2 enables enhanced understanding of multimodal data for downstream tasks, from retrieval and classification to clustering and recommendationsSlide 1 of 5Natively multimodalUnderstands different modalities and interleaved inputs, eliminating the need for separate embedding models and reducing pipeline complexity.Contextual grounding for RAGAnchors agents and applications by providing the deep context necessary for accurate, grounded retrieval.Semantic alignment across modalitiesPowers multimodal search and clustering without requiring users to have cross-modality aligned data.Powerful performance, minimal footprintSupports large input length and uses Matryoshka Representation Learning for flexible output dimensions that maintain accuracy at smaller sizes.Multilingual understandingCaptures conceptual meaning in over 100 languages, resulting in more consistent representations for cross-lingual tasks. PerformanceState-of-the-art results on a range of cross-modal benchmarksMetric typeMetric nameGeminiEmbedding 2gemini-embedding-001 Legacy text-only Google modelmultimodalembedding@001 Legacy multimodal Google modelAmazon Nova 2Multimodal EmbeddingsVoyageMultimodal 3.5Text-TextMTEB (Multilingual) Mean (Task)69.968.4—63.8**58.5***MTEB (Code) Mean (Task)84.076.0—**Text-ImageTextCaps recall@189.6—74.076.079.4Docci recall@193.4——84.083.8Image-TextTextCaps recall@197.4—88.188.988.6Docci recall@191.3——76.577.4Text-DocumentViDoRe v2 ndcg@1064.9—28.960.665.5**Text-VideoVatex ndcg@1068.8—54.960.355.2MSR-VTT ndcg@1068.0—57.967.063.0**Youcook2 ndcg@1052.5—34.934.731.4**Speech-TextMSEB mrr@1073.9——*—MSEB (ASR)**** mrr@1070.4——*—* score not available** self-reported*** voyage-3.5**** ASR model converts audio queries to textHands-onGenerate embeddings and explore how you can use themMultimodal Search with Gemini Embedding 2Surface the most relevant matches across modalities by calculating semantic similarity. Try in Google AI Studio ShowcaseFrom enhancing RAG systems to unlocking deeper data insights, companies are already using Gemini Embedding 2 to unlock high-value multimodal applicationsSlide 1 of 4“Empowering our teams to seamlessly search past and present content has increasingly driven us to vector search. While initially seeing great results with traditional large text embeddings (3,072 dim), crowding in vector space quickly took over; the right results couldn't reliably surface their way up from the noise. Gemini's new Embedding 2 model completely changed the game. Text queries can now pinpoint untranscribed micro-expressions, and we can even leverage existing media, such as a photo or B-roll clip, as the search input to instantly retrieve matching video assets. This propelled our text-to-video Recall@1 rate to 85.3%.”Seth GeorgionVP Technology Innovation, Paramount Skydance“We chose Gemini Embedding 2 to help legal professionals find critical information during the discovery process in litigation – a highly technical challenge in a high-stakes setting, and one Gemini excels at. In our most recent tests, Gemini's multi-modal embedding model improves precision and recall across millions of records, while unlocking powerful new search functionality for images and videos. For legal professionals, these new capabilities open up entirely novel ways to quickly understand case materials in even the largest matters.”Max ChristoffCTO, Everlaw“Gemini Embedding 2 is the foundation for Sparkonomony’s Creative Economic Equality Engine. Its native multi-modality slashes our latency by up to 70% by removing LLM inference and nearly doubles semantic similarity scores for text-image and text-video pairs-leaping from 0.4 to 0.8. This powers our proprietary Creator Genome to index millions of minutes of video, alongside images and text, with unprecedented precision—unlocking unbiased brand collaborations and democratizing economic success for every creator.”Guneet SinghCo-founder, Sparkonomy“The API continuity is excellent. Gemini Embedding 2 drops right into our existing workflow with minimal changes. We're testing new ways to embed text-based conversational memories together with audio and visual embed-dings, especially assistant question-and-answer pairs, and seeing a 20% lift in top-1 recall for our personal wellness app.”Ertuğrul ÇavuşoğluCo-founder, Mindlid Model informationNameEmbedding 2StatusGenerally availableInputTextImageVideoAudioDocuments text_snippet image videocam mic docsOutputEmbeddingsInput tokens8,192Dimension sizes128 - 3072AvailabilityGemini APIGemini Enterprise Agent PlatformDocumentation View Gemini API docs View Google Cloud docs
归纳
本文探讨了法律领域中“嵌入”概念的多维应用，涵盖数据嵌入、合同嵌入及技术嵌入等场景。文章分析了嵌入行为在知识产权、数据隐私及合同履行中的法律性质，指出其可能引发的侵权责任与合规挑战。同时，文章援引了相关司法实践中的典型案例，如某数据嵌入案中法院对“实质性相似”的认定标准，以及合同嵌入条款的效力争议。文章还讨论了监管机构对嵌入技术的态度，强调需平衡创新与风险防控。整体而言，本文为法律从业者提供了关于嵌入概念的系统性参考，有助于理解其在现代法律实践中的复杂角色。
点评
数据嵌入场景下“实质性相似”认定标准模糊，可能引发跨国知识产权侵权与数据合规双重风险。
法律视角点评
AI 生成 · 人工审核核心关切
数据嵌入场景下“实质性相似”认定标准模糊，可能引发跨国知识产权侵权与数据合规双重风险。
实务启示
中国法律人应关注数据嵌入行为中“实质性相似”的国内司法认定趋势，并在合同嵌入条款中明确数据使用边界以规避侵权责任。