他表示，当前AI应用的核心挑战已经从模型能力的竞争，转向如何高效利用企业私域数据。模型能力的趋同，意味着其本身难以形成垄断优势。 他强调，未来的路径是发展"KAG（Knowledge Augmented Generation，知识增强生成）"。这要求企业像管理数据一样，系统性地对知识进行治 理、建模与应用。具体而言，需要通过VLM、知识图谱等技术，融合多模态、多结构化的知识，并构建"数据湖"与"知识湖"双湖并重的架构。最 终目标是让AI真正"掌握"而非仅仅"看到"企业知识，从而在专业领域问答、智能写作与合规创作等场景中发挥可靠价值，完成从数字化到智能化 的关键跨越。 以下为演讲精彩观点： 12月20日，在华尔街见闻和中欧国际工商学院联合主办的「Alpha峰会」上，金山办公助理总裁、资深技术专家朱熠锷发表题为《WPS AI:迈向更 高质量的知识增强生成》的演讲。 他强调，真正决定AI应用价值的关键，在于将企业内大量复杂、非结构化的文档数据，转化为高质量、可被模型理解的知识资产。传统RAG面 临"文档不等于知识"以及"语义相似不等于逻辑相关"的根本局限，因此必须推动技术范式从"以模型为中心"升级为"以数据/知识为 ...

Core Insights - Kioxia America, Inc. has integrated its AiSAQ technology into Milvus, enhancing the capabilities of one of the most widely adopted open-source vector databases for AI applications [1][2][3] Group 1: Technology Integration - The integration of Kioxia's AiSAQ technology into Milvus (starting with version 2.6.4) provides developers and enterprises with a cost-effective solution for scaling AI applications without high memory costs [1][2] - AiSAQ technology significantly reduces DRAM requirements while enabling high-quality vector search, making large-scale Retrieval Augmented Generation (RAG) deployments more accessible and affordable [2][3] Group 2: Market Demand and Challenges - As organizations adopt larger AI models and develop complex RAG pipelines, the demand for vector databases is increasing, with DRAM cost becoming a major barrier to growth [2][3] - Milvus now supports SSD-optimized vector indexing due to the integration of AiSAQ, which dramatically reduces memory usage while maintaining high-quality search performance [3][4] Group 3: Future Developments - Kioxia's AiSAQ technology is designed to enhance vector scalability by storing all RAG database elements on SSDs, with tuning options available to prioritize performance or scalability [4][5] - The ongoing development of AiSAQ aims to support trillion-vector scale, further advancing the capabilities of AI applications [4][5]

Core Insights - The future of data management is shifting towards integrating unstructured data with structured data, emphasizing the need for advanced data platforms that can handle both types effectively [1][15]. Group 1: Unstructured Data Challenges - By 2025, CEOs will prioritize insights from unstructured data, such as vendor contracts in PDF format, over traditional structured data queries [3]. - The current disconnect in data management stems from the lack of efficient connections between vector databases and relational databases, complicating the retrieval of specific information from unstructured sources [4]. - The processing of unstructured data is costly, with estimates suggesting that handling 1 PB of unstructured text for retrieval-augmented generation (RAG) could incur API costs up to $150,000 if not optimized [6]. Group 2: Solutions and Recommendations - Experts recommend building a model routing system that utilizes smaller language models for basic extraction tasks, reserving more complex models for intricate reasoning tasks [6]. - Investment in better data ingestion layers is crucial, as improved parsers yield a return on investment ten times greater than enhancements in language learning models [9]. - The importance of metadata is highlighted, as successful data teams will embed structured attributes into unstructured data before it enters vector storage [10]. Group 3: Evolution of Data Products - Documents are evolving from mere data blocks to data products, with a focus on extracting actionable insights from contracts and other unstructured formats [12]. - The emergence of a "universal data lake" is anticipated, where various data types coexist and are managed under a single directory, enhancing accessibility and usability [12]. - Companies are advised to audit their data directories to ensure that search results yield relevant data formats, indicating the effectiveness of their data management systems [13].

Accessibility StatementSkip Navigation Enterprise-ready foundation integrates with AWS agentic AI services through a Coveo-hosted MCP Server, helping ensure every agentic response is factual, contextual, and compliant MONTREAL, Dec. 1, 2025 /PRNewswire/ -Â Coveo, the leader in AI-Relevance, delivering best-in-class AI- search and generative experiences, today announced Retrieval Augmented Generation (RAG)-as-a-Service for AWS agentic AI services through a Coveo-hosted MCP Server, a new cloud-native offering ...

Core Idea - Contextualized chunk embedding models, such as voyage-context-3, process entire documents to embed chunks, leading to document-aware embeddings [1] - This approach enables semantically aware retrieval in Retrieval-Augmented Generation (RAG) systems [1] Technology - Voyage-context-3 is highlighted as an example of a contextualized chunk embedding model [1] - The method contrasts with producing independent chunk embeddings [1] Collaboration - The MongoDB team is acknowledged for their collaboration on this topic [1]

[Music] All right. Uh, thank you everyone. We're excited for to be here and thank you for uh, coming to our talk.Uh, my name is Chong. I'm the CEO and co-founder of LANCB. I've been making data tools for machine learning and data science for about 20 years.I was one of the co-authors of pandas library and I'm working on LANCB today for all of that data that doesn't fit neatly into those pandas data frames. And I'm Calvin. I lead one of the teams at Harvey Aai working on rag um tough rag problems across mass ...

Financial Data and Key Metrics Changes - Total revenue in Q4 was $388 million, growing 16% year-over-year on an as-reported and constant currency basis [30] - Subscription revenue in Q4 totaled $362 million, also growing 16% as reported and 17% in constant currency [30] - Elastic Cloud revenue grew 23% on an as-reported and constant currency basis [30] - Non-GAAP operating margin for Q4 was 15%, with a gross margin of 77% [35][36] - Adjusted free cash flow margin improved by approximately 600 basis points to end the year at 19% [36] Business Line Data and Key Metrics Changes - The number of customers with over $1 million in annual contract value grew approximately 27%, adding about 45 net new customers [34] - Customers with over $100,000 in annual contract value grew approximately 14%, adding about 180 net new customers [34] - Subscription revenue excluding Monthly Cloud was $315 million, growing 19% in Q4 [32] Market Data and Key Metrics Changes - Strong growth was observed in the APJ region, followed by EMEA and The Americas, while some pressure was noted in the U.S. Public sector [34] - Over 2,000 Elastic Cloud customers are using Elastic for Gen AI use cases, with over 30% of these customers spending $100,000 or more annually [12] Company Strategy and Development Direction - The company is focusing on leveraging AI to automate business processes and drive innovation, positioning itself as a strategic partner for enterprises [11][18] - Elastic aims to strengthen its position as the preferred vector database, enhancing its offerings with new technologies like better binary quantization [13][19] - The company is committed to maintaining a balance between growth and profitability while continuing to innovate and expand its product offerings [40][43] Management's Comments on Operating Environment and Future Outlook - Management acknowledged potential uncertainty in the macro environment but expressed confidence in the healthy pipeline and demand signals [39] - The company expects continued growth and strong margins in FY 2026, projecting total revenue in the range of $1.655 billion to $1.670 billion [42] Other Important Information - Elastic Cloud now accounts for over 50% of subscription revenue, with strong growth in cloud adoption [18] - The company announced a strategic collaboration agreement with AWS to enhance solution integrations and accelerate AI innovation [25] Q&A Session Summary Question: Guidance and Metrics - Inquiry about the conservativeness of guidance and leading indicators of business performance [45] - Response highlighted the balance of positive demand signals with macro uncertainty, emphasizing the importance of CRPO and subscription revenue metrics [46][49] Question: Partnerships and Market Opportunities - Question regarding the impact of recent partnerships, particularly with AWS and NVIDIA, on market opportunities [53] - Management noted the growing acceptance of Elastic as a leading vector database and the importance of partnerships for driving cloud adoption [54] Question: Retrieval Augmented Generation (RAG) - Inquiry about the durability of RAG architectures and Elastic's positioning [59] - Management affirmed the critical role of retrieval in enterprise data management and the growing adoption of their vector database for RAG use cases [60][61] Question: Cloud Performance and Consumption Hesitation - Question about the sequential growth in cloud performance and the impact of the leap year [62] - Management clarified that the leap year and fewer days in Q4 affected consumption rates, but normalized growth rates remained strong [64][66] Question: Go-to-Market Strategy and Changes - Inquiry about the effectiveness of go-to-market changes made in the previous fiscal year [69] - Management confirmed that the changes have settled and are yielding positive results, with plans to continue hiring sales capacity [70][72] Question: AI Commitments and Emerging Use Cases - Question about the $1 million AI commitments and emerging use cases [93] - Management clarified that 25% of $1 million customers are using Elastic for AI workloads, with a variety of sophisticated use cases emerging across industries [94][96]