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the output includes citations and confidence scores for human-in-the-loop validation when compliance requires it. The output comes back as structured markdown, JSON, or HTML with metadata, rather than raw text you need to parse yourself. Picking the Right Tool The short version: Clean typed documents, controlled quality, high volume: Tesseract or PaddleOCR; Complex layouts, multilingual, need better accuracy: Surya or PaddleOCR; Irregular layouts, forms, need contextual understanding: LLM-based

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lumn names are intelligently extracted from header rows; Extra data: Data in your spreadsheet that doesn't explicitly belong in a structured data table (notes, titles, etc.); Cell metadata: 40+ features per cell including formatting (font bold , background color rgb ), position (row number , coordinate ), data types (is date like , is percentage ), and layout (is merged cell , horizontal alignment ); Sheet context: Optional LLM-generated titles and descriptions for each worksheet and extracted t

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We've also improved the output structure. The expand parameter gives you precise control over what parsed content gets returned, whether that's text, markdown, structured JSON, or metadata. And the returned content has more consistent typing and structure, so you (and your coding agents) can confidently navigate text, structured items, and everything in between with full type safety. Using Parse API v2 in the new SDK: python Using Parse API v1 in the old SDK: python Migrating to LlamaParse API v

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of: New IngestionPipline concept for ingesting and transforming data; Data ingestion and transforms are now automatically cached; Updated interface for node parsing/text splitting/metadata extraction modules; Changes to the default tokenizer, as well as customizing the tokenizer; Packaging/Installation changes with PyPi (reduced bloat, new install options); More predictable and consistent import paths; Plus, in beta: MultiModal RAG Modules for handling text and images! Have questions or concerns

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o struggle when faced with highly structured or densely formatted documents. For example, when parsing image-rich pages, embedded charts, merged cell tables or small-font embedded metadata, screenshot-only approaches still fail or hallucinate values. Even advanced vision-language models can drop subtle content when document pages are large or resolution is reduced. Missing Enterprise Metadata Raw LLM APIs often return simple text or basic JSON. In production document workflows you often need mor

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ax on another's, net of credits on a third. One schema field, three definitions, and the extraction is correct by its own lights every time. The same pattern runs through contract metadata, where effective date, execution date, and commencement date appear under overlapping labels, and through lab reports, where one analyte carries different names by instrument vendor. The fix is semantic: the field description has to carry the disambiguation, because the layout can't. Documents are full of repe

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These two functions are related but distinct. Classification is typically about routing: this is an invoice, this is a contract, this is a patient intake form. Tagging adds richer metadata: this contract contains an indemnity clause, this invoice is flagged for three-way matching, this medical record is associated with a specific procedure code. Together they give document workflows the structured information they need to operate without manual intervention. The difference between AI classificat

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piece of extracted information is linked back to its source location in the original document, with a confidence score and a citation. LlamaParse includes verifiable outputs with metadata, (confidence scores and citations) specifically for this reason. When an agent isn't certain, the system knows it, and the output includes the metadata needed for a human to verify it quickly rather than re-reading the entire document. Security and Privacy in Document Workflows Legal documents and financial re

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or a fleet of custom templates. It processes documents across formats and layouts using the same model, validates its own output, and returns results with the confidence scores and spatial metadata that reviewers need. If your pipeline has a manual review queue that's grown too large to ignore, that's usually a signal that your extraction layer is producing more uncertainty than your team can absorb. LlamaParse is free to try. Start with 10,000 credits and run it against your hardest documents.

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ternal consistency checks flag documents where the numbers do not add up correctly. Cross-document validation catches cases where submitted documents do not agree with each other. Metadata analysis identifies documents where the creation date does not match the purported pay period. Pattern recognition flags documents that match known fraudulent templates. LlamaParse surfaces the data and confidence scores that make these checks possible. An extracted pay stub where the year-to-date total is inc

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e connectors have support for ingesting the permissions info for each file ingested from that data-source. This permissions info is ingested into the downstream vector database as metadata values. From there, retrieval requests against the vector database can be scoped to only embeddings from files the end-user has access to by attaching the right permissions-related metadata filters. Document Parsing As you’ve probably heard from us before, document parsing is critical for enterprise RAG. Enter

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emantic chunking was incorporated by LangChain [14], and Aurelio AI developed its own version [15]. Additionally, Unstructured explored chunking in financial contexts, focusing on metadata about chunks based on their document position rather than optimal text partitioning [16]. These efforts highlight the emerging interest evaluations of chunking for RAG, yet there remains a significant gap in comprehensive evaluation, which our works is intended to address. We introduce our evaluation framework

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t to generate and execute code (SQL queries, pandas operations, regex pipelines) would open up search over structured data that current tools cannot effectively handle. Schema and metadata discovery. Real corpora come with metadata: timestamps, authors, document types, categorical labels. The agent currently has no mechanism to discover or leverage this structure. Exposing metadata schemas and allowing the agent to construct filtered queries would improve both precision and efficiency, particula

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伙伴之一是与阿拉伯联合酋长国，在那里同性恋是非法的，批评政府是被禁止的。在回应置评请求时，OpenAI 的一位发言人指出了该公司的模型规范，其中解释说，本地化不会凌驾于公司的人权准则之上，“除非涉及法律合规”，并且每当从回复中删除或添加信息时，它都会始终予以披露。 在其他地方，AI审查已经开始扎根。中国模型当然受到高度审查——这并不令人意外。但今年早些时候，Meta监督委员会发现，来自Anthropic、Google和OpenAI的五款广泛使用的模型更有可能拒绝与专制政府相关的查询。该委员会发现，相比对英国国王查尔斯三世（英国没有冒犯君主法），这些模型更不愿意制作批评泰国国王（泰国有冒犯君主法）的宣传册。他们表示，这些结果表明，这些模型不知何故已将压制性的国家言论限制内化。Anthropic和Google未回应置评请求。 随着拒绝技术的改进，它们可能扩大国家的审查触角。公司声称，一些模型现在能够在长时间对话中检测用户是否心怀不轨，或只是略显可疑——即使所使用的单个词语组合都没有明显危险。微软负责任AI首席产品官莎拉·伯德告诉我，Copilot和许多聊天机器人一样，运行一套用于分析用户身

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is the first frontier AI provider to release an agent that connects to enterprise workflows, illustrating how the cloud provider is differentiating itself in the AI race. Related:Meta joins enterprise agent race with Muse Wonder, facing a trust gap Digital workspace “This is a play on digital workspace,” said Mark Beccue, an analyst at Omdia, a division of Informa TechTarget. “It’s an everyday use of a personal assistant in your everyday work.” While Google is the first frontier AI developer wi

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on’ Mahmoud Ramin , a research director at Info-Tech Research Group, noted that Google’s announcement does not create a whole new category, since OpenAI, Anthropic, Microsoft, and Meta have already positioned multiagent capabilities in their platforms. What is interesting with Gemini, though, is that not only can it operate directly in multiple Google products like Gmail, Drive, Sheets, and Slides, it also connects to external systems. It is, of course, a good fit for those already using the Goo

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mini+; 周刊; 数据通 金融我闻 商圈 大盘脉搏 地缘图志 企业数据库 沪深股市 数据通Claw🦞 10月8日，Manus母公司蝴蝶效应宣布，于近日完成超5亿美元新一轮融资，由博裕投资、IDG 资本领投，老股东腾讯、红杉中国、真格基金继续加持，但并未披露本轮融资估值。据彭博社9月17日报道，Manus最新5亿美元融资接近完成，对应估值约40亿美元，约为Meta收购时估值的2倍。 谷歌云推出Gemini办公智能体 当地时间10月8日，谷歌云（Google Cloud）推出基于Gemini的办公智能体。据介绍，该智能体能够根据用户设定的目标规划工作步骤、调用工具并执行任务，同时连接Google Workspace、Microsoft 365、Slack、Jira等办公应用，协助用户处理日常工作。该智能体可为每项任务选择最佳模型，目前主要使用谷歌的Gemini模型和Anthropic Claude模型。 推荐进入财新数据库，可随时查阅公司股价走势、结构人员变化等投资信息。 财新机器人产业指数(RII)已发布，点击了解行业动态 金融｜失能老人一年花费12万元 长护险能赔多少; 政经｜黄奇

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rocess containers, plus Quicksand, a QEMU-based library (Willison).Unsloth adoption: Unsloth added OS-level sandboxing with under 100ms per tool call (Unsloth); Research:RoboJEPA (Meta/Mila): An 8B JEPA trained on 15K hours of robot video across 12 embodiments. Scaling laws fit on 22M–2B models predict the 4B and 8B results. It reaches 67% zero-shot grasping vs 5% for π0.5, though π0.5 still wins pick-and-place (summary); DeLM: Decentralized multi-agent coordination via a shared queue gives up t

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day Integration System User read access; A Databricks administrator creates one OAuth connection and a foreign catalog in Unity Catalog; Unity Catalog resolves the foreign catalog metadata, and Databricks compute reads the corresponding data from Workday; A Databricks administrator governs access to the foreign catalog through Unity Catalog’s robust table-level access controls; Users query the resulting catalog with standard SQL in Databricks or in natural-language with Genie This means you can:

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the strangest. Walk into a life-sciences R&D organization today and the real frontier isn't any single data type. It's multi-omics, the genomics, transcriptomics, proteomics, and metabolomics that each arrive with their own formats and silos. It's real-world and clinical-trial data, EHRs and registries and waveforms. It's medical affairs and the literature. The questions that actually move a program, which patients respond and why, what the image plus the molecular signature plus the outcome sa

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