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m ingestion pipelines. Amazon Bedrock Managed Knowledge Base now supports ServiceNow, Confluence Data Center, Salesforce, and Zendesk as native connectors to handle data crawling, metadata extraction, and incremental sync automatically. These connectors reduce the amount of custom ingestion code you need to build and maintain support content, internal documentation, and operational knowledge. Explore Amazon Bedrock, deploy agents with the AgentCore CLI, or build your first agent with the Strands

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mes after the current iteration of the tech. Trump adopting “super intelligence” might seem good for companies that had already hitched their wagon to superintelligence, including Meta and Microsoft. And as public opinion turns against AI, some in the industry may desire a rebrand. But forcing immediate adoption of SI also undercuts companies’ ability to promote big, new, flashy AI products as “superintelligent”, instead applying the term to everything from a future machine god to years-old musi

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bout as well as a robot can. And Instinct’s founder, Noah Shinn, is unfazed by the rising competition. “AI assistants are all we know,” Shinn tells me over email. While OpenAI and Meta build AI products for a range of customers, Shinn believes Instinct’s focus sets it apart. “We have been solely focused on building a personal assistant for everyday life that just gets it.” Shinn was an early employee of Sierra — another highly valued startup with a serious Silicon Valley pedigree — before striki

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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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ngerous work on offshore rigs — with human specialists calling the shots from a safe distance. Also: Agilink’s robot hand takes up a 2,500-year-old craft, and a microbot turns the metal beneath its feet into a power source. In today’s robotics rundown: Minerva builds a bomb-disposal humanoid; Agilink teaches a robot hand embroidery; Every step charges this tiny robot; MIT gives drones a mathematical survival instinct LATEST DEVELOPMENTS MINERVA HUMANOIDS 💣 Minerva builds a bomb-disposal humanoid

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