All posts tagged: Multimodal

Google Gemini API 2026 Update: Multimodal RAG and Page-Level Citations

Google Gemini API 2026 Update: Multimodal RAG and Page-Level Citations

Published by skeptic

Google’s Gemini API introduces multimodal retrieval, allowing users to query both text and image data within a shared vector space. This capability supports complex use cases, such as analyzing PDFs with diagrams or scanned pages, by integrating features like page-level citations and metadata-based filtering. According to Prompt Engineering, these features enhance precision by allowing targeted searches, such as identifying specific sections in legal documents or extracting insights from technical reports that combine text and visuals. Explore this explainer to gain insight into the mechanics of metadata filtering for narrowing search results, understand how multimodal embeddings integrate diverse data formats and learn how the API’s structured pipeline processes mixed content efficiently. These topics provide a clear framework for applying the Gemini API to tasks involving enterprise documents, visual analysis and cross-format synthesis. TL;DR Key Takeaways : The Gemini API now supports advanced multimodal retrieval, allowing simultaneous querying of text and image data within a unified vector space, enhancing workflows like retrieval-augmented generation (RAG). New features include metadata-based filtering for refined searches and page-level citations for precise …

Google’s Gemini Embedding 2 arrives with native multimodal support to cut costs and speed up your enterprise data stack

Google’s Gemini Embedding 2 arrives with native multimodal support to cut costs and speed up your enterprise data stack

Published by skeptic

Yesterday amid a flurry of enterprise AI product updates, Google announced arguably its most significant one for enterprise customers: the public preview availability of Gemini Embedding 2, its new embeddings model — a significant evolution in how machines represent and retrieve information across different media types. While previous embedding models were largely restricted to text, this new model natively integrates text, images, video, audio, and documents into a single numerical space — reducing latency by as much as 70% for some customers and reducing total cost for enterprises who use AI models powered by their own data to complete business tasks. VentureBeat collaborator Sam Witteveen, co-founder of AI and ML training company Red Dragon AI, received early access to Gemini Embedding 2 and published a video of his impressions on YouTube. Watch it below: Who needs and uses an embedding model? For those who have encountered the term “embeddings” in AI discussions but find it abstract, a useful analogy is that of a universal library. In a traditional library, books are organized by metadata: author, …

Black Forest Labs’ new Self-Flow technique makes training multimodal AI models 2.8x more efficient

Black Forest Labs’ new Self-Flow technique makes training multimodal AI models 2.8x more efficient

Published by skeptic

To create coherent images or videos, generative AI diffusion models like Stable Diffusion or FLUX have typically relied on external “teachers”—frozen encoders like CLIP or DINOv2—to provide the semantic understanding they couldn’t learn on their own. But this reliance has come at a cost: a “bottleneck” where scaling up the model no longer yields better results because the external teacher has hit its limit. Today, German AI startup Black Forest Labs (maker of the FLUX series of AI image models) has announced a potential end to this era of academic borrowing with the release of Self-Flow, a self-supervised flow matching framework that allows models to learn representation and generation simultaneously. By integrating a novel Dual-Timestep Scheduling mechanism, Black Forest Labs has demonstrated that a single model can achieve state-of-the-art results across images, video, and audio without any external supervision. The technology: breaking the “semantic gap” The fundamental problem with traditional generative training is that it’s a “denoising” task. The model is shown noise and asked to find an image; it has very little incentive to …

DeepSeek V4 Adds Native Multimodal Input and 1M Token Context Window

DeepSeek V4 Adds Native Multimodal Input and 1M Token Context Window

Published by skeptic

The release of DeepSeek V4 introduces notable advancements in AI capabilities, emphasizing scalability and efficiency. One key feature is the 1 million token context window, which allows the system to process large datasets, such as full research papers or extensive codebases, without the need for segmentation. According to Universe of AI, this enhancement supports more comprehensive and faster analysis, making it particularly useful for professionals managing complex data workflows. Additionally, the integration of Nvidia’s Blackwell SM100 architecture improves computational performance while addressing energy efficiency concerns. You’ll learn how DeepSeek V4’s native multimodal integration supports the simultaneous processing of text, images and other data types, streamlining diverse tasks within a single system. The guide also examines how these updates impact sectors like healthcare, education and finance, offering practical examples of their application. Finally, it explores the ethical considerations surrounding these developments, providing a balanced view of the challenges and opportunities in AI deployment. DeepSeek V4 Highlights TL;DR Key Takeaways : DeepSeek V4 introduces new features, including a 1 million token context window, native multimodal integration and …