MAROKO133 Hot ai: Why Google’s File Search could displace DIY RAG stacks in the enterprise

📌 MAROKO133 Update ai: Why Google’s File Search could displace DIY RAG stacks in t

By now, enterprises understand that retrieval augmented generation (RAG) allows applications and agents to find the best, most grounded information for queries. However, typical RAG setups could be an engineering challenge and also exhibit undesirable traits. 

To help solve this, Google released the File Search Tool on the Gemini API, a fully managed RAG system “that abstracts away the retrieval pipeline.” File Search removes much of the tool and application-gathering involved in setting up RAG pipelines, so engineers don’t need to stitch together things like storage solutions and embedding creators.  

This tool competes directly with enterprise RAG products from OpenAI, AWS and Microsoft, which also aim to simplify RAG architecture. Google, though, claims its offering requires less orchestration and is more standalone. 

“File Search provides a simple, integrated and scalable way to ground Gemini with your data, delivering responses that are more accurate, relevant and verifiable,” Google said in a blog post. 

Enterprises can access some features of File Search, such as storage and embedding generation, for free at query time. Users will begin paying for embeddings when these files are indexed at a fixed rate of $0.15 per 1 million tokens. 

Google’s Gemini Embedding model, which eventually became the top embedding model on the Massive Text Embedding Benchmark, powers File Search. 

File Search and integrated experiences 

Google said File Search works “by handling the complexities of RAG for you.” 

File Search manages file storage, chunking strategies and embeddings. Developers can invoke File Search within the existing generateContent API, which Google said makes the tool easier to adopt. 

File Search uses vector search to “understand the meaning and context of a user’s query.” Ideally, it will find the relevant information to answer a query from documents, even if the prompt contains inexact words. 

The feature has built-in citations that point to the specific parts of a document it used to generate answers, and also supports a variety of file formats. These include PDF, Docx, txt, JSON and “many common programming language file types," Google says.

Continuous RAG experimentation 

Enterprises may have already begun building out a RAG pipeline as they lay the groundwork for their AI agents to actually tap the correct data and make informed decisions. 

Because RAG represents a key part of how enterprises maintain accuracy and tap into insights about their business, organizations must quickly have visibility into this pipeline. RAG can be an engineering pain because orchestrating multiple tools together can become complicated. 

Building “traditional” RAG pipelines means organizations must assemble and fine-tune a file ingestion and parsing program, including chunking, embedding generation and updates. They must then contract a vector database like Pinecone, determine its retrieval logic, and fit it all within a model’s context window. Additionally, they can, if desired, add source citations. 

File Search aims to streamline all of that, although competitor platforms offer similar features. OpenAI’s Assistants API allows developers to utilize a file search feature, guiding an agent to relevant documents for responses. AWS’s Bedrock unveiled a data automation managed service in December. 

While File Search stands similarly to these other platforms, Google’s offering abstracts all, rather than just some, elements of the RAG pipeline creation. 

Phaser Studio, the creator of AI-driven game generation platform Beam, said in Google’s blog that it used File Search to sift through its library of 3,000 files.

“File Search allows us to instantly surface the right material, whether that’s a code snippet for bullet patterns, genre templates or architectural guidance from our Phaser ‘brain’ corpus,” said Phaser CTO Richard Davey. “The result is ideas that once took days to prototype now become playable in minutes.”

Since the announcement, several users expressed interest in using the feature.

🔗 Sumber: venturebeat.com

📌 MAROKO133 Eksklusif ai: More fuel-efficient aircraft could be developed, new met

A proposed computational approach could help develop more fuel-efficient aircraft, according to researchers. The method can provide aerodynamic drag data more efficiently during the early stages of aircraft design. This can reduce time needed for extensive wind tunnel testing or large-scale computer simulations.

The team claimed that being able to predict drag accurately at an early design stage helps deliver a design that boosts the fuel efficiency of an aircraft. Reliable early estimates can also reduce the need for extensive wind tunnel testing or large-scale computer simulations. 

Called AeroMap, the framework is developed by researchers at the University of Surrey. AeroMap estimates drag for different wing-body configurations operating at speeds close to the speed of sound.

AeroMap provides datasets up to 10 to 100 times faster

In their study, researchers have shown how AeroMap provides datasets up to 10 to 100 times faster than high-fidelity simulations currently on the market, while maintaining good accuracy.

“Our goal was to develop a method that provides reliable transonic aerodynamic predictions for a range of configurations, without the high computational cost of full-scale simulations,” said Dr Rejish Jesudasan, research fellow at the University of Surrey and lead author of the study.

“By providing reliable results earlier in the design process, AeroMap reduces the need for costly redesigns and repeated wind-tunnel testing. It also delivers the level of detail engineers need to refine concepts more efficiently and with greater confidence.”

Approach enables AeroMap to capture the main effects of drag

Based on a viscous-coupled full potential method, AeroMap combines a reduced form of the Navier–Stokes equations that describe airflow with a model of the thin boundary layer of air that moves along an aircraft’s surface.

The research team claimed that this approach enables AeroMap to capture the main effects of drag without the high computing demands of more detailed simulations. As a result, it provides a practical tool for the early stages of aircraft design, when engineers need results that are both reliable and rapid. 

Many existing models still rely on empirical methods developed several decades ago. Although these remain widely used, they can be less accurate when applied to modern, high-efficiency wing designs. AeroMap has been validated against NASA wind tunnel data, with results showing close agreement between its predictions and experimental measurements, indicating its suitability for sustainable aircraft development, according to a press release.

Predicting the transonic performance of aircraft configurations

“Accurately predicting the transonic performance of aircraft configurations, during early concept studies, remains a significant challenge,” said Dr Simao Marques, senior lecturer.

“AeroMap combines established aerodynamic principles in a way that improves the reliability of drag predictions during early development, helping engineers make better-informed design decisions.”

Published in Aerospace Science and Technology, the study describes that the Accurate prediction of aerodynamic drag characteristics across a wide range of wing-body configurations is crucial in the early design stages of transonic commercial transport aircraft.

Researchers present the AeroMap framework to rapidly generate aerodynamic performance maps for evaluating both on- and off-design aerodynamic characteristics of wing-body configurations.

The framework’s predictions of drag divergence onset across various wing-body configurations highlights the importance of considering viscous-compressibility interactions and the spanwise progression of shock strength, factors that are not captured by the Korn-Lock-Mason method, according to researchers.

“With computational costs at least one to two orders of magnitude lower than high-fidelity solvers, AeroMap is suitable for configuration trade studies during the early design phase,” said researchers in the study.

🔗 Sumber: interestingengineering.com