What is Edge AI?Edge AI (also: “on-device AI”, “local AI”) brings artificial intelligence to applications at the network’s edge, such as mobile devices, IoT, and other embedded systems like, e.g., interactive kiosks. Edge AI combines AI with Edge Computing, a decentralized paradigm designed to bring computing as close as possible to where data is generated and utilized.
What is Cloud AI? As opposed to this, cloud AI refers to an architecture where applications rely on data and AI models hosted on distant cloud infrastructure. The cloud offers extensive storage and processing power.
An Edge for Edge AI: The CloudÂ
Example: Edge-Cloud AI setup with a secure, two-way Data Sync architecture
Today, there is a broad spectrum of application architectures combining Edge Computing and Cloud Computing, and the same applies to AI. For example, “Apple Intelligence” performs many AI tasks directly on the phone (on-device AI) while sending more complex requests to a private, secure cloud. This approach combines the best of both worlds – with the cloud giving an edge to the local AI rather than the other way around. Let’s have a look at the advantages on-device AI brings to the table.
Benefits of Local AI on the Edge
Enhanced Privacy. Local data processing reduces the risk of breaches.
Faster Response Rates. Processing data locally cuts down travel time for data, speeding up responses.
Increased Availability. On-device processing makes apps fully offline-capable. Operations can continue smoothly during internet or data center disruptions.
Sustainability/costs. Keeping data where it is produced and used minimizes data transfers, cutting networking costs and reducing energy consumption—and with it, CO2 emissions.
Challenges of Local AI on the Edge
Data Storage and Processing: Local AI requires an on-device database that runs on a wide variety of edge devices (Mobile,IoT, Embedded) and performs complex tasks such as vector search locally on the device with minimal resource consumption.
Data Sync: It’s vital to keep data consistent across devices, necessitating robust bi-directional Data Sync solutions. Implementing such a solution oneself requires specialized tech talent, is non-trivial and time-consuming, and will be an ongoing maintenance factor.Â
Small Language Models: Small Language Models (SLMs) like Phi-2 (Microsoft Research), TinyStories (HuggingFace), and Mini-Giants (arXiv) are efficient and resource-friendly but often need enhancement with local vector databases for better response accuracy. An on-device vector database allows on-device semantic search with private, contextual information, reducing latency while enabling faster and more relevant outputs. For complex queries requiring larger models, a database that works both on-device and in the cloud (or a large on-premise server) is perfect for scalability and flexibility in on-device AI applications.
On-device AI Use Cases
On-device AI is revolutionizing numerous sectors by enabling real-time data processing wherever and whenever it’s needed. It enhances security systems, improves customer experiences in retail, supports predictive maintenance in industrial environments, and facilitates immediate medical diagnostics. On-device AI is essential for personalizing in-car experiences, delivering reliable remote medical care, and powering personal AI assistants on mobile devices—always keeping user privacy intact.
Personalized In-Car Experience: Features like climate control, lighting, and entertainment can be adjusted dynamically in vehicles based on real-time inputs and user habits, improving comfort and satisfaction. Recent studies, such as one by MHP, emphasize the increasing consumer demand for these AI-enabled features. This demand is driven by a desire for smarter, more responsive vehicle technology.
Remote Care: In healthcare, on-device AI enables on-device data processing that’s crucial for swift diagnostics and treatment. This secure, offline-capable technology aligns with health regulations like HIPAA and boosts emergency response speeds and patient care quality.
Personal AI Assistants: Today’s personal AI assistants often depend on the cloud, raising privacy issues. However, some companies, including Apple, are shifting towards on-device processing for basic tasks and secure, anonymized cloud processing for more complex functions, enhancing user privacy.
ObjectBox for On-Device AI – an edge for everyone
The continuum from Edge to Cloud
ObjectBox supports AI applications from Edge to cloud. It stands out as the first on-device vector database, enabling powerful Edge AI on mobile, IoT, and other embedded devices with minimal hardware needs. It works offline and supports efficient, private AI applications with a seamless bi-directional Data Sync solution, completely on-premise, and optional integration with MongoDB for enhanced backend features and cloud AI.
 Interested in extending your AI to the edge? Let’s connect to explore how we can transform your applications.
In today’s fast-paced, decentralized world valuable data is generated by everything, everywhere, and all at once. To harness the vast opportunities offered by this data for data-driven organizations and AI applications, you need to be able to access the data and seamlessly distribute it to when and where it’s needed.
The key to achieving this lies in efficient, offline-first on-device data storage, reliable bi-directional data sync, and a scalable data management backend in the cloud. In other words, you need the infrastructure to manage data flows bi-directionally to tap into fresh data throughout your organization, processes, and applications at the right time.
Together, MongoDB and ObjectBox provide developers with a robust solution to empower seamless workload and data flows on the edge and from the edge to the cloud. ObjectBox seamlessly syncs data bi-directionally across devices even without Internet and syncs back to the cloud and MongoDB when connected. With ObjectBox devices stay in sync also in environments with intermittent connectivity, high latency, or flaky networks. Capture and unlock the value of all your data, anytime, anywhere, without relying on a constant Internet connection, with MongoDB + ObjectBox.
Seamless Offline-First Data Sync for Edge Devices
Maintaining service continuity is essential, even when devices are offline. Your customers, users, operations, and employees need to be able to rely on essential data at all times. That’s where ObjectBox comes in. It comprises of two key components: the ObjectBox Database and ObjectBox Data Sync.
The ObjectBox Database is a lightweight, on-device solution that is highly resource-efficient and fast on restricted hardware like mobile, IoT, and embedded devices, and even in the cloud.
ObjectBox Data Sync enables seamless bi-directional data synchronization between devices. By handling only incremental changes in a compressed binary format, ObjectBox Sync ensures minimal data transfer, automatic conflict resolution, and a seamless user experience even in fluctuating network conditions. This approach effectively simplifies the development process by offering complex sync logic via easy native-language APIs, allowing developers to focus on core app functionality.
Once a connection is available, ObjectBox Data Sync instantly synchronizes changes with MongoDB, providing real-time, bi-directional data sync between edge devices and MongoDB’s robust cloud backend.
The Benefits of Offline-First and Real-Time Data Sync with MongoDB and ObjectBox:
Resource-efficiency & Highspeed: ObjectBox excels at consuming minimal computational resources (CPU, power, memory, …) while delivering data persistence speed that is typically on-par with in-memory caches for read operations.
Offline-First Operation: Ensure continuous app performance, even with no internet connection. ObjectBox stores and syncs data bi-directionally on the edge and additionally with MongoDB once connected.
Real-Time Data Sync: Get reliable, bi-directional data synchronization across devices and MongoDB, enabling real-time updates and data consistency.
Scalable Edge: Easily handle 100k operations / second on a single device. Host the Sync server on any edge device (like a phone) and easily handle 3M clients with a three-node cluster.
Scalable Cloud Backend: With MongoDB, businesses can scale their applications to handle growing data and performance demands, seamlessly syncing data between millions of devices and the cloud.
Flexible Setup Scenarios: Tailor Data Sync to Your Needs
ObjectBox and MongoDB offer flexible setup scenarios to meet different application needs. The two main setup options are the central sync and the edge sync setup.
The Central Sync Setup syncs data between edge devices and MongoDB in the cloud, providing centralized data management while retaining offline-first functionality. The ObjectBox Sync Server runs in the cloud or on-premise.
The Edge Sync Setup allows devices to operate and sync data efficiently offline between ObjectBox instances within an edge, e.g. within one location, or within a car. When reconnected, changes are synchronized back to MongoDB making it ideal for environments with intermittent connectivity or distributed devices that need to function independently while syncing back to the cloud when possible.
This structure offers a flexible approach to integrating edge and cloud systems, empowering organizations to choose the setup that best fits their specific use case. More details.
Use Cases for MongoDB + ObjectBox :
Data-Driven Organizations: In a data-driven organization, every decision relies on access to relevant, up-to-date data. ObjectBox enables real-time data collection and synchronization from edge devices, ensuring access to critical data, even when devices are intermittently connected. This streamlines operations, improves decision-making, and enhances analysis across distributed teams and IoT systems. With MongoDB’s scalable cloud infrastructure, decentralized data integrates seamlessly with the cloud backend for efficient management.
Point-of-Sale (PoS) & Retail Edge Computing: A seamless customer experience and the ability to keep selling and never lose a transaction, even during internet outages, are essential for PoS systems / in retail. ObjectBox enables offline-first data storage and syncing for PoS systems, allowing transactions to be processed locally, even without internet connectivity. When connectivity returns, ObjectBox syncs transaction data back to MongoDB in real time, ensuring data consistency across multiple locations. Retailers can then leverage MongoDB’s analytics to gain insights into customer behavior and optimize inventory management.
Software-Defined Vehicle (SDV) & Connected Cars: Modern vehicles generate vast amounts of data from sensors and onboard systems. ObjectBox enables efficient on-device storage and processing, providing real-time access to data for navigation, diagnostics, and infotainment systems. ObjectBox Data Sync ensures that local data is synced back to MongoDB when connectivity is available, supporting centralized analytics, fleet management, and predictive maintenance, optimizing performance and safety while enhancing the user experience.
Manufacturing & Smart Shopfloor Apps: In smart factories, machines and sensors continuously generate data that must be analyzed and processed in real time. ObjectBox enables local data storage and fast data sync on-premise without the necessity for an Internet connection, ensuring that critical systems that are not connected to the Internet can run smoothly on-site. With a connected instance, ObjectBox takes care of synchronizing this data with the cloud and MongoDB for further analysis and central dashboards.
AI-Applications with On-device Vector Search: ObjectBox is the first and only on-device vector database, empowering developers to run AI locally on mobile, IoT, embedded, and other commodity devices (Edge AI). In combination with a Small Language Model (SLM), this allows developers to build local AI applications (e.g. RAG, genAI) that run directly on the device—without needing a cloud connection. By syncing with MongoDB, businesses can combine the power of on-device AI with centralized cloud data for even greater insights and performance. This is especially beneficial in scenarios requiring real-time decision-making, such as personalized customer experiences and predictive maintenance.
In today’s data-driven world, a data-first strategy requires seamless integration between edge and cloud data management. The combination of MongoDB and ObjectBox unlocks the full potential of your data. MongoDB’s powerful cloud platform, together with ObjectBox’s efficient on-device database and offline-first capabilities, is ideal for capturing the value of your data from anywhere, including distributed edge devices where valuable data is generated all the time. This partnership empowers businesses to seamlessly handle decentralized data, enabling fast and reliable operations at the edge while syncing back to the cloud for centralized management. Whether on IoT devices, mobile, embedded systems, or commodity hardware, ObjectBox and MongoDB ensure optimal performance everywhere. From remote areas to bad networks, our joint solution keeps data flowing reliably between the edge and the MongoDB backend, even when connectivity or nodes are lost.
As artificial intelligence (AI) continues to evolve, companies, researchers, and developers are increasingly recognizing that bigger isn’t always better. Therefore, the era of ever-expanding model sizes is giving way to more efficient, compact models, so-called Small Language Models (SLMs). SLMs offer several key advantages that address both the growing complexity of AI and the practical challenges of deploying large-scale models. In this article, we’ll explore why the race for larger models is slowing down and how SLMs are emerging as the sustainable solution for the future of AI.Â
From Bigger to Better: The End of the Large Model Race
Up until 2023, the focus was on expanding models to unprecedented scales. But the era of creating ever-larger models appears to be coming to an end. Many newer models like Grok or Llama 3 are smaller in size yet maintain or even improve performance compared to models from just a year ago. The drive now is to reduce model size, optimize resources, and maintain power.Â
The Plateau of Large Language Models (LLMs)
Why Bigger No Longer Equals Better
As models become larger, developers are realizing that the performance improvements aren’t always worth the additional computational cost. Breakthroughs in knowledge distillation and fine-tuning enable smaller models to compete with and even outperform their larger predecessors in specific tasks. For example, medium-sized models like Llama with 70B parameters and Gemma-2 with 27B parameters are among the top 30 models in the chatbot arena, outperforming even much larger models like GPT-3.5 with 175B parameters.
The Shift Towards Small Language Models (SLMs)
In parallel with the optimization of LLMs, the rise of SLMs presents a new trend (see Figure). These models require fewer computational resources, offer faster inference times, and have the potential to run directly on devices. In combination with an on-device database, this enables powerful local GenAI and on-device RAG apps on all kinds of embedded devices, like on mobile phones, Raspberry Pis, commodity laptops, IoT, and robotics.
Advantages of SLMs
Despite the growing complexity of AI systems, SLMs offer several key advantages that make them essential in today’s AI landscape:Â
Accessibility As SLMs are less resource-hungry (less hardware requirements, less CPU, memory, power needs), they are more accessible for companies and developers with smaller budgets. Because the model and data can be used locally, on-device / on-premise, there is no need for cloud infatstructure and they are also usable for use cases with high privacy requirements. All in all, SLMs democratize AI development and empower smaller teams and individual developers to deploy advanced models on more affordable hardware.
Cost Reduction and Sustainability Training and deploying large models require immense computational and financial resources, and comes with high operational costs. SLMs drastically reduce the cost of training, deployment, and operation as well as the carbon footprint, making AI more financially and environmentally sustainable.
On-Device AI for Privacy and Security SLMs are becoming compact enough for deployment on edge devices like smartphones, IoT sensors, and wearable tech. This reduces the need for sensitive data to be sent to external servers, ensuring that user data stays local. With the rise of on-device vector databases, SLMs can now handle use-case-specific, personal, and private data directly on the device. This allows more advanced AI apps, like those using RAG, to interact with personal documents and perform tasks without sending data to the cloud. With a local, on-device vector database users get personalized, secure AI experiences while keeping their data private.
 The Future: Fit-for-Purpose Models: From Tiny to Small to Large Language models
 The future of AI will likely see the rise of models that are neither massive nor minimal but fit-for-purpose. This “right-sizing” reflects a broader shift toward models that balance scale with practicality. SLMs are becoming the go-to choice for environments where specialization is key and resources are limited. Medium-sized models (20-70 billion parameters) are becoming the standard choice for balancing computational efficiency and performance on general AI tasks. At the same time, SLMs are proving their worth in areas that require low latency and high privacy.
Innovations in model compression, parameter-efficient fine-tuning, and new architecture designs are enabling these smaller models to match or even outperform their predecessors. The focus on optimization rather than expansion will continue to be the driving force behind AI development in the coming years.
 Conclusion: Scaling Smart is the New Paradigm
As the field of AI moves beyond the era of “bigger is better,” SLMs and medium-sized models are becoming more important than ever. These models represent the future of scalable and efficient AI. They serve as the workhorses of an industry that is looking to balance performance with sustainability and efficiency. The focus on smaller, more optimized models demonstrates that innovation in AI isn’t just about scaling up; it’s about scaling smart.
Artificial Intelligence (AI) has become an integral part of our daily lives in recent years. However, it has been tied to running in huge, centralized cloud data centers. This year, “local AI”, also known as “on-device AI” or “Edge AI”, is gaining momentum. Local vector databases, efficient language models (so-called Small Language Models, SLMs), and AI algorithms are becoming smaller, more efficient, and less compute-heavy. As a result, they can now run on a wide variety of devices, locally.
Figure 1. Evolution of language model’s size with time. Large language models (LLMs) are marked as celadon circles, and small language models (SLMs) as blue ones.
What is Local AI (on-device AI, Edge AI)?
Local AI refers to running AI applications directly on a device, locally, instead of relying on (distant) cloud servers. Such an on-deivce AI works in real-time on commodity hardware (e.g. old PCs), consumer devices (e.g. smartphones, wearables), and other types of embedded devices (e.g. robots and point-of-sale (POS) systems used in shops and restaurants). An interest in local Artificial Intelligence is growing (see Figure 2).
Figure 2. Interest over time according to Google Trends.
Why use Local AI: Benefits
Local AI addresses many of the concerns and challenges of current cloud-based AI applications. The main reasons for the advancement of local AI are:
Privacy / Data Security – Data stays on the device and under one’s control
Accessibility – AI works independently from an internet connection
Sustainability – AI consumes significantly less energy compared to cloud setups
On top, local AI reduces:
latency, enabling real-time apps
data transmission and cloud costs, enabling commodity business cases
In a world where data privacy concerns are increasing, local AI offers a solution. Since data is processed directly on the device, sensitive information remains local, minimizing the risk of breaches or misuse of personal data. No need for data sharing, and data ownership is clear. This is the key to using AI responsibly in industries like healthcare, where sensitive data needs to be processed and used without being sent to external servers. For example, medical data analysis or diagnostic tools can run locally on a doctor’s device and be synchronized to other on-premise, local devices (like e.g. PCs, on-premise servers, specific medical equipment) as needed. This ensures that patient data never leaves the clinic, and data processing is compliant with strict privacy regulations like GDPR or HIPAA.
Accessibility: AI for Anyone, Anytime
One of the most significant advantages of local AI is its ability to function without an internet connection. This opens up a world of opportunities for users in remote locations or those with unreliable connectivity. Imagine having access to language translation, image recognition, or predictive text tools on your phone without needing to connect to the internet. Or a point-of-sale (POS) system in a retail store that operates seamlessly, even when there’s no internet. These AI-powered systems can still analyze customer buying habits, manage inventory, or suggest product recommendations offline, ensuring businesses don’t lose operational efficiency due to connectivity issues. Local AI makes this a reality. In combination with little hardware requirements, it makes AI accessible for anyone, anytime. Therefore, local AI is an integral ingredient in making AI more inclusive and to democratize AI.
Sustainability: Energy Efficiency
Cloud-based AI requires massive server farms that consume enormous amounts of energy. Despite strong efficiency improvements, in 2022, data centers globally consumed between 240 and 340 terawatt-hours (TWh) of electricity. To put this in perspective, data centers now use more electricity than entire countries like Argentina or Egypt. This growing energy demand places considerable pressure on global energy resources and contributes to around 1% of energy-related CO2 emissions. The rise of AI has amplified these trends. AI workloads alone could drive a 160% increase in data center energy demand by 2030, with some estimates suggesting that AI could consume 500% more energy in the UK than it does today. By that time, data centers may account for up to 8% of total energy consumption in the United States. In contrast, local AI presents a more sustainable alternative, e.g. by leveraging Small Language Models, which require less power to train and run. Since computations happen directly on the device, local AI significantly reduces the need for constant data transmission and large-scale server infrastructure. This not only lowers energy use but also helps decrease the overall carbon footprint. Additionally, integrating a local vector database can further enhance efficiency by minimizing reliance on power-hungry data centers, contributing to more energy-efficient and environmentally friendly technology solutions.
When to use local AI: Use case examples
Local AI enables an infinite number of new use cases. Thanks to advancements in AI models and vector databases, AI apps can be run cost-effectively on less capable hardware, e.g. commodity PCs, without the need for an internet connection and data sharing. This opens up the opportunity for offline AI, real-time AI, and private AI applications on a wide variety of devices. From smartphones and smartwatches to industrial equipment and even cars, local AI is becoming accessible to a broad range of users.
Consumer Use Cases (B2C): Everyday apps like photo editors, voice assistants, and fitness trackers can integrate AI to offer faster and more personalized services (local RAG), or integrate generative AI capabilities.
Business Use Cases (B2B): Retailers, manufacturers, and service providers can use local AI for data analysis, process automation, and real-time decision-making, even in offline environments. This improves efficiency and user experience without needing constant cloud connectivity.
Conclusion
Local AI is a powerful alternative to cloud-based solutions, making AI more accessible, private, and sustainable. With Small Language Models and on-device vector databases like ObjectBox, it is now possible to bring AI onto everyday devices. From the individual user who is looking for convenient, always-available tools to large businesses seeking to improve operations and create new services without relying on the cloud – local AI is transforming how we interact with technology everywhere.
Easily empower your iOS and macOS apps with fast, private, and sustainable AI features. All you need is a Small Language Model (SLM; aka “small LLM”) and ObjectBox – our on-device vector database built for Swift apps. This gives you a local semantic index for fast on-device AI features like RAG or GenAI that run without an internet connection and keep data private.
The recently demonstrated “Apple Intelligence” features are precisely that: a combination of on-device AI models and a vector database (semantic index). Now, ObjectBox Swift enables you to add the same kind of AI features easily and quickly to your iOS apps right now.
Typical AI apps use data (e.g. user-specific data, or company-specific data) and multiple queries to enhance and personalize the quality of the model’s response and perform complex tasks. And now, for the very first time, with the release of ObjectBox 4.0, this will be possible locally on restricted devices.
Local AI Tech Stack Example for on-device RAG
Swift on-device Vector Database and search for iOS and MacOS
With the ObjectBox Swift 4.0 release, it is possible to create a scalable vector index on floating point vector properties. It’s a very special index that uses an algorithm called HNSW. It’s scalable because it can find relevant data within millions of entries in a matter of milliseconds. Let’s pick up the cities example from our vector search documentation. Here, we use cities with a location vector and want to find the closest cities (a proximity search). The Swift class for the City entity shows how to define an HNSW index on the location:
1
2
3
4
5
6
7
8
9
10
11
// objectbox: entity
classCity{
varid:Id=0
varname:String?
// objectbox:hnswIndex: dimensions=2
varlocation:[Float]?
}
Inserting City objects with a float vector and HNSW index works as usual, the indexing happens behind the scenes:
1
2
3
4
5
6
7
8
let box:Box<City>=store.box()
trybox.put([
City("Barcelona",[41.385063,2.173404]),
City("Nairobi",[-1.292066,36.821945]),
City("Salzburg",[47.809490,13.055010]),
])
To then find cities closest to a location, we do a nearest neighbor search using the new query condition and “find with scores” methods. The nearest neighbor condition accepts a query vector, e.g. the coordinates of Madrid, and a count to limit the number of results of the nearest neighbor search, here we want at max 2 cities. The find with score methods are like a regular find, but in addition return a score. This score is the distance of each result to the query vector. In our case, it is the distance of each city to Madrid.
1
2
3
4
5
6
7
8
9
10
11
12
let madrid=[40.416775,-3.703790]// query vector
// Prepare a Query object to search for the 2 closest neighbors:
The ObjectBox on-device vector database empowers AI models to seamlessly interact with user-specific data — like texts and images — directly on the device, without relying on an internet connection. With ObjectBox, data never needs to leave the device, ensuring data privacy.
Thus, it’s the perfect solution for developers looking to create smarter apps that are efficient and reliable in any environment. It enhances everything from personalized banking apps to robust automotive systems.
ObjectBox: Optimized for Resource Efficiency
At ObjectBox, we specialize on efficiency that comes from optimized code. Our hearts beat for creating highly efficient and capable software that outperforms alternatives on small and big hardware. ObjectBox maximizes speed while minimizing resource use, extending battery life, and reducing CO2 emissions.
With this expertise, we took a unique approach to vector search. The result is not only a vector database that runs efficiently on constrained devices but also one that outperforms server-side vector databases (see first benchmark results; on-device benchmarks coming soon). We believe this is a significant achievement, especially considering that ObjectBox still upholds full ACID properties (guaranteeing data integrity).
Cloud/server vector databases vs. On-device/Edge vector databases
Also, keep in mind that ObjectBox is a fully capable database. It allows you to store complex data objects along with vectors. Thus, you have the full feature set of a database at hand. It empowers hybrid search, traceability, and powerful queries.
Use Cases / App ideas
ObjectBox can be used for a million different things, from empowering generative AI features in mobile apps to predictive maintenance on ECUs in cars to AI-enhanced games. For iOS apps, we expect to see the following on-device AI use cases very soon:
Across all categories we’ll see Chat-with-files apps:
Travel: Imagine chatting to your favorite travel guide offline, anytime, anywhere. No need to carry bulky paper books, or scroll through a long PDF on your mobile.
Research: Picture yourself chatting with all the research papers in your field. Easily compare studies and findings, and quickly locate original quotes.
Lifestyle:
Health: Apps offering personalized recommendations based on scientific research, your preferences, habits, and individual health data. This includes data tracked from your device, lab results, and doctoral diagnosis.
Productivity: Personal assistants for all areas of life.
Family Management: Interact with assistants tailored to specific roles. Imagine a parent’s assistant that monitors school channels, chat groups, emails, and calendars. Its goal is to automatically add events like school plays, remind you about forgotten gym bags, and even suggest birthday gifts for your child’s friends.
Professional Assistants: Imagine being a busy sales rep on the go, juggling appointments and travel. A powerful on-device sales assistant can do more than just automation. It can prepare contextual and personalized follow-ups instantly. For example, by summarizing talking points, attaching relevant company documents, and even suggesting who to CC in your emails.
Educational:
Educational apps featuring “chat-with-your-files” functionality for learning materials and research papers. But going beyond that, they generate quizzes and practice questions to help people solidify knowledge.
Run the local AI Stack with a Language Model (SLM, LLM)
Recent Small Language Models (SMLs) already demonstrate impressive capabilities while being small enough to run on e.g. mobile phones. To run the model on-device of an iPhone or a macOS computer, you need a model runtime. On Apple Silicone the best choice in terms of performance typically MLX – a framework brought to you by Apple machine learning research. It supports the hardware very efficiently by supporting CPU/GPU and unified memory.
To summarize, you need these three components to run on-device AI with an semantic index:
ObjectBox: vector database for the semantic index
Models: choose an embedding model and a language model to matching your requirements
Retrieval Augmented Generation (RAG) is a technique to enhance the intelligence of large language models (LLMs) with additional knowledge, such as reliable facts from specific sources, private or personal information not available to others, or just fresh news to improve their answers. Typically, the additional knowledge is provided to the model from a vector database. For example, you can add internal data from your company, the latest news or the data from your personal devices to get responses that use your context. It can truly help you like an expert instead of giving generalized answers. This technique also reduces hallucinations.Â
Why RAG?
Let’s take a look at the key benefits that RAG in general offers:
Customization and Adaptation: RAG helps LLMs to tailor responses to specific domains or use cases by using vector databases to store and retrieve domain-specific information. It turns general intelligence into expert intelligence.
Contextual Relevance: By incorporating information retrieved from a large corpus of text,RAG models can generate contextually relevant responses. It improves the quality of generated responses compared to traditional generation models.
Accuracy and diversity: Incorporation of external information also helps to generate more informative and accurate responses and keep LLM up-to-date. This also helps to avoid repetitive or generic responses and allows for more diverse and interesting conversations.
Cost-effective implementation: RAG requires less task-specific training data compared to fine-tuning the foundation models. When we compare retrieval augmented generation vs fine-tuning, RAG’s ability to use external knowledge stands out. While fine-tuning requires lots of labeled data, RAG can rely on external sources. This can be particularly beneficial in scenarios where annotated training data is limited or expensive to obtain, thus, providing a cost-effective implementation.
Transparency: RAG models provide transparency in their responses by explicitly indicating the source of retrieved information. This allows users to understand how the model arrived at its response and helps enhance trust in the generated output.
Therefore, RAG is suitable for applications where access to a vast amount of specialized data is necessary. For example, a customer support bot that pulls details from FAQs and generates coherent, conversational responses. Another example is an email drafting tool that fetches information about recent meetings and generates a personalized summary.
How retrieval augmented generation works
Let’s discuss the mechanics of how RAG operates with databases, covering its main stages from dataset creation to response generation (see figure).
Retrieval augmented generation diagram
DB creation: Creation of external dataset
Before the real use, the vector database should be created. The new data, that lies outside the training dataset of LLM, should be identified and added to the dataset (e.g. up-to-date information or specific information). This dataset is then transferred into vector embeddings via an AI model (embedding language models) and is stored in the vector database.
DB in use: Retrieval of relevant information Once a query comes in, it is also transferred into a vector / embedding. It is used then to retrieve the most relevant result from the database. To achieve this, RAG uses semantic search techniques also known as vector search to understand the user’s query and/or context, retrieving contextually relevant information from a large dataset. Vector search goes beyond keyword matching and focuses on semantic relationships, improving the quality of the retrieved information and the overall performance of the RAG system in generating contextually relevant responses.
DB in use: Augmentation At this stage, the user’s query is augmented by adding the relevant data retrieved in the previous stage. Often, only the top responses from vector search are considered as relevant data. Many databases have additional filtering techniques in place here.
Generation The augmented query is sent to the LLM to generate an accurate answer.
The Role of Long Context Windows
The rise of the new LLMs with long (1+million tokens) context windows, like Gemini 1.5, raised the discussion on whether long context windows will replace RAG. A long context window enables users to directly incorporate huge amounts of data into a query. Thus, it increases context to the LLM to improve its efficiency.
While RAG offers numerous benefits, there are still opportunities for improvement. Researchers are exploring ways to enhance RAG by combining it with other techniques. These include fine-tuning (RAFT) or the long context window (in combination with reranking). Another direction of research is expanding RAG capabilities by advancing data handling (including multimodal data), evaluation methodologies, and scalability. Finally, RAG is also affected by the new advances in optimizing LLMs to run locally on restricted devices (mobile, IoT), along with the emergence of the first on-device vector database. Now, RAG can be performed directly on your mobile device, prioritizing privacy, low latency, and offline capabilities.
We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it.Ok
