#Vector Data

[archiesoniconline]

http://archiesoniconline.com/?page_id=29085

New Data File Alert! 2025 is the 30th Anniversary of Knuckles' Chaotix, and we're celebrating! Check out the new Data File for the team and look forward for more of Knux and friends in the coming weeks! It's crunch time! Art by @drawloverlala

[kaiserouo]

(prev | next | first)

nom

[conceptuma]

humanized gabriel doodles

[why-the-heck-not]

truly my best brain time is in the middle of the night caffeine & sugar rush. I think I just understood math, like some part of the general pattern of math if that makes sense. something clicked somewhere in my brain and I felt it

[100-art]

Liquid Crystal Art

[darkstarcore]

“I’m the greatest programmer ever” to “stupidest coding bug known to man” pipeline

[vectordatabasecode]

Advanced Retrieval Techniques

Retrieval-Augmented Generation with Citations - Explore how augmentation with citations can significantly improve the depth and reliability of generated content.

Similarity Metrics for Vector Search - Understand different metrics that drive the effectiveness of vector searches, crucial for refining retrieval systems.

Local Agentic RAG with Langraph and Llama3 - Discover the integration of local datasets with advanced retrieval frameworks for enhanced performance.

Multimodal RAG with CLIP, Llama3, and Milvus - A deep dive into a multimodal approach, combining textual and visual data for rich content generation.

Practical Guides for Developers

A Beginner's Guide to Using Llama 3 with Ollama, Milvus, LangChain - Perfect for developers new to our frameworks, offering step-by-step guidance.

Getting Started with a Milvus Connection and Getting Started: Pgvector Guide for Developers Exploring Vector Databases - These guides are essential for setting up and beginning work with vector databases.

Educational Articles on Embedding Techniques and Applications

Sparse and Dense Embeddings - A look at different embedding types, offering insights into their use-cases and benefits.

Mastering BM25: A Deep Dive into the Algorithm and Application in Milvus - An in-depth exploration of BM25, a core algorithm for understanding document relevance.

Comparing SPLADE Sparse Vectors with BM25 - Comparative analysis that helps in selecting the right tool for specific retrieval tasks.

Training Your Own Text Embedding Model - Empower your projects by creating custom models tailored to your specific data needs.

Implementing and Optimizing RAG

Guide to Chunking Strategies for RAG and Experimenting with Different Chunking Strategies via LangChain - Both resources provide strategic insights into segmenting text for better retrieval outcomes.

Optimize RAG with Rerankers: The Role and Tradeoffs - Detailed discussion on the optimization of retrieval systems for balance between accuracy and performance.

[juliebowie]

Understanding What are Vector Databases and their Importance

Summary: Vector databases manage high-dimensional data efficiently, using advanced indexing for fast similarity searches. They are essential for handling unstructured data and are widely used in applications like recommendation systems and NLP.

Introduction

Vector databases store and manage data as high-dimensional vectors, enabling efficient similarity searches and complex queries. They excel in handling unstructured data, such as images, text, and audio, by transforming them into numerical vectors for rapid retrieval and analysis.

In today's data-driven world, understanding vector databases is crucial because they power advanced technologies like recommendation systems, semantic search, and machine learning applications. This blog aims to clarify how vector databases work, their benefits, and their growing significance in modern data management and analysis.

Read Blog: Exploring Differences: Database vs Data Warehouse.

What are Vector Databases?

Vector databases are specialised databases designed to store and manage high-dimensional data. Unlike traditional databases that handle structured data, vector databases focus on representing data as vectors in a multidimensional space. This representation allows for efficient similarity searches and complex data retrieval operations, making them essential for unstructured or semi-structured data applications.

Key Features

Vector databases excel at managing high-dimensional data, which is crucial for tasks involving large feature sets or complex data representations. These databases can handle various applications, from image and text analysis to recommendation systems, by converting data into vector format.

One of the standout features of vector databases is their ability to perform similarity searches. They allow users to find items most similar to a given query vector, making them ideal for content-based search and personalisation applications.

To handle vast amounts of data, vector databases utilise advanced indexing mechanisms such as KD-trees and locality-sensitive hashing (LSH). These indexing techniques enhance search efficiency by quickly narrowing down the possible matches, thus optimising retrieval times and resource usage.

How Vector Databases Work

Understanding how vector databases function requires a closer look at their data representation, indexing mechanisms, and query processing methods. These components work together to enable efficient and accurate retrieval of high-dimensional data.

Data Representation

In vector databases, data is represented as vectors, which are arrays of numbers. Each vector encodes specific features of an item, such as the attributes of an image or the semantic meaning of a text. 

For instance, in image search, each image might be transformed into a vector that captures its visual characteristics. Similarly, text documents are converted into vectors based on their semantic content. This vector representation allows the database to handle complex, high-dimensional data efficiently.

Indexing Mechanisms

Vector databases utilise various indexing techniques to speed up the search and retrieval processes. One common method is the KD-tree, which partitions the data space into regions, making it quicker to locate points of interest. 

Another technique is Locality-Sensitive Hashing (LSH), which hashes vectors into buckets based on their proximity, allowing for rapid approximate nearest neighbor searches. These indexing methods help manage large datasets by reducing the number of comparisons needed during a query.

Query Processing

Query processing in vector databases focuses on similarity searches and nearest neighbor retrieval. When a query vector is submitted, the database uses the indexing structure to quickly find vectors that are close to the query vector. 

This involves calculating distances or similarities between vectors, such as using Euclidean distance or cosine similarity. The database returns results based on the proximity of the vectors, allowing users to retrieve items that are most similar to the query, whether they are images, texts, or other data types.

By combining these techniques, vector databases offer powerful and efficient tools for managing and querying high-dimensional data.

Use Cases of Vector Databases

Vector databases excel in various practical applications by leveraging their ability to handle high-dimensional data efficiently. Here’s a look at some key use cases:

Recommendation Systems

Vector databases play a crucial role in recommendation systems by enabling personalised suggestions based on user preferences. By representing user profiles and items as vectors, these databases can quickly identify and recommend items similar to those previously interacted with. This method enhances user experience by providing highly relevant recommendations.

Image and Video Search

In visual search engines, vector databases facilitate quick and accurate image and video retrieval. By converting images and videos into vector representations, these databases can perform similarity searches, allowing users to find visually similar content. This is particularly useful in applications like reverse image search and content-based image retrieval.

Natural Language Processing

Vector databases are integral to natural language processing (NLP) tasks, such as semantic search and language models. They store vector embeddings of words, phrases, or documents, enabling systems to understand and process text based on semantic similarity. This capability improves the accuracy of search results and enhances language understanding in various applications.

Anomaly Detection

For anomaly detection, vector databases help in identifying outliers by comparing the vector representations of data points. By analysing deviations from typical patterns, these databases can detect unusual or unexpected data behavior, which is valuable for fraud detection, network security, and system health monitoring.

Benefits of Vector Databases

Vector databases offer several key advantages that make them invaluable for modern data management. They enhance both performance and adaptability, making them a preferred choice for many applications.

Efficiency: Vector databases significantly boost search speed and accuracy by leveraging advanced indexing techniques and optimised algorithms for similarity searches.

Scalability: These databases excel at handling large-scale data efficiently, ensuring that performance remains consistent even as data volumes grow.

Flexibility: They adapt well to various data types and queries, supporting diverse applications from image recognition to natural language processing.

Challenges and Considerations

Vector databases present unique challenges that can impact their effectiveness:

Complexity: Setting up and managing vector databases can be intricate, requiring specialised knowledge of vector indexing and data management techniques.

Data Quality: Ensuring high-quality data involves meticulous preprocessing and accurate vector representation, which can be challenging to achieve.

Performance: Optimising performance necessitates careful consideration of computational resources and tuning to handle large-scale data efficiently.

Addressing these challenges is crucial for leveraging the full potential of vector databases in real-world applications.

Future Trends and Developments

As vector databases continue to evolve, several exciting trends and technological advancements are shaping their future. These developments are expected to enhance their capabilities and broaden their applications.

Advancements in Vector Databases

One of the key trends is the integration of advanced machine learning algorithms with vector databases. This integration enhances the accuracy of similarity searches and improves the efficiency of indexing large datasets. 

Additionally, the rise of distributed vector databases allows for more scalable solutions, handling enormous volumes of data with reduced latency. Innovations in hardware, such as GPUs and TPUs, also contribute to faster processing and real-time data analysis.

Potential Impact

These advancements are set to revolutionise various industries. In e-commerce, improved recommendation systems will offer more personalised user experiences, driving higher engagement and sales. 

In healthcare, enhanced data retrieval capabilities will support better diagnostics and personalised treatments. Moreover, advancements in vector databases will enable more sophisticated AI and machine learning models, leading to breakthroughs in natural language processing and computer vision. 

As these technologies mature, they will unlock new opportunities and applications across diverse sectors, significantly impacting how businesses and organisations leverage data.

Frequently Asked Questions

What are vector databases? 

Vector databases store data as high-dimensional vectors, enabling efficient similarity searches and complex queries. They are ideal for handling unstructured data like images, text, and audio by transforming it into numerical vectors.

How do vector databases work? 

Vector databases represent data as vectors and use advanced indexing techniques, like KD-trees and Locality-Sensitive Hashing (LSH), for fast similarity searches. They calculate distances between vectors to retrieve the most similar items.

What are the benefits of using vector databases? 

Vector databases enhance search speed and accuracy with advanced indexing techniques. They are scalable, flexible, and effective for applications like recommendation systems, image search, and natural language processing.

Conclusion

Vector databases play a crucial role in managing and querying high-dimensional data. They excel in handling unstructured data types, such as images, text, and audio, by converting them into vectors. 

Their advanced indexing techniques and efficient similarity searches make them indispensable for modern data applications, including recommendation systems and NLP. As technology evolves, vector databases will continue to enhance data management, driving innovations across various industries.

[archiesoniconline]

New Data File Alert!

The hip croc is in the house! Check out our new Data File for Vector!

Art by @drawloverlala

https://archiesoniconline.com/?page_id=29207

[krawdad]

I'm scouring compositing information there has to be a way to automatically add motion blur to stop motion by now

[bugendaitechgerman]

Importance of Vector Database in Generative AI

Today, databases, including Vector databases in Generative AI, continue to serve as the backbone of the software industry. Moreover, the quick rise of digitalization, fueled by the increase in remote work, has made databases even more critical. But there's a big problem we need to deal with—the issue of unstructured data challenges. And this refers to the vast amount of data globally. And it lacks proper formatting or organization for efficient search and retrieval.   

The Unstructured Data Challenges   

Unstructured data, constituting up to 80% of stored information, poses significant hurdles in sorting, searching, and utilizing data.   

To understand this,   

Consider structured data as information that is neatly organized into spreadsheet columns. Unstructured data is information that is randomly arranged in the first column. In addition, this lack of structure introduces errors and inefficiencies. And it demands manual intervention for data organization. 

The Burden of Manual Review   

Manual review of unstructured data is a common problem that consumes significant time and resources. And this problem is wider than the digital arena; even librarians categorize books.  

The fundamental problem lies in classifying information for efficient storage and use. And overcoming this hurdle is crucial for unleashing the true potential of data.   

The Promise of Vector Databases   

Vector databases present an exciting solution by using vector embeddings. It is a concept derived from machine learning and deep learning. And these embeddings represent words as high-dimensional vectors, capturing semantic similarities. In databases, vector embeddings represent properties to be measured. And that enables unique searching and data handling.

How Vector Embeddings Work   

Vector embeddings are a key element in the synergy of Vector embeddings and AI. They are created through trained machine-learning models. Moreover, they monitor specific properties within a dataset. The resulting numerical representation is plotted on a graph, with each property forming a dimension. Furthermore, searching involves planning a search query's embedding on the chart to find the nearest matches. This process shows that AI-driven data retrieval relies on complex relationships rather than only keywords.   

Applications and Benefits   

Vector databases redefine data storage and search by allowing searches based on overall similarity rather than just keywords. And this revolutionary change enhances productivity across various sectors:   

Recommendation Systems  

E-commerce and streaming platforms can use embeddings to enhance recommendation systems. Also, it can uncover hidden connections among products or content. As a result, it drives more engaging user experiences.   

Semantic Search  

Vector databases' capacity to understand context enables accurate search results despite variations in phrasing. And it makes searches more intuitive and effective.   

Question Answering  

Chatbots and virtual assistants can now provide more relevant answers. And they do it by mapping user queries to complex knowledge base entries. As a result, they create more satisfying interactions.   

Fraud Detection  

Comparing vectors that represent user behavior patterns detects anomalies efficiently. Therefore, it allows for a faster response to potential threats.    

Personalized Searches  

Storing user preferences as vectors leads to more customized and relevant search results. This enhances customer satisfaction.   

Reduced Manual Intervention  

Vector databases can automate many of the tasks involved in unstructured data management. It includes data classification, labeling, and search. Furthermore, this can free up resources for more strategic initiatives.  

Vector Databases vs. Traditional Databases  

Vector databases outshine traditional databases in several critical aspects:  

Support for Diverse Data Types 

Beyond text, images, and audio, vectors can represent a wide range of data types. As a result, it opens doors to new possibilities in various industries.  

  High Performance 

Vector databases are optimized for high-dimensional data. And it excels in performing complex mathematical operations. As a result, it becomes well-suited for demanding AI applications.   

  Efficient Storage 

Vector compression techniques help cut storage needs. And that results in addressing the challenges posed by the exponential growth of data.  

  Contextual Search 

By capturing semantic meaning and relationships, vector databases enhance search accuracy and relevance. And this is one of the crucial semantic search benefits that vector databases offer over traditional databases.  

  Scalability 

The real-time processing abilities of vector databases make them vital for handling and processing large datasets. 

  Generative AI insights 

Vector databases can store and retrieve high-dimensional data more efficiently. Therefore, it is vital for training and deploying Generative AI models.   

Leading Vector Databases  

Several vector databases offer unique solutions that cater to diverse needs:   

Weaviate  

Weaviate is well-suited for AI applications that demand sophisticated AI- driven data retrieval techniques.   

Milvus  

As a scalable vector database, Milvus shines in scenarios requiring extensive similarity searches. And it is critical for tasks such as image recognition and many more.   

Pinecone  

Pinecone stands out with its managed solution that definitely focuses on data connectivity. Also, it integrates generative AI models, pushing the boundaries of AI-driven insights.   

Vespa  

Providing support for vector, lexical, and structured searches within a single query, Vespa simplifies and enhances the search experience across various data types.   

Qdrant  

Tailored for neural network and semantic-based matching, Qdrant is at the forefront of leveraging cutting-edge AI technologies for robust data retrieval.   

Chroma  

It is a platform that simplifies the integration of Large Language Models. Further, Chroma bridges the gap between advanced language processing and efficient data handling.   

Vald  

Vald plays a vital role in applications demanding rapid and accurate data retrieval. It is designed to handle high-volume, high-dimensional data searches,   

Faiss  

Faiss is known for its efficient similarity search and clustering capabilities. As a result, it becomes an essential tool for extracting insights from complex data.   

Elasticsearch  

With its added support for vector similarity search, Elasticsearch continues to evolve as a versatile solution for various data handling needs.   

Conclusion   

As the complexity of data continues to grow, traditional storage and search methods face limitations in handling this influx. Vector databases, empowered by embeddings and similarity-based retrieval, introduce a new paradigm for efficient data management and AI integration. Also, vector databases offer several semantic search benefits, including improved accuracy and relevance of search results.    

 From enhancing recommendation systems to bolstering fraud detection capabilities, vector databases unlock the potential of unstructured data management. As a result, it propels businesses into a future driven by profound insights and intelligent interactions.  

In a world where data reigns supreme, embracing the capabilities of vector databases emerges as a pivotal strategy for staying ahead in the ever-accelerating data-centric race. The transformative power of vector databases is reshaping the landscape of data utilization and AI innovation, paving the way for more intelligent, more informed decision-making across industries.

[hydralisk98]

Prospero (OS-dev? software development suggestions? Nth braindump for sure)

Inspirations & references...

AROS

ZealOS

Paradise + Lain

Microdot Linux

Zen Linux kernel with Liquorix?

KDE Plasma desktop environment minified to Liquid & KWin

Fish-shell

Es

Rio

8½

Cardfile

Symbian

DIBOL

Lotus 1-2-3

VisiCalc

WordStar

COS-310

Acme

Nim

Zig

C 2023+?

GNU Common Lisp

LibertyEiffel

TROPIX

ChrysaLisp

MINIX

Tlick

GNU Hurd

PhantomOS

Haiku

xv6

RISC-V

IBM PC-DOS

ITS

CDE?

AIX

z/OS (Hypervisor?)

Inferno

Plan9

OpenGenera

Elbrus

OpenPOWER

SPARC

OpenVMS

illumos OpenIndiana

Xerox GlobalView

OpenHarmony

OpenBSD

Project actual specifications, targets ...

Sasha (Es, Fish, Parade, ZealOS, ChrysaLisp, Wish "command shells")

LainFS (transparent-data multimedia libre filesystem / format)

Devi (scripting symbolic data editor & hypervisor)

Tal (interactive programming language deriving from GitHub's MAL repository & taking hints from Swift, F#, REXX & SBCL)

VUE (Visual Union Environment) compositing window manager? (imitating CDE, Haiku's, KDE Plasma, GlobalView...)

Xerxes (Hypervisor & multi-agent sandbox ecosystem)

Zorua (animated SVG & symbolic vector computation library)

Ava (synthetic-tier android individual built from such technical stack)

Maskoch (cute little black bear cub mascot)

Personalized shell environment (aesthetically and practically too)

{ Es (Plan9's newer shell), Fish (friendly interactive shell), Kate, K3B, Okteta, KDE Partition Manager, Devine Lu Linvega's Parade/Paradise, ZealOS', ChrysaLisp, Wish; } = Sasha (symbolic analytical shell A)

"Tal" as the Lisp dialect to script so much of whatever happens in "Sasha" the command shell, "LainFS" as multimedia filesystem + format, "Zorua" as animated SVG + OGV + OpenEXR USD-tier inclusive-embedding full-version-control-source archive of save-state instances (great for animating filesystem changes across multiple timelines & interpolating transition data between them?), "Xerxes" = hypervisor;

As far as what I intend to use such for, "Sasha" is a real-time "sandbox filesystem" virtual environment's REPL with which I desire to record multi-agent social simulation stories, using a custom Lisp dialect REPL (aka a lambda-calculus-like multimedia DSL), with cool X3D environements + 2D animated SVG illustrations / icons, interpolated as necessary, taking advantage of version-control mechanisms as well as direct-mode editing to make really customizable long-term "manifestation toybox" scenarios. It seems similar to existing NetLogo and symbolic GAI research stuff, but I want to personalize specific simulation steps / instances in a overtly transparent and open manner...

Like, let's imagine I generate lively / immersive TS2-like stories with MegaOCEAN NPCs, as to eventually import into QGIS+OSM or whatever game engine I so choose later... (I really mean it such to help goal manifestation in the data visualizations manner, but observing and documenting life scripts for scientific analysis would be fine.)

I really do think of this as a GLOSS data-respecting alternative to the ChatGPT / AutoGPT / LLM-based game dev stuff that Big Tech pushes onto us. Self-hosted, lightweight on the REPL, easy-to-compute / explain & useful for spiritually-minded individuals seeking historical validation or mindful whatever. (Sure does my blend of Geo-Syndicalism shine with my statements here...)

So you know, I will find a way to get to a decent response to this kind of proposition (not for game dev, rather for statistical / demographic history simulation & arbitrary long-term social timelines...); Hence my 16^12 stuff needing some computational assistance without compromising the ethos / integrity I would rather preserve.

Stay tuned!

[cyoza]

I swore to myself that I would never go back to education and here I am doing a MASTERS in MATHS nonetheless am I literally insane

[ari-ana-bel-la]

Hi lovely, I absolutely love your stories. I was wondering if you could write one for Lewis, he has a daughter who is 16-17 and is absolutely smart, like Einstein smart and it's her first time in the Ferrari garage since Lewis moved and she saw a fault in some engineering work and helped fixing it and shocked her father and the whole garage. Thank you

The Future of Ferrari

Ferrari’s Maranello garage was a symphony of whirring drills, clanking tools, and intense Italian chatter. The team was hard at work preparing for the weekend’s qualifying session, red and black suits moving in well-practiced rhythm. Amid the organized chaos, one presence stood out—not because of noise, but because of the absolute silence and awe she left in her wake.

A girl with thick curls pulled into a loose bun and wide, observant brown eyes stood at the edge of the garage. She wore an oversized red hoodie with the Ferrari emblem on the chest, and a lanyard hung from her neck, swinging gently with her movements. Her expression was sharp, analyzing every corner of the room like she was mentally dissecting the internal combustion engine of the SF-24 just by looking at it.

“Daaaad,” she called out, trying not to sound impatient. “Where do you keep the drinks around here? I’m thirsty.”

Lewis turned around, helmet under his arm, his eyes immediately softening at the sight of his daughter. “Over there, near the data screens. Just don’t unplug anything or they’ll have a meltdown,” he teased, pointing her toward the crew’s refreshment corner.

She smirked. “Please, I could rewire this place blindfolded.”

He chuckled and shook his head. “That’s the confidence of a teenager with three physics awards.”

“Five,” she corrected, walking off.

As she moved across the garage, a few of the engineers took notice, recognizing her as Lewis’s daughter. Most had heard rumors of her intellect. She had attended MIT lectures for fun while vacationing in the States and was known for winning national-level science competitions in Europe. But seeing her in the flesh, in their sacred garage? That was new.

She sipped a bottle of water and leaned casually against a pillar, eyes drifting over the open rear of the car. Something wasn’t sitting right. She tilted her head, stepped forward a bit, and squinted at the gearbox housing.

A technician walked past her, carrying a tablet. “Excuse me,” she said, stepping closer to the car. “Is that the final mount design for the differential casing?”

The man blinked at her. “Uh… yes?”

She pointed to a specific joint just behind the casing. “That’s going to cause micro-vibrations under torque load. The fastener's alignment is 1.3 degrees off. It’s subtle, but enough to affect the car's handling mid-corner. Especially if it's hot.”

The tech frowned, unsure if he should laugh or worry.

“Sorry, who are you again?”

“Just his daughter,” she replied, nodding toward Lewis, who was now talking with his race engineer.

“Do you want to… maybe sit down?” he asked awkwardly.

But she stepped past him, crouched slightly, and gestured at a younger engineer who was watching curiously.

“Can I borrow your torque data? Just real quick.”

The engineer hesitated, then handed her the tablet.

She began typing, pulling up schematics, calculations appearing rapidly on the screen. Her thumbs moved like lightning, her brow furrowed in concentration. A few other engineers were gathering now, whispering among themselves.

“I recalculated the stress vector. See?” she turned the tablet toward them. “It looks fine in theory, but under compound load—especially with the way the aero package is set up—it’ll shift. You’ll get slight inconsistencies in traction, which is bad news during qualifying laps.”

The older technician who’d first questioned her stepped forward again. “Are you saying we need to rework this section?”

“I’m saying you need to adjust the mounting bracket by 1.3 degrees, shift the load path just slightly to the left, and reinforce it with carbon-composite washers. If you do that, you’ll stabilize the torque vector and improve rear-end consistency in Sector 3.”

There was a beat of stunned silence.

Then—

“Where did you learn that?” one of the senior mechanics asked, blinking.

She shrugged. “I read a paper about torque distribution in high-speed cornering last week. Got bored on the flight here.”

Someone stifled a laugh. Another said under his breath, “Bloody hell…”

“Oi!” Lewis called, finally noticing the growing crowd. “What’s going on?”

The head of engineering, a stern Italian named Matteo, stepped forward and gestured for Lewis to come over.

“Your daughter,” he began slowly, still sounding amazed, “just found a design flaw we didn’t catch. One that would’ve possibly cost you two-tenths per lap. Maybe more.”

Lewis stared. “Wait. What?”

Matteo pointed at her. “She’s… she’s like a walking CFD simulator. She even pulled up our own torque data.”

Lewis turned to her, his face a mixture of disbelief and fatherly pride. “Sweetheart, what did you do?”

She looked up innocently. “I fixed your car. You’re welcome.”

A round of laughter broke out, but it was warm, appreciative. The crew clapped her on the back, some shaking their heads in awe.

“She’s incredible,” Matteo said to Lewis. “You sure she’s not secretly part of Red Bull’s spy program?”

Lewis laughed. “Trust me, if she were, we’d all be in trouble. She’s probably smarter than half the grid already.”

“I’m smarter than you,” she teased.

“Absolutely no doubt about that,” he replied with a grin, ruffling her hair.

She smoothed it down with a roll of her eyes. “So dramatic.”

The engineers quickly got to work implementing her suggestions. Matteo kept glancing back at her like she was some kind of wizard. Lewis watched with arms folded, his heart swelling.

After a while, she stood beside him, watching the updated component go onto the car.

“So… what did you think?” he asked gently.

She tilted her head. “It’s loud. Smells like oil. Half the men here don’t know how to hold a tablet properly.”

Lewis laughed. “Welcome to Formula One.”

She smiled. “It’s cool, though. I like it.”

He nudged her shoulder. “You ever think about working in this world someday? Engineering, maybe?”

She glanced at him, then back at the car. “Maybe. If they can keep up.”

He chuckled again. “No pressure, but… you made me proud today.”

She looked at him seriously. “You’re always proud.”

“True. But today, I’m blown away. You just walked into one of the most elite garages on the planet and made a critical engineering correction before lunch.”

She gave a shy smile, shrugging. “Just saw something wrong and fixed it.”

He wrapped an arm around her shoulders. “You’ve always done that. In your own way.”

As the car roared to life for testing, the modified part holding firm, Lewis and his daughter stood side by side, two Hamiltons—one a living legend of the track, the other a rising genius who might just change the sport in her own quiet, brilliant way.

And somewhere behind them, Matteo whispered to a fellow engineer, “Keep an eye on her. She’s the future.”

♡♡♡♡♡♡♡♡♡♡♡♡♥︎♡♡♡♡♡♡♡♡♡♡♡♡

Authors Note: Hey loves. I hope you enjoyed reading this story. My requests are always open for you!

-♡○♡