The demand for efficient and intuitive search capabilities has never been higher. Traditional keyword-based search methods, while effective for certain tasks, often fall short when it comes to understanding the nuances of language or visual information. This is where Elastic vector search steps in, offering a shift in how businesses approach information retrieval and recommendation systems. In this article, we will take a closer look at the different use cases in which vector search can bring added value. 

A. Why Vector Search Matters for Businesses

At its core, vector search is driven by the concept of similarity. Instead of relying solely on exact keyword matches, it understands the semantic context of data, allowing for more accurate and relevant results. This capability opens up a world of possibilities for businesses across various industries. 

1. Semantic search

E-commerce: Imagine a shopping experience where you’re not limited by specific keywords. With vector search, customers can find products based on their preferences, even if they express them differently. For instance, Shopify has launched a semantic search tool for its customers to make the client’s online shopping experience more intuitive. Read more here.

(Picture source: Shopify) 

Media & entertainment: Discovering content becomes a breeze as vector search analyzes audio or textual features, enabling recommendations based on mood, genre, or style. Think about Spotify, which makes playlists based on your listening behavior.  

(Picture source: Promoly)

Recommendation Systems: 

News & articles: Vector search goes beyond surface-level recommendations, understanding the underlying themes or sentiments of content to offer personalized suggestions. 

E-commerce: By considering diverse factors like demographics and browsing behavior, businesses can enhance product recommendations, leading to higher engagement and conversions.  For example, you are browsing through a clothing website and clicked on a pair of pants, then the website will recommend other similar pants or other clothing items which you might be interested in. 

(Screenshot source: Zalando)

Customer Service Chatbots  

Providing tailored responses to user queries becomes seamless with vector search, enhancing customer satisfaction and reducing response times. This personalized approach enhances customer satisfaction and significantly reduces response times, ensuring a seamless and efficient experience. 

Natural Language Processing (NLP) Tasks: 

From document classification to sentiment analysis, vector search streamlines NLP tasks by deciphering the meaning and tone of text, facilitating more efficient data processing. 

An example focused on document classification illustrates the following: Suppose we have a collection of news articles categorized as “Technology” and “Sports.” Using vector search, we represent each article as a numerical vector based on its content. When a new article is introduced, vector search compares it with existing articles and assigns it (automatically) to the category with the most similar articles, enabling efficient document classification.  

To illustrate this example, we searched for an article related to ‘Tiktok’ on Forbes, and automatically got related Tiktok news articles on their website:  

(Screenshot source: Forbes)

2. Image search

Vector search cannot only be used for semantic search use cases. Moreover, image similarity search can bring a lot of added value for different industries.

Image Search:
From identifying objects and scenes to aiding accessibility for visually impaired users, image search powered by vector technology transforms visual information into actionable insights. Let’s explore the example of PcFruit. In the agricultural industry, image search technology revolutionizes berry farming by enabling quick and accurate identification of berry varieties. By capturing images of berries and analyzing key features like shape and color, farmers can optimize harvesting schedules and improve inventory management. Explore the full case here. 

(Picture source: Brainjar)

B. Generative AI Integration

By harnessing the capabilities of generative AI alongside semantic search, businesses can streamline processes like customer service, document summarization, and information synthesis, boosting productivity and decision-making efficiency.  

Examples of a synergy: generative AI and semantic search 

Enhanced Customer Service: GenAI-driven question-answer solutions empower service desk employees and customers alike, improving response accuracy and efficiency.

Document Synthesis: Vector search combined with generative AI can synthesize findings from disparate sources, enabling quick access to relevant information. 

Information Summarization: By summarizing key insights from extensive research, businesses can make informed decisions faster, driving innovation and growth.

Automated Legal Research: Legal firms can utilize semantic search to sift through vast amounts of legal documents and precedents. By integrating generative AI, they can automatically generate briefs, summaries, or analyses based on specific case details, saving time and resources. 

Medical Diagnosis Support: Healthcare providers can employ semantic search to analyze patient records, medical literature, and diagnostic reports. Combined with generative AI, this technology can assist in generating differential diagnoses or treatment recommendations, aiding physicians in decision-making processes.

Financial Analysis and Forecasting: In the finance industry, semantic search can be used to extract insights from financial reports, news articles, and market data. When integrated with generative AI, it can assist in generating financial models, forecasting trends, and automating report generation for investment analysis or risk assessment. 

Conclusion: Embracing the Future of Information Retrieval  

Incorporating vector search into business processes isn’t just about staying ahead of the curve, it is about reshaping the way we interact with data. By embracing the power of semantic understanding and image recognition, businesses can deliver personalized experiences, streamline operations, and unlock new avenues of growth. As technology continues to evolve, the possibilities of vector search and its synergies with generative AI are limitless, promising a future where information retrieval is not just efficient but truly transformative. 

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AI has been around for some time. Use cases were sometimes not achievable due to the complexity of the implementation or limits within AI or computing power. Elastic technology makes it easier to enjoy the benefits of AI. Elastic makes it less complicated for developers to implement semantic search, image search and more. The proof of the pudding is in the eating. That’s the reason why we did an experiment and developed image similarity in Elastic. In this blog we will talk about the how and most importantly the impressive results achieved from the experiment.  

This article explores Elastic’s application in image similarity searches, focusing on icons like the recycle symbol and the European letter ‘E’. Specific experiments with these symbols are conducted, alongside discussions on technical challenges and solutions. This article particularly highlights the promising results achieved with the recycle icon and the European letter ‘E’. The potential of Image similarity technology for future projects is promising, underlining its simplicity and effectiveness in embedding images and text for searching purposes.  

Fun fact: The implementation of this experiment with the Elastic stack took less than a day, including the repairments and adjustments that were needed during the development process.   

First we will explain the results in this article, afterwards we will provide the technical details how we realized the image similarity search. To conclude will provide some insights with respect to sizing. 

We have a dataset of 47 images to be precise:

When searching for an image, typing “European E” we receive European E’s as the first results, which is good.

Instead of using text terms to search for images, we also used one of these “European E” symbols to search by clicking “Find similar images”. This also provided the expected similar images as search results. 

Green recycle symbol 

In our dataset, we have several recycle symbols of which a few are green. We did a test searching for “green recycle”. This did in fact only return our green recycle icons in the dataset, which is a great result:

2) The technical journey: how we achieved results 

The start 

We started our exploration based on this informative blog post from Elastic. 

We meticulously followed the instructions outlined in Elastic’s blog post and GitHub repository. After thorough perusal of the README.md file, we proceeded to clone the repository and integrated the required model from Hugging Face into our cloud instance using Elastic’s eland from Github. Keep in mind that this model does not need to be used. Other models can be used as well however then the backend would need to be modified.

Modifying the code 

Encountering outdated packages during the process, we swiftly modified the requirements.txt file, ensuring compatibility and smooth installation. Additionally, we addressed image pixel limitations and fine-tuned settings for optimal performance. Below illustrates the improved requirements.txt file: 

Following these adjustments, the pip install process proceeded seamlessly. Additionally, the ‘.env file’ was updated with the necessary credentials for our cloud instance. However, while attempting to generate image embeddings and ingest them, a subsequent issue was encountered. This challenge, which will be elaborated upon shortly, came from an oversaturation of pixels within our images. To address this, a simple line addition under the imports section of the create-image-embeddings.py file sufficed. Note: The code makes sure there is no maximum image pixels limit so watch out with how you use this (Decompression bomb):  

The embedding has 512 dimensions. Keep in mind that this image went through the clip-ViT-B-32 model. This is a free public model and is sufficient for our use case. The interface has a search box, which when submitted sends text to elastic which goes through the model (clip-ViT-B-32-multilingual-v1) that was imported with Elastic eland. This happens in the background (Flask Backend) and therefore, there is no need to look at that in this demo application, nor will we look at the Flask backend in this blog post. The interface has an image upload field. This can be used to upload images and search for other images that are similar to the uploaded one. 

Size of Images 

As mentioned above our dataset consists of 47 images. These Images have a combined size of around 3.6Mb. When looking at the index with the embedded values, the size is 469.9Kb.   

Size of Text  

We compared the sizing of embeddings for images with embeddings for text. We used a dataset containing about 8000 documents and having a size of 17.7Mb in total. Each document has only a few lines of text. When ingesting this data for ‘text search’ into an index, the index takes 36.3Mb. When ingesting for semantic search, using the E5 model the index takes 119.8Mb. In case both indexes are used for example to use RRF (Reciprocal Rank Fusion), the total index storage is 156.1Mb. 

Why did the size decrease for the images but increase for Text 

This is because of the dimensions and number of documents. The images index only had to keep 47 documents while the Text dataset’s index had to keep around 8000 documents. The Images dataset had the size of 9,99Kb per document While the Text index had around 14.97Kb per document. Which is relatively close to the Images index. If we take a look at the total vector dimensions, the Images index has 512 dimensions. However the Text index has around 768 dimensions, both the title and overview embedded fields have 384 dimensions. So after all it depends on how much dimensions are being used, if we used only one field to embed the size would greatly decrease, same can be said for the images index, if we used another model that generates more dimensions the size would greatly increase. 

If we go into even deeper detail, the images index has about 19.51bytes per dimension while the text index has 19.49bytes per dimension. As can be seen here they are about the same. So by this logic we could take 19.5b as average for each vector dimension. Keep in mind these are dense vectors, not sparse vectors.  

We were highly impressed with the ‘Image Similarity Search’ functionality, particularly appreciating the straightforward process of obtaining a model from Huggingface, embedding both images and text, and initiating searches.  

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Real-world Gen AI use cases

Gen AI innovations, particularly in semantic search, present a myriad of opportunities for the public and private sectors. From personalized responses for students, citizens, and customers to streamlining workflows for employees.

Read our other blogs about real-world use cases:
HelpdeskGPT & HrGPT

The challenge however lies in leveraging internal data to ensure relevance, accuracy, and security.

Your Data + Generative AI = Context-Rich Answers

The integration of Gen AI with private data is crucial for achieving mission value. While publicly available Gen AI applications are limited to internet data and prone to inaccuracies, the Elastic Stack platform ensures privacy-first Gen AI experiences. By prioritizing security, delivering hyper-relevant content, and reducing hallucinations, Elastic enables real-time, scalable, and secure Gen AI applications.

The Elastic Generative AI Value Proposition

To create business value with Gen AI, leveraging proprietary data is essential. Gen AI, using context windows, integrates with unstructured, structured, or semi-structured data to deliver highly relevant and contextual responses. ElasticSearch, trusted by over 50% of Fortune 500, ensures privacy-first Gen AI experiences.

• Prioritizes Security and Confidentiality: ElasticSearch implements clearance-level access, removing private information.
• Delivers Hyper-Relevant, Reliable Content: Ensures the most relevant content from proprietary data informs Gen AI responses.
• Reduces GAI Hallucinations: Infrequent inaccuracies as ElasticSearch uses mission-specific information.
• Lower Costs: By providing information most relevant to queries, ElasticSearch minimizes compute and storage resources.
• Unified Platform for AI Apps: ElasticSearch offers an end-to-end platform for building and delivering AI search applications.
• Real-time Guidance: The Elastic AI Assistant aids security teams in tasks like alert investigation, incident response, and query generation.
• Maintains Security and Confidentiality: ElasticSearch recognizes and implements appropriate access, removing private information.

Conclusion

In the dynamic landscape of AI-driven search results, whether or not combined with a Gen AI’s question/answer solution, the synergy between ElasticSearch and Gen AI unlocks unprecedented capabilities.
Whether serving the public sector or reshaping experiences in the private sector, the collaboration between data and Generative AI is a powerful force driving innovation and efficiency. Elastic’s commitment to security, relevance, and cost-effectiveness positions it as a cornerstone in the transformative journey of Generative AI.

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Single-Sided Platform

A single-sided platform focuses on bringing together a single group of users or participants. These platforms enable interactions and transactions among members of the same user base. A classic example of a one-sided platform is a social media network like Facebook or Twitter, where users connect, share content and interact within the same community.

Elasticsearch can significantly enhance the performance of one-sided platforms by providing robust search capabilities and efficient data retrieval. For instance, in a social media network, Elasticsearch can power the search functionality, allowing users to find relevant posts, users, and hashtags quickly. Its real-time indexing and search capabilities ensure that users receive up-to-date and accurate search results, leading to a more engaging user experience.

Two-Sided Platform

Two-sided platforms, often also referred to as multi-sided platforms, cater to two distinct user groups that are interconnected by the platform’s services. These platforms create value by facilitating interactions between the two groups. A classic example of a two-sided platform is Uber, which connects drivers and riders, enabling ride-sharing transactions.

For two-sided platforms, Elasticsearch can play a crucial role in optimizing user experiences and operational efficiency. In the case of Uber, Elasticsearch is employed to enhance the rider and driver matching process. It can consider various factors such as location, availability, and user preferences to quickly identify suitable matches, resulting in reduced wait times and improved customer satisfaction. Furthermore Elastic will also manage the access levels for the different user levels and types. Empowering privacy and data security.

Multi-Sided Platform

The multi-sided platform is a broader term and can go beyond connecting only two user groups; they involve three or more distinct user segments, each contributing to the platform’s overall value proposition. These platforms often create a network effect, where the value of the platform increases exponentially with the number of participating segments. An example of a multi-sided platform is Airbnb, which connects hosts, travelers, and local service providers.

In multi-sided platforms like Airbnb, Elasticsearch’s capabilities can be harnessed to create more personalized and relevant recommendations for users across various segments. By analyzing user preferences, historical data, and location-based information, Elasticsearch can provide tailored suggestions for accommodations, experiences, and services. This level of personalization enhances user engagement and encourages cross-segment interactions.

user level/roles data access

Summary

Digital platforms will continue to shape industries and redefine business models, understanding the differences between one-sided, two-sided, and multi-sided platforms is essential for effective platform design and management.
Elasticsearch, with its advanced search and analytics capabilities, can significantly enhance the performance of these platforms by improving data retrieval, user matching, and personalized recommendations.
By harnessing the power of Elasticsearch, platform operators can create seamless and value-driven experiences for their users, leading to increased engagement, satisfaction, and ultimately, business success.

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Telecom companies generate massive volumes of data daily, including call records, usage patterns, billing information, and customer interactions. Elastic’s capability to ingest and index diverse data sources enables real-time analysis, allowing companies to identify early warning signs of potential churn.

Elastic facilitates the creation of a comprehensive customer profile by integrating data from various touchpoints. By analyzing historical behavior, network usage, and customer feedback, telecom providers can gain insights into customer preferences and sentiments, aiding in predicting which customers are more likely to churn.

Elastic comes with machine learning, enabling telecom companies to develop churn prediction models. By training models on historical churn data and relevant features, Elastic assists in identifying patterns and factors that contribute to customer attrition. These models can then be used to forecast churn probabilities for individual customers.

Elastic’s anomaly detection features help identify unusual behavior that might indicate an imminent churn. Sudden spikes in customer complaints, decreased activity, or changes in usage patterns can trigger alerts, allowing companies to take proactive measures to retain those customers.

Elastic’s capabilities in segmenting customers based on attributes and behavior aid in tailoring retention strategies. By understanding the unique needs and preferences of different customer groups, telecom providers can implement personalized offers and incentives to prevent churn.

Elastic’s real-time monitoring and visualization tools empower telecom companies to track key performance indicators and promptly respond to emerging trends. This agility is crucial in preventing churn, as companies can swiftly address issues and concerns raised by customers.

Elastic’s iterative approach to analysis and modeling allows telecom providers to refine their churn prediction strategies over time. The more data is ingested in Elastic the accuracy of churn predictions improves, leading to more effective customer retention efforts.

Elastic’s powerful capabilities in data aggregation, real-time analysis, machine learning integration, and personalized strategies provide telecom companies with the tools needed to foresee and prevent customer churn.
By leveraging Elastic’s versatility, businesses can develop insights that guide strategic decisions, ultimately improving customer satisfaction and retention rates.
In an industry where customer loyalty is the key to success, Elastic emerges as a game-changing solution for predicting churn and enhancing customer engagement.

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Real-time Issue Tracking & Analysis

By leveraging Elasticsearch, a highly scalable and distributed search and analytics engine, helpdesk systems can capture and index vast amounts of log data, user interactions, and support ticket information.
This real-time indexing enables the system to respond rapidly to user queries and retrieve relevant information efficiently. Elastic Observability further enhances this capability by providing comprehensive insights into system health, performance metrics, and log analysis.
These analytics can assist helpdesk agents in identifying trends, root causes, and patterns, allowing them to proactively address issues before they escalate.

For example, when a customer reports a recurring problem, the integrated system can quickly search through historical data and identify similar cases. Helpdesk agents can use this information to provide more accurate and targeted solutions, which will reduce resolution times, and therefore, increase customer satisfaction.

Intelligent Chatbot Assistance

Integrating ChatGPT with Elasticsearch and Elastic Observability empowers helpdesk chatbots to deliver more intelligent and context-aware responses. The integration will be able to understand natural language queries, analyze user intent, and retrieve relevant knowledge base articles, support documents, or troubleshooting guides from the Elasticsearch index.
By combining this capability with the real-time analytics provided by Elastic Observability, chatbots can offer personalized and data-driven assistance to users.

For instance, if a user asks a question about a specific product feature, the chatbot can utilize Elasticsearch to search for relevant articles or Elastic Observability to identify any known issues or recent updates related to that feature.
The chatbot can then provide accurate information or suggest appropriate solutions, reducing the need for human intervention.

Beyond the Chatbot

Imagine the power further integrated with a text-to-speech assistant like Alexa, Siri, or google assistant, as we mentioned in a previous article.
This will give the user a whole new interface to communicate with and give true conversational capabilities.

Examples of use cases are abundant since most helpdesks only work during office hours. A data-driven, conversational, and auditive chatbot can ‘guard’ the remaining 16 hours of the day and night. Afterward, this bot will be able to report and help to sift through what customer tickets have to be prioritized and require human intervention the very next morning.
This will give 24/7 support services possible on smaller budgets.

AI-assisted Knowledge database

The combination of Elastic with ChatGPT can help formulate answers – taken from the knowledge base – to customer queries so that the helpdesk operator spends less time researching and keeping the customer on hold or waiting.

It will also vastly improve the quality and speed of new hires to function optimally as a helpdesk agent. Relying on suggested replies, based on the knowledge base of senior helpdesk agents, technical data, or past and resolved issues.

Predictive Analytics for Improved Decision Making

Elastic Observability offers advanced data visualization and analytics capabilities that helpdesk managers and stakeholders can leverage for data-driven decision-making. By monitoring and analyzing various metrics such as response times, ticket volumes, customer satisfaction ratings, and agent performance, the system can identify patterns and trends that enable proactive decision-making.

For example, the integrated system can detect an increasing number of support tickets related to a specific software version or hardware component. Armed with this information, helpdesk managers can allocate resources, plan training sessions for agents, or escalate the issue to the development team for a timely resolution. This proactive approach reduces downtime, improves efficiency, and again, enhances customer satisfaction.

So what’s in it for me?

The integration of Elasticsearch and Elastic Observability with ChatGPT brings countless benefits to the helpdesk industry, revolutionizing the way customer support is delivered. Through real-time issue tracking and analysis, helpdesk systems can proactively identify and resolve problems, resulting in faster response times and increased customer satisfaction.
The intelligent chatbot assistance capabilities provided by combining ChatGPT & Elastic ensure personalized and context-aware support, delivering accurate information and solutions to users.

As technology continues to evolve, the integration of advanced tools like Elasticsearch and Elastic Observability with AI models like ChatGPT will play an increasingly significant role in transforming the helpdesk industry. The future of helpdesk services lies in harnessing the power of intelligent data-driven conversational AI. The integration of the Elastic stack with ChatGPT is a significant leap in that direction.

Elk Factory – Elastic Premier Partner

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Intelligent & fast Employee Support

When integrating ChatGPT with Elasticsearch, HR professionals can create a powerful and intuitive chat interface for employees to seek assistance, obtain information, and resolve queries, based on your company’s own data and policies.

Imagine you have an accident with your company car in the middle of a weekend night. At this dire moment, HR won’t be available to answer your questions promptly. A conversational interface, however is available 24/7 and will be able to answer all or most questions swiftly, or even guide you and comfort you.

Or consider an employee seeking guidance on the company’s vacation policy. Instead of having to browse through lengthy policy documents or waiting for HR to respond, they can simply engage with the chat interface.

It is clear this integration can and will reduce response times, increase the employees’ experience, and free up HR personnel for more challenging tasks.

When HR departments deal with high volumes of recruitment, for example working with temps, day or seasonal laborers, the combination of these two technologies can help during the onboarding phase. Guiding new hires through the necessary paperwork, company policies, and FAQs.
This reduces administrative overhead, provides consistent and accurate information, and allows HR personnel to devote more time to fostering positive employee experiences.

Increasing the experience with
Alexa, Siri, or Google Assistant

If a mear textual communication is not enough, imagine combining the power of Elastic, ChatGPT, and a voice assistant like Alexa, Siri, or Google Assistant.

Combining Elastic & ChatGPT, and the text-to-speech and auditive power of the assistant technologies can further improve the experience and the possibilities on how to access the correct data. You will be able to ask questions and have fluent conversations with this voice-assisted interface. This can be useful in certain work environments, where textual conversations are impossible or not allowed. For example the transportation sector or any place where monitors, keyboards, and similar input devices are unavailable.

*More on this later, as we are currently working on this

As we mentioned before, however, the generative capabilities of ChatGPT and other technologies like Google’s Bard are already a technological marvel, surely set to revolutionize the world.
Caution has to be maintained as they can be susceptive to giving false data. We are convinced however that in a short span of time, these hallucinations will be a thing of the past.

The same goes for the security concerns on personal data. Luckily Elastic already offers an extensive solution, where you are able to finely tune and gate the data access to the field level.

Integrating ChatGPT further enhances HR operations by offering seamless employee support, and streamlined onboarding experiences. As technology continues to evolve, the collaboration between Elastic and advanced models like ChatGPT will undoubtedly shape the future of HR.

The combination of corporate data via Elastic and ChatGPT presents a game-changing opportunity for HR departments.
By harnessing Elasticsearch’s powerful search and analytics capabilities, HR professionals can gain valuable insights from employee data, enabling data-driven decision-making and strategic planning.

Embracing these tools will empower HR professionals to work smarter and deliver better employee experiences, which will drive organizational success.

Elk Factory – Elastic Premier Partner

Elk Factory is the Elastic partner to implement the Elastic platform. We aim for a win-win! We look at how this platform can make your company benefit the best, in return we can enjoy another satisfied customer!

Get to know us and contact us without obligation.

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Challenges, Solutions, and Opportunities

Speed

Research* shows that as many as 79% of visitors switch to a competing site if it takes too long to find the desired products. Time and speed are therefore highly important for online shoppers. This emphasizes the importance of an efficient and effective search experience on e-commerce platforms.

According to the research, possible frustrations or obstacles during searches include irrelevant search results, no ability to compare or find similar products, and no results based on descriptions (you need to know the product name).

In addition to the traditional full-text search, Elastic Enterprise Search offers numerous functionalities that help online shoppers obtain relevant results quickly:

Last but not least, from a technical standpoint, Elastic is an incredibly fast technology, even when dealing with large volumes of data.

Personalization

Quality over price

While price remains an important factor in online shopping, a significant portion of online shoppers still prioritize finding the right product, even if it means paying a little more.
According to research, 53% of online shoppers prefer finding the right product over the lowest price. This emphasizes the importance of a good search functionality focused on providing relevant search results rather than just price competition.

Since Elastic allows tuning the order of results, for example, the ranking of results can be based on reviews.

Measure and know

Elastic provides numerous statistics that offer insights into how users search, what they are searching for, and whether they are truly finding what they need.

Here are a few examples:

• Statistics show the words that are searched for but have no available results. This can be solved, for example, by setting a synonym or providing additional content.
• Statistics show the words that are searched for and then which pages are clicked on and how long users stay on those pages. This provides insight into the relevance of the results and whether you need to improve the ranking of the results.
• Suppose search statistics indicate that there is a high demand for a specific type of smartphone that is not yet released. In that case, you could launch a pre-registration to capture all this potential revenue.

*Wakefield research paper

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