Retrieving specific data from large data volumes is a pain. So, enterprises quickly adopt workplace search solutions to find hidden information within their vast datasets.
Elasticsearch and Vector Search are two popular choices for enterprises looking to enhance their search capabilities. Both possess unique strengths and challenges and are best suited for specific applications.
This article will explore the use cases, benefits, limitations, and core differences between Elasticsearch and Vector Search. Stay tuned to find out which one is ideal for your organization.
What is Elasticsearch in an AI workplace?
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Elasticsearch is an open-source, RESTful search and analytics engine designed to search, store, retrieve, and analyze large data volumes within seconds. It is built on Apache Lucene, an open-source search engine library, and supported by Elastic.
Elasticsearch has become one of the top choices in search and indexing engines. It helps you with backend searches and find specific metrics and logs. Other than these, here are the top Elasticsearch use cases:
Full-text search engine
Elasticsearch’s core search function includes the full-text search that supports multiple query types: phrase searches, fuzzy searches, autocomplete, user-defined searches, wildcard searches, and range searches.
Use Elasticsearch to power website searches and perform Google-like searches on sites or applications such as e-commerce websites, social networks, and document management systems to get fast and accurate search results.
Crawling and document processing
Elasticsearch can be used to crawl numerous websites, index their contents, and provide search functionality for the content based on user queries. Further, you can use the mapper-attachments plugin to convert documents like PDFs or Word files to plain text for indexing within Elasticsearch.
Real-time log analytics
Elasticsearch can be used to collect fragmented application-generated log data within the infrastructure. It centralizes the data and provides automation to sort the logs and identify information such as access requests, runtime errors, security issues, etc. Elasticsearch helps spot problems and troubleshoot quickly by providing valuable insights into system performance.
Metrics analysis
Elasticsearch’s API, paired with other Elastic Stack tools, can help you analyze metrics data like time series data, application events, and numbered values. Using Elasticsearch with a data visualization platform like Kibana, you can perform data analysis and visualize it to gain deeper insights into usage monitoring, behavior analytics, performance evaluation, and more.
What is Vector Search in an AI workplace?
Vector search is a data retrieval method used in generative AI and machine learning to find similar items from large datasets. This method focuses on similarity and semantic meaning instead of specific keywords and ensures accurate search results by representing data as vectors in high-dimensional space.
While vector search is the foundation of LLM applications, it has use cases beyond generative AI. Here are a few:
Similarity search
Vector search can be used for semantic or similarity searching to find similar content without relying on keyword matches. It captures the context and meaning of the search query and works with texts (documents), images, and audio to find items similar or related to the query. For instance, Google Lens uses vector search to find similar images based on user queries.
Personalized recommendations
Vector search provides contextual, personalized understanding to personalize content for each user. It powers models to recognize similar documents and their vectors and offer recommendations based on what users like or search for. For instance, applications like Amazon use vector search to recommend products similar to the ones you buy.
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Question-Answering systems
Vector search converts documents to text embeddings and delivers accurate full-text answers to user queries. This enables users to get answers to their questions quickly instead of reading long scripts or manuals.
Unstructured data browsing
Vector search allows you to search unstructured data like images, audio, videos, and texts. You can generate embeddings for your data to increase the possibilities for high-scale database applications.
Elasticsearch vs. Vector Search: What are the Differences?
Elasticsearch and Vector Search are powerful contenders among workplace search solutions. Let's discuss their differences to determine which suits your organization's needs.
Speed and efficiency
Elasticsearch and Vector Search can process search queries and deliver results within seconds. Elasticsearch requires fewer computational resources and supports a broader range of querying capabilities, including boolean queries, phrase matching, fuzzy matching, and relevance scoring. Elasticsearch's querying flexibility allows enterprises to fine-tune their search parameters and retrieve the most accurate results for their specific use cases.
On the other hand, Vector Search has transformed the way similarity searches are conducted. With vector embedding models, you can search through vector databases to find similar items. However, performing an exact search that brings the most similar vectors to the query involves costly methods. Also, the processing may take hours which is not so feasible in real-world applications.
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Scalability
Elasticsearch is renowned for its superb distributed architecture and scalability. It can horizontally scale across numerous nodes to handle large amounts of data and high query volumes. Sharding and replication algorithms integrated into the system guarantee fault tolerance and high availability. Due to its distributed architecture, Elasticsearch is appropriate for environments with big clusters and high throughput demands.
For example, to increase scalability, you can run Elasticsearch on different cloud platforms, including Microsoft Azure, Amazon Web Services (AWS), and Google Cloud. With cloud-based Elasticsearch, you can quickly scale clusters up or down to meet changing data needs without worrying about hardware constraints.
On the other hand, despite vector embeddings' processing demands, Vector Search operates best with smaller datasets. Nonetheless, it can effectively handle moderately large datasets with advancements like approximate nearest neighbor search. Nearest neighbor search is a technique to find data points closest to a given query point in a high-dimensional space.
Vector Search identifies the vector embeddings that are most similar to query embedding, while K-nearest neighbor (KNN) search is a variation that retrieves the top K closest data points. In Vector Search, KNN search fetches the top K most similar embeddings to a query.
Support and maintenance
Elasticsearch, a well-established open-source project, enjoys having a large and active developer community. This community supports and contributes to the project by sharing knowledge and developing plugins and extensions to enhance its functionality.
With a sizable user base, businesses can tap into a vast knowledge base, lively forums, and frequent updates, ensuring a reliable and well-maintained search system. Elastic, the organization behind Elasticsearch, also offers commercial support and additional functionality, further bolstering the support available to businesses.
In contrast, Vector Search, a relatively newer approach, may not have the same level of developer community support as Elasticsearch. However, with the increasing adoption of machine learning and deep learning techniques, the developer community for Vector Search is growing.
Thanks to increasing resources, tutorials, and community support, enterprises now have access to guidance for adequately deploying and sustaining Vector Search. The rising need for this technology is anticipated to draw more developers, leading to additional community support and collaboration opportunities.
Pricing
Elasticsearch is a cost-effective choice for enterprises wanting to adopt powerful search capabilities. The fundamental Elasticsearch software is free, but there may be charges for commercial support and extra features.
Vector Search is costlier than Elasticsearch. Depending on the specific frameworks and libraries used, Vector Search may require additional computational resources and infrastructure, such as GPUs (Graphics Processing Units), to generate and store vector embeddings effectively.
Since GPUs are expensive specialized hardware with parallel processing capabilities, their inclusion in the infrastructure increases implementation and maintenance costs, including power and cooling expenses. Enterprises should carefully assess their budget and resource requirements when considering this approach.
Pros and Cons of Elasticsearch
Elasticsearch offers quite a few benefits. However, it also has its downsides.
Pros
- Highly flexible Query API that integrates with both search and real-time analytics
- Distributed architecture enables parallel query execution across different shards, resulting in rapid responses
- Makes it possible to analyze billions of records in split seconds
- Provides aggregations to explore data patterns and trends
- Has a large community of users and contributors
Cons
- Setting up and maintaining Elasticsearch clusters can be complex
- Elasticsearch is resource-intensive, requiring sufficient memory, CPU, and storage to function effectively
- Has a steep learning curve with its many features and functionalities
- Data inconsistencies and documentation gaps
Pros and Cons of Vector Search
Vector Search is great for relevance, but it is not suitable for all query types. Here are the pros and cons of using Vector Search.
Pros
- Delivers highly relevant and accurate results
- It is versatile. Accommodates several use cases and content
- Enables search for text, videos, images, and audio
- Provides accurate product recommendations and personalized content
Cons
- Complex and pricey implementation process requiring significant computational resources
- Not suitable for searching for specialized or niche items
- Has speed and scale limitations that affect performance
The Final Verdict
Elasticsearch and Vector Search offer distinct approaches to workplace search, each with strengths and challenges. While Elasticsearch excels in text-based search and scalability and is ideal for online shopping, location-based services, etc., Vector Search works great for similarity-based searches. It is valuable for recommendation systems, content filtering, and internal knowledge base searches.
When choosing, consider search and scalability needs, data volume, and budget to implement the most suitable workplace search solution. You can also combine both to create a comprehensive search strategy.
Regardless of the chosen approach, you can use AI to find the perfect answer to your search query. With Findr’s knowledge management solution, you can quickly find the information you’re looking for within your organization’s internal database. Access universal search across all applications, build custom AI assistants, and easily integrate Findr into your tool stack to find everything in one place.
Want to know how Findr works?
FAQs
How do Elasticsearch and Vector Search differ?
Vector Search uses machine learning to offer similarity-based search using vector embeddings, whereas Elasticsearch is a document-oriented search engine with robust querying capabilities.
Which solution is better for text-based search?
Elasticsearch has strong querying capabilities and scalability for handling massive volumes of textual data, making it a good choice for full-text search applications.
In what scenarios does Vector Search excel?
Applications like recommendation systems, content-based filtering, and multimedia search, which call for enhanced search capabilities beyond keyword matching, excel when using Vector Search.
What is enterprise-level vector search?
Enterprise-level vector search is a method large-scale companies use to find and retrieve information based on semantic concepts or similarity instead of simple keyword matching.
