Author Introduction: I am Mansi Tibude, an electronics and communication engineer. I have worked in the IT industry for about 3 years as a Systems Engineer in a previous organization. I have worked with various technologies, and delivered results on time. I am a hard-worker as well as a smart worker and can quickly learn any technologies and can apply it to build real-time applications.
Abstract: Vector search is AI -powered search and has more advanced search features. It not only gives search for text, but can even give search results for Audios, Videos and Images too.
Elastic search has a major advantage over other search engines. It gives search outcomes as hybrid search which is a combination of semantic search and vector search and gives more accurate and 10x faster results. Vector search gives outcomes in the form of Vector Data not plain text which is beneficial for storing user searches in table format. We already know a lot of features of Elastic-Search and how it is different from other search engines, but what Blogathon expecting is that how we can add more features and how we can innovate in already built-in Elastic Search engine especially in Vector Search, Hybrid search and Semantic search using ELK.
Content Body: Vector search, Hybrid Search and Semantic search play a major role in giving more relevant results as per the user’s expectation but what if we need to increase the accuracy of the results along with adding more features in search query. As in vector search, the query results are stored and the results are given in Vector format.
The question is how we can scale up Vector Search, especially Hybrid Search?
Vector search can be optimized by either hybrid scaling or vertical scaling. Vector search is used for searching content from different documents by searching keywords, then analyzing the keywords and storing the search data in the Vector Database. The search data is converted into vector form (Vector Embedding) and then stored. In simple words, we can say:
Vectors search performs the below mentioned process:
-Converting the documents and search queries into vectors
-Store vectors, so as to facilitate vector math
-And performing mathematical operations by using different vectors match functions quickly and efficiently
The KNN – K- Nearest Neighbor ML model is used for Vector Searching and RAG (Retrieval Augmented Generation) is used to convert data into numerical vector search along with this Re-ranking is used to search algorithms by reordering the to improve the searching and get more accurate results. Now, comes the role of Vector database, a highly efficient database used to store vector search data.
Vector search specifications:
-Manual configuration
-Self-Embedding
-Giving directly vector search similarity matching
How Vector Search works, explanation is here. After searching in Vector search, search engines, the search data is encoded by generating embeddings with AI models, and the data is then indexed and converted into vectors and after this, search understands the context and gives the results as per the user’s search.
How Vector searches have overcome the challenges faced by other searching methodologies? The actual challenges overcome are: Semantic searches (understanding the context of search), having multi-model search capabilities and personalization and recommendations as per user’s requirements.
Vector Database: Vector database is used to store high-dimensional Vectors. What makes a vector database very useful in storing vector search data? Well, we can share the features of vector database:
-Scalability
-Indexing and search performance
-Hybrid search support
-Tech Stack integration
There are various ways of hybrid scaling and vertical scaling. Elastic-search can have three kinds of searching:
-Index and search basics
-Keyword and search with python
-Semantic search
-Vector Search
-Hybrid Search
But our focus is on optimizing the Vector Search. There are different ways by scaling up the Vector Search and these ways are mentioned below:
Vertical Scaling: Vertical scaling of elastic-search can be done by increasing the number of CPU’s cores and along with proper storage ways i.e. using caching, SSD improvements and increasing the processing speed for better outcomes.
Different caching mechanism for optimizing the vector search:
-Storage-level caching
-Embedding caching
-Query -level caching
-LLM output caching
We can increase the number of CPUs cores, or optimize ML models, we can use TPUs too, latest hardware development for increasing processing speeds.
Horizontal Scaling: Horizontal scaling of elastic-search can be implemented by increasing the nodes and the shards where the actual data is stored. This can help in dividing the load of data and increasing vector search speed.
Different horizontal scaling criteria for optimizing vector search can be increasing nodes, and shards for distributing the data input load. We can take reference from micro-services architecture by distributing the load.
Optimizing the vector search plays a major role in searching efficiency with faster results and also, managing the data we collect from user’s searches.
Applications:
Real-world use cases and scenarios where Vector searches along with elastic search is used. The summarize view of real-life applications of Vector search using elastic search is mentioned below:
Docusign: It is an Intelligent Agreement Management (IAM) company with millions of users. This organization helps various other companies, how businesses create, manage and analyze contracts. Before the introduction of IAM, users searched across multiple platforms to locate agreements.
Docusign used Elasticsearch along with vector search, to handle billions of new agreements which Docusign gets every single day and to deliver quick results to its customers.
Vector search builds using elastic-search technology have innovated the searching technology and search input can be text, image, keywords, audios and videos too. We can add one feature as well by extracting context from handwritings and artworks like paintings, to understand the meaning and to get the results from it.
Natural Languages Processing can be used to extract context for searching from handwriting and artworks too for getting desired results and nearly similar results.
Optimizing and adding more features to Vector search for better results:
Vector search basic designs uses various technologies including Semantic search, Vector database, Elastic-Search and many more. We can have two more features in Vector search criteria which will be more beneficial with for other kinds of inputs as context for searching. The modified architecture design for adding two more features:
This is modified vector search architecture along with taking input as images, audio, videos, along with taking input as handwriting and artworks. The algorithm which can be used for taking input as images, audios, and videos is KNN – K nearest neighbor and for taking inputs handwriting and artwork is CNN algorithm which is the part of Natural Language processing.
Conclusion/Takeaways:
Vector search and Semantic search changes the game of searching by handling millions of search queries by customers and managing them quite efficiently. Semantic search makes the search results by improving the search context like by giving input as text, audio and videos and that too in lesser time as compared to other search engines.
The major advantage is that the search query is stored in vector data in vector forms and as per the user’s requirements can be used for training our machine learning models. Elasticsearch has not only revolutionized the search criteria but also, increases giving more contextual results.
Disclosure - This Blog was submitted as part of the Elastic Blogathon.
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