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Overall Relevance |
Vectara has purpose-built a best-in-class LLM for neural retrieval and ranking and can take advantage of multiple relevance strategies to deliver the most relevant search results.
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Algolia’s offers relevance enhancing features.
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Underlying Retrieval Technology |
Zero-shot LLM-powered search: A native multi-model NLP pipeline using Vectara-created LLMs purpose built for fast, cost-effective retrieval with high precision and recall.
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Keyword-based search technology: Retrieval and recall is based on keyword matching between the query and data in the index and often yields less relevant results.
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Understanding of Prompts and Queries |
Semantic and contextual understanding of prompts and queries based on LLMs.
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Keyword matching; contextual understanding is limited.
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Language Configuration and Other Set-Up and Maintenance |
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Complex set-up that requires language configuration such as typo tolerance management, synonym management, stemming, and stop words; configurations require frequent maintenance and updating.
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Ease of Use |
Using Vectara requires no specialized search engineering or AI/ML knowledge. You can index your first set of data and be up and running on using Vectara within 30 minutes.
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Developer skills necessary to build a search function using Algolia include implementing language configuration rules, backend development, API integration, data modeling, front-end development, testing, and debugging.
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Cost |
Vectara offers a generous free version of its service. Users can upload a 50 MB of data into their indexes and use 15,000 queries each month for free. Upgrading to the paid plan is inexpensive.
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The process Algolia uses in combining counts of queries and documents makes Algolia expensive, especially for small companies.
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Build for the Future |
Vectara’s LLM-powered platform will enable you to take advantage of a continuously improving, fully integrated service.
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Algolia's Recommend feature leverages multiple AI techniques to provide personalized product recommendations that are tailored to each user's interests and preferences.
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| Learn More |
Algolia is a search, discovery, and recommendation search-as-a-service platform that enables companies to build and optimize their digital experiences. Algolia is API-centric; data for the client site or application is pushed from the client to Algolia via a RESTful JSON API, then the search box is added to the client's web pages or application. As Algolia's search can be tailored to the client site or application, the search offered can be more specific than a generalized web text search. This improves the relevance of search results as searching may take more of the context of site content or application data into account.
Algolia is designed for use by ecommerce businesses, marketplaces, media and entertainment companies, and other organizations that require fast and relevant search results for their websites or applications.
Algolia has customers of all sizes. The majority are smaller companies, but they also have mid-sized customers and enterprises.
Web and application developers are the primary users of Algolia, as they are the ones responsible for implementing and optimizing search functionality within sites and applications. Developer skills necessary to build a search function using Algolia include implementing language configuration rules, backend development, API integration, data modeling, front-end development, testing, and debugging.
Implementation of Algolia and maintenance while using it requires language configuration. Algolia’s search technology is based primarily on traditional keyword search approaches which require detailed management of language configuration rules. These rules are necessary to ensure that the search function works effectively for users in different languages and contexts and to make sure the intent of the query is not misinterpreted.
They include:
See Vectara’s blog posts on these language configuration rules topics — Search by Meaning and Stop Stopping.Implementation of Algolia and maintenance while using it requires language configuration. Algolia’s search technology is based primarily on traditional keyword search approaches which require detailed management of language configuration rules. These rules are necessary to ensure that the search function works effectively for users in different languages and contexts and to make sure the intent of the query is not misinterpreted.
Algolia’s search may also struggle to accurately interpret complex queries. For example, if a user enters a long or multi-part search query, Algolia may not be able to accurately understand what they are looking for, potentially leading to irrelevant or incorrect results.
Additionally, if the data being searched is not well-organized or tagged appropriately, Algolia may struggle to accurately return relevant results. Without careful organization and tagging of the indexed data, there is often the possibility of false positives or negatives in Algolia’s search results, which can reduce the overall accuracy of the product.
Algolia offers a wide range of customization options to its customers and allows developers to create many customized search experiences. Because of the language configuration requirements, this customization may require substantial effort in implementation and maintenance. There could also be a steep learning curve for those who are new to the platform or the concepts behind it.
Algolia offers a free plan for search with the following limits:
These combined limits require movement to a paid plan for any substantial volume. It has paid plans (Grow and Premium) designed for customers who are ready to scale their search function. All Recommendation features are paid features. In its paid Grow plan, Algolia charges $0.50/ 1,000 search requests per month over 10,000 and $0.40/1,000 records over 100,000.
Some specifics about how Algolia counts queries can be important. When auto-complete is turned on, every character typed counts as a unique query. This obviously makes their popular type-ahead feature expensive to use.
Additionally, for any record over 10 KB Algolia charges you for an additional record. So, if you index a document that is 51 KB, that counts as 6 Algolia records. This document size limitation in pricing can make building your index expensive.
The way their pricing counts both queries and records can make Algolia expensive, particularly for small companies. Some small businesses with limited resources have suggested that Algolia’s pricing can be expensive compared to other search providers.
Vectara is LLM-powered search-as-a-service. Using advanced research in AI, Vectara applies large language models to information retrieval (rather than using keywords) to deliver highly relevant results. Developers can efficiently embed NLP models for app and site search. It is a cloud-native, LLM-powered search platform built to serve developers at companies of all sizes and enable them to build or improve search functions in their sites and applications that will operate at fast speeds.
Vectara’s features include:
Vectara uses Zero-shot models in LLM-powered search, a multi-model, neural network-based information retrieval pipeline built using Vectara-created LLMs for fast, cost-effective retrieval with high precision and recall.
Vectara is API-first. It features quick set-up and easy-to-use APIs in a platform that enables developers to easily build, debug and test applications of semantic search. This is a unified API set with associated documentation and playground that allows full control over the entire pipeline, not just the database element or the embeddings element or the reranker or the text extractor, etc.
API-based features include:
Vectara’s InstantIndex feature allows developers to ingest and process new data through a full-service neural indexing pipeline in less than one second. Likewise, Vectara can directly ingest raw files like PDFs and Word documents to make content extraction a cinch.
Vectara can index most types of files and data. Vectara automatically extracts text from documents of nearly any type, with auto-detection of file formats and multi-stage extraction routines. Vectara can accurately extract text, index it, and create vector embeddings from documents in formats including PDF, Microsoft Word, Microsoft Powerpoint, Open Office, HTML, JSON, XML, email in RFC822, text, RTF, ePUB, or CommonMark. Vectara extracts text from tables, images and other document elements automatically.
Vectara’s LLM-powered re-ranking is a embedded feature. It is a part of Vectara’s multi-model AI architecture and allows users to re-rank retrieved documents for further precision around a given query.
Another customization feature, Rules-based AI, allows you to define and control the responses you provide to users.
Vectara also provides generative AI features like its LLM-powered summarization.
Vectara is language agnostic. It enables multi-language search and cross-language search. A user on the same site can search in multiple languages to find results in each of those languages. Developers can also use Vectara to provide users with the ability to search in one language for content written in another language.
Vectara security features extend across the entirety of the full pipeline at all times.
Security features include:
Finally, Vectara’s admin console UI provides users and administrators with access to manage user accounts, API keys, corpora, index data, and queries. An administrator has visibility to all the elements, users, and activities across all components of the pipeline within a single UI.
Vectara’s LLM-powered information retrieval model provides more relevance through its better contextual understanding of the questions being asked and their relationship to the information indexed.
Examples of how Vectara’s LLM-powered search might yield more relevant results than Algolia’s keyword-based search, imagine a user searching for “what are the best restaurants in the big apple?” or “which street are the not bisy in San Francisco on the weekend?” using traditional keyword-search. Any document that does not include the phrase “the big apple” or contain the misspelled word “bisy” will be considered of low relevance to the query.
Vectara’s LLM-powered search understands langugage including variants and mis-spellings and thus can overcome these challenges and match the documents to the actual intent of the query.
Some of the most common Vectara use cases for supporting marketing or enhancing your customer experience include:
Use Vectara to build a chatbot that understands questions no matter how they are asked and provides relevant answers, or empower your support team to quickly find answers to the most complex questions customers are asking.
Use Vectara to enable your website visitors to find what they are looking for no matter how they ask. Understand what they are asking for and provide it to them right away. Users can search across site content in many formats to include HTML JSON, and PDF. Build loyalty and improve conversion rates by dramatically improving your customer experience with a LLM-powered search.
Use Vectara to provide an eCommerce search function across all the products in your online store, and increase conversion rates and transactions. Allow shoppers to find what they are looking for as well as related products and products that other users like them purchased.
Use Vectara to improve your customer experience by helping users find related content and discover new ideas that are relevant to their question.
Common Vectara IT use cases include:
Use Vectara to enable employees in their workplace to search across documents of all types - files, emails, and other important data - to efficiently find the information they need to do their jobs.
Use Vectara to enable users to search in one language across content written in other languages and get accurate, relevant results.
Use Vectara to find more relevant and accurate information in your research. See an example of using Vectara to conduct financial research and analysis based on a company’s quarterly financial reports.
Use Vectara to enable your team to search across your Slack application and find relevant information with great accuracy. See an example of neural search Vectara built for its Slack application.
Vectara has developed solutions for these common Developer use cases as well:
Use Vectara to build a content discovery function across your applications that allows them to find the content they are looking for by better understanding the query and providing answers based on concepts, not keywords.
Use Vectara to answer semantic questions with concise, accurate answers. Vectara will first uses LLMs to understand what the user is looking for and return a relevant set of information, then use another LLM to summarize that information into a singular answer.
Use Vectara to create a real-time reporting database that is separate from your production database, and use that reporting DB to run your reporting queries and yield highly accurate results.
Vectara is designed for use by application developers, web developers, and data engineers at companies of all sizes that need to build or improve a search function or want to take advantage of Vectara’s LLM-powered information retrieval capabilities. Vectara’s platform enables developers to build solutions for marketing teams, IT teams, support teams, and sales teams and provide immediate access to answers for their toughest questions. Vectara also provides an LLM-powered platform that will enable companies who use it to build for the future by taking advantage of current and future generative AI features.
Vectara enables you to deliver a higher level of relevance, to answer your users’ questions better and faster. Vectara uses cutting edge zero-shot models that are pushing the boundaries of general purpose natural language processing. It developed its neural rank model for semantic search using large language models trained across the world’s leading languages and delivers a remarkable breadth of understanding no matter the question, who is asking it, or how it is asked.
You can get started using Vectara for free. You just open an account by signing-up, logging-in, and creating a corpora to start indexing your data.
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