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features
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support

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Description

Achieve optimal results through efficient and adaptable query-time sorting, allowing you to position specific records strategically for enhanced visibility or promotion. Enable users to discover pants when they search for trousers, and vice versa, by setting them as synonyms. Consolidate multiple users’ data within a single index and issue unique API keys to ensure that each user can only access their own information. Dynamically sort records by any field in your documents, such as price or popularity, eliminating the need for duplicate indices. Enhance result diversity by grouping similar items together, like combining all color variations of a shirt into one entry. Retrieve only those records that align with specified filters, and perform aggregate functions to compute counts, minimums, maximums, and averages across your records. Additionally, facilitate search and sorting capabilities within a specified distance from a particular latitude and longitude or within a defined polygon area. By following a few straightforward steps, you can build a robust and reliable production-grade search service that meets your needs. Ultimately, this approach ensures a seamless and efficient user experience, promoting greater satisfaction and engagement.

Description

Vectara offers LLM-powered search as-a-service. The platform offers a complete ML search process, from extraction and indexing to retrieval and re-ranking as well as calibration. API-addressable for every element of the platform. Developers can embed the most advanced NLP model for site and app search in minutes. Vectara automatically extracts text form PDF and Office to JSON HTML XML CommonMark, and many other formats. Use cutting-edge zero-shot models that use deep neural networks to understand language to encode at scale. Segment data into any number indexes that store vector encodings optimized to low latency and high recall. Use cutting-edge, zero shot neural network models to recall candidate results from millions upon millions of documents. Cross-attentional neural networks can increase the precision of retrieved answers. They can merge and reorder results. Focus on the likelihood that the retrieved answer is a probable answer to your query.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

IBM watsonx.data
Datavolo
Firebase
FormKiQ
Gatsby
Langflow
Laravel Herd
Prepr
ToolJet
WordPress

Integrations

IBM watsonx.data
Datavolo
Firebase
FormKiQ
Gatsby
Langflow
Laravel Herd
Prepr
ToolJet
WordPress

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Typesense

Country

United States

Website

typesense.org

Vendor Details

Company Name

Vectara

Founded

2020

Country

United States

Website

vectara.com

Product Features

Product Features

Enterprise Search

AI / Machine Learning
Faceted Search / Filtering
Full Text Search
Fuzzy Search
Indexing
Text Analytics
eDiscovery

Alternatives

Alternatives

Semantee Reviews

Semantee

Semantee.AI
Site Search ONE Reviews

Site Search ONE

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