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Average Ratings 0 Ratings

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ease
features
design
support

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Description

Sphinx is a high-performance open-source full-text search engine specifically designed to prioritize efficiency, search quality, and ease of integration. Built using C++, it operates seamlessly across various platforms including Linux (such as RedHat and Ubuntu), Windows, MacOS, Solaris, FreeBSD, and several others. Sphinx supports both batch indexing and on-the-fly searching of data from SQL databases, NoSQL systems, or even plain files, allowing for a flexible approach similar to querying a traditional database server. The platform offers numerous text processing capabilities that facilitate the customization of its functions to meet the distinct needs of different applications, while multiple relevance tuning options help enhance the quality of search results. Implementing searches through SphinxAPI requires only three lines of code, and using SphinxQL is even more straightforward, enabling users to write search queries in familiar SQL syntax. Remarkably, Sphinx can index between 10 to 15 MB of text in a second for each CPU core, translating to over 60 MB per second on a dedicated indexing server. With its robust features and efficient performance, Sphinx stands out as an excellent choice for developers seeking a search solution tailored to their specific requirements.

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

Crowdin
Dash
Datavolo
IBM watsonx.data
Langflow
Mermaid Chart
MySQL
Read the Docs

Integrations

Crowdin
Dash
Datavolo
IBM watsonx.data
Langflow
Mermaid Chart
MySQL
Read the Docs

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

Sphinx

Founded

2007

Country

United States

Website

sphinxsearch.com

Vendor Details

Company Name

Vectara

Founded

2020

Country

United States

Website

vectara.com

Product Features

Enterprise Search

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

Product Features

Enterprise Search

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

Alternatives

Alternatives

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