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Description

Gensim is an open-source Python library that specializes in unsupervised topic modeling and natural language processing, with an emphasis on extensive semantic modeling. It supports the development of various models, including Word2Vec, FastText, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), which aids in converting documents into semantic vectors and in identifying documents that are semantically linked. With a strong focus on performance, Gensim features highly efficient implementations crafted in both Python and Cython, enabling it to handle extremely large corpora through the use of data streaming and incremental algorithms, which allows for processing without the need to load the entire dataset into memory. This library operates independently of the platform, functioning seamlessly on Linux, Windows, and macOS, and is distributed under the GNU LGPL license, making it accessible for both personal and commercial applications. Its popularity is evident, as it is employed by thousands of organizations on a daily basis, has received over 2,600 citations in academic works, and boasts more than 1 million downloads each week, showcasing its widespread impact and utility in the field. Researchers and developers alike have come to rely on Gensim for its robust features and ease of use.

Description

NLWeb is a collaborative initiative by Microsoft designed to facilitate the creation of an intuitive, natural language interface for websites, utilizing any chosen model alongside proprietary data. The primary objective of NLWeb, which stands for Natural Language Web, is to provide the quickest and simplest means of transforming a website into an AI application, enabling users to interact with the site's content through natural language queries, akin to engaging with an AI assistant or Copilot. Each instance of NLWeb functions as a Model Context Protocol (MCP) server, giving websites the option to make their information discoverable and accessible to various agents and participants within the MCP framework. By leveraging semi-structured data formats such as Schema.org and RSS, which many websites already employ, NLWeb integrates these with LLM-powered tools to facilitate natural language interfaces that cater to both humans and AI agents, ultimately enhancing user interaction and engagement. This innovative approach not only streamlines the integration process but also broadens the accessibility of web content for a diverse audience.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

C
Cython
Eventbrite
Inception Labs
Microsoft Copilot
Milvus
NumPy
Python
Qdrant
RSS
Schema
Shopify
Snowflake
Tripadvisor
fastText
word2vec

Integrations

C
Cython
Eventbrite
Inception Labs
Microsoft Copilot
Milvus
NumPy
Python
Qdrant
RSS
Schema
Shopify
Snowflake
Tripadvisor
fastText
word2vec

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
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

Radim Řehůřek

Founded

2009

Country

Czech Republic

Website

radimrehurek.com/gensim/

Vendor Details

Company Name

Microsoft

Founded

1975

Country

United States

Website

news.microsoft.com/source/features/company-news/introducing-nlweb-bringing-conversational-interfaces-directly-to-the-web/

Product Features

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

Product Features

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

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Alternatives

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