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About

Asimov is a foundational AI-search and vector-search platform built for developers to upload content sources (documents, logs, files, etc.), auto-chunk and embed them, and expose them via a single API to power semantic search, filtering, and relevance for AI agents or applications. It removes the burden of managing separate vector-databases, embedding pipelines, or re-ranking systems by handling ingestion, metadata parameterization, usage tracking, and retrieval logic within a unified architecture. With support for adding content via a REST API and performing semantic search queries with custom filtering parameters, Asimov enables teams to build “search-across-everything” functionality with minimal infrastructure. It is designed to handle metadata, automatic chunking, embedding, and storage (e.g., into MongoDB) and provides developer-friendly tools, including a dashboard, usage analytics, and seamless integration.

About

E5 Text Embeddings, developed by Microsoft, are advanced models designed to convert textual data into meaningful vector representations, enhancing tasks like semantic search and information retrieval. These models are trained using weakly-supervised contrastive learning on a vast dataset of over one billion text pairs, enabling them to capture intricate semantic relationships across multiple languages. The E5 family includes models of varying sizes—small, base, and large—offering a balance between computational efficiency and embedding quality. Additionally, multilingual versions of these models have been fine-tuned to support diverse languages, ensuring broad applicability in global contexts. Comprehensive evaluations demonstrate that E5 models achieve performance on par with state-of-the-art, English-only models of similar sizes.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Developers and engineering teams in need of a solution to power semantic search and retrieval across large unstructured content sets for AI-driven applications

Audience

E5 Text Embeddings are designed for AI researchers, machine learning engineers, and developers seeking high-quality text representations for applications like semantic search, information retrieval, and multilingual NLP tasks

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

$20 per month
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Asimov
United States
www.asimov.mov/

Company Information

Microsoft
Founded: 1975
United States
github.com/microsoft/unilm/tree/master/e5

Alternatives

Alternatives

word2vec

word2vec

Google
Gensim

Gensim

Radim Řehůřek
txtai

txtai

NeuML
GloVe

GloVe

Stanford NLP

Categories

Categories

Integrations

MongoDB

Integrations

MongoDB
Claim Asimov and update features and information
Claim Asimov and update features and information
Claim E5 Text Embeddings and update features and information
Claim E5 Text Embeddings and update features and information