+
+

Related Products

  • Gemini Enterprise Agent Platform
    1,161 Ratings
    Visit Website
  • RaimaDB
    12 Ratings
    Visit Website
  • Gemini Credit Card
    2 Ratings
    Visit Website
  • Evertune
    1 Rating
    Visit Website
  • Google Workspace
    69,271 Ratings
    Visit Website
  • FISPAN
    5 Ratings
    Visit Website
  • Wallester
    277 Ratings
    Visit Website
  • Planview AdaptiveWork
    718 Ratings
    Visit Website
  • ScreenMeet
    35 Ratings
    Visit Website
  • AthenaHQ
    50 Ratings
    Visit Website

About

Gemini Embedding’s first text model (gemini-embedding-001) is now generally available via the Gemini API and Gemini Enterprise Agent Platform, having held a top spot on the Massive Text Embedding Benchmark Multilingual leaderboard since its experimental launch in March, thanks to superior performance across retrieval, classification, and other embedding tasks compared to both legacy Google and external proprietary models. Exceptionally versatile, it supports over 100 languages with a 2,048‑token input limit and employs the Matryoshka Representation Learning (MRL) technique to let developers choose output dimensions of 3072, 153,6, or 768 for optimal quality, performance, and storage efficiency.

About

VectorDB is a lightweight Python package for storing and retrieving text using chunking, embedding, and vector search techniques. It provides an easy-to-use interface for saving, searching, and managing textual data with associated metadata and is designed for use cases where low latency is essential. Vector search and embeddings are essential when working with large language models because they enable efficient and accurate retrieval of relevant information from massive datasets. By converting text into high-dimensional vectors, these techniques allow for quick comparisons and searches, even when dealing with millions of documents. This makes it possible to find the most relevant results in a fraction of the time it would take using traditional text-based search methods. Additionally, embeddings capture the semantic meaning of the text, which helps improve the quality of the search results and enables more advanced natural language processing tasks.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Data scientists wanting a solution for multilingual retrieval, classification and other advanced AI applications

Audience

Anyone in need of a tool to save, search, store, manage, and retrieve text

Support

Phone Support Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

$0.15 per 1M input tokens
Free Version Not Supported
Free Trial Not Supported

Pricing

Free
Free Version Supported
Free Trial Not Supported

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

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 Supported
Webinars Not Supported
Live Online Not Supported
In Person Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Google
Founded: 1998
United States
developers.googleblog.com/en/gemini-embedding-available-gemini-api/

Company Information

VectorDB
United States
vectordb.com

Alternatives

Alternatives

Categories

Embedding Models Supported

Categories

Vector Databases Supported

Integrations

Python Supported
Gemini Supported
Gemini Enterprise Supported
Gemini Enterprise Agent Platform Supported
Google AI Studio Supported
Lamatic.ai Not Supported
Mimasa AI Supported

Integrations

Python Supported
Gemini Not Supported
Gemini Enterprise Not Supported
Gemini Enterprise Agent Platform Not Supported
Google AI Studio Not Supported
Lamatic.ai Supported
Mimasa AI Not Supported
Claim Gemini Embedding and update features and information
Claim Gemini Embedding and update features and information
Claim VectorDB and update features and information
Claim VectorDB and update features and information