Faiss

Faiss

Meta
+
+

Related Products

  • MongoDB Atlas
    1,649 Ratings
    Visit Website
  • Cloudflare
    1,995 Ratings
    Visit Website
  • NINJIO
    415 Ratings
    Visit Website
  • Windocks
    7 Ratings
    Visit Website
  • Wiz
    1,446 Ratings
    Visit Website
  • Lenso.ai
    2 Ratings
    Visit Website
  • Apify
    1,242 Ratings
    Visit Website
  • D&B Hoovers
    1,211 Ratings
    Visit Website
  • Guardz
    117 Ratings
    Visit Website
  • SDS Manager
    4 Ratings
    Visit Website

About

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research.

About

Gemini Embedding models, including the newer Gemini Embedding 2, are part of Google’s Gemini AI ecosystem and are designed to convert text, phrases, sentences, and code into numerical vector representations that capture their semantic meaning. Unlike generative models that produce new content, the embedding model transforms input data into dense vectors that represent meaning in a mathematical format, allowing computers to compare and analyze information based on conceptual similarity rather than exact wording. These embeddings enable applications such as semantic search, recommendation systems, document retrieval, clustering, classification, and retrieval-augmented generation pipelines. The model can process input in more than 100 languages and supports up to 2048 tokens per request, allowing it to embed longer pieces of text or code while maintaining strong contextual understanding.

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 interested in a similarity search library

Audience

AI developers and data engineers who need a high-performance embedding model to convert text or code into semantic vectors for search, retrieval, and AI applications

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

Pricing

Free
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

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
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Meta
Founded: 2004
United States
faiss.ai/

Company Information

Google
Founded: 1998
United States
blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/

Alternatives

Alternatives

txtai

txtai

NeuML

Categories

Categories

Integrations

Gemini
Gemini Enterprise
Google AI Studio
Haystack
LLMWare.ai
Python
Vertex AI

Integrations

Gemini
Gemini Enterprise
Google AI Studio
Haystack
LLMWare.ai
Python
Vertex AI
Claim Faiss and update features and information
Claim Faiss and update features and information
Claim Gemini Embedding 2 and update features and information
Claim Gemini Embedding 2 and update features and information