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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.

About

Weaviate is an open-source, AI-native vector database for building search, RAG, and agentic AI applications. Store data objects alongside vector embeddings from your favorite ML models and scale seamlessly into billions of objects. Bring your own vectors or use built-in vectorization, then combine vector, keyword, and hybrid search for state-of-the-art results, even with filters. Pipe results through leading LLMs to power next-generation, retrieval-augmented experiences. Weaviate goes beyond the database: the Query Agent turns natural language into precise, cited queries, Engram provides managed memory for AI agents, and Weaviate Embeddings handles vectorization for you. Run it yourself under an open-source license, or use fully managed Weaviate Cloud on AWS, GCP, or Azure, with SOC 2 Type II compliance, multi-tenancy, and RBAC built in. Use any generative model with your own data to build chatbots, semantic search, recommendation, and agentic workflows.

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

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

Audience

Developers and AI teams building search, RAG, and agentic 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
Open source (free); free Weaviate Cloud tier; paid Cloud plans from $45/mo.
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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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

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

Company Information

Weaviate
Founded: 2019
The Netherlands
weaviate.io

Alternatives

Alternatives

Embeddinghub

Embeddinghub

Featureform
txtai

txtai

NeuML

Categories

Categories

Integrations

Anthropic
Anyscale
Astro by Astronomer
Boomi
Claude Code
Cohere
Comet
Confluent
FriendliAI
Gemini
Gemini Enterprise Agent Platform
GitHub
Jina AI
LangChain
Mem0
Microsoft Azure
Mistral AI
Nomic Atlas
OpenAI
Replicate

Integrations

Anthropic
Anyscale
Astro by Astronomer
Boomi
Claude Code
Cohere
Comet
Confluent
FriendliAI
Gemini
Gemini Enterprise Agent Platform
GitHub
Jina AI
LangChain
Mem0
Microsoft Azure
Mistral AI
Nomic Atlas
OpenAI
Replicate
Claim Gemini Embedding 2 and update features and information
Claim Gemini Embedding 2 and update features and information
Claim Weaviate and update features and information
Claim Weaviate and update features and information