Gemini Embedding 2Google
|
||||||
Related Products
|
||||||
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
TextBlob is a Python library for processing textual data, offering a simple API to perform common natural language processing tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, and classification. It stands on the giant shoulders of NLTK and Pattern, and plays nicely with both. Key features include tokenization (splitting text into words and sentences), word and phrase frequencies, parsing, n-grams, word inflection (pluralization and singularization) lemmatization, spelling correction, and WordNet integration. TextBlob is compatible with Python versions 2.7 and above, and 3.5 and above. It is actively developed on GitHub and is licensed under the MIT License. Comprehensive documentation, including a quick start guide and tutorials, is available to assist users in implementing various NLP tasks.
|
|||||
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
Python developers and data scientists seeking a solution for performing a wide range of natural language processing 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 |
|||||
Pricing
Free
Free Version
Free Trial
|
Pricing
No information available.
Free Version
Free Trial
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
|||||
Company InformationGoogle
Founded: 1998
United States
blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/
|
Company InformationTextBlob
United States
textblob.readthedocs.io/en/dev/
|
|||||
Alternatives |
Alternatives |
|||||
|
|
||||||
|
|
|
|||||
|
|
||||||
|
|
||||||
Categories |
Categories |
|||||
Integrations
Python
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
NLTK
|
Integrations
Python
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
NLTK
|
|||||
|
|
|