Actian VectorAI DBActian
|
Universal Sentence EncoderTensorflow
|
|||||
Related Products
|
||||||
About
Actian VectorAI DB is a portable, local-first vector database designed for AI systems that need to run close to their data, including edge, on-premises, and hybrid environments. It enables developers to deploy semantic search, retrieval-augmented generation (RAG), and AI-powered applications without relying on cloud infrastructure, avoiding latency, network dependency, and per-query costs. It provides native vector storage and high-performance similarity search using techniques such as approximate nearest neighbor indexing and algorithms like HNSW, allowing efficient retrieval across large embedding datasets while balancing speed and accuracy. It delivers low-latency search directly on devices ranging from laptops to embedded systems like Raspberry Pi, supporting real-time decision-making and autonomous behaviors without a network round-trip.
|
About
The Universal Sentence Encoder (USE) encodes text into high-dimensional vectors that can be utilized for tasks such as text classification, semantic similarity, and clustering. It offers two model variants: one based on the Transformer architecture and another on Deep Averaging Network (DAN), allowing a balance between accuracy and computational efficiency. The Transformer-based model captures context-sensitive embeddings by processing the entire input sequence simultaneously, while the DAN-based model computes embeddings by averaging word embeddings, followed by a feedforward neural network. These embeddings facilitate efficient semantic similarity calculations and enhance performance on downstream tasks with minimal supervised training data. The USE is accessible via TensorFlow Hub, enabling seamless integration into various applications.
|
|||||
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 engineers building edge or on-prem applications who need fast, local semantic search and scalable vector data processing without cloud dependency
|
Audience
Data scientists and machine learning engineers seeking a tool to optimize their natural language processing models with robust sentence embeddings
|
|||||
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
No information available.
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 InformationActian
Founded: 1980
United States
www.actian.com/databases/vectorai-db/
|
Company InformationTensorflow
Founded: 2015
United States
www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder
|
|||||
Alternatives |
Alternatives |
|||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Categories |
Categories |
|||||
Integrations
Cohere
Docker
Google Colab
Hugging Face
OpenAI
Raspberry Pi OS
TensorFlow
|
Integrations
Cohere
Docker
Google Colab
Hugging Face
OpenAI
Raspberry Pi OS
TensorFlow
|
|||||
|
|
|