txtai

txtai

NeuML
+
+

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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.

About

txtai is an all-in-one open source embeddings database designed for semantic search, large language model orchestration, and language model workflows. It unifies vector indexes (both sparse and dense), graph networks, and relational databases, providing a robust foundation for vector search and serving as a powerful knowledge source for LLM applications. With txtai, users can build autonomous agents, implement retrieval augmented generation processes, and develop multi-modal workflows. Key features include vector search with SQL support, object storage integration, topic modeling, graph analysis, and multimodal indexing capabilities. It supports the creation of embeddings for various data types, including text, documents, audio, images, and video. Additionally, txtai offers pipelines powered by language models that handle tasks such as LLM prompting, question-answering, labeling, transcription, translation, and summarization.

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

Data scientists and machine learning engineers seeking a tool to optimize their natural language processing models with robust sentence embeddings

Audience

Developers and data engineers requiring a solution to implement semantic search, orchestrate large language models, and build complex language model workflows

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

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

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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

Tensorflow
Founded: 2015
United States
www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder

Company Information

NeuML
United States
neuml.github.io/txtai/

Alternatives

word2vec

word2vec

Google

Alternatives

Exa

Exa

Exa.ai
txtai

txtai

NeuML
Cohere

Cohere

Cohere AI

Categories

Categories

Integrations

AWS Lambda
Docker
FastAPI
Go
Google Colab
Hugging Face
Java
JavaScript
Knative
Kubernetes
Python
Rust
SQL
TensorFlow
YAML

Integrations

AWS Lambda
Docker
FastAPI
Go
Google Colab
Hugging Face
Java
JavaScript
Knative
Kubernetes
Python
Rust
SQL
TensorFlow
YAML
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