txtai 3.0.0

AI-powered search engine

AI-powered search engine

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txtai executes machine-learning workflows to transform data and build AI-powered text indices to perform similarity search.

Summary of txtai features:

• 🔎 Large-scale similarity search with multiple index backends (Faiss, Annoy, Hnswlib)
• 📄 Create embeddings for text snippets, documents, audio and images. Supports transformers and word vectors.
• 💡 Machine-learning pipelines to run extractive question-answering, zero-shot labeling, transcription, translation, summarization and text extraction
• ↪️️ Workflows that join pipelines together to aggregate business logic. txtai processes can be microservices or full-fledged indexing workflows.
• 🔗 API bindings for JavaScript, Java, Rust and Go
• ☁️ Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes)

txtai and/or the concepts behind it has already been used to power the Natural Language Processing (NLP) applications listed below:

Application	Description
paperai	AI-powered literature discovery and review engine for medical/scientific papers
tldrstory	AI-powered understanding of headlines and story text
neuspo	Fact-driven, real-time sports event and news site
codequestion	Ask coding questions directly from the terminal

txtai is built with Python 3.6+, Hugging Face Transformers, Sentence Transformers and FastAPI

Installation

The easiest way to install is via pip and PyPI

pip install txtai

You can also install txtai directly from GitHub. Using a Python Virtual Environment is recommended.

pip install git+https://github.com/neuml/txtai

Python 3.6+ is supported. txtai has the following environment specific prerequisites.

Linux

Optional audio transcription requires a system library to be installed

macOS

Run brew install libomp see this link

Windows

Install C++ Build Tools

Examples

The examples directory has a series of notebooks and applications giving an overview of txtai. See the sections below.

Notebooks

Notebook	Description
Introducing txtai	Overview of the functionality provided by txtai
Build an Embeddings index with Hugging Face Datasets	Index and search Hugging Face Datasets
Build an Embeddings index from a data source	Index and search a data source with word embeddings
Add semantic search to Elasticsearch	Add semantic search to existing search systems
Extractive QA with txtai	Introduction to extractive question-answering with txtai
Extractive QA with Elasticsearch	Run extractive question-answering queries with Elasticsearch
Apply labels with zero shot classification	Use zero shot learning for labeling, classification and topic modeling
API Gallery	Using txtai in JavaScript, Java, Rust and Go
Building abstractive text summaries	Run abstractive text summarization
Extract text from documents	Extract text from PDF, Office, HTML and more
Transcribe audio to text	Convert audio files to text
Translate text between languages	Streamline machine translation and language detection
Similarity search with images	Embed images and text into the same space for search
Run pipeline workflows	Simple yet powerful constructs to efficiently process data

Applications

Application	Description
Workflow builder	Build and execute txtai workflows. Connect summarization, text extraction, transcription, translation and similarity search pipelines together to run unified workflows.
Image search	Image similarity search application. Index a directory of images and run searches to identify images similar to the input query
Demo query shell	Basic similarity search example. Used in the original txtai demo.

Documentation

Full documentation on txtai including configuration settings for pipelines, workflows, indexing and the API.

Contributing

For those who would like to contribute to txtai, please see this guide.