AI-powered search engine
Project description
AI-powered search engine
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.
Project details
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