Build AI-powered semantic search applications
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications.
Traditional search systems use keywords to find data. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords.
Backed by state-of-the-art machine learning models, data is transformed into vector representations for search (also known as embeddings). Innovation is happening at a rapid pace, models can understand concepts in documents, audio, images and more.
Summary of txtai features:
- 🔎 Large-scale similarity search with multiple index backends (Faiss, Annoy, Hnswlib)
- 📄 Create embeddings for text snippets, documents, audio, images and video. 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.
- ☁️ Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes)
Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. Semantic workflows transform and find data driven by user intent.
The following applications are powered by txtai.
|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|