neuml / txtai Public

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.

Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. The following applications are powered by txtai.

See the detailed install instructions for more information covering installing from source, environment specific prerequisites and optional dependencies.

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