This article is part of a tutorial series on txtai, an AI-powered semantic search platform. One of the main use cases of txtai is semantic search over a corpus of data. Semantic search provides an understanding of natural language and identifies results that have the same meaning, not necessarily the same keywords. Within an Embeddings instance sits a wealth of implied knowledge and relationships between rows. Many approximate nearest neighbor (ANN) indexes are even backed by graphs.

What's New @ NeuML publishes interesting content covering our open source projects, services and insights. The scheduled frequency is weekly to monthly. Work is underway for txtai 5.0. Here is an illustration that further explains graph path traversal. The chart below shows GitHub star growth to date with annotations.

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.

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

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