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Update documentation with new demo and diagrams, closes #313
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davidmezzetti
@@ -35,6 +35,9 @@ txtai executes machine-learning workflows to transform data and build AI-powered
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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.
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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.
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Summary of txtai features:
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@@ -46,7 +49,12 @@ Summary of txtai features:
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- 🔗 API bindings for [JavaScript](https://github.com/neuml/txtai.js), [Java](https://github.com/neuml/txtai.java), [Rust](https://github.com/neuml/txtai.rs) and [Go](https://github.com/neuml/txtai.go)
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- ☁️ Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes)
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Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. The following applications are powered by txtai.
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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.
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The following applications are powered by txtai.
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After Width: | Height: | Size: 856 KiB |
+9
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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.
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@@ -48,7 +51,12 @@ Summary of txtai features:
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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.
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The following applications are powered by txtai.
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Reference in New Issue
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