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This is a [txtai](https://github.com/neuml/txtai) embeddings index for the [Swedish edition of Wikipedia](https://sv.wikipedia.org/).
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Embeddings is the engine that delivers semantic search. Data is transformed into embeddings vectors where similar concepts will produce similar vectors.
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An embeddings index generated by txtai is a fully encapsulated index format. It
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This index is built from the [Wikipedia Februari 2024 dataset](https://huggingface.co/datasets/burgerbee/wikipedia-sv-20240220).
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Only the first two paragraph from each article is included. The Wikipedia index works well as a fact-based context source for retrieval augmented generation (RAG). It also uses [Wikipedia Page Views](https://dumps.wikimedia.org/other/pageviews/readme.html) data to add a `percentile` field. The `percentile` field can be used
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This is a [txtai](https://github.com/neuml/txtai) embeddings index for the [Swedish edition of Wikipedia](https://sv.wikipedia.org/).
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Embeddings is the engine that delivers semantic search. Data is transformed into embeddings vectors where similar concepts will produce similar vectors.
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An embeddings index generated by txtai is a fully encapsulated index format. It dosen't require a database server.
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This index is built from the [Wikipedia Februari 2024 dataset](https://huggingface.co/datasets/burgerbee/wikipedia-sv-20240220).
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Only the first two paragraph from each article is included. The Wikipedia index works well as a fact-based context source for retrieval augmented generation (RAG). It also uses [Wikipedia Page Views](https://dumps.wikimedia.org/other/pageviews/readme.html) data to add a `percentile` field. The `percentile` field can be used
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