[DOCS][101] Add BYO vectors ingestion tutorial #115112
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kderusso
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Oct 18, 2024
| <titleabbrev>Bring your own vector embeddings</titleabbrev> | ||
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| This tutorial demonstrates how to index documents that already have dense vector embeddings into {es}. |
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Is it worth adding an example for sparse_vector embeddings here as well?
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I think it would be best to keep this tightly focused to dense vectors and investigate demand for sparse vector equivalent going forward.
| [[bring-your-own-vectors-search-documents]] | ||
| === Step 3: Search documents with embeddings | ||
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| Now you can query these document vectors using a <<knn-retriever,`knn` retriever>>. |
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Nice to see retriever examples! 🎉
Added explanation for dims parameter Separated single and bulk document indexing examples Improved explanations and wording throughout Added tip for beginners about semantic search Mentioned client-side vector generation as an alternative
jeffvestal
reviewed
Oct 23, 2024
| [TIP] | ||
| ==== | ||
| The `dense_vector` type supports quantization to reduce the memory footprint required when searching float vectors. | ||
| Learn more about balancing performance and accuracy in <<dense-vector-quantization,Dense vector quantization>>. |
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Is it worth mentioning that we auto-quantize with int8_hnsw by default for dense_vector field type?
leemthompo
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Pinging @elastic/es-docs (Team:Docs) |
szabosteve
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