[DOCS] 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?
| "properties": { | ||
| "review_vector": { | ||
| "type": "dense_vector", | ||
| "dims": 8, <1> |
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We could technically omit some of these, as dims can be dynamically calculated.
| PUT /amazon-reviews/_doc/1 | ||
| { | ||
| "review_text": "This product is lifechanging! I'm telling all my friends about it.", | ||
| "review_vector": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8] |
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Maybe add a note here emphasizing that the size of the review_vector array is 8 matching the dims count?
| [[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! 🎉
| } | ||
| } | ||
| ---- | ||
| // TEST[skip:flakeyknnerror] |
| } | ||
| ---- | ||
| // TEST[skip:flakeyknnerror] | ||
| <1> In this toy example, we're sending a raw vector as the query text. In a real-world scenario, you'll need to generate vectors for queries using an embedding model. |
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| <1> In this toy example, we're sending a raw vector as the query text. In a real-world scenario, you'll need to generate vectors for queries using an embedding model. | |
| <1> In this simple example, we're sending a raw vector as the query text. In a real-world scenario, you'll need to generate vectors for queries using an embedding model. |
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| This was a simple example to help you understand the syntax for indexing a set of existing embeddings into {es}. | ||
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| In this toy example, we're sending a raw vector for the query text. |
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Suggested change
| In this toy example, we're sending a raw vector for the query text. | |
| In this simple example, we're sending a raw vector for the query text. |
| In a real-world scenario you won't know the query text ahead of time. | ||
| You'll need to generate vectors for queries, on the fly, using an embedding model. | ||
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| For this you'll need to deploy a text embedding model in {es} and use the <<knn-query-top-level-parameters,`query_vector_builder` parameter>>. |
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It's also legitimate to do this client side and just send the vectors in with the request.
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