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Update vector-search-tutorial.adoc #1241

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Oct 7, 2024
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Update vector-search-tutorial.adoc #1241

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Update vector-search-tutorial.adoc
wrmay Aug 2, 2024
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Merge branch 'v/5.5' into wrmay-patch-1
k-jamroz Aug 27, 2024
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Merge branch 'v/5.5' into wrmay-patch-1
oliverhowell Oct 7, 2024
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4 changes: 2 additions & 2 deletions docs/modules/data-structures/pages/vector-search-tutorial.adoc
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Expand Up @@ -105,7 +105,7 @@ hazelcast:

```
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* `hazelcast.partition.count: Vector search performs better with fewer partitions. On the other hand, fewer partitions means larger partitions, which can cause problems during migration. A discussion of the tradeoffs can be found here:
* `hazelcast.partition.count`: Vector search performs better with fewer partitions. On the other hand, fewer partitions means larger partitions, which can cause problems during migration. A discussion of the tradeoffs can be found here:
(https://docs.hazelcast.com/hazelcast/latest/data-structures/vector-search-overview#partition-count-impact).
* `jet`: This is the Hazelcast stream processing engine. Hazelcast pipelines are a scalable way to rapidly ingest or process large amounts of data. This example uses a pipeline to compute embeddings and load them into a vector collection, so stream processing must be enabled.
* `vector-collection`: If you are using a vector collection, you must configure the index settings. There are no defaults. In this case, the name of the collection is `images` and it has one index, which is called `semantic-search`. The dimension and distance metric are dependent on the embedding being used. The `dimension` must match the size of the vectors produced by the embedding. The `metric` defines the algorithm used to compute the distance between 2 vectors and it must match the one used to train the embedding. This tutorial uses the CLIP sentence transformer for embeddings. CLIP uses a dimension of 512 and cosine distance metric (literally the cosine of the angle between 2 vectors, adjusted to be non-negative). For more detail on supported options, see xref:data-structures:vector-collections.adoc[].
Expand Down Expand Up @@ -156,7 +156,7 @@ A solution pipeline is available in the

You need to use a Jupyter notebook for the remaining steps.

. Start the Jupyter process inside Docker.
. Retrieve the login URL for Jupyter from the logs
+
```sh
docker compose logs jupyter
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