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docs/stack/get-started/vector-database.md

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@@ -168,7 +168,7 @@ You can find further details about all these options in the [vector reference do
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### 2. Check the state of the index
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As soon as you execute the [FT.CREATE(https://redis.io/commands/ft.create/) command, the indexing process runs in the background. In a short time, all JSON documents should be indexed and ready to be queried. To validate that, you can use the [FT.INFO](https://redis.io/commands/ft.info/) command, which provides details and statistics about the index. Of particular interest are the number of documents successfully indexed and the number of failures:
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As soon as you execute the [FT.CREATE](https://redis.io/commands/ft.create/) command, the indexing process runs in the background. In a short time, all JSON documents should be indexed and ready to be queried. To validate that, you can use the [FT.INFO](https://redis.io/commands/ft.info/) command, which provides details and statistics about the index. Of particular interest are the number of documents successfully indexed and the number of failures:
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{{< clients-example search_vss validate_index >}}
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FT_INFO idx:bikes_vss
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1. You can learn more about the query options, such as pre-filters and radius queries, by reading the [vector reference documentation](/docs/interact/search-and-query/advanced-concepts/vectors/).
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2. The complete [search and query documentation](https://redis.io/docs/interact/search-and-query/) might be interesting for you.
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3. If you want to follow the code examples more interactively, then you can use the [Jupyter notebook](https://github.com/RedisVentures/redis-vss-getting-started/blob/main/vector_similarity_with_redis.ipynb) that inspired this quick start guide.
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3. If you want to follow the code examples more interactively, then you can use the [Jupyter notebook](https://github.com/RedisVentures/redis-vss-getting-started/blob/main/vector_similarity_with_redis.ipynb) that inspired this quick start guide.

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