fixing relrefs

content/commands/ft.create/index.md

@@ -228,7 +228,7 @@ after the SCHEMA keyword, declares which fields to index:
 
  - `GEO` - Allows radius range queries against the value (point) in this attribute. The value of the attribute must be a string containing a longitude (first) and latitude separated by a comma.
 
- - `VECTOR` - Allows vector queries against the value in this attribute. This requires [query dialect 2]({{< relref "/develop/interact/search-and-query/advanced-concepts/dialects#dialect-2" >}}) or above (introduced in [RediSearch v2.4](https://github.com/RediSearch/RediSearch/releases/tag/v2.4.3)). For more information, see [Vector Fields]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}).
+ - `VECTOR` - Allows vector queries against the value in this attribute. This requires [query dialect 2]({{< relref "/develop/interact/search-and-query/advanced-concepts/dialects#dialect-2" >}}) or above (introduced in [RediSearch v2.4](https://github.com/RediSearch/RediSearch/releases/tag/v2.4.3)). For more information, see [Vector Fields]({{< relref "/develop/ai/vector-fields" >}}).
 
  - `GEOSHAPE`- Allows polygon queries against the value in this attribute. The value of the attribute must follow a [WKT notation](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry) list of 2D points representing the polygon edges `POLYGON((x1 y1, x2 y2, ...)` separated by a comma. A `GEOSHAPE` field type can be followed by one of the following coordinate systems:
    - `SPHERICAL` for Geographic longitude and latitude coordinates

content/develop/ai/_index.md

@@ -15,7 +15,7 @@ Redis stores and indexes vector embeddings that semantically represent unstructu
 
 | Vector | RAG | RedisVL |
 | :-- | :-- | :-- |
-| {{<image filename="images/ai-cube.png" alt="AI Redis icon.">}}[Redis vector database quick start guide]({{< relref "/develop/get-started/vector-database" >}}) |{{<image filename="images/ai-brain.png" alt="AI Redis icon.">}} [Retrieval-Augmented Generation quick start guide]({{< relref "/develop/get-started/rag" >}}) | {{<image filename="images/ai-lib.png" alt="AI Redis icon.">}}[Redis vector Python client library documentation]({{< relref "/integrate/redisvl/" >}}) |
+| {{<image filename="images/ai-cube.png" alt="AI Redis icon.">}}[Redis vector database quick start guide]({{< relref "/develop/ai/vector-database" >}}) |{{<image filename="images/ai-brain.png" alt="AI Redis icon.">}} [Retrieval-Augmented Generation quick start guide]({{< relref "/develop/ai/rag" >}}) | {{<image filename="images/ai-lib.png" alt="AI Redis icon.">}}[Redis vector Python client library documentation]({{< relref "/integrate/redisvl/" >}}) |
 
 #### Overview
 
@@ -30,10 +30,7 @@ This page organized into a few sections depending on what you’re trying to do:
 
 ## How to's
 
-1. [**Create a vector index**]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#create-a-vector-index" >}}): Redis maintains a secondary index over your data with a defined schema (including vector fields and metadata). Redis supports [`FLAT`]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#flat-index" >}}) and [`HNSW`]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#hnsw-index" >}}) vector index types.
-1. [**Store and update vectors**]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#store-and-update-vectors" >}}): Redis stores vectors and metadata in hashes or JSON objects.
-1. [**Search with vectors**]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#search-with-vectors" >}}): Redis supports several advanced querying strategies with vector fields including k-nearest neighbor ([KNN]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#knn-vector-search" >}})), [vector range queries]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#vector-range-queries" >}}), and [metadata filters]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#filters" >}}).
-1. [**Configure vector queries at runtime**]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#runtime-query-parameters" >}}). Select the best filter mode to optimize query execution.
+1. [**Create a vector index**]({{< relref "develop/ai/vector-fields" >}}): Redis maintains a secondary index over your data with a defined schema (including vector fields and metadata). Redis supports [`FLAT`]({{< relref "develop/ai/vector-fields#flat-index" >}}) and [`HNSW`]({{< relref "develop/ai/vector-fields#hnsw-index" >}}) vector index types.
 
 #### Learn how to index and query vector embeddings
 * [redis-py (Python)]({{< relref "/develop/clients/redis-py/vecsearch" >}})
@@ -48,7 +45,7 @@ Learn to perform vector search and use gateways and semantic caching in your AI/
 
 | Search | LLM memory | Semantic caching | Semantic routing | AI Gateways |
 | :-- | :-- | :-- | :-- | :-- |
-| {{<image filename="images/ai-search.png" alt="AI Redis icon.">}}[Vector search guide]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) | {{<image filename="images/ai-LLM-memory.png" alt="LLM memory icon.">}}[Store memory for LLMs](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{<image filename="images/ai-brain-2.png" alt="AI Redis icon.">}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | {{<image filename="images/ai-semantic-routing.png" alt="Semantic routing icon.">}}[Semantic routing chooses the best tool](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{<image filename="images/ai-model.png" alt="AI Redis icon.">}}[Deploy an enhanced gateway with Redis](https://redis.io/blog/ai-gateways-what-are-they-how-can-you-deploy-an-enhanced-gateway-with-redis/) | {{<image filename="images/ai-brain-2.png" alt="AI Redis icon.">}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) |
+| {{<image filename="images/ai-search.png" alt="AI Redis icon.">}}[Vector search guide]({{< relref "/develop/ai/vector-search" >}}) | {{<image filename="images/ai-LLM-memory.png" alt="LLM memory icon.">}}[Store memory for LLMs](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{<image filename="images/ai-brain-2.png" alt="AI Redis icon.">}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | {{<image filename="images/ai-semantic-routing.png" alt="Semantic routing icon.">}}[Semantic routing chooses the best tool](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{<image filename="images/ai-model.png" alt="AI Redis icon.">}}[Deploy an enhanced gateway with Redis](https://redis.io/blog/ai-gateways-what-are-they-how-can-you-deploy-an-enhanced-gateway-with-redis/) | {{<image filename="images/ai-brain-2.png" alt="AI Redis icon.">}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) |
 
 ## Quickstarts
 

content/develop/ai/rag.md

@@ -17,7 +17,7 @@ weight: 4
 aliases: /develop/get-started/rag
 ---
 ### What is Retrieval Augmented Generation (RAG)?
-Large Language Models (LLMs) generate human-like text but are limited by the data they were trained on. RAG enhances LLMs by integrating them with external, domain-specific data stored in a Redis [vector database]({{< relref "/develop/get-started/vector-database" >}}).
+Large Language Models (LLMs) generate human-like text but are limited by the data they were trained on. RAG enhances LLMs by integrating them with external, domain-specific data stored in a Redis [vector database]({{< relref "/develop/ai/vector-database" >}}).
 
 RAG involves three main steps:
 

content/develop/ai/vector-database.md

@@ -187,7 +187,7 @@ Here is a breakdown of the `VECTOR` field definition:
 * `DIM 768`: The length or dimension of the embeddings, determined by the chosen embedding model.
 * `DISTANCE_METRIC COSINE`: The chosen distance function: [cosine distance](https://en.wikipedia.org/wiki/Cosine_similarity).
 
-You can find further details about all these options in the [vector reference documentation]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}).
+You can find further details about all these options in the [vector reference documentation]({{< relref "/develop/ai/vector-fields" >}}).
 
 ### 2. Check the state of the index
 
@@ -278,7 +278,7 @@ From the description, this bike is an excellent match for younger children, and
 
 ## Next steps
 
-1. You can learn more about the query options, such as filters and vector range queries, by reading the [vector reference documentation]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}).
+1. You can learn more about the query options, such as filters and vector range queries, by reading the [vector reference documentation]({{< relref "/develop/ai/vector-fields" >}}).
 2. The complete [Redis Query Engine documentation]({{< relref "/develop/interact/search-and-query/" >}}) might be interesting for you.
 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.
 4. If you want to see more advanced examples of a Redis vector database in action, visit the [Redis AI Resources](https://github.com/redis-developer/redis-ai-resources) page on GitHub.

content/develop/ai/vector-fields.md

@@ -19,16 +19,16 @@ aliases: /develop/interact/search-and-query/advanced-concepts/vectors
 
 Redis includes a [high-performance vector database](https://redis.io/blog/benchmarking-results-for-vector-databases/) that lets you perform semantic searches over vector embeddings. You can augment these searches with filtering over text, numerical, geospatial, and tag metadata.
 
-To quickly get started, check out the [Redis vector quickstart guide]({{< relref "develop/get-started/vector-database" >}}) and the [Redis AI Resources](https://github.com/redis-developer/redis-ai-resources) Github repo.
+To quickly get started, check out the [Redis vector quickstart guide]({{< relref "develop/ai/vector-database" >}}) and the [Redis AI Resources](https://github.com/redis-developer/redis-ai-resources) Github repo.
 
 
 ## Overview
 
-1. [**Create a vector index**]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#create-a-vector-index" >}}): Redis maintains a secondary index over your data with a defined schema (including vector fields and metadata). Redis supports [`FLAT`]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#flat-index" >}}) and [`HNSW`]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#hnsw-index" >}}) vector index types.
-1. [**Store and update vectors**]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#store-and-update-vectors" >}}): Redis stores vectors and metadata in hashes or JSON objects.
-1. [**Search with vectors**]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#search-with-vectors" >}}): Redis supports several advanced querying strategies with vector fields including k-nearest neighbor ([KNN]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#knn-vector-search" >}})), [vector range queries]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#vector-range-queries" >}}), and [metadata filters]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#filters" >}}).
-1. [**Configure vector queries at runtime**]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#runtime-query-params" >}}).
-1. [**Vector search examples**]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#vector-search-examples" >}}): Explore several vector search examples that cover different use cases and techniques.
+1. [**Create a vector index**]({{< relref "develop/ai/vector-fields#create-a-vector-index" >}}): Redis maintains a secondary index over your data with a defined schema (including vector fields and metadata). Redis supports [`FLAT`]({{< relref "develop/ai/vector-fields#flat-index" >}}) and [`HNSW`]({{< relref "develop/ai/vector-fields#hnsw-index" >}}) vector index types.
+1. [**Store and update vectors**]({{< relref "develop/ai/vector-fields#store-and-update-vectors" >}}): Redis stores vectors and metadata in hashes or JSON objects.
+1. [**Search with vectors**]({{< relref "develop/ai/vector-fields#search-with-vectors" >}}): Redis supports several advanced querying strategies with vector fields including k-nearest neighbor ([KNN]({{< relref "develop/ai/vector-fields#knn-vector-search" >}})), [vector range queries]({{< relref "develop/ai/vector-fields#vector-range-queries" >}}), and [metadata filters]({{< relref "develop/ai/vector-fields#filters" >}}).
+1. [**Configure vector queries at runtime**]({{< relref "develop/ai/vector-fields#runtime-query-params" >}}).
+1. [**Vector search examples**]({{< relref "develop/ai/vector-fields#vector-search-examples" >}}): Explore several vector search examples that cover different use cases and techniques.
 
 ## Create a vector index
 
@@ -231,11 +231,11 @@ FT.SEARCH <index_name>
 | Parameter         | Description                                                                                       |
 |:------------------|:--------------------------------------------------------------------------------------------------|
 | `index_name`  | Name of the index.  |
-| `primary_filter_query`  | [Filter]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#filters" >}}) criteria. Use `*` when no filters are required.  |
+| `primary_filter_query`  | [Filter]({{< relref "develop/ai/vector-fields#filters" >}}) criteria. Use `*` when no filters are required.  |
 | `top_k` | Number of nearest neighbors to fetch from the index.  |
 | `vector_field`  | Name of the vector field to search against.  |
 | `vector_blob_param`  | The query vector, passed in as a blob of raw bytes. The blob's byte size must match the vector field's dimensions and type.  |
-| `vector_query_params` (optional) | An optional section for marking one or more vector query parameters passed through the `PARAMS` section. Valid parameters should be provided as key-value pairs. See which [runtime query params]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#runtime-query-params" >}}) are supported for each vector index type.  |
+| `vector_query_params` (optional) | An optional section for marking one or more vector query parameters passed through the `PARAMS` section. Valid parameters should be provided as key-value pairs. See which [runtime query params]({{< relref "develop/ai/vector-fields#runtime-query-params" >}}) are supported for each vector index type.  |
 | `distance_field` (optional) | The optional distance field name used in the response and/or for sorting. By default, the distance field name is `__<vector_field>_score` and it can be used for sorting without using `AS <distance_field>` in the query.  |
 | `vector_query_params_count` | The number of vector query parameters.  |
 | `vector_query_param_name` | The name of the vector query parameter.  |
@@ -282,7 +282,7 @@ FT.SEARCH <index_name>
 | `vector_field`  | Name of the vector field in the index. |
 | `radius` or `radius_param` | The maximum semantic distance allowed between the query vector and indexed vectors. You can provide the value directly in the query, passed to the `PARAMS` section, or as a query attribute.
 | `vector_blob_param`  | The query vector, passed in as a blob of raw bytes. The blob's byte size must match the vector field's dimensions and type. |
-| `vector_query_params` (optional) | An optional section for marking one or more vector query parameters passed through the `PARAMS` section. Valid parameters should be provided as key-value pairs. See which [runtime query params]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#runtime-query-params" >}}) are supported for each vector index type.  |
+| `vector_query_params` (optional) | An optional section for marking one or more vector query parameters passed through the `PARAMS` section. Valid parameters should be provided as key-value pairs. See which [runtime query params]({{< relref "develop/ai/vector-fields#runtime-query-params" >}}) are supported for each vector index type.  |
 | `vector_query_params_count` | The number of vector query parameters.  |
 | `vector_query_param_name` | The name of the vector query parameter.  |
 | `vector_query_param_value` | The value of the vector query parameter.  |
@@ -393,7 +393,7 @@ Optional runtime parameters for HNSW indexes are:
 
 ## Vector search examples
 
-Below are a number of examples to help you get started. For more comprehensive walkthroughs, see the [Redis vector quickstart guide]({{< relref "develop/get-started/vector-database" >}}) and the [Redis AI Resources](https://github.com/redis-developer/redis-ai-resources) Github repo.
+Below are a number of examples to help you get started. For more comprehensive walkthroughs, see the [Redis vector quickstart guide]({{< relref "develop/ai/vector-database" >}}) and the [Redis AI Resources](https://github.com/redis-developer/redis-ai-resources) Github repo.
 
 ### KNN vector search examples
 

content/develop/ai/vector-search.md

@@ -15,7 +15,7 @@ title: Vector search examples
 weight: 2
 ---
 
-This article gives you a good overview of how to perform vector search queries with Redis Stack. See the [Redis as a vector database quick start guide]({{< relref "/develop/get-started/vector-database" >}}) for more information about Redis as a vector database. You can also find more detailed information about all the parameters in the [vector reference documentation]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}).
+This article gives you a good overview of how to perform vector search queries with Redis Stack. See the [Redis as a vector database quick start guide]({{< relref "/develop/ai/vector-database" >}}) for more information about Redis as a vector database. You can also find more detailed information about all the parameters in the [vector reference documentation]({{< relref "/develop/ai/vector-fields" >}}).
 
 A vector search query on a vector field allows you to find all vectors in a vector space that are close to a given vector. You can query for the k-nearest neighbors or vectors within a given radius.
 
@@ -82,7 +82,7 @@ Here is a more detailed explanation of this query:
 
 1. **Range query**: the syntax of a radius query is very similar to the regular range query, except for the keyword `VECTOR_RANGE`. You can also combine a vector radius query with other queries in the same way as regular range queries.  See [combined queries article]({{< relref "/develop/interact/search-and-query/query/combined" >}}) for more details.
 2. **Additional step**: the `=>` arrow means that the range query is followed by evaluating additional parameters.
-3. **Range query parameters**: parameters such as `$YIELD_DISTANCE_AS` can be found in the [vectors reference documentation]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}).
+3. **Range query parameters**: parameters such as `$YIELD_DISTANCE_AS` can be found in the [vectors reference documentation]({{< relref "/develop/ai/vector-fields" >}}).
 4. **Vector binary data**: you need to use `PARAMS` to pass the binary representation of the vector.
 5. **Dialect**: vector search has been available since version two of the query dialect.