From ae798dea91cfe18f08cc906ecd21ce84418ace77 Mon Sep 17 00:00:00 2001 From: Michelle Luna Date: Wed, 16 Apr 2025 13:39:29 -0700 Subject: [PATCH] fixing relrefs --- content/commands/ft.create/index.md | 2 +- content/develop/ai/_index.md | 9 +++------ content/develop/ai/rag.md | 2 +- content/develop/ai/vector-database.md | 4 ++-- content/develop/ai/vector-fields.md | 20 +++++++++---------- content/develop/ai/vector-search.md | 4 ++-- content/develop/clients/dotnet/vecsearch.md | 14 ++++++------- content/develop/clients/go/vecsearch.md | 12 +++++------ .../develop/clients/hiredis/issue-commands.md | 2 +- content/develop/clients/jedis/vecsearch.md | 14 ++++++------- content/develop/clients/nodejs/vecsearch.md | 12 +++++------ content/develop/clients/redis-py/vecsearch.md | 12 +++++------ content/develop/get-started/data-store.md | 2 +- .../develop/get-started/document-database.md | 4 ++-- .../administration/configuration.md | 4 ++-- .../advanced-concepts/_index.md | 4 ++-- .../advanced-concepts/query_syntax.md | 4 ++-- .../field-and-type-options.md | 4 ++-- .../search-and-query/indexing/_index.md | 2 +- content/develop/whats-new/_index.md | 8 ++++---- content/get-started/_index.md | 8 ++++---- content/operate/rc/rc-quickstart.md | 4 ++-- 22 files changed, 74 insertions(+), 77 deletions(-) diff --git a/content/commands/ft.create/index.md b/content/commands/ft.create/index.md index 9a367d7db..1c3573042 100644 --- a/content/commands/ft.create/index.md +++ b/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 diff --git a/content/develop/ai/_index.md b/content/develop/ai/_index.md index 6b6ddc028..30953c6bf 100644 --- a/content/develop/ai/_index.md +++ b/content/develop/ai/_index.md @@ -15,7 +15,7 @@ Redis stores and indexes vector embeddings that semantically represent unstructu | Vector | RAG | RedisVL | | :-- | :-- | :-- | -| {{AI Redis icon.}}[Redis vector database quick start guide]({{< relref "/develop/get-started/vector-database" >}}) |{{AI Redis icon.}} [Retrieval-Augmented Generation quick start guide]({{< relref "/develop/get-started/rag" >}}) | {{AI Redis icon.}}[Redis vector Python client library documentation]({{< relref "/integrate/redisvl/" >}}) | +| {{AI Redis icon.}}[Redis vector database quick start guide]({{< relref "/develop/ai/vector-database" >}}) |{{AI Redis icon.}} [Retrieval-Augmented Generation quick start guide]({{< relref "/develop/ai/rag" >}}) | {{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 | | :-- | :-- | :-- | :-- | :-- | -| {{AI Redis icon.}}[Vector search guide]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) | {{LLM memory icon.}}[Store memory for LLMs](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{AI Redis icon.}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | {{Semantic routing icon.}}[Semantic routing chooses the best tool](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{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/) | {{AI Redis icon.}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | +| {{AI Redis icon.}}[Vector search guide]({{< relref "/develop/ai/vector-search" >}}) | {{LLM memory icon.}}[Store memory for LLMs](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{AI Redis icon.}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | {{Semantic routing icon.}}[Semantic routing chooses the best tool](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{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/) | {{AI Redis icon.}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | ## Quickstarts diff --git a/content/develop/ai/rag.md b/content/develop/ai/rag.md index dfc5e6c02..c1df2cb3f 100644 --- a/content/develop/ai/rag.md +++ b/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: diff --git a/content/develop/ai/vector-database.md b/content/develop/ai/vector-database.md index 5ea17c3db..596b15277 100644 --- a/content/develop/ai/vector-database.md +++ b/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. diff --git a/content/develop/ai/vector-fields.md b/content/develop/ai/vector-fields.md index 8285e0232..9b6f70e29 100644 --- a/content/develop/ai/vector-fields.md +++ b/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 | 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 `___score` and it can be used for sorting without using `AS ` 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 | `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 diff --git a/content/develop/ai/vector-search.md b/content/develop/ai/vector-search.md index a283e264f..09df1dbb6 100644 --- a/content/develop/ai/vector-search.md +++ b/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. diff --git a/content/develop/clients/dotnet/vecsearch.md b/content/develop/clients/dotnet/vecsearch.md index 5b309d6a6..b7e9a8ad2 100644 --- a/content/develop/clients/dotnet/vecsearch.md +++ b/content/develop/clients/dotnet/vecsearch.md @@ -18,11 +18,11 @@ weight: 3 [Redis Query Engine]({{< relref "/develop/interact/search-and-query" >}}) lets you index vector fields in [hash]({{< relref "/develop/data-types/hashes" >}}) or [JSON]({{< relref "/develop/data-types/json" >}}) objects (see the -[Vectors]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}) +[Vectors]({{< relref "/develop/ai/vector-fields" >}}) reference page for more information). Among other things, vector fields can store *text embeddings*, which are AI-generated vector representations of the semantic information in pieces of text. The -[vector distance]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#distance-metrics" >}}) +[vector distance]({{< relref "/develop/ai/vector-fields#distance-metrics" >}}) between two embeddings indicates how similar they are semantically. By comparing the similarity of an embedding generated from some query text with embeddings stored in hash or JSON fields, Redis can retrieve documents that closely match the query in terms @@ -237,9 +237,9 @@ The schema in the example below includes three fields: the text content to index [tag]({{< relref "/develop/interact/search-and-query/advanced-concepts/tags" >}}) field to represent the "genre" of the text, and the embedding vector generated from the original text content. The `embedding` field specifies -[HNSW]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#hnsw-index" >}}) +[HNSW]({{< relref "/develop/ai/vector-fields#hnsw-index" >}}) indexing, the -[L2]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#distance-metrics" >}}) +[L2]({{< relref "/develop/ai/vector-fields#distance-metrics" >}}) vector distance metric, `Float32` values to represent the vector's components, and 150 dimensions, as required by our embedding model. @@ -328,10 +328,10 @@ sorted to rank them in order of ascending distance. The code below creates the query embedding using the `GetEmbedding()` method, as with the indexing, and passes it as a parameter when the query executes (see -[Vector search]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) +[Vector search]({{< relref "/develop/ai/vector-search" >}}) for more information about using query parameters with embeddings). The query is a -[K nearest neighbors (KNN)]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#knn-vector-search" >}}) +[K nearest neighbors (KNN)]({{< relref "/develop/ai/vector-fields#knn-vector-search" >}}) search that sorts the results in order of vector distance from the query vector. (As before, replace `GetEmbedding()` with `GetEmbeddingFromAzure()` if you are using @@ -417,6 +417,6 @@ is the result that is most similar in meaning to the query text ## Learn more See -[Vector search]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) +[Vector search]({{< relref "/develop/ai/vector-search" >}}) for more information about the indexing options, distance metrics, and query format for vectors. diff --git a/content/develop/clients/go/vecsearch.md b/content/develop/clients/go/vecsearch.md index c0be20c26..abf169dcd 100644 --- a/content/develop/clients/go/vecsearch.md +++ b/content/develop/clients/go/vecsearch.md @@ -18,11 +18,11 @@ weight: 3 [Redis Query Engine]({{< relref "/develop/interact/search-and-query" >}}) lets you index vector fields in [hash]({{< relref "/develop/data-types/hashes" >}}) or [JSON]({{< relref "/develop/data-types/json" >}}) objects (see the -[Vectors]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}) +[Vectors]({{< relref "/develop/ai/vector-fields" >}}) reference page for more information). Among other things, vector fields can store *text embeddings*, which are AI-generated vector representations of the semantic information in pieces of text. The -[vector distance]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#distance-metrics" >}}) +[vector distance]({{< relref "/develop/ai/vector-fields#distance-metrics" >}}) between two embeddings indicates how similar they are semantically. By comparing the similarity of an embedding generated from some query text with embeddings stored in hash or JSON fields, Redis can retrieve documents that closely match the query in terms @@ -131,9 +131,9 @@ three fields: the text content to index, a [tag]({{< relref "/develop/interact/search-and-query/advanced-concepts/tags" >}}) field to represent the "genre" of the text, and the embedding vector generated from the original text content. The `embedding` field specifies -[HNSW]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#hnsw-index" >}}) +[HNSW]({{< relref "/develop/ai/vector-fields#hnsw-index" >}}) indexing, the -[L2]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#distance-metrics" >}}) +[L2]({{< relref "/develop/ai/vector-fields#distance-metrics" >}}) vector distance metric, `Float32` values to represent the vector's components, and 384 dimensions, as required by the `all-MiniLM-L6-v2` embedding model. @@ -248,7 +248,7 @@ results in order of this numeric similarity value. The code below creates the query embedding using `Embed()`, as with the indexing, and passes it as a parameter when the query executes (see -[Vector search]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) +[Vector search]({{< relref "/develop/ai/vector-search" >}}) for more information about using query parameters with embeddings). ```go @@ -313,6 +313,6 @@ is the result that is most similar in meaning to the query text ## Learn more See -[Vector search]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) +[Vector search]({{< relref "/develop/ai/vector-search" >}}) for more information about the indexing options, distance metrics, and query format for vectors. diff --git a/content/develop/clients/hiredis/issue-commands.md b/content/develop/clients/hiredis/issue-commands.md index 036ebd695..800e0d78f 100644 --- a/content/develop/clients/hiredis/issue-commands.md +++ b/content/develop/clients/hiredis/issue-commands.md @@ -67,7 +67,7 @@ redisReply *reply = redisCommand(c, "SET key:%s %s", myKeyNumber, myValue); ``` You may need to include binary data in the command (for example, to store -[vector embeddings]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}) +[vector embeddings]({{< relref "/develop/ai/vector-fields" >}}) in fields of a [hash]({{< relref "/develop/data-types/hashes" >}})) object. To do this, use the `%b` format specifier and pass a pointer to the data buffer, followed by a `size_t` value indicating its length in bytes. diff --git a/content/develop/clients/jedis/vecsearch.md b/content/develop/clients/jedis/vecsearch.md index 6b4eccb62..be5191fea 100644 --- a/content/develop/clients/jedis/vecsearch.md +++ b/content/develop/clients/jedis/vecsearch.md @@ -18,11 +18,11 @@ weight: 3 [Redis Query Engine]({{< relref "/develop/interact/search-and-query" >}}) lets you index vector fields in [hash]({{< relref "/develop/data-types/hashes" >}}) or [JSON]({{< relref "/develop/data-types/json" >}}) objects (see the -[Vectors]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}) +[Vectors]({{< relref "/develop/ai/vector-fields" >}}) reference page for more information). Among other things, vector fields can store *text embeddings*, which are AI-generated vector representations of the semantic information in pieces of text. The -[vector distance]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#distance-metrics" >}}) +[vector distance]({{< relref "/develop/ai/vector-fields#distance-metrics" >}}) between two embeddings indicates how similar they are semantically. By comparing the similarity of an embedding generated from some query text with embeddings stored in hash or JSON fields, Redis can retrieve documents that closely match the query in terms @@ -140,9 +140,9 @@ The schema in the example below includes three fields: the text content to index [tag]({{< relref "/develop/interact/search-and-query/advanced-concepts/tags" >}}) field to represent the "genre" of the text, and the embedding vector generated from the original text content. The `embedding` field specifies -[HNSW]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#hnsw-index" >}}) +[HNSW]({{< relref "/develop/ai/vector-fields#hnsw-index" >}}) indexing, the -[L2]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#distance-metrics" >}}) +[L2]({{< relref "/develop/ai/vector-fields#distance-metrics" >}}) vector distance metric, `Float32` values to represent the vector's components, and 768 dimensions, as required by the `all-mpnet-base-v2` embedding model. @@ -226,10 +226,10 @@ sorted to rank them in order of ascending distance. The code below creates the query embedding using the `encode()` method, as with the indexing, and passes it as a parameter when the query executes (see -[Vector search]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) +[Vector search]({{< relref "/develop/ai/vector-search" >}}) for more information about using query parameters with embeddings). The query is a -[K nearest neighbors (KNN)]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#knn-vector-search" >}}) +[K nearest neighbors (KNN)]({{< relref "/develop/ai/vector-fields#knn-vector-search" >}}) search that sorts the results in order of vector distance from the query vector. ```java @@ -284,6 +284,6 @@ is the result judged to be most similar in meaning to the query text ## Learn more See -[Vector search]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) +[Vector search]({{< relref "/develop/ai/vector-search" >}}) for more information about the indexing options, distance metrics, and query format for vectors. diff --git a/content/develop/clients/nodejs/vecsearch.md b/content/develop/clients/nodejs/vecsearch.md index 874e3e63b..437d23129 100644 --- a/content/develop/clients/nodejs/vecsearch.md +++ b/content/develop/clients/nodejs/vecsearch.md @@ -18,11 +18,11 @@ weight: 3 [Redis Query Engine]({{< relref "/develop/interact/search-and-query" >}}) lets you index vector fields in [hash]({{< relref "/develop/data-types/hashes" >}}) or [JSON]({{< relref "/develop/data-types/json" >}}) objects (see the -[Vectors]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}) +[Vectors]({{< relref "/develop/ai/vector-fields" >}}) reference page for more information). Among other things, vector fields can store *text embeddings*, which are AI-generated vector representations of the semantic information in pieces of text. The -[vector distance]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#distance-metrics" >}}) +[vector distance]({{< relref "/develop/ai/vector-fields#distance-metrics" >}}) between two embeddings indicates how similar they are semantically. By comparing the similarity of an embedding generated from some query text with embeddings stored in hash or JSON fields, Redis can retrieve documents that closely match the query in terms @@ -100,9 +100,9 @@ three fields: the text content to index, a [tag]({{< relref "/develop/interact/search-and-query/advanced-concepts/tags" >}}) field to represent the "genre" of the text, and the embedding vector generated from the original text content. The `embedding` field specifies -[HNSW]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#hnsw-index" >}}) +[HNSW]({{< relref "/develop/ai/vector-fields#hnsw-index" >}}) indexing, the -[L2]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#distance-metrics" >}}) +[L2]({{< relref "/develop/ai/vector-fields#distance-metrics" >}}) vector distance metric, `Float32` values to represent the vector's components, and 768 dimensions, as required by the `all-distilroberta-v1` embedding model. @@ -197,7 +197,7 @@ distance value. The code below creates the query embedding using `pipe()`, as with the indexing, and passes it as a parameter during execution (see -[Vector search]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) +[Vector search]({{< relref "/develop/ai/vector-search" >}}) for more information about using query parameters with embeddings). The query returns an array of objects representing the documents @@ -250,6 +250,6 @@ is the result that is most similar in meaning to the query text ## Learn more See -[Vector search]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) +[Vector search]({{< relref "/develop/ai/vector-search" >}}) for more information about the indexing options, distance metrics, and query format for vectors. diff --git a/content/develop/clients/redis-py/vecsearch.md b/content/develop/clients/redis-py/vecsearch.md index be4bacd34..3377bdfcb 100644 --- a/content/develop/clients/redis-py/vecsearch.md +++ b/content/develop/clients/redis-py/vecsearch.md @@ -18,11 +18,11 @@ weight: 4 [Redis Query Engine]({{< relref "/develop/interact/search-and-query" >}}) lets you index vector fields in [hash]({{< relref "/develop/data-types/hashes" >}}) or [JSON]({{< relref "/develop/data-types/json" >}}) objects (see the -[Vectors]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}) +[Vectors]({{< relref "/develop/ai/vector-fields" >}}) reference page for more information). Among other things, vector fields can store *text embeddings*, which are AI-generated vector representations of the semantic information in pieces of text. The -[vector distance]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#distance-metrics" >}}) +[vector distance]({{< relref "/develop/ai/vector-fields#distance-metrics" >}}) between two embeddings indicates how similar they are semantically. By comparing the similarity of an embedding generated from some query text with embeddings stored in hash or JSON fields, Redis can retrieve documents that closely match the query in terms @@ -92,9 +92,9 @@ three fields: the text content to index, a [tag]({{< relref "/develop/interact/search-and-query/advanced-concepts/tags" >}}) field to represent the "genre" of the text, and the embedding vector generated from the original text content. The `embedding` field specifies -[HNSW]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#hnsw-index" >}}) +[HNSW]({{< relref "/develop/ai/vector-fields#hnsw-index" >}}) indexing, the -[L2]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors#distance-metrics" >}}) +[L2]({{< relref "/develop/ai/vector-fields#distance-metrics" >}}) vector distance metric, `Float32` values to represent the vector's components, and 384 dimensions, as required by the `all-MiniLM-L6-v2` embedding model. @@ -169,7 +169,7 @@ results in order of this numeric similarity value. The code below creates the query embedding using `model.encode()`, as with the indexing, and passes it as a parameter when the query executes (see -[Vector search]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) +[Vector search]({{< relref "/develop/ai/vector-search" >}}) for more information about using query parameters with embeddings). ```python @@ -229,6 +229,6 @@ is the result that is most similar in meaning to the query text ## Learn more See -[Vector search]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) +[Vector search]({{< relref "/develop/ai/vector-search" >}}) for more information about the indexing options, distance metrics, and query format for vectors. diff --git a/content/develop/get-started/data-store.md b/content/develop/get-started/data-store.md index 6b558122b..2b90f0961 100644 --- a/content/develop/get-started/data-store.md +++ b/content/develop/get-started/data-store.md @@ -95,7 +95,7 @@ SCAN 0 MATCH "bike:*" COUNT 100 You can address more use cases with Redis by reading these additional quick start guides: * [Redis as a document database]({{< relref "/develop/get-started/document-database" >}}) -* [Redis as a vector database]({{< relref "/develop/get-started/vector-database" >}}) +* [Redis as a vector database]({{< relref "/develop/ai/vector-database" >}}) ## Continue learning with Redis University diff --git a/content/develop/get-started/document-database.md b/content/develop/get-started/document-database.md index 787a6ea06..880fb4b83 100644 --- a/content/develop/get-started/document-database.md +++ b/content/develop/get-started/document-database.md @@ -175,8 +175,8 @@ Please see the [query documentation]({{< relref "/develop/interact/search-and-qu You can learn more about how to use Redis Community Edition as a vector database in the following quick start guide: -* [Redis as a vector database]({{< relref "/develop/get-started/vector-database" >}}) +* [Redis as a vector database]({{< relref "/develop/ai/vector-database" >}}) ## Continue learning with Redis University -{{< university-links >}} \ No newline at end of file +{{< university-links >}} diff --git a/content/develop/interact/search-and-query/administration/configuration.md b/content/develop/interact/search-and-query/administration/configuration.md index a08b44823..1b24c1e9d 100644 --- a/content/develop/interact/search-and-query/administration/configuration.md +++ b/content/develop/interact/search-and-query/administration/configuration.md @@ -513,7 +513,7 @@ search-upgrade-index idx PREFIX 1 tt LANGUAGE french LANGUAGE_FIELD MyLang SCORE Added in v2.4.8. The maximum memory resize (in bytes) for vector indexes. -The maximum memory resize (in bytes) for vector indexes. This value will override default memory limits if you need to allow for a large [`BLOCK_SIZE`]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors/#creation-attributes-per-algorithm" >}}). +The maximum memory resize (in bytes) for vector indexes. This value will override default memory limits if you need to allow for a large [`BLOCK_SIZE`]({{< relref "/develop/ai/vector-fields#creation-attributes-per-algorithm" >}}). Type: integer @@ -584,4 +584,4 @@ FT.CONFIG GET OPT1 FT.CONFIG GET * ``` -Values set using [`FT.CONFIG SET`]({{< relref "/commands/ft.config-set/" >}}) are not persisted after server restart. \ No newline at end of file +Values set using [`FT.CONFIG SET`]({{< relref "/commands/ft.config-set/" >}}) are not persisted after server restart. diff --git a/content/develop/interact/search-and-query/advanced-concepts/_index.md b/content/develop/interact/search-and-query/advanced-concepts/_index.md index c4d72551c..c100efd9d 100644 --- a/content/develop/interact/search-and-query/advanced-concepts/_index.md +++ b/content/develop/interact/search-and-query/advanced-concepts/_index.md @@ -39,7 +39,7 @@ Redis Community Edition supports the following Redis Query Engine features. This * Exact phrase search and slop-based search * Numeric filters and ranges * Geo-filtering using Redis [geo commands]({{< relref "/commands/" >}}?group=geo) -* [Vector search]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}) +* [Vector search]({{< relref "/develop/ai/vector-fields" >}}) ## Full-text search features @@ -61,4 +61,4 @@ Redis Community Edition is developed and tested on Linux and macOS on x86_64 CPU Atom CPUs are not supported. -
\ No newline at end of file +
diff --git a/content/develop/interact/search-and-query/advanced-concepts/query_syntax.md b/content/develop/interact/search-and-query/advanced-concepts/query_syntax.md index b551b8acf..d6a22fe2e 100644 --- a/content/develop/interact/search-and-query/advanced-concepts/query_syntax.md +++ b/content/develop/interact/search-and-query/advanced-concepts/query_syntax.md @@ -281,7 +281,7 @@ The general syntax for hybrid query is `{some filter query}=>[ KNN {num|$num} @v `@vector_field:[VECTOR_RANGE 0.5 $query_vec]` -As of v2.4, the KNN vector search can be used at most once in a query, while, as of v2.6, the vector range filter can be used multiple times in a query. For more information on vector similarity syntax, see [Querying vector fields]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}), and [Vector search examples]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#vector-search-examples" >}}) sections. +As of v2.4, the KNN vector search can be used at most once in a query, while, as of v2.6, the vector range filter can be used multiple times in a query. For more information on vector similarity syntax, see [Querying vector fields]({{< relref "/develop/ai/vector-fields" >}}), and [Vector search examples]({{< relref "develop/ai/vector-fields#vector-search-examples" >}}) sections. ## Prefix matching @@ -388,7 +388,7 @@ The supported attributes are: As of v2.6.1, the query attributes syntax supports these additional attributes: * **$yield_distance_as**: specifies the distance field name, used for later sorting and/or returning, for clauses that yield some distance metric. It is currently supported for vector queries only (both KNN and range). -* **vector query params**: pass optional parameters for [vector queries]({{< relref "develop/interact/search-and-query/advanced-concepts/vectors#querying-vector-fields" >}}) in key-value format. +* **vector query params**: pass optional parameters for [vector queries]({{< relref "develop/ai/vector-fields#querying-vector-fields" >}}) in key-value format. ## A few query examples diff --git a/content/develop/interact/search-and-query/basic-constructs/field-and-type-options.md b/content/develop/interact/search-and-query/basic-constructs/field-and-type-options.md index 3f1912a20..114ad9666 100644 --- a/content/develop/interact/search-and-query/basic-constructs/field-and-type-options.md +++ b/content/develop/interact/search-and-query/basic-constructs/field-and-type-options.md @@ -170,7 +170,7 @@ Notice that `{count}` represents the total number of attribute pairs passed in t * `{attribute_name} {attribute_value}` are algorithm attributes for the creation of the vector index. Every algorithm has its own mandatory and optional attributes. -For more information about vector fields, see [vector fields]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}). +For more information about vector fields, see [vector fields]({{< relref "/develop/ai/vector-fields" >}}). ## Tag fields @@ -259,4 +259,4 @@ redis> FT.SEARCH idx '@text:(πŸ˜€πŸ˜πŸ™‚*)' NOCONTENT redis> FT.SEARCH idx '@text:(%πŸ˜€πŸ˜πŸ™ƒ%)' NOCONTENT 1) (integer) 0 -``` \ No newline at end of file +``` diff --git a/content/develop/interact/search-and-query/indexing/_index.md b/content/develop/interact/search-and-query/indexing/_index.md index 2663b58b2..1b9872ee3 100644 --- a/content/develop/interact/search-and-query/indexing/_index.md +++ b/content/develop/interact/search-and-query/indexing/_index.md @@ -378,7 +378,7 @@ Now you can search for the two headphones that are most similar to an image embe ``` Note that `0.771500051022` is the L2 distance between the query vector and `[-0.8,-0.15,0.33,-0.01]`, which is the second element in the embedding array, and it is lower than the L2 distance between the query vector and `[-0.7,-0.51,0.88,0.14]`, which is the first element in the embedding array. -For more information on vector similarity syntax, see [Vector fields]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}). +For more information on vector similarity syntax, see [Vector fields]({{< relref "/develop/ai/vector-fields" >}}). ## Index JSON objects diff --git a/content/develop/whats-new/_index.md b/content/develop/whats-new/_index.md index 08a372828..88059abc6 100644 --- a/content/develop/whats-new/_index.md +++ b/content/develop/whats-new/_index.md @@ -30,8 +30,8 @@ weight: 10 - [.NET]({{< relref "/develop/clients/dotnet/vecsearch.md" >}}) - Updated AI integrations: - [AI overview]({{< relref "/develop/ai/index.md" >}}) - - [RAG intro]({{< relref "/develop/get-started/rag.md" >}}) - - [Redis in AI]({{< relref "/develop/get-started/redis-in-ai.md" >}}) + - [RAG intro]({{< relref "/develop/ai/rag.md" >}}) + - [GenAI tutorials and demos]({{< relref "/develop/ai/genai-apps.md" >}}) --- @@ -129,10 +129,10 @@ weight: 10 ## Q3 2024 (July - September) Updates -* Updated the [RAG with Redis quick start guide]({{< relref "/develop/get-started/rag" >}}). +* Updated the [RAG with Redis quick start guide]({{< relref "/develop/ai/rag" >}}). * Updates for [Redis Community Edition version 7.4]({{< relref "/operate/oss_and_stack/stack-with-enterprise/release-notes/redisce" >}}). * Added new [Redis Insight debugging]({{< relref "/develop/tools/insight/debugging" >}}) page. -* Completed a major re-write/restructuring of the [vector indexing page]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}). +* Completed a major re-write/restructuring of the [vector indexing page]({{< relref "/develop/ai/vector-fields" >}}). * Added new [client-side caching page]({{< relref "/develop/clients/client-side-caching" >}}). * Added new documentation for the [RDI in Redis Insight feature]({{< relref "/develop/tools/insight/rdi-connector" >}}). * Added new documentation for the [Redis for VS Code feature]({{< relref "/develop/tools/redis-for-vscode/" >}}). diff --git a/content/get-started/_index.md b/content/get-started/_index.md index 0dbfcd260..362f3f7cb 100644 --- a/content/get-started/_index.md +++ b/content/get-started/_index.md @@ -5,7 +5,7 @@ linkTitle: Community Edition title: Community Edition type: develop --- -Redis is an [in-memory data store]({{< relref "/develop/get-started/data-store" >}}) used by millions of developers as a cache, [vector database]({{< relref "/develop/get-started/vector-database" >}}), [document database]({{< relref "/develop/get-started/document-database" >}}), [streaming engine]({{< relref "/develop/data-types/streams" >}}), and message broker. Redis has built-in replication and different levels of [on-disk persistence]({{< relref "/operate/oss_and_stack/management/persistence" >}}). It supports complex [data types]({{< relref "/develop/data-types/" >}}) (for example, strings, hashes, lists, sets, sorted sets, and JSON), with atomic operations defined on those data types. +Redis is an [in-memory data store]({{< relref "/develop/get-started/data-store" >}}) used by millions of developers as a cache, [vector database]({{< relref "/develop/ai/vector-database" >}}), [document database]({{< relref "/develop/get-started/document-database" >}}), [streaming engine]({{< relref "/develop/data-types/streams" >}}), and message broker. Redis has built-in replication and different levels of [on-disk persistence]({{< relref "/operate/oss_and_stack/management/persistence" >}}). It supports complex [data types]({{< relref "/develop/data-types/" >}}) (for example, strings, hashes, lists, sets, sorted sets, and JSON), with atomic operations defined on those data types. You can install Redis from source or from an executable/distribution for your OS. @@ -22,9 +22,9 @@ The following quick start guides will show you how to use Redis for the followin - [Data structure store]({{< relref "/develop/get-started/data-store" >}}) - [Document database]({{< relref "/develop/get-started/document-database" >}}) -- [Vector database]({{< relref "/develop/get-started/vector-database" >}}) -- [AI agents and chatbots]({{< relref "/develop/get-started/redis-in-ai" >}}) -- [Retrieval Augmented Generation (RAG) with Redis]({{< relref "/develop/get-started/rag" >}}) +- [Vector database]({{< relref "/develop/ai/vector-database" >}}) +- [AI agents and chatbots]({{< relref "/develop/ai/genai-apps" >}}) +- [Retrieval Augmented Generation (RAG) with Redis]({{< relref "/develop/ai/rag" >}}) ## Data integration tools, libraries, and frameworks - [Client API libraries]({{< relref "/develop/clients" >}}) diff --git a/content/operate/rc/rc-quickstart.md b/content/operate/rc/rc-quickstart.md index 94c846014..cd4f65f8e 100644 --- a/content/operate/rc/rc-quickstart.md +++ b/content/operate/rc/rc-quickstart.md @@ -161,8 +161,8 @@ See [Clients]({{< relref "/develop/clients" >}}) to learn how to connect with th See the following guides to get started with different Redis use cases: - [Data structure store]({{< relref "/develop/get-started/data-store" >}}) - [Document database]({{< relref "/develop/get-started/document-database" >}}) -- [Vector database]({{< relref "/develop/get-started/vector-database" >}}) -- [RAG with Redis]({{< relref "/develop/get-started/rag" >}}) +- [Vector database]({{< relref "/develop/ai/vector-database" >}}) +- [RAG with Redis]({{< relref "/develop/ai/rag" >}}) - [Redis for AI]({{< relref "/develop/ai" >}}) ### redis-cli {#using-rediscli}