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Link search and RAG pages to the AI agent builder #3804

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1 change: 1 addition & 0 deletions content/develop/ai/search-and-query/_index.md
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Expand Up @@ -46,6 +46,7 @@ Here are the next steps to get you started:
1. Learn how to [create an index]({{< relref "/develop/ai/search-and-query/indexing/" >}}).
1. Learn how to [query your data]({{< relref "/develop/ai/search-and-query/query/" >}}).
1. [Install Redis Insight]({{< relref "/operate/redisinsight" >}}), connect it to your Redis database, and then use [Redis Copilot]({{< relref "/develop/tools/insight" >}}#redis-copilot) to help you learn how to execute complex queries against your own data using simple, plain language prompts.
1. Want working code to start from? Open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template to generate a complete RAG agent built on Redis vector search.

{{< alert title="Try it out" >}}
Experiment with Redis Search interactively in the [Redis playground](https://redis.io/try/sandbox) — no installation required.
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4 changes: 4 additions & 0 deletions content/develop/ai/search-and-query/query/vector-search.md
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Expand Up @@ -21,6 +21,10 @@ This article gives you a good overview of how to perform vector search queries w

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.

{{< tip >}}
Want a working example instead of building from scratch? Open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template to generate a complete RAG agent that uses the vector search queries described here.

@andy-stark-redis andy-stark-redis Aug 13, 2026

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A tersely worded question like this isn't our usual style but it is the kind of thing that AI often generates now. It's a matter of whether we're going to try to "correct" stuff like this (to teach AI how we prefer to express these things) or whether we're just letting this go now. Same basic issue applies with two of the other changed files, btw.

{{< /tip >}}

The examples in this article use a schema with the following fields:

| JSON field | Field alias | Field type | Description |
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2 changes: 1 addition & 1 deletion content/develop/ai/search-and-query/vectors/_index.md
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Expand Up @@ -22,7 +22,7 @@ weight: 8

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/search-tutorial/vector-search" >}}) 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/get-started/search-tutorial/vector-search" >}}) and the [Redis AI Resources](https://github.com/redis-developer/redis-ai-resources) Github repo. For a ready-to-run example, open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template.

{{< alert title="See vector search in action" >}}
[Redis Repo Memory](https://github.com/marketplace/actions/redis-repo-memory) is a GitHub Action that gives your repository a memory: on every pull request it embeds the change and uses Redis KNN vector search to surface semantically related past PRs, issues, and commits — a concise, real-world example of the concepts on this page. See the [source on GitHub](https://github.com/redis-learn/redis-repo-memory).
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4 changes: 4 additions & 0 deletions content/develop/get-started/rag.md
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Expand Up @@ -29,6 +29,10 @@ RAG involves three main steps:
RAG enables LLMs to use real-time information, improving the accuracy and relevance of generated content.
Redis is ideal for RAG due to its speed, versatility, and familiarity.

{{< tip >}}
Prefer to start from working code? Open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template — it generates a complete RAG agent with document ingestion, hybrid retrieval, and citations.
{{< /tip >}}

### The role of Redis in RAG

Redis provides a robust platform for managing real-time data. It supports the storage and retrieval of vectors, essential for handling large-scale, unstructured data and performing similarity searches. Key features and components of Redis that make it suitable for RAG include:
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