Merge pull request #1310 from redis/ai-restructure

Ai restructure

Author: mich-elle-luna

README.md

@@ -1,6 +1,6 @@
 # Redis Docs
 
-OPEN SOURCE LICENSE VS. TRADEMARKS. The three-clause BSD license gives you the right to redistribute and use the software in source and binary forms, with or without modification, under certain conditions. However, open source licenses like the three-clause BSD license do not address trademarks. For further details please read the [Redis Trademark Policy](https://redis.io/legal/trademark-policy/)."
+OPEN SOURCE LICENSE VS. TRADEMARKS. The three-clause BSD license gives you the right to redistribute and use the software in source and binary forms, with or without modification, under certain conditions. However, open source licenses like the three-clause BSD license do not address trademarks. For further details please read the [Redis Trademark Policy](https://redis.io/legal/trademark-policy/).
 
 ## A note for Issue and PR submitters
 
@@ -204,4 +204,4 @@ nil pointer evaluating page.Page.Params
 ```
 
 This happened for us when Hugo executed the `bannerText` logic, which inspected all parent pages by checking if they have a banner text set. The problem was that one of the parent folders was missing an `_index.md` file, which meant the folder couldn't be identified as a page. The solution was to add an `_index.md` file to the folder that didn't have it yet.
-<!--  -->
+<!--  -->

content/develop/ai/index.md renamed to content/develop/ai/_index.md

@@ -8,13 +8,14 @@ categories:
 - rc
 description: An overview of Redis for AI documentation
 linkTitle: Redis for AI
+hideListLinks: true
 weight: 40
 ---
-Redis stores and indexes vector embeddings that semantically represent unstructured data including text passages, images, videos, or audio. Store vectors and the associated metadata within [hashes]({{< relref "/develop/data-types/hashes" >}}) or [JSON]({{< relref "/develop/data-types/json" >}}) documents for [indexing]({{< relref "/develop/interact/search-and-query/indexing" >}}) and [querying]({{< relref "/develop/interact/search-and-query/query" >}}).
+Redis stores and indexes vector embeddings that semantically represent unstructured data including text passages, images, videos, or audio. Store vectors and the associated metadata within [hashes]({{< relref "/develop/data-types/hashes" >}}) or [JSON]({{< relref "/develop/data-types/json" >}}) documents for [indexing]({{< relref "/develop/interact/search-and-query/indexing" >}}) and [querying]({{< relref "/develop/interact/search-and-query/query" >}}). Use the [vector sets]({{< relref "/develop/data-types/vector-sets" >}}) preview data type to add items to a set, and retrieve a subset of items that are the most similar to a specified vector. 
 
 | 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
 
@@ -29,10 +30,14 @@ 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.
+[**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` and `HNSW` vector index types. Redis stores vectors and metadata in hashes or JSON objects. Redis supports several advanced querying strategies with vector fields including k-nearest neighbor KNN, vector range queries, and metadata filters.
+
+#### Learn how to index and query vector embeddings
+* [redis-py (Python)]({{< relref "/develop/clients/redis-py/vecsearch" >}})
+* [NRedisStack (C#/.NET)]({{< relref "/develop/clients/dotnet/vecsearch" >}})
+* [node-redis (JavaScript)]({{< relref "/develop/clients/nodejs/vecsearch" >}}) 
+* [Jedis (Java)]({{< relref "/develop/clients/jedis/vecsearch" >}})
+* [go-redis (Go)]({{< relref "/develop/clients/go/vecsearch" >}}) 
 
 ## Concepts 
 
@@ -44,43 +49,9 @@ Learn to perform vector search and use gateways and semantic caching in your AI/
 
 ## Quickstarts
 
-Quickstarts or recipes are useful when you are trying to build specific functionality. For example, you might want to do RAG with LangChain or set up LLM memory for you AI agent. Get started with the following Redis Python notebooks.
-
-* [The place to start if you are brand new to Redis](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/redis-intro/00_redis_intro.ipynb)
-
-#### Hybrid and vector search
-Vector search retrieves results based on the similarity of high-dimensional numerical embeddings, while hybrid search combines this with traditional keyword or metadata-based filtering for more comprehensive results.
-* [Implementing hybrid search with Redis](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/02_hybrid_search.ipynb)
-* [Vector search with Redis Python client](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/00_redispy.ipynb) 
-* [Vector search with Redis Vector Library](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/01_redisvl.ipynb)
-
-#### RAG  
-Retrieval Augmented Generation (aka RAG) is a technique to enhance the ability of an LLM to respond to user queries. The retrieval part of RAG is supported by a vector database, which can return semantically relevant results to a user’s query, serving as contextual information to augment the generative capabilities of an LLM.
-* [RAG from scratch with the Redis Vector Library](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/01_redisvl.ipynb) 
-* [RAG using Redis and LangChain](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/02_langchain.ipynb) 
-* [RAG using Redis and LlamaIndex](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/03_llamaindex.ipynb) 
-* [Advanced RAG with redisvl](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) 
-* [RAG using Redis and Nvidia](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb) 
-* [Utilize RAGAS framework to evaluate RAG performance](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/06_ragas_evaluation.ipynb)
-* [Vector search with Azure](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/vector-similarity-search-with-azure-cache-for-redis-enterprise/3822059)
-* [RAG with Spring AI](https://redis.io/blog/building-a-rag-application-with-redis-and-spring-ai/)
-* [RAG with Vertex AI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main)
-* [Notebook for additional tips and techniques to improve RAG quality](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) 
-
-#### Agents
-AI agents can act autonomously to plan and execute tasks for the user.
-* [Notebook to get started with LangGraph and agents](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) 
-* [Build a collaborative movie recommendation system using Redis for data storage, CrewAI for agent-based task execution, and LangGraph for workflow management.](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/01_crewai_langgraph_redis.ipynb)
-
-#### LLM memory
-LLMs are stateless. To maintain context within a conversation chat sessions must be stored and resent to the LLM. Redis manages the storage and retrieval of chat sessions to maintain context and conversational relevance.
-* [LLM session manager with semantic similarity](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/llm-session-manager/00_llm_session_manager.ipynb) 
-* [Handle multiple simultaneous chats with one instance](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/llm-session-manager/01_multiple_sessions.ipynb)
+Get started with the following Redis Python notebook.
 
-#### Semantic caching  
-An estimated 31% of LLM queries are potentially redundant. Redis enables semantic caching to help cut down on LLM costs quickly.
-* [Build a semantic cache using the Doc2Cache framework and Llama3.1](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/doc2cache_llama3_1.ipynb) 
-* [Build a semantic cache with Redis and Google Gemini](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/semantic_caching_gemini.ipynb)
+* [The place to start if you are brand new to Redis](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/redis-intro/00_redis_intro.ipynb). See the [Redis AI notebooks collection]({{< relref "/develop/ai/notebooks-collection" >}}) for the complete list.
 
 #### Computer vision
 Build a facial recognition system using the Facenet embedding model and RedisVL.
@@ -97,8 +68,13 @@ Need a deeper-dive through different use cases and topics?
 * [Agentic RAG](https://github.com/redis-developer/agentic-rag) - A tutorial focused on agentic RAG with LlamaIndex and Amazon Bedrock
 * [RAG on Vertex AI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main) - A RAG tutorial featuring Redis with Vertex AI
 * [RAG workbench](https://github.com/redis-developer/redis-rag-workbench) - A development playground for exploring RAG techniques with Redis
+* [Vector search with Azure](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/vector-similarity-search-with-azure-cache-for-redis-enterprise/3822059)
+* [RAG with Spring AI](https://redis.io/blog/building-a-rag-application-with-redis-and-spring-ai/)
+* [RAG with Vertex AI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main)
 
 #### Recommendation system
+* [Intro content filtering example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/00_content_filtering.ipynb) 
+* [Intro collaborative filtering example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/01_collaborative_filtering.ipynb) 
 * [Recommendation systems w/ NVIDIA Merlin & Redis](https://github.com/redis-developer/redis-nvidia-recsys) - Three examples, each escalating in complexity, showcasing the process of building a realtime recsys with NVIDIA and Redis
 * [Redis product search](https://github.com/redis-developer/redis-product-search) - Build a real-time product search engine using features like full-text search, vector similarity, and real-time data updates
 

content/develop/get-started/redis-in-ai.md renamed to content/develop/ai/genai-apps.md

@@ -1,29 +1,25 @@
 ---
+aliases: /develop/get-started/redis-in-ai
 Title: Redis for GenAI apps
 alwaysopen: false
 categories:
 - docs
 - develop
 description: Understand key benefits of using Redis for AI.
-linktitle: GenAI apps
+linktitle: GenAI tutorials and demos
 weight: 20
 ---
 
-Redis enables high-performance, scalable, and reliable data management, making it a key component for GenAI apps, chatbots, and AI agents. By leveraging Redis for fast data retrieval, caching, and vector search capabilities, you can enhance AI-powered interactions, reduce latency, and improve user experience.
-
-Redis excels in storing and indexing vector embeddings that semantically represent unstructured data. With vector search, Redis retrieves similar questions and relevant data, lowering LLM inference costs and latency. It fetches pertinent portions of chat history, enriching context for more accurate and relevant responses. These features make Redis an ideal choice for RAG systems and GenAI apps requiring fast data access.
+Redis supports storing and indexing vector embeddings—dense numeric representations of unstructured data like text, images, or audio. These embeddings capture semantic meaning, which makes it possible to perform similarity searches using approximate nearest neighbor (ANN) algorithms and K-Nearest Neighbor (KNN) searches. You can use Redis vector search to retrieve relevant content based on a query vector, such as similar questions or documents. This approach is useful for augmenting prompts in large language model (LLM) workflows. By retrieving only the most relevant data before calling the LLM, you can reduce inference costs and improve latency.
 
 ## Key Benefits of Redis in GenAI Apps
 
 - **Performance**: low-latency data access enables real-time interactions critical for AI-driven applications.
-- **Scalability**: designed to handle numerous concurrent connections, Redis is perfect for high-demand GenAI apps.
+- **Scalability**: Redis is designed to handle numerous concurrent connections, so it is perfect for high-demand GenAI apps.
 - **Caching**: efficiently stores frequently accessed data and responses, reducing primary database load and accelerating response times.
 - **Session Management**: in-memory data structures simplify managing session states in conversational AI scenarios.
 - **Flexibility**: Redis supports diverse data structures (for example, strings, hashes, lists, sets), allowing tailored solutions for GenAI apps.
-
-[RedisVL]({{< relref "/integrate/redisvl" >}}) is a Python library with an integrated CLI, offering seamless integration with Redis to enhance GenAI applications.
-
----
+- **Easy integration**: [RedisVL]({{< relref "/integrate/redisvl" >}}) is a Python library with an integrated CLI, offering seamless integration with Redis to enhance GenAI applications.
 
 ## Redis Use Cases in GenAI Apps
 
@@ -61,8 +57,6 @@ Redis enhances natural language understanding by:
 
 See the [Streaming LLM Output Using Redis Streams](https://redis.io/learn/howtos/solutions/streams/streaming-llm-output) tutorial for an in-depth walkthrough.
 
-Redis is a powerful tool to elevate your GenAI applications, enabling them to deliver superior performance, scalability, and user satisfaction.
-
 ## Resources
 
 Check out the [Redis for AI]({{< relref "/develop/ai" >}}) documentation for getting started guides, concepts, ecosystem integrations, examples, and Python notebooks.