INT: remove RedisVL docs and point to new RTD site

Author: dwdougherty

content/develop/ai/index.md

@@ -14,7 +14,7 @@ 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/overview/" >}}) |
+| {{<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/" >}}) |
 
 #### Overview
 

content/integrate/redisvl/_index.md

@@ -8,9 +8,10 @@ categories:
 - rc
 - oss
 - clients
-description: This is the Redis vector library.
+description: This is the Redis vector library (RedisVL).
 group: library
 hidden: false
+hideListLinks: true
 linkTitle: RedisVL
 summary: RedisVL provides a powerful, dedicated Python client library for using Redis
   as a vector database. Leverage Redis's speed, reliability, and vector-based semantic
@@ -19,4 +20,12 @@ title: RedisVL
 type: integration
 weight: 1
 ---
+RedisVL is a powerful, dedicated Python client library for Redis that enables seamless integration and management of high-dimensional vector data.
+Built to support machine learning and artificial intelligence workflows, RedisVL simplifies the process of storing, searching, and analyzing vector embeddings, which are commonly used for tasks like recommendation systems, semantic search, and anomaly detection.
 
+Key features of RedisVL include:
+
+- Vector Similarity Search: Efficiently find nearest neighbors in high-dimensional spaces using algorithms like HNSW (Hierarchical Navigable Small World).
+- Integration with AI Frameworks: RedisVL works seamlessly with popular frameworks such as TensorFlow, PyTorch, and Hugging Face, making it easy to deploy AI models.
+- Scalable and Fast: Leveraging Redis's in-memory architecture, RedisVL provides low-latency access to vector data, even at scale.
+- By bridging the gap between data storage and AI model deployment, RedisVL empowers developers to build intelligent, real-time applications with minimal infrastructure complexity.