Update for redisvl 0.8.2

Author: redisdocsapp[bot]

content/develop/ai/redisvl/0.8.2/api/message_history.md

@@ -1,7 +1,7 @@
 ---
 linkTitle: LLM message history
 title: LLM Message History
-url: '/develop/ai/redisvl/0.8.2/api/essage_history/'
+url: '/develop/ai/redisvl/0.8.2/api/message_history/'
 ---
 
 

content/develop/ai/redisvl/0.8.2/install.md

@@ -1,7 +1,6 @@
 ---
-description: Install RedisVL
-linkTitle: Install
-title: Install
+linkTitle: Install RedisVL
+title: Install RedisVL
 weight: 2
 aliases:
 - /integrate/redisvl/install

content/develop/ai/redisvl/0.8.2/user_guide/getting_started.md

@@ -2,7 +2,7 @@
 linkTitle: Getting started with RedisVL
 title: Getting Started with RedisVL
 weight: 01
-url: '/develop/ai/redisvl/0.8.2/user_guide/getting_starte/'
+url: '/develop/ai/redisvl/0.8.2/user_guide/getting_started/'
 ---
 
 `redisvl` is a versatile Python library with an integrated CLI, designed to enhance AI applications using Redis. This guide will walk you through the following steps:

content/develop/ai/redisvl/0.8.2/user_guide/llmcache.md

@@ -1,28 +1,11 @@
 ---
-linkTitle: LLM caching
-title: LLM Caching
-aliases:
-- /integrate/redisvl/user_guide/03_llmcache
+linkTitle: First, we will import [openai](https://platform.openai.com) to use their API for responding to user prompts. we will also create a simple `ask_openai` helper method to assist.
+title: First, we will import [OpenAI](https://platform.openai.com) to use their API for responding to user prompts. We will also create a simple `ask_openai` helper method to assist.
 weight: 03
+url: '/develop/ai/redisvl/0.8.2/user_guide/llmcache/'
 ---
 
 
-This notebook demonstrates how to use RedisVL's `SemanticCache` to cache LLM responses based on semantic similarity. Semantic caching can significantly reduce API costs and latency by retrieving cached responses for semantically similar prompts instead of making redundant API calls.
-
-Key features covered:
-- Basic cache operations (store, check, clear)
-- Customizing semantic similarity thresholds
-- TTL policies for cache expiration
-- Performance benchmarking
-- Access controls with tags and filters for multi-user scenarios
-
-Prerequisites:
-- Ensure `redisvl` is installed in your Python environment
-- Have a running instance of [Redis Stack](https://redis.io/docs/install/install-stack/) or [Redis Cloud](https://redis.io/cloud)
-- OpenAI API key for the examples
-
-First, we will import [OpenAI](https://platform.openai.com) to use their API for responding to user prompts. We will also create a simple `ask_openai` helper method to assist.
-
 
 ```python
 import os

content/develop/ai/redisvl/0.8.2/user_guide/message_history.md

@@ -2,7 +2,7 @@
 linkTitle: LLM message history
 title: LLM Message History
 weight: 07
-url: '/develop/ai/redisvl/0.8.2/user_guide/essage_history/'
+url: '/develop/ai/redisvl/0.8.2/user_guide/message_history/'
 ---