docs: update README

Author: bsbodden

README.md

@@ -38,11 +38,10 @@ Redis Vector Library (RedisVL) is the production-ready Python client for AI appl
 
 ### **Built for Modern AI Workloads**
 
-- **RAG Pipelines** → Real-time retrieval with hybrid search capabilities  
-- **AI Agents** → Short term & long term memory and semantic routing for intent-based decisions  
+- **RAG Pipelines** → Real-time retrieval with hybrid search capabilities
+- **AI Agents** → Short term & long term memory and semantic routing for intent-based decisions
 - **Recommendation Systems** → Fast retrieval and reranking
 
-
 # 💪 Getting Started
 
 ## Installation
@@ -52,29 +51,38 @@ Install `redisvl` into your Python (>=3.9) environment using `pip`:
 ```bash
 pip install redisvl
 ```
+
 > For more detailed instructions, visit the [installation guide](https://docs.redisvl.com/en/stable/overview/installation.html).
 
 ## Redis
 
 Choose from multiple Redis deployment options:
 
-
 1. [Redis Cloud](https://redis.io/try-free): Managed cloud database (free tier available)
 2. [Redis Stack](https://redis.io/docs/getting-started/install-stack/docker/): Docker image for development
+
     ```bash
     docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
     ```
+
 3. [Redis Enterprise](https://redis.io/enterprise/): Commercial, self-hosted database
-4. [Azure Managed Redis](https://azure.microsoft.com/en-us/products/managed-redis): Fully managed Redis Enterprise on Azure
+4. [Redis Sentinel](https://redis.io/docs/management/sentinel/): High availability with automatic failover
 
-> Enhance your experience and observability with the free [Redis Insight GUI](https://redis.io/insight/).
+    ```python
+    # Connect via Sentinel
+    redis_url="redis+sentinel://sentinel1:26379,sentinel2:26379/mymaster"
+    ```
 
+5. [Azure Managed Redis](https://azure.microsoft.com/en-us/products/managed-redis): Fully managed Redis Enterprise on Azure
 
-# Overview
+> Enhance your experience and observability with the free [Redis Insight GUI](https://redis.io/insight/).
 
+# Overview
 
 ## Index Management
+
 1. [Design a schema for your use case](https://docs.redisvl.com/en/stable/user_guide/01_getting_started.html#define-an-indexschema) that models your dataset with built-in Redis  and indexable fields (*e.g. text, tags, numerics, geo, and vectors*). [Load a schema](https://docs.redisvl.com/en/stable/user_guide/01_getting_started.html#example-schema-creation) from a YAML file:
+
     ```yaml
     index:
       name: user-idx
@@ -86,6 +94,12 @@ Choose from multiple Redis deployment options:
         type: tag
       - name: credit_score
         type: tag
+      - name: job_title
+        type: text
+        attrs:
+          sortable: true
+          no_index: false  # Index for search (default)
+          unf: false       # Normalize case for sorting (default)
       - name: embedding
         type: vector
         attrs:
@@ -94,12 +108,15 @@ Choose from multiple Redis deployment options:
           distance_metric: cosine
           datatype: float32
     ```
+
     ```python
     from redisvl.schema import IndexSchema
 
     schema = IndexSchema.from_yaml("schemas/schema.yaml")
     ```
+
     Or load directly from a Python dictionary:
+
     ```python
     schema = IndexSchema.from_dict({
         "index": {
@@ -110,6 +127,15 @@ Choose from multiple Redis deployment options:
         "fields": [
             {"name": "user", "type": "tag"},
             {"name": "credit_score", "type": "tag"},
+            {
+                "name": "job_title",
+                "type": "text",
+                "attrs": {
+                    "sortable": True,
+                    "no_index": False,  # Index for search
+                    "unf": False        # Normalize case for sorting
+                }
+            },
             {
                 "name": "embedding",
                 "type": "vector",
@@ -125,6 +151,7 @@ Choose from multiple Redis deployment options:
     ```
 
 2. [Create a SearchIndex](https://docs.redisvl.com/en/stable/user_guide/01_getting_started.html#create-a-searchindex) class with an input schema to perform admin and search operations on your index in Redis:
+
     ```python
     from redis import Redis
     from redisvl.index import SearchIndex
@@ -135,10 +162,12 @@ Choose from multiple Redis deployment options:
     # Create the index in Redis
     index.create()
     ```
+
     > An async-compatible index class also available: [AsyncSearchIndex](https://docs.redisvl.com/en/stable/api/searchindex.html#redisvl.index.AsyncSearchIndex).
 
 3. [Load](https://docs.redisvl.com/en/stable/user_guide/01_getting_started.html#load-data-to-searchindex)
 and [fetch](https://docs.redisvl.com/en/stable/user_guide/01_getting_started.html#fetch-an-object-from-redis) data to/from your Redis instance:
+
     ```python
     data = {"user": "john", "credit_score": "high", "embedding": [0.23, 0.49, -0.18, 0.95]}
 
@@ -168,6 +197,7 @@ Define queries and perform advanced searches over your indices, including the co
     ```
 
     Incorporate complex metadata filters on your queries:
+
     ```python
     from redisvl.query.filter import Tag
 
@@ -184,14 +214,16 @@ Define queries and perform advanced searches over your indices, including the co
 - [RangeQuery](https://docs.redisvl.com/en/stable/api/query.html#rangequery) - Vector search within a defined range paired with customizable filters
 - [FilterQuery](https://docs.redisvl.com/en/stable/api/query.html#filterquery) - Standard search using filters and the full-text search
 - [CountQuery](https://docs.redisvl.com/en/stable/api/query.html#countquery) - Count the number of indexed records given attributes
+- [TextQuery](https://docs.redisvl.com/en/stable/api/query.html#textquery) - Full-text search with support for field weighting and BM25 scoring
 
 > Read more about building [advanced Redis queries](https://docs.redisvl.com/en/stable/user_guide/02_hybrid_queries.html).
 
-
 ## Dev Utilities
 
 ### Vectorizers
+
 Integrate with popular embedding providers to greatly simplify the process of vectorizing unstructured data for your index and queries:
+
 - [AzureOpenAI](https://docs.redisvl.com/en/stable/api/vectorizer.html#azureopenaitextvectorizer)
 - [Cohere](https://docs.redisvl.com/en/stable/api/vectorizer.html#coheretextvectorizer)
 - [Custom](https://docs.redisvl.com/en/stable/api/vectorizer.html#customtextvectorizer)
@@ -220,17 +252,18 @@ embeddings = co.embed_many(
 
 > Learn more about using [vectorizers]((https://docs.redisvl.com/en/stable/user_guide/04_vectorizers.html)) in your embedding workflows.
 
-
 ### Rerankers
-[Integrate with popular reranking providers](https://docs.redisvl.com/en/stable/user_guide/06_rerankers.html) to improve the relevancy of the initial search results from Redis
 
+[Integrate with popular reranking providers](https://docs.redisvl.com/en/stable/user_guide/06_rerankers.html) to improve the relevancy of the initial search results from Redis
 
 ## Extensions
+
 We're excited to announce the support for **RedisVL Extensions**. These modules implement interfaces exposing best practices and design patterns for working with LLM memory and agents. We've taken the best from what we've learned from our users (that's you) as well as bleeding-edge customers, and packaged it up.
 
-*Have an idea for another extension? Open a PR or reach out to us at applied.ai@redis.com. We're always open to feedback.*
+*Have an idea for another extension? Open a PR or reach out to us at <applied.ai@redis.com>. We're always open to feedback.*
 
 ### Semantic Caching
+
 Increase application throughput and reduce the cost of using LLM models in production by leveraging previously generated knowledge with the [`SemanticCache`](https://docs.redisvl.com/en/stable/api/cache.html#semanticcache).
 
 ```python
@@ -254,13 +287,15 @@ llmcache.store(
 response = llmcache.check(prompt="What is France's capital city?")
 print(response[0]["response"])
 ```
+
 ```stdout
 >>> Paris
 ```
 
 > Learn more about [semantic caching]((https://docs.redisvl.com/en/stable/user_guide/03_llmcache.html)) for LLMs.
 
 ### Embedding Caching
+
 Reduce computational costs and improve performance by caching embedding vectors with their associated text and metadata using the [`EmbeddingsCache`](https://docs.redisvl.com/en/stable/api/cache.html#embeddingscache).
 
 ```python
@@ -286,6 +321,7 @@ embedding = vectorizer.embed("What is machine learning?")
 # Subsequent calls retrieve from cache (much faster!)
 cached_embedding = vectorizer.embed("What is machine learning?")
 ```
+
 ```stdout
 >>> Cache hit! Retrieved from Redis in <1ms
 ```
@@ -305,31 +341,49 @@ history = SemanticMessageHistory(
     distance_threshold=0.7
 )
 
+# Supports roles: system, user, llm, tool
+# Optional metadata field for additional context
 history.add_messages([
     {"role": "user", "content": "hello, how are you?"},
-    {"role": "assistant", "content": "I'm doing fine, thanks."},
+    {"role": "llm", "content": "I'm doing fine, thanks."},
     {"role": "user", "content": "what is the weather going to be today?"},
-    {"role": "assistant", "content": "I don't know"}
+    {"role": "llm", "content": "I don't know", "metadata": {"model": "gpt-4"}}
 ])
 ```
+
 Get recent chat history:
+
 ```python
 history.get_recent(top_k=1)
 ```
+
 ```stdout
->>> [{"role": "assistant", "content": "I don't know"}]
+>>> [{"role": "llm", "content": "I don't know", "metadata": {"model": "gpt-4"}}]
 ```
+
 Get relevant chat history (powered by vector search):
+
 ```python
 history.get_relevant("weather", top_k=1)
 ```
+
 ```stdout
 >>> [{"role": "user", "content": "what is the weather going to be today?"}]
 ```
-> Learn more about [LLM memory]((https://docs.redisvl.com/en/stable/user_guide/07_message_history.html)).
 
+Filter messages by role:
+
+```python
+# Get only user messages
+history.get_recent(role="user")
+# Or multiple roles
+history.get_recent(role=["user", "system"])
+```
+
+> Learn more about [LLM memory]((https://docs.redisvl.com/en/stable/user_guide/07_message_history.html)).
 
 ### Semantic Routing
+
 Build fast decision models that run directly in Redis and route user queries to the nearest "route" or "topic".
 
 ```python
@@ -360,12 +414,15 @@ router = SemanticRouter(
 
 router("Hi, good morning")
 ```
+
 ```stdout
 >>> RouteMatch(name='greeting', distance=0.273891836405)
 ```
+
 > Learn more about [semantic routing](https://docs.redisvl.com/en/stable/user_guide/08_semantic_router.html).
 
 ## Command Line Interface
+
 Create, destroy, and manage Redis index configurations from a purpose-built CLI interface: `rvl`.
 
 ```bash
@@ -387,17 +444,18 @@ Redis is a proven, high-performance database that excels at real-time workloads.
 
 Built on the [Redis Python](https://github.com/redis/redis-py/tree/master) client, RedisVL provides an intuitive interface for vector search, LLM caching, and conversational AI memory - all the core components needed for modern AI workloads.
 
-
 ## 😁 Helpful Links
 
 For additional help, check out the following resources:
- - [Getting Started Guide](https://docs.redisvl.com/en/stable/user_guide/01_getting_started.html)
- - [API Reference](https://docs.redisvl.com/en/stable/api/index.html)
- - [Redis AI Recipes](https://github.com/redis-developer/redis-ai-resources)
 
+- [Getting Started Guide](https://docs.redisvl.com/en/stable/user_guide/01_getting_started.html)
+- [API Reference](https://docs.redisvl.com/en/stable/api/index.html)
+- [Redis AI Recipes](https://github.com/redis-developer/redis-ai-resources)
 
 ## 🫱🏼‍🫲🏽 Contributing
+
 Please help us by contributing PRs, opening GitHub issues for bugs or new feature ideas, improving documentation, or increasing test coverage. [Read more about how to contribute!](CONTRIBUTING.md)
 
 ## 🚧 Maintenance
+
 This project is supported by [Redis, Inc](https://redis.io) on a good faith effort basis. To report bugs, request features, or receive assistance, please [file an issue](https://github.com/redis/redis-vl-python/issues).