improve readme vibes

Author: tylerhutcherson

README.md

@@ -1,58 +1,60 @@
-<div align="center" dir="auto">
-    <img width="300" src="https://raw.githubusercontent.com/redis/redis-vl-python/main/docs/_static/Redis_Logo_Red_RGB.svg" style="max-width: 100%" alt="Redis">
-    <h1>🔥 Vector Library</h1>
+<div align="center">
+    <img width="300" src="https://raw.githubusercontent.com/redis/redis-vl-python/main/docs/_static/Redis_Logo_Red_RGB.svg" alt="Redis">
+    <h1>Redis Vector Library</h1>
+    <p><strong>The AI-native Redis Python client</strong></p>
 </div>
 
-
-<div align="center" style="margin-top: 20px;">
-    <span style="display: block; margin-bottom: 10px;">The *AI-native* Redis Python client</span>
-    <br />
-
+<div align="center">
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-![Language](https://img.shields.io/github/languages/top/redis/redis-vl-python)
-[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
-![GitHub last commit](https://img.shields.io/github/last-commit/redis/redis-vl-python)
 [![pypi](https://badge.fury.io/py/redisvl.svg)](https://pypi.org/project/redisvl/)
 ![PyPI - Downloads](https://img.shields.io/pypi/dm/redisvl)
 [![GitHub stars](https://img.shields.io/github/stars/redis/redis-vl-python)](https://github.com/redis/redis-vl-python/stargazers)
 
-</div>
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+![Language](https://img.shields.io/github/languages/top/redis/redis-vl-python)
+![GitHub last commit](https://img.shields.io/github/last-commit/redis/redis-vl-python)
+
+**[Documentation](https://docs.redisvl.com)** • **[Recipes](https://github.com/redis-developer/redis-ai-resources)** • **[GitHub](https://github.com/redis/redis-vl-python)**
 
-<div align="center">
-<div display="inline-block">
-    <a href="https://github.com/redis/redis-vl-python"><b>Home</b></a>&nbsp;&nbsp;&nbsp;
-    <a href="https://docs.redisvl.com"><b>Documentation</b></a>&nbsp;&nbsp;&nbsp;
-    <a href="https://github.com/redis-developer/redis-ai-resources"><b>Recipes</b></a>&nbsp;&nbsp;&nbsp;
-  </div>
-    <br />
 </div>
 
+---
+
+## Introduction
+
+Redis Vector Library (RedisVL) is the production-ready Python client for AI applications built on Redis. **Lightning-fast vector search meets enterprise-grade reliability.**
+
+<div align="center">
 
-# Introduction
+| **🎯 Core Capabilities** | **🚀 AI Extensions** | **🛠️ Dev Utilities** |
+|:---:|:---:|:---:|
+| **[Index Management](#index-management)**<br/>*Schema design, data loading, CRUD ops* | **[Semantic Caching](#semantic-caching)**<br/>*Reduce LLM costs & boost throughput* | **[CLI](#command-line-interface)**<br/>*Index management from terminal* |
+| **[Vector Search](#retrieval)**<br/>*Similarity search with metadata filters* | **[LLM Memory](#llm-memory)**<br/>*Agentic AI context management* | **Async Support**<br/>*Async indexing and search for improved performance* |
+| **[Hybrid Queries](#retrieval)**<br/>*Vector + text + metadata combined* | **[Semantic Routing](#semantic-routing)**<br/>*Intelligent query classification* | **[Vectorizers](#vectorizers)**<br/>*8+ embedding provider integrations* |
+| **[Multi-Query Types](#retrieval)**<br/>*Vector, Range, Filter, Count queries* | **[Embedding Caching](#embedding-caching)**<br/>*Cache embeddings for efficiency* | **[Rerankers](#rerankers)**<br/>*Improve search result relevancy* |
 
-Redis Vector Library is the ultimate Python client designed for AI-native applications harnessing the power of [Redis](https://redis.io).
+</div>
 
-[redisvl](https://pypi.org/project/redisvl/) is your go-to client for:
+### **Built for Modern AI Workloads**
 
-- Lightning-fast information retrieval & vector similarity search
-- Real-time RAG pipelines
-- Agentic memory structures
-- Smart recommendation engines
+- **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
 
-Install `redisvl` into your Python (>=3.8) environment using `pip`:
+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).
 
-## Setting up Redis
+## Redis
 
 Choose from multiple Redis deployment options:
 
@@ -71,7 +73,7 @@ Choose from multiple Redis deployment options:
 # Overview
 
 
-## 🗃️ Redis Index Management
+## 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:
@@ -147,7 +149,7 @@ and [fetch](https://docs.redisvl.com/en/stable/user_guide/01_getting_started.htm
     john = index.fetch("john")
     ```
 
-## 🔍 Retrieval
+## Retrieval
 
 Define queries and perform advanced searches over your indices, including the combination of vectors, metadata filters, and more.
 
@@ -186,7 +188,7 @@ Define queries and perform advanced searches over your indices, including the co
 > Read more about building [advanced Redis queries](https://docs.redisvl.com/en/stable/user_guide/02_hybrid_queries.html).
 
 
-## 🔧  Utilities
+## Dev Utilities
 
 ### Vectorizers
 Integrate with popular embedding providers to greatly simplify the process of vectorizing unstructured data for your index and queries:
@@ -223,12 +225,12 @@ embeddings = co.embed_many(
 [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
+## 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 [email protected]. We're always open to feedback.*
 
-### LLM Semantic Caching
+### 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
@@ -258,9 +260,41 @@ print(response[0]["response"])
 
 > Learn more about [semantic caching]((https://docs.redisvl.com/en/stable/user_guide/03_llmcache.html)) for LLMs.
 
-### LLM Memory History
+### 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
+from redisvl.extensions.cache.embeddings import EmbeddingsCache
+from redisvl.utils.vectorize import HFTextVectorizer
 
-Improve personalization and accuracy of LLM responses by providing user message history as context. Manage access to message history data using recency or relevancy, *powered by vector search* with the [`MessageHistory`](https://docs.redisvl.com/en/stable/api/message_history.html).
+# Initialize embedding cache
+embed_cache = EmbeddingsCache(
+    name="embed_cache",
+    redis_url="redis://localhost:6379",
+    ttl=3600  # 1 hour TTL
+)
+
+# Initialize vectorizer with cache
+vectorizer = HFTextVectorizer(
+    model="sentence-transformers/all-MiniLM-L6-v2",
+    cache=embed_cache
+)
+
+# First call computes and caches the embedding
+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
+```
+
+> Learn more about [embedding caching](https://docs.redisvl.com/en/stable/user_guide/10_embeddings_cache.html) for improved performance.
+
+### LLM Memory
+
+Improve personalization and accuracy of LLM responses by providing user conversation context. Manage access to memory data using recency or relevancy, *powered by vector search* with the [`MessageHistory`](https://docs.redisvl.com/en/stable/api/message_history.html).
 
 ```python
 from redisvl.extensions.message_history import SemanticMessageHistory
@@ -292,10 +326,10 @@ history.get_relevant("weather", top_k=1)
 ```stdout
 >>> [{"role": "user", "content": "what is the weather going to be today?"}]
 ```
-> Learn more about [LLM message history]((https://docs.redisvl.com/en/stable/user_guide/07_message_history.html)).
+> Learn more about [LLM memory]((https://docs.redisvl.com/en/stable/user_guide/07_message_history.html)).
 
 
-### LLM Semantic Routing
+### Semantic Routing
 Build fast decision models that run directly in Redis and route user queries to the nearest "route" or "topic".
 
 ```python
@@ -331,7 +365,7 @@ router("Hi, good morning")
 ```
 > Learn more about [semantic routing](https://docs.redisvl.com/en/stable/user_guide/08_semantic_router.html).
 
-## 🖥️ Command Line Interface
+## Command Line Interface
 Create, destroy, and manage Redis index configurations from a purpose-built CLI interface: `rvl`.
 
 ```bash
@@ -349,25 +383,20 @@ Commands:
 
 ## 🚀 Why RedisVL?
 
-In the age of GenAI, **vector databases** and **LLMs** are transforming information retrieval systems. With emerging and popular frameworks like [LangChain](https://github.com/langchain-ai/langchain) and [LlamaIndex](https://www.llamaindex.ai/), innovation is rapid. Yet, many organizations face the challenge of delivering AI solutions **quickly** and at **scale**.
+Redis is a proven, high-performance database that excels at real-time workloads. With RedisVL, you get a production-ready Python client that makes Redis's vector search, caching, and session management capabilities easily accessible for AI applications.
 
-Enter [Redis](https://redis.io) – a cornerstone of the NoSQL world, renowned for its versatile [data structures](https://redis.io/docs/data-types/) and [processing engines](https://redis.io/docs/interact/). Redis excels in real-time workloads like caching, session management, and search. It's also a powerhouse as a vector database for RAG, an LLM cache, and a chat session memory store for conversational AI.
-
-The Redis Vector Library bridges the gap between the AI-native developer ecosystem and Redis's robust capabilities. With a lightweight, elegant, and intuitive interface, RedisVL makes it easy to leverage Redis's power. Built on the [Redis Python](https://github.com/redis/redis-py/tree/master) client, `redisvl` transforms Redis's features into a grammar perfectly aligned with the needs of today's AI/ML Engineers and Data Scientists.
+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)
- - [Example Gallery](https://docs.redisvl.com/en/stable/examples/index.html)
  - [Redis AI Recipes](https://github.com/redis-developer/redis-ai-resources)
- - [Official Redis Vector API Docs](https://redis.io/docs/interact/search-and-query/advanced-concepts/vectors/)
 
 
 ## 🫱🏼‍🫲🏽 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