update readme and some doc strings

Author: tylerhutcherson

README.md

@@ -1,8 +1,12 @@
-<h1 align="center">🔥 Redis Vector Library</h1>
+<div align="center" style="margin-bottom: 20px;">
+    <div><img src="docs/_static/Redis_Logo_Red_RGB.svg" style="width: 200px; margin-bottom: 20px;"></div>
+    <h1>🔥 Vector Library</h1>
+</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">
-        <span style="font-size: smaller;">the AI-native Redis Python client</span>
-        <br />
 
 [![Codecov](https://img.shields.io/codecov/c/github/redis/redis-vl-python/dev?label=Codecov&logo=codecov&token=E30WxqBeJJ)](https://codecov.io/gh/redis/redis-vl-python)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
@@ -18,33 +22,33 @@
 <div display="inline-block">
     <a href="https://github.com/redis/redis-vl-python"><b>Home</b></a>&nbsp;&nbsp;&nbsp;
     <a href="https://www.redisvl.com"><b>Documentation</b></a>&nbsp;&nbsp;&nbsp;
-    <a href="https://github.com/redis-developer"><b>More Projects</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
 
-The Python Redis Vector Library (RedisVL) is a tailor-made client for AI applications leveraging [Redis](https://redis.com).
+Welcome to the Redis Vector Library (`redisvl`) – the ultimate Python client designed for AI applications harnessing the power of [Redis](https://redis.io).
 
-It's specifically designed for:
+`redisvl` is your go-to tool for:
 
-- Information retrieval & vector similarity search
+- Lightning-fast information retrieval & vector similarity search
 - Real-time RAG pipelines
-- Recommendation engines
+- Agentic memory structures
+- Smart recommendation engines
 
-Enhance your applications with Redis' **speed**, **flexibility**, and **reliability**, incorporating capabilities like vector-based semantic search, full-text search, and geo-spatial search.
+## 🚀 Why RedisVL?
 
-# 🚀 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 soaring. Yet, many organizations face the challenge of delivering AI solutions **quickly** and at **scale**.
 
-The emergence of the modern GenAI stack, including **vector databases** and **LLMs**, has become increasingly popular due to accelerated innovation & research in information retrieval, the ubiquity of tools & frameworks (e.g. [LangChain](https://github.com/langchain-ai/langchain), [LlamaIndex](https://www.llamaindex.ai/), [EmbedChain](https://github.com/embedchain/embedchain)), and the never-ending stream of business problems addressable by AI.
+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.
 
-However, organizations still struggle with delivering reliable solutions **quickly** (*time to value*) at **scale** (*beyond a demo*).
+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.
 
-[Redis](https://redis.io) has been a staple for over a decade in the NoSQL world, and boasts a number of flexible [data structures](https://redis.io/docs/data-types/) and [processing engines](https://redis.io/docs/interact/) to handle realtime application workloads like caching, session management, and search. Most notably, Redis has been used as a vector database for RAG, as an LLM cache, and chat session memory store for conversational AI applications.
+Unleash the full potential of Redis for your AI projects with `redisvl`.
 
-The vector library **bridges the gap between** the emerging AI-native developer ecosystem and the capabilities of Redis by providing a lightweight, elegant, and intuitive interface. Built on the back of the popular Python client, [`redis-py`](https://github.com/redis/redis-py/tree/master), it abstracts the features Redis into a grammar that is more aligned to the needs of today's AI/ML Engineers or Data Scientists.
 
 # 💪 Getting Started
 
@@ -62,24 +66,24 @@ pip install redisvl
 Choose from multiple Redis deployment options:
 
 
-1. [Redis Cloud](https://redis.com/try-free): Managed cloud database (free tier available)
+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.com/redis-enterprise/): Commercial, self-hosted database
+3. [Redis Enterprise](https://redis.io/enterprise/): Commercial, self-hosted database
 4. [Azure Cache for Redis Enterprise](https://learn.microsoft.com/azure/azure-cache-for-redis/quickstart-create-redis-enterprise): Fully managed Redis Enterprise on Azure
 
 > Enhance your experience and observability with the free [Redis Insight GUI](https://redis.com/redis-enterprise/redis-insight/).
 
 
-## What's included?
+# Overview
 
 
-### 🗃️ Redis Index Management
+## 🗃️ Redis Index Management
 1. [Design an `IndexSchema`](https://www.redisvl.com/user_guide/getting_started_01.html#define-an-indexschema) that models your dataset with built-in Redis [data structures](https://www.redisvl.com/user_guide/hash_vs_json_05.html) (*Hash or JSON*) and indexable fields (*e.g. text, tags, numerics, geo, and vectors*).
 
-    [Load a schema](https://www.redisvl.com/user_guide/getting_started_01.html#example-schema-creation) from a [YAML file](schemas/schema.yaml):
+    [Load a schema](https://www.redisvl.com/user_guide/getting_started_01.html#example-schema-creation) from a YAML file:
     ```yaml
     index:
         name: user-index-v1
@@ -155,7 +159,7 @@ and [fetch](https://www.redisvl.com/user_guide/getting_started_01.html#fetch-an-
     john = index.fetch("john")
     ```
 
-### 🔍 Realtime Search
+## 🔍 Realtime Search
 
 Define queries and perform advanced searches over your indices, including the combination of vectors, metadata filters, and more.
 
@@ -191,10 +195,10 @@ Define queries and perform advanced searches over your indices, including the co
 - [FilterQuery](https://www.redisvl.com/api/query.html#filterquery) - Standard search using filters and the full-text search
 - [CountQuery](https://www.redisvl.com/api/query.html#countquery) - Count the number of indexed records given attributes
 
-> Read more about building advanced Redis queries [here](https://www.redisvl.com/user_guide/hybrid_queries_02.html).
+> Read more about building [advanced Redis queries](https://www.redisvl.com/user_guide/hybrid_queries_02.html).
 
 
-### 🖥️ Command Line Interface
+## 🖥️ Command Line Interface
 Create, destroy, and manage Redis index configurations from a purpose-built CLI interface: `rvl`.
 
 ```bash
@@ -208,15 +212,19 @@ Commands:
         stats       Obtain statistics about an index
 ```
 
-> Read more about using the `redisvl` CLI [here](https://www.redisvl.com/user_guide/cli.html).
+> Read more about using the `redisvl` [CLI](https://www.redisvl.com/user_guide/cli.html).
+
+## 🔧  Utilities
 
-### ⚡  Community Integrations
-Integrate with popular embedding models and providers to greatly simplify the process of vectorizing unstructured data for your index and queries:
-- [Cohere](https://www.redisvl.com/api/vectorizer/html#coheretextvectorizer)
+### Vectorizers
+Integrate with popular embedding providers to greatly simplify the process of vectorizing unstructured data for your index and queries:
+- [AzureOpenAI](https://www.redisvl.com/api/vectorizer.html#azureopenaitextvectorizer)
+- [Cohere](https://www.redisvl.com/api/vectorizer.html#coheretextvectorizer)
+- [Custom](https://www.redisvl.com/api/vectorizer.html#customtextvectorizer)
+- [GCP VertexAI](https://www.redisvl.com/api/vectorizer.html#vertexaitextvectorizer)
+- [HuggingFace](https://www.redisvl.com/api/vectorizer.html#hftextvectorizer)
 - [Mistral](https://www.redisvl.com/api/vectorizer/html#mistralaitextvectorizer)
 - [OpenAI](https://www.redisvl.com/api/vectorizer.html#openaitextvectorizer)
-- [HuggingFace](https://www.redisvl.com/api/vectorizer.html#hftextvectorizer)
-- [GCP VertexAI](https://www.redisvl.com/api/vectorizer.html#vertexaitextvectorizer)
 
 ```python
 from redisvl.utils.vectorize import CohereTextVectorizer
@@ -235,53 +243,124 @@ embeddings = co.embed_many(
 )
 ```
 
-> Learn more about using `redisvl` Vectorizers in your workflows [here](https://www.redisvl.com/user_guide/vectorizers_04.html).
+> Learn more about using `redisvl` [vectorizers]((https://www.redisvl.com/user_guide/vectorizers_04.html)) in your embedding workflows.
 
-### 💫 Beyond Vector Search
-In order to perform well in production, modern GenAI applications require much more than vector search for retrieval. `redisvl` provides some common extensions that
-aim to improve applications working with LLMs:
 
-- **LLM Semantic Caching** is designed to increase application throughput and reduce the cost of using LLM models in production by leveraging previously generated knowledge.
+### Rerankers
+Integrate with popular reranking providers to improve the relevancy of the initial search results from Redis:
 
-    ```python
-    from redisvl.extensions.llmcache import SemanticCache
 
-    # init cache with TTL (expiration) policy and semantic distance threshhold
-    llmcache = SemanticCache(
-        name="llmcache",
-        ttl=360,
-        redis_url="redis://localhost:6379"
-    )
-    llmcache.set_threshold(0.2) # can be changed on-demand
 
-    # store user queries and LLM responses in the semantic cache
-    llmcache.store(
-        prompt="What is the capital city of France?",
-        response="Paris",
-        metadata={}
-    )
+## 💫 Extensions
+To improve production performance in GenAI applications, `redisvl` provides some common extensions:
 
-    # quickly check the cache with a slightly different prompt (before invoking an LLM)
-    response = llmcache.check(prompt="What is France's capital city?")
-    print(response[0]["response"])
-    ```
-    ```stdout
-    >>> "Paris"
-    ```
+### LLM Semantic Caching
+Increase application throughput and reduce the cost of using LLM models in production by leveraging previously generated knowledge with the [`SemanticCache`](https://www.redisvl.com/api/cache.html#semanticcache).
+
+```python
+from redisvl.extensions.llmcache import SemanticCache
+
+# init cache with TTL and semantic distance threshold
+llmcache = SemanticCache(
+    name="llmcache",
+    ttl=360,
+    redis_url="redis://localhost:6379",
+    distance_threshold=0.1
+)
+
+# store user queries and LLM responses in the semantic cache
+llmcache.store(
+    prompt="What is the capital city of France?",
+    response="Paris",
+    metadata={}
+)
+
+# quickly check the cache with a slightly different prompt (before invoking an LLM)
+response = llmcache.check(prompt="What is France's capital city?")
+print(response[0]["response"])
+```
+```stdout
+>>> "Paris"
+```
 
-    > Learn more about Semantic Caching [here](https://www.redisvl.com/user_guide/llmcache_03.html).
+> Learn more about [semantic caching]((https://www.redisvl.com/user_guide/llmcache_03.html)) for LLMs with `redisvl`.
 
-- **LLM Session Management (COMING SOON)** aims to improve personalization and accuracy of the LLM application by providing user chat session information and conversational memory.
-- **LLM Contextual Access Control (COMING SOON)** aims to improve security concerns by preventing malicious, irrelevant, or problematic user input from reaching LLMs and infrastructure.
+### LLM Session Management
 
+Improve personalization and accuracy of LLM responses by providing user chat history as context. Manage access to the session data using recency or relevancy, *powered by vector search* with the [`SemanticSessionManager`]().
+
+```python
+from redisvl.extensions.session_manager import SemanticSessionManager
+
+session = SemanticSessionManager(
+    name="my-session",
+    redis_url="redis://localhost:6379"
+)
+
+session.add_messages([
+    {"role": "user", "content": "hello, how are you?"},
+    {"role": "assistant", "content": "I'm doing fine, thanks."},
+    {"role": "user", "content": "what is the weather going to be today?"},
+    {"role": "assistant", "content": "I don't know"}
+])
+```
+```python
+session.get_recent(top_k=1)
+```
+```stdout
+>>> {"role": "assistant", "content": "I don't know"}
+```
+```python
+session.get_relevant("weather", top_k=1)
+```
+```stdout
+>>> {"role": "user", "content": "what is the weather going to be today?"}
+```
+> Learn more about [LLM session management]((https://www.redisvl.com/user_guide/session_manager_07.html)) with `redisvl`.
+
+
+### LLM Semantic Routing
+Build fast decision models that run directly in Redis and route user queries to the nearest "route" or "topic".
+
+```python
+routes = [
+    Route(
+        name="greeting",
+        references=["hello", "hi"],
+        metadata={"type": "greeting"},
+        distance_threshold=0.3,
+    )
+    Route(
+        name="farewell",
+        references=["bye", "goodbye"],
+        metadata={"type": "farewell"},
+        distance_threshold=0.3,
+    ),
+]
+
+# build semantic router from routes
+router = SemanticRouter(
+    name="topic-router",
+    routes=routes,
+    redis_url="redis://localhost:6379",
+)
+
+
+router("Hi, good morning")
+```
+```stdout
+>>> RouteMatch(name='greeting', distance=0.09384023)
+```
+> Learn more about [semantic routing](https://www.redisvl.com/user_guide/semantic_router_08.html) in `redisvl`.
 
-## Helpful Links
+## 😁 Helpful Links
 
-To get started, check out the following guides:
+For additional help, check out the following resources:
  - [Getting Started Guide](https://www.redisvl.com/user_guide/getting_started_01.html)
  - [API Reference](https://www.redisvl.com/api/index.html)
  - [Example Gallery](https://www.redisvl.com/examples/index.html)
- - [Official Redis Vector Search Docs](https://redis.io/docs/interact/search-and-query/advanced-concepts/vectors/)
+ - [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

redisvl/extensions/session_manager/semantic_session.py

@@ -198,7 +198,7 @@ def get_relevant(
             as_text (bool): Whether to return the prompts and responses as text
             or as JSON
             top_k (int): The number of previous messages to return. Default is 5.
-            fallback (bool): Whether to drop back to recent conversation history
+            fall_back (bool): Whether to drop back to recent conversation history
                 if no relevant context is found.
             session_tag (str): Tag of entries linked to a specific session.
             user_tag (str): Tag of entries linked to a specific user.

redisvl/utils/vectorize/text/custom.py

@@ -44,7 +44,6 @@ def __init__(
         embed_many: Optional[Callable] = None,
         aembed: Optional[Callable] = None,
         aembed_many: Optional[Callable] = None,
-        model: str = "custom_vectorizer",
     ):
         """Initialize the Custom vectorizer.
 
@@ -72,7 +71,7 @@ def __init__(
             self._validate_aembed_many(aembed_many)
             self._aembed_many_func = aembed_many
 
-        super().__init__(model=model, dims=self._set_model_dims())
+        super().__init__(model=self.type, dims=self._set_model_dims())
 
     def _validate_embed(self, func: Callable):
         """calls the func with dummy input and validates that it returns a vector"""