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# RedisVL

<div align="center">
<div display="inline-block">
<b>🔥 Redis Vector Library: the AI-native Redis Python client</b>&nbsp;&nbsp;&nbsp;
</div>
<div style="display: inline-block; text-align: center; margin-bottom: 10px;">
<span style="font-size: larger;"><b>🔥 Redis Vector Library:</b></span>
<br />
<span style="font-size: smaller;">the AI-native Redis Python client</span>
</div>
<br />
</div>



<div align="center">

[![Codecov](https://img.shields.io/codecov/c/github/RedisVentures/RedisVL/dev?label=Codecov&logo=codecov&token=E30WxqBeJJ)](https://codecov.io/gh/RedisVentures/RedisVL)
Expand All @@ -30,29 +31,29 @@
</div>


## Introduction
# Introduction

`redisvl` is a Python client library, tailor-made for AI applications leveraging [Redis](https://redis.com). It's designed for use in:

- Information retrieval & semantic search apps
- Information retrieval & vector similarity search
- Real-time RAG pipelines
- Recommendation engines

Enhance your AI applications with Redis's **speed**, **flexibility**, and **reliability**, incorporating capabilities like vector-based semantic search, full-text search, and geo-spatial search.

## 🚀 Why RedisVL?
# 🚀 Why RedisVL?

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](), [LlamaIndex](), [EmbedChain]()), and the never-ending stream of business problems addressable by AI.
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.

However, organizations struggle with delivering reliable solutions **quickly** (*time to value*) at **scale** (*beyond a demo*).
However, organizations still struggle with delivering reliable solutions **quickly** (*time to value*) at **scale** (*beyond a demo*).

[Redis](https://redis.io) has been a staple for over a decade in the NoSQL world, and boasts a number of flexible [data structures]() and [processing engines]() to handle realtime application workloads like [caching](), [session management](), [job queueing]() and [search]().
[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, LLM cache, and chat session memory store for conversational AI applications

`redisvl` 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`](), it extends the core features of Redis into a grammar that is more aligned to the needs of today's AI/ML engineers or scientists.
`redisvl` **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 extends the core caching and search features of Redis into a grammar that is more aligned to the needs of today's AI/ML engineers or scientists.

## 💪 Getting Started
# 💪 Getting Started

### Installation
## Installation

Install `redisvl` into your Python (>=3.8) environment using `pip`:

Expand All @@ -61,7 +62,7 @@ pip install redisvl
```
> For more instructions, visit the `redisvl` [installation guide](https://www.redisvl.com/overview/installation.html).
### Setting up Redis
## Setting up Redis

Choose from multiple Redis deployment options:

Expand All @@ -76,30 +77,53 @@ Choose from multiple Redis deployment options:
> Enhance your experience and obersvability with the free [Redis Insight GUI](https://redis.com/redis-enterprise/redis-insight/).


## Features and Usage
## What's included?


### 🗃️ 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 YAML file or from a Python dictionary:
### 🗃️ 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*).

```python
from redisvl.schema import IndexSchema
[Load a schema](https://www.redisvl.com/user_guide/getting_started_01.html#example-schema-creation) from a [YAML file](schemas/schema.yaml):
```yaml
version: '0.1.0'
index:
name: user-index-v1
prefix: user
key_separator: ':'
storage_type: json
fields:
- name: user
type: tag
- name: credit_score
type: tag
- name: embedding
type: vector
attrs:
algorithm: flat
dims: 3
distance_metric: cosine
datatype: float32
```
Load schema from a [YAML file](schemas/schema.yaml):
```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": {
"name": "my-index",
"prefix": "docs",
"name": "user-index-v1",
"prefix": "user",
"storage_type": "json"
},
"fields": [
{"name": "content", "type": "text"},
{"name": "user", "type": "tag"},
{"name": "credit_score", "type": "tag"},
{
"name": "content-embedding",
"name": "embedding",
"type": "vector",
"attrs": {
"algorithm": "flat",
Expand All @@ -124,6 +148,19 @@ Choose from multiple Redis deployment options:
# Create the index in Redis
index.create()
```
> Async-compliant search index class also available: `AsyncSearchIndex`

3. [Load](https://www.redisvl.com/user_guide/getting_started_01.html#load-data-to-searchindex)
and [fetch](https://www.redisvl.com/user_guide/getting_started_01.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]}
# load list of dictionaries, specify the id-field
index.load([data], id_field="user")
# fetch
john = index.fetch("john")
```

### 🔍 Realtime Search

Expand All @@ -136,7 +173,7 @@ Define queries and perform advanced searches over your indices, including the co
query = VectorQuery(
vector=[0.16, -0.34, 0.98, 0.23],
vector_field_name="content-embedding",
vector_field_name="embedding",
num_results=3
)
Expand All @@ -145,15 +182,15 @@ 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 Text
from redisvl.query.filter import Tag
# define a text filter
text_filter = Text("content") % "foo"
# define a tag match filter
tag_filter = Tag("user") == "john"
# update query definition
query.set_filter(text_filter)
query.set_filter(tag_filter)
# execute
# execute query
results = index.query(query)
```

Expand Down Expand Up @@ -188,7 +225,7 @@ Integrate with popular embedding models and providers to greatly simplify the pr
- [GCP VertexAI](https://www.redisvl.com/api/vectorizer.html#vertexaitextvectorizer)

```python
from redisvl.vectorize import CohereTextVectorizer
from redisvl.utils.vectorize import CohereTextVectorizer
# set COHERE_API_KEY in your environment
co = CohereTextVectorizer()
Expand All @@ -209,12 +246,12 @@ embeddings = co.embed_many(
### 💫 Beyond Vector Search
Modern GenAI applications require much more than RAG-style vector search in order
to perform well in production. `redisvl` provides some common extensions that
improve applications working with LLMs:
aim to improve applications working with LLMs:

- **LLM Semantic Caching** is designed to increase the request QPS, reduce the cost of using LLM models in production, and drive towards more compliant + consistent responses, robust to nuanced input.

```python
from redisvl.llmcache import SemanticCache
from redisvl.extensions.llmcache import SemanticCache
# init cache with TTL (expiration) policy and semantic distance threshhold
llmcache = SemanticCache(
Expand All @@ -241,9 +278,7 @@ improve applications working with LLMs:

> Learn more about Semantic Caching in `redisvl` [here](https://www.redisvl.com/user_guide/llmcache_03.html).

- **LLM Session Management**
COMING SOON

- **LLM Session Management** COMING SOON
- **LLM Contextual Access Control** COMING SOON


Expand Down

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