redisvl 0.3.4

Python client library and CLI for using Redis as a vector database

🔥 Vector Library

the *AI-native* Redis Python client

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Introduction

Welcome to the Redis Vector Library – the ultimate Python client designed for AI applications harnessing the power of Redis.

redisvl is your go-to tool for:

• Lightning-fast information retrieval & vector similarity search
• Real-time RAG pipelines
• Agentic memory structures
• Smart recommendation engines

💪 Getting Started

Installation

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

pip install redisvl

For more detailed instructions, visit the installation guide.

Setting up Redis

Choose from multiple Redis deployment options:

1. Redis Cloud: Managed cloud database (free tier available)
2. Redis Stack: Docker image for development

   docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
   

3. Redis Enterprise: Commercial, self-hosted database
4. Azure Cache for Redis Enterprise: Fully managed Redis Enterprise on Azure

Enhance your experience and observability with the free Redis Insight GUI.

Overview

🗃️ Redis Index Management

1. Design a schema for your use case that models your dataset with built-in Redis and indexable fields (e.g. text, tags, numerics, geo, and vectors). Load a schema from a YAML file:

   index:
     name: user-idx
     prefix: user
     storage_type: json
   
   fields:
     - name: user
       type: tag
     - name: credit_score
       type: tag
     - name: embedding
       type: vector
       attrs:
         algorithm: flat
         dims: 4
         distance_metric: cosine
         datatype: float32
   

   from redisvl.schema import IndexSchema
   
   schema = IndexSchema.from_yaml("schemas/schema.yaml")
   

   Or load directly from a Python dictionary:

   schema = IndexSchema.from_dict({
       "index": {
           "name": "user-idx",
           "prefix": "user",
           "storage_type": "json"
       },
       "fields": [
           {"name": "user", "type": "tag"},
           {"name": "credit_score", "type": "tag"},
           {
               "name": "embedding",
               "type": "vector",
               "attrs": {
                   "algorithm": "flat",
                   "datatype": "float32",
                   "dims": 4,
                   "distance_metric": "cosine"
               }
           }
       ]
   })
   

2. Create a SearchIndex class with an input schema and client connection in order to perform admin and search operations on your index in Redis:

   from redis import Redis
   from redisvl.index import SearchIndex
   
   # Establish Redis connection and define index
   client = Redis.from_url("redis://localhost:6379")
   index = SearchIndex(schema, client)
   
   # Create the index in Redis
   index.create()
   

   Async compliant search index class also available: AsyncSearchIndex.

3. Load and fetch data to/from your Redis instance:

   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 by "id"
   john = index.fetch("john")
   

🔍 Retrieval

Define queries and perform advanced searches over your indices, including the combination of vectors, metadata filters, and more.

• VectorQuery - Flexible vector queries with customizable filters enabling semantic search:

  from redisvl.query import VectorQuery
  
  query = VectorQuery(
    vector=[0.16, -0.34, 0.98, 0.23],
    vector_field_name="embedding",
    num_results=3
  )
  # run the vector search query against the embedding field
  results = index.query(query)
  

  Incorporate complex metadata filters on your queries:

  from redisvl.query.filter import Tag
  
  # define a tag match filter
  tag_filter = Tag("user") == "john"
  
  # update query definition
  query.set_filter(tag_filter)
  
  # execute query
  results = index.query(query)
  

• RangeQuery - Vector search within a defined range paired with customizable filters

• FilterQuery - Standard search using filters and the full-text search

• CountQuery - Count the number of indexed records given attributes

Read more about building advanced Redis queries.

🔧 Utilities

Vectorizers

Integrate with popular embedding providers to greatly simplify the process of vectorizing unstructured data for your index and queries:

• AzureOpenAI
• Cohere
• Custom
• GCP VertexAI
• HuggingFace
• Mistral
• OpenAI

from redisvl.utils.vectorize import CohereTextVectorizer

# set COHERE_API_KEY in your environment
co = CohereTextVectorizer()

embedding = co.embed(
    text="What is the capital city of France?",
    input_type="search_query"
)

embeddings = co.embed_many(
    texts=["my document chunk content", "my other document chunk content"],
    input_type="search_document"
)

Learn more about using vectorizers in your embedding workflows.

Rerankers

Integrate with popular reranking providers 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 [e-mail]. We're always open to feedback.

LLM Semantic Caching

Increase application throughput and reduce the cost of using LLM models in production by leveraging previously generated knowledge with the SemanticCache.

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"
)

# 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"])

>>> Paris

Learn more about semantic caching for LLMs.

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.

from redisvl.extensions.session_manager import SemanticSessionManager

session = SemanticSessionManager(
    name="my-session",
    redis_url="redis://localhost:6379",
    distance_threshold=0.7
)

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"}
])

Get recent chat history:

session.get_recent(top_k=1)

>>> [{"role": "assistant", "content": "I don't know"}]

Get relevant chat history (powered by vector search):

session.get_relevant("weather", top_k=1)

>>> [{"role": "user", "content": "what is the weather going to be today?"}]

Learn more about LLM session management.

LLM Semantic Routing

Build fast decision models that run directly in Redis and route user queries to the nearest "route" or "topic".

from redisvl.extensions.router import Route, SemanticRouter

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")

>>> RouteMatch(name='greeting', distance=0.273891836405)

Learn more about semantic routing.

🖥️ Command Line Interface

Create, destroy, and manage Redis index configurations from a purpose-built CLI interface: rvl.

$ rvl -h

usage: rvl <command> [<args>]

Commands:
        index       Index manipulation (create, delete, etc.)
        version     Obtain the version of RedisVL
        stats       Obtain statistics about an index

Read more about using the CLI.

🚀 Why RedisVL?

In the age of GenAI, vector databases and LLMs are transforming information retrieval systems. With emerging and popular frameworks like LangChain and LlamaIndex, innovation is rapid. Yet, many organizations face the challenge of delivering AI solutions quickly and at scale.

Enter Redis – a cornerstone of the NoSQL world, renowned for its versatile data structures and processing engines. 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 client, redisvl transforms Redis's features into a grammar perfectly aligned with the needs of today's AI/ML Engineers and Data Scientists.

😁 Helpful Links

For additional help, check out the following resources:

• Getting Started Guide
• API Reference
• Example Gallery
• Redis AI Recipes
• Official Redis Vector API Docs

🫱🏼‍🫲🏽 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!

🚧 Maintenance

This project is supported by Redis, Inc on a good faith effort basis. To report bugs, request features, or receive assistance, please file an issue.