Redis Vector Library (RedisVL) is the production-ready Python client for AI applications built on Redis. Lightning-fast vector search meets enterprise-grade reliability.
| 🎯 Core Capabilities | 🚀 AI Extensions | 🛠️ Dev Utilities |
|---|---|---|
| Index Management Schema design, data loading, CRUD ops |
Semantic Caching Reduce LLM costs & boost throughput |
CLI Index management from terminal |
| Vector Search Similarity search with metadata filters |
LLM Memory Agentic AI context management |
Async Support Async indexing and search for improved performance |
| Hybrid Queries Vector + text + metadata combined |
Semantic Routing Intelligent query classification |
Vectorizers 8+ embedding provider integrations |
| Multi-Query Types Vector, Range, Filter, Count queries |
Embedding Caching Cache embeddings for efficiency |
Rerankers Improve search result relevancy |
- 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
Install redisvl into your Python (>=3.9) environment using pip:
pip install redisvlFor more detailed instructions, visit the installation guide.
Choose from multiple Redis deployment options:
- Redis Cloud: Managed cloud database (free tier available)
- Redis Stack: Docker image for development
docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
- Redis Enterprise: Commercial, self-hosted database
- Azure Managed Redis: Fully managed Redis Enterprise on Azure
Enhance your experience and observability with the free Redis Insight GUI.
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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" } } ] })
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Create a SearchIndex class with an input schema to perform admin and search operations on your index in Redis:
from redis import Redis from redisvl.index import SearchIndex # Define the index index = SearchIndex(schema, redis_url="redis://localhost:6379") # Create the index in Redis index.create()
An async-compatible index class also available: AsyncSearchIndex.
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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")
Define queries and perform advanced searches over your indices, including the combination of vectors, metadata filters, and more.
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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)
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RangeQuery - Vector search within a defined range paired with customizable filters
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FilterQuery - Standard search using filters and the full-text search
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CountQuery - Count the number of indexed records given attributes
Read more about building advanced Redis queries.
Integrate with popular embedding providers to greatly simplify the process of vectorizing unstructured data for your index and queries:
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.
Integrate with popular reranking providers to improve the relevancy of the initial search results from Redis
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.
Increase application throughput and reduce the cost of using LLM models in production by leveraging previously generated knowledge with the SemanticCache.
from redisvl.extensions.cache.llm 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.
Reduce computational costs and improve performance by caching embedding vectors with their associated text and metadata using the EmbeddingsCache.
from redisvl.extensions.cache.embeddings import EmbeddingsCache
from redisvl.utils.vectorize import HFTextVectorizer
# 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?")>>> Cache hit! Retrieved from Redis in <1ms
Learn more about embedding caching for improved performance.
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.
from redisvl.extensions.message_history import SemanticMessageHistory
history = SemanticMessageHistory(
name="my-session",
redis_url="redis://localhost:6379",
distance_threshold=0.7
)
history.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:
history.get_recent(top_k=1)>>> [{"role": "assistant", "content": "I don't know"}]
Get relevant chat history (powered by vector search):
history.get_relevant("weather", top_k=1)>>> [{"role": "user", "content": "what is the weather going to be today?"}]
Learn more about LLM memory.
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.
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 indexRead more about using the CLI.
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.
Built on the Redis Python client, RedisVL provides an intuitive interface for vector search, LLM caching, and conversational AI memory - all the core components needed for modern AI workloads.
For additional help, check out the following resources:
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!
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.