Redis Vector Library (RedisVL) is the production-ready Python client for AI applications built on Redis. Lightning-fast vector search meets enterprise-grade reliability.
Perfect for building RAG pipelines with real-time retrieval, AI agents with memory and semantic routing, and recommendation systems with fast search and reranking.
| 🎯 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 |
| Complex Filtering Combine multiple filter types |
Semantic Routing Intelligent query classification |
Vectorizers 8+ embedding provider integrations |
| Hybrid Search Combine semantic & full-text signals |
Embedding Caching Cache embeddings for efficiency |
Rerankers Improve search result relevancy |
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 Cloud offers a fully managed Redis service with a free tier, perfect for getting started quickly.
Redis Stack - Docker image for development
Run Redis Stack locally using Docker:
docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latestThis includes Redis with vector search capabilities and Redis Insight GUI.
Redis Enterprise - Commercial, self-hosted database
Redis Enterprise provides enterprise-grade features for production deployments.
Redis Sentinel - High availability with automatic failover
Configure Redis Sentinel for high availability:
# Connect via Sentinel
redis_url="redis+sentinel://sentinel1:26379,sentinel2:26379/mymaster"Azure Managed Redis - Fully managed Redis Enterprise on Azure
Azure Managed Redis provides fully managed Redis Enterprise on Microsoft Azure.
💡 Tip: Enhance your experience and observability with the free Redis Insight GUI.
-
Design a schema for your use case that models your dataset with built-in Redis indexable fields (e.g. text, tags, numerics, geo, and vectors).
Load schema from YAML file
index: name: user-idx prefix: user storage_type: json fields: - name: user type: tag - name: credit_score type: tag - name: job_title type: text attrs: sortable: true no_index: false # Index for search (default) unf: false # Normalize case for sorting (default) - 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")
Load schema from Python dictionary
from redisvl.schema import IndexSchema schema = IndexSchema.from_dict({ "index": { "name": "user-idx", "prefix": "user", "storage_type": "json" }, "fields": [ {"name": "user", "type": "tag"}, {"name": "credit_score", "type": "tag"}, { "name": "job_title", "type": "text", "attrs": { "sortable": True, "no_index": False, # Index for search "unf": False # Normalize case for sorting } }, { "name": "embedding", "type": "vector", "attrs": { "algorithm": "flat", "datatype": "float32", "dims": 4, "distance_metric": "cosine" } } ] })
📚 Learn more about schema design and schema creation.
-
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.
-
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 vector search, complex filtering, and hybrid search combining semantic and full-text signals.
Quick Reference: Query Types
| Query Type | Use Case | Description |
|---|---|---|
VectorQuery |
Semantic similarity search | Find similar vectors with optional filters |
RangeQuery |
Distance-based search | Vector search within a defined distance range |
FilterQuery |
Metadata filtering | Filter and search using metadata fields |
TextQuery |
Full-text search | BM25-based keyword search with field weighting |
HybridQuery |
Combined search | Combine semantic + full-text signals (Redis 8.4.0+) |
CountQuery |
Counting records | Count documents matching filter criteria |
-
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, # Optional: tune search performance with runtime parameters ef_runtime=100 # HNSW: higher for better recall ) # run the vector search query against the embedding field results = index.query(query)
-
RangeQuery - Vector search within a defined range paired with customizable filters
Build complex filtering queries by combining multiple filter types (tags, numerics, text, geo, timestamps) using logical operators:
```python
from redisvl.query import VectorQuery
from redisvl.query.filter import Tag, Num
# Combine multiple filter types
tag_filter = Tag("user") == "john"
price_filter = Num("price") >= 100
# Create complex filtering query with combined filters
query = VectorQuery(
vector=[0.16, -0.34, 0.98, 0.23],
vector_field_name="embedding",
filter_expression=tag_filter & price_filter,
num_results=10
)
results = index.query(query)
```
- FilterQuery - Standard search using filters and full-text search
- CountQuery - Count the number of indexed records given attributes
- TextQuery - Full-text search with support for field weighting and BM25 scoring
Learn more about building complex filtering queries.
Combine semantic (vector) search with full-text (BM25) search signals for improved search quality:
-
HybridQuery - Native hybrid search combining text and vector similarity (Redis 8.4.0+):
from redisvl.query import HybridQuery hybrid_query = HybridQuery( text="running shoes", text_field_name="description", vector=[0.1, 0.2, 0.3], vector_field_name="embedding", combination_method="LINEAR", # or "RRF" num_results=10 ) results = index.query(hybrid_query)
-
AggregateHybridQuery - Hybrid search using aggregation (compatible with earlier Redis versions)
Learn more about hybrid search.
Integrate with popular embedding providers to greatly simplify the process of vectorizing unstructured data for your index and queries.
Supported Vectorizer Providers
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
RedisVL Extensions provide production-ready modules implementing best practices and design patterns for working with LLM memory and agents. These extensions encapsulate learnings from our user community and enterprise customers.
💡 Have an idea for another extension? Open a PR or reach out to us at applied.ai@redis.com. 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.
Example: Semantic Cache Usage
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 # Redis COSINE distance [0-2], lower is stricter
)
# 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.
Example: Embedding Cache Usage
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.
Example: Message History Usage
from redisvl.extensions.message_history import SemanticMessageHistory
history = SemanticMessageHistory(
name="my-session",
redis_url="redis://localhost:6379",
distance_threshold=0.7
)
# Supports roles: system, user, llm, tool
# Optional metadata field for additional context
history.add_messages([
{"role": "user", "content": "hello, how are you?"},
{"role": "llm", "content": "I'm doing fine, thanks."},
{"role": "user", "content": "what is the weather going to be today?"},
{"role": "llm", "content": "I don't know", "metadata": {"model": "gpt-4"}}
])
# Get recent chat history
history.get_recent(top_k=1)
# >>> [{"role": "llm", "content": "I don't know", "metadata": {"model": "gpt-4"}}]
# 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?"}]
# Filter messages by role
history.get_recent(role="user") # Get only user messages
history.get_recent(role=["user", "system"]) # Or multiple rolesLearn more about LLM memory.
Build fast decision models that run directly in Redis and route user queries to the nearest "route" or "topic".
Example: Semantic Router Usage
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.
