llama-index-vector-stores-redis
llama-index vector_stores redis integration
Decision gist · record as of 2026-08-14
Yes, if you are building LlamaIndex applications and have Redis infrastructure available or planned. The package has low install friction, active maintenance, permissive licensing, and no known vulnerabilities. Install only if you intend to use Redis as your vector store backend; it is not useful standalone without LlamaIndex and a Redis instance.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10,<3.14 and an accessible Redis server instance.
- Low install friction with only two runtime dependencies (llama-index-core and redisvl).
- Package is actively maintained with a recent release cycle.
License · maintenance · safety
MIT (permissive) — MIT license permits use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
last release 2026-03-17 (150 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 79,704 downloads/mo, #14,339 on PyPI
Alternatives
Verify before relying
pip install llama-index-vector-stores-redis
from llama_index.vector_stores.redis import RedisVectorStore
from redisvl.index import SearchIndex
# Initialize Redis vector store with connection details
vector_store = RedisVectorStore(index_name="my_index")- Whether Redis server setup or configuration is required beyond installing this package
- Performance characteristics and scalability limits for large embedding datasets
- Compatibility with specific versions of llama-index-core and redisvl beyond the stated Python version range
What it is and what it does
This package provides a LlamaIndex integration that uses Redis as the backend storage for vector embeddings. It bridges LlamaIndex's vector store abstraction with Redis's vector capabilities through the redisvl library, allowing developers to build retrieval-augmented generation (RAG) systems and semantic search applications that persist embeddings in Redis.
The package is designed for applications that need fast, scalable vector similarity search without requiring a separate specialized vector database. It fits into the LlamaIndex ecosystem as a drop-in vector store option, handling embedding storage, indexing, and retrieval operations against a Redis instance.
Use it for
- Build RAG pipelines that store document embeddings in Redis for fast retrieval during question-answering workflows.
- Implement semantic search over large document collections using Redis as the vector index backend.
- Integrate vector search into existing applications already using Redis for caching or session storage.
- Prototype and deploy LlamaIndex applications with Redis as a cost-effective vector store alternative.
- Scale vector search workloads by leveraging Redis's in-memory performance and clustering capabilities.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building LlamaIndex applications and have Redis infrastructure available or planned.
The package has low install friction, active maintenance, permissive licensing, and no known vulnerabilities. Install only if you intend to use Redis as your vector store backend; it is not useful standalone without LlamaIndex and a Redis instance.
Install
llama-index-vector-stores-redis on PyPI
Before you install
Low install friction with only two runtime dependencies (llama-index-core and redisvl). Package is actively maintained with a recent release cycle.
Requires Python >=3.10,<3.14 and an accessible Redis server instance.
License in practice
MIT license permits use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
Quickstart
pip install llama-index-vector-stores-redis
from llama_index.vector_stores.redis import RedisVectorStore
from redisvl.index import SearchIndex
# Initialize Redis vector store with connection details
vector_store = RedisVectorStore(index_name="my_index")Verify before relying
- Whether Redis server setup or configuration is required beyond installing this package
- Performance characteristics and scalability limits for large embedding datasets
- Compatibility with specific versions of llama-index-core and redisvl beyond the stated Python version range
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesllama-index-coreredisvl |
| Maintenance | Actively maintained 150 days since the last release |
| First released | |
| Downloads | 79,704 / month, #14,339 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_vector_stores_redis-0.8.0-py3-none-any.whl
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