llama-index-vector-stores-faiss
llama-index vector_stores faiss integration
Decision gist · record as of 2026-08-14
Yes. This is a lightweight, actively maintained integration package with no known vulnerabilities, permissive licensing, and low installation friction. Install it if you are building a LlamaIndex application and want to use FAISS for vector storage and retrieval.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; FAISS may have platform-specific build requirements.
- Low friction installation with a single runtime dependency on llama-index-core.
- Active maintenance status with recent release activity.
License · maintenance · safety
MIT (permissive) — MIT license permits use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2026-03-12 (155 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 150,222 downloads/mo, #10,971 on PyPI
Alternatives
Verify before relying
pip install llama-index-vector-stores-faiss
from llama_index.vector_stores.faiss import FaissVectorStore
vector_store = FaissVectorStore()- Whether FAISS itself requires system-level dependencies or compilation on target platforms
- Performance characteristics and scalability limits for different index sizes
- Specific LlamaIndex version compatibility beyond the Python version requirement
What it is and what it does
This package provides a LlamaIndex integration for FAISS, a library for efficient similarity search over high-dimensional vectors. It acts as a bridge between LlamaIndex's document and embedding management layer and FAISS's vector indexing capabilities, allowing you to store embeddings and perform fast nearest-neighbor queries within a LlamaIndex application.
The package is designed for developers building retrieval-augmented generation (RAG) systems, semantic search applications, or other AI workflows that need to index and query embeddings. It depends only on llama-index-core and installs with low friction. The MIT license and active maintenance status make it a straightforward choice for integrating FAISS into LlamaIndex-based projects.
Use it for
- Store and retrieve document embeddings in retrieval-augmented generation (RAG) pipelines using LlamaIndex.
- Build semantic search applications that find similar documents or passages based on embedding similarity.
- Integrate efficient in-memory or disk-backed vector indexing into LlamaIndex workflows without external database infrastructure.
- Prototype and develop AI applications that need fast approximate nearest-neighbor search over embeddings.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
This is a lightweight, actively maintained integration package with no known vulnerabilities, permissive licensing, and low installation friction. Install it if you are building a LlamaIndex application and want to use FAISS for vector storage and retrieval.
Install
llama-index-vector-stores-faiss on PyPI
Before you install
Low friction installation with a single runtime dependency on llama-index-core. Active maintenance status with recent release activity.
Requires Python 3.10 or later; FAISS may have platform-specific build requirements.
License in practice
MIT license permits use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install llama-index-vector-stores-faiss
from llama_index.vector_stores.faiss import FaissVectorStore
vector_store = FaissVectorStore()Verify before relying
- Whether FAISS itself requires system-level dependencies or compilation on target platforms
- Performance characteristics and scalability limits for different index sizes
- Specific LlamaIndex version compatibility beyond the Python version requirement
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagellama-index-core |
| Maintenance | Actively maintained 155 days since the last release |
| First released | |
| Downloads | 150,222 / month, #10,971 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_faiss-0.6.0-py3-none-any.whl
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See also llama-index-vector-stores-qdrant · llama-index-retrievers-bm25 · llama-index-vector-stores-postgres · llama-index-vector-stores-chroma · llama-index-vector-stores-redis · llama-index-vector-stores-milvus · llama-index-vector-stores-pinecone · llama-index-vector-stores-azureaisearch · llama-index-vector-stores-lancedb · llama-index-storage-docstore-postgres