FAISS Vector Store

Import: from selectools.rag.stores import FAISSVectorStore Stability: beta Added in: v0.21.0

FAISSVectorStore wraps Facebook AI's FAISS library to provide a fast, in-process vector index that lives entirely in memory but can be persisted to disk. It's ideal when you want zero-server RAG with millions of vectors and have plenty of RAM.

from selectools.embeddings import OpenAIEmbeddingProvider
from selectools.rag import Document
from selectools.rag.stores import FAISSVectorStore

embedder = OpenAIEmbeddingProvider()
store = FAISSVectorStore(embedder=embedder)
store.add_documents([
    Document(text="Selectools is a Python AI agent framework."),
    Document(text="FAISS does fast similarity search."),
])

# search() takes a query embedding, not a string — embed the query first
query_vec = embedder.embed_query("agent framework")
results = store.search(query_vec, top_k=2)
for r in results:
    print(r.score, r.document.text)

store.save("faiss_index")  # writes index + documents

!!! tip "See Also" - Qdrant - Self-hosted vector store with REST + gRPC - pgvector - PostgreSQL-backed vector store - RAG - High-level retrieval pipeline

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Install

pip install "selectools[rag]"

faiss-cpu>=1.7.0 is part of the [rag] optional extras. If you want GPU acceleration, install faiss-gpu separately.

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Constructor

FAISSVectorStore(
    embedder: EmbeddingProvider | None = None,
    dimension: int | None = None,
)

Parameter	Description
embedder	Any selectools.embeddings.EmbeddingProvider. May be None when loading a persisted index that already contains pre-computed vectors.
dimension	Vector dimension. If None, inferred from the first batch of add_documents().

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Persistence

store.save("path/to/index")   # writes index file + sidecar JSON for documents
loaded = FAISSVectorStore.load("path/to/index", embedder=OpenAIEmbedder())

save() persists both the FAISS index and the parallel Document list so search results can return original text/metadata after reload.

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Thread Safety

FAISS itself is not thread-safe for writes. FAISSVectorStore wraps every mutation in a threading.Lock, so concurrent add_documents() and search() calls from multiple agent threads are safe.

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API Reference

Method	Description
add_documents(docs)	Embed and add documents to the index
search(query, top_k)	Cosine similarity search; returns List[SearchResult]
delete(ids)	Remove documents by ID
clear()	Wipe the index
save(path)	Persist index + documents to disk
load(path, embedder)	Class method: rehydrate a persisted store

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Related Examples

#	Script	Description
77	77_faiss_vector_store.py	FAISS quickstart with embeddings + persistence