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.

faiss_quick.py

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

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.

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()`.

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.

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.

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

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