FAISSDocumentStore

API reference	FAISS
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/faiss

FAISSDocumentStore is a local Document Store backed by FAISS for vector similarity search. It keeps vectors in a FAISS index and stores document data in memory, with optional persistence to disk.

FAISSDocumentStore is a good fit for local development and small to medium-sized datasets where you want a lightweight setup without running an external database service.

Installation

Install the FAISS integration:

shell

pip install faiss-haystack

Initialization

Create a FAISSDocumentStore instance and write embedded documents:

python

from haystack import Document
from haystack.document_stores.types import DuplicatePolicy
from haystack_integrations.document_stores.faiss import FAISSDocumentStore

document_store = FAISSDocumentStore(
    index_path="my_faiss_index",  # Optional: enables persistence on disk
    index_string="Flat",
    embedding_dim=768,
)

document_store.write_documents(
    [
        Document(content="This is first", embedding=[0.1] * 768),
        Document(content="This is second", embedding=[0.2] * 768),
    ],
    policy=DuplicatePolicy.OVERWRITE,
)

print(document_store.count_documents())

# Persist index and metadata files (`.faiss` and `.json`)
document_store.save("my_faiss_index")

Persistence

If you provide index_path when initializing FAISSDocumentStore, it tries to load existing persisted files (.faiss and .json) from that path. You can also explicitly call:

• save(index_path) to write index and metadata to disk.
• load(index_path) to load them later.

Example of loading from a previously saved folder/path:

python

from haystack_integrations.document_stores.faiss import FAISSDocumentStore

# This loads `my_faiss_index.faiss` and `my_faiss_index.json` if they exist
document_store = FAISSDocumentStore(index_path="my_faiss_index")

# Alternatively, initialize first and then load explicitly
another_store = FAISSDocumentStore(embedding_dim=768)
another_store.load("my_faiss_index")

Supported Retrievers

FAISSEmbeddingRetriever: Retrieves documents from FAISSDocumentStore based on query embeddings.