Integration: FAISS
A Document Store for vector search using FAISS
The integration provides FAISSDocumentStore, which uses
FAISS (Facebook AI Similarity Search) for vector search and a simple JSON file for metadata storage. It is suitable for small to medium-sized datasets where simplicity is preferred over scalability, and supports optional persistence by saving the FAISS index to a .faiss file and documents to a .json file. Use FAISSEmbeddingRetriever for semantic retrieval in your pipelines.
Installation
Install the package with pip:
pip install faiss-haystack
For GPU-accelerated FAISS, install faiss-gpu separately and use it in place of the default faiss-cpu dependency where applicable.
The examples below use
Sentence Transformers for embeddings. Install with: pip install "sentence-transformers>=5.0.0".
Usage
In-memory document store
Create an in-memory document store (no persistence):
from haystack_integrations.document_stores.faiss import FAISSDocumentStore
document_store = FAISSDocumentStore(embedding_dim=768)
Persisted document store
To save and load the index and documents from disk, pass index_path:
from haystack_integrations.document_stores.faiss import FAISSDocumentStore
document_store = FAISSDocumentStore(
index_path="./my_faiss_index",
index_string="Flat",
embedding_dim=768,
)
# After writing documents, persist with:
# document_store.save("./my_faiss_index")
# Later, create the store with the same index_path to load from disk.
Writing documents
Use an indexing pipeline to write documents (with embeddings) to the store. This example uses Sentence Transformers (768 dimensions).
from haystack import Pipeline
from haystack.components.converters import TextFileToDocument
from haystack.components.writers import DocumentWriter
from haystack.components.embedders import SentenceTransformersDocumentEmbedder
from haystack_integrations.document_stores.faiss import FAISSDocumentStore
document_store = FAISSDocumentStore(
index_path="./my_faiss_index",
index_string="Flat",
embedding_dim=768,
)
indexing = Pipeline()
indexing.add_component("converter", TextFileToDocument())
indexing.add_component("embedder", SentenceTransformersDocumentEmbedder())
indexing.add_component("writer", DocumentWriter(document_store))
indexing.connect("converter", "embedder")
indexing.connect("embedder", "writer")
indexing.run({"converter": {"sources": file_paths}})
# If using persistence, save after indexing
# document_store.save("./my_faiss_index")
Retrieval with FAISSEmbeddingRetriever
Build a query pipeline using FAISSEmbeddingRetriever for semantic search:
from haystack import Pipeline
from haystack.components.embedders import SentenceTransformersTextEmbedder
from haystack_integrations.components.retrievers.faiss import FAISSEmbeddingRetriever
query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
query_pipeline.add_component(
"retriever",
FAISSEmbeddingRetriever(document_store=document_store, top_k=10),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
results = query_pipeline.run({"text_embedder": {"text": "your query"}})
documents = results["retriever"]["documents"]
License
faiss-haystack is distributed under the terms of the
Apache-2.0 license.