FAISSEmbeddingRetriever

An embedding-based Retriever compatible with the FAISSDocumentStore.

Most common position in a pipeline	1. After a Text Embedder and before a PromptBuilder in a RAG pipeline 2. The last component in a semantic search pipeline 3. After a Text Embedder and before an ExtractiveReader in an extractive QA pipeline
Mandatory init variables	document_store: An instance of a FAISSDocumentStore
Mandatory run variables	query_embedding: A vector representing the query (a list of floats)
Output variables	documents: A list of documents
API reference	FAISS
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/faiss

Overview

The FAISSEmbeddingRetriever is an embedding-based Retriever that queries a FAISSDocumentStore. It compares the query embedding to document embeddings stored in FAISS and returns the most similar documents.

This Retriever expects precomputed embeddings in the Document Store and a query embedding at runtime. You can generate them with a Document Embedder in your indexing pipeline and a Text Embedder in your query pipeline.

In addition to query_embedding, you can pass:

• top_k: The maximum number of documents to return.
• filters: Metadata filters to restrict retrieved documents.

You can also configure default filters and filter_policy at initialization.

Usage

On its own

python

from haystack_integrations.document_stores.faiss import FAISSDocumentStore
from haystack_integrations.components.retrievers.faiss import FAISSEmbeddingRetriever

document_store = FAISSDocumentStore(embedding_dim=768)
retriever = FAISSEmbeddingRetriever(document_store=document_store, top_k=5)

# Example query embedding
result = retriever.run(query_embedding=[0.1] * 768)
print(result["documents"])

In a pipeline

python

from haystack import Document, Pipeline
from haystack.components.embedders import (
    SentenceTransformersDocumentEmbedder,
    SentenceTransformersTextEmbedder,
)
from haystack.document_stores.types import DuplicatePolicy
from haystack_integrations.document_stores.faiss import FAISSDocumentStore
from haystack_integrations.components.retrievers.faiss import FAISSEmbeddingRetriever

document_store = FAISSDocumentStore(embedding_dim=768)

documents = [
    Document(content="There are over 7,000 languages spoken around the world today."),
    Document(
        content="Elephants have been observed to behave in a way that indicates a high level of intelligence.",
    ),
    Document(
        content="In certain places, you can witness the phenomenon of bioluminescent waves.",
    ),
]

document_embedder = SentenceTransformersDocumentEmbedder()
document_embedder.warm_up()
documents_with_embeddings = document_embedder.run(documents)["documents"]
document_store.write_documents(
    documents_with_embeddings,
    policy=DuplicatePolicy.OVERWRITE,
)

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
query_pipeline.add_component(
    "retriever",
    FAISSEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "How many languages are there?"
result = query_pipeline.run({"text_embedder": {"text": query}})

print(result["retriever"]["documents"][0])