LlamaIndex Readers Integration: Faiss

Overview

Faiss Reader retrieves documents through an existing in-memory Faiss index. These documents can then be used in a downstream LlamaIndex data structure. If you wish to use Faiss itself as an index to organize documents, insert documents, and perform queries on them, please use VectorStoreIndex with FaissVectorStore.

Installation

You can install Faiss Reader via pip:

pip install llama-index-readers-faiss

Usage

from llama_index.readers.faiss import FaissReader

# Initialize FaissReader with an existing Faiss Index object
reader = FaissReader(index="<Faiss Index Object>")

# Load data from Faiss
documents = reader.load_data(
    query="<Query Vector>",  # 2D numpy array of query vectors
    id_to_text_map={"<ID>": "<Text>"},  # A map from IDs to text
    k=4,  # Number of nearest neighbors to retrieve
    separate_documents=True,  # Whether to return separate documents
)

This loader is designed to be used as a way to load data into LlamaIndex.