langchain/tests/integration_tests/test_faiss.py

"""Test FAISS functionality."""
from typing import List

import pytest

from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.faiss import FAISS


class FakeEmbeddings(Embeddings):
    """Fake embeddings functionality for testing."""

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Return simple embeddings."""
        return [[i] * 10 for i in range(len(texts))]

    def embed_query(self, text: str) -> List[float]:
        """Return simple embeddings."""
        return [0] * 10


def test_faiss() -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings())
    expected_docstore = InMemoryDocstore(
        {
            "0": Document(page_content="foo"),
            "1": Document(page_content="bar"),
            "2": Document(page_content="baz"),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    output = docsearch.similarity_search("foo", k=1)
    assert output == [Document(page_content="foo")]


def test_faiss_search_not_found() -> None:
    """Test what happens when document is not found."""
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings())
    # Get rid of the docstore to purposefully induce errors.
    docsearch.docstore = InMemoryDocstore({})
    with pytest.raises(ValueError):
        docsearch.similarity_search("foo")