"""Test FAISS functionality."""
import datetime
import math
import tempfile
from typing import Union

import pytest
from langchain_core.documents import Document

from langchain_community.docstore.base import Docstore
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain_community.vectorstores.faiss import FAISS
from langchain_community.vectorstores.utils import DistanceStrategy
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings

_PAGE_CONTENT = """This is a page about LangChain.

It is a really cool framework.

What isn't there to love about langchain?

Made in 2022."""


class FakeDocstore(Docstore):
    """Fake docstore for testing purposes."""

    def search(self, search: str) -> Union[str, Document]:
        """Return the fake document."""
        document = Document(page_content=_PAGE_CONTENT)
        return document


@pytest.mark.requires("faiss")
def test_faiss() -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings())
    index_to_id = docsearch.index_to_docstore_id
    expected_docstore = InMemoryDocstore(
        {
            index_to_id[0]: Document(page_content="foo"),
            index_to_id[1]: Document(page_content="bar"),
            index_to_id[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")]


@pytest.mark.requires("faiss")
async def test_faiss_afrom_texts() -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings())
    index_to_id = docsearch.index_to_docstore_id
    expected_docstore = InMemoryDocstore(
        {
            index_to_id[0]: Document(page_content="foo"),
            index_to_id[1]: Document(page_content="bar"),
            index_to_id[2]: Document(page_content="baz"),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    output = await docsearch.asimilarity_search("foo", k=1)
    assert output == [Document(page_content="foo")]


@pytest.mark.requires("faiss")
def test_faiss_vector_sim() -> None:
    """Test vector similarity."""
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings())
    index_to_id = docsearch.index_to_docstore_id
    expected_docstore = InMemoryDocstore(
        {
            index_to_id[0]: Document(page_content="foo"),
            index_to_id[1]: Document(page_content="bar"),
            index_to_id[2]: Document(page_content="baz"),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    query_vec = FakeEmbeddings().embed_query(text="foo")
    output = docsearch.similarity_search_by_vector(query_vec, k=1)
    assert output == [Document(page_content="foo")]


@pytest.mark.requires("faiss")
async def test_faiss_async_vector_sim() -> None:
    """Test vector similarity."""
    texts = ["foo", "bar", "baz"]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings())
    index_to_id = docsearch.index_to_docstore_id
    expected_docstore = InMemoryDocstore(
        {
            index_to_id[0]: Document(page_content="foo"),
            index_to_id[1]: Document(page_content="bar"),
            index_to_id[2]: Document(page_content="baz"),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    query_vec = await FakeEmbeddings().aembed_query(text="foo")
    output = await docsearch.asimilarity_search_by_vector(query_vec, k=1)
    assert output == [Document(page_content="foo")]


@pytest.mark.requires("faiss")
def test_faiss_vector_sim_with_score_threshold() -> None:
    """Test vector similarity."""
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings())
    index_to_id = docsearch.index_to_docstore_id
    expected_docstore = InMemoryDocstore(
        {
            index_to_id[0]: Document(page_content="foo"),
            index_to_id[1]: Document(page_content="bar"),
            index_to_id[2]: Document(page_content="baz"),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    query_vec = FakeEmbeddings().embed_query(text="foo")
    output = docsearch.similarity_search_by_vector(query_vec, k=2, score_threshold=0.2)
    assert output == [Document(page_content="foo")]


@pytest.mark.requires("faiss")
async def test_faiss_vector_async_sim_with_score_threshold() -> None:
    """Test vector similarity."""
    texts = ["foo", "bar", "baz"]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings())
    index_to_id = docsearch.index_to_docstore_id
    expected_docstore = InMemoryDocstore(
        {
            index_to_id[0]: Document(page_content="foo"),
            index_to_id[1]: Document(page_content="bar"),
            index_to_id[2]: Document(page_content="baz"),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    query_vec = await FakeEmbeddings().aembed_query(text="foo")
    output = await docsearch.asimilarity_search_by_vector(
        query_vec, k=2, score_threshold=0.2
    )
    assert output == [Document(page_content="foo")]


@pytest.mark.requires("faiss")
def test_similarity_search_with_score_by_vector() -> None:
    """Test vector similarity with score by vector."""
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings())
    index_to_id = docsearch.index_to_docstore_id
    expected_docstore = InMemoryDocstore(
        {
            index_to_id[0]: Document(page_content="foo"),
            index_to_id[1]: Document(page_content="bar"),
            index_to_id[2]: Document(page_content="baz"),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    query_vec = FakeEmbeddings().embed_query(text="foo")
    output = docsearch.similarity_search_with_score_by_vector(query_vec, k=1)
    assert len(output) == 1
    assert output[0][0] == Document(page_content="foo")


@pytest.mark.requires("faiss")
async def test_similarity_async_search_with_score_by_vector() -> None:
    """Test vector similarity with score by vector."""
    texts = ["foo", "bar", "baz"]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings())
    index_to_id = docsearch.index_to_docstore_id
    expected_docstore = InMemoryDocstore(
        {
            index_to_id[0]: Document(page_content="foo"),
            index_to_id[1]: Document(page_content="bar"),
            index_to_id[2]: Document(page_content="baz"),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    query_vec = await FakeEmbeddings().aembed_query(text="foo")
    output = await docsearch.asimilarity_search_with_score_by_vector(query_vec, k=1)
    assert len(output) == 1
    assert output[0][0] == Document(page_content="foo")


@pytest.mark.requires("faiss")
def test_similarity_search_with_score_by_vector_with_score_threshold() -> None:
    """Test vector similarity with score by vector."""
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings())
    index_to_id = docsearch.index_to_docstore_id
    expected_docstore = InMemoryDocstore(
        {
            index_to_id[0]: Document(page_content="foo"),
            index_to_id[1]: Document(page_content="bar"),
            index_to_id[2]: Document(page_content="baz"),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    query_vec = FakeEmbeddings().embed_query(text="foo")
    output = docsearch.similarity_search_with_score_by_vector(
        query_vec,
        k=2,
        score_threshold=0.2,
    )
    assert len(output) == 1
    assert output[0][0] == Document(page_content="foo")
    assert output[0][1] < 0.2


@pytest.mark.requires("faiss")
async def test_sim_asearch_with_score_by_vector_with_score_threshold() -> None:
    """Test vector similarity with score by vector."""
    texts = ["foo", "bar", "baz"]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings())
    index_to_id = docsearch.index_to_docstore_id
    expected_docstore = InMemoryDocstore(
        {
            index_to_id[0]: Document(page_content="foo"),
            index_to_id[1]: Document(page_content="bar"),
            index_to_id[2]: Document(page_content="baz"),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    query_vec = await FakeEmbeddings().aembed_query(text="foo")
    output = await docsearch.asimilarity_search_with_score_by_vector(
        query_vec,
        k=2,
        score_threshold=0.2,
    )
    assert len(output) == 1
    assert output[0][0] == Document(page_content="foo")
    assert output[0][1] < 0.2


@pytest.mark.requires("faiss")
def test_faiss_mmr() -> None:
    texts = ["foo", "foo", "fou", "foy"]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings())
    query_vec = FakeEmbeddings().embed_query(text="foo")
    # make sure we can have k > docstore size
    output = docsearch.max_marginal_relevance_search_with_score_by_vector(
        query_vec, k=10, lambda_mult=0.1
    )
    assert len(output) == len(texts)
    assert output[0][0] == Document(page_content="foo")
    assert output[0][1] == 0.0
    assert output[1][0] != Document(page_content="foo")


@pytest.mark.requires("faiss")
async def test_faiss_async_mmr() -> None:
    texts = ["foo", "foo", "fou", "foy"]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings())
    query_vec = await FakeEmbeddings().aembed_query(text="foo")
    # make sure we can have k > docstore size
    output = await docsearch.amax_marginal_relevance_search_with_score_by_vector(
        query_vec, k=10, lambda_mult=0.1
    )
    assert len(output) == len(texts)
    assert output[0][0] == Document(page_content="foo")
    assert output[0][1] == 0.0
    assert output[1][0] != Document(page_content="foo")


@pytest.mark.requires("faiss")
def test_faiss_mmr_with_metadatas() -> None:
    texts = ["foo", "foo", "fou", "foy"]
    metadatas = [{"page": i} for i in range(len(texts))]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    query_vec = FakeEmbeddings().embed_query(text="foo")
    output = docsearch.max_marginal_relevance_search_with_score_by_vector(
        query_vec, k=10, lambda_mult=0.1
    )
    assert len(output) == len(texts)
    assert output[0][0] == Document(page_content="foo", metadata={"page": 0})
    assert output[0][1] == 0.0
    assert output[1][0] != Document(page_content="foo", metadata={"page": 0})


@pytest.mark.requires("faiss")
async def test_faiss_async_mmr_with_metadatas() -> None:
    texts = ["foo", "foo", "fou", "foy"]
    metadatas = [{"page": i} for i in range(len(texts))]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    query_vec = await FakeEmbeddings().aembed_query(text="foo")
    output = await docsearch.amax_marginal_relevance_search_with_score_by_vector(
        query_vec, k=10, lambda_mult=0.1
    )
    assert len(output) == len(texts)
    assert output[0][0] == Document(page_content="foo", metadata={"page": 0})
    assert output[0][1] == 0.0
    assert output[1][0] != Document(page_content="foo", metadata={"page": 0})


@pytest.mark.requires("faiss")
def test_faiss_mmr_with_metadatas_and_filter() -> None:
    texts = ["foo", "foo", "fou", "foy"]
    metadatas = [{"page": i} for i in range(len(texts))]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    query_vec = FakeEmbeddings().embed_query(text="foo")
    output = docsearch.max_marginal_relevance_search_with_score_by_vector(
        query_vec, k=10, lambda_mult=0.1, filter={"page": 1}
    )
    assert len(output) == 1
    assert output[0][0] == Document(page_content="foo", metadata={"page": 1})
    assert output[0][1] == 0.0
    assert output == docsearch.max_marginal_relevance_search_with_score_by_vector(
        query_vec, k=10, lambda_mult=0.1, filter=lambda di: di["page"] == 1
    )


@pytest.mark.requires("faiss")
async def test_faiss_async_mmr_with_metadatas_and_filter() -> None:
    texts = ["foo", "foo", "fou", "foy"]
    metadatas = [{"page": i} for i in range(len(texts))]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    query_vec = await FakeEmbeddings().aembed_query(text="foo")
    output = await docsearch.amax_marginal_relevance_search_with_score_by_vector(
        query_vec, k=10, lambda_mult=0.1, filter={"page": 1}
    )
    assert len(output) == 1
    assert output[0][0] == Document(page_content="foo", metadata={"page": 1})
    assert output[0][1] == 0.0
    assert (
        output
        == await docsearch.amax_marginal_relevance_search_with_score_by_vector(
            query_vec, k=10, lambda_mult=0.1, filter=lambda di: di["page"] == 1
        )
    )


@pytest.mark.requires("faiss")
def test_faiss_mmr_with_metadatas_and_list_filter() -> None:
    texts = ["foo", "foo", "fou", "foy"]
    metadatas = [{"page": i} if i <= 3 else {"page": 3} for i in range(len(texts))]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    query_vec = FakeEmbeddings().embed_query(text="foo")
    output = docsearch.max_marginal_relevance_search_with_score_by_vector(
        query_vec, k=10, lambda_mult=0.1, filter={"page": [0, 1, 2]}
    )
    assert len(output) == 3
    assert output[0][0] == Document(page_content="foo", metadata={"page": 0})
    assert output[0][1] == 0.0
    assert output[1][0] != Document(page_content="foo", metadata={"page": 0})
    assert output == docsearch.max_marginal_relevance_search_with_score_by_vector(
        query_vec, k=10, lambda_mult=0.1, filter=lambda di: di["page"] in [0, 1, 2]
    )


@pytest.mark.requires("faiss")
async def test_faiss_async_mmr_with_metadatas_and_list_filter() -> None:
    texts = ["foo", "foo", "fou", "foy"]
    metadatas = [{"page": i} if i <= 3 else {"page": 3} for i in range(len(texts))]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    query_vec = await FakeEmbeddings().aembed_query(text="foo")
    output = await docsearch.amax_marginal_relevance_search_with_score_by_vector(
        query_vec, k=10, lambda_mult=0.1, filter={"page": [0, 1, 2]}
    )
    assert len(output) == 3
    assert output[0][0] == Document(page_content="foo", metadata={"page": 0})
    assert output[0][1] == 0.0
    assert output[1][0] != Document(page_content="foo", metadata={"page": 0})
    assert output == (
        await docsearch.amax_marginal_relevance_search_with_score_by_vector(
            query_vec, k=10, lambda_mult=0.1, filter=lambda di: di["page"] in [0, 1, 2]
        )
    )


@pytest.mark.requires("faiss")
def test_faiss_with_metadatas() -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    metadatas = [{"page": i} for i in range(len(texts))]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    expected_docstore = InMemoryDocstore(
        {
            docsearch.index_to_docstore_id[0]: Document(
                page_content="foo", metadata={"page": 0}
            ),
            docsearch.index_to_docstore_id[1]: Document(
                page_content="bar", metadata={"page": 1}
            ),
            docsearch.index_to_docstore_id[2]: Document(
                page_content="baz", metadata={"page": 2}
            ),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    output = docsearch.similarity_search("foo", k=1)
    assert output == [Document(page_content="foo", metadata={"page": 0})]


@pytest.mark.requires("faiss")
async def test_faiss_async_with_metadatas() -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    metadatas = [{"page": i} for i in range(len(texts))]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    expected_docstore = InMemoryDocstore(
        {
            docsearch.index_to_docstore_id[0]: Document(
                page_content="foo", metadata={"page": 0}
            ),
            docsearch.index_to_docstore_id[1]: Document(
                page_content="bar", metadata={"page": 1}
            ),
            docsearch.index_to_docstore_id[2]: Document(
                page_content="baz", metadata={"page": 2}
            ),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    output = await docsearch.asimilarity_search("foo", k=1)
    assert output == [Document(page_content="foo", metadata={"page": 0})]


@pytest.mark.requires("faiss")
def test_faiss_with_metadatas_and_filter() -> None:
    texts = ["foo", "bar", "baz"]
    metadatas = [{"page": i} for i in range(len(texts))]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    expected_docstore = InMemoryDocstore(
        {
            docsearch.index_to_docstore_id[0]: Document(
                page_content="foo", metadata={"page": 0}
            ),
            docsearch.index_to_docstore_id[1]: Document(
                page_content="bar", metadata={"page": 1}
            ),
            docsearch.index_to_docstore_id[2]: Document(
                page_content="baz", metadata={"page": 2}
            ),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    output = docsearch.similarity_search("foo", k=1, filter={"page": 1})
    # make sure it returns the result that matches the filter.
    # Not the one who's text matches better.
    assert output == [Document(page_content="bar", metadata={"page": 1})]
    assert output == docsearch.similarity_search(
        "foo", k=1, filter=lambda di: di["page"] == 1
    )


@pytest.mark.requires("faiss")
async def test_faiss_async_with_metadatas_and_filter() -> None:
    texts = ["foo", "bar", "baz"]
    metadatas = [{"page": i} for i in range(len(texts))]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    expected_docstore = InMemoryDocstore(
        {
            docsearch.index_to_docstore_id[0]: Document(
                page_content="foo", metadata={"page": 0}
            ),
            docsearch.index_to_docstore_id[1]: Document(
                page_content="bar", metadata={"page": 1}
            ),
            docsearch.index_to_docstore_id[2]: Document(
                page_content="baz", metadata={"page": 2}
            ),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    output = await docsearch.asimilarity_search("foo", k=1, filter={"page": 1})
    # make sure it returns the result that matches the filter.
    # Not the one who's text matches better.
    assert output == [Document(page_content="bar", metadata={"page": 1})]
    assert output == await docsearch.asimilarity_search(
        "foo", k=1, filter=lambda di: di["page"] == 1
    )


@pytest.mark.requires("faiss")
def test_faiss_with_metadatas_and_list_filter() -> None:
    texts = ["foo", "bar", "baz", "foo", "qux"]
    metadatas = [{"page": i} if i <= 3 else {"page": 3} for i in range(len(texts))]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    expected_docstore = InMemoryDocstore(
        {
            docsearch.index_to_docstore_id[0]: Document(
                page_content="foo", metadata={"page": 0}
            ),
            docsearch.index_to_docstore_id[1]: Document(
                page_content="bar", metadata={"page": 1}
            ),
            docsearch.index_to_docstore_id[2]: Document(
                page_content="baz", metadata={"page": 2}
            ),
            docsearch.index_to_docstore_id[3]: Document(
                page_content="foo", metadata={"page": 3}
            ),
            docsearch.index_to_docstore_id[4]: Document(
                page_content="qux", metadata={"page": 3}
            ),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    output = docsearch.similarity_search("foor", k=1, filter={"page": [0, 1, 2]})
    assert output == [Document(page_content="foo", metadata={"page": 0})]
    assert output == docsearch.similarity_search(
        "foor", k=1, filter=lambda di: di["page"] in [0, 1, 2]
    )


@pytest.mark.requires("faiss")
async def test_faiss_async_with_metadatas_and_list_filter() -> None:
    texts = ["foo", "bar", "baz", "foo", "qux"]
    metadatas = [{"page": i} if i <= 3 else {"page": 3} for i in range(len(texts))]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings(), metadatas=metadatas)
    expected_docstore = InMemoryDocstore(
        {
            docsearch.index_to_docstore_id[0]: Document(
                page_content="foo", metadata={"page": 0}
            ),
            docsearch.index_to_docstore_id[1]: Document(
                page_content="bar", metadata={"page": 1}
            ),
            docsearch.index_to_docstore_id[2]: Document(
                page_content="baz", metadata={"page": 2}
            ),
            docsearch.index_to_docstore_id[3]: Document(
                page_content="foo", metadata={"page": 3}
            ),
            docsearch.index_to_docstore_id[4]: Document(
                page_content="qux", metadata={"page": 3}
            ),
        }
    )
    assert docsearch.docstore.__dict__ == expected_docstore.__dict__
    output = await docsearch.asimilarity_search("foor", k=1, filter={"page": [0, 1, 2]})
    assert output == [Document(page_content="foo", metadata={"page": 0})]
    assert output == await docsearch.asimilarity_search(
        "foor", k=1, filter=lambda di: di["page"] in [0, 1, 2]
    )


@pytest.mark.requires("faiss")
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")


@pytest.mark.requires("faiss")
async def test_faiss_async_search_not_found() -> None:
    """Test what happens when document is not found."""
    texts = ["foo", "bar", "baz"]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings())
    # Get rid of the docstore to purposefully induce errors.
    docsearch.docstore = InMemoryDocstore({})
    with pytest.raises(ValueError):
        await docsearch.asimilarity_search("foo")


@pytest.mark.requires("faiss")
def test_faiss_add_texts() -> None:
    """Test end to end adding of texts."""
    # Create initial doc store.
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings())
    # Test adding a similar document as before.
    docsearch.add_texts(["foo"])
    output = docsearch.similarity_search("foo", k=2)
    assert output == [Document(page_content="foo"), Document(page_content="foo")]


@pytest.mark.requires("faiss")
async def test_faiss_async_add_texts() -> None:
    """Test end to end adding of texts."""
    # Create initial doc store.
    texts = ["foo", "bar", "baz"]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings())
    # Test adding a similar document as before.
    await docsearch.aadd_texts(["foo"])
    output = await docsearch.asimilarity_search("foo", k=2)
    assert output == [Document(page_content="foo"), Document(page_content="foo")]


@pytest.mark.requires("faiss")
def test_faiss_add_texts_not_supported() -> None:
    """Test adding of texts to a docstore that doesn't support it."""
    docsearch = FAISS(FakeEmbeddings(), None, FakeDocstore(), {})
    with pytest.raises(ValueError):
        docsearch.add_texts(["foo"])


@pytest.mark.requires("faiss")
async def test_faiss_async_add_texts_not_supported() -> None:
    """Test adding of texts to a docstore that doesn't support it."""
    docsearch = FAISS(FakeEmbeddings(), None, FakeDocstore(), {})
    with pytest.raises(ValueError):
        await docsearch.aadd_texts(["foo"])


@pytest.mark.requires("faiss")
def test_faiss_local_save_load() -> None:
    """Test end to end serialization."""
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(texts, FakeEmbeddings())
    temp_timestamp = datetime.datetime.utcnow().strftime("%Y%m%d-%H%M%S")
    with tempfile.TemporaryDirectory(suffix="_" + temp_timestamp + "/") as temp_folder:
        docsearch.save_local(temp_folder)
        new_docsearch = FAISS.load_local(
            temp_folder, FakeEmbeddings(), allow_dangerous_deserialization=True
        )
    assert new_docsearch.index is not None


@pytest.mark.requires("faiss")
async def test_faiss_async_local_save_load() -> None:
    """Test end to end serialization."""
    texts = ["foo", "bar", "baz"]
    docsearch = await FAISS.afrom_texts(texts, FakeEmbeddings())
    temp_timestamp = datetime.datetime.utcnow().strftime("%Y%m%d-%H%M%S")
    with tempfile.TemporaryDirectory(suffix="_" + temp_timestamp + "/") as temp_folder:
        docsearch.save_local(temp_folder)
        new_docsearch = FAISS.load_local(
            temp_folder, FakeEmbeddings(), allow_dangerous_deserialization=True
        )
    assert new_docsearch.index is not None


@pytest.mark.requires("faiss")
def test_faiss_similarity_search_with_relevance_scores() -> None:
    """Test the similarity search with normalized similarities."""
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(
        texts,
        FakeEmbeddings(),
        relevance_score_fn=lambda score: 1.0 - score / math.sqrt(2),
    )
    outputs = docsearch.similarity_search_with_relevance_scores("foo", k=1)
    output, score = outputs[0]
    assert output == Document(page_content="foo")
    assert score == 1.0


@pytest.mark.requires("faiss")
async def test_faiss_async_similarity_search_with_relevance_scores() -> None:
    """Test the similarity search with normalized similarities."""
    texts = ["foo", "bar", "baz"]
    docsearch = await FAISS.afrom_texts(
        texts,
        FakeEmbeddings(),
        relevance_score_fn=lambda score: 1.0 - score / math.sqrt(2),
    )
    outputs = await docsearch.asimilarity_search_with_relevance_scores("foo", k=1)
    output, score = outputs[0]
    assert output == Document(page_content="foo")
    assert score == 1.0


@pytest.mark.requires("faiss")
def test_faiss_similarity_search_with_relevance_scores_with_threshold() -> None:
    """Test the similarity search with normalized similarities with score threshold."""
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(
        texts,
        FakeEmbeddings(),
        relevance_score_fn=lambda score: 1.0 - score / math.sqrt(2),
    )
    outputs = docsearch.similarity_search_with_relevance_scores(
        "foo", k=2, score_threshold=0.5
    )
    assert len(outputs) == 1
    output, score = outputs[0]
    assert output == Document(page_content="foo")
    assert score == 1.0


@pytest.mark.requires("faiss")
async def test_faiss_asimilarity_search_with_relevance_scores_with_threshold() -> None:
    """Test the similarity search with normalized similarities with score threshold."""
    texts = ["foo", "bar", "baz"]
    docsearch = await FAISS.afrom_texts(
        texts,
        FakeEmbeddings(),
        relevance_score_fn=lambda score: 1.0 - score / math.sqrt(2),
    )
    outputs = await docsearch.asimilarity_search_with_relevance_scores(
        "foo", k=2, score_threshold=0.5
    )
    assert len(outputs) == 1
    output, score = outputs[0]
    assert output == Document(page_content="foo")
    assert score == 1.0


@pytest.mark.requires("faiss")
def test_faiss_invalid_normalize_fn() -> None:
    """Test the similarity search with normalized similarities."""
    texts = ["foo", "bar", "baz"]
    docsearch = FAISS.from_texts(
        texts, FakeEmbeddings(), relevance_score_fn=lambda _: 2.0
    )
    with pytest.warns(Warning, match="scores must be between"):
        docsearch.similarity_search_with_relevance_scores("foo", k=1)


@pytest.mark.requires("faiss")
async def test_faiss_async_invalid_normalize_fn() -> None:
    """Test the similarity search with normalized similarities."""
    texts = ["foo", "bar", "baz"]
    docsearch = await FAISS.afrom_texts(
        texts, FakeEmbeddings(), relevance_score_fn=lambda _: 2.0
    )
    with pytest.warns(Warning, match="scores must be between"):
        await docsearch.asimilarity_search_with_relevance_scores("foo", k=1)


@pytest.mark.requires("faiss")
def test_missing_normalize_score_fn() -> None:
    """Test doesn't perform similarity search without a valid distance strategy."""
    texts = ["foo", "bar", "baz"]
    faiss_instance = FAISS.from_texts(texts, FakeEmbeddings(), distance_strategy="fake")
    with pytest.raises(ValueError):
        faiss_instance.similarity_search_with_relevance_scores("foo", k=2)


@pytest.mark.skip(reason="old relevance score feature")
@pytest.mark.requires("faiss")
def test_ip_score() -> None:
    embedding = FakeEmbeddings()
    vector = embedding.embed_query("hi")
    assert vector == [1] * 9 + [0], f"FakeEmbeddings() has changed, produced {vector}"

    db = FAISS.from_texts(
        ["sundays coming so i drive my car"],
        embedding=FakeEmbeddings(),
        distance_strategy=DistanceStrategy.MAX_INNER_PRODUCT,
    )
    scores = db.similarity_search_with_relevance_scores("sundays", k=1)
    assert len(scores) == 1, "only one vector should be in db"
    _, score = scores[0]
    assert (
        score == 1
    ), f"expected inner product of equivalent vectors to be 1, not {score}"


@pytest.mark.requires("faiss")
async def test_async_missing_normalize_score_fn() -> None:
    """Test doesn't perform similarity search without a valid distance strategy."""
    texts = ["foo", "bar", "baz"]
    faiss_instance = await FAISS.afrom_texts(
        texts, FakeEmbeddings(), distance_strategy="fake"
    )
    with pytest.raises(ValueError):
        await faiss_instance.asimilarity_search_with_relevance_scores("foo", k=2)


@pytest.mark.requires("faiss")
def test_delete() -> None:
    """Test the similarity search with normalized similarities."""
    ids = ["a", "b", "c"]
    docsearch = FAISS.from_texts(["foo", "bar", "baz"], FakeEmbeddings(), ids=ids)
    docsearch.delete(ids[1:2])

    result = docsearch.similarity_search("bar", k=2)
    assert sorted([d.page_content for d in result]) == ["baz", "foo"]
    assert docsearch.index_to_docstore_id == {0: ids[0], 1: ids[2]}


@pytest.mark.requires("faiss")
async def test_async_delete() -> None:
    """Test the similarity search with normalized similarities."""
    ids = ["a", "b", "c"]
    docsearch = await FAISS.afrom_texts(
        ["foo", "bar", "baz"], FakeEmbeddings(), ids=ids
    )
    docsearch.delete(ids[1:2])

    result = await docsearch.asimilarity_search("bar", k=2)
    assert sorted([d.page_content for d in result]) == ["baz", "foo"]
    assert docsearch.index_to_docstore_id == {0: ids[0], 1: ids[2]}


@pytest.mark.requires("faiss")
def test_faiss_with_duplicate_ids() -> None:
    """Test whether FAISS raises an exception for duplicate ids."""
    texts = ["foo", "bar", "baz"]
    duplicate_ids = ["id1", "id1", "id2"]

    with pytest.raises(ValueError) as exc_info:
        FAISS.from_texts(texts, FakeEmbeddings(), ids=duplicate_ids)

    assert "Duplicate ids found in the ids list." in str(exc_info.value)