import uuid
from typing import Optional

import pytest
from langchain_core.documents import Document

from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
    ConsistentFakeEmbeddings,
)
from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals


@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_add_documents_extends_existing_collection(
    batch_size: int, vector_name: Optional[str]
) -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    docsearch: Qdrant = Qdrant.from_texts(
        texts,
        ConsistentFakeEmbeddings(),
        location=":memory:",
        batch_size=batch_size,
        vector_name=vector_name,
    )

    new_texts = ["foobar", "foobaz"]
    docsearch.add_documents(
        [Document(page_content=content) for content in new_texts], batch_size=batch_size
    )
    output = docsearch.similarity_search("foobar", k=1)
    # ConsistentFakeEmbeddings return the same query embedding as the first document
    # embedding computed in `embedding.embed_documents`. Thus, "foo" embedding is the
    # same as "foobar" embedding
    assert_documents_equals(output, [Document(page_content="foobar")])


@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_returns_all_ids(batch_size: int) -> None:
    """Test end to end Qdrant.add_texts returns unique ids."""
    docsearch: Qdrant = Qdrant.from_texts(
        ["foobar"],
        ConsistentFakeEmbeddings(),
        location=":memory:",
        batch_size=batch_size,
    )

    ids = docsearch.add_texts(["foo", "bar", "baz"])
    assert 3 == len(ids)
    assert 3 == len(set(ids))


@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_add_texts_stores_duplicated_texts(vector_name: Optional[str]) -> None:
    """Test end to end Qdrant.add_texts stores duplicated texts separately."""
    from qdrant_client import QdrantClient
    from qdrant_client.http import models as rest

    client = QdrantClient(":memory:")
    collection_name = uuid.uuid4().hex
    vectors_config = rest.VectorParams(size=10, distance=rest.Distance.COSINE)
    if vector_name is not None:
        vectors_config = {vector_name: vectors_config}
    client.recreate_collection(collection_name, vectors_config=vectors_config)

    vec_store = Qdrant(
        client,
        collection_name,
        embeddings=ConsistentFakeEmbeddings(),
        vector_name=vector_name,
    )
    ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])

    assert 2 == len(set(ids))
    assert 2 == client.count(collection_name).count


@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_stores_ids(batch_size: int) -> None:
    """Test end to end Qdrant.add_texts stores provided ids."""
    from qdrant_client import QdrantClient
    from qdrant_client.http import models as rest

    ids = [
        "fa38d572-4c31-4579-aedc-1960d79df6df",
        "cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
    ]

    client = QdrantClient(":memory:")
    collection_name = uuid.uuid4().hex
    client.recreate_collection(
        collection_name,
        vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
    )

    vec_store = Qdrant(client, collection_name, ConsistentFakeEmbeddings())
    returned_ids = vec_store.add_texts(["abc", "def"], ids=ids, batch_size=batch_size)

    assert all(first == second for first, second in zip(ids, returned_ids))
    assert 2 == client.count(collection_name).count
    stored_ids = [point.id for point in client.scroll(collection_name)[0]]
    assert set(ids) == set(stored_ids)


@pytest.mark.parametrize("vector_name", ["custom-vector"])
def test_qdrant_add_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
    """Test end to end Qdrant.add_texts stores named vectors if name is provided."""
    from qdrant_client import QdrantClient
    from qdrant_client.http import models as rest

    collection_name = uuid.uuid4().hex

    client = QdrantClient(":memory:")
    client.recreate_collection(
        collection_name,
        vectors_config={
            vector_name: rest.VectorParams(size=10, distance=rest.Distance.COSINE)
        },
    )

    vec_store = Qdrant(
        client,
        collection_name,
        ConsistentFakeEmbeddings(),
        vector_name=vector_name,
    )
    vec_store.add_texts(["lorem", "ipsum", "dolor", "sit", "amet"])

    assert 5 == client.count(collection_name).count
    assert all(
        vector_name in point.vector
        for point in client.scroll(collection_name, with_vectors=True)[0]
    )

import uuid
from typing import Optional

import pytest
from langchain_core.documents import Document

from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
    ConsistentFakeEmbeddings,
)
from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals


@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_add_documents_extends_existing_collection(
    batch_size: int, vector_name: Optional[str]
) -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    docsearch: Qdrant = Qdrant.from_texts(
        texts,
        ConsistentFakeEmbeddings(),
        location=":memory:",
        batch_size=batch_size,
        vector_name=vector_name,
    )

    new_texts = ["foobar", "foobaz"]
    docsearch.add_documents(
        [Document(page_content=content) for content in new_texts], batch_size=batch_size
    )
    output = docsearch.similarity_search("foobar", k=1)
    # ConsistentFakeEmbeddings return the same query embedding as the first document
    # embedding computed in `embedding.embed_documents`. Thus, "foo" embedding is the
    # same as "foobar" embedding
    assert_documents_equals(output, [Document(page_content="foobar")])


@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_returns_all_ids(batch_size: int) -> None:
    """Test end to end Qdrant.add_texts returns unique ids."""
    docsearch: Qdrant = Qdrant.from_texts(
        ["foobar"],
        ConsistentFakeEmbeddings(),
        location=":memory:",
        batch_size=batch_size,
    )

    ids = docsearch.add_texts(["foo", "bar", "baz"])
    assert 3 == len(ids)
    assert 3 == len(set(ids))


@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_add_texts_stores_duplicated_texts(vector_name: Optional[str]) -> None:
    """Test end to end Qdrant.add_texts stores duplicated texts separately."""
    from qdrant_client import QdrantClient
    from qdrant_client.http import models as rest

    client = QdrantClient(":memory:")
    collection_name = uuid.uuid4().hex
    vectors_config = rest.VectorParams(size=10, distance=rest.Distance.COSINE)
    if vector_name is not None:
        vectors_config = {vector_name: vectors_config}  # type: ignore[assignment]
    client.recreate_collection(collection_name, vectors_config=vectors_config)

    vec_store = Qdrant(
        client,
        collection_name,
        embeddings=ConsistentFakeEmbeddings(),
        vector_name=vector_name,
    )
    ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])

    assert 2 == len(set(ids))
    assert 2 == client.count(collection_name).count


@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_stores_ids(batch_size: int) -> None:
    """Test end to end Qdrant.add_texts stores provided ids."""
    from qdrant_client import QdrantClient
    from qdrant_client.http import models as rest

    ids = [
        "fa38d572-4c31-4579-aedc-1960d79df6df",
        "cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
    ]

    client = QdrantClient(":memory:")
    collection_name = uuid.uuid4().hex
    client.recreate_collection(
        collection_name,
        vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
    )

    vec_store = Qdrant(client, collection_name, ConsistentFakeEmbeddings())
    returned_ids = vec_store.add_texts(["abc", "def"], ids=ids, batch_size=batch_size)

    assert all(first == second for first, second in zip(ids, returned_ids))
    assert 2 == client.count(collection_name).count
    stored_ids = [point.id for point in client.scroll(collection_name)[0]]
    assert set(ids) == set(stored_ids)


@pytest.mark.parametrize("vector_name", ["custom-vector"])
def test_qdrant_add_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
    """Test end to end Qdrant.add_texts stores named vectors if name is provided."""
    from qdrant_client import QdrantClient
    from qdrant_client.http import models as rest

    collection_name = uuid.uuid4().hex

    client = QdrantClient(":memory:")
    client.recreate_collection(
        collection_name,
        vectors_config={
            vector_name: rest.VectorParams(size=10, distance=rest.Distance.COSINE)
        },
    )

    vec_store = Qdrant(
        client,
        collection_name,
        ConsistentFakeEmbeddings(),
        vector_name=vector_name,
    )
    vec_store.add_texts(["lorem", "ipsum", "dolor", "sit", "amet"])

    assert 5 == client.count(collection_name).count
    assert all(
        vector_name in point.vector  # type: ignore[operator]
        for point in client.scroll(collection_name, with_vectors=True)[0]
    )
