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qdrant: init package (#21146)
## Description This PR introduces the new `langchain-qdrant` partner package, intending to deprecate the community package. ## Changes - Moved the Qdrant vector store implementation `/libs/partners/qdrant` with integration tests. - The conditional imports of the client library are now regular with minor implementation improvements. - Added a deprecation warning to `langchain_community.vectorstores.qdrant.Qdrant`. - Replaced references/imports from `langchain_community` with either `langchain_core` or by moving the definitions to the `langchain_qdrant` package itself. - Updated the Qdrant vector store documentation to reflect the changes. ## Testing - `QDRANT_URL` and [`QDRANT_API_KEY`](583e36bf6b) env values need to be set to [run integration tests](d608c93d1f) in the [cloud](https://cloud.qdrant.tech). - If a Qdrant instance is running at `http://localhost:6333`, the integration tests will use it too. - By default, tests use an [`in-memory`](https://github.com/qdrant/qdrant-client?tab=readme-ov-file#local-mode) instance(Not comprehensive). --------- Co-authored-by: Erick Friis <erick@langchain.dev> Co-authored-by: Erick Friis <erickfriis@gmail.com>
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288
libs/partners/qdrant/tests/integration_tests/test_from_texts.py
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288
libs/partners/qdrant/tests/integration_tests/test_from_texts.py
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import tempfile
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import uuid
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from typing import Optional
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import pytest
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from langchain_core.documents import Document
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from langchain_qdrant import Qdrant
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from langchain_qdrant.vectorstores import QdrantException
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from tests.integration_tests.common import (
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ConsistentFakeEmbeddings,
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assert_documents_equals,
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)
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from tests.integration_tests.fixtures import qdrant_locations
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def test_qdrant_from_texts_stores_duplicated_texts() -> None:
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"""Test end to end Qdrant.from_texts stores duplicated texts separately."""
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from qdrant_client import QdrantClient
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collection_name = uuid.uuid4().hex
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with tempfile.TemporaryDirectory() as tmpdir:
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vec_store = Qdrant.from_texts(
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["abc", "abc"],
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ConsistentFakeEmbeddings(),
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collection_name=collection_name,
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path=str(tmpdir),
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)
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del vec_store
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client = QdrantClient(path=str(tmpdir))
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assert 2 == client.count(collection_name).count
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_from_texts_stores_ids(
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batch_size: int, vector_name: Optional[str]
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) -> None:
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"""Test end to end Qdrant.from_texts stores provided ids."""
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from qdrant_client import QdrantClient
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collection_name = uuid.uuid4().hex
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with tempfile.TemporaryDirectory() as tmpdir:
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ids = [
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"fa38d572-4c31-4579-aedc-1960d79df6df",
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"cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
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]
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vec_store = Qdrant.from_texts(
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["abc", "def"],
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ConsistentFakeEmbeddings(),
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ids=ids,
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collection_name=collection_name,
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path=str(tmpdir),
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batch_size=batch_size,
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vector_name=vector_name,
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)
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del vec_store
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client = QdrantClient(path=str(tmpdir))
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assert 2 == client.count(collection_name).count
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stored_ids = [point.id for point in client.scroll(collection_name)[0]]
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assert set(ids) == set(stored_ids)
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@pytest.mark.parametrize("vector_name", ["custom-vector"])
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def test_qdrant_from_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
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"""Test end to end Qdrant.from_texts stores named vectors if name is provided."""
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from qdrant_client import QdrantClient
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collection_name = uuid.uuid4().hex
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with tempfile.TemporaryDirectory() as tmpdir:
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vec_store = Qdrant.from_texts(
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["lorem", "ipsum", "dolor", "sit", "amet"],
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ConsistentFakeEmbeddings(),
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collection_name=collection_name,
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path=str(tmpdir),
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vector_name=vector_name,
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)
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del vec_store
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client = QdrantClient(path=str(tmpdir))
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assert 5 == client.count(collection_name).count
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assert all(
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vector_name in point.vector # type: ignore[operator]
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for point in client.scroll(collection_name, with_vectors=True)[0]
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)
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@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
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def test_qdrant_from_texts_reuses_same_collection(vector_name: Optional[str]) -> None:
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"""Test if Qdrant.from_texts reuses the same collection"""
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from qdrant_client import QdrantClient
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collection_name = uuid.uuid4().hex
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embeddings = ConsistentFakeEmbeddings()
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with tempfile.TemporaryDirectory() as tmpdir:
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vec_store = Qdrant.from_texts(
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["lorem", "ipsum", "dolor", "sit", "amet"],
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embeddings,
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collection_name=collection_name,
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path=str(tmpdir),
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vector_name=vector_name,
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)
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del vec_store
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vec_store = Qdrant.from_texts(
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["foo", "bar"],
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embeddings,
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collection_name=collection_name,
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path=str(tmpdir),
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vector_name=vector_name,
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)
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del vec_store
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client = QdrantClient(path=str(tmpdir))
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assert 7 == client.count(collection_name).count
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@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
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def test_qdrant_from_texts_raises_error_on_different_dimensionality(
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vector_name: Optional[str],
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) -> None:
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"""Test if Qdrant.from_texts raises an exception if dimensionality does not match"""
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collection_name = uuid.uuid4().hex
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with tempfile.TemporaryDirectory() as tmpdir:
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vec_store = Qdrant.from_texts(
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["lorem", "ipsum", "dolor", "sit", "amet"],
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ConsistentFakeEmbeddings(dimensionality=10),
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collection_name=collection_name,
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path=str(tmpdir),
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vector_name=vector_name,
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)
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del vec_store
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with pytest.raises(QdrantException):
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Qdrant.from_texts(
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["foo", "bar"],
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ConsistentFakeEmbeddings(dimensionality=5),
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collection_name=collection_name,
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path=str(tmpdir),
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vector_name=vector_name,
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)
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@pytest.mark.parametrize(
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["first_vector_name", "second_vector_name"],
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[
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(None, "custom-vector"),
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("custom-vector", None),
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("my-first-vector", "my-second_vector"),
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],
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)
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def test_qdrant_from_texts_raises_error_on_different_vector_name(
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first_vector_name: Optional[str],
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second_vector_name: Optional[str],
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) -> None:
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"""Test if Qdrant.from_texts raises an exception if vector name does not match"""
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collection_name = uuid.uuid4().hex
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with tempfile.TemporaryDirectory() as tmpdir:
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vec_store = Qdrant.from_texts(
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["lorem", "ipsum", "dolor", "sit", "amet"],
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ConsistentFakeEmbeddings(dimensionality=10),
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collection_name=collection_name,
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path=str(tmpdir),
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vector_name=first_vector_name,
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)
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del vec_store
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with pytest.raises(QdrantException):
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Qdrant.from_texts(
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["foo", "bar"],
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ConsistentFakeEmbeddings(dimensionality=5),
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collection_name=collection_name,
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path=str(tmpdir),
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vector_name=second_vector_name,
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)
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def test_qdrant_from_texts_raises_error_on_different_distance() -> None:
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"""Test if Qdrant.from_texts raises an exception if distance does not match"""
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collection_name = uuid.uuid4().hex
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with tempfile.TemporaryDirectory() as tmpdir:
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vec_store = Qdrant.from_texts(
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["lorem", "ipsum", "dolor", "sit", "amet"],
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ConsistentFakeEmbeddings(),
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collection_name=collection_name,
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path=str(tmpdir),
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distance_func="Cosine",
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)
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del vec_store
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with pytest.raises(QdrantException) as excinfo:
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Qdrant.from_texts(
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["foo", "bar"],
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ConsistentFakeEmbeddings(),
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collection_name=collection_name,
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path=str(tmpdir),
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distance_func="Euclid",
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)
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expected_message = (
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"configured for COSINE similarity, but requested EUCLID. Please set "
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"`distance_func` parameter to `COSINE`"
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)
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assert expected_message in str(excinfo.value)
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@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
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def test_qdrant_from_texts_recreates_collection_on_force_recreate(
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vector_name: Optional[str],
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) -> None:
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"""Test if Qdrant.from_texts recreates the collection even if config mismatches"""
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from qdrant_client import QdrantClient
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collection_name = uuid.uuid4().hex
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with tempfile.TemporaryDirectory() as tmpdir:
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vec_store = Qdrant.from_texts(
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["lorem", "ipsum", "dolor", "sit", "amet"],
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ConsistentFakeEmbeddings(dimensionality=10),
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collection_name=collection_name,
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path=str(tmpdir),
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vector_name=vector_name,
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)
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del vec_store
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vec_store = Qdrant.from_texts(
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["foo", "bar"],
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ConsistentFakeEmbeddings(dimensionality=5),
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collection_name=collection_name,
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path=str(tmpdir),
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vector_name=vector_name,
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force_recreate=True,
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)
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del vec_store
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client = QdrantClient(path=str(tmpdir))
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assert 2 == client.count(collection_name).count
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
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@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
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def test_qdrant_from_texts_stores_metadatas(
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batch_size: int, content_payload_key: str, metadata_payload_key: str
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) -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": i} for i in range(len(texts))]
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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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metadatas=metadatas,
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location=":memory:",
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content_payload_key=content_payload_key,
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metadata_payload_key=metadata_payload_key,
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batch_size=batch_size,
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)
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output = docsearch.similarity_search("foo", k=1)
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assert_documents_equals(
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output, [Document(page_content="foo", metadata={"page": 0})]
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)
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@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
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def test_from_texts_passed_optimizers_config_and_on_disk_payload(location: str) -> None:
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from qdrant_client import models
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collection_name = uuid.uuid4().hex
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": i} for i in range(len(texts))]
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optimizers_config = models.OptimizersConfigDiff(memmap_threshold=1000)
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vec_store = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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metadatas=metadatas,
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optimizers_config=optimizers_config,
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on_disk_payload=True,
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on_disk=True,
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collection_name=collection_name,
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location=location,
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)
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collection_info = vec_store.client.get_collection(collection_name)
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assert collection_info.config.params.vectors.on_disk is True # type: ignore
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assert collection_info.config.optimizer_config.memmap_threshold == 1000
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assert collection_info.config.params.on_disk_payload is True
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