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470 lines
15 KiB
Python
470 lines
15 KiB
Python
"""Test Qdrant functionality."""
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import tempfile
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from typing import Callable, Optional
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import pytest
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from qdrant_client.http import models as rest
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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from langchain.vectorstores import Qdrant
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from tests.integration_tests.vectorstores.fake_embeddings import (
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ConsistentFakeEmbeddings,
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)
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize(
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["content_payload_key", "metadata_payload_key"],
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[
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(Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
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("foo", "bar"),
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(Qdrant.CONTENT_KEY, "bar"),
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("foo", Qdrant.METADATA_KEY),
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],
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)
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def test_qdrant_similarity_search(
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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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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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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 output == [Document(page_content="foo")]
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize(
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["content_payload_key", "metadata_payload_key"],
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[
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(Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
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("foo", "bar"),
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(Qdrant.CONTENT_KEY, "bar"),
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("foo", Qdrant.METADATA_KEY),
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],
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)
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def test_qdrant_similarity_search_by_vector(
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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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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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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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embeddings = ConsistentFakeEmbeddings().embed_query("foo")
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output = docsearch.similarity_search_by_vector(embeddings, k=1)
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assert output == [Document(page_content="foo")]
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize(
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["content_payload_key", "metadata_payload_key"],
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[
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(Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
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("foo", "bar"),
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(Qdrant.CONTENT_KEY, "bar"),
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("foo", Qdrant.METADATA_KEY),
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],
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)
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def test_qdrant_similarity_search_with_score_by_vector(
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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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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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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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embeddings = ConsistentFakeEmbeddings().embed_query("foo")
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output = docsearch.similarity_search_with_score_by_vector(embeddings, k=1)
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assert len(output) == 1
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document, score = output[0]
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assert document == Document(page_content="foo")
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assert score >= 0
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@pytest.mark.parametrize("batch_size", [1, 64])
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def test_qdrant_add_documents(batch_size: int) -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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docsearch: Qdrant = Qdrant.from_texts(
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texts, ConsistentFakeEmbeddings(), location=":memory:", batch_size=batch_size
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)
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new_texts = ["foobar", "foobaz"]
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docsearch.add_documents(
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[Document(page_content=content) for content in new_texts], batch_size=batch_size
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)
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output = docsearch.similarity_search("foobar", k=1)
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# StatefulFakeEmbeddings return the same query embedding as the first document
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# embedding computed in `embedding.embed_documents`. Thus, "foo" embedding is the
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# same as "foobar" embedding
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assert output == [Document(page_content="foobar")] or output == [
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Document(page_content="foo")
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]
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@pytest.mark.parametrize("batch_size", [1, 64])
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def test_qdrant_add_texts_returns_all_ids(batch_size: int) -> None:
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docsearch: Qdrant = Qdrant.from_texts(
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["foobar"],
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ConsistentFakeEmbeddings(),
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location=":memory:",
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batch_size=batch_size,
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)
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ids = docsearch.add_texts(["foo", "bar", "baz"])
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assert 3 == len(ids)
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assert 3 == len(set(ids))
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize(
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["content_payload_key", "metadata_payload_key"],
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[
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(Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
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("test_content", "test_payload"),
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(Qdrant.CONTENT_KEY, "payload_test"),
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("content_test", Qdrant.METADATA_KEY),
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],
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)
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def test_qdrant_with_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 output == [Document(page_content="foo", metadata={"page": 0})]
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@pytest.mark.parametrize("batch_size", [1, 64])
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def test_qdrant_similarity_search_filters(batch_size: int) -> 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 = [
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{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
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for i in range(len(texts))
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]
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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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batch_size=batch_size,
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)
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output = docsearch.similarity_search(
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"foo", k=1, filter={"page": 1, "metadata": {"page": 2, "pages": [3]}}
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)
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assert output == [
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Document(
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page_content="bar",
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metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}},
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)
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]
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def test_qdrant_similarity_search_with_relevance_score_no_threshold() -> 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 = [
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{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
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for i in range(len(texts))
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]
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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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)
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output = docsearch.similarity_search_with_relevance_scores(
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"foo", k=3, score_threshold=None
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)
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assert len(output) == 3
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for i in range(len(output)):
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assert round(output[i][1], 2) >= 0
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assert round(output[i][1], 2) <= 1
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def test_qdrant_similarity_search_with_relevance_score_with_threshold() -> 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 = [
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{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
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for i in range(len(texts))
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]
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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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)
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score_threshold = 0.98
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kwargs = {"score_threshold": score_threshold}
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output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
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assert len(output) == 1
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assert all([score >= score_threshold for _, score in output])
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def test_qdrant_similarity_search_with_relevance_score_with_threshold_and_filter() -> (
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None
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):
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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metadatas = [
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{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
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for i in range(len(texts))
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]
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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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)
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score_threshold = 0.99 # for almost exact match
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# test negative filter condition
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negative_filter = {"page": 1, "metadata": {"page": 2, "pages": [3]}}
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kwargs = {"filter": negative_filter, "score_threshold": score_threshold}
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output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
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assert len(output) == 0
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# test positive filter condition
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positive_filter = {"page": 0, "metadata": {"page": 1, "pages": [2]}}
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kwargs = {"filter": positive_filter, "score_threshold": score_threshold}
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output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
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assert len(output) == 1
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assert all([score >= score_threshold for _, score in output])
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def test_qdrant_similarity_search_filters_with_qdrant_filters() -> 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 = [
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{"page": i, "details": {"page": i + 1, "pages": [i + 2, -1]}}
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for i in range(len(texts))
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]
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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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)
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qdrant_filter = rest.Filter(
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must=[
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rest.FieldCondition(
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key="metadata.page",
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match=rest.MatchValue(value=1),
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),
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rest.FieldCondition(
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key="metadata.details.page",
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match=rest.MatchValue(value=2),
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),
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rest.FieldCondition(
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key="metadata.details.pages",
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match=rest.MatchAny(any=[3]),
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),
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]
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)
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output = docsearch.similarity_search("foo", k=1, filter=qdrant_filter)
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assert output == [
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Document(
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page_content="bar",
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metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}},
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)
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]
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize(
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["content_payload_key", "metadata_payload_key"],
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[
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(Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
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("test_content", "test_payload"),
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(Qdrant.CONTENT_KEY, "payload_test"),
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("content_test", Qdrant.METADATA_KEY),
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],
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)
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def test_qdrant_max_marginal_relevance_search(
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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 MRR 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.max_marginal_relevance_search("foo", k=2, fetch_k=3)
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assert output == [
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Document(page_content="foo", metadata={"page": 0}),
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Document(page_content="bar", metadata={"page": 1}),
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]
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@pytest.mark.parametrize(
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["embeddings", "embedding_function"],
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[
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(ConsistentFakeEmbeddings(), None),
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(ConsistentFakeEmbeddings().embed_query, None),
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(None, ConsistentFakeEmbeddings().embed_query),
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],
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)
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def test_qdrant_embedding_interface(
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embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
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) -> None:
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from qdrant_client import QdrantClient
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client = QdrantClient(":memory:")
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collection_name = "test"
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Qdrant(
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client,
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collection_name,
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embeddings=embeddings,
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embedding_function=embedding_function,
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)
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@pytest.mark.parametrize(
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["embeddings", "embedding_function"],
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[
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(ConsistentFakeEmbeddings(), ConsistentFakeEmbeddings().embed_query),
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(None, None),
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],
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)
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def test_qdrant_embedding_interface_raises(
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embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
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) -> None:
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from qdrant_client import QdrantClient
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client = QdrantClient(":memory:")
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collection_name = "test"
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with pytest.raises(ValueError):
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Qdrant(
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client,
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collection_name,
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embeddings=embeddings,
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embedding_function=embedding_function,
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)
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def test_qdrant_stores_duplicated_texts() -> None:
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from qdrant_client import QdrantClient
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from qdrant_client.http import models as rest
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client = QdrantClient(":memory:")
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collection_name = "test"
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client.recreate_collection(
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collection_name,
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vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
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)
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vec_store = Qdrant(
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client,
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collection_name,
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embeddings=ConsistentFakeEmbeddings(),
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)
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ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])
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assert 2 == len(set(ids))
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assert 2 == client.count(collection_name).count
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def test_qdrant_from_texts_stores_duplicated_texts() -> None:
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from qdrant_client import QdrantClient
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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="test",
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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("test").count
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@pytest.mark.parametrize("batch_size", [1, 64])
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def test_qdrant_from_texts_stores_ids(batch_size: int) -> None:
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from qdrant_client import QdrantClient
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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="test",
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path=str(tmpdir),
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batch_size=batch_size,
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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("test").count
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stored_ids = [point.id for point in client.scroll("test")[0]]
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assert set(ids) == set(stored_ids)
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@pytest.mark.parametrize("batch_size", [1, 64])
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def test_qdrant_add_texts_stores_ids(batch_size: int) -> None:
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from qdrant_client import QdrantClient
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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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client = QdrantClient(":memory:")
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collection_name = "test"
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client.recreate_collection(
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collection_name,
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vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
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)
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vec_store = Qdrant(client, "test", ConsistentFakeEmbeddings())
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returned_ids = vec_store.add_texts(["abc", "def"], ids=ids, batch_size=batch_size)
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assert all(first == second for first, second in zip(ids, returned_ids))
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assert 2 == client.count("test").count
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stored_ids = [point.id for point in client.scroll("test")[0]]
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assert set(ids) == set(stored_ids)
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