mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
1f83b5f47e
This PR changes the behavior of `Qdrant.from_texts` so the collection is reused if not requested to recreate it. Previously, calling `Qdrant.from_texts` or `Qdrant.from_documents` resulted in removing the old data which was confusing for many.
686 lines
23 KiB
Python
686 lines
23 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 numpy as np
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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 langchain.vectorstores.qdrant import QdrantException
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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("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_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("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_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("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_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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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_add_documents(batch_size: int, vector_name: Optional[str]) -> 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,
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ConsistentFakeEmbeddings(),
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location=":memory:",
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batch_size=batch_size,
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vector_name=vector_name,
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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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"""Test end to end Qdrant.add_texts returns unique ids."""
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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("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_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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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_similarity_search_with_relevance_score_no_threshold(
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vector_name: Optional[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 = [
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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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vector_name=vector_name,
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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("content_payload_key", [Qdrant.CONTENT_KEY, "test_content"])
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@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "test_metadata"])
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_max_marginal_relevance_search(
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batch_size: int,
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content_payload_key: str,
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metadata_payload_key: str,
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vector_name: Optional[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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vector_name=vector_name,
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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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"""Test Qdrant may accept different types for embeddings."""
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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_value_error(
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embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
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) -> None:
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"""Test Qdrant requires only one method for embeddings."""
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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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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_add_texts_stores_duplicated_texts(vector_name: Optional[str]) -> None:
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"""Test end to end Qdrant.add_texts stores duplicated texts separately."""
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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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vectors_config = rest.VectorParams(size=10, distance=rest.Distance.COSINE)
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if vector_name is not None:
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vectors_config = {vector_name: vectors_config} # type: ignore[assignment]
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client.recreate_collection(collection_name, vectors_config=vectors_config)
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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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vector_name=vector_name,
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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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"""Test end to end Qdrant.from_texts stores duplicated texts separately."""
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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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@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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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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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("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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"""Test end to end Qdrant.add_texts stores provided ids."""
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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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|
@pytest.mark.parametrize("vector_name", ["custom-vector"])
|
|
def test_qdrant_from_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
|
|
"""Test end to end Qdrant.from_texts stores named vectors if name is provided."""
|
|
from qdrant_client import QdrantClient
|
|
|
|
collection_name = "test"
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
vec_store = Qdrant.from_texts(
|
|
["lorem", "ipsum", "dolor", "sit", "amet"],
|
|
ConsistentFakeEmbeddings(),
|
|
collection_name=collection_name,
|
|
path=str(tmpdir),
|
|
vector_name=vector_name,
|
|
)
|
|
del vec_store
|
|
|
|
client = QdrantClient(path=str(tmpdir))
|
|
assert 5 == client.count("test").count
|
|
assert all(
|
|
vector_name in point.vector # type: ignore[operator]
|
|
for point in client.scroll(collection_name, with_vectors=True)[0]
|
|
)
|
|
|
|
|
|
@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
|
|
|
|
collection_name = "test"
|
|
|
|
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("test").count
|
|
assert all(
|
|
vector_name in point.vector # type: ignore[operator]
|
|
for point in client.scroll(collection_name, with_vectors=True)[0]
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("batch_size", [1, 64])
|
|
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
|
|
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
|
|
def test_qdrant_similarity_search_with_relevance_scores(
|
|
batch_size: int, content_payload_key: str, metadata_payload_key: str
|
|
) -> None:
|
|
"""Test end to end construction and search."""
|
|
texts = ["foo", "bar", "baz"]
|
|
docsearch = Qdrant.from_texts(
|
|
texts,
|
|
ConsistentFakeEmbeddings(),
|
|
location=":memory:",
|
|
content_payload_key=content_payload_key,
|
|
metadata_payload_key=metadata_payload_key,
|
|
batch_size=batch_size,
|
|
)
|
|
output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
|
|
|
|
assert all(
|
|
(1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
|
|
def test_qdrant_from_texts_reuses_same_collection(vector_name: Optional[str]) -> None:
|
|
"""Test if Qdrant.from_texts reuses the same collection"""
|
|
from qdrant_client import QdrantClient
|
|
|
|
collection_name = "test"
|
|
embeddings = ConsistentFakeEmbeddings()
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
vec_store = Qdrant.from_texts(
|
|
["lorem", "ipsum", "dolor", "sit", "amet"],
|
|
embeddings,
|
|
collection_name=collection_name,
|
|
path=str(tmpdir),
|
|
vector_name=vector_name,
|
|
)
|
|
del vec_store
|
|
|
|
vec_store = Qdrant.from_texts(
|
|
["foo", "bar"],
|
|
embeddings,
|
|
collection_name=collection_name,
|
|
path=str(tmpdir),
|
|
vector_name=vector_name,
|
|
)
|
|
del vec_store
|
|
|
|
client = QdrantClient(path=str(tmpdir))
|
|
assert 7 == client.count(collection_name).count
|
|
|
|
|
|
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
|
|
def test_qdrant_from_texts_raises_error_on_different_dimensionality(
|
|
vector_name: Optional[str],
|
|
) -> None:
|
|
"""Test if Qdrant.from_texts raises an exception if dimensionality does not match"""
|
|
collection_name = "test"
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
vec_store = Qdrant.from_texts(
|
|
["lorem", "ipsum", "dolor", "sit", "amet"],
|
|
ConsistentFakeEmbeddings(dimensionality=10),
|
|
collection_name=collection_name,
|
|
path=str(tmpdir),
|
|
vector_name=vector_name,
|
|
)
|
|
del vec_store
|
|
|
|
with pytest.raises(QdrantException):
|
|
Qdrant.from_texts(
|
|
["foo", "bar"],
|
|
ConsistentFakeEmbeddings(dimensionality=5),
|
|
collection_name=collection_name,
|
|
path=str(tmpdir),
|
|
vector_name=vector_name,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
["first_vector_name", "second_vector_name"],
|
|
[
|
|
(None, "custom-vector"),
|
|
("custom-vector", None),
|
|
("my-first-vector", "my-second_vector"),
|
|
],
|
|
)
|
|
def test_qdrant_from_texts_raises_error_on_different_vector_name(
|
|
first_vector_name: Optional[str],
|
|
second_vector_name: Optional[str],
|
|
) -> None:
|
|
"""Test if Qdrant.from_texts raises an exception if vector name does not match"""
|
|
collection_name = "test"
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
vec_store = Qdrant.from_texts(
|
|
["lorem", "ipsum", "dolor", "sit", "amet"],
|
|
ConsistentFakeEmbeddings(dimensionality=10),
|
|
collection_name=collection_name,
|
|
path=str(tmpdir),
|
|
vector_name=first_vector_name,
|
|
)
|
|
del vec_store
|
|
|
|
with pytest.raises(QdrantException):
|
|
Qdrant.from_texts(
|
|
["foo", "bar"],
|
|
ConsistentFakeEmbeddings(dimensionality=5),
|
|
collection_name=collection_name,
|
|
path=str(tmpdir),
|
|
vector_name=second_vector_name,
|
|
)
|
|
|
|
|
|
def test_qdrant_from_texts_raises_error_on_different_distance() -> None:
|
|
"""Test if Qdrant.from_texts raises an exception if distance does not match"""
|
|
collection_name = "test"
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
vec_store = Qdrant.from_texts(
|
|
["lorem", "ipsum", "dolor", "sit", "amet"],
|
|
ConsistentFakeEmbeddings(dimensionality=10),
|
|
collection_name=collection_name,
|
|
path=str(tmpdir),
|
|
distance_func="Cosine",
|
|
)
|
|
del vec_store
|
|
|
|
with pytest.raises(QdrantException):
|
|
Qdrant.from_texts(
|
|
["foo", "bar"],
|
|
ConsistentFakeEmbeddings(dimensionality=5),
|
|
collection_name=collection_name,
|
|
path=str(tmpdir),
|
|
distance_func="Euclid",
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
|
|
def test_qdrant_from_texts_recreates_collection_on_force_recreate(
|
|
vector_name: Optional[str],
|
|
) -> None:
|
|
"""Test if Qdrant.from_texts recreates the collection even if config mismatches"""
|
|
from qdrant_client import QdrantClient
|
|
|
|
collection_name = "test"
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
vec_store = Qdrant.from_texts(
|
|
["lorem", "ipsum", "dolor", "sit", "amet"],
|
|
ConsistentFakeEmbeddings(dimensionality=10),
|
|
collection_name=collection_name,
|
|
path=str(tmpdir),
|
|
vector_name=vector_name,
|
|
)
|
|
del vec_store
|
|
|
|
vec_store = Qdrant.from_texts(
|
|
["foo", "bar"],
|
|
ConsistentFakeEmbeddings(dimensionality=5),
|
|
collection_name=collection_name,
|
|
path=str(tmpdir),
|
|
vector_name=vector_name,
|
|
force_recreate=True,
|
|
)
|
|
del vec_store
|
|
|
|
client = QdrantClient(path=str(tmpdir))
|
|
assert 2 == client.count(collection_name).count
|