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429 lines
15 KiB
Python
429 lines
15 KiB
Python
"""Test TimescaleVector functionality."""
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import os
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from datetime import datetime, timedelta
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from typing import List
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from langchain_core.documents import Document
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from langchain_community.vectorstores.timescalevector import TimescaleVector
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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SERVICE_URL = TimescaleVector.service_url_from_db_params(
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host=os.environ.get("TEST_TIMESCALE_HOST", "localhost"),
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port=int(os.environ.get("TEST_TIMESCALE_PORT", "5432")),
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database=os.environ.get("TEST_TIMESCALE_DATABASE", "postgres"),
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user=os.environ.get("TEST_TIMESCALE_USER", "postgres"),
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password=os.environ.get("TEST_TIMESCALE_PASSWORD", "postgres"),
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)
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ADA_TOKEN_COUNT = 1536
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class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
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"""Fake embeddings functionality for testing."""
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Return simple embeddings."""
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return [
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[float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(i)] for i in range(len(texts))
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]
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def embed_query(self, text: str) -> List[float]:
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"""Return simple embeddings."""
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return [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(0.0)]
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def test_timescalevector() -> 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 = TimescaleVector.from_texts(
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texts=texts,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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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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def test_timescalevector_from_documents() -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
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docsearch = TimescaleVector.from_documents(
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documents=docs,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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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={"a": "b"})]
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async def test_timescalevector_afrom_documents() -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
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docsearch = await TimescaleVector.afrom_documents(
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documents=docs,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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output = await docsearch.asimilarity_search("foo", k=1)
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assert output == [Document(page_content="foo", metadata={"a": "b"})]
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def test_timescalevector_embeddings() -> None:
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"""Test end to end construction with embeddings and search."""
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texts = ["foo", "bar", "baz"]
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text_embeddings = FakeEmbeddingsWithAdaDimension().embed_documents(texts)
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text_embedding_pairs = list(zip(texts, text_embeddings))
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docsearch = TimescaleVector.from_embeddings(
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text_embeddings=text_embedding_pairs,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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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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async def test_timescalevector_aembeddings() -> None:
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"""Test end to end construction with embeddings and search."""
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texts = ["foo", "bar", "baz"]
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text_embeddings = FakeEmbeddingsWithAdaDimension().embed_documents(texts)
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text_embedding_pairs = list(zip(texts, text_embeddings))
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docsearch = await TimescaleVector.afrom_embeddings(
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text_embeddings=text_embedding_pairs,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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output = await docsearch.asimilarity_search("foo", k=1)
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assert output == [Document(page_content="foo")]
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def test_timescalevector_with_metadatas() -> 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": str(i)} for i in range(len(texts))]
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docsearch = TimescaleVector.from_texts(
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texts=texts,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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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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def test_timescalevector_with_metadatas_with_scores() -> 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": str(i)} for i in range(len(texts))]
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docsearch = TimescaleVector.from_texts(
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texts=texts,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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output = docsearch.similarity_search_with_score("foo", k=1)
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assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
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async def test_timescalevector_awith_metadatas_with_scores() -> 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": str(i)} for i in range(len(texts))]
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docsearch = await TimescaleVector.afrom_texts(
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texts=texts,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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output = await docsearch.asimilarity_search_with_score("foo", k=1)
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assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
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def test_timescalevector_with_filter_match() -> 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": str(i)} for i in range(len(texts))]
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docsearch = TimescaleVector.from_texts(
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texts=texts,
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collection_name="test_collection_filter",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "0"})
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assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
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def test_timescalevector_with_filter_distant_match() -> 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": str(i)} for i in range(len(texts))]
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docsearch = TimescaleVector.from_texts(
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texts=texts,
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collection_name="test_collection_filter",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"})
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assert output == [
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(Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406)
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]
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def test_timescalevector_with_filter_no_match() -> 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": str(i)} for i in range(len(texts))]
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docsearch = TimescaleVector.from_texts(
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texts=texts,
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collection_name="test_collection_filter",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "5"})
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assert output == []
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def test_timescalevector_with_filter_in_set() -> 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": str(i)} for i in range(len(texts))]
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docsearch = TimescaleVector.from_texts(
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texts=texts,
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collection_name="test_collection_filter",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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output = docsearch.similarity_search_with_score(
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"foo", k=2, filter=[{"page": "0"}, {"page": "2"}]
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)
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assert output == [
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(Document(page_content="foo", metadata={"page": "0"}), 0.0),
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(Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406),
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]
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def test_timescalevector_relevance_score() -> None:
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"""Test to make sure the relevance score is scaled to 0-1."""
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": str(i)} for i in range(len(texts))]
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docsearch = TimescaleVector.from_texts(
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texts=texts,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
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assert output == [
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(Document(page_content="foo", metadata={"page": "0"}), 1.0),
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(Document(page_content="bar", metadata={"page": "1"}), 0.9996744261675065),
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(Document(page_content="baz", metadata={"page": "2"}), 0.9986996093328621),
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]
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async def test_timescalevector_relevance_score_async() -> None:
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"""Test to make sure the relevance score is scaled to 0-1."""
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": str(i)} for i in range(len(texts))]
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docsearch = await TimescaleVector.afrom_texts(
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texts=texts,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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output = await docsearch.asimilarity_search_with_relevance_scores("foo", k=3)
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assert output == [
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(Document(page_content="foo", metadata={"page": "0"}), 1.0),
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(Document(page_content="bar", metadata={"page": "1"}), 0.9996744261675065),
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(Document(page_content="baz", metadata={"page": "2"}), 0.9986996093328621),
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]
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def test_timescalevector_retriever_search_threshold() -> None:
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"""Test using retriever for searching with threshold."""
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": str(i)} for i in range(len(texts))]
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docsearch = TimescaleVector.from_texts(
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texts=texts,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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retriever = docsearch.as_retriever(
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search_type="similarity_score_threshold",
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search_kwargs={"k": 3, "score_threshold": 0.999},
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)
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output = retriever.get_relevant_documents("summer")
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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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def test_timescalevector_retriever_search_threshold_custom_normalization_fn() -> None:
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"""Test searching with threshold and custom normalization function"""
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": str(i)} for i in range(len(texts))]
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docsearch = TimescaleVector.from_texts(
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texts=texts,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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relevance_score_fn=lambda d: d * 0,
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)
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retriever = docsearch.as_retriever(
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search_type="similarity_score_threshold",
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search_kwargs={"k": 3, "score_threshold": 0.5},
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)
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output = retriever.get_relevant_documents("foo")
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assert output == []
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def test_timescalevector_delete() -> None:
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"""Test deleting functionality."""
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texts = ["bar", "baz"]
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docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
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docsearch = TimescaleVector.from_documents(
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documents=docs,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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texts = ["foo"]
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meta = [{"b": "c"}]
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ids = docsearch.add_texts(texts, meta)
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output = docsearch.similarity_search("bar", k=10)
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assert len(output) == 3
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docsearch.delete(ids)
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output = docsearch.similarity_search("bar", k=10)
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assert len(output) == 2
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docsearch.delete_by_metadata({"a": "b"})
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output = docsearch.similarity_search("bar", k=10)
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assert len(output) == 0
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def test_timescalevector_with_index() -> None:
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"""Test deleting functionality."""
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texts = ["bar", "baz"]
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docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
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docsearch = TimescaleVector.from_documents(
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documents=docs,
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collection_name="test_collection",
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embedding=FakeEmbeddingsWithAdaDimension(),
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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)
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texts = ["foo"]
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meta = [{"b": "c"}]
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docsearch.add_texts(texts, meta)
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docsearch.create_index()
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output = docsearch.similarity_search("bar", k=10)
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assert len(output) == 3
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docsearch.drop_index()
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docsearch.create_index(
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index_type=TimescaleVector.IndexType.TIMESCALE_VECTOR,
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max_alpha=1.0,
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num_neighbors=50,
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)
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docsearch.drop_index()
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docsearch.create_index("tsv", max_alpha=1.0, num_neighbors=50)
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docsearch.drop_index()
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docsearch.create_index("ivfflat", num_lists=20, num_records=1000)
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docsearch.drop_index()
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docsearch.create_index("hnsw", m=16, ef_construction=64)
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def test_timescalevector_time_partitioning() -> None:
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"""Test deleting functionality."""
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from timescale_vector import client
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texts = ["bar", "baz"]
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docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
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docsearch = TimescaleVector.from_documents(
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documents=docs,
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collection_name="test_collection_time_partitioning",
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embedding=FakeEmbeddingsWithAdaDimension(),
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service_url=SERVICE_URL,
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pre_delete_collection=True,
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time_partition_interval=timedelta(hours=1),
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)
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texts = ["foo"]
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meta = [{"b": "c"}]
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ids = [client.uuid_from_time(datetime.now() - timedelta(hours=3))]
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docsearch.add_texts(texts, meta, ids)
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output = docsearch.similarity_search("bar", k=10)
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assert len(output) == 3
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output = docsearch.similarity_search(
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"bar", k=10, start_date=datetime.now() - timedelta(hours=1)
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)
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assert len(output) == 2
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output = docsearch.similarity_search(
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"bar", k=10, end_date=datetime.now() - timedelta(hours=1)
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)
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assert len(output) == 1
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output = docsearch.similarity_search(
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"bar", k=10, start_date=datetime.now() - timedelta(minutes=200)
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)
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assert len(output) == 3
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output = docsearch.similarity_search(
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"bar",
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k=10,
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start_date=datetime.now() - timedelta(minutes=200),
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time_delta=timedelta(hours=1),
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)
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assert len(output) == 1
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