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https://github.com/hwchase17/langchain
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e05997c25e
Co-authored-by: Changgeng Zhao <changgeng@nyu.edu> Co-authored-by: Changgeng Zhao <zhaochanggeng.zcg@alibaba-inc.com>
143 lines
5.1 KiB
Python
143 lines
5.1 KiB
Python
"""Test Hologres functionality."""
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import os
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from typing import List
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from langchain.docstore.document import Document
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from langchain.vectorstores.hologres import Hologres
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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CONNECTION_STRING = Hologres.connection_string_from_db_params(
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host=os.environ.get("TEST_HOLOGRES_HOST", "localhost"),
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port=int(os.environ.get("TEST_HOLOGRES_PORT", "80")),
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database=os.environ.get("TEST_HOLOGRES_DATABASE", "postgres"),
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user=os.environ.get("TEST_HOLOGRES_USER", "postgres"),
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password=os.environ.get("TEST_HOLOGRES_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_hologres() -> 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 = Hologres.from_texts(
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texts=texts,
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table_name="test_table",
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embedding=FakeEmbeddingsWithAdaDimension(),
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connection_string=CONNECTION_STRING,
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pre_delete_table=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_hologres_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 = Hologres.from_embeddings(
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text_embeddings=text_embedding_pairs,
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table_name="test_table",
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embedding=FakeEmbeddingsWithAdaDimension(),
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connection_string=CONNECTION_STRING,
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pre_delete_table=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_hologres_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 = Hologres.from_texts(
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texts=texts,
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table_name="test_table",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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connection_string=CONNECTION_STRING,
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pre_delete_table=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_hologres_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 = Hologres.from_texts(
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texts=texts,
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table_name="test_table",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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connection_string=CONNECTION_STRING,
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pre_delete_table=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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def test_hologres_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 = Hologres.from_texts(
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texts=texts,
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table_name="test_table_filter",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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connection_string=CONNECTION_STRING,
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pre_delete_table=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_hologres_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 = Hologres.from_texts(
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texts=texts,
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table_name="test_table_filter",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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connection_string=CONNECTION_STRING,
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pre_delete_table=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 == [(Document(page_content="baz", metadata={"page": "2"}), 4.0)]
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def test_hologres_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 = Hologres.from_texts(
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texts=texts,
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table_name="test_table_filter",
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embedding=FakeEmbeddingsWithAdaDimension(),
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metadatas=metadatas,
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connection_string=CONNECTION_STRING,
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pre_delete_table=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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