Spaces:
Sleeping
Sleeping
File size: 4,616 Bytes
cfd3735 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
"""Test Annoy functionality."""
import tempfile
import pytest
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.vectorstores.annoy import Annoy
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_annoy() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Annoy.from_texts(texts, FakeEmbeddings())
index_to_id = docsearch.index_to_docstore_id
expected_docstore = InMemoryDocstore(
{
index_to_id[0]: Document(page_content="foo"),
index_to_id[1]: Document(page_content="bar"),
index_to_id[2]: Document(page_content="baz"),
}
)
assert docsearch.docstore.__dict__ == expected_docstore.__dict__
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_annoy_vector_sim() -> None:
"""Test vector similarity."""
texts = ["foo", "bar", "baz"]
docsearch = Annoy.from_texts(texts, FakeEmbeddings())
index_to_id = docsearch.index_to_docstore_id
expected_docstore = InMemoryDocstore(
{
index_to_id[0]: Document(page_content="foo"),
index_to_id[1]: Document(page_content="bar"),
index_to_id[2]: Document(page_content="baz"),
}
)
assert docsearch.docstore.__dict__ == expected_docstore.__dict__
query_vec = FakeEmbeddings().embed_query(text="foo")
output = docsearch.similarity_search_by_vector(query_vec, k=1)
assert output == [Document(page_content="foo")]
# make sure we can have k > docstore size
output = docsearch.max_marginal_relevance_search_by_vector(query_vec, k=10)
assert len(output) == len(texts)
def test_annoy_vector_sim_by_index() -> None:
"""Test vector similarity."""
texts = ["foo", "bar", "baz"]
docsearch = Annoy.from_texts(texts, FakeEmbeddings())
index_to_id = docsearch.index_to_docstore_id
expected_docstore = InMemoryDocstore(
{
index_to_id[0]: Document(page_content="foo"),
index_to_id[1]: Document(page_content="bar"),
index_to_id[2]: Document(page_content="baz"),
}
)
assert docsearch.docstore.__dict__ == expected_docstore.__dict__
output = docsearch.similarity_search_by_index(2, k=1)
assert output == [Document(page_content="baz")]
def test_annoy_with_metadatas() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Annoy.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
expected_docstore = InMemoryDocstore(
{
docsearch.index_to_docstore_id[0]: Document(
page_content="foo", metadata={"page": 0}
),
docsearch.index_to_docstore_id[1]: Document(
page_content="bar", metadata={"page": 1}
),
docsearch.index_to_docstore_id[2]: Document(
page_content="baz", metadata={"page": 2}
),
}
)
assert docsearch.docstore.__dict__ == expected_docstore.__dict__
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": 0})]
def test_annoy_search_not_found() -> None:
"""Test what happens when document is not found."""
texts = ["foo", "bar", "baz"]
docsearch = Annoy.from_texts(texts, FakeEmbeddings())
# Get rid of the docstore to purposefully induce errors.
docsearch.docstore = InMemoryDocstore({})
with pytest.raises(ValueError):
docsearch.similarity_search("foo")
def test_annoy_add_texts() -> None:
"""Test end to end adding of texts."""
# Create initial doc store.
texts = ["foo", "bar", "baz"]
docsearch = Annoy.from_texts(texts, FakeEmbeddings())
# Test adding a similar document as before.
with pytest.raises(NotImplementedError):
docsearch.add_texts(["foo"])
def test_annoy_local_save_load() -> None:
"""Test end to end serialization."""
texts = ["foo", "bar", "baz"]
docsearch = Annoy.from_texts(texts, FakeEmbeddings())
temp_dir = tempfile.TemporaryDirectory()
docsearch.save_local(temp_dir.name)
loaded_docsearch = Annoy.load_local(temp_dir.name, FakeEmbeddings())
assert docsearch.index_to_docstore_id == loaded_docsearch.index_to_docstore_id
assert docsearch.docstore.__dict__ == loaded_docsearch.docstore.__dict__
assert loaded_docsearch.index is not None
|