forked from Archives/langchain
48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
"""Test FAISS functionality."""
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from typing import List
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import pytest
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from langchain.docstore.document import Document
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from langchain.docstore.in_memory import InMemoryDocstore
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from langchain.embeddings.base import Embeddings
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from langchain.faiss import FAISS
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class FakeEmbeddings(Embeddings):
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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 [[i] * 10 for i in range(len(texts))]
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def embed_query(self, text: str) -> List[float]:
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"""Return simple embeddings."""
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return [0] * 10
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def test_faiss() -> 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 = FAISS.from_texts(texts, FakeEmbeddings())
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expected_docstore = InMemoryDocstore(
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{
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"0": Document(page_content="foo"),
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"1": Document(page_content="bar"),
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"2": Document(page_content="baz"),
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}
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)
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assert docsearch.docstore.__dict__ == expected_docstore.__dict__
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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_faiss_search_not_found() -> None:
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"""Test what happens when document is not found."""
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texts = ["foo", "bar", "baz"]
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docsearch = FAISS.from_texts(texts, FakeEmbeddings())
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# Get rid of the docstore to purposefully induce errors.
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docsearch.docstore = InMemoryDocstore({})
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with pytest.raises(ValueError):
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docsearch.similarity_search("foo")
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