forked from Archives/langchain
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
"""Test Qdrant functionality."""
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from typing import List
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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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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 [[1.0] * 9 + [float(i)] 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 [1.0] * 9 + [0.0]
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def test_qdrant() -> 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(texts, FakeEmbeddings(), host="localhost")
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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_qdrant_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": i} for i in range(len(texts))]
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docsearch = Qdrant.from_texts(
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texts,
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FakeEmbeddings(),
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metadatas=metadatas,
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host="localhost",
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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_qdrant_max_marginal_relevance_search() -> 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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FakeEmbeddings(),
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metadatas=metadatas,
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host="localhost",
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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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