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43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
"""Test ElasticSearch 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.elastic_vector_search import ElasticVectorSearch
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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 + [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_elasticsearch() -> 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 = ElasticVectorSearch.from_texts(
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texts, FakeEmbeddings(), elasticsearch_url="http://localhost:9200"
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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_elasticsearch_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 = ElasticVectorSearch.from_texts(
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texts,
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FakeEmbeddings(),
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metadatas=metadatas,
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elasticsearch_url="http://localhost:9200",
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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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