forked from Archives/langchain
54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
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"""Test Milvus functionality."""
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from typing import List, Optional
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from langchain.docstore.document import Document
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from langchain.vectorstores import Milvus
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from tests.integration_tests.vectorstores.fake_embeddings import (
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FakeEmbeddings,
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fake_texts,
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)
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def _milvus_from_texts(metadatas: Optional[List[dict]] = None) -> Milvus:
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return Milvus.from_texts(
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fake_texts,
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FakeEmbeddings(),
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metadatas=metadatas,
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connection_args={"host": "127.0.0.1", "port": "19530"},
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)
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def test_milvus() -> None:
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"""Test end to end construction and search."""
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docsearch = _milvus_from_texts()
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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_milvus_with_score() -> None:
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"""Test end to end construction and search with scores and IDs."""
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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 = _milvus_from_texts(metadatas=metadatas)
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output = docsearch.similarity_search_with_score("foo", k=3)
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docs = [o[0] for o in output]
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scores = [o[1] for o in output]
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assert docs == [
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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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Document(page_content="baz", metadata={"page": 2}),
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]
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assert scores[0] < scores[1] < scores[2]
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def test_milvus_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 = _milvus_from_texts(metadatas=metadatas)
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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="baz", metadata={"page": 2}),
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]
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