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98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
"""Test Pinecone functionality."""
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import pinecone
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from langchain.docstore.document import Document
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from langchain.vectorstores.pinecone import Pinecone
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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pinecone.init(api_key="YOUR_API_KEY", environment="YOUR_ENV")
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# if the index already exists, delete it
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try:
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pinecone.delete_index("langchain-demo")
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except Exception:
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pass
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index = pinecone.Index("langchain-demo")
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def test_pinecone() -> 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 = Pinecone.from_texts(
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texts, FakeEmbeddings(), index_name="langchain-demo", namespace="test"
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)
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output = docsearch.similarity_search("foo", k=1, namespace="test")
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assert output == [Document(page_content="foo")]
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def test_pinecone_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 = Pinecone.from_texts(
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texts,
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FakeEmbeddings(),
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index_name="langchain-demo",
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metadatas=metadatas,
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namespace="test-metadata",
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)
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output = docsearch.similarity_search("foo", k=1, namespace="test-metadata")
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assert output == [Document(page_content="foo", metadata={"page": 0})]
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def test_pinecone_with_scores() -> 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 = Pinecone.from_texts(
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texts,
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FakeEmbeddings(),
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index_name="langchain-demo",
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metadatas=metadatas,
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namespace="test-metadata-score",
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)
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output = docsearch.similarity_search_with_score(
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"foo", k=3, namespace="test-metadata-score"
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)
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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_pinecone_with_namespaces() -> None:
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"Test that namespaces are properly handled." ""
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# Create two indexes with the same name but different namespaces
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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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Pinecone.from_texts(
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texts,
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FakeEmbeddings(),
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index_name="langchain-demo",
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metadatas=metadatas,
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namespace="test-namespace",
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)
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texts = ["foo2", "bar2", "baz2"]
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metadatas = [{"page": i} for i in range(len(texts))]
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Pinecone.from_texts(
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texts,
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FakeEmbeddings(),
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index_name="langchain-demo",
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metadatas=metadatas,
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namespace="test-namespace2",
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)
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# Search with namespace
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docsearch = Pinecone.from_existing_index(
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"langchain-demo", embedding=FakeEmbeddings(), namespace="test-namespace"
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)
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output = docsearch.similarity_search("foo", k=6)
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# check that we don't get results from the other namespace
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page_contents = [o.page_content for o in output]
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assert set(page_contents) == set(["foo", "bar", "baz"])
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