langchain/tests/integration_tests/vectorstores/test_pinecone.py

"""Test Pinecone functionality."""
import pinecone

from langchain.docstore.document import Document
from langchain.vectorstores.pinecone import Pinecone
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings

pinecone.init(api_key="YOUR_API_KEY", environment="YOUR_ENV")

# if the index already exists, delete it
try:
    pinecone.delete_index("langchain-demo")
except Exception:
    pass
index = pinecone.Index("langchain-demo")


def test_pinecone() -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    docsearch = Pinecone.from_texts(
        texts, FakeEmbeddings(), index_name="langchain-demo", namespace="test"
    )
    output = docsearch.similarity_search("foo", k=1, namespace="test")
    assert output == [Document(page_content="foo")]


def test_pinecone_with_metadatas() -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    metadatas = [{"page": i} for i in range(len(texts))]
    docsearch = Pinecone.from_texts(
        texts,
        FakeEmbeddings(),
        index_name="langchain-demo",
        metadatas=metadatas,
        namespace="test-metadata",
    )
    output = docsearch.similarity_search("foo", k=1, namespace="test-metadata")
    assert output == [Document(page_content="foo", metadata={"page": 0})]


def test_pinecone_with_scores() -> None:
    """Test end to end construction and search with scores and IDs."""
    texts = ["foo", "bar", "baz"]
    metadatas = [{"page": i} for i in range(len(texts))]
    docsearch = Pinecone.from_texts(
        texts,
        FakeEmbeddings(),
        index_name="langchain-demo",
        metadatas=metadatas,
        namespace="test-metadata-score",
    )
    output = docsearch.similarity_search_with_score(
        "foo", k=3, namespace="test-metadata-score"
    )
    docs = [o[0] for o in output]
    scores = [o[1] for o in output]
    assert docs == [
        Document(page_content="foo", metadata={"page": 0}),
        Document(page_content="bar", metadata={"page": 1}),
        Document(page_content="baz", metadata={"page": 2}),
    ]
    assert scores[0] > scores[1] > scores[2]


def test_pinecone_with_namespaces() -> None:
    "Test that namespaces are properly handled." ""
    # Create two indexes with the same name but different namespaces
    texts = ["foo", "bar", "baz"]
    metadatas = [{"page": i} for i in range(len(texts))]
    Pinecone.from_texts(
        texts,
        FakeEmbeddings(),
        index_name="langchain-demo",
        metadatas=metadatas,
        namespace="test-namespace",
    )

    texts = ["foo2", "bar2", "baz2"]
    metadatas = [{"page": i} for i in range(len(texts))]
    Pinecone.from_texts(
        texts,
        FakeEmbeddings(),
        index_name="langchain-demo",
        metadatas=metadatas,
        namespace="test-namespace2",
    )

    # Search with namespace
    docsearch = Pinecone.from_existing_index(
        "langchain-demo", embedding=FakeEmbeddings(), namespace="test-namespace"
    )
    output = docsearch.similarity_search("foo", k=6)
    # check that we don't get results from the other namespace
    page_contents = [o.page_content for o in output]
    assert set(page_contents) == set(["foo", "bar", "baz"])