"""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") 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]