"""Test Clarifai vectore store functionality.""" import time from langchain.docstore.document import Document from langchain.vectorstores import Clarifai def test_clarifai_with_from_texts() -> None: """Test end to end construction and search.""" texts = ["foo", "bar", "baz"] USER_ID = "minhajul" APP_ID = "test-lang-2" NUMBER_OF_DOCS = 1 docsearch = Clarifai.from_texts( user_id=USER_ID, app_id=APP_ID, texts=texts, pat=None, number_of_docs=NUMBER_OF_DOCS, ) time.sleep(2.5) output = docsearch.similarity_search("foo") assert output == [Document(page_content="foo")] def test_clarifai_with_from_documents() -> None: """Test end to end construction and search.""" # Initial document content and id initial_content = "foo" # Create an instance of Document with initial content and metadata original_doc = Document(page_content=initial_content, metadata={"page": "0"}) USER_ID = "minhajul" APP_ID = "test-lang-2" NUMBER_OF_DOCS = 1 docsearch = Clarifai.from_documents( user_id=USER_ID, app_id=APP_ID, documents=[original_doc], pat=None, number_of_docs=NUMBER_OF_DOCS, ) time.sleep(2.5) output = docsearch.similarity_search("foo") assert output == [Document(page_content=initial_content, metadata={"page": "0"})] def test_clarifai_with_metadatas() -> None: """Test end to end construction and search with metadata.""" texts = ["oof", "rab", "zab"] metadatas = [{"page": str(i)} for i in range(len(texts))] USER_ID = "minhajul" APP_ID = "test-lang-2" NUMBER_OF_DOCS = 1 docsearch = Clarifai.from_texts( user_id=USER_ID, app_id=APP_ID, texts=texts, pat=None, number_of_docs=NUMBER_OF_DOCS, metadatas=metadatas, ) time.sleep(2.5) output = docsearch.similarity_search("oof", k=1) assert output == [Document(page_content="oof", metadata={"page": "0"})] def test_clarifai_with_metadatas_with_scores() -> None: """Test end to end construction and scored search.""" texts = ["oof", "rab", "zab"] metadatas = [{"page": str(i)} for i in range(len(texts))] USER_ID = "minhajul" APP_ID = "test-lang-2" NUMBER_OF_DOCS = 1 docsearch = Clarifai.from_texts( user_id=USER_ID, app_id=APP_ID, texts=texts, pat=None, number_of_docs=NUMBER_OF_DOCS, metadatas=metadatas, ) time.sleep(2.5) output = docsearch.similarity_search_with_score("oof", k=1) assert output[0][0] == Document(page_content="oof", metadata={"page": "0"}) assert abs(output[0][1] - 1.0) < 0.001