refactor: move tests folder structure and update pytest commands (#2785)
* refactor: move tests folder to src/backend * chore(Makefile): update pytest commands to run tests from the correct directory paths for unit and integration tests * refactor: update file path in test_custom_component.py The file path in the test_custom_component.py file has been updated to use the correct relative path to the component_multiple_outputs.py file. This change ensures that the test code can access the correct file and improves the reliability of the test.
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
parent
afdff210b3
commit
0122a50a35
56 changed files with 5 additions and 5 deletions
0
src/backend/tests/integration/__init__.py
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0
src/backend/tests/integration/__init__.py
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src/backend/tests/integration/astra/__init__.py
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src/backend/tests/integration/astra/__init__.py
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src/backend/tests/integration/astra/test_astra_component.py
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src/backend/tests/integration/astra/test_astra_component.py
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import os
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from langchain_astradb import AstraDBVectorStore, CollectionVectorServiceOptions
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from langflow.components.embeddings.AstraVectorize import AstraVectorizeComponent
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import pytest
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from integration.utils import MockEmbeddings, check_env_vars
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from langchain_core.documents import Document
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# from langflow.components.memories.AstraDBMessageReader import AstraDBMessageReaderComponent
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# from langflow.components.memories.AstraDBMessageWriter import AstraDBMessageWriterComponent
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from langflow.components.vectorstores.AstraDB import AstraVectorStoreComponent
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from langflow.schema.data import Data
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COLLECTION = "test_basic"
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SEARCH_COLLECTION = "test_search"
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# MEMORY_COLLECTION = "test_memory"
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VECTORIZE_COLLECTION = "test_vectorize"
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VECTORIZE_COLLECTION_OPENAI = "test_vectorize_openai"
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VECTORIZE_COLLECTION_OPENAI_WITH_AUTH = "test_vectorize_openai_auth"
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@pytest.fixture()
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def astra_fixture(request):
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"""
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Sets up the astra collection and cleans up after
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"""
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try:
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from langchain_astradb import AstraDBVectorStore
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except ImportError:
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raise ImportError(
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"Could not import langchain Astra DB integration package. Please install it with `pip install langchain-astradb`."
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)
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store = AstraDBVectorStore(
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collection_name=request.param,
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embedding=MockEmbeddings(),
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api_endpoint=os.getenv("ASTRA_DB_API_ENDPOINT"),
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token=os.getenv("ASTRA_DB_APPLICATION_TOKEN"),
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)
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yield
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store.delete_collection()
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@pytest.mark.skipif(
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not check_env_vars("ASTRA_DB_APPLICATION_TOKEN", "ASTRA_DB_API_ENDPOINT"),
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reason="missing astra env vars",
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)
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@pytest.mark.parametrize("astra_fixture", [COLLECTION], indirect=True)
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def test_astra_setup(astra_fixture):
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application_token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
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api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
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embedding = MockEmbeddings()
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component = AstraVectorStoreComponent()
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component.build(
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token=application_token,
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api_endpoint=api_endpoint,
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collection_name=COLLECTION,
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embedding=embedding,
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)
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component.build_vector_store()
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@pytest.mark.skipif(
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not check_env_vars("ASTRA_DB_APPLICATION_TOKEN", "ASTRA_DB_API_ENDPOINT"),
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reason="missing astra env vars",
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)
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@pytest.mark.parametrize("astra_fixture", [SEARCH_COLLECTION], indirect=True)
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def test_astra_embeds_and_search(astra_fixture):
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application_token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
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api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
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embedding = MockEmbeddings()
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documents = [Document(page_content="test1"), Document(page_content="test2")]
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records = [Data.from_document(d) for d in documents]
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component = AstraVectorStoreComponent()
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component.build(
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token=application_token,
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api_endpoint=api_endpoint,
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collection_name=SEARCH_COLLECTION,
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embedding=embedding,
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ingest_data=records,
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search_input="test1",
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number_of_results=1,
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)
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component.build_vector_store()
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records = component.search_documents()
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assert len(records) == 1
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@pytest.mark.skipif(
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not check_env_vars("ASTRA_DB_APPLICATION_TOKEN", "ASTRA_DB_API_ENDPOINT"),
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reason="missing astra env vars",
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)
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def test_astra_vectorize():
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store = None
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try:
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options = {"provider": "nvidia", "modelName": "NV-Embed-QA", "parameters": {}, "authentication": {}}
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store = AstraDBVectorStore(
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collection_name=VECTORIZE_COLLECTION,
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api_endpoint=os.getenv("ASTRA_DB_API_ENDPOINT"),
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token=os.getenv("ASTRA_DB_APPLICATION_TOKEN"),
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collection_vector_service_options=CollectionVectorServiceOptions.from_dict(options),
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)
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application_token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
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api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
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documents = [Document(page_content="test1"), Document(page_content="test2")]
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records = [Data.from_document(d) for d in documents]
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vectorize = AstraVectorizeComponent()
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vectorize.build(provider="NVIDIA", model_name="NV-Embed-QA")
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vectorize_options = vectorize.build_options()
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component = AstraVectorStoreComponent()
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component.build(
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token=application_token,
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api_endpoint=api_endpoint,
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collection_name=VECTORIZE_COLLECTION,
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ingest_data=records,
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embedding=vectorize_options,
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search_input="test",
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number_of_results=2,
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)
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component.build_vector_store()
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records = component.search_documents()
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assert len(records) == 2
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finally:
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if store is not None:
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store.delete_collection()
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@pytest.mark.skipif(
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not check_env_vars("ASTRA_DB_APPLICATION_TOKEN", "ASTRA_DB_API_ENDPOINT", "OPENAI_API_KEY"),
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reason="missing env vars",
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)
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def test_astra_vectorize_with_provider_api_key():
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"""tests vectorize using an openai api key"""
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store = None
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try:
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application_token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
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api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
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options = {"provider": "openai", "modelName": "text-embedding-3-small", "parameters": {}, "authentication": {}}
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store = AstraDBVectorStore(
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collection_name=VECTORIZE_COLLECTION_OPENAI,
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api_endpoint=api_endpoint,
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token=application_token,
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collection_vector_service_options=CollectionVectorServiceOptions.from_dict(options),
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collection_embedding_api_key=os.getenv("OPENAI_API_KEY"),
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)
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documents = [Document(page_content="test1"), Document(page_content="test2")]
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records = [Data.from_document(d) for d in documents]
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vectorize = AstraVectorizeComponent()
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vectorize.build(
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provider="OpenAI", model_name="text-embedding-3-small", provider_api_key=os.getenv("OPENAI_API_KEY")
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)
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vectorize_options = vectorize.build_options()
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component = AstraVectorStoreComponent()
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component.build(
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token=application_token,
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api_endpoint=api_endpoint,
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collection_name=VECTORIZE_COLLECTION_OPENAI,
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ingest_data=records,
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embedding=vectorize_options,
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search_input="test",
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)
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component.build_vector_store()
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records = component.search_documents()
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assert len(records) == 2
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finally:
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if store is not None:
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store.delete_collection()
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@pytest.mark.skipif(
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not check_env_vars("ASTRA_DB_APPLICATION_TOKEN", "ASTRA_DB_API_ENDPOINT", "OPENAI_API_KEY"),
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reason="missing env vars",
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)
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def test_astra_vectorize_passes_authentication():
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"""tests vectorize using the authentication parameter"""
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store = None
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try:
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application_token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
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api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
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options = {
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"provider": "openai",
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"modelName": "text-embedding-3-small",
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"parameters": {},
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"authentication": {"providerKey": "providerKey"},
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}
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store = AstraDBVectorStore(
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collection_name=VECTORIZE_COLLECTION_OPENAI_WITH_AUTH,
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api_endpoint=api_endpoint,
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token=application_token,
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collection_vector_service_options=CollectionVectorServiceOptions.from_dict(options),
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)
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documents = [Document(page_content="test1"), Document(page_content="test2")]
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records = [Data.from_document(d) for d in documents]
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vectorize = AstraVectorizeComponent()
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vectorize.build(
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provider="OpenAI", model_name="text-embedding-3-small", authentication={"providerKey": "providerKey"}
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)
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vectorize_options = vectorize.build_options()
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component = AstraVectorStoreComponent()
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component.build(
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token=application_token,
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api_endpoint=api_endpoint,
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collection_name=VECTORIZE_COLLECTION_OPENAI_WITH_AUTH,
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ingest_data=records,
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embedding=vectorize_options,
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search_input="test",
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)
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component.build_vector_store()
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records = component.search_documents()
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assert len(records) == 2
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finally:
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if store is not None:
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store.delete_collection()
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# @pytest.mark.skipif(
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# not check_env_vars("ASTRA_DB_APPLICATION_TOKEN", "ASTRA_DB_API_ENDPOINT"),
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# reason="missing astra env vars",
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# )
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# def test_astra_memory():
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# application_token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
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# api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
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# writer = AstraDBMessageWriterComponent()
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# reader = AstraDBMessageReaderComponent()
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# input_value = Data.from_document(
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# Document(
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# page_content="memory1",
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# metadata={"session_id": 1, "sender": "human", "sender_name": "Bob"},
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# )
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# )
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# writer.build(
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# input_value=input_value,
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# session_id=1,
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# token=application_token,
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# api_endpoint=api_endpoint,
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# collection_name=MEMORY_COLLECTION,
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# )
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# # verify reading w/ same session id pulls the same record
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# records = reader.build(
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# session_id=1,
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# token=application_token,
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# api_endpoint=api_endpoint,
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# collection_name=MEMORY_COLLECTION,
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# )
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# assert len(records) == 1
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# assert isinstance(records[0], Data)
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# content = records[0].get_text()
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# assert content == "memory1"
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# # verify reading w/ different session id does not pull the same record
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# records = reader.build(
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# session_id=2,
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# token=application_token,
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# api_endpoint=api_endpoint,
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# collection_name=MEMORY_COLLECTION,
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# )
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# assert len(records) == 0
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# # Cleanup store - doing here rather than fixture (see https://github.com/langchain-ai/langchain-datastax/pull/36)
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# try:
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# from langchain_astradb import AstraDBVectorStore
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# except ImportError:
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# raise ImportError(
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# "Could not import langchain Astra DB integration package. Please install it with `pip install langchain-astradb`."
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# )
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# store = AstraDBVectorStore(
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# collection_name=MEMORY_COLLECTION,
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# embedding=MockEmbeddings(),
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# api_endpoint=api_endpoint,
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# token=application_token,
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# )
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# store.delete_collection()
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88
src/backend/tests/integration/langflow.py
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88
src/backend/tests/integration/langflow.py
Normal file
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from uuid import uuid4
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import pytest
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from fastapi import status
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from fastapi.testclient import TestClient
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from langflow.graph.schema import RunOutputs
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from langflow.initial_setup.setup import load_starter_projects
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from langflow.load import run_flow_from_json
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@pytest.mark.api_key_required
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def test_run_flow_with_caching_success(client: TestClient, starter_project, created_api_key):
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flow_id = starter_project["id"]
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headers = {"x-api-key": created_api_key.api_key}
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payload = {
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"input_value": "value1",
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"input_type": "text",
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"output_type": "text",
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"tweaks": {"parameter_name": "value"},
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"stream": False,
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}
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response = client.post(f"/api/v1/run/{flow_id}", json=payload, headers=headers)
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assert response.status_code == status.HTTP_200_OK
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data = response.json()
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assert "outputs" in data
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assert "session_id" in data
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@pytest.mark.api_key_required
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def test_run_flow_with_caching_invalid_flow_id(client: TestClient, created_api_key):
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invalid_flow_id = uuid4()
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headers = {"x-api-key": created_api_key.api_key}
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payload = {"input_value": "", "input_type": "text", "output_type": "text", "tweaks": {}, "stream": False}
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response = client.post(f"/api/v1/run/{invalid_flow_id}", json=payload, headers=headers)
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assert response.status_code == status.HTTP_404_NOT_FOUND
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data = response.json()
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assert "detail" in data
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assert f"Flow identifier {invalid_flow_id} not found" in data["detail"]
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@pytest.mark.api_key_required
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def test_run_flow_with_caching_invalid_input_format(client: TestClient, starter_project, created_api_key):
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flow_id = starter_project["id"]
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headers = {"x-api-key": created_api_key.api_key}
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payload = {"input_value": {"key": "value"}, "input_type": "text", "output_type": "text", "tweaks": {}}
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response = client.post(f"/api/v1/run/{flow_id}", json=payload, headers=headers)
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assert response.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY
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@pytest.mark.api_key_required
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def test_run_flow_with_invalid_tweaks(client, starter_project, created_api_key):
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headers = {"x-api-key": created_api_key.api_key}
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flow_id = starter_project["id"]
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payload = {
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"input_value": "value1",
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"input_type": "text",
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"output_type": "text",
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"tweaks": {"invalid_tweak": "value"},
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}
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response = client.post(f"/api/v1/run/{flow_id}", json=payload, headers=headers)
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assert response.status_code == status.HTTP_200_OK
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@pytest.mark.api_key_required
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def test_run_with_inputs_and_outputs(client, starter_project, created_api_key):
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headers = {"x-api-key": created_api_key.api_key}
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flow_id = starter_project["id"]
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payload = {
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"input_value": "value1",
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"input_type": "text",
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"output_type": "text",
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"tweaks": {"parameter_name": "value"},
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"stream": False,
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}
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response = client.post(f"/api/v1/run/{flow_id}", json=payload, headers=headers)
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assert response.status_code == status.HTTP_200_OK, response.text
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@pytest.mark.noclient
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@pytest.mark.api_key_required
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def test_run_flow_from_json_object():
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"""Test loading a flow from a json file and applying tweaks"""
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_, projects = zip(*load_starter_projects())
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project = [project for project in projects if "Basic Prompting" in project["name"]][0]
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results = run_flow_from_json(project, input_value="test", fallback_to_env_vars=True)
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assert results is not None
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assert all(isinstance(result, RunOutputs) for result in results)
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35
src/backend/tests/integration/utils.py
Normal file
35
src/backend/tests/integration/utils.py
Normal file
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import os
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from typing import List
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from langflow.field_typing import Embeddings
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def check_env_vars(*vars):
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"""
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Check if all specified environment variables are set.
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Args:
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*vars (str): The environment variables to check.
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Returns:
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bool: True if all environment variables are set, False otherwise.
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"""
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return all(os.getenv(var) for var in vars)
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class MockEmbeddings(Embeddings):
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def __init__(self):
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self.embedded_documents = None
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self.embedded_query = None
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@staticmethod
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def mock_embedding(text: str):
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return [len(text) / 2, len(text) / 5, len(text) / 10]
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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self.embedded_documents = texts
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return [self.mock_embedding(text) for text in texts]
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def embed_query(self, text: str) -> List[float]:
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self.embedded_query = text
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return self.mock_embedding(text)
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