test: add astra integration test (#2189)

* add first astra integ test framework

* use fixtures

* remove old tests from merge

* Add correct sender type

* chore: Update unit test command in GitHub workflow

---------

Co-authored-by: ogabrielluiz <gabriel@langflow.org>
This commit is contained in:
Jordan Frazier 2024-06-15 19:50:38 -07:00 committed by GitHub
commit ca660cf8df
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GPG key ID: B5690EEEBB952194
31 changed files with 211 additions and 12 deletions

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@ -42,4 +42,4 @@ jobs:
poetry install
- name: Run unit tests
run: |
make tests args="-n auto"
make unit_tests args="-n auto"

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@ -55,9 +55,11 @@ coverage: ## run the tests and generate a coverage report
# allow passing arguments to pytest
tests: ## run the tests
poetry run pytest tests --instafail -ra -n auto -m "not api_key_required" $(args)
unit_tests:
poetry run pytest tests/unit --instafail -ra -n auto -m "not api_key_required" $(args)
integration_tests:
poetry run pytest tests/integration --instafail -ra -n auto $(args)
format: ## run code formatters
poetry run ruff check . --fix

10
poetry.lock generated
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@ -2438,8 +2438,8 @@ files = [
[package.dependencies]
cffi = {version = ">=1.12.2", markers = "platform_python_implementation == \"CPython\" and sys_platform == \"win32\""}
greenlet = [
{version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.11\""},
{version = ">=3.0rc3", markers = "platform_python_implementation == \"CPython\" and python_version >= \"3.11\""},
{version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.11\""},
]
"zope.event" = "*"
"zope.interface" = "*"
@ -2566,12 +2566,12 @@ files = [
google-auth = ">=2.14.1,<3.0.dev0"
googleapis-common-protos = ">=1.56.2,<2.0.dev0"
grpcio = [
{version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
{version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
{version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
]
grpcio-status = [
{version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
{version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
{version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
]
proto-plus = ">=1.22.3,<2.0.0dev"
protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0"
@ -4550,8 +4550,8 @@ psutil = ">=5.9.1"
pywin32 = {version = "*", markers = "platform_system == \"Windows\""}
pyzmq = ">=25.0.0"
requests = [
{version = ">=2.26.0", markers = "python_version <= \"3.11\""},
{version = ">=2.32.2", markers = "python_version > \"3.11\""},
{version = ">=2.26.0", markers = "python_version <= \"3.11\""},
]
tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
Werkzeug = ">=2.0.0"
@ -6019,9 +6019,9 @@ files = [
[package.dependencies]
numpy = [
{version = ">=1.26.0,<2", markers = "python_version >= \"3.12\""},
{version = ">=1.22.4,<2", markers = "python_version < \"3.11\""},
{version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
{version = ">=1.26.0,<2", markers = "python_version >= \"3.12\""},
]
python-dateutil = ">=2.8.2"
pytz = ">=2020.1"

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@ -89,6 +89,7 @@ class AstraDBMessageWriterComponent(BaseMemoryComponent):
sender_name=sender_name,
metadata=metadata,
session_id=session_id,
type=sender,
)
]

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@ -163,4 +163,3 @@ class AstraDBVectorStoreComponent(CustomComponent):
)
return vector_store
return vector_store

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@ -4,19 +4,21 @@ from typing import TYPE_CHECKING, List, Optional, Union
import duckdb
from langflow.services.base import Service
from langflow.services.monitor.schema import MessageModel, TransactionModel, VertexBuildModel
from langflow.services.monitor.utils import add_row_to_table, drop_and_create_table_if_schema_mismatch
from loguru import logger
from platformdirs import user_cache_dir
if TYPE_CHECKING:
from langflow.services.settings.manager import SettingsService
from langflow.services.monitor.schema import MessageModel, TransactionModel, VertexBuildModel
class MonitorService(Service):
name = "monitor_service"
def __init__(self, settings_service: "SettingsService"):
from langflow.services.monitor.schema import MessageModel, TransactionModel, VertexBuildModel
self.settings_service = settings_service
self.base_cache_dir = Path(user_cache_dir("langflow"))
self.db_path = self.base_cache_dir / "monitor.duckdb"
@ -45,7 +47,7 @@ class MonitorService(Service):
def add_row(
self,
table_name: str,
data: Union[dict, TransactionModel, MessageModel, VertexBuildModel],
data: Union[dict, "TransactionModel", "MessageModel", "VertexBuildModel"],
):
# Make sure the model passed matches the table
@ -127,7 +129,7 @@ class MonitorService(Service):
return self.exec_query(query, read_only=False)
def add_message(self, message: MessageModel):
def add_message(self, message: "MessageModel"):
self.add_row("messages", message)
def get_messages(

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@ -0,0 +1,160 @@
import os
import pytest
from integration.utils import MockEmbeddings, check_env_vars
from langflow.components.memories.AstraDBMessageReader import (
AstraDBMessageReaderComponent,
)
from langflow.components.memories.AstraDBMessageWriter import (
AstraDBMessageWriterComponent,
)
from langflow.components.vectorsearch.AstraDBSearch import AstraDBSearchComponent
from langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent
from langflow.schema.record import Record
from langchain_core.documents import Document
COLLECTION = "test_basic"
SEARCH_COLLECTION = "test_search"
MEMORY_COLLECTION = "test_memory"
@pytest.fixture()
def astra_fixture(request):
"""
Sets up the astra collection and cleans up after
"""
try:
from langchain_astradb import AstraDBVectorStore
except ImportError:
raise ImportError(
"Could not import langchain Astra DB integration package. Please install it with `pip install langchain-astradb`."
)
store = AstraDBVectorStore(
collection_name=request.param,
embedding=MockEmbeddings(),
api_endpoint=os.getenv("ASTRA_DB_API_ENDPOINT"),
token=os.getenv("ASTRA_DB_APPLICATION_TOKEN"),
)
yield
store.delete_collection()
@pytest.mark.skipif(
not check_env_vars("ASTRA_DB_APPLICATION_TOKEN", "ASTRA_DB_API_ENDPOINT"),
reason="missing astra env vars",
)
@pytest.mark.parametrize("astra_fixture", [COLLECTION], indirect=True)
def test_astra_setup(astra_fixture):
application_token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
embedding = MockEmbeddings()
component = AstraDBVectorStoreComponent()
component.build(
token=application_token,
api_endpoint=api_endpoint,
collection_name=COLLECTION,
embedding=embedding,
)
@pytest.mark.skipif(
not check_env_vars("ASTRA_DB_APPLICATION_TOKEN", "ASTRA_DB_API_ENDPOINT"),
reason="missing astra env vars",
)
@pytest.mark.parametrize("astra_fixture", [SEARCH_COLLECTION], indirect=True)
def test_astra_embeds_and_search(astra_fixture):
application_token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
embedding = MockEmbeddings()
documents = [Document(page_content="test1"), Document(page_content="test2")]
records = [Record.from_document(d) for d in documents]
component = AstraDBVectorStoreComponent()
component.build(
token=application_token,
api_endpoint=api_endpoint,
collection_name=SEARCH_COLLECTION,
embedding=embedding,
inputs=records,
)
component = AstraDBSearchComponent()
records = component.build(
token=application_token,
api_endpoint=api_endpoint,
collection_name=SEARCH_COLLECTION,
embedding=embedding,
input_value="test1",
number_of_results=1,
)
assert len(records) == 1
@pytest.mark.skipif(
not check_env_vars("ASTRA_DB_APPLICATION_TOKEN", "ASTRA_DB_API_ENDPOINT"),
reason="missing astra env vars",
)
def test_astra_memory():
application_token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
writer = AstraDBMessageWriterComponent()
reader = AstraDBMessageReaderComponent()
input_value = Record.from_document(
Document(
page_content="memory1",
metadata={"session_id": 1, "sender": "human", "sender_name": "Bob"},
)
)
writer.build(
input_value=input_value,
session_id=1,
token=application_token,
api_endpoint=api_endpoint,
collection_name=MEMORY_COLLECTION,
)
# verify reading w/ same session id pulls the same record
records = reader.build(
session_id=1,
token=application_token,
api_endpoint=api_endpoint,
collection_name=MEMORY_COLLECTION,
)
assert len(records) == 1
assert isinstance(records[0], Record)
content = records[0].get_text()
assert content == "memory1"
# verify reading w/ different session id does not pull the same record
records = reader.build(
session_id=2,
token=application_token,
api_endpoint=api_endpoint,
collection_name=MEMORY_COLLECTION,
)
assert len(records) == 0
# Cleanup store - doing here rather than fixture (see https://github.com/langchain-ai/langchain-datastax/pull/36)
try:
from langchain_astradb import AstraDBVectorStore
except ImportError:
raise ImportError(
"Could not import langchain Astra DB integration package. Please install it with `pip install langchain-astradb`."
)
store = AstraDBVectorStore(
collection_name=MEMORY_COLLECTION,
embedding=MockEmbeddings(),
api_endpoint=api_endpoint,
token=application_token,
)
store.delete_collection()

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@ -0,0 +1,35 @@
import os
from typing import List
from langflow.field_typing import Embeddings, VectorStore
def check_env_vars(*vars):
"""
Check if all specified environment variables are set.
Args:
*vars (str): The environment variables to check.
Returns:
bool: True if all environment variables are set, False otherwise.
"""
return all(os.getenv(var) for var in vars)
class MockEmbeddings(Embeddings):
def __init__(self):
self.embedded_documents = None
self.embedded_query = None
@staticmethod
def mock_embedding(text: str):
return [len(text) / 2, len(text) / 5, len(text) / 10]
def embed_documents(self, texts: List[str]) -> List[List[float]]:
self.embedded_documents = texts
return [self.mock_embedding(text) for text in texts]
def embed_query(self, text: str) -> List[float]:
self.embedded_query = text
return self.mock_embedding(text)

0
tests/unit/__init__.py Normal file
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