64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
import json
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import hashlib
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from pathlib import Path
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import dill
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import tempfile
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from langflow.cache.utils import compute_hash, load_cache, save_cache, PREFIX
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from langflow.interface.run import load_langchain_object, process_graph
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import pytest
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def get_graph(_type="basic"):
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"""Get a graph from a json file"""
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if _type == "basic":
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path = pytest.BASIC_EXAMPLE_PATH
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elif _type == "complex":
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path = pytest.COMPLEX_EXAMPLE_PATH
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elif _type == "openapi":
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path = pytest.OPENAPI_EXAMPLE_PATH
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with open(path, "r") as f:
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flow_graph = json.load(f)
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return flow_graph["data"]
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@pytest.fixture
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def basic_data_graph():
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return get_graph()
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@pytest.fixture
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def complex_data_graph():
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return get_graph("complex")
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@pytest.fixture
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def openapi_data_graph():
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return get_graph("openapi")
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def langchain_objects_are_equal(obj1, obj2):
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return str(obj1) == str(obj2)
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def test_cache_creation(basic_data_graph):
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# Compute hash for the input data_graph
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computed_hash = compute_hash(basic_data_graph)
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# Call process_graph function to build and cache the langchain_object
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_ = load_langchain_object(basic_data_graph)
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# Check if the cache file exists
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cache_file = Path(tempfile.gettempdir()) / f"{PREFIX}_{computed_hash}.dill"
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assert cache_file.exists()
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def test_cache_reuse(basic_data_graph):
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# Call process_graph function to build and cache the langchain_object
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result1 = load_langchain_object(basic_data_graph)
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# Call process_graph function again to use the cached langchain_object
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result2 = load_langchain_object(basic_data_graph)
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# Compare the results to ensure the same langchain_object was used
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assert langchain_objects_are_equal(result1, result2)
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