feat: implemented caching of agent

tests/test_cache.py

@@ -0,0 +1,64 @@
+import json
+import hashlib
+from pathlib import Path
+import dill
+import tempfile
+from langflow.cache.utils import compute_hash, load_cache, save_cache, PREFIX
+from langflow.interface.run import load_langchain_object, process_graph
+import pytest
+
+
+def get_graph(_type="basic"):
+    """Get a graph from a json file"""
+    if _type == "basic":
+        path = pytest.BASIC_EXAMPLE_PATH
+    elif _type == "complex":
+        path = pytest.COMPLEX_EXAMPLE_PATH
+    elif _type == "openapi":
+        path = pytest.OPENAPI_EXAMPLE_PATH
+
+    with open(path, "r") as f:
+        flow_graph = json.load(f)
+    return flow_graph["data"]
+
+
+@pytest.fixture
+def basic_data_graph():
+    return get_graph()
+
+
+@pytest.fixture
+def complex_data_graph():
+    return get_graph("complex")
+
+
+@pytest.fixture
+def openapi_data_graph():
+    return get_graph("openapi")
+
+
+def langchain_objects_are_equal(obj1, obj2):
+    return str(obj1) == str(obj2)
+
+
+def test_cache_creation(basic_data_graph):
+    # Compute hash for the input data_graph
+    computed_hash = compute_hash(basic_data_graph)
+
+    # Call process_graph function to build and cache the langchain_object
+    _ = load_langchain_object(basic_data_graph)
+
+    # Check if the cache file exists
+    cache_file = Path(tempfile.gettempdir()) / f"{PREFIX}_{computed_hash}.dill"
+    assert cache_file.exists()
+
+
+def test_cache_reuse(basic_data_graph):
+    # Call process_graph function to build and cache the langchain_object
+    result1 = load_langchain_object(basic_data_graph)
+
+    # Call process_graph function again to use the cached langchain_object
+    result2 = load_langchain_object(basic_data_graph)
+
+    # Compare the results to ensure the same langchain_object was used
+    assert langchain_objects_are_equal(result1, result2)