Add Chat Agent Regression Tests

tests/regression_tests/test_chat_agents.py

@@ -0,0 +1,50 @@
+"""Test chat agents in various scenarios."""
+
+from typing import Set
+
+import pytest
+
+from langchain.agents.agent_types import AgentType
+from langchain.agents.initialize import initialize_agent
+from langchain.agents.tools import Tool
+from langchain.chains.llm_math.base import LLMMathChain
+from langchain.chat_models.openai import ChatOpenAI
+from langchain.tools.ddg_search.tool import DuckDuckGoSearchRun
+from langchain.tools.plugin import AIPluginTool
+
+TEST_CASES = [
+    (
+        "What's the current time in NYC?",
+        {"DuckDuckGo Search"},
+    ),
+    ("What is a shoe that's available on Klarna?", {"KlarnaProducts"}),
+    ("What's 3*4.2*1.7", {"Calculator"}),
+]
+
+
+@pytest.mark.parametrize("query, used_tools", TEST_CASES)
+def test_chat_agent(query: str, used_tools: Set[str]) -> None:
+    """Test chat agent."""
+    llm = ChatOpenAI(temperature=0)
+    llm_math_chain = LLMMathChain(llm=llm)
+    tools = [
+        DuckDuckGoSearchRun(),
+        AIPluginTool.from_plugin_url(
+            "https://www.klarna.com/.well-known/ai-plugin.json"
+        ),
+        Tool(
+            name="Calculator",
+            func=llm_math_chain.run,
+            description="useful for doing calculations",
+        ),
+    ]
+    agent_executor = initialize_agent(
+        tools,
+        llm,
+        AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
+        return_intermediate_steps=True,
+    )
+    result = agent_executor({"input": query})
+    intermediate_steps = result["intermediate_steps"]
+    tool_sequences = [act.tool for act, _ in intermediate_steps]
+    assert set(tool_sequences) == used_tools