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standard-tests: split tool calling test #20803

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merged 2 commits into from
Apr 23, 2024
Merged

standard-tests: split tool calling test #20803

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standard-tests: split tool calling test
efriis Apr 23, 2024
1bcbac7
Merge branch 'master' into erick/standard-tests-split-tool-calling-test
efriis Apr 23, 2024
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121 changes: 70 additions & 51 deletions libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,69 +117,88 @@ async def test_abatch(
assert isinstance(result.content, str)
assert len(result.content) > 0

def test_tool_message_histories(
def test_tool_message_histories_string_content(
self,
chat_model_class: Type[BaseChatModel],
chat_model_params: dict,
chat_model_has_tool_calling: bool,
) -> None:
"""Test that message histories are compatible across providers."""
"""
Test that message histories are compatible with string tool contents
(e.g. OpenAI).
"""
if not chat_model_has_tool_calling:
pytest.skip("Test requires tool calling.")
model = chat_model_class(**chat_model_params)
model_with_tools = model.bind_tools([my_adder_tool])
function_name = "my_adder_tool"
function_args = {"a": "1", "b": "2"}

human_message = HumanMessage(content="What is 1 + 2")
tool_message = ToolMessage(
name=function_name,
content=json.dumps({"result": 3}),
tool_call_id="abc123",
)

# String content (e.g., OpenAI)
string_content_msg = AIMessage(
content="",
tool_calls=[
{
"name": function_name,
"args": function_args,
"id": "abc123",
},
],
)
messages = [
human_message,
string_content_msg,
tool_message,
messages_string_content = [
HumanMessage(content="What is 1 + 2"),
# string content (e.g. OpenAI)
AIMessage(
content="",
tool_calls=[
{
"name": function_name,
"args": function_args,
"id": "abc123",
},
],
),
ToolMessage(
name=function_name,
content=json.dumps({"result": 3}),
tool_call_id="abc123",
),
]
result = model_with_tools.invoke(messages)
assert isinstance(result, AIMessage)
result_string_content = model_with_tools.invoke(messages_string_content)
assert isinstance(result_string_content, AIMessage)

# List content (e.g., Anthropic)
list_content_msg = AIMessage(
content=[
{"type": "text", "text": "some text"},
{
"type": "tool_use",
"id": "abc123",
"name": function_name,
"input": function_args,
},
],
tool_calls=[
{
"name": function_name,
"args": function_args,
"id": "abc123",
},
],
)
messages = [
human_message,
list_content_msg,
tool_message,
def test_tool_message_histories_list_content(
self,
chat_model_class: Type[BaseChatModel],
chat_model_params: dict,
chat_model_has_tool_calling: bool,
) -> None:
"""
Test that message histories are compatible with list tool contents
(e.g. Anthropic).
"""
if not chat_model_has_tool_calling:
pytest.skip("Test requires tool calling.")
model = chat_model_class(**chat_model_params)
model_with_tools = model.bind_tools([my_adder_tool])
function_name = "my_adder_tool"
function_args = {"a": 1, "b": 2}

messages_list_content = [
HumanMessage(content="What is 1 + 2"),
# List content (e.g., Anthropic)
AIMessage(
content=[
{"type": "text", "text": "some text"},
{
"type": "tool_use",
"id": "abc123",
"name": function_name,
"input": function_args,
},
],
tool_calls=[
{
"name": function_name,
"args": function_args,
"id": "abc123",
},
],
),
ToolMessage(
name=function_name,
content=json.dumps({"result": 3}),
tool_call_id="abc123",
),
]
result = model_with_tools.invoke(messages)
assert isinstance(result, AIMessage)
result_list_content = model_with_tools.invoke(messages_list_content)
assert isinstance(result_list_content, AIMessage)
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