together, standard-tests: specify tool_choice in standard tests

libs/partners/groq/tests/integration_tests/test_chat_models.py

@@ -14,6 +14,7 @@
 )
 from langchain_core.outputs import ChatGeneration, LLMResult
 from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.tools import tool
 
 from langchain_groq import ChatGroq
 from tests.unit_tests.fake.callbacks import (
@@ -393,6 +394,42 @@ class Joke(BaseModel):
     assert len(result.punchline) != 0
 
 
+def test_tool_calling_no_arguments() -> None:
+    # Note: this is a variant of a test in langchain_standard_tests
+    # that as of 2024-08-19 fails with "Failed to call a function. Please
+    # adjust your prompt." when `tool_choice="any"` is specified, but
+    # passes when `tool_choice` is not specified.
+    model = ChatGroq(model="llama-3.1-70b-versatile", temperature=0)  # type: ignore[call-arg]
+
+    @tool
+    def magic_function_no_args() -> int:
+        """Calculates a magic function."""
+        return 5
+
+    model_with_tools = model.bind_tools([magic_function_no_args])
+    query = "What is the value of magic_function()? Use the tool."
+    result = model_with_tools.invoke(query)
+    assert isinstance(result, AIMessage)
+    assert len(result.tool_calls) == 1
+    tool_call = result.tool_calls[0]
+    assert tool_call["name"] == "magic_function_no_args"
+    assert tool_call["args"] == {}
+    assert tool_call["id"] is not None
+    assert tool_call["type"] == "tool_call"
+
+    # Test streaming
+    full: Optional[BaseMessageChunk] = None
+    for chunk in model_with_tools.stream(query):
+        full = chunk if full is None else full + chunk  # type: ignore
+    assert isinstance(full, AIMessage)
+    assert len(full.tool_calls) == 1
+    tool_call = full.tool_calls[0]
+    assert tool_call["name"] == "magic_function_no_args"
+    assert tool_call["args"] == {}
+    assert tool_call["id"] is not None
+    assert tool_call["type"] == "tool_call"
+
+
 # Groq does not currently support N > 1
 # @pytest.mark.scheduled
 # def test_chat_multiple_completions() -> None:

libs/partners/groq/tests/integration_tests/test_standard.py

@@ -1,6 +1,6 @@
 """Standard LangChain interface tests"""
 
-from typing import Type
+from typing import Optional, Type
 
 import pytest
 from langchain_core.language_models import BaseChatModel
@@ -28,11 +28,22 @@ class TestGroqLlama(BaseTestGroq):
     @property
     def chat_model_params(self) -> dict:
         return {
-            "model": "llama-3.1-70b-versatile",
+            "model": "llama-3.1-8b-instant",
             "temperature": 0,
             "rate_limiter": rate_limiter,
         }
 
+    @property
+    def tool_choice_value(self) -> Optional[str]:
+        """Value to use for tool choice when used in tests."""
+        return "any"
+
+    @pytest.mark.xfail(
+        reason=("Fails with 'Failed to call a function. Please adjust your prompt.'")
+    )
+    def test_tool_calling_with_no_arguments(self, model: BaseChatModel) -> None:
+        super().test_tool_calling_with_no_arguments(model)
+
     @pytest.mark.xfail(
         reason=("Fails with 'Failed to call a function. Please adjust your prompt.'")
     )

libs/partners/mistralai/tests/integration_tests/test_standard.py

@@ -1,6 +1,6 @@
 """Standard LangChain interface tests"""
 
-from typing import Type
+from typing import Optional, Type
 
 from langchain_core.language_models import BaseChatModel
 from langchain_standard_tests.integration_tests import (  # type: ignore[import-not-found]
@@ -18,3 +18,8 @@ def chat_model_class(self) -> Type[BaseChatModel]:
     @property
     def chat_model_params(self) -> dict:
         return {"model": "mistral-large-latest", "temperature": 0}
+
+    @property
+    def tool_choice_value(self) -> Optional[str]:
+        """Value to use for tool choice when used in tests."""
+        return "any"

libs/partners/together/tests/integration_tests/test_chat_models_standard.py

@@ -1,6 +1,6 @@
 """Standard LangChain interface tests"""
 
-from typing import Type
+from typing import Optional, Type
 
 import pytest
 from langchain_core.language_models import BaseChatModel
@@ -28,9 +28,10 @@ def chat_model_params(self) -> dict:
             "rate_limiter": rate_limiter,
         }
 
-    @pytest.mark.xfail(reason=("May not call a tool."))
-    def test_tool_calling_with_no_arguments(self, model: BaseChatModel) -> None:
-        super().test_tool_calling_with_no_arguments(model)
+    @property
+    def tool_choice_value(self) -> Optional[str]:
+        """Value to use for tool choice when used in tests."""
+        return "tool_name"
 
     @pytest.mark.xfail(reason="Not yet supported.")
     def test_usage_metadata_streaming(self, model: BaseChatModel) -> None:

libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py

@@ -170,7 +170,11 @@ def test_stop_sequence(self, model: BaseChatModel) -> None:
     def test_tool_calling(self, model: BaseChatModel) -> None:
         if not self.has_tool_calling:
             pytest.skip("Test requires tool calling.")
-        model_with_tools = model.bind_tools([magic_function])
+        if self.tool_choice_value == "tool_name":
+            tool_choice: Optional[str] = "magic_function"
+        else:
+            tool_choice = self.tool_choice_value
+        model_with_tools = model.bind_tools([magic_function], tool_choice=tool_choice)
 
         # Test invoke
         query = "What is the value of magic_function(3)? Use the tool."
@@ -188,7 +192,13 @@ def test_tool_calling_with_no_arguments(self, model: BaseChatModel) -> None:
         if not self.has_tool_calling:
             pytest.skip("Test requires tool calling.")
 
-        model_with_tools = model.bind_tools([magic_function_no_args])
+        if self.tool_choice_value == "tool_name":
+            tool_choice: Optional[str] = "magic_function_no_args"
+        else:
+            tool_choice = self.tool_choice_value
+        model_with_tools = model.bind_tools(
+            [magic_function_no_args], tool_choice=tool_choice
+        )
         query = "What is the value of magic_function()? Use the tool."
         result = model_with_tools.invoke(query)
         _validate_tool_call_message_no_args(result)
@@ -212,7 +222,11 @@ def test_bind_runnables_as_tools(self, model: BaseChatModel) -> None:
             name="greeting_generator",
             description="Generate a greeting in a particular style of speaking.",
         )
-        model_with_tools = model.bind_tools([tool_])
+        if self.tool_choice_value == "tool_name":
+            tool_choice: Optional[str] = "greeting_generator"
+        else:
+            tool_choice = self.tool_choice_value
+        model_with_tools = model.bind_tools([tool_], tool_choice=tool_choice)
         query = "Using the tool, generate a Pirate greeting."
         result = model_with_tools.invoke(query)
         assert isinstance(result, AIMessage)

libs/standard-tests/langchain_standard_tests/unit_tests/chat_models.py

@@ -96,6 +96,11 @@ def model(self) -> BaseChatModel:
     def has_tool_calling(self) -> bool:
         return self.chat_model_class.bind_tools is not BaseChatModel.bind_tools
 
+    @property
+    def tool_choice_value(self) -> Optional[str]:
+        """Value to use for tool choice when used in tests."""
+        return None
+
     @property
     def has_structured_output(self) -> bool:
         return (