core, anthropic[patch]: support streaming tool calls when function has no arguments (#23915)

resolves https://github.com/langchain-ai/langchain/issues/23911

When an AIMessageChunk is instantiated, we attempt to parse tool calls
off of the tool_call_chunks.

Here we add a special-case to this parsing, where `""` will be parsed as
`{}`.

This is a reaction to how Anthropic streams tool calls in the case where
a function has no arguments:
```
{'id': 'toolu_01J8CgKcuUVrMqfTQWPYh64r', 'input': {}, 'name': 'magic_function', 'type': 'tool_use', 'index': 1}
{'partial_json': '', 'type': 'tool_use', 'index': 1}
```
The `partial_json` does not accumulate to a valid json string-- most
other providers tend to emit `"{}"` in this case.

libs/core/langchain_core/messages/ai.py

@@ -241,7 +241,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
         invalid_tool_calls = []
         for chunk in values["tool_call_chunks"]:
             try:
-                args_ = parse_partial_json(chunk["args"])
+                args_ = parse_partial_json(chunk["args"]) if chunk["args"] != "" else {}
                 if isinstance(args_, dict):
                     tool_calls.append(
                         ToolCall(

libs/core/tests/unit_tests/test_messages.py

@@ -121,6 +121,12 @@ def test_message_chunks() -> None:
     assert ai_msg_chunk + tool_calls_msg_chunk == tool_calls_msg_chunk
     assert tool_calls_msg_chunk + ai_msg_chunk == tool_calls_msg_chunk
 
+    ai_msg_chunk = AIMessageChunk(
+        content="",
+        tool_call_chunks=[ToolCallChunk(name="tool1", args="", id="1", index=0)],
+    )
+    assert ai_msg_chunk.tool_calls == [ToolCall(name="tool1", args={}, id="1")]
+
     # Test token usage
     left = AIMessageChunk(
         content="",

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

@@ -34,6 +34,12 @@ class TestGroqMixtral(BaseTestGroq):
     def test_structured_output(self, model: BaseChatModel) -> None:
         super().test_structured_output(model)
 
+    @pytest.mark.xfail(
+        reason=("May pass arguments: {'properties': {}, 'type': 'object'}")
+    )
+    def test_tool_calling_with_no_arguments(self, model: BaseChatModel) -> None:
+        super().test_tool_calling_with_no_arguments(model)
+
 
 class TestGroqLlama(BaseTestGroq):
     @property

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

@@ -2,6 +2,7 @@
 
 from typing import Type
 
+import pytest
 from langchain_core.language_models import BaseChatModel
 from langchain_standard_tests.integration_tests import (  # type: ignore[import-not-found]
     ChatModelIntegrationTests,  # type: ignore[import-not-found]
@@ -18,3 +19,7 @@ class TestTogetherStandard(ChatModelIntegrationTests):
     @property
     def chat_model_params(self) -> dict:
         return {"model": "mistralai/Mistral-7B-Instruct-v0.1"}
+
+    @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)

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

@@ -8,6 +8,7 @@ from langchain_core.language_models import BaseChatModel
 from langchain_core.messages import (
     AIMessage,
     AIMessageChunk,
+    BaseMessage,
     BaseMessageChunk,
     HumanMessage,
     SystemMessage,
@@ -28,7 +29,13 @@ def magic_function(input: int) -> int:
     return input + 2
 
 
-def _validate_tool_call_message(message: AIMessage) -> None:
+@tool
+def magic_function_no_args() -> int:
+    """Calculates a magic function."""
+    return 5
+
+
+def _validate_tool_call_message(message: BaseMessage) -> None:
     assert isinstance(message, AIMessage)
     assert len(message.tool_calls) == 1
     tool_call = message.tool_calls[0]
@@ -37,6 +44,15 @@ def _validate_tool_call_message(message: AIMessage) -> None:
     assert tool_call["id"] is not None
 
 
+def _validate_tool_call_message_no_args(message: BaseMessage) -> None:
+    assert isinstance(message, AIMessage)
+    assert len(message.tool_calls) == 1
+    tool_call = message.tool_calls[0]
+    assert tool_call["name"] == "magic_function_no_args"
+    assert tool_call["args"] == {}
+    assert tool_call["id"] is not None
+
+
 class ChatModelIntegrationTests(ChatModelTests):
     def test_invoke(self, model: BaseChatModel) -> None:
         result = model.invoke("Hello")
@@ -131,7 +147,6 @@ class ChatModelIntegrationTests(ChatModelTests):
         # Test invoke
         query = "What is the value of magic_function(3)? Use the tool."
         result = model_with_tools.invoke(query)
-        assert isinstance(result, AIMessage)
         _validate_tool_call_message(result)
 
         # Test stream
@@ -141,6 +156,21 @@ class ChatModelIntegrationTests(ChatModelTests):
         assert isinstance(full, AIMessage)
         _validate_tool_call_message(full)
 
+    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])
+        query = "What is the value of magic_function()? Use the tool."
+        result = model_with_tools.invoke(query)
+        _validate_tool_call_message_no_args(result)
+
+        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)
+        _validate_tool_call_message_no_args(full)
+
     def test_structured_output(self, model: BaseChatModel) -> None:
         if not self.has_tool_calling:
             pytest.skip("Test requires tool calling.")