aws[patch]: support snake_case arguments in tools in ChatBedrockConverse (#441)

Following #437

Author: ccurme

libs/aws/langchain_aws/chat_models/bedrock_converse.py

@@ -592,7 +592,7 @@ def _generate(
         logger.debug(f"input message to bedrock: {bedrock_messages}")
         logger.debug(f"System message to bedrock: {system}")
         params = self._converse_params(
-            stop=stop, **_snake_to_camel_keys(kwargs, excluded_keys={"inputSchema", "function"})
+            stop=stop, **_snake_to_camel_keys(kwargs, excluded_keys={"inputSchema", "properties"})
         )
         logger.debug(f"Input params: {params}")
         logger.info("Using Bedrock Converse API to generate response")
@@ -613,7 +613,7 @@ def _stream(
     ) -> Iterator[ChatGenerationChunk]:
         bedrock_messages, system = _messages_to_bedrock(messages)
         params = self._converse_params(
-            stop=stop, **_snake_to_camel_keys(kwargs, excluded_keys={"inputSchema", "function"})
+            stop=stop, **_snake_to_camel_keys(kwargs, excluded_keys={"inputSchema", "properties"})
         )
         response = self.client.converse_stream(
             messages=bedrock_messages, system=system, **params

libs/aws/tests/integration_tests/chat_models/test_bedrock_converse.py

@@ -1,11 +1,11 @@
 """Standard LangChain interface tests"""
 
-from typing import Literal, Type
+from typing import Literal, Optional, Type
 
 import pytest
 from langchain_core.exceptions import OutputParserException
 from langchain_core.language_models import BaseChatModel
-from langchain_core.messages import HumanMessage
+from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage
 from langchain_core.tools import BaseTool
 from langchain_tests.integration_tests import ChatModelIntegrationTests
 from pydantic import BaseModel, Field
@@ -181,6 +181,30 @@ def test_structured_output_snake_case() -> None:
         assert isinstance(chunk, ClassifyQuery)
 
 
+def test_tool_calling_snake_case() -> None:
+    model = ChatBedrockConverse(model="anthropic.claude-3-sonnet-20240229-v1:0")
+
+    def classify_query(query_type: Literal["cat", "dog"]) -> None:
+        pass
+
+    chat = model.bind_tools([classify_query], tool_choice="any")
+    response = chat.invoke("How big are cats?")
+    assert isinstance(response, AIMessage)
+    assert len(response.tool_calls) == 1
+    tool_call = response.tool_calls[0]
+    assert tool_call["name"] == "classify_query"
+    assert tool_call["args"] == {"query_type": "cat"}
+
+    full = None
+    for chunk in chat.stream("How big are cats?"):
+        full = chunk if full is None else full + chunk  # type: ignore[assignment]
+    assert isinstance(full, AIMessageChunk)
+    assert len(full.tool_calls) == 1
+    tool_call = full.tool_calls[0]
+    assert tool_call["name"] == "classify_query"
+    assert tool_call["args"] == {"query_type": "cat"}
+
+
 def test_structured_output_streaming() -> None:
     model = ChatBedrockConverse(
         model="anthropic.claude-3-sonnet-20240229-v1:0", temperature=0