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fix(bedrock): parse Nexus Claude text tool calls in converse responses
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GitHub Copilot
parent
d04373f4ce
commit
26f3216db8
2 changed files with 410 additions and 10 deletions
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@ -4,9 +4,10 @@ Translating between OpenAI's `/chat/completion` format and Amazon's `/converse`
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import copy
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import json
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import re
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import time
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import types
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from typing import List, Literal, Optional, Tuple, Union, cast, overload
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from typing import Any, List, Literal, Optional, Tuple, Union, cast, overload
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import httpx
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@ -1777,6 +1778,131 @@ class AmazonConverseConfig(BaseConfig):
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tool_set.add(_name)
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return list(tool_set)
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@staticmethod
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def _extract_xml_tag_text(content: str, tag_name: str) -> Optional[str]:
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match = re.search(
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rf"<{tag_name}>\s*(.*?)\s*</{tag_name}>",
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content,
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flags=re.DOTALL | re.IGNORECASE,
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)
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if match is None:
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return None
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return match.group(1).strip()
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@staticmethod
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def _parse_json_string_if_possible(value: str) -> Any:
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value = value.strip()
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if value == "":
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return ""
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try:
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return json.loads(value)
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except Exception:
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return value
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@staticmethod
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def _resolve_nexus_tool_name(
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raw_name: Optional[str], tool_call_names: set[str]
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) -> Optional[str]:
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if raw_name is None:
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return None
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normalized_name = raw_name.strip()
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if normalized_name in tool_call_names:
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return normalized_name
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short_name = normalized_name.split(".")[-1]
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if short_name in tool_call_names:
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return short_name
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return None
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def _nexus_text_tool_call_to_openai_tool_call(
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self,
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content: str,
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tools: Optional[
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Union[List[ToolBlock], List[OpenAIChatCompletionToolParam]]
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] = None,
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) -> Optional[ChatCompletionMessageToolCall]:
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tool_call_names = set(self.get_tool_call_names(tools))
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if content.strip() == "" or not tool_call_names:
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return None
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function_match = re.search(
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r"<function>\s*(.*?)\s*</function>",
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content,
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flags=re.DOTALL | re.IGNORECASE,
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)
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if function_match is not None:
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raw_function_content = function_match.group(1)
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parameter_matches = re.findall(
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r'<parameter\s+name=["\']([^"\']+)["\']\s*>(.*?)</parameter>',
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raw_function_content,
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flags=re.DOTALL | re.IGNORECASE,
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)
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function_name: Optional[str] = None
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function_args: dict[str, Any] = {}
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for key, value in parameter_matches:
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normalized_key = key.strip()
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parsed_value = self._parse_json_string_if_possible(value)
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if normalized_key in {"name", "command", "tool_name", "tool"}:
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function_name = str(parsed_value).strip()
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else:
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function_args[normalized_key] = parsed_value
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resolved_function_name = self._resolve_nexus_tool_name(
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function_name, tool_call_names
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)
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if resolved_function_name is None:
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return None
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return ChatCompletionMessageToolCall(
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function=Function(
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name=resolved_function_name,
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arguments=json.dumps(function_args),
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)
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)
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tool_use_match = re.search(
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r"<tool_use>\s*(.*?)\s*</tool_use>",
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content,
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flags=re.DOTALL | re.IGNORECASE,
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)
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if tool_use_match is None:
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return None
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raw_tool_use_content = tool_use_match.group(1).strip()
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raw_tool_name = self._extract_xml_tag_text(raw_tool_use_content, "tool_name")
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raw_tool_input = self._extract_xml_tag_text(raw_tool_use_content, "input")
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if raw_tool_name is None:
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tool_use_lines = [
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line.strip() for line in raw_tool_use_content.splitlines() if line.strip()
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]
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if len(tool_use_lines) == 0:
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return None
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raw_tool_name = tool_use_lines[0]
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raw_tool_input = "\n".join(tool_use_lines[1:]).strip()
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resolved_tool_name = self._resolve_nexus_tool_name(
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raw_tool_name, tool_call_names
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)
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if resolved_tool_name is None:
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return None
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parsed_tool_input: Any = {}
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if raw_tool_input:
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parsed_tool_input = self._parse_json_string_if_possible(raw_tool_input)
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if not isinstance(parsed_tool_input, dict):
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return None
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return ChatCompletionMessageToolCall(
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function=Function(
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name=resolved_tool_name,
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arguments=json.dumps(parsed_tool_input),
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)
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)
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def apply_tool_call_transformation_if_needed(
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self,
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message: Message,
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@ -1795,6 +1921,7 @@ class AmazonConverseConfig(BaseConfig):
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return message, returned_finish_reason
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if message.content is not None:
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parsed_tool_call: Optional[ChatCompletionMessageToolCall] = None
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try:
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tool_call_names = self.get_tool_call_names(tools)
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json_content = json.loads(message.content)
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@ -1802,15 +1929,19 @@ class AmazonConverseConfig(BaseConfig):
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json_content.get("type") == "function"
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and json_content.get("name") in tool_call_names
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):
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tool_calls = [
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ChatCompletionMessageToolCall(function=Function(**json_content))
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]
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message.tool_calls = tool_calls
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message.content = None
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returned_finish_reason = "tool_calls"
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parsed_tool_call = ChatCompletionMessageToolCall(
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function=Function(**json_content)
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)
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except Exception:
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pass
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parsed_tool_call = self._nexus_text_tool_call_to_openai_tool_call(
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content=message.content,
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tools=tools,
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)
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if parsed_tool_call is not None:
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message.tool_calls = [parsed_tool_call]
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message.content = None
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returned_finish_reason = "tool_calls"
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return message, returned_finish_reason
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@ -2094,6 +2225,30 @@ class AmazonConverseConfig(BaseConfig):
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## HANDLE TOOL CALLS
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_message = Message(**chat_completion_message)
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initial_finish_reason = map_finish_reason(completion_response["stopReason"])
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response_tools = optional_params.get("tools")
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if response_tools is None:
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logging_details = (
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getattr(logging_obj, "model_call_details", None)
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if logging_obj is not None
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else None
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)
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if isinstance(logging_details, dict):
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response_tools = logging_details.get("tools")
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if response_tools is None:
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response_data: Optional[dict] = None
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if isinstance(data, dict):
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response_data = data
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elif isinstance(data, str):
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try:
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parsed_data = json.loads(data)
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if isinstance(parsed_data, dict):
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response_data = parsed_data
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except Exception:
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pass
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if response_data is not None:
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response_tools = (response_data.get("toolConfig") or {}).get("tools")
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# When json_mode filtered out all synthetic tool calls the response
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# is plain content, not a pending tool invocation. Fix finish_reason
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@ -2106,7 +2261,7 @@ class AmazonConverseConfig(BaseConfig):
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returned_finish_reason,
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) = self.apply_tool_call_transformation_if_needed(
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message=_message,
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tools=optional_params.get("tools"),
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tools=response_tools,
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initial_finish_reason=initial_finish_reason,
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)
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model_response.choices = [
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@ -206,6 +206,251 @@ def test_apply_tool_call_transformation_if_needed():
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)
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def test_apply_tool_call_transformation_if_needed_with_nexus_function_text():
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from litellm.types.utils import Message
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config = AmazonConverseConfig()
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tools = [
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{
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"type": "function",
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"function": {
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"name": "read_file",
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"parameters": {
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"type": "object",
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"properties": {
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"path": {"type": "string"},
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},
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"required": ["path"],
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},
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},
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}
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]
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message = Message(
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role="assistant",
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content='\n<function>\n<parameter name="name">read_file</parameter>\n<parameter name="path">README.md</parameter>\n</function>',
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)
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transformed_message, transformed_finish_reason = (
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config.apply_tool_call_transformation_if_needed(
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message=message,
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tools=tools,
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initial_finish_reason="stop",
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)
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)
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assert transformed_message.content is None
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assert transformed_finish_reason == "tool_calls"
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assert transformed_message.tool_calls is not None
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assert transformed_message.tool_calls[0].function.name == "read_file"
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assert transformed_message.tool_calls[0].function.arguments == json.dumps(
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{"path": "README.md"}
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)
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def test_apply_tool_call_transformation_if_needed_with_nexus_tool_use_line_format():
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from litellm.types.utils import Message
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config = AmazonConverseConfig()
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tools = [
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{
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"type": "function",
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"function": {
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"name": "read_file",
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"parameters": {
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"type": "object",
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"properties": {
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"path": {"type": "string"},
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},
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"required": ["path"],
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},
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},
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}
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]
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message = Message(
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role="assistant",
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content='<tool_use>desktop-commander.read_file\n{"path":"README.md"}</tool_use>',
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)
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transformed_message, transformed_finish_reason = (
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config.apply_tool_call_transformation_if_needed(
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message=message,
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tools=tools,
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initial_finish_reason="stop",
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)
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)
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assert transformed_message.content is None
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assert transformed_finish_reason == "tool_calls"
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assert transformed_message.tool_calls is not None
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assert transformed_message.tool_calls[0].function.name == "read_file"
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assert transformed_message.tool_calls[0].function.arguments == json.dumps(
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{"path": "README.md"}
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)
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def test_apply_tool_call_transformation_if_needed_with_nexus_tool_use_xml_format():
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from litellm.types.utils import Message
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config = AmazonConverseConfig()
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tools = [
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{
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"type": "function",
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"function": {
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"name": "read_file",
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"parameters": {
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"type": "object",
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"properties": {
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"path": {"type": "string"},
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},
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"required": ["path"],
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},
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},
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}
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]
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message = Message(
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role="assistant",
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content='<tool_use><server_name>desktop-commander</server_name><tool_name>read_file</tool_name><input>{"path":"README.md"}</input></tool_use>',
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)
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transformed_message, transformed_finish_reason = (
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config.apply_tool_call_transformation_if_needed(
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message=message,
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tools=tools,
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initial_finish_reason="stop",
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)
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)
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assert transformed_message.content is None
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assert transformed_finish_reason == "tool_calls"
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assert transformed_message.tool_calls is not None
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assert transformed_message.tool_calls[0].function.name == "read_file"
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assert transformed_message.tool_calls[0].function.arguments == json.dumps(
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{"path": "README.md"}
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)
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def test_apply_tool_call_transformation_if_needed_ignores_unknown_nexus_tool_name():
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from litellm.types.utils import Message
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config = AmazonConverseConfig()
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tools = [
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{
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"type": "function",
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"function": {
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"name": "write_file",
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"parameters": {
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"type": "object",
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"properties": {
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"path": {"type": "string"},
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},
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"required": ["path"],
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},
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},
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}
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]
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message = Message(
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role="assistant",
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content='\n<function>\n<parameter name="name">read_file</parameter>\n<parameter name="path">README.md</parameter>\n</function>',
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)
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transformed_message, transformed_finish_reason = (
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config.apply_tool_call_transformation_if_needed(
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message=message,
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tools=tools,
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initial_finish_reason="stop",
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)
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)
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assert transformed_message.content == message.content
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assert transformed_finish_reason == "stop"
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assert transformed_message.tool_calls is None
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def test_transform_response_uses_logging_tools_when_optional_tools_missing():
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import httpx
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from litellm.types.utils import ModelResponse
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response_json = {
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"output": {
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"message": {
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"role": "assistant",
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"content": [
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{
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"text": '\n<function>\n<parameter name="name">read_file</parameter>\n<parameter name="path">README.md</parameter>\n</function>'
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}
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],
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}
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},
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"stopReason": "end_turn",
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"usage": {
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"inputTokens": 10,
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"outputTokens": 5,
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"totalTokens": 15,
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},
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}
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response = httpx.Response(200, json=response_json)
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config = AmazonConverseConfig()
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logging_obj = MagicMock()
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logging_obj.model_call_details = {
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"tools": [
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{
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"type": "function",
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"function": {
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"name": "read_file",
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"parameters": {
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"type": "object",
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"properties": {
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"path": {"type": "string"},
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}
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},
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},
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}
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]
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}
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request_data = {
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"toolConfig": {
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"tools": [
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{
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"toolSpec": {
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"name": "read_file",
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"inputSchema": {
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"json": {
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"type": "object",
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"properties": {
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"path": {"type": "string"},
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},
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}
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},
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}
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}
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]
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}
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}
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result = config._transform_response(
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model="bedrock/converse/claude-opus-4.6",
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response=response,
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model_response=ModelResponse(),
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stream=False,
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logging_obj=logging_obj,
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optional_params={},
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api_key=None,
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data=json.dumps(request_data),
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messages=[],
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encoding=None,
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)
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assert result.choices[0].finish_reason == "tool_calls"
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assert result.choices[0].message.content is None
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assert result.choices[0].message.tool_calls is not None
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assert result.choices[0].message.tool_calls[0].function.name == "read_file"
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assert result.choices[0].message.tool_calls[0].function.arguments == json.dumps(
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{"path": "README.md"}
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)
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def test_transform_tool_call_with_cache_control():
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from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig
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