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add test_function_calling_with_tool_response to base llm tests

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This commit is contained in:
Ishaan Jaff 2025-04-16 09:01:26 -07:00
parent a743b6fc1f
commit aa1e13f65b
4 changed files with 62 additions and 15 deletions
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40
litellm/litellm_core_utils/prompt_templates/factory.py
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@ -15,6 +15,12 @@ from litellm import verbose_logger
from litellm.llms.custom_httpx.http_handler import HTTPHandler, get_async_httpx_client
from litellm.types.llms.anthropic import *
from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock
from litellm.types.llms.bedrock import ToolBlock as BedrockToolBlock
from litellm.types.llms.bedrock import (
ToolInputSchemaBlock as BedrockToolInputSchemaBlock,
)
from litellm.types.llms.bedrock import ToolJsonSchemaBlock as BedrockToolJsonSchemaBlock
from litellm.types.llms.bedrock import ToolSpecBlock as BedrockToolSpecBlock
from litellm.types.llms.custom_http import httpxSpecialProvider
from litellm.types.llms.ollama import OllamaVisionModelObject
from litellm.types.llms.openai import (
@ -1041,10 +1047,10 @@ def convert_to_gemini_tool_call_invoke(
if tool_calls is not None:
for tool in tool_calls:
if "function" in tool:
gemini_function_call: Optional[
VertexFunctionCall
] = _gemini_tool_call_invoke_helper(
function_call_params=tool["function"]
gemini_function_call: Optional[VertexFunctionCall] = (
_gemini_tool_call_invoke_helper(
function_call_params=tool["function"]
)
)
if gemini_function_call is not None:
_parts_list.append(
@ -1139,7 +1145,7 @@ def convert_to_gemini_tool_call_result(
def convert_to_anthropic_tool_result(
message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage]
message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage],
) -> AnthropicMessagesToolResultParam:
"""
OpenAI message with a tool result looks like:
@ -1449,9 +1455,9 @@ def anthropic_messages_pt( # noqa: PLR0915
)
if "cache_control" in _content_element:
_anthropic_content_element[
"cache_control"
] = _content_element["cache_control"]
_anthropic_content_element["cache_control"] = (
_content_element["cache_control"]
)
user_content.append(_anthropic_content_element)
elif m.get("type", "") == "text":
m = cast(ChatCompletionTextObject, m)
@ -1502,9 +1508,9 @@ def anthropic_messages_pt( # noqa: PLR0915
)
if "cache_control" in _content_element:
_anthropic_content_text_element[
"cache_control"
] = _content_element["cache_control"]
_anthropic_content_text_element["cache_control"] = (
_content_element["cache_control"]
)
user_content.append(_anthropic_content_text_element)
@ -2491,7 +2497,7 @@ def _convert_to_bedrock_tool_call_invoke(
def _convert_to_bedrock_tool_call_result(
message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage]
message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage],
) -> BedrockContentBlock:
"""
OpenAI message with a tool result looks like:
@ -2664,7 +2670,7 @@ def get_user_message_block_or_continue_message(
def return_assistant_continue_message(
assistant_continue_message: Optional[
Union[str, ChatCompletionAssistantMessage]
] = None
] = None,
) -> ChatCompletionAssistantMessage:
if assistant_continue_message and isinstance(assistant_continue_message, str):
return ChatCompletionAssistantMessage(
@ -3462,7 +3468,13 @@ def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]:
for _, value in defs_copy.items():
unpack_defs(value, defs_copy)
unpack_defs(parameters, defs_copy)
tool_input_schema = BedrockToolInputSchemaBlock(json=parameters)
tool_input_schema = BedrockToolInputSchemaBlock(
json=BedrockToolJsonSchemaBlock(
type=parameters.get("type", ""),
properties=parameters.get("properties", {}),
required=parameters.get("required", []),
)
)
tool_spec = BedrockToolSpecBlock(
inputSchema=tool_input_schema, name=name, description=description
)