phoenix/litellm-mirror
0
0
Fork 1
mirror of https://github.com/BerriAI/litellm.git synced 2025-04-27 11:43:54 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

add test_function_calling_with_tool_response to base llm tests

Browse source
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
Download patch file Download diff file Expand all files Collapse all files

40
litellm/litellm_core_utils/prompt_templates/factory.py
View file

@ -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
)

13
litellm/types/llms/bedrock.py
View file

@ -125,8 +125,19 @@ class ConverseResponseBlock(TypedDict):
usage: ConverseTokenUsageBlock
class ToolJsonArgsBlock(TypedDict, total=False):
type: str
description: str
class ToolJsonSchemaBlock(TypedDict, total=False):
type: Literal["object"]
properties: dict
required: List[str]
class ToolInputSchemaBlock(TypedDict):
json: Optional[dict]
json: Optional[ToolJsonSchemaBlock]
class ToolSpecBlock(TypedDict, total=False):

2
tests/llm_translation/base_llm_unit_tests.py
View file

@ -1037,6 +1037,7 @@ class BaseLLMChatTest(ABC):
def test_function_calling_with_tool_response(self):
from litellm.utils import supports_function_calling
from litellm import completion
litellm._turn_on_debug()
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
@ -1056,6 +1057,7 @@ class BaseLLMChatTest(ABC):
"name": "get_weather",
"description": "Get the weather in a city",
"parameters": {
"$id": "https://some/internal/name",
"type": "object",
"properties": {
"city": {

22
tests/llm_translation/test_bedrock_completion.py
View file

@ -2226,6 +2226,28 @@ class TestBedrockConverseChatNormal(BaseLLMChatTest):
"""
pass
class TestBedrockConverseNovaTestSuite(BaseLLMChatTest):
def get_base_completion_call_args(self) -> dict:
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
litellm.add_known_models()
return {
"model": "bedrock/us.amazon.nova-lite-v1:0",
"aws_region_name": "us-east-1",
}
def test_tool_call_no_arguments(self, tool_call_no_arguments):
"""Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833"""
pass
def test_multilingual_requests(self):
"""
Bedrock API raises a 400 BadRequest error when the request contains invalid utf-8 sequences.
Todo: if litellm.modify_params is True ensure it's a valid utf-8 sequence
"""
pass
class TestBedrockRerank(BaseLLMRerankTest):
def get_custom_llm_provider(self) -> litellm.LlmProviders: