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commented
6 days ago Hey @jordan-BAIC! I'm here to help you with any bugs, questions, or contributions you have for the repository. Let's tackle this together!
To resolve the issue where the tool_choice parameter is ignored in the FunctionCallingProgram class, leading to a ValueError when no tool calls are made, you should ensure that the tool_choice parameter is properly processed using the resolve_tool_choice function. This function checks if the tool name exists in the provided tools and validates the tool_choice.
Here is the relevant code snippet for the resolve_tool_choice function:
from typing import Union, List, Optional
def resolve_tool_choice(
tools: Optional[List[dict]] = None, tool_choice: str = "none"
) -> Union[str, dict]:
"""Resolve tool choice.
Check if tool_name exists in tools.
Note that unlike in OpenAI specification, 'auto' will ALWAYS choose the tool for you.
Set to 'none' explicitly if do not wish to use tool.
"""
valid_tool_choices = ["none", "auto"] + [t["function"]["name"] for t in tools or []]
if tool_choice not in valid_tool_choices:
raise ValueError(
f"{tool_choice} is not a valid tool_choice. Must be one of {valid_tool_choices}"
)
return tool_choiceMake sure that the FunctionCallingProgram class uses this function to handle the tool_choice parameter correctly. For example:
class FunctionCallingProgram:
def __init__(self, tools: Optional[List[dict]] = None, tool_choice: str = "none"):
self.tool_choice = resolve_tool_choice(tools, tool_choice)
# other initialization code
def execute(self):
if self.tool_choice == "none":
raise ValueError("No tool calls made.")
# other execution codeAdditionally, ensure that the FunctionCallingProgram class properly integrates with the call_function and acall_function methods to handle tool calls. Here is an example of how these methods can be used:
from llama_index.legacy.tools import BaseTool, ToolOutput
from llama_index.legacy.llms.openai_utils import OpenAIToolCall
from llama_index.legacy.core.llms.types import ChatMessage, MessageRole
def call_function(
tools: List[BaseTool],
tool_call: OpenAIToolCall,
verbose: bool = False,
) -> Tuple[ChatMessage, ToolOutput]:
"""Call a function and return the output as a string."""
# validations to get passed mypy
assert tool_call.id is not None
assert tool_call.function is not None
assert tool_call.function.name is not None
assert tool_call.function.arguments is not None
id_ = tool_call.id
function_call = tool_call.function
name = tool_call.function.name
arguments_str = tool_call.function.arguments
if verbose:
print("=== Calling Function ===")
print(f"Calling function: {name} with args: {arguments_str}")
tool = get_function_by_name(tools, name)
argument_dict = json.loads(arguments_str)
# Call tool
# Use default error message
output = call_tool_with_error_handling(tool, argument_dict, error_message=None)
if verbose:
print(f"Got output: {output!s}")
print("========================\n")
return (
ChatMessage(
content=str(output),
role=MessageRole.TOOL,
additional_kwargs={
"name": name,
"tool_call_id": id_,
},
),
output,
)For asynchronous calls, you can use the acall_function method:
async def acall_function(
tools: List[BaseTool], tool_call: OpenAIToolCall, verbose: bool = False
) -> Tuple[ChatMessage, ToolOutput]:
"""Call a function and return the output as a string."""
# validations to get passed mypy
assert tool_call.id is not None
assert tool_call.function is not None
assert tool_call.function.name is not None
assert tool_call.function.arguments is not None
id_ = tool_call.id
function_call = tool_call.function
name = tool_call.function.name
arguments_str = tool_call.function.arguments
if verbose:
print("=== Calling Function ===")
print(f"Calling function: {name} with args: {arguments_str}")
tool = get_function_by_name(tools, name)
async_tool = adapt_to_async_tool(tool)
argument_dict = json.loads(arguments_str)
output = await async_tool.acall(**argument_dict)
if verbose:
print(f"Got output: {output!s}")
print("========================\n")
return (
ChatMessage(
content=str(output),
role=MessageRole.TOOL,
additional_kwargs={
"name": name,
"tool_call_id": id_,
},
),
output,
)By using the resolve_tool_choice function and ensuring proper integration with the call_function and acall_function methods, you can ensure that the tool_choice parameter is properly interpreted, which should prevent the ValueError when no tool calls are made [1][2][3][4].
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sabir012
Bug Description
The FunctionCallingProgram class takes in a tool_choice parameter but does not use it.
The use varies slightly for OpenAI and Anthropic models: https://platform.openai.com/docs/guides/function-calling https://docs.anthropic.com/en/docs/build-with-claude/tool-use
Version
0.10.37
Steps to Reproduce