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"""Different methods for rendering Tools to be passed to LLMs. | ||
Depending on the LLM you are using and the prompting strategy you are using, | ||
you may want Tools to be rendered in a different way. | ||
This module contains various ways to render tools. | ||
""" | ||
from langchain_core.tools import BaseTool | ||
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from langchain_community.utils.openai_functions import ( | ||
FunctionDescription, | ||
ToolDescription, | ||
convert_pydantic_to_openai_function, | ||
from langchain_community.tools.convert_to_openai import ( | ||
format_tool_to_openai_function, | ||
format_tool_to_openai_tool, | ||
) | ||
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def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription: | ||
"""Format tool into the OpenAI function API.""" | ||
if tool.args_schema: | ||
return convert_pydantic_to_openai_function( | ||
tool.args_schema, name=tool.name, description=tool.description | ||
) | ||
else: | ||
return { | ||
"name": tool.name, | ||
"description": tool.description, | ||
"parameters": { | ||
# This is a hack to get around the fact that some tools | ||
# do not expose an args_schema, and expect an argument | ||
# which is a string. | ||
# And Open AI does not support an array type for the | ||
# parameters. | ||
"properties": { | ||
"__arg1": {"title": "__arg1", "type": "string"}, | ||
}, | ||
"required": ["__arg1"], | ||
"type": "object", | ||
}, | ||
} | ||
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def format_tool_to_openai_tool(tool: BaseTool) -> ToolDescription: | ||
"""Format tool into the OpenAI function API.""" | ||
function = format_tool_to_openai_function(tool) | ||
return {"type": "function", "function": function} | ||
# backward compatibility | ||
def __getattr__(name): | ||
if name == "format_tool_to_openai_function": | ||
return format_tool_to_openai_function | ||
if name == "format_tool_to_openai_tool": | ||
return format_tool_to_openai_tool |