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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,139 @@ | ||
| """Methods for creating function specs in the style of Bedrock Functions | ||
| for supported model providers""" | ||
|
|
||
| import json | ||
| from typing import ( | ||
| Any, | ||
| Callable, | ||
| Dict, | ||
| List, | ||
| Literal, | ||
| Type, | ||
| Union, | ||
| ) | ||
|
|
||
| from langchain_core.pydantic_v1 import BaseModel | ||
| from langchain_core.tools import BaseTool | ||
| from langchain_core.utils.function_calling import convert_to_openai_tool | ||
| from typing_extensions import TypedDict | ||
|
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||
| PYTHON_TO_JSON_TYPES = { | ||
| "str": "string", | ||
| "int": "integer", | ||
| "float": "number", | ||
| "bool": "boolean", | ||
| } | ||
|
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| SYSTEM_PROMPT_FORMAT = """In this environment you have access to a set of tools you can use to answer the user's question. | ||
| You may call them like this: | ||
| <function_calls> | ||
| <invoke> | ||
| <tool_name>$TOOL_NAME</tool_name> | ||
| <parameters> | ||
| <$PARAMETER_NAME>$PARAMETER_VALUE</$PARAMETER_NAME> | ||
| ... | ||
| </parameters> | ||
| </invoke> | ||
| </function_calls> | ||
| Here are the tools available: | ||
| <tools> | ||
| {formatted_tools} | ||
| </tools>""" # noqa: E501 | ||
|
|
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| TOOL_FORMAT = """<tool_description> | ||
| <tool_name>{tool_name}</tool_name> | ||
| <description>{tool_description}</description> | ||
| <parameters> | ||
| {formatted_parameters} | ||
| </parameters> | ||
| </tool_description>""" | ||
|
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| TOOL_PARAMETER_FORMAT = """<parameter> | ||
| <name>{parameter_name}</name> | ||
| <type>{parameter_type}</type> | ||
| <description>{parameter_description}</description> | ||
| </parameter>""" | ||
|
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|
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| class AnthropicTool(TypedDict): | ||
| name: str | ||
| description: str | ||
| input_schema: Dict[str, Any] | ||
|
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|
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| def _get_type(parameter: Dict[str, Any]) -> str: | ||
| if "type" in parameter: | ||
| return parameter["type"] | ||
| if "anyOf" in parameter: | ||
| return json.dumps({"anyOf": parameter["anyOf"]}) | ||
| if "allOf" in parameter: | ||
| return json.dumps({"allOf": parameter["allOf"]}) | ||
| return json.dumps(parameter) | ||
|
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|
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| def get_system_message(tools: List[AnthropicTool]) -> str: | ||
| tools_data: List[Dict] = [ | ||
| { | ||
| "tool_name": tool["name"], | ||
| "tool_description": tool["description"], | ||
| "formatted_parameters": "\n".join( | ||
| [ | ||
| TOOL_PARAMETER_FORMAT.format( | ||
| parameter_name=name, | ||
| parameter_type=_get_type(parameter), | ||
| parameter_description=parameter.get("description"), | ||
| ) | ||
| for name, parameter in tool["input_schema"]["properties"].items() | ||
| ] | ||
| ), | ||
| } | ||
| for tool in tools | ||
| ] | ||
| tools_formatted = "\n".join( | ||
| [ | ||
| TOOL_FORMAT.format( | ||
| tool_name=tool["tool_name"], | ||
| tool_description=tool["tool_description"], | ||
| formatted_parameters=tool["formatted_parameters"], | ||
| ) | ||
| for tool in tools_data | ||
| ] | ||
| ) | ||
| return SYSTEM_PROMPT_FORMAT.format(formatted_tools=tools_formatted) | ||
|
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||
|
|
||
| class FunctionDescription(TypedDict): | ||
| """Representation of a callable function to send to an LLM.""" | ||
|
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| name: str | ||
| """The name of the function.""" | ||
| description: str | ||
| """A description of the function.""" | ||
| parameters: dict | ||
| """The parameters of the function.""" | ||
|
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|
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| class ToolDescription(TypedDict): | ||
| """Representation of a callable function to the OpenAI API.""" | ||
|
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| type: Literal["function"] | ||
| function: FunctionDescription | ||
|
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|
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| def convert_to_anthropic_tool( | ||
| tool: Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool], | ||
| ) -> AnthropicTool: | ||
| # already in Anthropic tool format | ||
| if isinstance(tool, dict) and all( | ||
| k in tool for k in ("name", "description", "input_schema") | ||
| ): | ||
| return AnthropicTool(tool) # type: ignore | ||
| else: | ||
| formatted = convert_to_openai_tool(tool)["function"] | ||
| return AnthropicTool( | ||
| name=formatted["name"], | ||
| description=formatted["description"], | ||
| input_schema=formatted["parameters"], | ||
| ) |
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