"""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,
Optional,
Union,
cast,
)
from langchain_core.messages import ToolCall
from langchain_core.output_parsers import BaseGenerationOutputParser
from langchain_core.outputs import ChatGeneration, Generation
from langchain_core.prompts.chat import AIMessage
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 langchain_core.utils.pydantic import TypeBaseModel
from typing_extensions import TypedDict
PYTHON_TO_JSON_TYPES = {
"str": "string",
"int": "integer",
"float": "number",
"bool": "boolean",
}
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
TOOL_FORMAT = """<tool_description>
<tool_name>{tool_name}</tool_name>
<description>{tool_description}</description>
<parameters>
{formatted_parameters}
</parameters>
</tool_description>"""
TOOL_PARAMETER_FORMAT = """<parameter>
<name>{parameter_name}</name>
<type>{parameter_type}</type>
<description>{parameter_description}</description>
</parameter>"""
def _tools_in_params(params: dict) -> bool:
return "tools" in params or (
"extra_body" in params and params["extra_body"].get("tools")
)
class _AnthropicToolUse(TypedDict):
type: Literal["tool_use"]
name: str
input: dict
id: str
def _lc_tool_calls_to_anthropic_tool_use_blocks(
tool_calls: List[ToolCall],
) -> List[_AnthropicToolUse]:
blocks = []
for tool_call in tool_calls:
blocks.append(
_AnthropicToolUse(
type="tool_use",
name=tool_call["name"],
input=tool_call["args"],
id=cast(str, tool_call["id"]),
)
)
return blocks
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)
[docs]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)
[docs]class FunctionDescription(TypedDict):
"""Representation of a callable function to send to an LLM."""
name: str
"""The name of the function."""
description: str
"""A description of the function."""
parameters: dict
"""The parameters of the function."""