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# Copyright 2025 the LlamaFactory team. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import json | |
import re | |
from abc import ABC, abstractmethod | |
from dataclasses import dataclass | |
from datetime import datetime | |
from typing import Any, NamedTuple, Union | |
from typing_extensions import override | |
class FunctionCall(NamedTuple): | |
name: str | |
arguments: str | |
DEFAULT_TOOL_PROMPT = ( | |
"You have access to the following tools:\n{tool_text}" | |
"Use the following format if using a tool:\n" | |
"```\n" | |
"Action: tool name (one of [{tool_names}])\n" | |
"Action Input: the input to the tool, in a JSON format representing the kwargs " | |
"""(e.g. ```{{"input": "hello world", "num_beams": 5}}```)\n""" | |
"```\n" | |
) | |
GLM4_TOOL_PROMPT = ( | |
"你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的," | |
"你的任务是针对用户的问题和要求提供适当的答复和支持。# 可用工具{tool_text}" | |
) | |
LLAMA3_TOOL_PROMPT = ( | |
"Cutting Knowledge Date: December 2023\nToday Date: {date}\n\n" | |
"You have access to the following functions. To call a function, please respond with JSON for a function call. " | |
"""Respond in the format {{"name": function name, "parameters": dictionary of argument name and its value}}. """ | |
"Do not use variables.\n\n{tool_text}" | |
) | |
QWEN_TOOL_PROMPT = ( | |
"\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n" | |
"You are provided with function signatures within <tools></tools> XML tags:\n<tools>{tool_text}" | |
"\n</tools>\n\nFor each function call, return a json object with function name and arguments within " | |
"""<tool_call></tool_call> XML tags:\n<tool_call>\n{{"name": <function-name>, """ | |
""""arguments": <args-json-object>}}\n</tool_call>""" | |
) | |
class ToolUtils(ABC): | |
"""Base class for tool utilities.""" | |
def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
r"""Generate the system message describing all the available tools.""" | |
... | |
def function_formatter(functions: list["FunctionCall"]) -> str: | |
r"""Generate the assistant message including all the tool calls.""" | |
... | |
def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
r"""Extract all the function calls from the assistant message. | |
It should be an inverse function of `function_formatter`. | |
""" | |
... | |
class DefaultToolUtils(ToolUtils): | |
r"""Default tool using template.""" | |
def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
tool_text = "" | |
tool_names = [] | |
for tool in tools: | |
param_text = "" | |
for name, param in tool["parameters"]["properties"].items(): | |
required, enum, items = "", "", "" | |
if name in tool["parameters"].get("required", []): | |
required = ", required" | |
if param.get("enum", None): | |
enum = ", should be one of [{}]".format(", ".join(param["enum"])) | |
if param.get("items", None): | |
items = ", where each item should be {}".format(param["items"].get("type", "")) | |
param_text += " - {name} ({type}{required}): {desc}{enum}{items}\n".format( | |
name=name, | |
type=param.get("type", ""), | |
required=required, | |
desc=param.get("description", ""), | |
enum=enum, | |
items=items, | |
) | |
tool_text += "> Tool Name: {name}\nTool Description: {desc}\nTool Args:\n{args}\n".format( | |
name=tool["name"], desc=tool.get("description", ""), args=param_text | |
) | |
tool_names.append(tool["name"]) | |
return DEFAULT_TOOL_PROMPT.format(tool_text=tool_text, tool_names=", ".join(tool_names)) | |
def function_formatter(functions: list["FunctionCall"]) -> str: | |
function_text = "" | |
for name, arguments in functions: | |
function_text += f"Action: {name}\nAction Input: {arguments}\n" | |
return function_text | |
def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
regex = re.compile(r"Action:\s*([a-zA-Z0-9_]+)\s*Action Input:\s*(.+?)(?=\s*Action:|\s*$)", re.DOTALL) | |
action_match: list[tuple[str, str]] = re.findall(regex, content) | |
if not action_match: | |
return content | |
results = [] | |
for match in action_match: | |
tool_name = match[0].strip() | |
tool_input = match[1].strip().strip('"').strip("```") | |
try: | |
arguments = json.loads(tool_input) | |
results.append(FunctionCall(tool_name, json.dumps(arguments, ensure_ascii=False))) | |
except json.JSONDecodeError: | |
return content | |
return results | |
class GLM4ToolUtils(ToolUtils): | |
r"""GLM-4 tool using template.""" | |
def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
tool_text = "" | |
for tool in tools: | |
tool_text += "\n\n## {name}\n\n{body}\n在调用上述函数时,请使用 Json 格式表示调用的参数。".format( | |
name=tool["name"], body=json.dumps(tool, indent=4, ensure_ascii=False) | |
) | |
return GLM4_TOOL_PROMPT.format(tool_text=tool_text) | |
def function_formatter(functions: list["FunctionCall"]) -> str: | |
if len(functions) > 1: | |
raise ValueError("GLM-4 does not support parallel functions.") | |
return f"{functions[0].name}\n{functions[0].arguments}" | |
def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
if "\n" not in content: | |
return content | |
tool_name, tool_input = content.split("\n", maxsplit=1) | |
try: | |
arguments = json.loads(tool_input.strip()) | |
except json.JSONDecodeError: | |
return content | |
return [FunctionCall(tool_name, json.dumps(arguments, ensure_ascii=False))] | |
class Llama3ToolUtils(ToolUtils): | |
r"""Llama 3.x tool using template with `tools_in_user_message=False`. | |
Reference: https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/#json-based-tool-calling | |
""" | |
def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
date = datetime.now().strftime("%d %b %Y") | |
tool_text = "" | |
for tool in tools: | |
wrapped_tool = {"type": "function", "function": tool} | |
tool_text += json.dumps(wrapped_tool, indent=4, ensure_ascii=False) + "\n\n" | |
return LLAMA3_TOOL_PROMPT.format(date=date, tool_text=tool_text) | |
def function_formatter(functions: list["FunctionCall"]) -> str: | |
if len(functions) > 1: | |
raise ValueError("Llama-3 does not support parallel functions.") | |
return f'{{"name": "{functions[0].name}", "parameters": {functions[0].arguments}}}' | |
def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
try: | |
tool = json.loads(content.strip()) | |
except json.JSONDecodeError: | |
return content | |
if "name" not in tool or "parameters" not in tool: | |
return content | |
return [FunctionCall(tool["name"], json.dumps(tool["parameters"], ensure_ascii=False))] | |
class MistralToolUtils(ToolUtils): | |
r"""Mistral v0.3 tool using template.""" | |
def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
wrapped_tools = [] | |
for tool in tools: | |
wrapped_tools.append({"type": "function", "function": tool}) | |
return "[AVAILABLE_TOOLS] " + json.dumps(wrapped_tools, ensure_ascii=False) + "[/AVAILABLE_TOOLS]" | |
def function_formatter(functions: list["FunctionCall"]) -> str: | |
function_texts = [] | |
for name, arguments in functions: | |
function_texts.append(f'{{"name": "{name}", "arguments": {arguments}}}') | |
return "[" + ", ".join(function_texts) + "]" | |
def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
try: | |
tools = json.loads(content.strip()) | |
except json.JSONDecodeError: | |
return content | |
if not isinstance(tools, list): | |
tools = [tools] | |
results = [] | |
for tool in tools: | |
if "name" not in tool or "arguments" not in tool: | |
return content | |
results.append(FunctionCall(tool["name"], json.dumps(tool["arguments"], ensure_ascii=False))) | |
return results | |
class QwenToolUtils(ToolUtils): | |
r"""Qwen 2.5 tool using template.""" | |
def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
tool_text = "" | |
for tool in tools: | |
wrapped_tool = {"type": "function", "function": tool} | |
tool_text += "\n" + json.dumps(wrapped_tool, ensure_ascii=False) | |
return QWEN_TOOL_PROMPT.format(tool_text=tool_text) | |
def function_formatter(functions: list["FunctionCall"]) -> str: | |
function_texts = [] | |
for name, arguments in functions: | |
function_texts.append( | |
"<tool_call>\n" + f'{{"name": "{name}", "arguments": {arguments}}}' + "\n</tool_call>" | |
) | |
return "\n".join(function_texts) | |
def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
regex = re.compile(r"<tool_call>(.+?)</tool_call>(?=\s*<tool_call>|\s*$)", re.DOTALL) | |
tool_match: list[str] = re.findall(regex, content) | |
if not tool_match: | |
return content | |
results = [] | |
for tool in tool_match: | |
try: | |
tool = json.loads(tool.strip()) | |
except json.JSONDecodeError: | |
return content | |
if "name" not in tool or "arguments" not in tool: | |
return content | |
results.append(FunctionCall(tool["name"], json.dumps(tool["arguments"], ensure_ascii=False))) | |
return results | |
TOOLS = { | |
"default": DefaultToolUtils(), | |
"glm4": GLM4ToolUtils(), | |
"llama3": Llama3ToolUtils(), | |
"mistral": MistralToolUtils(), | |
"qwen": QwenToolUtils(), | |
} | |
def get_tool_utils(name: str) -> "ToolUtils": | |
tool_utils = TOOLS.get(name, None) | |
if tool_utils is None: | |
raise ValueError(f"Tool utils `{name}` not found.") | |
return tool_utils | |