596 lines
26 KiB
Python
596 lines
26 KiB
Python
from abc import ABC, abstractmethod
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from enum import Enum
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from functools import wraps
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import jinja2
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import json
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from pathlib import Path
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import random
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import re
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import sys
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from typing import Any, Dict, Literal, Optional, Tuple, Callable, Union
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from pydantic import BaseModel
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from typeguard import typechecked
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from examples.json_schema_to_grammar import SchemaConverter
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from examples.openai.api import Tool, Message, FunctionCall, ToolCall
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from examples.openai.gguf_kvs import GGUFKeyValues, Keys
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from examples.openai.ts_converter import SchemaToTypeScriptConverter
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@typechecked
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def raise_exception(msg: str):
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raise Exception(msg)
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@typechecked
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class ChatTemplate(BaseModel):
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template: str
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@property
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def tool_style(self) -> 'ToolsPromptStyle':
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return self._tool_style
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def __init__(self, template: str, eos_token: str, bos_token: str):
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super().__init__(template=template
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)
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env = jinja2.Environment(loader=jinja2.BaseLoader(), trim_blocks=True, lstrip_blocks=True)
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self._template = env.from_string(template)
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self._eos_token = eos_token
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self._bos_token = bos_token
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self._strict_user_assistant_alternation = "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception" in template
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if "<|recipient|>' + tool_call['function']['name']" in template:
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self._tool_style = ToolsPromptStyle.TYPESCRIPT_FUNCTIONARY_V2
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else:
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self._tool_style = ToolsPromptStyle.TOOLS_BESPOKE
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# self._tool_style = ToolsPromptStyle.TOOLS_LONG
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# TODO: Test whether the template supports formatting tool_calls
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delimiter = '<%$[SAMPLE]$%>'
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user_msg = Message(role="user", content="Hey")
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empty_prompt = self.render([user_msg], add_generation_prompt=True).strip()
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planted_prompt = self.render([user_msg, Message(role="assistant", content=delimiter)], add_generation_prompt=False).strip()
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assert planted_prompt.startswith(empty_prompt), f"Planted prompt does not start with empty prompt: {planted_prompt} vs {empty_prompt}"
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[prefix, suffix] = planted_prompt[len(empty_prompt):].split(delimiter)
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sys.stderr.write(f"\n# prefix={prefix}\n# suffix={suffix}\n\n")
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self._prefix = prefix
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self._suffix = suffix
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def strip_suffix(self, s: str) -> str:
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if s.endswith(self._suffix):
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return s[:-len(self._suffix)]
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else:
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sys.stderr.write(f"Expected suffix ({self._suffix}) not found: {s}\n")
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return s
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def __str__(self):
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return f"ChatTemplate(template={self.template}, eos_token={self._eos_token}, bos_token={self._bos_token})"
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def add_system_prompt(self, messages: list[Message], system_prompt: Message) -> list[Message]:
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assert system_prompt.role == "system"
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# TODO: add to last system message, or create a new one just before the last user message
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system_message = next(((i, m) for i, m in enumerate(messages) if m.role == "system"), None)
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if system_message is not None:
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(i, m) = system_message
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return messages[:i] + [Message(role="system", content=system_prompt.content + '\n' + m.content)] + messages[i+1:]
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else:
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return [system_prompt] + messages
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@staticmethod
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def from_gguf(metadata: GGUFKeyValues):
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tokens = metadata[Keys.Tokenizer.LIST]
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return ChatTemplate(
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template = metadata[Keys.Tokenizer.CHAT_TEMPLATE],
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bos_token = tokens[metadata[Keys.Tokenizer.BOS_ID]],
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eos_token = tokens[metadata[Keys.Tokenizer.EOS_ID]])
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def render(self, messages: list[Message], add_generation_prompt: bool, omit_bos: bool = False):
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if self._strict_user_assistant_alternation and any(m.role not in ('user', 'assistant') for m in messages):
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new_messages=[]
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i = 0
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n = len(messages)
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while i < n:
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if messages[i].role == 'system':
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assert messages[i+1].role == 'user'
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new_messages.append(Message(
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role="user",
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content=f'[SYS]{messages[i].content}[/SYS]\n{messages[i+1].content}'
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))
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i += 2
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elif messages[i].role == 'assistant' and messages[i].tool_calls and messages[i].content:
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tc = '\n'.join(f'<tool_call>{json.dumps(tc.model_dump())}</tool_call>' for tc in messages[i].tool_calls)
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new_messages.append(Message(
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role="assistant",
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content=f'{messages[i].content}\n{tc}'
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))
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i += 1
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else:
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new_messages.append(messages[i])
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i += 1
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# print(f'new_messages={json.dumps(new_messages, indent=2)}')
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messages = new_messages
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# print(f'messages={messages}')
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result = self._template.render(
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messages=messages,
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eos_token=self._eos_token,
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bos_token='' if omit_bos else self._bos_token,
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raise_exception=raise_exception,
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add_generation_prompt=add_generation_prompt,
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)
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sys.stderr.write(f'\n# RENDERED:\n\n{result}\n\n')
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return result
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# While the API will be usable with a generic tools usage like OpenAI,
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# (see https://cookbook.openai.com/examples/how_to_call_functions_with_chat_models),
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# each model may need specific prompting (and/or constrained output,
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# especially for models not fine-tuned for tool usage / function calling).
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class ToolsPromptStyle(Enum):
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# Short prompt w/ <tools>schemas</tools>, <tool_call>...</tool_call> output
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TOOLS_SHORT = 1
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# Longer prompt w/ <tools>schemas</tools>, <tool_call>...</tool_call> output
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TOOLS_LONG = 2
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# Bespoke constrained output format that favours thought and reasoning
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# while allowing unambiguous parsing of parallel tool calling.
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TOOLS_BESPOKE = 3
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# Large prompt for https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B
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# <tool_call>...</tool_call> output
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# Requires:
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# - git clone https://github.com/NousResearch/Hermes-Function-Calling examples/openai/hermes_function_calling
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# - Set large context length as their prompts are super long
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TOOLS_HERMES_2_PRO = 4
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# Seems to want to escape underscores in tool names and in the <tool\_call>...</tool\_call> tags
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TOOLS_MISTRAL = 5
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# Short prompt w/ TypeScript definitions for https://github.com/MeetKai/functionary
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# https://github.com/MeetKai/functionary/blob/main/functionary/prompt_template/prompt_template_v2.py
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# Note: see this prior attempt to support Functionary: https://github.com/ggerganov/llama.cpp/pull/5695
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TYPESCRIPT_FUNCTIONARY_V2 = 6
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class ChatHandlerArgs(BaseModel):
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chat_template: ChatTemplate
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response_schema: Optional[dict] = None
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tools: Optional[list[Tool]] = None
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class ChatHandler(ABC):
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def __init__(self, args: ChatHandlerArgs):
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self.args = args
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self.output_format_prompt: Optional[Message] = None
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self.grammar: Optional[str] = None
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@abstractmethod
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def parse(self, s: str) -> Optional[Message]:
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raise NotImplementedError()
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class NoToolsChatHandler(ChatHandler):
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def __init__(self, args: ChatHandlerArgs):
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super().__init__(args)
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assert not args.tools
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if args.response_schema:
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self.output_format_prompt = Message(
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role="system",
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content=_please_respond_with_schema(args.response_schema)
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)
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converter = SchemaConverter(prop_order={}, allow_fetch=False, dotall=False, raw_pattern=False)
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self.grammar = converter.visit(args.response_schema, '')
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else:
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self.output_format_prompt = None
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self.grammar = None
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@typechecked
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def parse(self, s: str) -> Optional[Message]:
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return Message(role="assistant", content=s)
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class ToolCallTagsChatHandler(ChatHandler):
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def __init__(self, args: ChatHandlerArgs, escapes_underscores: bool, allow_parallel_calls: bool):
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super().__init__(args)
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converter = SchemaConverter(prop_order={}, allow_fetch=False, dotall=False, raw_pattern=False)
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tool_rules = [
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converter.visit(
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dict(
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type="object",
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properties=dict(
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name=dict(type="string", pattern='^' + tool.function.name.replace('_', f'\\?_') + '$') if escapes_underscores \
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else dict(const=tool.function.name),
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arguments=tool.function.parameters,
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),
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required=['name', 'arguments']
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),
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f'{tool.function.name}-tool-call'
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)
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for tool in self.args.tools
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]
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def format_literal(s: str) -> str:
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if escapes_underscores:
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return ' "\\\\"? "_" '.join((converter._format_literal(part) for part in s.split('_')))
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else:
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return converter._format_literal(s)
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tool_call_rule = converter._add_rule(
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'tool_call',
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format_literal("<tool_call>") + " space (" +
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' | '.join(tool_rules) +
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") space " + format_literal("</tool_call>"))# + ' space')
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# Ideally we'd want a negative lookahead of /<tool\\?_call>/, but it's just too hard to express in GBNF for now.
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# So we just over-constrain the content rule to not contain literals dangerously getting close to <tool_call>
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content_rule = converter._add_rule('content', '[^<] | "<" [^t<] | "<t" [^o<]')
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# content_rule = converter._add_rule('content', converter.not_literal('<tool_call>'))
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converter._add_rule(
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'root',
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# tool_call_rule)
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f'{content_rule}* ({tool_call_rule}+ {content_rule}*)?' if allow_parallel_calls \
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else f'{content_rule}* {tool_call_rule}?')
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self.grammar = converter.format_grammar()
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# # Constrain the output to be a non-tool-call message (constrained to a JSON schema or not)
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# # OR a tool-call message respecting the schema of any of the tools
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# converter._add_rule(
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# "root",
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# converter._format_literal(prefix) + " (" +
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# (response_rule or converter.not_literal("<tool_call>")) + " | " +
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# converter._format_literal("<tool_call>") + " (" +
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# ' | '.join(tool_rules) +
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# ") " + converter._format_literal("</tool_call>") +
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# ")") # + converter._format_literal(suffix))
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@typechecked
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def parse(self, s: str) -> Optional[Message]:
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s = self.args.chat_template.strip_suffix(s)
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if r'<tool\_call>' in s:
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# Some weird escaping of underscores is happening w/ Mixtral 8x7B Instruct
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s = s.replace(r'\_', '_')
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parts = _tool_call_re.split(s)
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if len(parts) == 1:
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return Message(role="assistant", content=s)
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else:
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content = []
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tool_calls = []
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for i, part in enumerate(parts):
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if i % 2 == 0:
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content.append(part)
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else:
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try:
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fc = json.loads(part)
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except json.JSONDecodeError:
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raise ValueError(f'Failed to parse tool call as JSON: {part}\nFull string: {s}')
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tool_calls.append(
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ToolCall(
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id=gen_callid(),
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function=FunctionCall(**fc)))
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content = '\n'.join(content).strip()
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return Message(role="assistant", content=content if content else None, tool_calls=tool_calls)
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# if '<tool_call>'.startswith(ls) or ls.startswith('<tool_call>'):
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# if ls.startswith('<tool_call>') and ls.endswith('</tool_call>' + suffix):
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# tool_call = ls[len('<tool_call>'):-len('</tool_call>' + suffix)]
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# return Message(role="assistant", content=None, tool_calls=[json.loads(tool_call)])
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# return None
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# else:
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# return Message(role="assistant", content=s)
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class TemplatedToolsChatHandler(ToolCallTagsChatHandler):
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def __init__(self, args: ChatHandlerArgs, template: str, escapes_underscores=False, allow_parallel_calls=True):
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super().__init__(args, escapes_underscores=escapes_underscores, allow_parallel_calls=allow_parallel_calls)
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assert '{tools}' in template, 'Template must contain "{tools}"'
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self.output_format_prompt = Message(
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role="system",
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content=template.replace(
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'{tools}',
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'\n'.join(json.dumps(tool.model_dump(), indent=2) for tool in self.args.tools),
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)
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)
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class Hermes2ProToolsChatHandler(ToolCallTagsChatHandler):
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def __init__(self, args: ChatHandlerArgs):
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super().__init__(args, escapes_underscores=False, allow_parallel_calls=False)
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# Hackily import https://github.com/NousResearch/Hermes-Function-Calling
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path = str(Path(__file__).parent / "hermes_function_calling")
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if path not in sys.path: sys.path.insert(0, path)
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try:
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from examples.openai.hermes_function_calling.prompter import PromptManager
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except ImportError:
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raise ImportError(f"Please `git clone https://github.com/NousResearch/Hermes-Function-Calling {path}`")
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prompt = PromptManager().generate_prompt(user_prompt=[], tools=[json.dumps(tool) for tool in tools])
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assert len(prompt) == 1 and prompt[0]["role"] == "system"
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self.output_format_prompt = Message(**prompt[0])
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class FunctionaryToolsChatHandler(ChatHandler):
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def __init__(self, args: ChatHandlerArgs, allow_parallel_calls: bool):
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super().__init__(args)
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# Only allowing a single tool call at a time for now.
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# Note that if there were more, they'd be separated by a '<|from|>assistant' literal
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self.output_format_prompt = Message(
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role="system",
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content= '// Supported function definitions that should be called when necessary.\n' +
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_tools_typescript_signatures(args.tools)
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)
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converter = SchemaConverter(prop_order={}, allow_fetch=False, dotall=False, raw_pattern=False)
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tool_rules = [
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converter._add_rule(
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tool.function.name + '-call',
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converter._format_literal(tool.function.name) + ' ' + converter._format_literal('\n<|content|>\n') + ' ' +
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converter.visit(tool.function.parameters, tool.function.name + '-args') + ' ' +
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converter._format_literal('\n'))
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# converter.visit(
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# dict(
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# type="object",
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# properties=dict(
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# name=dict(const=tool.function.name),
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# arguments=tool.function.parameters,
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# ),
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# required=['name', 'arguments']
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# ),
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# f'{tool.function.name}-tool-call'
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# )
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for i, tool in enumerate(self.args.tools)
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]
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not_from_rule = converter._add_rule('not_from', converter.not_literal("<|from|>"))
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content_without_start_rule = converter._add_rule(
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'content_without_start',
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converter._format_literal("all\n<|content|>") + ' ' + not_from_rule + '*')
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start_rule = converter._add_rule('start', converter._format_literal('<|from|>assistant\n<|recipient|>'))
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content_rule = converter._add_rule('content', start_rule + ' ' + content_without_start_rule)
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tool_call_without_start_rule = converter._add_rule(
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'tool_call_without_start',
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' | '.join(tool_rules))
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# + ' ' +
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# converter.not_literal("all", dotall=False) + ' ' + converter._format_literal('\n<|content|>\n') + ' ' + not_from_rule + '*')
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tool_call_rule = converter._add_rule('tool_call', f'{start_rule} {tool_call_without_start_rule}')
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# converter._add_rule('root', f'({content_without_start_rule} ({content_rule})* ({tool_call_rule}+ {content_rule}*)? | {tool_call_without_start_rule} (* {tool_call_rule}{content_rule}*')
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converter._add_rule(
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'root',
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f'{content_without_start_rule} {content_rule}* ({tool_call_rule}+ {content_rule}*)? | '
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f'{tool_call_without_start_rule} {tool_call_rule}* {content_rule}*' if allow_parallel_calls \
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else f'{content_without_start_rule} {tool_call_rule}? | {tool_call_without_start_rule}')
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self.grammar = converter.format_grammar()
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# converter._add_rule(
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# "root",
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# converter._format_literal(prefix) + " (" +
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# (response_rule or converter.not_literal("<|recipient|>")) + " | " +
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# (' | '.join(
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# converter._format_literal(f"<|recipient|>{tool.function.name}\n<|content|>") + " " +
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# converter.visit(tool.function.parameters, tool.function.name + '-args')
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# for tool in tools
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# )) +
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# ") " +
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# ")") # + converter._format_literal(suffix))
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@typechecked
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def parse(self, s: str) -> Optional[Message]:
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s = self.args.chat_template.strip_suffix(s)
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parts = _recipient_content_re.split(s)
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if len(parts) == 1:
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return Message(role="assistant", content=s)
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else:
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text_content = []
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tool_calls: list[ToolCall] = []
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for i in range((len(parts) - 1) // 3):
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assert parts[i * 3].strip() == '', f'Unexpected content before tool call: {parts[i * 3]}'
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recipient = parts[i * 3 + 1].strip()
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content = parts[i * 3 + 2]
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if recipient == 'all':
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text_content.append(content)
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else:
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try:
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arguments = json.loads(content)
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except json.JSONDecodeError:
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raise ValueError(f'Failed to parse tool call content as JSON: {content}')
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tool_calls.append(
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ToolCall(
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id=gen_callid(),
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function=FunctionCall(name=recipient, arguments=arguments)))
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assert parts[-1].strip() in ('', '<|stop|>'), f'Unexpected content after tool calls: {parts[-1]}\nFull string: {s}'
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content = '\n'.join(text_content).strip()
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return Message(role="assistant", content=content if content else None, tool_calls=tool_calls if tool_calls else None)
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def _make_bespoke_schema(response_schema, tool_call_schema):
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return {
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"type": "object",
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"properties": {
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# "original_goal": {"title": "Original Goal", "type": "string"},
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"thought": {
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# "title": "Thought about how the next step brings us closer to achieving the original goal",
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"type": "string"
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},
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"next_step": {
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"title": "Next Step: either a result or one or more tool calls to achieve the original goal",
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"oneOf": [
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{
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"title": "Tool Calls",
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"properties": {
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# "type": {
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# "const": "tool_calls"
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# },
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"tool_calls": {
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"type": "array",
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"items": tool_call_schema
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}
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},
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"required": ["tool_calls"]
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},
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{
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"title": "Result (achieving original goal)",
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||
"properties": {
|
||
"result": response_schema,
|
||
},
|
||
"required": ["result"]
|
||
},
|
||
]
|
||
},
|
||
},
|
||
"required": ["original_goal", "thought", "next_step"]
|
||
}
|
||
|
||
class BespokeToolsChatHandler(ChatHandler):
|
||
def __init__(self, args: ChatHandlerArgs):
|
||
super().__init__(args)
|
||
|
||
# args.response_schema = args.response_schema or {}
|
||
converter = SchemaConverter(prop_order={}, allow_fetch=False, dotall=False, raw_pattern=False)
|
||
|
||
response_schema = args.response_schema or {"type": "string"}
|
||
converter.visit(
|
||
_make_bespoke_schema(
|
||
response_schema,
|
||
{
|
||
"oneOf": [
|
||
{
|
||
"type": "object",
|
||
"properties": {
|
||
"name": {"const": tool.function.name},
|
||
"arguments": tool.function.parameters,
|
||
},
|
||
"required": ["name", "arguments"]
|
||
}
|
||
for tool in self.args.tools
|
||
]
|
||
}
|
||
),
|
||
'',
|
||
)
|
||
self.grammar = converter.format_grammar()
|
||
|
||
self.output_format_prompt = Message(
|
||
role="system",
|
||
content='\n'.join([
|
||
'You are a function calling AI model.',
|
||
'Here are the tools available:',
|
||
_tools_schema_signatures(self.args.tools, indent=2),
|
||
_please_respond_with_schema(
|
||
_make_bespoke_schema(
|
||
response_schema,
|
||
{
|
||
"properties": {
|
||
"name": {
|
||
"title": "Name of the tool to call",
|
||
"type": "string"
|
||
},
|
||
"arguments": {
|
||
"title": "Arguments to pass to the tool",
|
||
"type": "object"
|
||
}
|
||
},
|
||
"required": ["name", "arguments"]
|
||
}
|
||
)
|
||
),
|
||
])
|
||
)
|
||
|
||
@typechecked
|
||
def parse(self, s: str) -> Optional[Message]:
|
||
s = self.args.chat_template.strip_suffix(s)
|
||
try:
|
||
data = json.loads(s)
|
||
except json.JSONDecodeError:
|
||
raise ValueError(f'Failed to parse data as JSON: {s}')
|
||
|
||
next_step = data['next_step']
|
||
if 'result' in next_step:
|
||
return Message(role="assistant", content=json.dumps(next_step['result']))
|
||
elif 'tool_calls' in next_step:
|
||
return Message(
|
||
role="assistant",
|
||
content=data["thought"],
|
||
tool_calls=[
|
||
ToolCall(id=gen_callid(), function=FunctionCall(**tc))
|
||
for tc in next_step['tool_calls']
|
||
]
|
||
)
|
||
else:
|
||
raise ValueError(f'Unexpected data: {data}')
|
||
|
||
_SHORT_TEMPLATE='\n'.join([
|
||
'Here are the tools available:',
|
||
'<tools>',
|
||
'{tools}',
|
||
'</tools>',
|
||
])
|
||
|
||
_LONG_TEMPLATE='\n'.join([
|
||
# '''You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags.''',
|
||
'You may call one or more functions to assist with the user query. Don\'t make assumptions about what values to plug into functions. Here are the available tools:',
|
||
'<tools>',
|
||
'{tools}',
|
||
'</tools>',
|
||
'',
|
||
# 'Use the following json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"}',
|
||
# '',
|
||
# 'For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:',
|
||
'To call each function, give its name and arguments within <tool_call></tool_call> XML tags as follows:',
|
||
'<tool_call>',
|
||
'{"name": <function-name>, "arguments": <args-dict>}',
|
||
'</tool_call>',
|
||
# 'This is not hypothetical, you're not asked what you would do. If you need a tool called, just call it with <tool_call>...</tool_call>.''',
|
||
])
|
||
|
||
def get_chat_handler(args: ChatHandlerArgs, allow_parallel_calls=False) -> ChatHandler:
|
||
if not args.tools:
|
||
return NoToolsChatHandler(args)
|
||
elif args.chat_template.tool_style == ToolsPromptStyle.TYPESCRIPT_FUNCTIONARY_V2:
|
||
return FunctionaryToolsChatHandler(args)
|
||
elif args.chat_template.tool_style == ToolsPromptStyle.TOOLS_SHORT:
|
||
return TemplatedToolsChatHandler(args, _SHORT_TEMPLATE, allow_parallel_calls=allow_parallel_calls)
|
||
elif args.chat_template.tool_style == ToolsPromptStyle.TOOLS_LONG:
|
||
return TemplatedToolsChatHandler(args, _LONG_TEMPLATE, allow_parallel_calls=allow_parallel_calls)
|
||
elif args.chat_template.tool_style == ToolsPromptStyle.TOOLS_MISTRAL:
|
||
return TemplatedToolsChatHandler(args, _LONG_TEMPLATE, escapes_underscores=True, allow_parallel_calls=allow_parallel_calls)
|
||
elif args.chat_template.tool_style == ToolsPromptStyle.TOOLS_BESPOKE:
|
||
return BespokeToolsChatHandler(args)
|
||
elif args.chat_template.tool_style == ToolsPromptStyle.TOOLS_HERMES_2_PRO:
|
||
return Hermes2ProToolsChatHandler(args)
|
||
else:
|
||
raise ValueError(f"Unsupported tool call style: {args.chat_template.tool_style}")
|
||
|
||
_ts_converter = SchemaToTypeScriptConverter()
|
||
|
||
def _please_respond_with_schema(schema: dict) -> str:
|
||
# sig = json.dumps(schema, indent=2)
|
||
sig = _ts_converter.visit(schema)
|
||
return f'Please respond in JSON format with the following schema: {sig}'
|
||
|
||
def _tools_typescript_signatures(tools: list[Tool]) -> str:
|
||
return 'namespace functions {' + '\n'.join(
|
||
'// ' + tool.function.description.replace('\n', '\n// ') + '\n' + ''
|
||
'type ' + tool.function.name + ' = (_: ' + _ts_converter.visit(tool.function.parameters) + ") => any;\n"
|
||
for tool in tools
|
||
) + '} // namespace functions'
|
||
|
||
def _tools_schema_signatures(tools: list[Tool], indent=None) -> str:
|
||
return '\n'.join(
|
||
json.dumps(tool.model_dump(), indent=indent)
|
||
for tool in tools
|
||
)
|
||
|
||
_tool_call_re = re.compile(
|
||
'<tool_call>(.*?)</tool_call>', re.DOTALL)
|
||
_recipient_content_re = re.compile(r'(?:(?:<\|(?:stop|from)\|>)+ *assistant\n<\|recipient\|>|^) *([^ <|>\n]+) *\n<\|content\|>(.*?)(?:$|<\|stop\|>\s*$|(?=(?:<\|(?:stop|from)\|>)+ *assistant\n))', re.DOTALL)
|
||
|
||
def gen_callid():
|
||
return f'call_{random.randint(0, 1000000)}'
|