biondizzle
a86d6d90a5
- Copied deepseek_v4_encoding.py from vLLM tree to encoding/ - Replaced hand-rolled prompt construction with encode_messages() - --chat-mode → --thinking-mode (thinking|chat) - The official encoder handles: BOS, User/Assistant tokens, thinking mode, tool calls, and all special token placement. It can't drift. - This is the same code path inference engines will use.
758 lines
28 KiB
Python
758 lines
28 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# ruff: noqa
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# fmt: off
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"""
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DeepSeek-V4 Encoding
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A self-contained implementation for encoding/decoding DeepSeek-V4 chat messages
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with tool calling, thinking mode, and quick instruction task support.
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"""
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from typing import Any, Dict, List, Union, Optional, Tuple
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import copy
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import json
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import regex as re
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# ============================================================
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# Special Tokens
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# ============================================================
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bos_token: str = "<｜begin▁of▁sentence｜>"
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eos_token: str = "<｜end▁of▁sentence｜>"
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thinking_start_token: str = "<think>"
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thinking_end_token: str = "</think>"
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dsml_token: str = "｜DSML｜"
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USER_SP_TOKEN = "<｜User｜>"
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ASSISTANT_SP_TOKEN = "<｜Assistant｜>"
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LATEST_REMINDER_SP_TOKEN = "<｜latest_reminder｜>"
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# Task special tokens for internal classification tasks
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DS_TASK_SP_TOKENS = {
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"action": "<｜action｜>",
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"query": "<｜query｜>",
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"authority": "<｜authority｜>",
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"domain": "<｜domain｜>",
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"title": "<｜title｜>",
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"read_url": "<｜read_url｜>",
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}
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VALID_TASKS = set(DS_TASK_SP_TOKENS.keys())
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# ============================================================
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# Templates
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# ============================================================
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system_msg_template: str = "{content}"
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user_msg_template: str = "{content}"
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latest_reminder_msg_template: str = "{content}"
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assistant_msg_template: str = "{reasoning}{content}{tool_calls}" + eos_token
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assistant_msg_wo_eos_template: str = "{reasoning}{content}{tool_calls}"
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thinking_template: str = "{reasoning}"
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response_format_template: str = (
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"## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n{schema}"
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)
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tool_call_template: str = (
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"<{dsml_token}invoke name=\"{name}\">\n{arguments}\n</{dsml_token}invoke>"
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)
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tool_calls_template = (
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"<{dsml_token}{tc_block_name}>\n{tool_calls}\n</{dsml_token}{tc_block_name}>"
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)
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tool_calls_block_name: str = "tool_calls"
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tool_output_template: str = (
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"<tool_result>{content}</tool_result>"
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)
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REASONING_EFFORT_MAX = (
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"Reasoning Effort: Absolute maximum with no shortcuts permitted.\n"
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"You MUST be very thorough in your thinking and comprehensively decompose the problem to resolve the root cause, rigorously stress-testing your logic against all potential paths, edge cases, and adversarial scenarios.\n"
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"Explicitly write out your entire deliberation process, documenting every intermediate step, considered alternative, and rejected hypothesis to ensure absolutely no assumption is left unchecked.\n\n"
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)
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TOOLS_TEMPLATE = """## Tools
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You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<{dsml_token}tool_calls>" block like the following:
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<{dsml_token}tool_calls>
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<{dsml_token}invoke name="$TOOL_NAME">
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<{dsml_token}parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</{dsml_token}parameter>
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...
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</{dsml_token}invoke>
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<{dsml_token}invoke name="$TOOL_NAME2">
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...
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</{dsml_token}invoke>
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</{dsml_token}tool_calls>
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String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.
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If thinking_mode is enabled (triggered by {thinking_start_token}), you MUST output your complete reasoning inside {thinking_start_token}...{thinking_end_token} BEFORE any tool calls or final response.
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Otherwise, output directly after {thinking_end_token} with tool calls or final response.
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### Available Tool Schemas
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{tool_schemas}
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You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
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"""
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# ============================================================
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# Utility Functions
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# ============================================================
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def to_json(value: Any) -> str:
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"""Serialize a value to JSON string."""
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try:
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return json.dumps(value, ensure_ascii=False)
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except Exception:
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return json.dumps(value, ensure_ascii=True)
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def tools_from_openai_format(tools):
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"""Extract function definitions from OpenAI-format tool list."""
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return [tool["function"] for tool in tools]
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def tool_calls_from_openai_format(tool_calls):
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"""Convert OpenAI-format tool calls to internal format."""
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return [
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{
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"name": tool_call["function"]["name"],
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"arguments": tool_call["function"]["arguments"],
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}
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for tool_call in tool_calls
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]
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def tool_calls_to_openai_format(tool_calls):
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"""Convert internal tool calls to OpenAI format."""
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return [
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{
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"type": "function",
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"function": {
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"name": tool_call["name"],
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"arguments": tool_call["arguments"],
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}
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}
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for tool_call in tool_calls
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]
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def encode_arguments_to_dsml(tool_call: Dict[str, Any]) -> str:
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"""
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Encode tool call arguments into DSML parameter format.
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Args:
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tool_call: Dict with "name" and "arguments" keys.
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Returns:
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DSML-formatted parameter string.
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"""
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p_dsml_template = '<{dsml_token}parameter name="{key}" string="{is_str}">{value}</{dsml_token}parameter>'
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P_dsml_strs = []
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if isinstance(tool_call["arguments"], str):
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arguments = json.loads(tool_call["arguments"])
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else:
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arguments = tool_call["arguments"]
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for k, v in arguments.items():
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p_dsml_str = p_dsml_template.format(
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dsml_token=dsml_token,
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key=k,
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is_str="true" if isinstance(v, str) else "false",
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value=v if isinstance(v, str) else to_json(v),
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)
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P_dsml_strs.append(p_dsml_str)
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return "\n".join(P_dsml_strs)
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def decode_dsml_to_arguments(tool_name: str, tool_args: Dict[str, Tuple[str, str]]) -> Dict[str, str]:
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"""
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Decode DSML parameters back to a tool call dict.
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Args:
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tool_name: Name of the tool.
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tool_args: Dict mapping param_name -> (value, is_string_flag).
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Returns:
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Dict with "name" and "arguments" (JSON string) keys.
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"""
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def _decode_value(key: str, value: str, string: str):
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if string == "true":
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value = to_json(value)
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return f"{to_json(key)}: {value}"
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tool_args_json = "{" + ", ".join([_decode_value(k, v, string=is_str) for k, (v, is_str) in tool_args.items()]) + "}"
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return dict(name=tool_name, arguments=tool_args_json)
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def render_tools(tools: List[Dict[str, Union[str, Dict[str, Any]]]]) -> str:
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"""
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Render tool schemas into the system prompt format.
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Args:
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tools: List of tool schema dicts (each with name, description, parameters).
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Returns:
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Formatted tools section string.
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"""
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tools_json = [to_json(t) for t in tools]
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return TOOLS_TEMPLATE.format(
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tool_schemas="\n".join(tools_json),
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dsml_token=dsml_token,
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thinking_start_token=thinking_start_token,
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thinking_end_token=thinking_end_token,
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)
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def find_last_user_index(messages: List[Dict[str, Any]]) -> int:
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"""Find the index of the last user/developer message."""
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last_user_index = -1
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for idx in range(len(messages) - 1, -1, -1):
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if messages[idx].get("role") in ["user", "developer"]:
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last_user_index = idx
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break
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return last_user_index
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# ============================================================
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# Message Rendering
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# ============================================================
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def render_message(index: int, messages: List[Dict[str, Any]], thinking_mode: str, drop_thinking: bool = True, reasoning_effort: Optional[str] = None) -> str:
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"""
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Render a single message at the given index into its encoded string form.
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This is the core function that converts each message in the conversation
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into the DeepSeek-V4 format.
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Args:
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index: Index of the message to render.
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messages: Full list of messages in the conversation.
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thinking_mode: Either "chat" or "thinking".
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drop_thinking: Whether to drop reasoning content from earlier turns.
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reasoning_effort: Optional reasoning effort level ("max", "high", or None).
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Returns:
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Encoded string for this message.
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"""
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assert 0 <= index < len(messages)
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assert thinking_mode in ["chat", "thinking"], f"Invalid thinking_mode `{thinking_mode}`"
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prompt = ""
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msg = messages[index]
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last_user_idx = find_last_user_index(messages)
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role = msg.get("role")
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content = msg.get("content")
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tools = msg.get("tools")
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response_format = msg.get("response_format")
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tool_calls = msg.get("tool_calls")
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reasoning = msg.get("reasoning")
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wo_eos = msg.get("wo_eos", False)
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if tools:
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tools = tools_from_openai_format(tools)
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if tool_calls:
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tool_calls = tool_calls_from_openai_format(tool_calls)
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# Reasoning effort prefix (only at index 0 in thinking mode with max effort)
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assert reasoning_effort in ['max', None, 'high'], f"Invalid reasoning effort: {reasoning_effort}"
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if index == 0 and thinking_mode == "thinking" and reasoning_effort == 'max':
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prompt += REASONING_EFFORT_MAX
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if role == "system":
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prompt += system_msg_template.format(content=content or "")
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if tools:
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prompt += "\n\n" + render_tools(tools)
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if response_format:
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prompt += "\n\n" + response_format_template.format(schema=to_json(response_format))
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elif role == "developer":
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assert content, f"Invalid message for role `{role}`: {msg}"
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content_developer = USER_SP_TOKEN
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content_developer += content
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if tools:
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content_developer += "\n\n" + render_tools(tools)
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if response_format:
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content_developer += "\n\n" + response_format_template.format(schema=to_json(response_format))
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prompt += user_msg_template.format(content=content_developer)
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elif role == "user":
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prompt += USER_SP_TOKEN
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# Handle content blocks (tool results mixed with text)
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content_blocks = msg.get("content_blocks")
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if content_blocks:
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parts = []
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for block in content_blocks:
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block_type = block.get("type")
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if block_type == "text":
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parts.append(block.get("text", ""))
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elif block_type == "tool_result":
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tool_content = block.get("content", "")
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if isinstance(tool_content, list):
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text_parts = []
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for b in tool_content:
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if b.get("type") == "text":
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text_parts.append(b.get("text", ""))
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else:
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text_parts.append(f"[Unsupported {b.get('type')}]")
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tool_content = "\n\n".join(text_parts)
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parts.append(tool_output_template.format(content=tool_content))
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else:
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parts.append(f"[Unsupported {block_type}]")
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prompt += "\n\n".join(parts)
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else:
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prompt += content or ""
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elif role == "latest_reminder":
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prompt += LATEST_REMINDER_SP_TOKEN + latest_reminder_msg_template.format(content=content)
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elif role == "tool":
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raise NotImplementedError("deepseek_v4 merges tool messages into user; please preprocess with merge_tool_messages()")
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elif role == "assistant":
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thinking_part = ""
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tc_content = ""
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if tool_calls:
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tc_list = [
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tool_call_template.format(
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dsml_token=dsml_token,
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name=tc.get("name"),
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arguments=encode_arguments_to_dsml(tc)
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)
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for tc in tool_calls
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]
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tc_content += '\n\n' + tool_calls_template.format(
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dsml_token=dsml_token,
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tool_calls="\n".join(tc_list),
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tc_block_name=tool_calls_block_name,
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)
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summary_content = content or ""
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reasoning = reasoning or ""
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# Check if previous message has a task - if so, this is a task output (no thinking)
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prev_has_task = index - 1 >= 0 and messages[index - 1].get("task") is not None
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if thinking_mode == "thinking" and not prev_has_task:
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if not drop_thinking or index > last_user_idx:
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thinking_part = thinking_template.format(reasoning=reasoning) + thinking_end_token
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else:
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thinking_part = ""
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if wo_eos:
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prompt += assistant_msg_wo_eos_template.format(
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reasoning=thinking_part,
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content=summary_content,
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tool_calls=tc_content,
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)
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else:
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prompt += assistant_msg_template.format(
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reasoning=thinking_part,
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content=summary_content,
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tool_calls=tc_content,
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)
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else:
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raise NotImplementedError(f"Unknown role: {role}")
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# Append transition tokens based on what follows
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if index + 1 < len(messages) and messages[index + 1].get("role") not in ["assistant", "latest_reminder"]:
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return prompt
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task = messages[index].get("task")
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if task is not None:
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# Task special token for internal classification tasks
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assert task in VALID_TASKS, f"Invalid task: '{task}'. Valid tasks are: {list(VALID_TASKS)}"
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task_sp_token = DS_TASK_SP_TOKENS[task]
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if task != "action":
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# Non-action tasks: append task sp token directly after the message
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prompt += task_sp_token
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else:
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# Action task: append Assistant + thinking token + action sp token
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prompt += ASSISTANT_SP_TOKEN
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prompt += thinking_end_token if thinking_mode != "thinking" else thinking_start_token
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prompt += task_sp_token
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elif messages[index].get("role") in ["user", "developer"]:
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# Normal generation: append Assistant + thinking token
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prompt += ASSISTANT_SP_TOKEN
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if not drop_thinking and thinking_mode == "thinking":
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prompt += thinking_start_token
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elif drop_thinking and thinking_mode == "thinking" and index >= last_user_idx:
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prompt += thinking_start_token
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else:
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prompt += thinking_end_token
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return prompt
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# ============================================================
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# Preprocessing
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# ============================================================
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def merge_tool_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""
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Merge tool messages into the preceding user message using content_blocks format.
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DeepSeek-V4 does not have a standalone "tool" role; instead, tool results
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are encoded as <tool_result> blocks within user messages.
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This function converts a standard OpenAI-format conversation (with separate
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"tool" role messages) into V4 format where tool results are merged into
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user messages.
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Args:
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messages: List of message dicts in OpenAI format.
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Returns:
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Processed message list with tool messages merged into user messages.
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"""
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merged: List[Dict[str, Any]] = []
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for msg in messages:
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msg = copy.deepcopy(msg)
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role = msg.get("role")
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if role == "tool":
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# Convert tool message to a user message with tool_result block
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tool_block = {
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"type": "tool_result",
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"tool_use_id": msg.get("tool_call_id", ""),
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"content": msg.get("content", ""),
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}
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# Merge into previous message if it's already a user (merged tool)
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if merged and merged[-1].get("role") == "user" and "content_blocks" in merged[-1]:
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merged[-1]["content_blocks"].append(tool_block)
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else:
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merged.append({
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"role": "user",
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"content_blocks": [tool_block],
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})
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elif role == "user":
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text_block = {"type": "text", "text": msg.get("content", "")}
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if merged and merged[-1].get("role") == "user" and "content_blocks" in merged[-1] and merged[-1].get("task") is None:
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merged[-1]["content_blocks"].append(text_block)
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else:
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new_msg = {
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"role": "user",
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"content": msg.get("content", ""),
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"content_blocks": [text_block],
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}
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# Preserve extra fields (task, wo_eos, mask, etc.)
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for key in ("task", "wo_eos", "mask"):
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if key in msg:
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new_msg[key] = msg[key]
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merged.append(new_msg)
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else:
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merged.append(msg)
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return merged
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def sort_tool_results_by_call_order(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""
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Sort tool_result blocks within user messages by the order of tool_calls
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in the preceding assistant message.
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Args:
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messages: Preprocessed message list (after merge_tool_messages).
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Returns:
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Message list with sorted tool result blocks.
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"""
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last_tool_call_order: Dict[str, int] = {}
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for msg in messages:
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role = msg.get("role")
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if role == "assistant" and msg.get("tool_calls"):
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last_tool_call_order = {}
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for idx, tc in enumerate(msg["tool_calls"]):
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tc_id = tc.get("id") or tc.get("function", {}).get("id", "")
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if tc_id:
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last_tool_call_order[tc_id] = idx
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elif role == "user" and msg.get("content_blocks"):
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tool_blocks = [b for b in msg["content_blocks"] if b.get("type") == "tool_result"]
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if len(tool_blocks) > 1 and last_tool_call_order:
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sorted_blocks = sorted(
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tool_blocks,
|
||
key=lambda b: last_tool_call_order.get(b.get("tool_use_id", ""), 0)
|
||
)
|
||
sorted_idx = 0
|
||
new_blocks = []
|
||
for block in msg["content_blocks"]:
|
||
if block.get("type") == "tool_result":
|
||
new_blocks.append(sorted_blocks[sorted_idx])
|
||
sorted_idx += 1
|
||
else:
|
||
new_blocks.append(block)
|
||
msg["content_blocks"] = new_blocks
|
||
|
||
return messages
|
||
|
||
|
||
# ============================================================
|
||
# Main Encoding Function
|
||
# ============================================================
|
||
|
||
def encode_messages(
|
||
messages: List[Dict[str, Any]],
|
||
thinking_mode: str,
|
||
context: Optional[List[Dict[str, Any]]] = None,
|
||
drop_thinking: bool = True,
|
||
add_default_bos_token: bool = True,
|
||
reasoning_effort: Optional[str] = None,
|
||
) -> str:
|
||
"""
|
||
Encode a list of messages into the DeepSeek-V4 prompt format.
|
||
|
||
This is the main entry point for encoding conversations. It handles:
|
||
- BOS token insertion
|
||
- Thinking mode with optional reasoning content dropping
|
||
- Tool message merging into user messages
|
||
- Multi-turn conversation context
|
||
|
||
Args:
|
||
messages: List of message dicts to encode.
|
||
thinking_mode: Either "chat" or "thinking".
|
||
context: Optional preceding context messages (already encoded prefix).
|
||
drop_thinking: If True, drop reasoning from earlier assistant turns
|
||
(only keep reasoning for messages after the last user message).
|
||
add_default_bos_token: Whether to prepend BOS token at conversation start.
|
||
reasoning_effort: Optional reasoning effort level ("max", "high", or None).
|
||
|
||
Returns:
|
||
The encoded prompt string.
|
||
"""
|
||
context = context if context else []
|
||
|
||
# Preprocess: merge tool messages and sort tool results
|
||
messages = merge_tool_messages(messages)
|
||
messages = sort_tool_results_by_call_order(context + messages)[len(context):]
|
||
if context:
|
||
context = merge_tool_messages(context)
|
||
context = sort_tool_results_by_call_order(context)
|
||
|
||
full_messages = context + messages
|
||
|
||
prompt = bos_token if add_default_bos_token and len(context) == 0 else ""
|
||
|
||
# Resolve drop_thinking: if any message has tools defined, don't drop thinking
|
||
effective_drop_thinking = drop_thinking
|
||
if any(m.get("tools") for m in full_messages):
|
||
effective_drop_thinking = False
|
||
|
||
if thinking_mode == "thinking" and effective_drop_thinking:
|
||
full_messages = _drop_thinking_messages(full_messages)
|
||
# After dropping, recalculate how many messages to render
|
||
# (context may have shrunk too)
|
||
num_to_render = len(full_messages) - len(_drop_thinking_messages(context))
|
||
context_len = len(full_messages) - num_to_render
|
||
else:
|
||
num_to_render = len(messages)
|
||
context_len = len(context)
|
||
|
||
for idx in range(num_to_render):
|
||
prompt += render_message(
|
||
idx + context_len,
|
||
full_messages,
|
||
thinking_mode=thinking_mode,
|
||
drop_thinking=effective_drop_thinking,
|
||
reasoning_effort=reasoning_effort,
|
||
)
|
||
|
||
return prompt
|
||
|
||
|
||
def _drop_thinking_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
||
"""
|
||
Drop reasoning and non-essential messages before the last user message.
|
||
|
||
Behavior:
|
||
- Messages with role in ["user", "system", "tool", "latest_reminder"] are always kept.
|
||
- Messages at or after the last user index are always kept.
|
||
- Assistant messages before the last user get reasoning removed.
|
||
- Developer messages before the last user are dropped entirely.
|
||
"""
|
||
last_user_idx = find_last_user_index(messages)
|
||
result = []
|
||
keep_roles = {"user", "system", "tool", "latest_reminder", "direct_search_results"}
|
||
|
||
for idx, msg in enumerate(messages):
|
||
role = msg.get("role")
|
||
if role in keep_roles or idx >= last_user_idx:
|
||
result.append(msg)
|
||
elif role == "assistant":
|
||
msg = copy.copy(msg)
|
||
msg.pop("reasoning", None)
|
||
result.append(msg)
|
||
# developer and other roles before last_user_idx are dropped
|
||
|
||
return result
|
||
|
||
|
||
# ============================================================
|
||
# Parsing (Decoding model output)
|
||
# ============================================================
|
||
|
||
def _read_until_stop(index: int, text: str, stop: List[str]) -> Tuple[int, str, Optional[str]]:
|
||
"""
|
||
Read text from index until one of the stop strings is found.
|
||
|
||
Returns:
|
||
Tuple of (new_index, content_before_stop, matched_stop_string_or_None).
|
||
"""
|
||
min_pos = len(text)
|
||
matched_stop = None
|
||
|
||
for s in stop:
|
||
pos = text.find(s, index)
|
||
if pos != -1 and pos < min_pos:
|
||
min_pos = pos
|
||
matched_stop = s
|
||
|
||
if matched_stop:
|
||
content = text[index:min_pos]
|
||
return min_pos + len(matched_stop), content, matched_stop
|
||
else:
|
||
content = text[index:]
|
||
return len(text), content, None
|
||
|
||
|
||
def parse_tool_calls(index: int, text: str) -> Tuple[int, Optional[str], List[Dict[str, str]]]:
|
||
"""
|
||
Parse DSML tool calls from text starting at the given index.
|
||
|
||
Args:
|
||
index: Starting position in text.
|
||
text: The full text to parse.
|
||
|
||
Returns:
|
||
Tuple of (new_index, last_stop_token, list_of_tool_call_dicts).
|
||
Each tool call dict has "name" and "arguments" keys.
|
||
"""
|
||
tool_calls: List[Dict[str, Any]] = []
|
||
stop_token = None
|
||
tool_calls_end_token = f"</{dsml_token}{tool_calls_block_name}>"
|
||
|
||
while index < len(text):
|
||
index, content_before, stop_token = _read_until_stop(index, text, [f"<{dsml_token}invoke", tool_calls_end_token])
|
||
if content_before != ">\n":
|
||
raise ValueError(f"Tool call format error: expected '>\\n' but got '{content_before}'")
|
||
|
||
if stop_token == tool_calls_end_token:
|
||
break
|
||
|
||
if stop_token is None:
|
||
raise ValueError("Missing special token in tool calls")
|
||
|
||
index, tool_name_content, stop_token = _read_until_stop(index, text, [f"<{dsml_token}parameter", f"</{dsml_token}invoke"])
|
||
|
||
p_tool_name = re.findall(r'^\s*name="(.*?)">\n$', tool_name_content, flags=re.DOTALL)
|
||
if len(p_tool_name) != 1:
|
||
raise ValueError(f"Tool name format error: '{tool_name_content}'")
|
||
tool_name = p_tool_name[0]
|
||
|
||
tool_args: Dict[str, Tuple[str, str]] = {}
|
||
while stop_token == f"<{dsml_token}parameter":
|
||
index, param_content, stop_token = _read_until_stop(index, text, [f"/{dsml_token}parameter"])
|
||
|
||
param_kv = re.findall(r'^ name="(.*?)" string="(true|false)">(.*?)<$', param_content, flags=re.DOTALL)
|
||
if len(param_kv) != 1:
|
||
raise ValueError(f"Parameter format error: '{param_content}'")
|
||
param_name, string, param_value = param_kv[0]
|
||
|
||
if param_name in tool_args:
|
||
raise ValueError(f"Duplicate parameter name: '{param_name}'")
|
||
tool_args[param_name] = (param_value, string)
|
||
|
||
index, content, stop_token = _read_until_stop(index, text, [f"<{dsml_token}parameter", f"</{dsml_token}invoke"])
|
||
if content != ">\n":
|
||
raise ValueError(f"Parameter format error: expected '>\\n' but got '{content}'")
|
||
|
||
tool_call = decode_dsml_to_arguments(tool_name=tool_name, tool_args=tool_args)
|
||
tool_calls.append(tool_call)
|
||
|
||
return index, stop_token, tool_calls
|
||
|
||
|
||
def parse_message_from_completion_text(text: str, thinking_mode: str) -> Dict[str, Any]:
|
||
"""
|
||
Parse a model completion text into a structured assistant message.
|
||
|
||
This function takes the raw text output from the model (a single assistant turn)
|
||
and extracts:
|
||
- reasoning (thinking block)
|
||
- content (summary/response)
|
||
- tool_calls (if any)
|
||
|
||
NOTE: This function is designed to parse only correctly formatted strings and
|
||
will raise ValueError for malformed output.
|
||
|
||
Args:
|
||
text: The raw completion text (including EOS token).
|
||
thinking_mode: Either "chat" or "thinking".
|
||
|
||
Returns:
|
||
Dict with keys: "role", "content", "reasoning", "tool_calls".
|
||
tool_calls are in OpenAI format.
|
||
"""
|
||
summary_content, reasoning = "", ""
|
||
tool_calls: List[Dict[str, str]] = []
|
||
index, stop_token = 0, None
|
||
tool_calls_start_token = f"\n\n<{dsml_token}{tool_calls_block_name}"
|
||
|
||
is_thinking = thinking_mode == "thinking"
|
||
is_tool_calling = False
|
||
|
||
if is_thinking:
|
||
index, content_delta, stop_token = _read_until_stop(index, text, [thinking_end_token, tool_calls_start_token])
|
||
reasoning = content_delta
|
||
if stop_token != thinking_end_token:
|
||
raise ValueError("Invalid thinking format: missing </think>")
|
||
|
||
index, content_delta, stop_token = _read_until_stop(index, text, [eos_token, tool_calls_start_token])
|
||
summary_content = content_delta
|
||
if stop_token == tool_calls_start_token:
|
||
is_tool_calling = True
|
||
else:
|
||
if stop_token != eos_token:
|
||
raise ValueError("Invalid format: missing EOS token")
|
||
|
||
if is_tool_calling:
|
||
index, stop_token, tool_calls = parse_tool_calls(index, text)
|
||
|
||
index, tool_ends_text, stop_token = _read_until_stop(index, text, [eos_token])
|
||
if tool_ends_text:
|
||
raise ValueError("Unexpected content after tool calls")
|
||
|
||
if len(text) != index or stop_token not in [eos_token, None]:
|
||
raise ValueError("Unexpected content at end")
|
||
|
||
for sp_token in [bos_token, eos_token, thinking_start_token, thinking_end_token, dsml_token]:
|
||
if sp_token in summary_content or sp_token in reasoning:
|
||
raise ValueError(f"Unexpected special token '{sp_token}' in content")
|
||
|
||
return {
|
||
"role": "assistant",
|
||
"content": summary_content,
|
||
"reasoning": reasoning,
|
||
"tool_calls": tool_calls_to_openai_format(tool_calls)
|
||
}
|
||
|
||
# fmt: on
|