vllm.tokenizers.deepseek_v4_encoding ¶

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

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 decode_dsml_to_arguments(tool_name: str, tool_args: Dict[str, Tuple[str, str]]) -> Dict[str, str]:
    """
    Decode DSML parameters back to a tool call dict.

    Args:
        tool_name: Name of the tool.
        tool_args: Dict mapping param_name -> (value, is_string_flag).

    Returns:
        Dict with "name" and "arguments" (JSON string) keys.
    """
    def _decode_value(key: str, value: str, string: str):
        if string == "true":
            value = to_json(value)
        return f"{to_json(key)}: {value}"

    tool_args_json = "{" + ", ".join([_decode_value(k, v, string=is_str) for k, (v, is_str) in tool_args.items()]) + "}"
    return dict(name=tool_name, arguments=tool_args_json)

def encode_arguments_to_dsml(tool_call: Dict[str, Any]) -> str:
    """
    Encode tool call arguments into DSML parameter format.

    Args:
        tool_call: Dict with "name" and "arguments" keys.

    Returns:
        DSML-formatted parameter string.
    """
    p_dsml_template = '<{dsml_token}parameter name="{key}" string="{is_str}">{value}</{dsml_token}parameter>'
    P_dsml_strs = []

    if isinstance(tool_call["arguments"], str):
        arguments = json.loads(tool_call["arguments"])
    else:
        arguments = tool_call["arguments"]

    for k, v in arguments.items():
        p_dsml_str = p_dsml_template.format(
            dsml_token=dsml_token,
            key=k,
            is_str="true" if isinstance(v, str) else "false",
            value=v if isinstance(v, str) else to_json(v),
        )
        P_dsml_strs.append(p_dsml_str)

    return "\n".join(P_dsml_strs)

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 find_last_user_index(messages: List[Dict[str, Any]]) -> int:
    """Find the index of the last user/developer message."""
    last_user_index = -1
    for idx in range(len(messages) - 1, -1, -1):
        if messages[idx].get("role") in ["user", "developer"]:
            last_user_index = idx
            break
    return last_user_index

def merge_tool_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Merge tool messages into the preceding user message using content_blocks format.

    DeepSeek-V4 does not have a standalone "tool" role; instead, tool results
    are encoded as <tool_result> blocks within user messages.

    This function converts a standard OpenAI-format conversation (with separate
    "tool" role messages) into V4 format where tool results are merged into
    user messages.

    Args:
        messages: List of message dicts in OpenAI format.

    Returns:
        Processed message list with tool messages merged into user messages.
    """
    merged: List[Dict[str, Any]] = []

    for msg in messages:
        msg = copy.deepcopy(msg)
        role = msg.get("role")

        if role == "tool":
            # Convert tool message to a user message with tool_result block
            tool_block = {
                "type": "tool_result",
                "tool_use_id": msg.get("tool_call_id", ""),
                "content": msg.get("content", ""),
            }
            # Merge into previous message if it's already a user (merged tool)
            if merged and merged[-1].get("role") == "user" and "content_blocks" in merged[-1]:
                merged[-1]["content_blocks"].append(tool_block)
            else:
                merged.append({
                    "role": "user",
                    "content_blocks": [tool_block],
                })
        elif role == "user":
            text_block = {"type": "text", "text": msg.get("content", "")}
            if merged and merged[-1].get("role") == "user" and "content_blocks" in merged[-1] and merged[-1].get("task") is None:
                merged[-1]["content_blocks"].append(text_block)
            else:
                new_msg = {
                    "role": "user",
                    "content": msg.get("content", ""),
                    "content_blocks": [text_block],
                }
                # Preserve extra fields (task, wo_eos, mask, etc.)
                for key in ("task", "wo_eos", "mask"):
                    if key in msg:
                        new_msg[key] = msg[key]
                merged.append(new_msg)
        else:
            merged.append(msg)

    return merged

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)
    }

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 render_message(index: int, messages: List[Dict[str, Any]], thinking_mode: str, drop_thinking: bool = True, reasoning_effort: Optional[str] = None) -> str:
    """
    Render a single message at the given index into its encoded string form.

    This is the core function that converts each message in the conversation
    into the DeepSeek-V4 format.

    Args:
        index: Index of the message to render.
        messages: Full list of messages in the conversation.
        thinking_mode: Either "chat" or "thinking".
        drop_thinking: Whether to drop reasoning content from earlier turns.
        reasoning_effort: Optional reasoning effort level ("max", "high", or None).

    Returns:
        Encoded string for this message.
    """
    assert 0 <= index < len(messages)
    assert thinking_mode in ["chat", "thinking"], f"Invalid thinking_mode `{thinking_mode}`"

    prompt = ""
    msg = messages[index]
    last_user_idx = find_last_user_index(messages)

    role = msg.get("role")
    content = msg.get("content")
    tools = msg.get("tools")
    response_format = msg.get("response_format")
    tool_calls = msg.get("tool_calls")
    reasoning = msg.get("reasoning")
    wo_eos = msg.get("wo_eos", False)

    if tools:
        tools = tools_from_openai_format(tools)
    if tool_calls:
        tool_calls = tool_calls_from_openai_format(tool_calls)

    # Reasoning effort prefix (only at index 0 in thinking mode with max effort)
    assert reasoning_effort in ['max', None, 'high'], f"Invalid reasoning effort: {reasoning_effort}"
    if index == 0 and thinking_mode == "thinking" and reasoning_effort == 'max':
        prompt += REASONING_EFFORT_MAX

    if role == "system":
        prompt += system_msg_template.format(content=content or "")
        if tools:
            prompt += "\n\n" + render_tools(tools)
        if response_format:
            prompt += "\n\n" + response_format_template.format(schema=to_json(response_format))

    elif role == "developer":
        assert content, f"Invalid message for role `{role}`: {msg}"

        content_developer = USER_SP_TOKEN
        content_developer += content

        if tools:
            content_developer += "\n\n" + render_tools(tools)
        if response_format:
            content_developer += "\n\n" + response_format_template.format(schema=to_json(response_format))

        prompt += user_msg_template.format(content=content_developer)

    elif role == "user":
        prompt += USER_SP_TOKEN

        # Handle content blocks (tool results mixed with text)
        content_blocks = msg.get("content_blocks")
        if content_blocks:
            parts = []
            for block in content_blocks:
                block_type = block.get("type")
                if block_type == "text":
                    parts.append(block.get("text", ""))
                elif block_type == "tool_result":
                    tool_content = block.get("content", "")
                    if isinstance(tool_content, list):
                        text_parts = []
                        for b in tool_content:
                            if b.get("type") == "text":
                                text_parts.append(b.get("text", ""))
                            else:
                                text_parts.append(f"[Unsupported {b.get('type')}]")
                        tool_content = "\n\n".join(text_parts)
                    parts.append(tool_output_template.format(content=tool_content))
                else:
                    parts.append(f"[Unsupported {block_type}]")
            prompt += "\n\n".join(parts)
        else:
            prompt += content or ""

    elif role == "latest_reminder":
        prompt += LATEST_REMINDER_SP_TOKEN + latest_reminder_msg_template.format(content=content)

    elif role == "tool":
        raise NotImplementedError("deepseek_v4 merges tool messages into user; please preprocess with merge_tool_messages()")

    elif role == "assistant":
        thinking_part = ""
        tc_content = ""

        if tool_calls:
            tc_list = [
                tool_call_template.format(
                    dsml_token=dsml_token,
                    name=tc.get("name"),
                    arguments=encode_arguments_to_dsml(tc)
                )
                for tc in tool_calls
            ]
            tc_content += '\n\n' + tool_calls_template.format(
                dsml_token=dsml_token,
                tool_calls="\n".join(tc_list),
                tc_block_name=tool_calls_block_name,
            )

        summary_content = content or ""
        reasoning = reasoning or ""

        # Check if previous message has a task - if so, this is a task output (no thinking)
        prev_has_task = index - 1 >= 0 and messages[index - 1].get("task") is not None

        if thinking_mode == "thinking" and not prev_has_task:
            if not drop_thinking or index > last_user_idx:
                thinking_part = thinking_template.format(reasoning=reasoning) + thinking_end_token
            else:
                thinking_part = ""

        if wo_eos:
            prompt += assistant_msg_wo_eos_template.format(
                reasoning=thinking_part,
                content=summary_content,
                tool_calls=tc_content,
            )
        else:
            prompt += assistant_msg_template.format(
                reasoning=thinking_part,
                content=summary_content,
                tool_calls=tc_content,
            )
    else:
        raise NotImplementedError(f"Unknown role: {role}")

    # Append transition tokens based on what follows
    if index + 1 < len(messages) and messages[index + 1].get("role") not in ["assistant", "latest_reminder"]:
        return prompt

    task = messages[index].get("task")
    if task is not None:
        # Task special token for internal classification tasks
        assert task in VALID_TASKS, f"Invalid task: '{task}'. Valid tasks are: {list(VALID_TASKS)}"
        task_sp_token = DS_TASK_SP_TOKENS[task]

        if task != "action":
            # Non-action tasks: append task sp token directly after the message
            prompt += task_sp_token
        else:
            # Action task: append Assistant + thinking token + action sp token
            prompt += ASSISTANT_SP_TOKEN
            prompt += thinking_end_token if thinking_mode != "thinking" else thinking_start_token
            prompt += task_sp_token

    elif messages[index].get("role") in ["user", "developer"]:
        # Normal generation: append Assistant + thinking token
        prompt += ASSISTANT_SP_TOKEN
        if not drop_thinking and thinking_mode == "thinking":
            prompt += thinking_start_token
        elif drop_thinking and thinking_mode == "thinking" and index >= last_user_idx:
            prompt += thinking_start_token
        else:
            prompt += thinking_end_token

    return prompt

def render_tools(tools: List[Dict[str, Union[str, Dict[str, Any]]]]) -> str:
    """
    Render tool schemas into the system prompt format.

    Args:
        tools: List of tool schema dicts (each with name, description, parameters).

    Returns:
        Formatted tools section string.
    """
    tools_json = [to_json(t) for t in tools]

    return TOOLS_TEMPLATE.format(
        tool_schemas="\n".join(tools_json),
        dsml_token=dsml_token,
        thinking_start_token=thinking_start_token,
        thinking_end_token=thinking_end_token,
    )

def sort_tool_results_by_call_order(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Sort tool_result blocks within user messages by the order of tool_calls
    in the preceding assistant message.

    Args:
        messages: Preprocessed message list (after merge_tool_messages).

    Returns:
        Message list with sorted tool result blocks.
    """
    last_tool_call_order: Dict[str, int] = {}

    for msg in messages:
        role = msg.get("role")
        if role == "assistant" and msg.get("tool_calls"):
            last_tool_call_order = {}
            for idx, tc in enumerate(msg["tool_calls"]):
                tc_id = tc.get("id") or tc.get("function", {}).get("id", "")
                if tc_id:
                    last_tool_call_order[tc_id] = idx

        elif role == "user" and msg.get("content_blocks"):
            tool_blocks = [b for b in msg["content_blocks"] if b.get("type") == "tool_result"]
            if len(tool_blocks) > 1 and last_tool_call_order:
                sorted_blocks = sorted(
                    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

def to_json(value: Any) -> str:
    """Serialize a value to JSON string."""
    try:
        return json.dumps(value, ensure_ascii=False)
    except Exception:
        return json.dumps(value, ensure_ascii=True)

def tool_calls_from_openai_format(tool_calls):
    """Convert OpenAI-format tool calls to internal format."""
    return [
        {
            "name": tool_call["function"]["name"],
            "arguments": tool_call["function"]["arguments"],
        }
        for tool_call in tool_calls
    ]

def tool_calls_to_openai_format(tool_calls):
    """Convert internal tool calls to OpenAI format."""
    return [
        {
            "type": "function",
            "function": {
                "name": tool_call["name"],
                "arguments": tool_call["arguments"],
            }
        }
        for tool_call in tool_calls
    ]

def tools_from_openai_format(tools):
    """Extract function definitions from OpenAI-format tool list."""
    return [tool["function"] for tool in tools]