fix(deepseek-v4): patch vendored dsv4 tokenizer reasoning_effort handling

The deepseek_v4 tokenizer in the sparkrun-vllm-ds4-gb10:fixed overlay
mishandles reasoning_effort: high was accepted but silently injected
nothing (only max triggered text), low/unknown values were coerced to
high (unreachable low, silent escalation), and a single REASONING_EFFORT_MAX
wording existed with max using the high wording.

Replace the single constant with a low/high/max dict using the full
model-card wording (deepseek-ai/DeepSeek-V4-Flash-0731 encoding/README.md),
fix the normalization so low is reachable and unknown values map to None,
and inject the prompt for both high and max. Verified on dgx3: lengths
increase low < high < max and the injected text is the complete detailed
prompt, not the truncated forum-patch version.

docker-images/deepseek-v4-flash-dspark/Dockerfile

@@ -13,3 +13,14 @@
 FROM aidendle94/sparkrun-vllm-ds4-gb10:production-3.75
 
 RUN pip install --no-cache-dir --no-deps --force-reinstall instanttensor==0.1.9
+
+# Fix reasoning_effort handling in the vendored deepseek_v4 tokenizer:
+# - high was accepted but silently injected nothing (only max triggered text)
+# - low / unknown values were coerced to high (unreachable low, silent escalation)
+# - only a single REASONING_EFFORT_MAX wording existed (max used the "high" text)
+# Replaces the single constant with a low/high/max dict using the full model-card
+# wording and injects for both high and max. See deepseek_v4_encoding.py gate.
+# (Mirrors the local-inference-lab encoding_dsv4.py reference implementation.)
+COPY patches/deepseek_v4.py \
+     patches/deepseek_v4_encoding.py \
+     /opt/venv/lib/python3.12/site-packages/vllm/tokenizers/

docker-images/deepseek-v4-flash-dspark/patches/deepseek_v4.py

@@ -0,0 +1,96 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import copy
+from typing import Any
+
+from transformers import PreTrainedTokenizerFast
+
+from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+
+from .deepseek_v4_encoding import encode_messages
+from .hf import HfTokenizer, get_cached_tokenizer
+from .protocol import TokenizerLike
+
+
+def get_deepseek_v4_tokenizer(tokenizer: HfTokenizer) -> HfTokenizer:
+    """
+    Wraps a tokenizer to use the custom DeepSeek V4 chat template encoding.
+    """
+    dsv4_tokenizer = copy.copy(tokenizer)
+
+    added_vocab = tokenizer.get_added_vocab()
+    added_vocab_size = len(added_vocab)
+    tokenizer_vocab_size = tokenizer.vocab_size
+
+    class _DeepseekV4Tokenizer(tokenizer.__class__):  # type: ignore
+        def apply_chat_template(
+            self,
+            messages: list["ChatCompletionMessageParam"],
+            tools: list[dict[str, Any]] | None = None,
+            **kwargs,
+        ) -> str | list[int]:
+            thinking = kwargs.get("thinking", False)
+            enable_thinking = kwargs.get("enable_thinking", False)
+            thinking = thinking or enable_thinking
+            thinking_mode = "thinking" if thinking else "chat"
+
+            conversation = kwargs.get("conversation", messages)
+            messages = conversation.copy()
+            if tools is not None and len(tools) > 0:
+                messages.insert(0, {"role": "system"})
+                messages[0]["tools"] = tools  # type: ignore[typeddict-unknown-key]
+
+            reasoning_effort = kwargs.get("reasoning_effort")
+            if not isinstance(reasoning_effort, str):
+                reasoning_effort = None
+            elif reasoning_effort == "none":
+                thinking_mode = "chat"
+                reasoning_effort = None
+            elif reasoning_effort in ("max", "xhigh"):
+                reasoning_effort = "max"
+            elif reasoning_effort not in ("low", "high", "max"):
+                reasoning_effort = None
+
+            encode_config = dict(
+                thinking_mode=thinking_mode,
+                drop_thinking=kwargs.get("drop_thinking", True),
+                reasoning_effort=reasoning_effort,
+            )
+
+            prompt_str = encode_messages(messages, **encode_config)  # type: ignore
+
+            if kwargs.get("tokenize", True):
+                tokenizer_kwargs = {
+                    k: kwargs[k] for k in ("truncation", "max_length") if k in kwargs
+                }
+                return self.encode(
+                    prompt_str,
+                    add_special_tokens=False,
+                    **tokenizer_kwargs,
+                )
+
+            return prompt_str
+
+        def num_special_tokens_to_add(self) -> int:
+            return len(self.encode(""))
+
+        def __len__(self) -> int:
+            return tokenizer_vocab_size + added_vocab_size
+
+        def get_added_vocab(self) -> dict[str, int]:
+            return added_vocab.copy()
+
+        def __reduce__(self):
+            return get_deepseek_v4_tokenizer, (tokenizer,)
+
+    _DeepseekV4Tokenizer.__name__ = f"DSV4{tokenizer.__class__.__name__}"
+
+    dsv4_tokenizer.__class__ = _DeepseekV4Tokenizer
+    return dsv4_tokenizer
+
+
+class DeepseekV4Tokenizer(TokenizerLike):
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs) -> HfTokenizer:
+        tokenizer = PreTrainedTokenizerFast.from_pretrained(*args, **kwargs)
+        return get_cached_tokenizer(get_deepseek_v4_tokenizer(tokenizer))

docker-images/deepseek-v4-flash-dspark/patches/deepseek_v4_encoding.py

@@ -0,0 +1,766 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa
+# fmt: off
+
+"""
+DeepSeek-V4 Encoding
+
+A self-contained implementation for encoding/decoding DeepSeek-V4 chat messages
+with tool calling, thinking mode, and quick instruction task support.
+"""
+
+from typing import Any, Dict, List, Union, Optional, Tuple
+import copy
+import json
+
+import regex as re
+
+# ============================================================
+# Special Tokens
+# ============================================================
+
+bos_token: str = "<｜begin▁of▁sentence｜>"
+eos_token: str = "<｜end▁of▁sentence｜>"
+thinking_start_token: str = "<think>"
+thinking_end_token: str = "</think>"
+dsml_token: str = "｜DSML｜"
+
+USER_SP_TOKEN = "<｜User｜>"
+ASSISTANT_SP_TOKEN = "<｜Assistant｜>"
+LATEST_REMINDER_SP_TOKEN = "<｜latest_reminder｜>"
+
+# Task special tokens for internal classification tasks
+DS_TASK_SP_TOKENS = {
+    "action": "<｜action｜>",
+    "query": "<｜query｜>",
+    "authority": "<｜authority｜>",
+    "domain": "<｜domain｜>",
+    "title": "<｜title｜>",
+    "read_url": "<｜read_url｜>",
+}
+VALID_TASKS = set(DS_TASK_SP_TOKENS.keys())
+
+# ============================================================
+# Templates
+# ============================================================
+
+system_msg_template: str = "{content}"
+user_msg_template: str = "{content}"
+latest_reminder_msg_template: str = "{content}"
+assistant_msg_template: str = "{reasoning}{content}{tool_calls}" + eos_token
+assistant_msg_wo_eos_template: str = "{reasoning}{content}{tool_calls}"
+thinking_template: str = "{reasoning}"
+
+response_format_template: str = (
+    "## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n{schema}"
+)
+tool_call_template: str = (
+    "<{dsml_token}invoke name=\"{name}\">\n{arguments}\n</{dsml_token}invoke>"
+)
+tool_calls_template = (
+    "<{dsml_token}{tc_block_name}>\n{tool_calls}\n</{dsml_token}{tc_block_name}>"
+)
+tool_calls_block_name: str = "tool_calls"
+
+tool_output_template: str = (
+    "<tool_result>{content}</tool_result>"
+)
+
+REASONING_EFFORT_PROMPTS = {
+    "low": "",
+    "high": (
+        "Reasoning Effort: Absolute maximum with no shortcuts permitted.\n"
+        "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"
+        "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"
+    ),
+    "max": (
+        "Reasoning Effort: Beyond maximum — exhaustive, relentless, and uncompromising.\n"
+        "You MUST reason with the utmost depth and rigor, leaving absolutely nothing to chance: exhaustively decompose the problem into its most fundamental components, trace every causal chain to its root, and resolve the underlying cause rather than any surface symptom.\n"
+        "Do not stop reasoning until you have independently verified the solution from multiple angles and are certain that no assumption remains unchecked and no error remains undiscovered.\n\n"
+    ),
+}
+
+TOOLS_TEMPLATE = """## Tools
+
+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:
+
+<{dsml_token}tool_calls>
+<{dsml_token}invoke name="$TOOL_NAME">
+<{dsml_token}parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</{dsml_token}parameter>
+...
+</{dsml_token}invoke>
+<{dsml_token}invoke name="$TOOL_NAME2">
+...
+</{dsml_token}invoke>
+</{dsml_token}tool_calls>
+
+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"`.
+
+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.
+
+Otherwise, output directly after {thinking_end_token} with tool calls or final response.
+
+### Available Tool Schemas
+
+{tool_schemas}
+
+You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
+"""
+
+# ============================================================
+# Utility Functions
+# ============================================================
+
+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 tools_from_openai_format(tools):
+    """Extract function definitions from OpenAI-format tool list."""
+    return [tool["function"] for tool in tools]
+
+
+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 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 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 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 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
+
+
+# ============================================================
+# Message Rendering
+# ============================================================
+
+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; "low" adds nothing)
+    assert reasoning_effort is None or reasoning_effort in REASONING_EFFORT_PROMPTS, \
+        f"Invalid reasoning effort: {reasoning_effort}"
+    if index == 0 and thinking_mode == "thinking" and reasoning_effort in ("high", "max"):
+        prompt += REASONING_EFFORT_PROMPTS[reasoning_effort]
+
+    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
+
+
+# ============================================================
+# Preprocessing
+# ============================================================
+
+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 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
+
+
+# ============================================================
+# 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