Losita/mai-bot
0
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

perf: stabilize maisaka prompt cache

Browse Source
This commit is contained in:
SengokuCola
2026-05-01 13:00:54 +08:00
parent badd4988b6
commit 88b895a925
4 changed files with 117 additions and 81 deletions
Download Patch File Download Diff File Expand all files Collapse all files

79
src/maisaka/chat_loop_service.py
View File

@@ -41,6 +41,11 @@ from .display.prompt_cli_renderer import PromptCLIVisualizer
from .visual_mode_utils import resolve_enable_visual_planner
TIMING_GATE_TOOL_NAMES = {"continue", "no_reply", "wait"}
REQUEST_TYPE_BY_REQUEST_KIND = {
"planner": "maisaka_planner",
"timing_gate": "maisaka_timing_gate",
}
CONTEXT_SELECTION_CACHE_STABILITY_RATIO = 2.0
@dataclass(slots=True)
@@ -212,7 +217,7 @@ class MaisakaChatLoopService:
self._chat_system_prompt = f"{self._personality_prompt}\n\nYou are a helpful AI assistant."
else:
self._chat_system_prompt = chat_system_prompt
self._llm_chat = LLMServiceClient(task_name="planner", request_type="maisaka_planner")
self._llm_chat_clients: dict[str, LLMServiceClient] = {}
@property
def personality_prompt(self) -> str:
@@ -220,6 +225,30 @@ class MaisakaChatLoopService:
return self._personality_prompt
@staticmethod
def _resolve_llm_request_type(request_kind: str) -> str:
"""根据 Maisaka 请求类型解析 LLM 统计口径。"""
normalized_request_kind = str(request_kind or "").strip()
return REQUEST_TYPE_BY_REQUEST_KIND.get(
normalized_request_kind,
f"maisaka_{normalized_request_kind}" if normalized_request_kind else "maisaka_planner",
)
def _get_llm_chat_client(self, request_kind: str) -> LLMServiceClient:
"""获取当前请求类型对应的 planner LLM 客户端。"""
request_type = self._resolve_llm_request_type(request_kind)
llm_client = self._llm_chat_clients.get(request_type)
if llm_client is None:
llm_client = LLMServiceClient(
task_name="planner",
request_type=request_type,
session_id=self._session_id,
)
self._llm_chat_clients[request_type] = llm_client
return llm_client
@staticmethod
def _get_runtime_manager() -> Any:
"""获取插件运行时管理器。
@@ -321,7 +350,13 @@ class MaisakaChatLoopService:
@staticmethod
def _build_time_block() -> str:
"""构建当前时间提示块。"""
"""构建静态时间提示块。"""
return "当前时间会在每次请求末尾以用户消息形式提供。"
@staticmethod
def _build_current_time_user_message() -> str:
"""构建追加到请求末尾的当前时间消息。"""
return f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
@@ -446,7 +481,11 @@ class MaisakaChatLoopService:
messages.append(llm_message)
normalized_injected_messages: List[Message] = []
for injected_message in injected_user_messages or []:
final_user_messages = [
*(injected_user_messages or []),
self._build_current_time_user_message(),
]
for injected_message in final_user_messages:
normalized_message = str(injected_message or "").strip()
if not normalized_message:
continue
@@ -458,31 +497,10 @@ class MaisakaChatLoopService:
)
if normalized_injected_messages:
insertion_index = self._resolve_injected_user_messages_insertion_index(messages)
messages[insertion_index:insertion_index] = normalized_injected_messages
messages.extend(normalized_injected_messages)
return messages
@staticmethod
def _resolve_injected_user_messages_insertion_index(messages: Sequence[Message]) -> int:
"""计算 injected meta user messages 在请求中的插入位置。
规则与 deferred attachment 更接近：
- 从尾部向前寻找最近的 stopping point；
- stopping point 为 assistant 消息或 tool 结果消息；
- 找到后插入到其后面；
- 若不存在 stopping point，则退回到 system 消息之后。
"""
for index in range(len(messages) - 1, -1, -1):
message = messages[index]
if message.role in {RoleType.Assistant, RoleType.Tool}:
return index + 1
if messages and messages[0].role == RoleType.System:
return 1
return 0
async def chat_loop_step(
self,
chat_history: List[LLMContextMessage],
@@ -575,7 +593,8 @@ class MaisakaChatLoopService:
tool_definitions=list(all_tools),
)
generation_result = await self._llm_chat.generate_response_with_messages(
llm_chat = self._get_llm_chat_client(request_kind)
generation_result = await llm_chat.generate_response_with_messages(
message_factory=message_factory,
options=LLMGenerationOptions(
tool_options=all_tools if all_tools else None,
@@ -654,7 +673,11 @@ class MaisakaChatLoopService:
chat_history,
request_kind=request_kind,
)
effective_context_size = max(1, int(max_context_size or global_config.chat.max_context_size))
base_context_size = max(1, int(max_context_size or global_config.chat.max_context_size))
effective_context_size = max(
base_context_size,
int(base_context_size * CONTEXT_SELECTION_CACHE_STABILITY_RATIO),
)
selected_indices: List[int] = []
counted_message_count = 0
@@ -690,9 +713,11 @@ class MaisakaChatLoopService:
selected_history, _ = normalize_tool_result_order(selected_history)
tool_message_count = sum(1 for message in selected_history if isinstance(message, ToolResultMessage))
normal_message_count = len(selected_history) - tool_message_count
stability_text = f"|cache_window {base_context_size}->{effective_context_size}"
selection_reason = (
f"实际发送 {len(selected_history)} 条消息"
f"|消息 {normal_message_count} 条|tool {tool_message_count} 条"
f"{stability_text}"
)
return (
selected_history,

78
src/maisaka/history_post_processor.py
View File

@@ -3,11 +3,11 @@
from dataclasses import dataclass
from math import ceil
from .context_messages import AssistantMessage, LLMContextMessage
from .context_messages import LLMContextMessage
from .history_utils import drop_leading_orphan_tool_results, drop_orphan_tool_results, normalize_tool_result_order
EARLY_TRIM_RATIO = 0.3
TRIM_THRESHOLD_RATIO = 1.2
TRIM_TARGET_RATIO = 1.0
TRIM_THRESHOLD_RATIO = 2.0
@dataclass(slots=True)
@@ -36,21 +36,16 @@ def process_chat_history_after_cycle(
compact_removed_count = 0
trim_threshold = ceil(max_context_size * TRIM_THRESHOLD_RATIO)
if remaining_context_count > trim_threshold:
removed_early_message_count = _remove_early_history_messages(processed_history)
processed_history, removed_after_message_trim_count, moved_after_message_trim_count = (
_normalize_history_structure(processed_history)
target_context_count = max(1, int(max_context_size * TRIM_TARGET_RATIO))
removed_early_message_count = _trim_history_to_context_target(
processed_history,
target_context_count=target_context_count,
)
removed_assistant_thought_count = _remove_early_assistant_thoughts(processed_history)
processed_history, removed_after_thought_trim_count, moved_after_thought_trim_count = (
_normalize_history_structure(processed_history)
processed_history, removed_after_trim_count, moved_after_trim_count = _normalize_history_structure(
processed_history
)
compact_removed_count = (
removed_early_message_count
+ removed_after_message_trim_count
+ removed_assistant_thought_count
+ removed_after_thought_trim_count
)
moved_tool_result_count += moved_after_message_trim_count + moved_after_thought_trim_count
compact_removed_count = removed_early_message_count + removed_after_trim_count
moved_tool_result_count += moved_after_trim_count
remaining_context_count = sum(1 for message in processed_history if message.count_in_context)
removed_count = normalized_removed_count + compact_removed_count
@@ -78,42 +73,27 @@ def _normalize_history_structure(
)
def _remove_early_history_messages(chat_history: list[LLMContextMessage]) -> int:
"""移除最早 30% 的全部历史消息。"""
def _trim_history_to_context_target(
chat_history: list[LLMContextMessage],
*,
target_context_count: int,
) -> int:
"""移除最早的一段历史，直到普通上下文消息数量降到目标值以内。"""
remaining_context_count = sum(1 for message in chat_history if message.count_in_context)
if remaining_context_count <= target_context_count:
return 0
remove_count = 0
for message in chat_history:
remove_count += 1
if message.count_in_context:
remaining_context_count -= 1
if remaining_context_count <= target_context_count:
break
remove_count = int(len(chat_history) * EARLY_TRIM_RATIO)
if remove_count <= 0:
return 0
del chat_history[:remove_count]
return remove_count
def _remove_early_assistant_thoughts(chat_history: list[LLMContextMessage]) -> int:
"""移除最早 30% 的非工具 assistant 思考内容。"""
candidate_indexes = [
index
for index, message in enumerate(chat_history)
if isinstance(message, AssistantMessage)
and not message.tool_calls
and message.source_kind != "perception"
and bool(message.content.strip())
]
remove_count = int(len(candidate_indexes) * EARLY_TRIM_RATIO)
if remove_count <= 0:
return 0
removed_indexes = set(candidate_indexes[:remove_count])
filtered_history: list[LLMContextMessage] = []
removed_total = 0
for index, message in enumerate(chat_history):
if index in removed_indexes:
removed_total += 1
continue
filtered_history.append(message)
chat_history[:] = filtered_history
return removed_total

9
src/maisaka/reasoning_engine.py
View File

@@ -52,7 +52,7 @@ if TYPE_CHECKING:
logger = get_logger("maisaka_reasoning_engine")
TIMING_GATE_CONTEXT_LIMIT = 24
TIMING_GATE_CONTEXT_DROP_HEAD_RATIO = 0.7
TIMING_GATE_MAX_TOKENS = 384
TIMING_GATE_MAX_ATTEMPTS = 3
TIMING_GATE_TOOL_NAMES = {"continue", "no_reply", "wait"}
@@ -124,7 +124,6 @@ class MaisakaReasoningEngine:
async def _run_timing_gate_sub_agent(
self,
*,
context_message_limit: int,
system_prompt: str,
tool_definitions: list[dict[str, Any]],
) -> Any:
@@ -134,7 +133,10 @@ class MaisakaReasoningEngine:
"""
return await self._runtime.run_sub_agent(
context_message_limit=context_message_limit,
context_message_limit=self._runtime._max_context_size,
drop_head_context_count=int(
self._runtime._max_context_size * TIMING_GATE_CONTEXT_DROP_HEAD_RATIO,
),
system_prompt=system_prompt,
request_kind="timing_gate",
interrupt_flag=None,
@@ -255,7 +257,6 @@ class MaisakaReasoningEngine:
invalid_tool_text = ""
for attempt_index in range(TIMING_GATE_MAX_ATTEMPTS):
response = await self._run_timing_gate_sub_agent(
context_message_limit=TIMING_GATE_CONTEXT_LIMIT,
system_prompt=self._build_timing_gate_system_prompt(),
tool_definitions=get_timing_tools(),
)

32
src/maisaka/runtime.py
View File

@@ -45,6 +45,7 @@ from .context_messages import (
from .display.display_utils import build_tool_call_summary_lines, format_token_count
from .display.prompt_cli_renderer import PromptCLIVisualizer
from .display.stage_status_board import remove_stage_status, update_stage_status
from .history_utils import drop_leading_orphan_tool_results
from .reasoning_engine import MaisakaReasoningEngine
from .reply_effect import ReplyEffectTracker
from .reply_effect.image_utils import extract_visual_attachments_from_sequence
@@ -583,6 +584,7 @@ class MaisakaHeartFlowChatting:
self,
*,
context_message_limit: int,
drop_head_context_count: int = 0,
system_prompt: str,
request_kind: str = "sub_agent",
extra_messages: Optional[Sequence[LLMContextMessage]] = None,
@@ -598,7 +600,10 @@ class MaisakaHeartFlowChatting:
request_kind=request_kind,
max_context_size=context_message_limit,
)
sub_agent_history = list(selected_history)
sub_agent_history = self._drop_head_context_messages(
selected_history,
drop_head_context_count,
)
if extra_messages:
sub_agent_history.extend(list(extra_messages))
@@ -616,6 +621,31 @@ class MaisakaHeartFlowChatting:
tool_definitions=[] if tool_definitions is None else tool_definitions,
)
@staticmethod
def _drop_head_context_messages(
chat_history: Sequence[LLMContextMessage],
drop_context_count: int,
) -> list[LLMContextMessage]:
"""从已选上下文头部丢弃指定数量的普通上下文消息。"""
if drop_context_count <= 0:
return list(chat_history)
first_kept_index = 0
dropped_context_count = 0
while (
first_kept_index < len(chat_history)
and dropped_context_count < drop_context_count
):
message = chat_history[first_kept_index]
if message.count_in_context:
dropped_context_count += 1
first_kept_index += 1
trimmed_history = list(chat_history[first_kept_index:])
trimmed_history, _ = drop_leading_orphan_tool_results(trimmed_history)
return trimmed_history
async def _run_reply_effect_judge(self, prompt: str) -> str:
"""运行回复效果观察器使用的临时 LLM 评审。"""