refactor: 移除 Python 后端源码,全面迁移至 Rust gateway 架构

- 删除全部 Python 源码 (src/) 及 Alembic 迁移脚本,归档至 _deprecated_py_src/
- 重构 Rust gateway ai_pipeline: 拆分 planner/finalize 模块,新增 contracts/adaptation 层
- 重组 handlers 模块为 admin/public/proxy/internal/shared 子模块结构
- 新增 executor 模块,引入 Rust 原生数据库迁移 (aether-data/migrations)
- 简化 CI/Docker 构建流程,移除 base image 二级构建,统一为单一 app image
- 移除 Python 相关基础设施文件 (entrypoint.sh, gunicorn_conf.py, Dockerfile.base)
This commit is contained in:
fawney19
2026-04-03 16:26:16 +08:00
parent 8f26e1a31f
commit 1d9c77522a
868 changed files with 1735 additions and 2433 deletions

View File

@@ -0,0 +1,7 @@
"""
消息处理服务模块
"""
from .thinking_rectifier import ThinkingRectifier
__all__ = ["ThinkingRectifier"]

View File

@@ -0,0 +1,314 @@
"""
Thinking 整流器Rectifier
采用 cc-switch 的"错误触发"模式,在遇到 Thinking 签名/结构错误时触发整流。
核心功能:
1. 移除所有 thinking 和 redacted_thinking 块
2. 移除非 thinking 块上的 signature 字段
3. 条件删除顶层 thinking 参数
使用场景:
当遇到 ThinkingSignatureException 时,调用 rectify() 整流请求体后重试一次。
"""
import copy
import json
from typing import Any
from src.core.logger import logger
class ThinkingRectifier:
"""
Thinking 整流器
在遇到 Thinking 签名/结构错误时,整流请求体以便重试。
采用"彻底清洗 + 条件禁用 thinking"策略。
"""
@staticmethod
def rectify(request_body: dict[str, Any]) -> tuple[dict[str, Any], bool]:
"""
整流请求体
执行以下操作:
1. 移除所有 thinking 和 redacted_thinking 块
2. 移除非 thinking 块上的 signature 字段
3. 条件删除顶层 thinking 参数
Args:
request_body: 原始请求体
Returns:
Tuple[整流后的请求体, 是否有修改]
"""
if not request_body:
return request_body, False
# 深拷贝以避免修改原始数据
rectified_body = copy.deepcopy(request_body)
modified = False
# 1. 整流 messages
messages = rectified_body.get("messages", [])
if messages:
rectified_messages, messages_modified = ThinkingRectifier._rectify_messages(messages)
if messages_modified:
rectified_body["messages"] = rectified_messages
modified = True
# 2. 条件删除顶层 thinking 参数(使用整流后的 messages 判断)
# 与 cc-switch 行为一致:在整流 messages 之后获取快照进行判断
if ThinkingRectifier._should_remove_top_level_thinking(rectified_body):
if "thinking" in rectified_body:
del rectified_body["thinking"]
modified = True
logger.info("ThinkingRectifier: 已移除顶层 thinking 参数")
return rectified_body, modified
@staticmethod
def rectify_signature_sensitive_blocks(
request_body: dict[str, Any],
) -> tuple[dict[str, Any], bool]:
"""Second-stage rectification for signature-related failures.
This is a more aggressive fallback than `rectify()`:
- Removes all thinking/redacted_thinking blocks
- Removes signature fields on remaining blocks
- Degrades tool_use/tool_result blocks into plain text blocks
- Disables top-level `thinking` when enabled
"""
if not request_body:
return request_body, False
rectified_body = copy.deepcopy(request_body)
modified = False
messages = rectified_body.get("messages", [])
if isinstance(messages, list) and messages:
new_messages: list[Any] = []
for message in messages:
if not isinstance(message, dict):
new_messages.append(message)
continue
new_message = dict(message)
content = message.get("content")
if isinstance(content, list):
new_content: list[Any] = []
for block in content:
if not isinstance(block, dict):
new_content.append(block)
continue
block_type = block.get("type")
if block_type in ("thinking", "redacted_thinking"):
modified = True
continue
if block_type == "tool_use":
# Degrade into text to avoid strict structure/signature validation.
name = block.get("name")
inp = block.get("input")
try:
inp_text = json.dumps(inp, ensure_ascii=False)
except Exception:
inp_text = str(inp)
new_content.append(
{
"type": "text",
"text": f"[tool_use] name={name} input={inp_text}",
}
)
modified = True
continue
if block_type == "tool_result":
raw = block.get("content")
try:
raw_text = json.dumps(raw, ensure_ascii=False)
except Exception:
raw_text = str(raw)
new_content.append(
{
"type": "text",
"text": f"[tool_result] {raw_text}",
}
)
modified = True
continue
# Remove signature field (for any non-thinking block).
if "signature" in block:
new_block = {k: v for k, v in block.items() if k != "signature"}
new_content.append(new_block)
modified = True
continue
new_content.append(block)
new_message["content"] = new_content
new_messages.append(new_message)
rectified_body["messages"] = new_messages
# Stage-2: disable top-level thinking unconditionally when enabled.
thinking_param = rectified_body.get("thinking")
if isinstance(thinking_param, dict) and thinking_param.get("type") == "enabled":
del rectified_body["thinking"]
modified = True
logger.info("ThinkingRectifier(stage2): 已移除顶层 thinking 参数")
return rectified_body, modified
@staticmethod
def _rectify_messages(messages: list[dict[str, Any]]) -> tuple[list[dict[str, Any]], bool]:
"""
整流消息列表
移除所有 thinking/redacted_thinking 块和 signature 字段
Args:
messages: 原始消息列表
Returns:
Tuple[整流后的消息列表, 是否有修改]
"""
if not messages:
return messages, False
modified = False
result_messages: list[dict[str, Any]] = []
thinking_removed = 0
signature_removed = 0
for message in messages:
# 类型保护:跳过非 dict 消息
if not isinstance(message, dict):
result_messages.append(message)
continue
# 消息级浅拷贝:外层 rectify() 已深拷贝整个 request_body
# content 会被重建为新列表,不会影响原始数据
new_message = dict(message)
content = message.get("content")
if isinstance(content, list):
new_content = []
for block in content:
if isinstance(block, dict):
block_type = block.get("type")
# 移除 thinking 和 redacted_thinking 块
if block_type in ("thinking", "redacted_thinking"):
thinking_removed += 1
modified = True
continue
# 移除非 thinking 块上的 signature 字段
if "signature" in block:
new_block = {k: v for k, v in block.items() if k != "signature"}
new_content.append(new_block)
signature_removed += 1
modified = True
continue
new_content.append(block)
else:
new_content.append(block)
# 更新 content
new_message["content"] = new_content
# 如果整流后 assistant 消息的 content 为空,记录警告
# (空 content 本身不是"修改",只是检测到的状态,不设置 modified
# 保留消息是必要的:跳过会破坏对话结构(后续 tool_result 消息需要前置 assistant 消息)
if new_message.get("role") == "assistant":
effective_content = new_message.get("content")
is_empty = not effective_content or (
isinstance(effective_content, list) and len(effective_content) == 0
)
if is_empty:
msg_idx = len(result_messages)
logger.warning(
f"ThinkingRectifier: assistant 消息整流后 content 为空 (message_index={msg_idx})"
)
result_messages.append(new_message)
if thinking_removed > 0 or signature_removed > 0:
logger.info(
f"ThinkingRectifier: 移除了 {thinking_removed} 个 thinking 块, "
f"{signature_removed} 个 signature 字段"
)
return result_messages, modified
@staticmethod
def _should_remove_top_level_thinking(body: dict[str, Any]) -> bool:
"""
判断是否应该删除顶层 thinking 参数
与 cc-switch 行为一致:只检查最后一条 assistant 消息
设计思路:
- body 中的 messages 是整流后的状态thinking 块已被移除
- Claude API 只校验最后一条 assistant 消息的结构
- 如果最后一条有 tool_use 但首块不是 thinking需要禁用 thinking 参数
Args:
body: 整流后的请求体
Returns:
是否应该删除顶层 thinking 参数
"""
# 条件 1: thinking 参数存在且已启用
thinking_param = body.get("thinking")
if not isinstance(thinking_param, dict) or thinking_param.get("type") != "enabled":
return False
# 从 body 中获取 messages
messages = body.get("messages", [])
# 类型保护:确保 messages 是 list
if not isinstance(messages, list) or not messages:
return False
# 条件 2: 找到最后一条 assistant 消息
last_assistant = None
for message in reversed(messages):
if isinstance(message, dict) and message.get("role") == "assistant":
last_assistant = message
break
if not last_assistant:
return False
content = last_assistant.get("content")
if not isinstance(content, list) or not content:
return False
# 注意:传入的 messages 是整流后的状态thinking 块已被移除
# 因此只需检查是否有 tool_use如果有则需要禁用 thinking 参数
# (因为整流后的 assistant 消息不再以 thinking 块开头)
# 检查是否有 tool_use
has_tool_use = any(
isinstance(block, dict) and block.get("type") == "tool_use" for block in content
)
# 整流后 assistant 消息不再以 thinking 块开头,如果有 tool_use 则需要禁用 thinking 参数
# Claude API 要求:启用 thinking 时,有 tool_use 的 assistant 消息必须以 thinking 块开头)
if has_tool_use:
logger.info(
"ThinkingRectifier: 整流后 assistant 消息有 tool_use 但无 thinking 前缀,"
"禁用 thinking 参数以通过 API 校验"
)
return True
logger.debug("ThinkingRectifier: 整流后 assistant 消息无 tool_use保留 thinking 参数")
return False