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feat: 添加了中英文动作插件模板，更新了摘要和智能思维导图插件，并简化了异步上下文压缩插件的模型阈值配置。

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fujie
2025-12-20 14:59:55 +08:00
parent 0c380b9fe4
commit 0ef4d67d09
9 changed files with 703 additions and 448 deletions
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2
plugins/filters/async-context-compression/README_CN.md
View File

@@ -65,7 +65,7 @@
| 参数 | 默认值 | 描述 |
| :--- | :--- | :--- |
| `summary_model` | `None` | 用于生成摘要的模型 ID。**强烈建议**配置一个快速、经济且上下文窗口较大的模型（如 `gemini-2.5-flash`, `deepseek-v3`）。如果留空，将尝试使用当前对话的模型。 |
| `summary_model` | `None` | 用于生成摘要的模型 ID。**强烈建议**配置一个快速、经济且上下文窗口较大的模型（如 `gemini-2.5-flash`）。如果留空，将尝试使用当前对话的模型。 |
| `max_summary_tokens` | `16384` | 生成摘要时允许的最大 Token 数。 |
| `summary_temperature` | `0.1` | 控制摘要生成的随机性，较低的值结果更稳定。 |

375
plugins/filters/async-context-compression/async_context_compression.py
View File

@@ -383,360 +383,7 @@ class Filter:
description="Hard limit for context. Exceeding this value will force removal of earliest messages (Global Default)",
)
model_thresholds: dict = Field(
default={
# Groq
"groq-openai/gpt-oss-20b": {
"max_context_tokens": 8000,
"compression_threshold_tokens": 5600,
},
"groq-openai/gpt-oss-120b": {
"max_context_tokens": 8000,
"compression_threshold_tokens": 5600,
},
# Qwen (ModelScope / CF)
"modelscope-Qwen/Qwen3-Coder-480B-A35B-Instruct": {
"max_context_tokens": 256000,
"compression_threshold_tokens": 179200,
},
"cfchatqwen-qwen3-max-search": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"modelscope-Qwen/Qwen3-235B-A22B-Thinking-2507": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-max": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-vl-plus-thinking": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-coder-plus-search": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"cfchatqwen-qwen3-vl-plus": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-coder-plus": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"cfchatqwen-qwen3-omni-flash-thinking": {
"max_context_tokens": 65536,
"compression_threshold_tokens": 45875,
},
"cfchatqwen-qwen3-omni-flash": {
"max_context_tokens": 65536,
"compression_threshold_tokens": 45875,
},
"cfchatqwen-qwen3-next-80b-a3b-thinking": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"modelscope-Qwen/Qwen3-VL-235B-A22B-Instruct": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-next-80b-a3b-thinking-search": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-next-80b-a3b": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-235b-a22b-thinking-search": {
"max_context_tokens": 131072,
"compression_threshold_tokens": 91750,
},
"cfchatqwen-qwen3-235b-a22b": {
"max_context_tokens": 131072,
"compression_threshold_tokens": 91750,
},
"cfchatqwen-qwen3-235b-a22b-thinking": {
"max_context_tokens": 131072,
"compression_threshold_tokens": 91750,
},
"cfchatqwen-qwen3-coder-flash-search": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-coder-flash": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-max-2025-10-30": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-max-2025-10-30-thinking": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-max-2025-10-30-thinking-search": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"modelscope-Qwen/Qwen3-235B-A22B-Instruct-2507": {
"max_context_tokens": 262144,
"compression_threshold_tokens": 183500,
},
"cfchatqwen-qwen3-vl-30b-a3b": {
"max_context_tokens": 131072,
"compression_threshold_tokens": 91750,
},
"cfchatqwen-qwen3-vl-30b-a3b-thinking": {
"max_context_tokens": 131072,
"compression_threshold_tokens": 91750,
},
# Gemini
"gemini-2.5-pro-search": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-2.5-flash-search": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-2.5-flash": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-2.5-flash-lite": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-2.5-flash-lite-search": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-2.5-pro": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-2.0-flash-search": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-2.0-flash": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-2.0-flash-exp": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-2.0-flash-lite": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"copilot-gemini-2.5-pro": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"gemini-pro-latest": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-3-pro-preview": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"gemini-pro-latest-search": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-flash-latest": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-flash-latest-search": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-flash-lite-latest-search": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-flash-lite-latest": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
"gemini-robotics-er-1.5-preview": {
"max_context_tokens": 1048576,
"compression_threshold_tokens": 734000,
},
# DeepSeek
"modelscope-deepseek-ai/DeepSeek-V3.1": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"cfdeepseek-deepseek-search": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"openrouter-deepseek/deepseek-r1-0528:free": {
"max_context_tokens": 163840,
"compression_threshold_tokens": 114688,
},
"modelscope-deepseek-ai/DeepSeek-V3.2-Exp": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"cfdeepseek-deepseek-r1-search": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"cfdeepseek-deepseek-r1": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"openrouter-deepseek/deepseek-chat-v3.1:free": {
"max_context_tokens": 163800,
"compression_threshold_tokens": 114660,
},
"modelscope-deepseek-ai/DeepSeek-R1-0528": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"cfdeepseek-deepseek": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
# Kimi (Moonshot)
"cfkimi-kimi-k2-search": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"cfkimi-kimi-k1.5-search": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"cfkimi-kimi-k1.5-thinking-search": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"cfkimi-kimi-research": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"openrouter-moonshotai/kimi-k2:free": {
"max_context_tokens": 32768,
"compression_threshold_tokens": 22937,
},
"cfkimi-kimi-k2": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"cfkimi-kimi-k1.5": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
# GPT / OpenAI
"gpt-4.1": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"gpt-4o": {
"max_context_tokens": 64000,
"compression_threshold_tokens": 44800,
},
"gpt-5": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"github-gpt-4.1": {
"max_context_tokens": 7500,
"compression_threshold_tokens": 5250,
},
"gpt-5-mini": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"gpt-5.1": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"gpt-5.1-codex": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"gpt-5.1-codex-mini": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"gpt-5-codex": {
"max_context_tokens": 200000,
"compression_threshold_tokens": 140000,
},
"github-gpt-4.1-mini": {
"max_context_tokens": 7500,
"compression_threshold_tokens": 5250,
},
"openrouter-openai/gpt-oss-20b:free": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
# Claude / Anthropic
"claude-sonnet-4.5": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"claude-haiku-4.5": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"copilot-claude-opus-41": {
"max_context_tokens": 80000,
"compression_threshold_tokens": 56000,
},
"copilot-claude-sonnet-4": {
"max_context_tokens": 80000,
"compression_threshold_tokens": 56000,
},
# Other / OpenRouter / OSWE
"oswe-vscode-insiders": {
"max_context_tokens": 256000,
"compression_threshold_tokens": 179200,
},
"modelscope-MiniMax/MiniMax-M2": {
"max_context_tokens": 204800,
"compression_threshold_tokens": 143360,
},
"oswe-vscode-prime": {
"max_context_tokens": 200000,
"compression_threshold_tokens": 140000,
},
"grok-code-fast-1": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"copilot-auto": {
"max_context_tokens": 128000,
"compression_threshold_tokens": 89600,
},
"modelscope-ZhipuAI/GLM-4.6": {
"max_context_tokens": 32000,
"compression_threshold_tokens": 22400,
},
"openrouter-x-ai/grok-4.1-fast:free": {
"max_context_tokens": 2000000,
"compression_threshold_tokens": 1400000,
},
"openrouter-qwen/qwen3-coder:free": {
"max_context_tokens": 262000,
"compression_threshold_tokens": 183400,
},
"openrouter-qwen/qwen3-235b-a22b:free": {
"max_context_tokens": 40960,
"compression_threshold_tokens": 28672,
},
},
default={},
description="Threshold override configuration for specific models. Only includes models requiring special configuration.",
)
@@ -847,7 +494,7 @@ class Filter:
return record.summary
return None
def _count_tokens(self, text: str, model: str = "gpt-3.5-turbo") -> int:
def _count_tokens(self, text: str) -> int:
"""Counts the number of tokens in the text."""
if not text:
return 0
@@ -866,9 +513,7 @@ class Filter:
# Fallback strategy: Rough estimation (1 token ≈ 4 chars)
return len(text) // 4
def _calculate_messages_tokens(
self, messages: List[Dict], model: str = "gpt-3.5-turbo"
) -> int:
def _calculate_messages_tokens(self, messages: List[Dict]) -> int:
"""Calculates the total tokens for a list of messages."""
total_tokens = 0
for msg in messages:
@@ -879,9 +524,9 @@ class Filter:
for part in content:
if isinstance(part, dict) and part.get("type") == "text":
text_content += part.get("text", "")
total_tokens += self._count_tokens(text_content, model)
total_tokens += self._count_tokens(text_content)
else:
total_tokens += self._count_tokens(str(content), model)
total_tokens += self._count_tokens(str(content))
return total_tokens
def _get_model_thresholds(self, model_id: str) -> Dict[str, int]:
@@ -1101,7 +746,7 @@ class Filter:
# Calculate Token count in a background thread
current_tokens = await asyncio.to_thread(
self._calculate_messages_tokens, messages, model
self._calculate_messages_tokens, messages
)
if self.valves.debug_mode:
@@ -1197,7 +842,7 @@ class Filter:
# Calculate current total Tokens (using summary model for counting)
total_tokens = await asyncio.to_thread(
self._calculate_messages_tokens, messages, summary_model_id
self._calculate_messages_tokens, messages
)
if total_tokens > max_context_tokens:
@@ -1213,9 +858,7 @@ class Filter:
while removed_tokens < excess_tokens and middle_messages:
msg_to_remove = middle_messages.pop(0)
msg_tokens = self._count_tokens(
str(msg_to_remove.get("content", "")), summary_model_id
)
msg_tokens = self._count_tokens(str(msg_to_remove.get("content", "")))
removed_tokens += msg_tokens
removed_count += 1
@@ -1269,7 +912,7 @@ class Filter:
{
"type": "status",
"data": {
"description": f"Context summary updated (Saved {len(middle_messages)} messages)",
"description": f"Context summary updated (Compressed {len(middle_messages)} messages)",
"done": True,
},
}

169
plugins/filters/async-context-compression/异步上下文压缩.py
View File

@@ -367,20 +367,26 @@ class Filter:
)
# Token 相关参数
compression_threshold_tokens: int = Field(
default=64000, ge=0, description="当上下文总 Token 数超过此值时，触发压缩 (全局默认值)"
default=64000,
ge=0,
description="当上下文总 Token 数超过此值时，触发压缩 (全局默认值)",
)
max_context_tokens: int = Field(
default=128000, ge=0, description="上下文的硬性上限。超过此值将强制移除最早的消息 (全局默认值)"
default=128000,
ge=0,
description="上下文的硬性上限。超过此值将强制移除最早的消息 (全局默认值)",
)
model_thresholds: dict = Field(
default={},
description="针对特定模型的阈值覆盖配置。仅包含需要特殊配置的模型。"
description="针对特定模型的阈值覆盖配置。仅包含需要特殊配置的模型。",
)
keep_first: int = Field(
default=1, ge=0, description="始终保留最初的 N 条消息。设置为 0 则不保留。"
)
keep_last: int = Field(default=6, ge=0, description="始终保留最近的 N 条完整消息。")
keep_last: int = Field(
default=6, ge=0, description="始终保留最近的 N 条完整消息。"
)
summary_model: str = Field(
default=None,
description="用于生成摘要的模型 ID。留空则使用当前对话的模型。用于匹配 model_thresholds 中的配置。",
@@ -404,15 +410,15 @@ class Filter:
session = self._SessionLocal()
try:
# 查找现有记录
existing = (
session.query(ChatSummary).filter_by(chat_id=chat_id).first()
)
existing = session.query(ChatSummary).filter_by(chat_id=chat_id).first()
if existing:
# [优化] 乐观锁检查：只有进度向前推进时才更新
if compressed_count <= existing.compressed_message_count:
if self.valves.debug_mode:
print(f"[存储] 跳过更新：新进度 ({compressed_count}) 不大于现有进度 ({existing.compressed_message_count})")
print(
f"[存储] 跳过更新：新进度 ({compressed_count}) 不大于现有进度 ({existing.compressed_message_count})"
)
return
# 更新现有记录
@@ -471,11 +477,11 @@ class Filter:
return record.summary
return None
def _count_tokens(self, text: str, model: str = "gpt-3.5-turbo") -> int:
def _count_tokens(self, text: str) -> int:
"""计算文本的 Token 数量"""
if not text:
return 0
if tiktoken:
try:
# 统一使用 o200k_base 编码 (适配最新模型)
@@ -484,11 +490,13 @@ class Filter:
except Exception as e:
if self.valves.debug_mode:
print(f"[Token计数] tiktoken 错误: {e}，回退到字符估算")
# 回退策略：粗略估算 (1 token ≈ 4 chars)
return len(text) // 4
def _calculate_messages_tokens(self, messages: List[Dict], model: str = "gpt-3.5-turbo") -> int:
def _calculate_messages_tokens(
self, messages: List[Dict]
) -> int:
"""计算消息列表的总 Token 数"""
total_tokens = 0
for msg in messages:
@@ -499,14 +507,14 @@ class Filter:
for part in content:
if isinstance(part, dict) and part.get("type") == "text":
text_content += part.get("text", "")
total_tokens += self._count_tokens(text_content, model)
total_tokens += self._count_tokens(text_content)
else:
total_tokens += self._count_tokens(str(content), model)
total_tokens += self._count_tokens(str(content))
return total_tokens
def _get_model_thresholds(self, model_id: str) -> Dict[str, int]:
"""获取特定模型的阈值配置
优先级：
1. 如果 model_thresholds 中存在该模型ID的配置，使用该配置
2. 否则使用全局参数 compression_threshold_tokens 和 max_context_tokens
@@ -516,14 +524,14 @@ class Filter:
if self.valves.debug_mode:
print(f"[配置] 使用模型特定配置: {model_id}")
return self.valves.model_thresholds[model_id]
# 使用全局默认配置
if self.valves.debug_mode:
print(f"[配置] 模型 {model_id} 未在 model_thresholds 中，使用全局参数")
return {
"compression_threshold_tokens": self.valves.compression_threshold_tokens,
"max_context_tokens": self.valves.max_context_tokens
"max_context_tokens": self.valves.max_context_tokens,
}
def _inject_summary_to_first_message(self, message: dict, summary: str) -> dict:
@@ -584,36 +592,36 @@ class Filter:
# 记录原始消息的目标压缩进度，供 outlet 使用
# 目标是压缩到倒数第 keep_last 条之前
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
# [优化] 简单的状态清理检查
if chat_id in self.temp_state:
if self.valves.debug_mode:
if self.valves.debug_mode:
print(f"[Inlet] ⚠️ 覆盖未消费的旧状态 (Chat ID: {chat_id})")
self.temp_state[chat_id] = target_compressed_count
if self.valves.debug_mode:
print(f"[Inlet] 记录目标压缩进度: {target_compressed_count}")
# 加载摘要记录
summary_record = await asyncio.to_thread(self._load_summary_record, chat_id)
final_messages = []
if summary_record:
# 存在摘要，构建视图：[Head] + [Summary Message] + [Tail]
# Tail 是从上次压缩点之后的所有消息
compressed_count = summary_record.compressed_message_count
# 确保 compressed_count 合理
if compressed_count > len(messages):
compressed_count = max(0, len(messages) - self.valves.keep_last)
# 1. 头部消息 (Keep First)
head_messages = []
if self.valves.keep_first > 0:
head_messages = messages[:self.valves.keep_first]
head_messages = messages[: self.valves.keep_first]
# 2. 摘要消息 (作为 User 消息插入)
summary_content = (
f"【系统提示：以下是历史对话的摘要，仅供参考上下文，请勿对摘要内容进行回复，直接回答后续的最新问题】\n\n"
@@ -622,14 +630,14 @@ class Filter:
f"以下是最近的对话："
)
summary_msg = {"role": "user", "content": summary_content}
# 3. 尾部消息 (Tail) - 从上次压缩点开始的所有消息
# 注意：这里必须确保不重复包含头部消息
start_index = max(compressed_count, self.valves.keep_first)
tail_messages = messages[start_index:]
final_messages = head_messages + [summary_msg] + tail_messages
# 发送状态通知
if __event_emitter__:
await __event_emitter__(
@@ -641,15 +649,17 @@ class Filter:
},
}
)
if self.valves.debug_mode:
print(f"[Inlet] 应用摘要: Head({len(head_messages)}) + Summary + Tail({len(tail_messages)})")
print(
f"[Inlet] 应用摘要: Head({len(head_messages)}) + Summary + Tail({len(tail_messages)})"
)
else:
# 没有摘要，使用原始消息
final_messages = messages
body["messages"] = final_messages
if self.valves.debug_mode:
print(f"[Inlet] 最终发送: {len(body['messages'])} 条消息")
print(f"{'='*60}\n")
@@ -701,17 +711,19 @@ class Filter:
"""
try:
messages = body.get("messages", [])
# 获取当前模型的阈值配置
thresholds = self._get_model_thresholds(model)
compression_threshold_tokens = thresholds.get("compression_threshold_tokens", self.valves.compression_threshold_tokens)
compression_threshold_tokens = thresholds.get(
"compression_threshold_tokens", self.valves.compression_threshold_tokens
)
if self.valves.debug_mode:
print(f"\n[🔍 后台计算] 开始 Token 计数...")
# 在后台线程中计算 Token 数
current_tokens = await asyncio.to_thread(
self._calculate_messages_tokens, messages, model
self._calculate_messages_tokens, messages
)
if self.valves.debug_mode:
@@ -762,18 +774,22 @@ class Filter:
if target_compressed_count is None:
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
if self.valves.debug_mode:
print(f"[🤖 异步摘要任务] ⚠️ 无法获取 inlet 状态，使用当前消息数估算进度: {target_compressed_count}")
print(
f"[🤖 异步摘要任务] ⚠️ 无法获取 inlet 状态，使用当前消息数估算进度: {target_compressed_count}"
)
# 2. 确定待压缩的消息范围 (Middle)
start_index = self.valves.keep_first
end_index = len(messages) - self.valves.keep_last
if self.valves.keep_last == 0:
end_index = len(messages)
# 确保索引有效
if start_index >= end_index:
if self.valves.debug_mode:
print(f"[🤖 异步摘要任务] 中间消息为空 (Start: {start_index}, End: {end_index})，跳过")
print(
f"[🤖 异步摘要任务] 中间消息为空 (Start: {start_index}, End: {end_index})，跳过"
)
return
middle_messages = messages[start_index:end_index]
@@ -784,36 +800,48 @@ class Filter:
# 3. 检查 Token 上限并截断 (Max Context Truncation)
# [优化] 使用摘要模型(如果有)的阈值来决定能处理多少中间消息
# 这样可以用长窗口模型(如 gemini-flash)来压缩超过当前模型窗口的历史记录
summary_model_id = self.valves.summary_model or body.get("model", "gpt-3.5-turbo")
summary_model_id = self.valves.summary_model or body.get("model")
thresholds = self._get_model_thresholds(summary_model_id)
# 注意：这里使用的是摘要模型的最大上下文限制
max_context_tokens = thresholds.get("max_context_tokens", self.valves.max_context_tokens)
max_context_tokens = thresholds.get(
"max_context_tokens", self.valves.max_context_tokens
)
if self.valves.debug_mode:
print(f"[🤖 异步摘要任务] 使用模型 {summary_model_id} 的上限: {max_context_tokens} Tokens")
print(
f"[🤖 异步摘要任务] 使用模型 {summary_model_id} 的上限: {max_context_tokens} Tokens"
)
# 计算当前总 Token (使用摘要模型进行计数)
total_tokens = await asyncio.to_thread(self._calculate_messages_tokens, messages, summary_model_id)
total_tokens = await asyncio.to_thread(
self._calculate_messages_tokens, messages, summary_model_id
)
if total_tokens > max_context_tokens:
excess_tokens = total_tokens - max_context_tokens
if self.valves.debug_mode:
print(f"[🤖 异步摘要任务] ⚠️ 总 Token ({total_tokens}) 超过摘要模型上限 ({max_context_tokens})，需要移除约 {excess_tokens} Token")
print(
f"[🤖 异步摘要任务] ⚠️ 总 Token ({total_tokens}) 超过摘要模型上限 ({max_context_tokens})，需要移除约 {excess_tokens} Token"
)
# 从 middle_messages 头部开始移除
removed_tokens = 0
removed_count = 0
while removed_tokens < excess_tokens and middle_messages:
msg_to_remove = middle_messages.pop(0)
msg_tokens = self._count_tokens(str(msg_to_remove.get("content", "")), summary_model_id)
msg_tokens = self._count_tokens(
str(msg_to_remove.get("content", ""))
)
removed_tokens += msg_tokens
removed_count += 1
if self.valves.debug_mode:
print(f"[🤖 异步摘要任务] 已移除 {removed_count} 条消息，共 {removed_tokens} Token")
print(
f"[🤖 异步摘要任务] 已移除 {removed_count} 条消息，共 {removed_tokens} Token"
)
if not middle_messages:
if self.valves.debug_mode:
print(f"[🤖 异步摘要任务] 截断后中间消息为空，跳过摘要生成")
@@ -824,7 +852,7 @@ class Filter:
# 5. 调用 LLM 生成新摘要
# 注意：这里不再传入 previous_summary，因为旧摘要（如果有）已经包含在 middle_messages 里了
# 发送开始生成摘要的状态通知
if __event_emitter__:
await __event_emitter__(
@@ -837,13 +865,17 @@ class Filter:
}
)
new_summary = await self._call_summary_llm(None, conversation_text, body, user_data)
new_summary = await self._call_summary_llm(
None, conversation_text, body, user_data
)
# 6. 保存新摘要
if self.valves.debug_mode:
print("[优化] 正在后台线程中保存摘要，以避免阻塞事件循环。")
await asyncio.to_thread(self._save_summary, chat_id, new_summary, target_compressed_count)
await asyncio.to_thread(
self._save_summary, chat_id, new_summary, target_compressed_count
)
# 发送完成状态通知
if __event_emitter__:
@@ -851,7 +883,7 @@ class Filter:
{
"type": "status",
"data": {
"description": f"上下文摘要已更新 (节省 {len(middle_messages)} 条消息)",
"description": f"上下文摘要已更新 (已压缩 {len(middle_messages)} 条消息)",
"done": True,
},
}
@@ -859,11 +891,14 @@ class Filter:
if self.valves.debug_mode:
print(f"[🤖 异步摘要任务] ✅ 完成！新摘要长度: {len(new_summary)} 字符")
print(f"[🤖 异步摘要任务] 进度更新: 已压缩至原始第 {target_compressed_count} 条消息")
print(
f"[🤖 异步摘要任务] 进度更新: 已压缩至原始第 {target_compressed_count} 条消息"
)
except Exception as e:
print(f"[🤖 异步摘要任务] ❌ 错误: {str(e)}")
import traceback
traceback.print_exc()
def _format_messages_for_summary(self, messages: list) -> str:
@@ -893,7 +928,11 @@ class Filter:
return "\n\n".join(formatted)
async def _call_summary_llm(
self, previous_summary: Optional[str], new_conversation_text: str, body: dict, user_data: dict
self,
previous_summary: Optional[str],
new_conversation_text: str,
body: dict,
user_data: dict,
) -> str:
"""
使用 Open WebUI 内置方法调用 LLM 生成摘要
@@ -960,7 +999,7 @@ class Filter:
if self.valves.debug_mode:
print("[优化] 正在后台线程中获取用户对象，以避免阻塞事件循环。")
user = await asyncio.to_thread(Users.get_user_by_id, user_id)
if not user:
raise ValueError(f"无法找到用户: {user_id}")